A supervision control method and system for construction engineering measurement

Abstract

The present invention provides a supervision control method and system for construction engineering measurement, including: obtaining original point cloud data of a construction site through three-dimensional laser scanning; processing the original point cloud data through a cloud computing platform to generate a digital terrain model and a digital surface model, and comparing and analyzing them with engineering design drawings; based on the results of the comparative analysis, identifying construction deviations, and sending the construction deviations to the terminal device of the on-site supervisor in real time. In the present invention, the distributed computing resources of the cloud computing platform are used to efficiently process massive data, generate high-precision digital terrain models and digital surface models, and accurately compare and analyze them with engineering design drawings, so as to quickly obtain construction deviations, so that the supervision of construction engineering measurement is improved in efficiency, avoiding the current defects of difficulty in timely discovering construction deviations and avoiding affecting the progress of the project.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a supervision and control method and system for construction engineering measurement. Background Art

[0002] The measurement and supervision of traditional construction projects mainly rely on manual work, which is not only inefficient but also prone to human errors, leading to construction deviations. With the development of science and technology, especially the application of 3D laser scanning technology, it is possible to improve the accuracy and efficiency of construction project measurement. 3D laser scanning technology can quickly and accurately obtain the original point cloud data of the construction site, which contains detailed terrain and structural information of the construction site.

[0003] However, a large amount of raw point cloud data requires powerful computing power to process, which is difficult for traditional single-machine processing methods to handle. Due to insufficient computing power, traditional supervision methods often have information lags, and the discovery and correction of construction deviations often takes a long time, which not only affects the progress of the project, but also adds additional costs. Summary of the invention

[0004] The main purpose of the present invention is to provide a supervision control method and system for construction engineering measurement, aiming to overcome the current defect that it is difficult to find construction deviations in time.

[0005] To achieve the above object, the present invention provides a supervision and control method for construction engineering measurement, comprising the following steps:

[0006] Obtain raw point cloud data of construction sites through 3D laser scanning;

[0007] Processing the raw point cloud data through a cloud computing platform to generate a digital terrain model and a digital surface model, and comparing and analyzing the model with the engineering design drawings;

[0008] Based on the results of the comparative analysis, construction deviations are identified and sent in real time to the terminal device of the on-site supervisor.

[0009] Furthermore, after the construction deviation is sent to the terminal device of the on-site supervisor in real time, the following steps are included:

[0010] After receiving the construction deviation, the terminal device uses augmented reality technology on the terminal device to display the deviation position and size at the construction site;

[0011] The terminal device issues rectification instructions to the construction team and records the rectification process of the construction team.

[0012] Furthermore, the processing of the original point cloud data through a cloud computing platform to generate a digital terrain model and a digital surface model includes:

[0013] Safely send raw point cloud data to the cloud computing platform;

[0014] The cloud computing platform uses machine learning algorithms to classify the raw point cloud data to obtain ground objects and ground points;

[0015] Using all ground points, a continuous digital terrain model is generated based on an interpolation algorithm;

[0016] All ground objects and ground points are used to generate a digital surface model that expresses the true shape of the surface.

[0017] Furthermore, before the cloud computing platform classifies the original point cloud data using the machine learning algorithm, it also includes:

[0018] The original point cloud data is preprocessed; wherein the preprocessing includes removing noise points and outliers.

[0019] Furthermore, the securely sending the original point cloud data to the cloud computing platform includes:

[0020] Identifying non-sensitive point cloud data and sensitive point cloud data from the original point cloud data;

[0021] Generate a verification code based on the non-sensitive point cloud data;

[0022] Sending the non-sensitive point cloud data to the cloud computing platform; wherein the cloud computing platform generates a request code based on the non-sensitive point cloud data;

[0023] The request code returned by the cloud computing platform is received. If the request code is consistent with the verification code, the sensitive point cloud data is encrypted and the encrypted sensitive point cloud data is sent to the cloud computing platform.

[0024] Furthermore, after sending the non-sensitive point cloud data to the cloud computing platform, the method further includes:

[0025] If the request code returned by the cloud computing platform is not received, it is prohibited to send the sensitive point cloud data to the cloud computing platform;

[0026] or,

[0027] A request code returned by the cloud computing platform is received. If the request code is inconsistent with the verification code, sending the sensitive point cloud data to the cloud computing platform is prohibited.

[0028] Further, generating a verification code based on the non-sensitive point cloud data includes:

[0029] Performing a hash operation on the non-sensitive point cloud data to obtain a hash value;

[0030] The characters at the designated positions of the hash value are extracted and combined, and the obtained character combination is used as the verification code.

[0031] Furthermore, encrypting the sensitive point cloud data includes:

[0032] Performing a hash operation on the non-sensitive point cloud data to obtain a hash value;

[0033] Add the characters in the hash value to a preset blank data table one by one in sequence to generate a data array with multiple rows and columns;

[0034] Deforming the data array based on the verification code to obtain a deformed data array;

[0035] Based on the deformation data array, an encryption password is generated; and based on the encryption password, the sensitive point cloud data is encrypted.

