A device and method for detecting using a laser signal

Abstract

The application provides a device and method for detecting by using a laser signal, the device comprising at least one laser radar for acquiring point cloud data, a communication module for transmitting and receiving data through a communication network, a modeling module for establishing a three-dimensional model according to a second laser signal, a vibration detection module for collecting vibration data, a bearing module for bearing a to-be-detected object and a control processing module, wherein the laser radar comprises a laser signal emitting module for emitting a first laser signal, an optoelectronic detection module for collecting the laser signal to convert the laser signal into an electric signal, a pre-amplification module for amplifying the electric signal, a high-pass filter module for reducing noise of the electric signal and a post-amplification circuit for optimizing the electric signal. According to the scheme of the embodiment of the application, high-precision laser point cloud data is obtained by using a laser signal, and the detection precision is improved.

Description

Technical Field

[0001] This invention relates to the field of measurement technology, and specifically to a device and method for detection using laser signals. Background Technology

[0002] In modern production and daily life, the inspection of 3D information is indispensable. Currently, a large part of the existing 3D inspection solutions are manual, which is relatively inefficient. Furthermore, for the inspection of some precision instruments, contact measuring equipment is not only difficult to install, but can also cause scratches on the surface of the object being measured. Therefore, optical non-contact measurement technology has developed rapidly and is now widely used in product design, manufacturing, quality inspection and other fields.

[0003] Existing optical-based 3D inspection solutions have low accuracy and are prone to errors. Summary of the Invention

[0004] Based on the above-mentioned problems, this invention proposes a device and method for detection using laser signals. Through the solution of the embodiments of this invention, the detection accuracy is improved by using laser signals to obtain high-precision laser point cloud data.

[0005] In view of this, one aspect of the present invention proposes a device for detection using laser signals, comprising at least one lidar for acquiring point cloud data, a communication module for transmitting and receiving data via a communication network, a modeling module for establishing a three-dimensional model based on a second laser signal, a vibration detection module for acquiring vibration data, a carrying module for carrying the object to be detected, and a control processing module. The lidar includes a laser signal transmitting module for emitting a first laser signal, a photoelectric detection module for acquiring the laser signal and converting it into an electrical signal, a pre-amplifier module for amplifying the electrical signal, a high-pass filter module for denoising the electrical signal, and a post-amplifier circuit for optimizing the electrical signal.

[0006] The control processing module is configured as follows:

[0007] Select multiple standard samples of the item to be tested;

[0008] Multiple first point cloud data of the multiple standard samples are acquired by scanning with a first lidar.

[0009] A baseline 3D model of the object to be tested is established by selecting the baseline point cloud data with the smallest difference from the standard physical parameters of the object to be tested from the plurality of first point cloud data.

[0010] Multiple reference 3D models of the object to be detected are established using the first point cloud data other than the reference point cloud data from the multiple first point cloud data.

[0011] A reference detection region is determined on the reference 3D model, and multiple corresponding reference detection regions are determined on each of the multiple reference 3D models.

[0012] The second point cloud data of the object to be detected is obtained by the second lidar;

[0013] The three-dimensional model of the object to be detected is reconstructed based on the second point cloud data;

[0014] Based on the total number of the baseline 3D model and the plurality of reference 3D models, the same number of the 3D model to be detected are copied to obtain a plurality of copied 3D models;

[0015] One of the multiple replicated 3D models is adjusted to be the same as the specification of the reference 3D model to obtain a first replicated 3D model, and the remaining replicated 3D models are adjusted to be the same as the specification of the multiple reference 3D models to obtain multiple second replicated 3D models.

[0016] A first detection region is determined from the first replicated 3D model based on the reference detection region;

[0017] Multiple second detection regions are determined from the multiple second replicated 3D models based on the multiple reference detection regions;

[0018] The reference detection area and the first detection area are compared to obtain a first comparison result, and / or the plurality of reference detection areas are compared with the corresponding plurality of second detection areas to obtain a second comparison result;

[0019] Output the detection report data based on the first comparison result and / or the second comparison result.

[0020] Optionally, in the step of determining a reference detection region on the reference 3D model and determining corresponding multiple reference detection regions on each of the multiple reference 3D models, the processing module is configured to:

[0021] Multiple first reference detection regions are determined on the reference 3D model, and corresponding multiple first reference detection regions are determined on each of the multiple reference 3D models.

[0022] Determine all first reference coordinate values ​​for each of the plurality of first reference detection regions, the first point association relationship between all the first reference coordinate values, and the first region association relationship between the plurality of first reference points and each of the first reference detection regions;

[0023] For each of the plurality of reference 3D models, determine all first reference coordinate values ​​of each first reference detection area in each of the plurality of first reference detection areas on each reference 3D model, the second point association relationship between all first reference coordinate values, and the second region association relationship between the plurality of first reference reference points and each first reference detection area;

[0024] The plurality of first reference detection regions are used as the reference detection regions;

[0025] The plurality of reference detection regions are obtained by taking the plurality of first reference detection regions on each of the plurality of reference 3D models as their respective reference detection regions on each of the plurality of reference 3D models.

[0026] Optionally, in the step of determining the first detection region from the first replicated 3D model based on the reference detection region, the processing module is configured to:

[0027] Based on the plurality of first reference points in the reference detection area, a plurality of first replication reference points on the first replication 3D model are determined;

[0028] Based on the multiple first copy reference points and the first point correlation relationship between all the first reference coordinate values, multiple first copy detection areas are determined;

[0029] The first detection region is determined based on the first region association relationship between the plurality of first copy detection regions and each first reference detection region.

