Laser data processing method, scanning device, three-dimensional scanning system and storage medium

Abstract

The invention relates to a laser data processing method, scanning equipment, a three-dimensional scanning system and a storage medium, and the method comprises the steps: obtaining to-be-recognized feature data; the to-be-identified feature data is data acquired by performing laser scanning on a scanning object; under the condition that the quantity of the to-be-identified feature data meets a preset data quantity condition, sorting gray values of the to-be-identified feature data to obtain a feature sequence; the data volume condition is determined based on clock resources of a built-in data processing module of the scanning equipment; identifying a front preset number of to-be-identified feature data with gray values from high to low in the feature sequence as laser feature data; and transmitting the laser characteristic data. Effective laser features can be screened out from to-be-identified feature data based on gray values, so that noise points which are wrongly identified in the to-be-identified feature data are eliminated, and the amount of data needing to be transmitted can be reduced.

Description

Technical Field

[0001] The present application relates to the field of three-dimensional scanning, and in particular to a laser data processing method, a scanning device, a three-dimensional scanning system and a storage medium. Background Art

[0002] Laser 3D scanning technology is a 3D measurement technology that uses the principle of laser ranging to collect coordinates of various locations on the surface of the object being measured, and then reconstructs the 3D model through the collected point cloud data. It is widely used in architectural surveying and mapping, industrial design, cultural relics protection and other fields.

[0003] At present, laser 3D scanning often requires the laser features of the entire image to be identified for data transmission. Due to the limitations of the recognition algorithm, there are cases where noise points in the image are identified as laser features. The more complex the scanning scene, the more noise points may be misidentified. The misidentified noise points, together with the correctly identified laser features, are transmitted as transmission data to subsequent modules for analysis and processing. Therefore, in this case, the amount of data transmitted will increase.

[0004] With regard to the problem of large amount of data transmission in laser three-dimensional scanning in related technologies, no effective solution has been proposed so far. Summary of the invention

[0005] In this embodiment, a laser data processing method, a scanning device, a three-dimensional scanning system and a storage medium are provided to solve the problem of a large amount of transmitted data in laser three-dimensional scanning in the related art.

[0006] In a first aspect, a laser data processing method is provided in this embodiment, which is used for a scanning device, and the method includes:

[0007] Acquire feature data to be identified; the feature data to be identified is data collected by laser scanning the scanned object;

[0008] In the case where the quantity of the feature data to be identified meets a preset data quantity condition, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence; the data quantity condition is determined based on a clock resource of a data processing module built into the scanning device;

[0009] Identify the first preset number of to-be-identified feature data with grayscale values ​​from high to low in the feature sequence as laser feature data;

[0010] The laser characteristic data is transmitted.

[0011] In some embodiments, when the amount of the feature data to be identified meets a preset data amount condition, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence, including:

[0012] When the quantity of the feature data to be identified is lower than a preset threshold value of the number of sorted features, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence.

[0013] In some of the embodiments, the preset data volume condition is determined based on a maximum number of clocks allocated by the data processing module for single-frame data processing.

[0014] In some embodiments, when the amount of the feature data to be identified meets a preset data amount condition, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence, including:

[0015] When the number of the feature data to be identified meets the preset data amount condition, the grayscale value of each feature data to be identified is taken as the target grayscale value in turn, and the following process is iterated until the sequence number of the target grayscale value in the current comparison sequence is determined, and the target grayscale value is updated into the current comparison sequence according to the sequence number:

[0016] Compare the target grayscale value with the midpoint value of the current comparison sequence, and determine the next comparison sequence according to the comparison result; the next comparison sequence is one of the subsequences of the current comparison sequence divided according to the midpoint value;

[0017] Taking the next comparison sequence as a new current comparison sequence, and determining a new next comparison sequence according to a comparison result between the target gray value and a midpoint value of the new current comparison sequence;

[0018] The initial comparison sequence is a set of values ​​whose values ​​are all initial values ​​and whose serial numbers increase in sequence.

[0019] In some embodiments, transmitting the laser characteristic data includes:

[0020] The laser characteristic data is transmitted to an associated processor in a wireless manner.

[0021] In some of the embodiments, when the amount of the feature data to be identified does not meet the preset data amount condition, the method further includes:

[0022] All feature data to be identified are transmitted together.

