Web-based infrastructure programmed modeling system

Abstract

The invention discloses a web-based infrastructure programmed modeling system, and relates to the technical field of data transmission. Obtaining a data file of a web end, and carrying out programmed modeling to obtain a target file; congestion detection is carried out on the transmission path, and if the transmission path is determined to be congested, path optimization is carried out to obtain a transmission strategy; performing spatial compression on the target file to obtain target point cloud data, converting the target point cloud data into structured data, and performing security detection on the structured data; and if the structured data is judged to be safe, performing data transmission according to the transmission strategy. According to the method, the structured data is subjected to security detection, and only the data judged to be secure can be transmitted according to the optimized transmission strategy, so that efficient and secure transmission of the data between the Web side and the cloud side is realized, and the integrity and transmission efficiency of the data are guaranteed.

Description

Technical Field

[0001] The invention belongs to the technical field of data transmission, and in particular relates to a web-based infrastructure programmatic modeling system. Background Art

[0002] With the rapid development of Internet technology, Web-based modeling systems have gradually become an important tool for the digital transformation of various industries. Early Web technology was mainly based on static pages, but with the continuous evolution of technologies such as HTML, CSS, and JavaScript, Web applications have gradually developed in the direction of dynamic and interactive. In recent years, the rise of Web3D technology has further promoted the development of Web modeling systems. Through technologies such as WebGL and WebGPU, users can directly render and interact with 3D models in the browser without the need for additional plug-ins. In addition, the integration of AI technology has also brought the possibility of automation and intelligence to Web modeling, which can accelerate model generation and optimization. In the field of systems engineering, model-based systems engineering (MBSE) methods are widely used, and Web modeling systems provide more convenient tool support for MBSE, making modeling more efficient and collaborative. In the existing technology, Web-based infrastructure faces the problems of difficulty in standardizing heterogeneous data formats, low efficiency caused by congested transmission paths, and insufficient security threatening data integrity during data transmission to the cloud. Summary of the invention

[0003] The purpose of the present invention is to solve the problems faced by Web-based infrastructure in the process of transmitting data to the cloud, such as difficulty in standardizing heterogeneous data formats, low efficiency caused by congested transmission paths, and insufficient security threatening data integrity, and to propose a web-based infrastructure programmatic modeling system.

[0004] In the first aspect of the implementation of the present invention, a web-based infrastructure programmatic modeling system is first proposed, the system comprising: a data modeling module, a path analysis module, a data transmission module and a task execution module: The data modeling module is used to obtain the data file on the web end, and perform programmatic modeling on the data file to obtain the target file; The path analysis module is used to perform congestion detection on the transmission path of the current cycle, and if the transmission path is determined to be congested, perform path optimization to obtain a transmission strategy; The data transmission module is used to perform spatial compression on the target file to obtain target point cloud data, convert the target point cloud data into structured data, and perform security detection on the structured data; The task execution module is used to perform data transmission according to the transmission strategy if the structured data is determined to be safe.

[0005] Optionally, the path analysis module includes: a data detection module, a delay calculation module and a weight calculation module: The data detection module is used to send a detection data packet to the web end and the cloud end respectively to obtain a first detection time and a second detection time, and the web end and the cloud end receive the detection data packet and send the detection data packet to each other to obtain a first verification time and a second verification time; The delay calculation module is used to calculate the link delay according to the first detection time, the second detection time, the first verification time and the second verification time, The weight calculation module is used to obtain the minimum remaining bandwidth of the target path, calculate the path delay according to the link delay of each link in the target path, and calculate the target path weight according to the minimum remaining bandwidth and the path delay.

[0006] The transmission paths are screened according to the target path weights to obtain a transmission path set, and a transmission strategy is determined according to the transmission path set.

[0007] Optionally, the path analysis module further includes a policy optimization module; the policy optimization module is used to obtain an expert data set and a historical log set, and input the path weight, link bandwidth, and historical log set into a preset model to obtain a transmission strategy; the policy optimization module is used to perform the following steps, which specifically include: Step 1: dividing the expert data set according to preset rules to obtain expert data subsets, and determining the expert strategy according to the expert data subsets; Step 2: Initialize the strategy to obtain the target strategy, and set the target strategy as the optimal solution of the reward function; Step 3: Save the dynamic distribution of the target strategy , update the reward function by online projected gradient method; Step 4: determine the corresponding historical log according to the target strategy, calculate the service quality evaluation value according to the link bandwidth, packet loss rate and historical log, and determine the transmission strategy according to the service quality evaluation value.

[0008] Optionally, the strategy optimization module further includes: an evaluation value calculation module and a strategy determination module: The evaluation value calculation module is used to calculate the service quality evaluation value according to the historical log, the cache packet transmission speed and the path weight, and select the transmission path according to the service quality evaluation value; the historical log includes: average delay and maximum packet loss rate; The strategy determination module is used to use the transmission path as the transmission strategy if the service quality evaluation value is greater than a preset threshold; Service quality calculation formula:

[0009] in, Represents the transmission quality, Represents the standard cache packet transmission speed, Represents the cache packet data transmission speed, represents the packet loss rate, represents the average delay, T represents the unit time, and W represents the path weight.