[0036] Further, the step of deforming the data array based on the verification code to obtain a deformed data array includes:

[0037] De-duplication of the verification code is performed to obtain a de-duplication verification code; the number of characters in the de-duplication verification code is obtained, and a first number corresponding to the number of characters is obtained;

[0038] Obtain all divisors of the first number as the second number;

[0039] All characters in the designated column of the data array are shifted leftward to the beginning of the column, and other columns are shifted backward to fill the complete data array, thereby obtaining the deformed data array; wherein the designated column is the column corresponding to the second number.

[0040] Further, generating an encrypted password based on the deformed data array includes:

[0041] Acquire the sending time of the non-sensitive point cloud data, and generate a virtual clock based on the sending time;

[0042] Superimposing the virtual clock on the deformed data array; wherein the center of the virtual clock overlaps with the center of the deformed data array, and the upper and lower edges of the virtual clock are tangent to the upper and lower edges of the deformed data array;

[0043] Obtaining the characters respectively passed by the hour hand and the minute hand of the virtual clock on the deformed data array as target characters;

[0044] According to the order of the target characters on the deformed data array, the target characters are combined in sequence to obtain the encrypted password.

[0045] The present invention also provides a supervision control system for construction engineering measurement, comprising:

[0046] An acquisition unit, used for acquiring original point cloud data of a construction site through three-dimensional laser scanning;

[0047] An analysis unit, used to process the raw point cloud data through a cloud computing platform, generate a digital terrain model and a digital surface model, and compare and analyze them with the engineering design drawings;

[0048] The supervision unit is used to identify the construction deviation based on the result of the comparative analysis, and send the construction deviation to the terminal device of the on-site supervisor in real time.

[0049] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0050] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0051] The present invention provides a supervision control method and system for construction engineering measurement, including: obtaining original point cloud data of a construction site through three-dimensional laser scanning; processing the original point cloud data through a cloud computing platform to generate a digital terrain model and a digital surface model, and comparing and analyzing them with engineering design drawings; based on the results of the comparative analysis, identifying construction deviations, and sending the construction deviations to the terminal device of the on-site supervisor in real time. In the present invention, the distributed computing resources of the cloud computing platform are used to efficiently process massive data, generate high-precision digital terrain models and digital surface models, and compare and analyze them accurately with engineering design drawings, so as to quickly obtain construction deviations, so that the supervision of construction engineering measurement is improved in efficiency, avoiding the current defects of difficulty in timely discovering construction deviations and avoiding affecting the progress of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the steps of a supervision control method for construction engineering measurement in one embodiment of the present invention;

[0053] Figure 2 It is a structural block diagram of a supervision control system for construction engineering measurement in one embodiment of the present invention;

[0054] Figure 3It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0055] The implementation, functional features and advantages of the present invention will be further described in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] Reference Figure 1 In one embodiment of the present invention, a supervision and control method for construction engineering measurement is provided, comprising the following steps:

[0058] Step S1, obtaining original point cloud data of the construction site through three-dimensional laser scanning;

[0059] Step S2, processing the original point cloud data through a cloud computing platform to generate a digital terrain model and a digital surface model, and comparing and analyzing them with the engineering design drawings;

[0060] Step S3, based on the results of the comparative analysis, the construction deviation is identified, and the construction deviation is sent to the terminal device of the on-site supervisor in real time.

[0061] In this embodiment, it specifically includes:

[0062] 3D laser scanning to obtain raw point cloud data: First, use 3D laser scanning technology to collect raw point cloud data on the construction site. 3D laser scanning is a non-contact measurement technology that can quickly and accurately capture the 3D shape and spatial position information of an object. On a construction site, this usually involves scanning the terrain and built structures of the entire site to generate a dense point cloud data set. These data sets contain the location coordinates of each point and can be used to reconstruct a 3D model of the construction site.

[0063] Cloud computing platform processes point cloud data: Next, the large amount of collected point cloud data is processed through the cloud computing platform. The cloud computing platform provides powerful computing power and storage resources, making large-scale data processing possible. At this stage, the original point cloud data is converted into two key digital models: digital terrain model (DTM) and digital surface model (DSM). The digital terrain model reflects the natural form of the ground, while the digital surface model contains the shape and height information of all objects on the ground. These two models are compared and analyzed with the engineering design drawings to verify whether the actual construction meets the design requirements.

[0064] Comparative analysis and identification of construction deviations: By comparing the digital terrain model, digital surface model and engineering design drawings, any construction deviations can be automatically identified. These deviations may include but are not limited to ground flatness, structural dimensions, position deviations, etc. Traditional supervision methods often rely on manual inspections, which are inefficient and prone to errors. This technical solution can quickly and accurately identify deviations through automated analysis, improving the efficiency and accuracy of supervision work.

