A method and system for predicting settlement of high-rise buildings based on chaotic system

Abstract

The present application relates to the field of engineering modeling technology, and in particular to a method and system for predicting the settlement of high-rise buildings based on a chaotic system, obtaining the current pile foundation data and completion effect data of the high-rise building, and constructing a continuous chaotic system; screening the effective numerical construction period of the high-rise building, and using the dual-construction period Lyapunov index diagram method to analyze the dynamic behavior changes of the three-dimensional chaotic system under different unknown construction period conditions; expanding the three-dimensional system into a four-dimensional state and applying it to the DNA high-rise building construction real-scene picture encryption system; inputting the continuous system according to the data of the specific high-rise building data, selecting and determining the actual rheological model construction period, and combining the instantaneous settlement of the pile foundation to obtain the corresponding relationship between the settlement of the high-rise building and time. The present invention is specifically aimed at high-rise buildings, and by changing parameters to compare the post-construction settlement under different conditions, the construction period is shortened and the construction cost is reduced, and the problem of difficult analysis and prediction of settlement of high-rise buildings is effectively solved.

Description

Technical Field

[0001] The present application relates to the technical field of engineering modeling, and in particular to a method and system for predicting settlement of high-rise buildings based on a chaotic system. Background Art

[0002] The delayed feedback scheme in the chaotic system construction method is a type of construction scheme that generates chaotic behavior by introducing time delay in the system feedback. This scheme is usually applied to biological, physical and engineering systems. Its basic idea is to use the delayed feedback term to make the current state of the system not only depend on the current input, but also be affected by the past state. However, the delayed feedback system pays more attention to emphasizing the dependence of the system state on the past state, is suitable for systems with time-delayed feedback, and is usually used in deterministic systems. Another random perturbation method usually introduces random perturbations or noise into the system to make the system exhibit chaotic characteristics. This type of method is not a strictly deterministic system, but can exhibit complex dynamic behaviors in a statistical sense. The random perturbation method emphasizes the complex behavior of the system under random perturbations by introducing random noise, and is suitable for random systems.

[0003] The purpose of building settlement observation is to check whether the settlement index exceeds the range allowed by the building design. If it exceeds the limit, the danger index of the building will increase, which will threaten people's lives in serious cases. With the increasing world population, the increasingly serious housing problem and the emergence of high-rise buildings, in order to ensure the normal use and safety of buildings and provide reliable observation data and corresponding settlement construction cycles for later construction, the necessity of building settlement prediction is becoming increasingly obvious. At present, the commonly used prediction models include regression models, time series models, etc. Most of these models cannot accurately simulate the changes in the settlement curve, resulting in poor accuracy of model prediction, which is difficult to meet the requirements in actual engineering. Summary of the invention

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] According to a first aspect of the present invention, the present invention claims a method for predicting settlement of a high-rise building based on a chaotic system, comprising:

[0006] S1, obtain the current pile foundation data and completion effect data of high-rise buildings, construct a continuous chaotic system, use state variables combined with the amplitude matrix to construct a unified form, and verify the feasibility of the completion effect data through the Lyapunov exponent;

[0007] S2, using a three-dimensional chaotic system with a single nonlinear structure, screened the effective numerical construction period of high-rise buildings through the Lyapunov index, and used bifurcation diagrams, attractor diagrams, 0-1 tests, and dual-construction period Lyapunov index diagrams to analyze the dynamic behavior changes of the three-dimensional chaotic system under different unknown construction period conditions;

[0008] S3, expand the three-dimensional system into a four-dimensional state and demonstrate the complex characteristics of the system at different complexities to verify the applicability of the high-rise building construction;

[0009] S4, applying the constructed three-dimensional and four-dimensional continuous systems to the DNA high-rise building construction real scene picture encryption system, and analyzing the robustness and security of the DNA high-rise building construction real scene picture encryption system;

[0010] S5, inputting the three-dimensional and four-dimensional continuous systems according to the data of specific high-rise building materials, selecting and determining the actual rheological model construction period, and combining the instantaneous settlement of the pile foundation to obtain the corresponding relationship between the settlement of the high-rise building and time.

[0011] Furthermore, in S1, from the perspective of combining state variables, using the amplitude matrix to be determined, adopting a unified form of construction, and verifying the feasibility of the completion effect data through the Lyapunov exponent, a continuous chaotic system is constructed, including:

[0012] Set the minimum and maximum values ​​of the construction period and define the initial construction period range;

[0013] Within the set construction period range, a set of construction periods is randomly selected as the starting value, and the corresponding initial Lyapunov exponent is calculated;

[0014] In each iteration, the scope of the construction period is narrowed to find a construction period that meets the construction requirements;

[0015] After each construction cycle range is updated, a construction cycle is randomly selected within the new range and its corresponding Lyapunov index is calculated. If this index is less than zero, the construction cycle is considered unreasonable and the attempt is terminated. Otherwise, other construction cycles are verified.

[0016] When a construction cycle that meets the requirements is found, that is, a construction cycle with a Lyapunov index greater than or equal to zero, the construction cycle is marked as reasonable and the screening process ends;

[0017] The filtered construction period interval is output as the construction period range.