[0030] Optionally, in the step of determining multiple second detection regions from the multiple second replicated 3D models based on the multiple reference detection regions, the processing module is configured to:

[0031] For each of the plurality of second replicated 3D models, the following steps are performed to determine the plurality of second detection regions:

[0032] Select the corresponding first reference reference point from the plurality of first reference reference points;

[0033] Based on the corresponding first reference point, determine the multiple second replication reference points on the corresponding second replication 3D model in the multiple second replication 3D models;

[0034] Based on the multiple second copy reference points and the second point correlation relationship between all the first reference coordinate values, multiple second copy detection areas are determined;

[0035] A second detection region is determined based on the second region association relationship between the plurality of second replication detection regions and each first reference detection region.

[0036] Optionally, in the steps of comparing the reference detection region and the first detection region to obtain a first comparison result, and / or comparing the plurality of reference detection regions and the corresponding plurality of second detection regions to obtain a second comparison result, the processing module is configured to:

[0037] Obtain the first detection coordinate values ​​of all detection points within the first detection area, and store the first detection coordinate values ​​into the first queue;

[0038] Obtain all the first reference coordinate values ​​within the reference detection area, and store the first reference coordinate values ​​into the second queue;

[0039] Calculate the distance between the coordinate points represented by the coordinate values ​​in the first queue and the coordinate points represented by the coordinate values ​​in the second queue one by one to obtain the first distance set;

[0040] Statistical analysis is performed on the first distance set to determine the first distance value whose concentration reaches the first preset concentration.

[0041] Select the coordinate point pairs whose distance value is the first distance value, and add a first number to the coordinate point pairs;

[0042] Sort all coordinate points within the reference detection area and the first detection area according to the first number to obtain the first reference detection coordinate set and the first detection coordinate set respectively;

[0043] The first benchmark detection coordinate set and the first detection coordinate set are fitted respectively, and the fitted results are compared to obtain the first comparison result;

[0044] When the first comparison result does not meet the first threshold, the plurality of reference detection regions and the corresponding plurality of second detection regions are compared to obtain the second comparison result.

[0045] Another aspect of the present invention provides a method for detection using laser signals, the method comprising:

[0046] Select multiple standard samples of the item to be tested;

[0047] Multiple first point cloud data of the multiple standard samples are acquired by scanning with a first lidar.

[0048] A baseline 3D model of the object to be tested is established by selecting the baseline point cloud data with the smallest difference from the standard physical parameters of the object to be tested from the plurality of first point cloud data.

[0049] Multiple reference 3D models of the object to be detected are established using the first point cloud data other than the reference point cloud data from the multiple first point cloud data.

[0050] A reference detection region is determined on the reference 3D model, and multiple corresponding reference detection regions are determined on each of the multiple reference 3D models.

[0051] The second point cloud data of the object to be detected is obtained by the second lidar;

[0052] The three-dimensional model of the object to be detected is reconstructed based on the second point cloud data;

[0053] Based on the total number of the baseline 3D model and the plurality of reference 3D models, the same number of the 3D model to be detected are copied to obtain a plurality of copied 3D models;

[0054] One of the multiple replicated 3D models is adjusted to be the same as the specification of the reference 3D model to obtain a first replicated 3D model, and the remaining replicated 3D models are adjusted to be the same as the specification of the multiple reference 3D models to obtain multiple second replicated 3D models.

[0055] A first detection region is determined from the first replicated 3D model based on the reference detection region;

[0056] Multiple second detection regions are determined from the multiple second replicated 3D models based on the multiple reference detection regions;

[0057] The reference detection area and the first detection area are compared to obtain a first comparison result, and / or the plurality of reference detection areas are compared with the corresponding plurality of second detection areas to obtain a second comparison result;

[0058] Output the detection report data based on the first comparison result and / or the second comparison result.

[0059] Optionally, the step of determining a reference detection region on the reference 3D model and determining corresponding reference detection regions on each of the plurality of reference 3D models includes:

[0060] Multiple first reference detection regions are determined on the reference 3D model, and corresponding multiple first reference detection regions are determined on each of the multiple reference 3D models.

[0061] Determine all first reference coordinate values ​​for each of the plurality of first reference detection regions, the first point association relationship between all the first reference coordinate values, and the first region association relationship between the plurality of first reference points and each of the first reference detection regions;

[0062] For each of the plurality of reference 3D models, determine all first reference coordinate values ​​of each first reference detection area in each of the plurality of first reference detection areas on each reference 3D model, the second point association relationship between all first reference coordinate values, and the second region association relationship between the plurality of first reference reference points and each first reference detection area;

[0063] The plurality of first reference detection regions are used as the reference detection regions;

[0064] The plurality of reference detection regions are obtained by taking the plurality of first reference detection regions on each of the plurality of reference 3D models as their respective reference detection regions on each of the plurality of reference 3D models.

[0065] Optionally, the step of determining the first detection region from the first replicated 3D model based on the reference detection region includes:

[0066] Based on the plurality of first reference points in the reference detection area, a plurality of first replication reference points on the first replication 3D model are determined;

[0067] Based on the multiple first copy reference points and the first point correlation relationship between all the first reference coordinate values, multiple first copy detection areas are determined;

[0068] The first detection region is determined based on the first region association relationship between the plurality of first copy detection regions and each first reference detection region.