[0065] Real-time deviation information: Once construction deviations are identified, relevant information is sent to the terminal device of the on-site supervisor in real time. This means that supervisors can receive alerts about deviations immediately without waiting for regular reports or inspections. Real-time communication ensures that problems can be paid attention to and handled in a timely manner, helping to avoid potential safety hazards and reduce the cost increase caused by delays.

[0066] In summary, the distributed computing resources of the cloud computing platform can be used to efficiently process massive data, generate high-precision digital terrain models and digital surface models, and accurately compare and analyze them with the engineering design drawings, so as to quickly obtain construction deviations, thereby improving the efficiency of construction engineering measurement supervision, avoiding the current defects of construction deviations that are difficult to discover in time and avoiding affecting the progress of the project.

[0067] The above technical solution provides a more efficient and accurate solution for construction project supervision by integrating 3D laser scanning, cloud computing and real-time communication technology. It not only improves the transparency of the construction process, but also promotes the optimization of site management and quality control, which ultimately helps to ensure the smooth progress and on-time delivery of construction projects.

[0068] In one embodiment, after the construction deviation is sent to the terminal device of the on-site supervisor in real time, the following steps are included:

[0069] After receiving the construction deviation, the terminal device uses augmented reality technology on the terminal device to display the deviation position and size at the construction site;

[0070] The terminal device issues rectification instructions to the construction team and records the rectification process of the construction team.

[0071] In this embodiment, it specifically includes:

[0072] When the terminal device receives the construction deviation information, it uses augmented reality technology to overlay virtual information in the supervisor's field of vision, directly pointing to the actual deviation position and size at the construction site. Augmented reality technology can integrate digital information with the real world, allowing supervisors to see the specific location of the deviation, as well as detailed information such as the size and shape of the deviation in a real environment. This method greatly facilitates the on-site inspection work of supervisors, reduces the time to find the deviation location, and improves the intuitiveness and accuracy of understanding the deviation.

[0073] The terminal device is not only used to display deviations, but also as a communication and command tool. Supervisors can issue rectification instructions directly to the construction team through the terminal device, clearly indicating the deviations that need to be corrected and their specific requirements. This instant communication capability ensures the rapid transmission of instructions and avoids information delays or misunderstandings that may exist in traditional methods.

[0074] In addition, the terminal device also has a recording function, which can record the rectification process of the construction team. This includes taking photos, recording videos, saving relevant documents, etc., to form a complete rectification file. These records not only serve as evidence of the rectification effect, but also facilitate subsequent quality review and responsibility tracing. At the same time, the records of the rectification process also provide valuable data for project management, which can be used to analyze the causes of deviations, optimize the construction process, and prevent similar problems from occurring in the future.

[0075] Comprehensive benefits: The above technical solution combines augmented reality technology and the application of mobile terminal devices to achieve real-time, visual and traceable construction deviation management, greatly improving the efficiency and quality of supervision work. It not only simplifies the process of deviation identification and rectification, but also enhances communication and collaboration among all parties in the project, which is of great significance to improving the management level of the entire construction project.

[0076] In summary, the above technical solutions demonstrate how digital transformation has profoundly changed the supervision practices in the construction industry and promoted the industry to develop in a more intelligent and efficient direction through technological innovation.

[0077] In one embodiment, the processing of the original point cloud data through a cloud computing platform to generate a digital terrain model and a digital surface model includes:

[0078] Safely send raw point cloud data to the cloud computing platform;

[0079] The cloud computing platform uses machine learning algorithms to classify the raw point cloud data to obtain ground objects and ground points;

[0080] Using all ground points, a continuous digital terrain model is generated based on an interpolation algorithm;

[0081] All ground objects and ground points are used to generate a digital surface model that expresses the true shape of the surface.

[0082] In this embodiment, it specifically includes:

[0083] The original point cloud data usually contains a large amount of spatial coordinate information collected from LiDAR, drones or other sensors. These data are huge and sensitive. The data is transmitted to the cloud computing platform through a secure network connection to ensure the integrity and security of the data during the transmission process. The cloud computing platform has become an ideal place to process large-scale point cloud data due to its powerful computing power and storage resources.

[0084] Once the data arrives at the cloud computing platform, machine learning algorithms come into play. These algorithms are able to automatically identify and distinguish different categories in the point cloud, such as vegetation, buildings, roads, and other objects, as well as true ground points. The classification process is a key step in generating accurate DTMs and DSMs, as it allows the separation of ground and non-ground features from complex point cloud data.

[0085] After completing the point cloud classification, all points marked as ground are used to construct a digital terrain model. DTM is a three-dimensional grid model that represents the ground elevation. It only reflects the morphology of the bare ground and excludes the influence of non-ground objects such as vegetation and buildings. The generation of DTM usually relies on an interpolation algorithm, which estimates the terrain features of adjacent unmeasured areas based on the height information of known ground points, thereby generating a continuous and smooth terrain surface.