[0018] Furthermore, the three-dimensional chaotic system with a single nonlinear structure is used in S2 to screen the effective numerical construction period of high-rise buildings through the Lyapunov index, and the bifurcation diagram, attractor diagram, 0-1 test and double construction period Lyapunov index diagram method are used to analyze the changes in the dynamic behavior of the system under different unknown construction period conditions, and also includes:

[0019] Randomly select initial conditions within the range of values ​​that meet the conditions, use bifurcation diagrams and Lyapunov index diagrams for comparative analysis, and observe the dynamic behavior of the system model;

[0020] Select the initial value as (0.1, 0.1, 0.1), and select the first construction period selection range as the construction period selection range. , the second construction period selection range , the third construction period selection range ; The dynamic behavior of the system under the influence of the construction period is analyzed through bifurcation diagrams and Lyapunov index diagrams;

[0021] Starting from the construction period, the system model structure is constructed, and the random amplitude is set as a fixed reference condition to obtain the specific simulation results of the unknown construction period for the dynamic behavior of the system model. The set amplitude includes the first amplitude , the second amplitude , the third amplitude , the fourth amplitude , the fifth amplitude , the sixth amplitude They are:

[0022] ;

[0023] The normal deviation percentage range of the unknown construction period is set as , and obtain the bifurcation diagram and attractor diagram under the corresponding construction period conditions.

[0024] Furthermore, the three-dimensional system is expanded into a four-dimensional state in S3, and the complex characteristics of the system under different complexities are demonstrated to verify the applicability of the proposed construction method, and also includes:

[0025] Generate time series about the system and the evolution of state variables over time;

[0026] Select the delay time and embedding dimension, and choose the appropriate delay time according to the time series. and embedding dimension , delay time represents the time interval between each element in the delay vector, embedded in the dimension represents the number of state variables used to construct the delay vector;

[0027] Create delay vectors. For the time series generated by the system, construct a delay vector sequence according to the selected delay time and embedding dimension;

[0028] Assume that the selected time series is , then the i-th element of the delay vector is ;

[0029] For each delay vector , generate a permutation based on the size relationship of the elements;

[0030] Count the frequencies of all occurrences of the permutations and get a frequency distribution ,in Indicates the result of the arrangement;

[0031] Using the formula Calculate the permutation entropy, where Traverse all the permutation results;

[0032] The entire time series is divided into multiple overlapping subsequences, and the permutation entropy of each subsequence is calculated, and finally the average value is taken.

[0033] Furthermore, the three-dimensional and four-dimensional continuous systems constructed in S4 are applied to the DNA high-rise building construction scene picture encryption system, and the robustness and security thereof are analyzed, and further include:

[0034] Select size MN The real scene of high-rise building construction is a plaintext image, which is divided into three two-dimensional matrices of R, G, and B. In order to ensure the rationality of the image size, the 0-filling operation is performed:

[0035] ;

[0036] In the formula, W is the block size;

[0037] Set the initial settings and construction period, select one dimension in the system Generating chaotic sequences as chaotic maps , the sequence length is equal to the image size MN , the selection of the initial value is based on the initial value in the three-dimensional system;

[0038] Transform the chaotic sequence and transform the one-dimensional chaotic sequence Convert to a two-dimensional matrix to ensure that the sequence size is the same as the image size, and normalize the sequence to between 0 and 255 for subsequent DNA operations;

[0039] Use the Runge-Kutta method to solve the four-dimensional system and generate four sequences of length MN ; Taking the two-dimensional matrix R as an example, the data value range is 0 to 255, and the data is represented by 8-bit binary numbers; using the initial value of the four-dimensional , and the initial conditions are calculated from different bits of the image;

[0040] DNA coding is performed to convert the image pixel value into a DNA sequence. Each pixel value corresponds to a DNA code. The generated chaotic sequence is used to perform an XOR operation on the DNA sequence. The DNA coding rule is given by Determined by; DNA operations are determined by Decide;

[0041] The encoded matrix is ​​permuted in rows and columns, and the permutation order is a chaotic sequence generated by a three-dimensional system. After the decision is made, the permuted matrix is ​​subjected to DNA operation again to obtain the final ciphertext image.

[0042] Furthermore, in said S4, the constructed three-dimensional and four-dimensional continuous systems are applied to the DNA high-rise building construction real scene picture encryption system for decryption when used, including:

[0043] Decompose the ciphertext image into three channels: R, G, and B;

[0044] Generate a chaotic sequence to obtain the same chaotic sequence of the three-dimensional chaotic system as that during encryption, and set the construction period to the initial value consistent with that during encryption;

[0045] Perform an inverse permutation operation on the matrices of the three channels of R, G, and B. The permutation order is opposite to that during encryption, from the last column to the first column and from the last row to the first row.

[0046] Solve the four-dimensional system and use the Runge-Kutta method to continue solving the four-dimensional system, generating the same four sequences as when encrypting ;

[0047] Perform DNA inverse operation on the inverse permuted matrix, and the decoding rule is given by Determined by, the inverse operation rule is given by Determine,remove the zero elements added during encryption and restore the original pixel value;

[0048] The decoded three two-dimensional matrices of R, G, and B are combined into a three-dimensional matrix to obtain the decrypted real-life construction scene of the high-rise building.