[0023] In a second aspect, a scanning device is provided in this embodiment, including: an image acquisition module and a data processing module; wherein:

[0024] The image acquisition module is used to perform laser scanning on the scanned object to acquire feature data to be identified;

[0025] The data processing module is used to execute the laser data processing method described in the first aspect.

[0026] In a third aspect, a three-dimensional scanning system is provided in this embodiment, comprising: a processor and the scanning device described in the second aspect; the processor and the scanning device are connected.

[0027] In a fourth aspect, an electronic device is provided in this embodiment, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the laser data processing method described in the first aspect when executing the computer program.

[0028] In a fifth aspect, in this embodiment, a storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the laser data processing method described in the first aspect is implemented.

[0029] Compared with the related art, the present embodiment provides a laser data processing method, a scanning device, a three-dimensional scanning system and a storage medium. The laser data processing method obtains feature data to be identified; the feature data to be identified is data collected by laser scanning the scanned object; when the number of feature data to be identified meets the preset data volume condition, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence; the data volume condition is determined based on the clock resources of the data processing module built into the scanning device; the first preset number of feature data to be identified in the feature sequence with grayscale values ​​from high to low are identified as laser feature data; and the laser feature data is transmitted. It can filter out effective laser features from the feature data to be identified based on the grayscale value, thereby eliminating the noise points that are misidentified, and thus reducing the amount of data that needs to be transmitted.

[0030] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0032] Figure 1 is a hardware structure block diagram of a terminal of the laser data processing method of this embodiment;

[0033] Figure 2 is a flow chart of the laser data processing method of this embodiment;

[0034] Figure 3is a flow chart of a laser data processing method of some embodiments;

[0035] Figure 4 is a schematic structural diagram of a scanning device of this embodiment;

[0036] Figure 5 Schematic diagram of the structure of the three-dimensional scanning system of this embodiment. DETAILED DESCRIPTION

[0037] In order to more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0038] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the general meaning understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the", "these" and the like in this application do not represent quantitative restrictions, and they can be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusions; for example, a process, method and system, product or device comprising a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether directly or indirectly. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. Usually, the character " / " indicates that the objects associated with each other are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0039] The method embodiment provided in this embodiment can be executed in a terminal, a computer or a similar computing device. For example, running on a terminal, Figure 1 FIG. 1 is a hardware structure diagram of a terminal of the laser data processing method of this embodiment. Figure 1 As shown, the terminal may include one or more ( Figure 1Only one is shown in the figure) processor 102 and memory 104 for storing data, wherein processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0040] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the laser data processing method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0041] The transmission device 106 is used to receive or send data via a network. The above network includes a wireless network provided by the communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0042] In this embodiment, a laser data processing method is provided. Figure 2 is a flow chart of the laser data processing method of this embodiment. Figure 2 As shown, the process includes the following steps:

[0043] Step S210, obtaining feature data to be identified; the feature data to be identified is data collected by laser scanning the scanned object.

[0044] Specifically, a scanning device can be used to perform laser scanning on the scanned object to obtain feature data to be identified, which includes both laser feature data formed on the actual scanned object surface and noise caused by factors such as fill light duration and scene light.

[0045] Step S220, when the number of feature data to be identified meets a preset data volume condition, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence; the data volume condition is determined based on the clock resources of the data processing module built into the scanning device.

[0046] The data volume condition is used to indicate whether the current number of feature data to be identified is suitable for gray value sorting. In the process of laser scanning, in order to reduce the amount of data transmitted, only the laser features in the collected feature data to be identified can be extracted, and the noise points therein can be eliminated. Considering that in the original image, the grayscale of the laser feature is higher than the grayscale of the noise points in the image, based on this characteristic, the noise points can be eliminated from the laser features. For a single frame image, there is an upper limit to the number of laser features. The upper limit of the number of laser features depends on the laser installed in the scanning device. For example, when the number of laser features required is 50, it is necessary to extract a preset number of laser features from the feature data to be identified based on this reference number of 50, and all other feature data that are in excess are identified as noise points for elimination. Exemplarily, the above-mentioned preset number can exceed the number of laser features required by a certain margin. For example, when the number of laser features required is 50, the above-mentioned preset number can be determined to be about 20% of 50. In addition, the above-mentioned preset number can also be set based on the required laser data in other ways, and this embodiment does not specifically limit this. Therefore, based on the above-mentioned characteristics of laser features and noise in grayscale, all feature data to be identified can be sorted by grayscale values, and the first preset number of feature data to be identified from high to low are determined as valid laser features, and the remaining points are regarded as noise points and removed.