[0086] The digital surface model contains the elevation information of all objects on the surface (such as trees, buildings, etc.), reflecting the true shape of the surface. When generating DSM, in addition to ground points, all points classified as land features are also included. DSM can provide richer terrain details and is suitable for scenarios that need to consider the height changes of surface cover, such as urban planning, disaster assessment and other fields.

[0087] By migrating the processing of raw point cloud data to the cloud computing platform and applying advanced machine learning algorithms, this technical solution can quickly and accurately generate DTM and DSM, greatly improving the efficiency and accuracy of geospatial data analysis. This has important application value in many fields such as land management, environmental monitoring, disaster prediction, and urban planning, and has promoted the development and application of geographic information system (GIS) technology.

[0088] In summary, the above technical solutions, through the combination of cloud computing and machine learning, have opened up a new way to process large-scale point cloud data, providing strong support for the deep mining and intelligent analysis of geospatial information.

[0089] In one embodiment, before the cloud computing platform classifies the original point cloud data using a machine learning algorithm, the cloud computing platform further includes:

[0090] The original point cloud data is preprocessed; wherein the preprocessing includes removing noise points and outliers.

[0091] In one embodiment, securely sending the original point cloud data to the cloud computing platform includes:

[0092] Identifying non-sensitive point cloud data and sensitive point cloud data from the original point cloud data;

[0093] Generate a verification code based on the non-sensitive point cloud data;

[0094] Sending the non-sensitive point cloud data to the cloud computing platform; wherein the cloud computing platform generates a request code based on the non-sensitive point cloud data;

[0095] The request code returned by the cloud computing platform is received. If the request code is consistent with the verification code, the sensitive point cloud data is encrypted and the encrypted sensitive point cloud data is sent to the cloud computing platform.

[0096] In this embodiment, it specifically includes:

[0097] Raw point cloud data contains various types of information, some of which involve privacy or business secrets and are therefore considered sensitive data. First, it is necessary to identify and separate non-sensitive data and sensitive data from the raw point cloud data. Non-sensitive data generally refers to those parts that do not contain personal identity information or key business data, while sensitive data includes environmental characteristics, engineering layout, specific location information, identity information of specific individuals, etc.

[0098] Verification code generation: Generate a verification code based on non-sensitive point cloud data. This verification code can be a string of characters obtained by performing some algorithm processing (such as hash operation) on non-sensitive data, which is used in the subsequent verification process.

[0099] Request code generation and verification: When non-sensitive point cloud data is sent to the cloud computing platform, the cloud computing platform will generate a request code based on the received non-sensitive data. Ideally, if both parties use the same algorithm, the verification code generated locally and the request code generated by the cloud computing platform should be consistent.

[0100] Consistency check: The local system receives the request code from the cloud computing platform and checks whether it matches the verification code generated previously. If the two are consistent, this indicates that the cloud computing platform has correctly and securely received the non-sensitive point cloud data.

[0101] Once the request code of the cloud computing platform is confirmed to be consistent with the local verification code, indicating that the communication channel between the two parties is secure, the sensitive point cloud data can be encrypted. Encryption is an important means of protecting data from unauthorized access, ensuring that even if the data is intercepted during transmission, it cannot be interpreted by a third party. The encrypted sensitive point cloud data is then sent to the cloud computing platform, and only the recipient with the correct decryption key can read the true content of the data.

[0102] In this embodiment, the above technical solution effectively improves the security of data transmission by dividing the point cloud data into non-sensitive and sensitive parts, combining the verification mechanism of verification code and request code, and encrypted transmission of sensitive data. This not only protects the data from potential malicious attacks, but also ensures the integrity of the data, allowing the cloud computing platform to further process and analyze the point cloud data while ensuring data security. This solution is particularly important for industries involving a large amount of sensitive geospatial information, which helps to build user trust and promote the legal and compliant use of data.

[0103] In one embodiment, after sending the non-sensitive point cloud data to the cloud computing platform, the method further includes:

[0104] If the request code returned by the cloud computing platform is not received, it is prohibited to send the sensitive point cloud data to the cloud computing platform;

[0105] or,

[0106] A request code returned by the cloud computing platform is received. If the request code is inconsistent with the verification code, sending the sensitive point cloud data to the cloud computing platform is prohibited.

[0107] In this embodiment, it specifically includes:

[0108] After sending the non-sensitive data, it enters the waiting state, expecting to receive the request code from the cloud computing platform. The request code is generated based on the processing of the non-sensitive point cloud data by the cloud computing platform, and it should match the verification code previously generated by the local.

[0109] Once the request code is received, the verification code and the request code are checked for consistency. This is to confirm whether the cloud computing platform has accurately received and processed the non-sensitive data, and also to verify the security of the communication channel.