[0049] Furthermore, in S5, the three-dimensional and four-dimensional continuous systems are input according to the data of the specific high-rise building information, the actual rheological model construction period is selected and determined, and the corresponding relationship between the settlement of the high-rise building and time is obtained in combination with the instantaneous settlement of the pile foundation, which also includes:

[0050] Collecting RGB time-series images measured by the three-dimensional and four-dimensional continuous systems, and preprocessing the RGB time-series images to obtain reference scene data under multiple scenes;

[0051] Establishing a temporal network model in each scenario respectively, slicing the reference scenario data into training data and verification data according to the structure required by the temporal network model, and obtaining a trained temporal network model;

[0052] Preprocessing the input data of the prediction period, inputting the trained time series network model to calculate the instantaneous settlement prediction value sequence of the pile foundation of the high-rise building under each scenario;

[0053] The instantaneous settlement prediction value sequence of the pile foundation in each scenario is integrated to obtain a complete time series of the instantaneous settlement prediction value of the pile foundation in the durability construction process;

[0054] The time series network model is regularly updated by using incremental learning to achieve dynamic mapping from the real building state to the simulated digital twin model, and the prediction accuracy of the time series network model is regularly evaluated.

[0055] According to a second aspect of the present invention, the present invention claims protection for a high-rise building settlement prediction system based on a chaotic system, comprising:

[0056] one or more processors;

[0057] A memory having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the high-rise building settlement prediction method based on a chaotic system.

[0058] The present application relates to the field of engineering modeling technology, and in particular to a method and system for predicting the settlement of high-rise buildings based on a chaotic system, obtaining the current pile foundation data and completion effect data of the high-rise building, and constructing a continuous chaotic system; screening the effective numerical construction period of the high-rise building, and using the dual-construction period Lyapunov index diagram method to analyze the dynamic behavior changes of the three-dimensional chaotic system under different unknown construction period conditions; expanding the three-dimensional system into a four-dimensional state and applying it to the DNA high-rise building construction real-scene picture encryption system; inputting the continuous system according to the data of the specific high-rise building data, selecting and determining the actual rheological model construction period, and combining the instantaneous settlement of the pile foundation to obtain the corresponding relationship between the settlement of the high-rise building and time. The present invention is specifically aimed at high-rise buildings, and by changing parameters to compare the post-construction settlement under different conditions, the construction period is shortened and the construction cost is reduced, and the problem of difficult analysis and prediction of settlement of high-rise buildings is effectively solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1A workflow diagram of a method for predicting settlement of a high-rise building based on a chaotic system as claimed in an embodiment of the present application;

[0060] Figure 2 A second working flow chart of a method for predicting settlement of a high-rise building based on a chaotic system as claimed in an embodiment of the present application;

[0061] Figure 3 The third workflow diagram of a high-rise building settlement prediction method based on a chaotic system as claimed in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] The terms "first", "second" and "third" in this application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of this application are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0064] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0065] According to the first embodiment of the present invention, the present invention claims a method for predicting the settlement of a high-rise building based on a chaotic system, referring to Figure 1 ,include:

[0066] S1, obtain the current pile foundation data and completion effect data of high-rise buildings, construct a continuous chaotic system, use state variables combined with the amplitude matrix to construct a unified form, and verify the feasibility of the completion effect data through the Lyapunov exponent;

[0067] S2, using a three-dimensional chaotic system with a single nonlinear structure, screened the effective numerical construction period of high-rise buildings through the Lyapunov index, and used bifurcation diagrams, attractor diagrams, 0-1 tests, and dual-construction period Lyapunov index diagrams to analyze the dynamic behavior changes of the three-dimensional chaotic system under different unknown construction period conditions;

[0068] S3, expand the three-dimensional system into a four-dimensional state and demonstrate the complex characteristics of the system at different complexities to verify the applicability of the high-rise building construction;

[0069] S4, applying the constructed three-dimensional and four-dimensional continuous systems to the DNA high-rise building construction real scene picture encryption system, and analyzing the robustness and security of the DNA high-rise building construction real scene picture encryption system;

[0070] S5, inputting the three-dimensional and four-dimensional continuous systems according to the data of specific high-rise building materials, selecting and determining the actual rheological model construction period, and combining the instantaneous settlement of the pile foundation to obtain the corresponding relationship between the settlement of the high-rise building and time.

[0071] In this embodiment, the DNA of a high-rise building is represented in the building environment, and the selections may include (but are not limited to): doors, walls, hardware. Preferably, each of the parts is completed to configure the needs of the builder, and these objects are conceptually considered to be the DNA of the building.

[0072] Based on the three-dimensional system, it is expanded to a four-dimensional state, and the complex characteristics of the system under different complexities are demonstrated, verifying the universal applicability of the proposed construction method.

[0073] For the four-dimensional state mentioned in S3, the system is expanded with the above system as the model basis to obtain a four-dimensional continuous chaotic system. The emergence of the four-dimensional continuous chaotic system can further verify the construction idea of ​​the system proposed by the present invention. The system can maintain a certain continuity in terms of initial conditions and topological structure by expanding the dimensions.