[0047] For the data processing module, clock resources are relatively tight. Compared with the process of not sorting, the gray value sorting of each feature data to be identified is an extra processing process, so the sorting process requires more clocks. It should also be noted that there is an upper limit on the processing time of a single frame of data (that is, the scanning device has a set frame rate, and the processing clock for each frame of data is limited). If the upper limit of the processing time is exceeded but the sorting is still not completed, the data transmission of the frame will be wrong, resulting in data mistransmission or leakage. In order to avoid this situation, an upper limit on the number of features allowed to enter the sorting can be set (the upper limit is set according to the clock resources). When the number of feature data to be identified is less than the upper limit, or the number of feature data to be identified is within a certain range, it is confirmed that the feature data to be identified meets the above-mentioned data volume condition, so that all feature data to be identified can be sorted and the transmission data volume can be compressed to a minimum; if the feature data to be identified is greater than the upper limit or is not within a certain range (does not meet the preset data volume condition), then the provided clock will not be sufficient to complete the sorting of all feature data to be identified, and there is also the possibility of losing laser feature data when partially sorting all feature data to be identified. Therefore, it is necessary to skip the sorting process and directly output all feature data to be identified.

[0048] In this step, it is further considered that the data sorting process needs to consume the clock of the data processing module of the scanning device, such as the clock of the field programmable gate array (FPGA), which will affect the efficiency, integrity and accuracy of data transmission as a whole. Therefore, it is necessary to make a conditional judgment on the number of feature data to be identified. When the number of feature data to be identified matches the number of clocks required to complete the sorting with the number of clocks that the data processing unit can provide, it is confirmed that the number of feature data to be identified meets the preset data quantity condition. In this case, the grayscale values ​​of the feature data to be identified are sorted.

[0049] When performing the sorting, in order to improve the utilization of the clock, improve the sorting efficiency and accuracy, the sorting algorithm can be selected according to the actual application scenario. For example, several sorting algorithms based on the binary idea can be used. After that, after the gray value sorting of the feature data to be identified is completed, a feature sequence from high to low is obtained.

[0050] Step S230, the first preset number of feature data to be identified in the feature sequence with grayscale values ​​from high to low are identified as laser feature data. Specifically, after obtaining the above feature sequence, according to the required number of laser features, the first preset number of feature data to be identified, such as the first 50 feature data to be identified, are selected as laser feature data.

[0051] Step S240, transmitting the laser characteristic data.

[0052] In the related art, due to the limitations of the recognition algorithm, according to the laser scanning of the scanning object by the scanning device, different degrees of noise points will be misidentified according to the different original images collected. In addition, as the complexity of the scanning scene increases, the higher the overall grayscale of the scanned original image, the more noise points will be identified. The more noise points are identified, plus the large number of laser feature data that exist, the larger the amount of data that needs to be transmitted. That is, in the related art, the misidentified noise points in the image are directly transmitted together with the correctly identified laser features, resulting in a large amount of transmitted data. Especially for wireless transmission scenarios, the transmission bandwidth of wireless network cards is often around 20 megabytes per second when the network is stable. Therefore, when the laser feature data and the marker points are transmitted at the same time, if there are a large number of noise points, the bandwidth of the wireless transmission will be insufficient to support high frame rate transmission, and it can only be processed by frame reduction, thereby affecting the integrity and accuracy of the transmitted data.

[0053] This embodiment is based on the difference in grayscale between laser feature data and noise points. The feature data to be identified is sorted according to the grayscale value to screen the laser feature data and eliminate the noise points, thereby effectively reducing the amount of data to be transmitted and adapting the transmission bandwidth in the wireless transmission scenario.