[0110] Request code not received: If the request code returned by the cloud computing platform is not received within the preset time, it means that there is a security risk or communication failure during the data transmission process. In this case, the sending of sensitive point cloud data will be automatically prohibited to avoid potential data leakage risks.

[0111] Request code and verification code are inconsistent: Even if the request code is received, if it is found that the request code is inconsistent with the previously generated verification code, this also indicates that there may be anomalies or security threats in the data transmission process. The transmission of sensitive data will be prohibited to prevent sensitive information from being sent under unsafe conditions.

[0112] The above control measures reflect the principle of least privilege and the concept of zero-trust network in data security, that is, strict control and verification are carried out at every stage of data transmission, and the security of any link is not compromised by default. Through such a design, sensitive information can be effectively prevented from being transmitted without authorization, thereby greatly reducing the risk of data leakage.

[0113] In summary, the above technical solution provides additional security for the transmission of sensitive point cloud data by setting conditions for receiving request codes and comparing verification codes. This not only enhances the reliability of the entire data transmission process, but also meets the high standards for data protection in the current network security environment.

[0114] In one embodiment, generating a verification code based on the non-sensitive point cloud data includes:

[0115] Performing a hash operation on the non-sensitive point cloud data to obtain a hash value;

[0116] The characters at the designated positions of the hash value are extracted and combined, and the obtained character combination is used as the verification code.

[0117] In this embodiment, it specifically includes:

[0118] First, the hash algorithm is used to operate on the non-sensitive point cloud data to generate a fixed-length hash value. The hash algorithm is a one-way encryption technology. Its main feature is that no matter how the size of the original data changes, the length of the output hash value remains unchanged, and even if the original data has only a slight change, the hash value will produce a significant difference, which makes the hash value a unique identifier of the data fingerprint.

[0119] After obtaining the hash value, the next step is to extract the characters at specific positions from the hash value. The specified positions here can be pre-set rules, such as extracting the first few digits, the last few digits, or certain intervals of the hash value. The selection of these positions should follow a certain degree of randomness and complexity to increase the difficulty of cracking.

[0120] Finally, the characters extracted from the hash value are combined to form a verification code. This verification code has the following characteristics:

[0121] Uniqueness: Since the hash values ​​are generated from non-sensitive point cloud data, which itself contains rich details, even small differences will result in different hash values ​​and thus different verification codes.

[0122] Unpredictability: Since the extracted character positions are pre-set but kept secret, and the hash value generation is highly random, it is difficult for a third party to predict or reverse deduce the specific content of the verification code.

[0123] One-time use: To enhance security, a verification code usually becomes invalid after one use, and a new verification code needs to be generated each time data is transmitted.

[0124] In this embodiment, the above technical solution of generating a verification code based on non-sensitive point cloud data has the following advantages:

[0125] Data security: Since only non-sensitive point cloud data is used to generate the verification code, even if the verification code is intercepted, the attacker cannot obtain sensitive information from it.

[0126] Anti-tampering capability: The generation of hash values ​​depends on the integrity and consistency of point cloud data. Any modification to the original data will cause the hash value to change, thereby invalidating the original verification code. This helps to detect the integrity of the data during transmission.

[0127] Efficiency: The hashing and character extraction processes are relatively fast and will not significantly affect data transmission efficiency.

[0128] To sum up, the above technical solution generates a verification code by combining hash operation and specific character extraction method. The technical solution provides a simple and effective data transmission security control method, which can ensure data security without affecting the efficiency of data transmission and user experience.

[0129] In one embodiment, encrypting the sensitive point cloud data includes:

[0130] Performing a hash operation on the non-sensitive point cloud data to obtain a hash value;

[0131] Add the characters in the hash value to a preset blank data table one by one in sequence to generate a data array with multiple rows and columns;

[0132] Deforming the data array based on the verification code to obtain a deformed data array;

[0133] Based on the deformation data array, an encryption password is generated; and based on the encryption password, the sensitive point cloud data is encrypted.

[0134] In this embodiment, it specifically includes:

[0135] First, the non-sensitive point cloud data is hashed to generate a fixed-length hash value through a hash algorithm. The hash algorithm ensures that even if the input data is slightly different, the output hash value will be significantly different, which provides a high-security foundation for subsequent steps.

[0136] Next, each character in the hash value is added one by one to a preset blank data table to form a multi-row and multi-column data array. This array can be viewed as a two-dimensional matrix, where each element is filled with the characters in the hash value. The structure and size of the data array depends on the specific application requirements and the length of the hash value.

[0137] After the data array is generated, the data array is deformed using the previously generated verification code. Here, the verification code affects the position adjustment or value change of the elements in the array, for example, by changing the arrangement order of the array elements by rotation, flipping, permutation, etc., or by converting the values ​​of the characters in the array (such as ASCII code conversion). This step enhances the randomness and complexity of the data array, adding an extra layer of protection for the subsequent encryption process.