[0074] Compared with the three-dimensional system, the state variables are newly added , the initial condition increases , the initial values ​​are selected as (0.1, 0.1, 0.1, 0.1). Using the above mentioned initial conditions as the basis for the study, the initial conditions of the system are obtained.

[0075] For the four-dimensional chaotic system mentioned in the present invention, it is very necessary to verify its complexity. SE (Shannon Entropy) and C0 complexity (C0 complexity), as two important methods for describing and analyzing chaotic systems, play an important role in analyzing the transformation of system dynamics behavior. SE complexity can quantitatively analyze the degree of chaos of the system by analyzing the time series of the system. High Shannon entropy indicates that the state of the system changes dramatically over time, with high uncertainty and complexity, indicating that the system is in a chaotic state. Low Shannon entropy indicates that the state of the system changes more regularly over time, with low information content, indicating that the system may be in a periodic or stable state. C0 complexity is an indicator for measuring the complexity of time series, especially for chaotic time series. Based on the arrangement pattern of the sequence, it reflects the complexity and structural characteristics of the sequence. C0 complexity evaluates the complexity of the sequence by calculating the frequency of occurrence of different arrangement patterns in the time series. A higher C0 complexity indicates that the time series of the system contains a large number of different arrangement patterns, and the sequence is complex and difficult to predict, indicating that the system may have chaotic characteristics.

[0076] Due to the significant advantages of SE complexity and C0 complexity in exploring the chaotic characteristics of chaotic systems, this paper uses SE complexity and C0 complexity to explore the changes in dynamic behavior caused by changes in random amplitude in the system. Combining the four-dimensional system with the initial conditions in the table, the effect diagram of the system under different conditions is obtained.

[0077] The constructed three-dimensional and four-dimensional continuous systems are applied to the DNA high-rise building construction scene image encryption system, and its robustness and security are analyzed. The results show that this method has significant advantages in practical applications and demonstrates theoretical innovation and practical value.

[0078] In S4, in order to further verify the characteristics of the system construction method proposed in the present invention in terms of combining state variables with unknown construction periods and using random construction periods as amplitudes in specific applications, a properly improved classic DNA high-rise building construction scene encryption scheme is used as an application research scheme. The application of the classic DNA high-rise building construction scene encryption scheme in a continuous chaotic system can make full use of the dynamic complexity of the chaotic system and the diversity of DNA computing to achieve a high-security, high-efficiency, and high-resistance image encryption scheme.

[0079] Since the high-rise building construction real-scene picture encryption system used in the present invention is mainly used to explore the chaotic characteristics of the above-mentioned three-dimensional and four-dimensional systems, the classical encryption and decryption processes used are described relatively simply.

[0080] In S4, information entropy is a method for calculating the uncertainty of image information and is often used to reflect the uncertainty of image information. Therefore, using information entropy to calculate the information entropy of plaintext images and ciphertext images can intuitively verify the uncertainty of ciphertext images. The calculation formula for information entropy is:

[0081] ;

[0082] In the above formula It represents the grayscale values ​​that may appear in the image. Since the grayscale values ​​of the real-life construction pictures of high-rise buildings used in the test of this embodiment are all 256, the theoretical value of information entropy is 8.

[0083] Usually, there is a close correlation between the pixels in the original image in all directions, while the encrypted image loses this correlation and presents a weak correlation. Therefore, we can evaluate the effectiveness of the encryption system by comparing the pixel correlation between the original image and the encrypted image in the horizontal, vertical, and diagonal directions.

[0084] Further, refer to Figure 2 In S1, from the perspective of state variable combination, the amplitude matrix to be determined is used, a construction with a unified form is adopted, and the feasibility of the completion effect data is verified by the Lyapunov exponent to construct a continuous chaotic system, including:

[0085] Set the minimum and maximum values ​​of the construction period and define the initial construction period range;

[0086] Within the set construction period range, a set of construction periods is randomly selected as the starting value, and the corresponding initial Lyapunov exponent is calculated;

[0087] In each iteration, the scope of the construction period is narrowed to find a construction period that meets the construction requirements;

[0088] After each construction cycle range is updated, a construction cycle is randomly selected within the new range and its corresponding Lyapunov index is calculated. If this index is less than zero, the construction cycle is considered unreasonable and the attempt is terminated. Otherwise, other construction cycles are verified.

[0089] When a construction cycle that meets the requirements is found, that is, a construction cycle with a Lyapunov index greater than or equal to zero, the construction cycle is marked as reasonable and the screening process ends;

[0090] The filtered construction period interval is output as the construction period range.