[0069] Optionally, the step of determining multiple second detection regions from the multiple second replicated 3D models based on the multiple reference detection regions includes:

[0070] For each of the plurality of second replicated 3D models, the following steps are performed to determine the plurality of second detection regions:

[0071] Select the corresponding first reference reference point from the plurality of first reference reference points;

[0072] Based on the corresponding first reference point, determine the multiple second replication reference points on the corresponding second replication 3D model in the multiple second replication 3D models;

[0073] Based on the multiple second copy reference points and the second point correlation relationship between all the first reference coordinate values, multiple second copy detection areas are determined;

[0074] A second detection region is determined based on the second region association relationship between the plurality of second replication detection regions and each first reference detection region.

[0075] Optionally, the steps of comparing the reference detection region and the first detection region to obtain a first comparison result, and / or comparing the plurality of reference detection regions and the corresponding plurality of second detection regions to obtain a second comparison result, include:

[0076] Obtain the first detection coordinate values ​​of all detection points within the first detection area, and store the first detection coordinate values ​​into the first queue;

[0077] Obtain all the first reference coordinate values ​​within the reference detection area, and store the first reference coordinate values ​​into the second queue;

[0078] Calculate the distance between the coordinate points represented by the coordinate values ​​in the first queue and the coordinate points represented by the coordinate values ​​in the second queue one by one to obtain the first distance set;

[0079] Statistical analysis is performed on the first distance set to determine the first distance value whose concentration reaches the first preset concentration.

[0080] Select the coordinate point pairs whose distance value is the first distance value, and add a first number to the coordinate point pairs;

[0081] Sort all coordinate points within the reference detection area and the first detection area according to the first number to obtain the first reference detection coordinate set and the first detection coordinate set respectively;

[0082] The first benchmark detection coordinate set and the first detection coordinate set are fitted respectively, and the fitted results are compared to obtain the first comparison result;

[0083] When the first comparison result does not meet the first threshold, the plurality of reference detection regions and the corresponding plurality of second detection regions are compared to obtain the second comparison result.

[0084] The device for detection using laser signals, employing the technical solution of this invention, includes at least one lidar for acquiring point cloud data, a communication module for sending and receiving data via a communication network, a modeling module for establishing a three-dimensional model based on a second laser signal, a vibration detection module for collecting vibration data, a support module for carrying the object to be detected, and a control and processing module. The lidar includes a laser signal emitting module for emitting a first laser signal, a photoelectric detection module for acquiring the laser signal and converting it into an electrical signal, a pre-amplifier module for amplifying the electrical signal, a high-pass filter module for noise reduction of the electrical signal, and a post-amplifier circuit for optimizing the electrical signal. Through the solution of this invention, high-precision laser point cloud data is obtained using laser signals, improving detection accuracy. Attached Figure Description

[0085] Figure 1 This is a schematic block diagram of a device for detection using laser signals according to an embodiment of the present invention;

[0086] Figure 2 This is a flowchart of a method for detection using laser signals provided in one embodiment of the present invention. Detailed Implementation

[0087] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0088] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0089] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0090] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0091] The following reference Figures 1 to 2 This invention describes an apparatus and method for detection using laser signals, provided by some embodiments of the present invention.

[0092] like Figure 1 As shown, one embodiment of the present invention provides a device for detection using laser signals, comprising at least one lidar for acquiring point cloud data, a communication module for sending and receiving data via a communication network, a modeling module for establishing a three-dimensional model based on a second laser signal, a vibration detection module for acquiring vibration data, a carrying module for carrying the object to be detected, and a control processing module. The lidar includes a laser signal emitting module for emitting a first laser signal, a photoelectric detection module for acquiring the laser signal and converting it into an electrical signal, a pre-amplifier module for amplifying the electrical signal, a high-pass filter module for noise reduction of the electrical signal, and a post-amplifier circuit for optimizing the electrical signal.

[0093] The control processing module is configured as follows:

[0094] Select multiple standard samples of the item to be tested;

[0095] Multiple first point cloud data of the multiple standard samples are acquired by scanning with a first lidar.

[0096] A baseline 3D model of the object to be tested is established by selecting the baseline point cloud data with the smallest difference from the standard physical parameters of the object to be tested from the plurality of first point cloud data.

[0097] Multiple reference 3D models of the object to be detected are established using the first point cloud data other than the reference point cloud data from the multiple first point cloud data.

[0098] A reference detection region is determined on the reference 3D model, and multiple corresponding reference detection regions are determined on each of the multiple reference 3D models.

[0099] The second point cloud data of the object to be detected is obtained by the second lidar;

[0100] The three-dimensional model of the object to be detected is reconstructed based on the second point cloud data;

[0101] Based on the total number of the baseline 3D model and the plurality of reference 3D models, the same number of the 3D model to be detected are copied to obtain a plurality of copied 3D models;

[0102] One of the multiple replicated 3D models is adjusted to be the same as the specification of the reference 3D model to obtain a first replicated 3D model, and the remaining replicated 3D models are adjusted to be the same as the specification of the multiple reference 3D models to obtain multiple second replicated 3D models.

[0103] A first detection region is determined from the first replicated 3D model based on the reference detection region;

[0104] Multiple second detection regions are determined from the multiple second replicated 3D models based on the multiple reference detection regions;

[0105] The reference detection area and the first detection area are compared to obtain a first comparison result, and / or the plurality of reference detection areas are compared with the corresponding plurality of second detection areas to obtain a second comparison result;

[0106] Output the detection report data based on the first comparison result and / or the second comparison result.