[0138] Based on the transformed data array, the final encryption password is generated through an algorithm (which can be a further mathematical or logical operation on the data array). This password is usually the key used in the encryption algorithm, and its strength and complexity are directly related to the security level of the encrypted data.

[0139] Finally, the sensitive point cloud data is encrypted using the encryption password generated above. This step can use a variety of encryption algorithms, such as symmetric encryption (such as AES), asymmetric encryption (such as RSA), or other algorithms specifically designed for data encryption. The encryption process converts sensitive point cloud data into an incomprehensible ciphertext form, ensuring that even if the data is intercepted during transmission, attackers cannot easily decipher its true content.

[0140] Advantages of the above technical solutions:

[0141] Security: By combining hash operations, data array deformation and encryption algorithms, this technical solution can generate complex encryption passwords, effectively improving the protection level of sensitive point cloud data.

[0142] Flexibility: The construction and deformation process of the data array allows adjustments based on actual application needs, such as changing the size of the array, the deformation method, or the encryption algorithm to adapt to different security requirements.

[0143] Reliability: The determinism of hash values ​​and the repeatability of data array deformation ensure the consistency of the encrypted password, and the integrity and accuracy of the data can be maintained even during multiple encryption and decryption processes.

[0144] In summary, the above technical solution provides an innovative encryption mechanism, which uses non-sensitive point cloud data to generate encryption passwords, thereby achieving effective protection of sensitive data and meeting the growing security needs in modern data processing and transmission.

[0145] In one embodiment, the step of deforming the data array based on the verification code to obtain a deformed data array includes:

[0146] De-duplication of the verification code is performed to obtain a de-duplication verification code; the number of characters in the de-duplication verification code is obtained, and a first number corresponding to the number of characters is obtained;

[0147] Obtain all divisors of the first number as the second number;

[0148] All characters in the designated column of the data array are shifted leftward to the beginning of the column, and other columns are shifted backward to fill the complete data array, thereby obtaining the deformed data array; wherein the designated column is the column corresponding to the second number.

[0149] In this embodiment, it specifically includes:

[0150] First, the original verification code is deduplicated, that is, repeated characters are removed, and a unique character sequence is retained to obtain a deduplicated verification code. Then, the number of characters remaining in the deduplicated verification code is calculated, and this number is the first number mentioned above.

[0151] Next, find all the factors of the first number, that is, all the positive integers that can divide the first number. These factors are collectively called the second number. For example, if the first number is 6, then its factors are 1, 2, 3, and 6.

[0152] Transformation of the data array:

[0153] In this step, select one or all values ​​of the second number (in this embodiment, all divisors are used as numbers corresponding to the designated column) and regard it as the designated column in the data array that needs special processing. Then, transform the data array according to the following rules:

[0154] Shift all characters in the specified column of the data array to the left to the leftmost position of the column, that is, the first position of the column.

[0155] Shift the columns in the data array except the specified column backward to fill the vacancies left by the characters in the specified column moving forward, thus ensuring that the data array remains intact.

[0156] After such deformation processing, the originally ordered data array will become a new array that appears to be randomly distributed, namely the deformed data array.

[0157] Advantages of the above technical solutions:

[0158] Increased complexity: By introducing the steps of deduplication and factor acquisition of the verification code, and the subsequent deformation of the data array, the complexity of the entire encryption process is significantly increased, making it more difficult for attackers to predict or crack the encryption password.

[0159] Utilize mathematical properties: The concept of factors in mathematics is used to determine the way in which the data array is deformed. This not only increases the unpredictability of the encryption algorithm, but also reflects the important application of mathematics in cryptography.

[0160] Security improvement: By dynamically selecting designated columns for special transformation, each encryption process may produce different results, greatly increasing the size of the password space and improving the security of the encryption system.

[0161] Flexibility and scalability: The selection of verification codes, deduplication, and determination of designated columns can all be flexibly adjusted according to actual conditions, making the technical solution highly scalable and adaptable, and able to meet the requirements for encryption strength in different scenarios.

[0162] Simple operation: Although the concept behind the algorithm involves relatively complex mathematical principles, the actual operation steps are relatively intuitive and easy to implement and maintain.

[0163] In summary, the above technical solution provides an innovative and secure method for generating encryption passwords by cleverly combining verification code processing, mathematical characteristics and data array deformation. It can not only effectively improve the complexity and security of the encryption process, but also maintain the simplicity and scalability of the algorithm implementation. It is a direction worthy of research and application in modern data encryption technology.