[0091] Furthermore, the three-dimensional chaotic system with a single nonlinear structure is used in S2 to screen the effective numerical construction period of high-rise buildings through the Lyapunov index, and the bifurcation diagram, attractor diagram, 0-1 test and double construction period Lyapunov index diagram method are used to analyze the changes in the dynamic behavior of the system under different unknown construction period conditions, and also includes:

[0092] Randomly select initial conditions within the range of values ​​that meet the conditions, use bifurcation diagrams and Lyapunov index diagrams for comparative analysis, and observe the dynamic behavior of the system model;

[0093] Select the initial value as (0.1, 0.1, 0.1), and select the first construction period selection range as the construction period selection range. , the second construction period selection range , the third construction period selection range ; The dynamic behavior of the system under the influence of the construction period is analyzed through bifurcation diagrams and Lyapunov index diagrams;

[0094] Starting from the construction period, the system model structure is constructed, and the random amplitude is set as a fixed reference condition to obtain the specific simulation results of the unknown construction period for the dynamic behavior of the system model. The set amplitude includes the first amplitude , the second amplitude , the third amplitude , the fourth amplitude , the fifth amplitude , the sixth amplitude They are:

[0095] ;

[0096] The normal deviation percentage range of the unknown construction period is set as , and obtain the bifurcation diagram and attractor diagram under the corresponding construction period conditions.

[0097] Furthermore, the three-dimensional system is expanded into a four-dimensional state in S3, and the complex characteristics of the system under different complexities are demonstrated to verify the applicability of the proposed construction method, and also includes:

[0098] Generate time series about the system and the evolution of state variables over time;

[0099] Select the delay time and embedding dimension, and choose the appropriate delay time according to the time series. and embedding dimension , delay time represents the time interval between each element in the delay vector, embedded in the dimension represents the number of state variables used to construct the delay vector;

[0100] Create delay vectors. For the time series generated by the system, construct a delay vector sequence according to the selected delay time and embedding dimension;

[0101] Assume that the selected time series is , then the i-th element of the delay vector is ;

[0102] For each delay vector , generate a permutation based on the size relationship of the elements;

[0103] Count the frequencies of all occurrences of the permutations and get a frequency distribution ,in Indicates the result of the arrangement;

[0104] Using the formula Calculate the permutation entropy, where Traverse all the permutation results;

[0105] The entire time series is divided into multiple overlapping subsequences, and the permutation entropy of each subsequence is calculated, and finally the average value is taken.

[0106] Furthermore, the three-dimensional and four-dimensional continuous systems constructed in S4 are applied to the DNA high-rise building construction scene picture encryption system, and the robustness and security thereof are analyzed, and further include:

[0107] Select size MN The real scene of high-rise building construction is a plaintext image, which is divided into three two-dimensional matrices of R, G, and B. In order to ensure the rationality of the image size, the 0-filling operation is performed:

[0108] ;

[0109] In the formula, W is the block size;

[0110] Set the initial settings and construction period, select one dimension in the system Generating chaotic sequences as chaotic maps , the sequence length is equal to the image size MN , the selection of the initial value is based on the initial value in the three-dimensional system;

[0111] Transform the chaotic sequence and transform the one-dimensional chaotic sequence Convert to a two-dimensional matrix to ensure that the sequence size is the same as the image size, and normalize the sequence to between 0 and 255 for subsequent DNA operations;

[0112] Use the Runge-Kutta method to solve the four-dimensional system and generate four sequences of length MN ; Taking the two-dimensional matrix R as an example, the data value range is 0 to 255, and the data is represented by 8-bit binary numbers; using the initial value of the four-dimensional , and the initial conditions are calculated from different bits of the image;

[0113] DNA coding is performed to convert the image pixel value into a DNA sequence. Each pixel value corresponds to a DNA code. The generated chaotic sequence is used to perform an XOR operation on the DNA sequence. The DNA coding rule is given by Determined by; DNA operations are determined by Decide;

[0114] The encoded matrix is ​​permuted in rows and columns, and the permutation order is a chaotic sequence generated by a three-dimensional system. After the decision is made, the permuted matrix is ​​subjected to DNA operation again to obtain the final ciphertext image.

[0115] Furthermore, in said S4, the constructed three-dimensional and four-dimensional continuous systems are applied to the DNA high-rise building construction real scene picture encryption system for decryption when used, including:

[0116] Decompose the ciphertext image into three channels: R, G, and B;

[0117] Generate a chaotic sequence to obtain the same chaotic sequence of the three-dimensional chaotic system as that during encryption, and set the construction period to the initial value consistent with that during encryption;

[0118] Perform an inverse permutation operation on the matrices of the three channels of R, G, and B. The permutation order is opposite to that during encryption, from the last column to the first column and from the last row to the first row.

[0119] Solve the four-dimensional system and use the Runge-Kutta method to continue solving the four-dimensional system, generating the same four sequences as when encrypting ;

[0120] Perform DNA inverse operation on the inverse permuted matrix, and the decoding rule is given by Determined by, the inverse operation rule is given by Determine,remove the zero elements added during encryption and restore the original pixel value;

[0121] The decoded three two-dimensional matrices of R, G, and B are combined into a three-dimensional matrix to obtain the decrypted real-life construction scene of the high-rise building.