[0107] It should be noted that, in real production and life, different individual products of the same item manufactured according to the same standard may have minor differences in physical parameters that do not affect the function. In order to avoid misjudgment due to such minor differences, in this embodiment, the data of multiple standard samples of the item to be tested are selected as the test comparison data to include products that fall within the aforementioned range of minor differences into the range of normal products as much as possible.

[0108] In embodiments of the present invention, a point cloud data containing three-dimensional coordinates of the object is obtained by scanning the object with a lidar. For example, multiple first point cloud data of the multiple standard samples are obtained by scanning with a first lidar, and a reference point cloud data is selected from the multiple first point cloud data to establish a reference three-dimensional model of the item to be inspected; multiple reference three-dimensional models of the item to be inspected are established using the other first point cloud data in the multiple first point cloud data besides the reference point cloud data; then, according to the inspection requirements, a reference inspection area (which can be a connected region or composed of multiple non-connected sub-regions) is determined on the reference three-dimensional model, and multiple corresponding reference inspection areas are determined on each of the multiple reference three-dimensional models (based on the aforementioned determined reference inspection area, correspondingly determined in each reference three-dimensional model, for example, after adjusting the reference three-dimensional model and multiple reference three-dimensional models to a uniform specification, the determination is based on the correspondence between coordinates). For example, a second point cloud data of the object to be detected is acquired using a second lidar, and then a 3D model of the object to be detected is reconstructed based on the second point cloud data; based on the total number of the baseline 3D model and the plurality of reference 3D models, the same number of copies of the 3D model to be detected are made to obtain a plurality of copied 3D models; one of the plurality of copied 3D models is adjusted to be the same as the specifications of the baseline 3D model to obtain a first copied 3D model, and the remaining copied 3D models are adjusted to be the same as the specifications of the plurality of reference 3D models (e.g., the same size, the same coordinate system, etc.) to obtain a plurality of second copied 3D models; a first detection area is determined from the first copied 3D model based on the baseline detection area; and a plurality of second detection areas are determined from the plurality of second copied 3D models based on the plurality of reference detection areas.

[0109] Finally, the first detection area and / or the plurality of second detection areas are compared with the benchmark detection area and the plurality of reference detection areas respectively to obtain the comparison results and thus the detection results.

[0110] The solution of this invention utilizes laser signals to obtain high-precision laser point cloud data, which not only improves detection accuracy but also avoids errors within a certain range.

[0111] Understandably, to enhance data security during transmission and storage, point cloud data can be encrypted in some embodiments of this invention. For example, for coordinate data in point cloud data, all X values ​​can be used as a first X set, all Y values ​​as a first Y set, and all Z values ​​as a first Z set. A first encryption algorithm is used to encrypt the first X set to obtain a second X set, a second encryption algorithm is used to encrypt the first Y set to obtain a second Y set, and a third encryption algorithm is used to encrypt the first Z set to obtain a second Z set. The first encryption algorithm can be based on the first Y set and the third encryption algorithm. The first encryption algorithm can be generated based on at least one of the first Y set and / or the first Z set (e.g., generating a random number sequence / pseudo-random number sequence by combining the first Y set and / or the first Z set, and constructing a first encryption algorithm based thereon); the second encryption algorithm can be generated based on at least one of the first X set and the first Z set (e.g., generating a random number sequence / pseudo-random number sequence by combining the first X set and / or the first Z set, and constructing a second encryption algorithm based thereon); the third encryption algorithm can be generated based on at least one of the first Y set and the first X set (e.g., generating a random number sequence / pseudo-random number sequence by combining the first Y set and / or the first X set, and constructing a third encryption algorithm based thereon). When transmitting or storing point cloud data after encrypting all X, Y, and Z values, the following steps are taken: Based on the second X set, a value is randomly selected from the second Y set and the second Z set, mixed with the second X set, to form the encrypted X value. This encrypted X value is then transmitted or stored in the first storage area via the first transmission channel. Similarly, based on the second Y set, a value is randomly selected from the second X set and the second Z set, mixed with the second Y set, to form the encrypted Y value. This encrypted Y value is then transmitted or stored in the second storage area via the second transmission channel. Likewise, based on the second Z set, a value is randomly selected from the second X set and the second Y set, mixed with the second Z set, to form the encrypted Z value. This encrypted Z value is then transmitted or stored in the third storage area via the third transmission channel. Of course, the reflection intensity data in the point cloud data can be encrypted using one or more different data types; the embodiments of this invention do not limit this.

[0112] It should be known that, Figure 1 The block diagram of the device for detection using laser signals shown is for illustrative purposes only, and the number of modules shown does not limit the scope of protection of this invention.

[0113] In some possible embodiments of the present invention, the processing module is configured to: determine a reference detection region on the reference 3D model and determine corresponding multiple reference detection regions on each of the multiple reference 3D models.

[0114] Multiple first reference detection regions are determined on the reference 3D model, and corresponding multiple first reference detection regions are determined on each of the multiple reference 3D models.

[0115] Determine all first reference coordinate values ​​for each of the plurality of first reference detection regions, the first point association relationship between all the first reference coordinate values, and the first region association relationship between the plurality of first reference points and each of the first reference detection regions;

[0116] For each of the plurality of reference 3D models, determine all first reference coordinate values ​​of each first reference detection area in each of the plurality of first reference detection areas on each reference 3D model, the second point association relationship between all first reference coordinate values, and the second region association relationship between the plurality of first reference reference points and each first reference detection area;

[0117] The plurality of first reference detection regions are used as the reference detection regions;

[0118] The plurality of reference detection regions are obtained by taking the plurality of first reference detection regions on each of the plurality of reference 3D models as their respective reference detection regions on each of the plurality of reference 3D models.