[0054] Therefore, in the above steps S210 to S240, the feature data to be identified are obtained; the feature data to be identified are data collected by laser scanning the scanned object; when the number of feature data to be identified meets the preset data volume condition, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence; the data volume condition is determined based on the clock resources of the data processing module built into the scanning device; the first preset number of feature data to be identified in the feature sequence with grayscale values ​​from high to low are identified as laser feature data; and the laser feature data is transmitted. It can filter out effective laser features from the feature data to be identified based on the grayscale value, thereby eliminating the noise points that are misidentified, and thus reducing the amount of data that needs to be transmitted.

[0055] In one embodiment, based on the above step S220, when the amount of the feature data to be identified meets the preset data amount condition, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence, which may specifically include:

[0056] When the number of feature data to be identified is lower than a preset threshold value of the number of sorted features, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence.

[0057] A sorting feature quantity threshold can be set according to the clock resource of the data processing module built into the scanning device. Afterwards, when the quantity of the feature data to be identified is lower than the sorting feature quantity threshold, it is confirmed that the quantity of the feature data to be identified meets the preset data quantity condition, and the grayscale value sorting is performed on the feature data to be identified; otherwise, it is confirmed that it does not meet the preset data quantity condition, and the grayscale value sorting is not performed.

[0058] In this step, whether to sort the feature data to be identified is limited based on the quantity threshold, which can avoid the influence of wrong transmission or missed transmission caused by the sorting process on data transmission.

[0059] Additionally, in one embodiment, the preset data volume condition is determined based on the maximum number of clocks allocated by the data processing module for single-frame data processing. Exemplarily, if the maximum number of clocks allocated for single-frame data processing is 600 clocks. Assuming that the number of clocks required for sorting each feature data to be identified is 4, then the number of feature data to be identified that can be processed, x, satisfies 4x=600, that is, x=150 features. On this basis, considering a certain tolerance, the threshold value of the number of features to be identified and sorted can be set to 120, that is, if the number of identified feature data to be identified is less than 120, all feature data to be identified can be sorted to minimize the amount of transmitted data; if the number of identified feature data to be identified is greater than or equal to 120, then the clock provided is not enough to complete the sorting of all features, and partial sorting of all features may also cause the loss of laser features, so the sorting process will be skipped and all features will be output directly.

[0060] In this embodiment, the data volume condition is determined based on the maximum number of clocks allocated by the data processing module for single-frame data processing, which can avoid data transmission errors and omissions due to the fact that the provided clock is insufficient to complete all feature sorting, thereby affecting the integrity and accuracy of the transmitted data.

[0061] In one embodiment, based on the above step S220, when the amount of the feature data to be identified meets the preset data amount condition, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence, which may specifically include:

[0062] When the number of feature data to be identified meets the preset data volume condition, the grayscale value of each feature data to be identified is taken as the target grayscale value in turn, and the following process is iterated until the sequence number of the target grayscale value in the current comparison sequence is determined, and the target grayscale value is updated into the current comparison sequence according to the sequence number:

[0063] Compare the target grayscale value with the midpoint value of the current comparison sequence, and determine the next comparison sequence according to the comparison result; the next comparison sequence is one of the subsequences of the current comparison sequence divided by the midpoint value; take the next comparison sequence as the new current comparison sequence, and determine the new next comparison sequence according to the comparison result of the target grayscale value with the midpoint value of the new current comparison sequence.

[0064] The initial comparison sequence is a set of values ​​whose values ​​are all initial values ​​and whose serial numbers increase in sequence.

[0065] Specifically, we can set a preset number of positions, such as 50 positions, before sorting begins, set the values ​​at these 50 positions to 0, and retain the serial numbers of the 50 positions to form an initial comparison sequence. Based on binary sorting, each feature data to be identified is updated into the initial comparison sequence in turn, that is, the position of each feature data to be identified in the above comparison sequence is determined. Specifically, assuming that the first target grayscale value to be compared is A, the current comparison sequence corresponding to A (greater than 0) is the initial comparison sequence. First, A is compared with the value 0 with serial number 25 in the middle of the initial comparison sequence. If A is greater than 0, then the next comparison sequence is confirmed to be the sequence of serial numbers 1 to 25, and enter the upper half. The comparison sequence of serial numbers 1 to 25 is used as the new current sequence, and then A is compared with the value 0 corresponding to serial number 12 to determine that the next comparison sequence is the sequence of serial numbers 1 to 12. The sequence of numbers 1 to 12 is used as the new current comparison sequence, and A is compared with the value 0 of number 6, and so on, and then compared with the values ​​of numbers 3, 2, and 1, and finally the target gray value A is determined to be 1, and a sorting is completed. A is updated into the initial comparison sequence, and the updated comparison sequence is a sequence with the first value A and the other values ​​0. The updated comparison sequence is used as the current comparison sequence of the initial iteration corresponding to the next target gray value. Based on this, the sorting of the gray values ​​of all feature data to be identified can be completed.