[0164] In one embodiment, generating an encrypted password based on the deformed data array comprises:

[0165] Acquire the sending time of the non-sensitive point cloud data, and generate a virtual clock based on the sending time;

[0166] Superimposing the virtual clock on the deformed data array; wherein the center of the virtual clock overlaps with the center of the deformed data array, and the upper and lower edges of the virtual clock are tangent to the upper and lower edges of the deformed data array;

[0167] Obtaining the characters respectively passed by the hour hand and the minute hand of the virtual clock on the deformed data array as target characters;

[0168] According to the order of the target characters on the deformed data array, the target characters are combined in sequence to obtain the encrypted password.

[0169] In this embodiment, it specifically includes:

[0170] First, a virtual clock is created based on the time point when the non-sensitive point cloud data is sent. The generation of the virtual clock is essentially to convert the time information into a visual clock model, the purpose of which is to prepare for the subsequent interaction with the deformed data array.

[0171] Next, the generated virtual clock is superimposed on the deformed data array, ensuring that the center of the clock completely coincides with the center of the data array, and the upper and lower edges of the clock are exactly tangent to the upper and lower edges of the data array. This precise superposition method ensures the accurate alignment of the clock pointer with the characters in the data array, so that specific character information can be extracted from it.

[0172] By analyzing the direction of the hour and minute hands of the virtual clock on the deformed data array, the characters they pass through are found. These characters are called target characters. The target characters here are the characters at the intersection of the hour and minute hands with the data array at a specific time. They are selected because of direct interaction with the virtual clock.

[0173] Finally, according to the natural order of the target characters in the deformed data array, these characters are combined in sequence to form the final encrypted password. The order here refers to the order of arrangement of the characters in the data array, not the movement trajectory of the clock hands.

[0174] Advantages of the above technical solutions

[0175] Introduction of the time dimension: By converting the sending time into a virtual clock, the technical solution cleverly integrates the time dimension into the password generation process, making the generated password closely related to a specific time point, increasing the dynamics and timeliness of the password.

[0176] Combination of visualization and mathematics: By utilizing the visual superposition of a virtual clock and a deformed data array, combined with the coordinate positioning principle in mathematics, the technical solution achieves the goal of accurately extracting characters from a two-dimensional data array, improving the accuracy and uniqueness of password generation.

[0177] Enhanced security: Since password generation relies on real-time changing time information and randomly deformed data arrays, this scheme greatly improves the unpredictability of passwords and enhances the overall security of the system.

[0178] High dynamism and personalization: Each generated password is associated with a specific point in time and a specific data array state. Even with the same initial conditions, the passwords generated at different points in time will be completely different, ensuring the personalization and dynamism of the password.

[0179] Easy to implement and scalable: Although it involves the interaction between the virtual clock and the data array, its implementation logic is clear and easy to program. At the same time, the design of the virtual clock or the deformation rules of the data array can be flexibly adjusted according to needs, showing good scalability and adaptability.

[0180] In summary, the above technical solution proposes an innovative encryption password generation mechanism by integrating the time dimension, visualization elements and mathematical operations. It not only effectively improves the security and personalization level of the password, but also demonstrates the possibility of combining multidisciplinary knowledge for technological innovation in the field of cryptography. For information encryption systems seeking higher security standards, this solution undoubtedly provides valuable reference and inspiration.

[0181] Reference Figure 2 In another embodiment of the present invention, a supervision control system for construction engineering measurement is provided, comprising:

[0182] An acquisition unit, used for acquiring original point cloud data of a construction site through three-dimensional laser scanning;

[0183] An analysis unit, used to process the raw point cloud data through a cloud computing platform, generate a digital terrain model and a digital surface model, and compare and analyze them with the engineering design drawings;

[0184] The supervision unit is used to identify the construction deviation based on the result of the comparative analysis, and send the construction deviation to the terminal device of the on-site supervisor in real time.

[0185] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0186] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0187] Those skilled in the art will understand that Figure 3The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0188] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0189] In summary, a supervision control method and system for construction engineering measurement provided in an embodiment of the present invention includes: obtaining original point cloud data of a construction site through three-dimensional laser scanning; processing the original point cloud data through a cloud computing platform to generate a digital terrain model and a digital surface model, and comparing and analyzing them with engineering design drawings; based on the results of the comparative analysis, identifying construction deviations, and sending the construction deviations to the terminal device of the on-site supervisor in real time. In the present invention, the distributed computing resources of the cloud computing platform are used to efficiently process massive data, generate high-precision digital terrain models and digital surface models, and accurately compare and analyze them with engineering design drawings, so as to quickly obtain construction deviations, so that the supervision of construction engineering measurement is improved in efficiency, avoiding the current defects of difficulty in timely discovering construction deviations and avoiding affecting the progress of the project.