[0119] It is understandable that, in order to perform targeted detection on specific regions and to achieve simultaneous detection of multiple specific regions to improve detection efficiency, in this embodiment, each benchmark detection region and each reference detection region are composed of multiple sub-regions (first benchmark detection region or first reference detection region). By determining all first reference coordinate values ​​(i.e., coordinate values ​​of all points within the first reference detection area) of each of the plurality of first reference detection areas, the first point association relationship between all the first reference coordinate values ​​(i.e., the relationship between points within the first reference detection area, such as positional relationship, coordinate relationship, etc.), multiple first reference points (one or more reference points determined from all points within each first reference detection area), and the first region association relationship between each of the first reference detection areas (i.e., the relationship between each sub-region, such as positional relationship, coordinate relationship, etc.); similarly, for each of the plurality of reference 3D models, by determining all first reference coordinate values, the second point association relationship between all the first reference coordinate values, the second region association relationship between multiple first reference reference points and each of the multiple first reference detection areas (i.e., sub-regions) on each reference 3D model, the reference detection area and the plurality of reference detection areas are further obtained.

[0120] In some possible embodiments of the present invention, in the step of determining the first detection region from the first replicated 3D model based on the reference detection region, the processing module is configured to:

[0121] Based on the plurality of first reference points in the reference detection area, a plurality of first replication reference points on the first replication 3D model are determined;

[0122] Based on the multiple first copy reference points and the first point correlation relationship between all the first reference coordinate values, multiple first copy detection areas are determined;

[0123] The first detection region is determined based on the first region association relationship between the plurality of first copy detection regions and each first reference detection region.

[0124] It is understood that in this embodiment, the first detection area is determined from the first replicated 3D model of the item to be detected using data from the reference detection area for targeted detection.

[0125] In some possible embodiments of the present invention, in the step of determining a plurality of second detection regions from the plurality of second replicated 3D models based on the plurality of reference detection regions, the processing module is configured to:

[0126] For each of the plurality of second replicated 3D models, the following steps are performed to determine the plurality of second detection regions:

[0127] Select the corresponding first reference reference point from the plurality of first reference reference points;

[0128] Based on the corresponding first reference point, determine the multiple second replication reference points on the corresponding second replication 3D model in the multiple second replication 3D models;

[0129] Based on the multiple second copy reference points and the second point correlation relationship between all the first reference coordinate values, multiple second copy detection areas are determined;

[0130] A second detection region is determined based on the second region association relationship between the plurality of second replication detection regions and each first reference detection region.

[0131] It is understood that, in this embodiment, in order to tolerate as many minor errors as possible within the normal range, the same number of second replicated 3D models (derived from the 3D model of the object to be detected) are configured according to the specific number of the plurality of reference 3D models. For each of the plurality of second replicated 3D models, a second detection area is determined for each second replicated 3D model, thereby obtaining the plurality of second detection areas (the number of which is consistent with the specific number of the plurality of reference 3D models).

[0132] In some possible embodiments of the present invention, the step of comparing the reference detection region and the first detection region to obtain a first comparison result, and / or comparing the plurality of reference detection regions and the corresponding plurality of second detection regions to obtain a second comparison result, wherein the processing module is configured to:

[0133] Obtain the first detection coordinate values ​​of all detection points within the first detection area, and store the first detection coordinate values ​​into the first queue;

[0134] Obtain all the first reference coordinate values ​​within the reference detection area, and store the first reference coordinate values ​​into the second queue;

[0135] Calculate the distance between the coordinate points represented by the coordinate values ​​in the first queue and the coordinate points represented by the coordinate values ​​in the second queue one by one to obtain the first distance set;

[0136] Statistical analysis is performed on the first distance set to determine the first distance value whose concentration reaches the first preset concentration.

[0137] Select the coordinate point pairs whose distance value is the first distance value, and add a first number to the coordinate point pairs;

[0138] Sort all coordinate points within the reference detection area and the first detection area according to the first number to obtain the first reference detection coordinate set and the first detection coordinate set respectively;

[0139] The first benchmark detection coordinate set and the first detection coordinate set are fitted respectively, and the fitted results are compared to obtain the first comparison result;

[0140] When the first comparison result does not meet the first threshold, the plurality of reference detection regions and the corresponding plurality of second detection regions are compared to obtain the second comparison result.

[0141] Understandably, in order to obtain accurate comparison results, in this embodiment, the coordinate values ​​of the points in the reference detection area and the first detection area are compared. Utilizing the relativity of the spatial positions of the two three-dimensional graphics, pairs of coordinate points at the same location are selected. These coordinate points are then numbered, fitted, and compared to obtain a first comparison result. When the first comparison result does not meet the first threshold, the plurality of reference detection areas and the corresponding plurality of second detection areas are compared using the same method to obtain a second comparison result.

[0142] Please see Figure 2 Another embodiment of the present invention provides a method for detection using laser signals, the method comprising:

[0143] Select multiple standard samples of the item to be tested;

[0144] Multiple first point cloud data of the multiple standard samples are acquired by scanning with a first lidar.

[0145] A baseline 3D model of the object to be tested is established by selecting the baseline point cloud data with the smallest difference from the standard physical parameters of the object to be tested from the plurality of first point cloud data.

[0146] Multiple reference 3D models of the object to be detected are established using the first point cloud data other than the reference point cloud data from the multiple first point cloud data.

[0147] A reference detection region is determined on the reference 3D model, and multiple corresponding reference detection regions are determined on each of the multiple reference 3D models.