[0122] Further, refer to Figure 3In S5, the three-dimensional and four-dimensional continuous systems are input according to the data of the specific high-rise building information, the actual rheological model construction period is selected and determined, and the corresponding relationship between the settlement of the high-rise building and time is obtained in combination with the instantaneous settlement of the pile foundation, and the following is also included:

[0123] Collecting RGB time-series images measured by the three-dimensional and four-dimensional continuous systems, and preprocessing the RGB time-series images to obtain reference scene data under multiple scenes;

[0124] Establishing a temporal network model in each scenario respectively, slicing the reference scenario data into training data and verification data according to the structure required by the temporal network model, and obtaining a trained temporal network model;

[0125] Preprocessing the input data of the prediction period, inputting the trained time series network model to calculate the instantaneous settlement prediction value sequence of the pile foundation of the high-rise building under each scenario;

[0126] The instantaneous settlement prediction value sequence of the pile foundation in each scenario is integrated to obtain a complete time series of the instantaneous settlement prediction value of the pile foundation in the durability construction process;

[0127] The time series network model is regularly updated by using incremental learning to achieve dynamic mapping from the real building state to the simulated digital twin model, and the prediction accuracy of the time series network model is regularly evaluated.

[0128] In this embodiment, the pile foundation instantaneous settlement prediction value sequences given by the models under multiple scenarios are merged according to the accumulated elapsed time to obtain a corresponding number of scenario matrices, which respectively correspond to the prediction results of the scenarios;

[0129] The scene matrix after column merging is spliced ​​row by row in time order, and the transition area is filled by linear interpolation to obtain a complete second-by-second prediction result;

[0130] The instantaneous settlement prediction value sequence of the pile foundation is restored to the original scale by the inverse transformation of the data referencing to obtain a complete time series;

[0131] Setting the corresponding time intervals for current training, verification and prediction, and performing training iteration updates on the temporal network model according to the cycle length between model iterations;

[0132] When the prediction time node of each iteration cycle arrives, the accuracy of the time series network model is evaluated, and the normalized root mean square error and determination coefficient evaluation indicators are selected to evaluate the gap between the prediction result and the actual value of the predicted value of the instantaneous average settlement of the pile foundation.

[0133] The expected benefits and commercial value of the technical solution of the present invention after transformation are:

[0134] Improve information security: By utilizing three-dimensional and four-dimensional continuous chaotic systems in the encryption of DNA high-rise building construction real-scene images, the present invention can significantly improve the complexity and robustness of data encryption, effectively prevent information leakage and tampering, and has broad application prospects in the field of information security.

[0135] Optimize the efficiency of encryption algorithms: This solution uses the unpredictability and complexity of chaotic systems to make the encryption process more resistant to cracking while maintaining high encryption and decryption efficiency. It is suitable for scenarios with high requirements for encryption speed, such as real-time image and video transmission.

[0136] Wide range of application scenarios: This technology is not only suitable for image encryption, but can also be applied to other data encryption scenarios that require high security, such as financial data transmission, confidential document protection, Internet of Things communication, etc., expanding the scope of its commercial application.

[0137] The technical solution of the present invention fills the technical gap in the industry:

[0138] Combining unknown and uncertain variables to construct chaotic systems: A method of combining unknown variables with uncertain variables to construct continuous chaotic systems is proposed, which is different from the traditional single state variable construction method. This innovative method makes the dynamic behavior of the system more complex and diverse, filling the relevant research gap in the field of chaotic system construction.

[0139] Construction cycle screening and system analysis based on Lyapunov index: The numerical construction cycle is screened by Lyapunov index, and the system is further analyzed in detail by combining bifurcation diagram, attractor diagram, 0-1 test and dual construction cycle Lyapunov index diagram, providing a comprehensive dynamic behavior research method. This method is innovative in dynamic system analysis and can effectively analyze the impact of construction cycle on the chaotic behavior of the system.

[0140] Scalability and applicability from three-dimensional to four-dimensional systems: This invention demonstrates a method for expanding the three-dimensional continuous chaotic system model to a four-dimensional system, and demonstrates its complex characteristics in higher dimensions. The scalability and universal applicability of this method fills the technical gap in the construction and analysis of high-dimensional chaotic systems in nonlinear dynamics.

[0141] Innovative image encryption method: By applying the constructed three-dimensional and four-dimensional chaotic systems to the DNA high-rise building construction scene encryption system, the present invention proposes a new solution in the field of information encryption. Compared with traditional encryption methods, this solution has significant advantages in robustness and anti-attack capabilities, filling the gap in the field of high-security chaotic encryption.

[0142] Using permutation entropy to evaluate changes in random behavior of the system: Using permutation entropy to conduct a detailed study on the changes in the chaotic behavior of the system under random amplitudes, it provides a new method and perspective for evaluating the impact of randomness on the behavior of chaotic systems, and supplements the deficiencies of existing chaotic system analysis methods.

[0143] The technical solution of the present invention overcomes the following technical prejudices:

[0144] Limitations of single state variable construction: Traditionally, the construction of chaotic systems mainly relies on fixed state variables, which has certain limitations on the flexibility and complexity of the system. This invention overcomes this bias by combining unknown variables with uncertain variables to construct chaotic systems, making the constructed system more adaptable and diverse in dealing with complex environments.

[0145] Inherent conservatism in the construction of high-dimensional systems: In the field of nonlinear dynamics, many studies usually focus only on three-dimensional systems, believing that the construction and analysis of high-dimensional systems are too complex and difficult to implement. This paper overcomes the conservative attitude towards the construction of high-dimensional systems by expanding the three-dimensional continuous chaotic system into a four-dimensional system and demonstrating its performance at different complexities, thus expanding the research space of high-dimensional chaotic systems.