[0066] By adopting the above binary sorting, even if the sorting data increases significantly, the clocks consumed will not increase too much. It should also be noted that the comparison objects of the sorting can be superimposed, and multiple numerical comparisons and judgments can be processed in the same clock, using more resource consumption in exchange for a reduction in the number of clocks. That is, multiple numerical comparisons can be completed within one clock. For example, the comparison of A with the above-mentioned serial numbers 25, 12, 6, and 3 can be completed within one clock; and then the comparison of serial numbers 2, 1, and 0 can be completed in the next clock.

[0067] Assuming that 200 laser feature data need to be transmitted each time, without sorting, only 200 clocks are needed to process and transmit the laser features each time. However, if sorting is performed, assuming that the sorting of each laser feature consumes 5 clocks, 1000 clocks are needed to complete the sorting, plus 50 clocks for transmission, a total of 1050 clocks are required. Compared with not sorting, a considerable number of clocks are added, which may lead to data mistransmission or omission.

[0068] Therefore, the sorting method based on the binary idea can be selected to minimize the clock consumed by sorting, and more sorting logic can perform multiple judgments within one clock, further compressing the required clock. For the above example, the sorting of the feature data to be identified once consumes three clocks, which can control the amount of data transmitted while reducing clock consumption.

[0069] Furthermore, in one embodiment, transmitting the laser characteristic data may include: transmitting the laser characteristic data to an associated processor in a wireless manner.

[0070] Additionally, in one embodiment, when the amount of feature data to be identified does not meet a preset data amount condition, the laser data processing method further includes: transmitting all feature data to be identified together.

[0071] Figure 3 is a flow chart of a laser data processing method in some embodiments, such as Figure 3 As shown, the laser data processing method includes the following steps:

[0072] Step S301, obtaining feature data to be identified; the feature data to be identified is data collected by laser scanning of a scanned object.

[0073] Step S302, when the number of feature data to be identified is lower than a preset sorting feature quantity threshold, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence; wherein the sorting feature quantity threshold is determined according to the maximum number of clocks allocated for single frame data processing.

[0074] Step S303, identifying the first preset number of to-be-identified feature data with grayscale values ​​from high to low in the feature sequence as laser feature data.

[0075] Step S304, transmitting the laser characteristic data by wireless transmission.

[0076] The above steps S301 to S304 can screen out effective laser features from the feature data to be identified based on the grayscale value, thereby eliminating the misidentified noise points therein, thereby reducing the amount of data to be transmitted.

[0077] In this embodiment, a scanning device is also provided. Figure 4 FIG. 4 is a schematic diagram of the structure of the scanning device 40 of this embodiment; Figure 4 As shown, the scanning device 40 includes: an image acquisition module 42 and a data processing module 44; wherein:

[0078] The image acquisition module 42 is used to perform laser scanning on the scanned object and acquire feature data to be identified; the data processing module 44 is used to execute the laser data processing method provided in the above embodiment.

[0079] In this embodiment, a three-dimensional scanning system is also provided. Figure 5 is a schematic diagram of the structure of the three-dimensional scanning system 50 of this embodiment, as shown in Figure 5 As shown, the three-dimensional scanning system 50 includes: a processor 52 and the scanning device 40 provided in the above embodiment; the processor 52 and the scanning device 40 are connected.