[0148] The second point cloud data of the object to be detected is obtained by the second lidar;

[0149] The three-dimensional model of the object to be detected is reconstructed based on the second point cloud data;

[0150] Based on the total number of the baseline 3D model and the plurality of reference 3D models, the same number of the 3D model to be detected are copied to obtain a plurality of copied 3D models;

[0151] One of the multiple replicated 3D models is adjusted to be the same as the specification of the reference 3D model to obtain a first replicated 3D model, and the remaining replicated 3D models are adjusted to be the same as the specification of the multiple reference 3D models to obtain multiple second replicated 3D models.

[0152] A first detection region is determined from the first replicated 3D model based on the reference detection region;

[0153] Multiple second detection regions are determined from the multiple second replicated 3D models based on the multiple reference detection regions;

[0154] The reference detection area and the first detection area are compared to obtain a first comparison result, and / or the plurality of reference detection areas are compared with the corresponding plurality of second detection areas to obtain a second comparison result;

[0155] Output the detection report data based on the first comparison result and / or the second comparison result.

[0156] It should be noted that, in real production and life, different individual products of the same item manufactured according to the same standard may have minor differences in physical parameters that do not affect the function. In order to avoid misjudgment due to such minor differences, in this embodiment, the data of multiple standard samples of the item to be tested are selected as the test comparison data to include products that fall within the aforementioned range of minor differences into the range of normal products as much as possible.

[0157] In embodiments of the present invention, a point cloud data containing three-dimensional coordinates of the object is obtained by scanning the object with a lidar. For example, multiple first point cloud data of the multiple standard samples are obtained by scanning with a first lidar, and a reference point cloud data is selected from the multiple first point cloud data to establish a reference three-dimensional model of the item to be inspected; multiple reference three-dimensional models of the item to be inspected are established using the other first point cloud data in the multiple first point cloud data besides the reference point cloud data; then, according to the inspection requirements, a reference inspection area (which can be a connected region or composed of multiple non-connected sub-regions) is determined on the reference three-dimensional model, and multiple corresponding reference inspection areas are determined on each of the multiple reference three-dimensional models (based on the aforementioned determined reference inspection area, correspondingly determined in each reference three-dimensional model, for example, after adjusting the reference three-dimensional model and multiple reference three-dimensional models to a uniform specification, the determination is based on the correspondence between coordinates). For example, a second point cloud data of the object to be detected is acquired using a second lidar, and then a 3D model of the object to be detected is reconstructed based on the second point cloud data; based on the total number of the baseline 3D model and the plurality of reference 3D models, the same number of copies of the 3D model to be detected are made to obtain a plurality of copied 3D models; one of the plurality of copied 3D models is adjusted to be the same as the specifications of the baseline 3D model to obtain a first copied 3D model, and the remaining copied 3D models are adjusted to be the same as the specifications of the plurality of reference 3D models (e.g., the same size, the same coordinate system, etc.) to obtain a plurality of second copied 3D models; a first detection area is determined from the first copied 3D model based on the baseline detection area; and a plurality of second detection areas are determined from the plurality of second copied 3D models based on the plurality of reference detection areas.

[0158] Finally, the first detection area and / or the plurality of second detection areas are compared with the benchmark detection area and the plurality of reference detection areas respectively to obtain the comparison results and thus the detection results.

[0159] The solution of this invention utilizes laser signals to obtain high-precision laser point cloud data, which not only improves detection accuracy but also avoids errors within a certain range.

[0160] Understandably, to enhance data security during transmission and storage, point cloud data can be encrypted in some embodiments of this invention. For example, for coordinate data in point cloud data, all X values ​​can be used as a first X set, all Y values ​​as a first Y set, and all Z values ​​as a first Z set. A first encryption algorithm is used to encrypt the first X set to obtain a second X set, a second encryption algorithm is used to encrypt the first Y set to obtain a second Y set, and a third encryption algorithm is used to encrypt the first Z set to obtain a second Z set. The first encryption algorithm can be based on the first Y set and the third encryption algorithm. The first encryption algorithm can be generated based on at least one of the first Y set and / or the first Z set (e.g., generating a random number sequence / pseudo-random number sequence by combining the first Y set and / or the first Z set, and constructing a first encryption algorithm based thereon); the second encryption algorithm can be generated based on at least one of the first X set and the first Z set (e.g., generating a random number sequence / pseudo-random number sequence by combining the first X set and / or the first Z set, and constructing a second encryption algorithm based thereon); the third encryption algorithm can be generated based on at least one of the first Y set and the first X set (e.g., generating a random number sequence / pseudo-random number sequence by combining the first Y set and / or the first X set, and constructing a third encryption algorithm based thereon). When transmitting or storing point cloud data after encrypting all X, Y, and Z values, the following steps are taken: Based on the second X set, a value is randomly selected from the second Y set and the second Z set, mixed with the second X set, to form the encrypted X value. This encrypted X value is then transmitted or stored in the first storage area via the first transmission channel. Similarly, based on the second Y set, a value is randomly selected from the second X set and the second Z set, mixed with the second Y set, to form the encrypted Y value. This encrypted Y value is then transmitted or stored in the second storage area via the second transmission channel. Likewise, based on the second Z set, a value is randomly selected from the second X set and the second Y set, mixed with the second Z set, to form the encrypted Z value. This encrypted Z value is then transmitted or stored in the third storage area via the third transmission channel. Of course, the reflection intensity data in the point cloud data can be encrypted using one or more different data types; the embodiments of this invention do not limit this.