[0146] Single application of Lyapunov index: Traditionally, Lyapunov index is mainly used to judge the chaotic nature of the system, but its potential in construction cycle screening and system behavior analysis is often not fully explored. This paper uses Lyapunov index to screen the construction cycle and combines other analysis methods (such as bifurcation diagram, attractor diagram, 0-1 test, etc.) to overcome this single perspective and present a more comprehensive system dynamics behavior research method.

[0147] Preference for linear methods in information encryption: The field of information encryption tends to use encryption methods based on algebraic operations or linear transformations, believing that these methods are easier to implement and more secure. This invention overcomes the preference for traditional linear encryption methods by applying the unpredictability and complexity of chaotic systems to the encryption of DNA high-rise building construction scenes, proving the advantages of nonlinear chaotic methods in the field of encryption.

[0148] Ignoring the influence of randomness on chaotic behavior: In traditional research, the influence of randomness on the behavior of chaotic systems is often not given enough attention and is often regarded as an uncontrollable factor. This paper overcomes this bias by using permutation entropy to conduct an in-depth study of the changes in the chaotic behavior of the system under random amplitudes, revealing the important role of randomness in system complexity and providing new ideas for more accurate system behavior control.

[0149] According to a second embodiment of the present invention, the present invention claims protection for a high-rise building settlement prediction system based on a chaotic system, comprising:

[0150] one or more processors;

[0151] A memory having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the high-rise building settlement prediction method based on a chaotic system.

[0152] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0153] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

[0154] The specific implementation methods of the invention are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for predicting settlement of high-rise buildings based on chaotic systems, characterized in that: include: S1, obtain the current pile foundation data and completion effect data of high-rise buildings, construct a continuous chaotic system, use state variables combined with the amplitude matrix to construct a unified form, and verify the feasibility of the completion effect data through the Lyapunov exponent; S2, using a three-dimensional chaotic system with a single nonlinear structure, screened the effective numerical construction period of high-rise buildings through the Lyapunov index, and used bifurcation diagrams, attractor diagrams, 0-1 tests, and dual-construction period Lyapunov index diagrams to analyze the dynamic behavior changes of the three-dimensional chaotic system under different unknown construction period conditions; S3, expand the three-dimensional system into a four-dimensional state and demonstrate the complex characteristics of the system at different complexities to verify the applicability of the high-rise building construction; S4, applying the constructed three-dimensional and four-dimensional continuous systems to the DNA high-rise building construction real scene picture encryption system, and analyzing the robustness and security of the DNA high-rise building construction real scene picture encryption system; S5, inputting the three-dimensional and four-dimensional continuous systems according to the data of specific high-rise building materials, selecting and determining the actual rheological model construction period, and combining the instantaneous settlement of the pile foundation to obtain the corresponding relationship between the settlement of the high-rise building and time; In S1, from the perspective of combining state variables, using the amplitude matrix to be determined, adopting a unified form of construction, and verifying the feasibility of the completion effect data through the Lyapunov exponent, a continuous chaotic system is constructed, including: Set the minimum and maximum values ​​of the construction period and define the initial construction period range; Within the set construction period range, a set of construction periods is randomly selected as the starting value, and the corresponding initial Lyapunov exponent is calculated; In each iteration, the scope of the construction period is narrowed to find a construction period that meets the construction requirements; After each construction cycle range is updated, a construction cycle is randomly selected within the new range and its corresponding Lyapunov index is calculated. If this index is less than zero, the construction cycle is considered unreasonable and the attempt is terminated. Otherwise, other construction cycles are verified. When a construction cycle that meets the requirements is found, that is, a construction cycle with a Lyapunov index greater than or equal to zero, the construction cycle is marked as reasonable and the screening process ends; The selected construction period interval is output as the construction period range; The S2 adopts a three-dimensional chaotic system with a single nonlinear structure, selects the effective numerical construction period of high-rise buildings through the Lyapunov index, and uses bifurcation diagrams, attractor diagrams, 0-1 tests and dual-construction period Lyapunov index diagram methods to analyze the changes in the dynamic behavior of the system under different unknown construction period conditions, and also includes: Randomly select initial conditions within the range of values ​​that meet the conditions, use bifurcation diagrams and Lyapunov index diagrams for comparative analysis, and observe the dynamic behavior of the system model; Select the initial value as (0.1, 0.1, 0.1), and select the first construction period selection range as the construction period selection range , the second construction period selection range , the third construction period selection range ; The dynamic behavior of the system under the influence of the construction period is analyzed through bifurcation diagrams and Lyapunov index diagrams; Starting from the construction period, the system model structure is constructed, and the random amplitude is set as a fixed reference condition to obtain the specific simulation results of the unknown construction period for the dynamic behavior of the system model. The set amplitude includes the first amplitude , the second amplitude 、The third , the fourth amplitude , the fifth amplitude , the sixth amplitude They are: ； The normal deviation percentage range of the unknown construction period is set as , and obtain the bifurcation diagram and attractor diagram under the corresponding construction period conditions; The S3 expands the three-dimensional system into a four-dimensional state and demonstrates the complex characteristics of the system under different complexities to verify the applicability of the proposed construction method. It also includes: Generate time series about the system and the evolution of state variables over time; Select the delay time and embedding dimension, and choose the appropriate delay time according to the time series. and embedding dimension m , delay time represents the time interval between each element in the delay vector, embedded in the dimension m represents the number of state variables used to construct the delay vector; Create delay vectors. For the time series generated by the system, construct a delay vector sequence according to the selected delay time and embedding dimension; Assume that the selected time series is , then the i-th element of the delay vector is ； For each delay vector , generate a permutation based on the size relationship of the elements; Count the frequencies of all occurrences of the permutations and get a frequency distribution ,in Indicates the result of the arrangement; Using the formula Calculate the permutation entropy, where Traverse all the permutation results; Divide the entire time series into multiple overlapping subsequences, calculate the permutation entropy of each subsequence, and finally take the average value; In S5, the data of specific high-rise building data is input into the three-dimensional and four-dimensional continuous systems, and the actual rheological model construction period is selected and determined, and the corresponding relationship between the settlement of the high-rise building and time is obtained in combination with the instantaneous settlement of the pile foundation, and the following is also included: Collecting RGB time-series images measured by the three-dimensional and four-dimensional continuous systems, and preprocessing the RGB time-series images to obtain reference scene data under multiple scenes; Establishing a temporal network model in each scenario respectively, slicing the reference scenario data into training data and verification data according to the structure required by the temporal network model, and obtaining a trained temporal network model; Preprocessing the input data of the prediction period, inputting the trained time series network model to calculate the instantaneous settlement prediction value sequence of the pile foundation of the high-rise building under each scenario; The instantaneous settlement prediction value sequence of the pile foundation in each scenario is integrated to obtain a complete time series of the instantaneous settlement prediction value of the pile foundation in the durability construction process; The time series network model is regularly updated by using incremental learning to achieve dynamic mapping from the real building state to the simulated digital twin model, and the prediction accuracy of the time series network model is regularly evaluated.