[0080] In this embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0081] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0082] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0083] S1, obtaining feature data to be identified; the feature data to be identified is data collected by laser scanning of the scanned object;

[0084] S2, when the number of feature data to be identified meets a preset data volume condition, sorting the grayscale values ​​of the feature data to be identified to obtain a feature sequence; the data volume condition is determined based on the clock resources of the data processing module built into the scanning device;

[0085] S3, identifying the first preset number of feature data to be identified in the feature sequence with grayscale values ​​from high to low as laser feature data;

[0086] S4, transmitting the laser characteristic data.

[0087] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and will not be repeated in this embodiment.

[0088] In addition, in combination with the laser data processing method provided in the above embodiments, a storage medium may be provided in this embodiment to implement the method. The storage medium stores a computer program; when the computer program is executed by a processor, any one of the laser data processing methods in the above embodiments is implemented.

[0089] It should be understood that the specific embodiments described herein are only used to explain the application, rather than to limit it. Based on the embodiments provided in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the protection scope of this application.

[0090] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0091] Obviously, the drawings are only some examples or embodiments of the present application. For ordinary technicians in the field, the present application can also be applied to other similar situations based on these drawings without creative work. In addition, it is understandable that although the work done in this development process may be complicated and lengthy, for ordinary technicians in the field, certain changes in design, manufacturing or production based on the technical content disclosed in this application are only conventional technical means and should not be regarded as insufficient content disclosed in this application.

[0092] The term "embodiment" in this application refers to a specific feature, structure or characteristic described in conjunction with the embodiment that can be included in at least one embodiment of the present application. The appearance of this phrase in various locations in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. It is clearly or implicitly understood by those of ordinary skill in the art that the embodiments described in this application can be combined with other embodiments without conflict.

[0093] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of patent protection. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the attached claims.

Claims

1. A laser data processing method, characterized in that: For scanning a device, the method comprises: Acquire feature data to be identified; the feature data to be identified is data collected by laser scanning the scanned object; In the case where the quantity of the feature data to be identified meets a preset data quantity condition, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence; the data quantity condition is determined based on a clock resource of a data processing module built into the scanning device; Identify the first preset number of to-be-identified feature data with grayscale values ​​from high to low in the feature sequence as laser feature data; The laser characteristic data is transmitted.

2. The laser data processing method according to claim 1, characterized in that: When the amount of the feature data to be identified meets the preset data amount condition, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence, including: When the quantity of the feature data to be identified is lower than a preset threshold value of the number of sorted features, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence.

3. The laser data processing method according to claim 1, characterized in that: The preset data volume condition is determined based on the maximum number of clocks allocated by the data processing module for single-frame data processing.

4. The laser data processing method according to any one of claims 1 to 3, characterized in that: When the amount of the feature data to be identified meets the preset data amount condition, the grayscale values ​​of the feature data to be identified are sorted to obtain a feature sequence, including: When the number of the feature data to be identified meets the preset data amount condition, the grayscale value of each feature data to be identified is taken as the target grayscale value in turn, and the following process is iterated until the sequence number of the target grayscale value in the current comparison sequence is determined, and the target grayscale value is updated into the current comparison sequence according to the sequence number: Compare the target grayscale value with the midpoint value of the current comparison sequence, and determine the next comparison sequence according to the comparison result; the next comparison sequence is one of the subsequences of the current comparison sequence divided according to the midpoint value; Taking the next comparison sequence as a new current comparison sequence, and determining a new next comparison sequence according to a comparison result between the target gray value and a midpoint value of the new current comparison sequence; The initial comparison sequence is a set of values ​​whose values ​​are all initial values ​​and whose serial numbers increase in sequence.

5. The laser data processing method according to claim 1, characterized in that: Transmitting the laser characteristic data includes: The laser characteristic data is transmitted to an associated processor in a wireless manner.

6. The laser data processing method according to claim 1, characterized in that: When the amount of the feature data to be identified does not meet the preset data amount condition, the method further includes: All feature data to be identified are transmitted together.

7. A scanning device, characterized in that: include: Image acquisition module and data processing module; wherein: The image acquisition module is used to perform laser scanning on the scanned object to acquire feature data to be identified; The data processing module is used to execute the laser data processing method according to any one of claims 1 to 6.

8. A three-dimensional scanning system, characterized in that: include: A processor and a scanning device as claimed in claim 7; said processor and said scanning device are connected.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the laser data processing method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the laser data processing method according to any one of claims 1 to 6 are implemented.

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