[0161] In some possible embodiments of the present invention, the step of determining a reference detection region on the reference 3D model and determining corresponding multiple reference detection regions on each of the multiple reference 3D models includes:

[0162] Multiple first reference detection regions are determined on the reference 3D model, and corresponding multiple first reference detection regions are determined on each of the multiple reference 3D models.

[0163] Determine all first reference coordinate values ​​for each of the plurality of first reference detection regions, the first point association relationship between all the first reference coordinate values, and the first region association relationship between the plurality of first reference points and each of the first reference detection regions;

[0164] For each of the plurality of reference 3D models, determine all first reference coordinate values ​​of each first reference detection area in each of the plurality of first reference detection areas on each reference 3D model, the second point association relationship between all first reference coordinate values, and the second region association relationship between the plurality of first reference reference points and each first reference detection area;

[0165] The plurality of first reference detection regions are used as the reference detection regions;

[0166] The plurality of reference detection regions are obtained by taking the plurality of first reference detection regions on each of the plurality of reference 3D models as their respective reference detection regions on each of the plurality of reference 3D models.

[0167] It is understandable that, in order to perform targeted detection on specific regions and to achieve simultaneous detection of multiple specific regions to improve detection efficiency, in this embodiment, each benchmark detection region and each reference detection region are composed of multiple sub-regions (first benchmark detection region or first reference detection region). By determining all first reference coordinate values ​​(i.e., coordinate values ​​of all points within the first reference detection area) of each of the plurality of first reference detection areas, the first point association relationship between all the first reference coordinate values ​​(i.e., the relationship between points within the first reference detection area, such as positional relationship, coordinate relationship, etc.), multiple first reference points (one or more reference points determined from all points within each first reference detection area), and the first region association relationship between each of the first reference detection areas (i.e., the relationship between each sub-region, such as positional relationship, coordinate relationship, etc.); similarly, for each of the plurality of reference 3D models, by determining all first reference coordinate values, the second point association relationship between all the first reference coordinate values, the second region association relationship between multiple first reference reference points and each of the multiple first reference detection areas (i.e., sub-regions) on each reference 3D model, the reference detection area and the plurality of reference detection areas are further obtained.

[0168] In some possible embodiments of the present invention, the step of determining the first detection region from the first replicated 3D model based on the reference detection region includes:

[0169] Based on the plurality of first reference points in the reference detection area, a plurality of first replication reference points on the first replication 3D model are determined;

[0170] Based on the multiple first copy reference points and the first point correlation relationship between all the first reference coordinate values, multiple first copy detection areas are determined;

[0171] The first detection region is determined based on the first region association relationship between the plurality of first copy detection regions and each first reference detection region.

[0172] It is understood that in this embodiment, the first detection area is determined from the first replicated 3D model of the item to be detected using data from the reference detection area for targeted detection.

[0173] In some possible embodiments of the present invention, the step of determining a plurality of second detection regions from the plurality of second replicated 3D models based on the plurality of reference detection regions includes:

[0174] For each of the plurality of second replicated 3D models, the following steps are performed to determine the plurality of second detection regions:

[0175] Select the corresponding first reference reference point from the plurality of first reference reference points;

[0176] Based on the corresponding first reference point, determine the multiple second replication reference points on the corresponding second replication 3D model in the multiple second replication 3D models;

[0177] Based on the multiple second copy reference points and the second point correlation relationship between all the first reference coordinate values, multiple second copy detection areas are determined;

[0178] A second detection region is determined based on the second region association relationship between the plurality of second copy detection regions and each first reference detection region.

[0179] It is understood that, in this embodiment, in order to tolerate as many minor errors as possible within the normal range, the same number of second replicated 3D models (derived from the 3D model of the object to be detected) are configured according to the specific number of the plurality of reference 3D models. For each of the plurality of second replicated 3D models, a second detection area is determined for each second replicated 3D model, thereby obtaining the plurality of second detection areas (the number of which is consistent with the specific number of the plurality of reference 3D models).

[0180] In some possible embodiments of the present invention, the steps of comparing the reference detection region and the first detection region to obtain a first comparison result, and / or comparing the plurality of reference detection regions and the corresponding plurality of second detection regions to obtain a second comparison result, include:

[0181] Obtain the first detection coordinate values ​​of all detection points within the first detection area, and store the first detection coordinate values ​​into the first queue;

[0182] Obtain all the first reference coordinate values ​​within the reference detection area, and store the first reference coordinate values ​​into the second queue;

[0183] Calculate the distance between the coordinate points represented by the coordinate values ​​in the first queue and the coordinate points represented by the coordinate values ​​in the second queue one by one to obtain the first distance set;

[0184] Statistical analysis is performed on the first distance set to determine the first distance value whose concentration reaches the first preset concentration.

[0185] Select the coordinate point pairs whose distance value is the first distance value, and add a first number to the coordinate point pairs;

[0186] Sort all coordinate points within the reference detection area and the first detection area according to the first number to obtain the first reference detection coordinate set and the first detection coordinate set respectively;

[0187] The first benchmark detection coordinate set and the first detection coordinate set are fitted respectively, and the fitted results are compared to obtain the first comparison result;

[0188] When the first comparison result does not meet the first threshold, the plurality of reference detection regions and the corresponding plurality of second detection regions are compared to obtain the second comparison result.

[0189] Understandably, in order to obtain accurate comparison results, in this embodiment, the coordinate values ​​of the points in the reference detection area and the first detection area are compared. Utilizing the relativity of the spatial positions of the two three-dimensional graphics, pairs of coordinate points at the same location are selected. These coordinate points are then numbered, fitted, and compared to obtain a first comparison result. When the first comparison result does not meet the first threshold, the plurality of reference detection areas and the corresponding plurality of second detection areas are compared using the same method to obtain a second comparison result.