[0190] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0191] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0192] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A supervision and control method for construction engineering measurement, characterized in that: The following steps are involved: Obtain raw point cloud data of construction sites through 3D laser scanning; Processing the raw point cloud data through a cloud computing platform to generate a digital terrain model and a digital surface model, and comparing and analyzing the model with the engineering design drawings; Based on the results of the comparative analysis, the construction deviation is identified and the construction deviation is sent to the terminal device of the on-site supervisor in real time; The processing of the original point cloud data through the cloud computing platform to generate a digital terrain model and a digital surface model includes: Safely send raw point cloud data to the cloud computing platform; The cloud computing platform uses machine learning algorithms to classify the raw point cloud data to obtain ground objects and ground points; Using all ground points, a continuous digital terrain model is generated based on an interpolation algorithm; Using all the objects and ground points, a digital surface model that expresses the true shape of the surface is generated; The securely sending the original point cloud data to the cloud computing platform includes: Identifying non-sensitive point cloud data and sensitive point cloud data from the original point cloud data; Generate a verification code based on the non-sensitive point cloud data; Sending the non-sensitive point cloud data to the cloud computing platform; wherein the cloud computing platform generates a request code based on the non-sensitive point cloud data; The request code returned by the cloud computing platform is received. If the request code is consistent with the verification code, the sensitive point cloud data is encrypted and the encrypted sensitive point cloud data is sent to the cloud computing platform.

2. The supervision and control method for construction engineering measurement according to claim 1, characterized in that: After the construction deviation is sent to the terminal device of the on-site supervisor in real time, it includes: After receiving the construction deviation, the terminal device uses augmented reality technology on the terminal device to display the deviation position and size at the construction site; The terminal device issues rectification instructions to the construction team and records the rectification process of the construction team.

3. The supervision and control method for construction engineering measurement according to claim 1 is characterized in that: Before the cloud computing platform classifies the original point cloud data using a machine learning algorithm, the cloud computing platform also includes: The original point cloud data is preprocessed; wherein the preprocessing includes removing noise points and outliers.

4. The supervision and control method for construction engineering measurement according to claim 1, characterized in that: After sending the non-sensitive point cloud data to the cloud computing platform, the method further includes: If the request code returned by the cloud computing platform is not received, it is prohibited to send the sensitive point cloud data to the cloud computing platform; or, A request code returned by the cloud computing platform is received. If the request code is inconsistent with the verification code, sending the sensitive point cloud data to the cloud computing platform is prohibited.

5. The supervision and control method for construction engineering measurement according to claim 1, characterized in that: Generating a verification code based on the non-sensitive point cloud data includes: Performing a hash operation on the non-sensitive point cloud data to obtain a hash value; The characters at the designated positions of the hash value are extracted and combined, and the obtained character combination is used as the verification code.

6. The supervision and control method for construction engineering measurement according to claim 1, characterized in that: Encrypting the sensitive point cloud data includes: Performing a hash operation on the non-sensitive point cloud data to obtain a hash value; Add the characters in the hash value to a preset blank data table one by one in sequence to generate a data array with multiple rows and columns; Deforming the data array based on the verification code to obtain a deformed data array; Based on the deformation data array, an encryption password is generated; and based on the encryption password, the sensitive point cloud data is encrypted.

7. The supervision and control method for construction engineering measurement according to claim 6 is characterized in that: The step of deforming the data array based on the verification code to obtain a deformed data array includes: De-duplication of the verification code is performed to obtain a de-duplication verification code; the number of characters in the de-duplication verification code is obtained, and a first number corresponding to the number of characters is obtained; Obtain all divisors of the first number as the second number; All characters in the designated column of the data array are shifted leftward to the beginning of the column, and other columns are shifted backward to fill the complete data array, thereby obtaining the deformed data array; wherein the designated column is the column corresponding to the second number.

8. A supervision control system for construction engineering measurement, characterized in that: include: An acquisition unit, used for acquiring original point cloud data of a construction site through three-dimensional laser scanning; An analysis unit, used to process the raw point cloud data through a cloud computing platform, generate a digital terrain model and a digital surface model, and compare and analyze them with the engineering design drawings; A supervision unit, used to identify construction deviations based on the results of comparative analysis, and send the construction deviations to a terminal device of an on-site supervisor in real time; The processing of the original point cloud data through the cloud computing platform to generate a digital terrain model and a digital surface model includes: Safely send raw point cloud data to the cloud computing platform; The cloud computing platform uses machine learning algorithms to classify the raw point cloud data to obtain ground objects and ground points; Using all ground points, a continuous digital terrain model is generated based on an interpolation algorithm; Using all the objects and ground points, a digital surface model that expresses the true shape of the surface is generated; The securely sending the original point cloud data to the cloud computing platform includes: Identifying non-sensitive point cloud data and sensitive point cloud data from the original point cloud data; Generate a verification code based on the non-sensitive point cloud data; Sending the non-sensitive point cloud data to the cloud computing platform; wherein the cloud computing platform generates a request code based on the non-sensitive point cloud data; The request code returned by the cloud computing platform is received. If the request code is consistent with the verification code, the sensitive point cloud data is encrypted and the encrypted sensitive point cloud data is sent to the cloud computing platform.

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