2. A high-rise building settlement prediction method based on chaotic system according to claim 1, characterized in that: In said S4, the constructed three-dimensional and four-dimensional continuous systems are applied to the DNA high-rise building construction scene picture encryption system, and its robustness and security are analyzed, and also include: Select size MN The real scene of high-rise building construction is a plaintext image, which is divided into three two-dimensional matrices of R, G, and B. In order to ensure the rationality of the image size, a zero-filling operation is performed; ； In the formula, W is the block size; Set the initial settings and construction period, select one dimension in the system Generating chaotic sequences as chaotic maps , the sequence length is equal to the image size MN , the selection of the initial value is based on the initial value in the three-dimensional system; Transform the chaotic sequence and transform the one-dimensional chaotic sequence Convert to a two-dimensional matrix to ensure that the sequence size is the same as the image size, and normalize the sequence to between 0 and 255 for subsequent DNA operations; Use the Runge-Kutta method to solve the four-dimensional system and generate four sequences of length MN ; Taking the two-dimensional matrix R as an example, the data value range is 0 to 255, and the data is represented by 8-bit binary numbers; using the initial value of the four-dimensional , and the initial conditions are calculated from different bits of the image; DNA coding is performed to convert the image pixel value into a DNA sequence. Each pixel value corresponds to a DNA code. The generated chaotic sequence is used to perform an XOR operation on the DNA sequence. The DNA coding rule is given by Determined by; DNA operations are determined by Decide; The encoded matrix is ​​permuted in rows and columns, and the permutation order is a chaotic sequence generated by a three-dimensional system. After the decision is made, the permuted matrix is ​​subjected to DNA operation again to obtain the final ciphertext image.

3. The method for predicting settlement of high-rise buildings based on chaotic systems according to claim 1 is characterized in that: In said S4, the constructed three-dimensional and four-dimensional continuous systems are applied to the DNA high-rise building construction real scene picture encryption system for decryption when used, including: Decompose the ciphertext image into three channels: R, G, and B; Generate a chaotic sequence to obtain the same chaotic sequence of the three-dimensional chaotic system as that during encryption, and set the construction period to the initial value consistent with that during encryption; Perform an inverse permutation operation on the matrices of the three channels of R, G, and B. The permutation order is opposite to that during encryption, from the last column to the first column and from the last row to the first row. Solve the four-dimensional system and use the Runge-Kutta method to continue solving the four-dimensional system, generating the same four sequences as when encrypting ; Perform DNA inverse operation on the inverse permuted matrix, and the decoding rule is given by Determined by, the inverse operation rule is given by Determine,remove the zero elements added during encryption and restore the original pixel value; The decoded three two-dimensional matrices of R, G, and B are combined into a three-dimensional matrix to obtain the decrypted real-life construction scene of the high-rise building.

4. A high-rise building settlement prediction system based on chaotic system, characterized in that: include: one or more processors; A memory having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement a high-rise building settlement prediction method based on a chaotic system according to any one of claims 1 to 3.

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