[0190] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0191] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0192] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0193] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0194] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0195] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0196] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0197] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

[0198] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can easily conceive of variations or substitutions without departing from the spirit and scope of the present invention, and various modifications and alterations can be made, including combinations of the different functions and implementation steps described above, as well as software and hardware implementation methods, all of which are within the protection scope of the present invention.

Claims

1. A method of detection using a laser signal, characterized in that, The method includes: Select multiple standard samples of the item to be tested; Multiple first point cloud data of the multiple standard samples are acquired by scanning with a first lidar. A baseline 3D model of the object to be tested is established by selecting the baseline point cloud data with the smallest difference from the standard physical parameters of the object to be tested from the plurality of first point cloud data. Multiple reference 3D models of the object to be detected are established using the first point cloud data other than the reference point cloud data from the multiple first point cloud data. A reference detection region is determined on the reference 3D model, and multiple corresponding reference detection regions are determined on each of the multiple reference 3D models. The second point cloud data of the object to be detected is obtained by the second lidar; The three-dimensional model of the object to be detected is reconstructed based on the second point cloud data; Based on the total number of the baseline 3D model and the plurality of reference 3D models, the same number of the 3D model to be detected are copied to obtain a plurality of copied 3D models; One of the multiple replicated 3D models is adjusted to be the same as the specification of the reference 3D model to obtain a first replicated 3D model, and the remaining replicated 3D models are adjusted to be the same as the specification of the multiple reference 3D models to obtain multiple second replicated 3D models. A first detection region is determined from the first replicated 3D model based on the reference detection region; Multiple second detection regions are determined from the multiple second replicated 3D models based on the multiple reference detection regions; The reference detection area and the first detection area are compared to obtain a first comparison result, and / or the plurality of reference detection areas are compared with the corresponding plurality of second detection areas to obtain a second comparison result; Output the detection report data based on the first comparison result and / or the second comparison result.

2. The method of claim 1, wherein, The step of determining a reference detection region on the reference 3D model and determining corresponding reference detection regions on each of the plurality of reference 3D models includes: Multiple first reference detection regions are determined on the reference 3D model, and corresponding multiple first reference detection regions are determined on each of the multiple reference 3D models. Determine all first reference coordinate values ​​for each of the plurality of first reference detection regions, the first point association relationship between all the first reference coordinate values, and the first region association relationship between the plurality of first reference points and each of the first reference detection regions; For each of the plurality of reference 3D models, determine all first reference coordinate values ​​of each first reference detection area in each of the plurality of first reference detection areas on each reference 3D model, the second point association relationship between all first reference coordinate values, and the second region association relationship between the plurality of first reference reference points and each first reference detection area; The plurality of first reference detection regions are used as the reference detection regions; The plurality of first reference detection regions on each of the plurality of reference 3D models are respectively used as the respective reference detection regions on each of the plurality of reference 3D models to obtain the plurality of reference detection regions.

3. The method of claim 2, wherein the laser signal is a laser beam. The step of determining the first detection region from the first replicated 3D model based on the reference detection region includes: Based on the plurality of first reference points in the reference detection area, a plurality of first replication reference points on the first replication 3D model are determined; Based on the multiple first copy reference points and the first point correlation relationship between all the first reference coordinate values, multiple first copy detection areas are determined; The first detection region is determined based on the first region association relationship between the plurality of first copy detection regions and each first reference detection region.

4. The method of claim 3, wherein the laser signal is a laser beam. The step of determining multiple second detection regions from the multiple second replicated 3D models based on the multiple reference detection regions includes: For each of the plurality of second replicated 3D models, the following steps are performed to determine the plurality of second detection regions: Select the corresponding first reference reference point from the plurality of first reference reference points; Based on the corresponding first reference point, determine the multiple second replication reference points on the corresponding second replication 3D model in the multiple second replication 3D models; Based on the multiple second copy reference points and the second point correlation relationship between all the first reference coordinate values, multiple second copy detection areas are determined; A second detection region is determined based on the second region association relationship between the plurality of second replication detection regions and each first reference detection region.

5. The method of detecting with a laser signal of claim 4, wherein, The steps of comparing the reference detection region and the first detection region to obtain a first comparison result, and / or comparing the plurality of reference detection regions and the corresponding plurality of second detection regions to obtain a second comparison result, include: Obtain the first detection coordinate values ​​of all detection points within the first detection area, and store the first detection coordinate values ​​into the first queue; Obtain all the first reference coordinate values ​​within the reference detection area, and store the first reference coordinate values ​​into the second queue; Calculate the distance between the coordinate points represented by the coordinate values ​​in the first queue and the coordinate points represented by the coordinate values ​​in the second queue one by one to obtain the first distance set; Statistical analysis is performed on the first distance set to determine the first distance value whose concentration reaches the first preset concentration. Select the coordinate point pairs whose distance value is the first distance value, and add a first number to the coordinate point pairs; Sort all coordinate points within the reference detection area and the first detection area according to the first number to obtain the first reference detection coordinate set and the first detection coordinate set respectively; The first benchmark detection coordinate set and the first detection coordinate set are fitted respectively, and the fitted results are compared to obtain the first comparison result; When the first comparison result does not meet a first threshold, the multiple reference detection regions and the corresponding multiple second detection regions are compared respectively to obtain a second comparison result.

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