[0010] Optionally, the data transmission module further includes: a file cutting module and a data compression module: The file cutting module is used to determine the Euclidean space of the target file, and divide the Euclidean space into multiple data blocks according to a preset length; The data compression module is used to set marking points in any data block according to preset rules if there is an intersection between any data block and the Euclidean space, and combine all marking points to obtain target point cloud data.

[0011] Optionally, the data compression module further includes: a data conversion module, a model training module and a security detection module: The data conversion module is used to convert the target point cloud data into structured data and obtain a historical data set; the historical data set includes: a historical training data set and a historical verification data set; The model training module is used to input the historical training data set into a preset model for training to obtain target model parameters, and update the model parameters of the preset model according to the target model parameters to obtain the target model; The safety detection module is used to input the historical verification data set into the target model for verification to obtain the pass rate. If the pass rate is greater than the preset pass rate, the model training is determined to be completed, and the structured data is input into the target model for safety detection. Otherwise, the target model is trained again.

[0012] Optionally, the task execution module is further configured to, if the structured data is determined to be dangerous, perform a risk assessment on the structured data to obtain a feedback report, and report the feedback report as a vulnerability.

[0013] Beneficial effects of the present invention: The present invention proposes a programmatic modeling system for infrastructure based on the web, which generates standardized target files by programmatically modeling the data files on the web side, and converts them into structured data by spatial compression, while detecting transmission path congestion and optimizing the path to ensure the efficiency and reliability of data transmission. Security detection is performed on structured data, and only data that is determined to be safe will be transmitted according to the optimized transmission strategy, thereby achieving efficient and secure transmission of data between the web side and the cloud, and ensuring data integrity and transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below in conjunction with the accompanying drawings.

[0015] Figure 1 A flowchart of a web-based infrastructure programmatic modeling system is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the present invention, the description of "first", "second", etc. is only used for descriptive purposes, and cannot be understood as indicating or implying its relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0017] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0018] The embodiment of the present invention provides a web-based infrastructure programmatic modeling system. Figure 1 , Figure 1 A flowchart of a web-based infrastructure programmatic modeling system provided by an embodiment of the present invention. The system includes the following modules: a data modeling module, a path analysis module, a data transmission module, and a task execution module: The data modeling module is used to obtain the data files on the web side and perform programmatic modeling on the data files to obtain the target files; The path analysis module is used to perform congestion detection on the transmission path of the current cycle. If the transmission path is determined to be congested, the path is optimized to obtain a transmission strategy; The data transmission module is used to spatially compress the target file to obtain the target point cloud data, convert the target point cloud data into structured data, and perform security detection on the structured data; The task execution module is used to transmit the structured data according to the transmission strategy if the structured data is determined to be safe.

[0019] A web-based infrastructure programmatic modeling system provided by an embodiment of the present invention generates standardized target files by programmatically modeling Web-side data files, and converts them into structured data through spatial compression, while detecting transmission path congestion and optimizing the path to ensure the efficiency and reliability of data transmission. Security detection is performed on structured data, and only data that is determined to be safe will be transmitted according to the optimized transmission strategy, thereby achieving efficient and secure data transmission between the Web side and the cloud, and ensuring data integrity and transmission efficiency.

[0020] In one implementation, by programmatically modeling the data files obtained from the Web, the original data can be converted into target files. The technical effect of this process is to achieve normalization and standardization of data. Programmatic modeling can automatically extract the key features of the data and generate formatted files suitable for subsequent processing, thereby improving the efficiency and accuracy of data processing. At the same time, this process can reduce manual intervention, ensure the consistency and integrity of the data, and provide a high-quality data foundation for subsequent transmission and analysis.

[0021] In one implementation, congestion detection is performed on the transmission path of the current cycle. If the path is determined to be congested, path optimization is performed to obtain a transmission strategy. The technical effect of this process is to dynamically adjust the transmission path to ensure the efficiency and reliability of data transmission. By monitoring the congestion of the path in real time, potential bottlenecks can be discovered in a timely manner, and a better transmission path can be selected through an optimization algorithm. This not only reduces transmission delays, but also improves the utilization of network resources and enhances the system's adaptive ability, thereby maintaining stable transmission performance in a complex network environment.

[0022] In one implementation, the target file is spatially compressed to obtain the target point cloud data, which is then converted into structured data. The technical effect of this process is to significantly reduce the storage and transmission costs of data while retaining the core information of the data. Spatial compression can remove redundant data and improve data transmission efficiency, while the structured conversion of point cloud data provides an adaptive format for subsequent safety inspections. This processing method not only optimizes the organization of data, but also facilitates subsequent analysis and processing, ensuring that data can be efficiently processed in safety inspections.

[0023] In one implementation, security testing is performed on structured data. The technical effect of this process is to ensure the security and integrity of data during transmission. Through security testing, anomalies or potential threats in the data, such as data tampering and malicious code injection, can be discovered in a timely manner. The implementation of security testing provides the last line of defense for data transmission, preventing sensitive information from being leaked or maliciously exploited. Only when the data is judged to be safe will it enter the next step of the transmission process, thereby effectively ensuring the security of the data.

[0024] In one implementation, if the structured data is determined to be safe, the data is transmitted according to the optimized transmission strategy. The technical effect of this process is to achieve efficient, safe and reliable data transmission. The implementation of the transmission strategy is based on the results of path optimization to ensure that the data is transmitted through the optimal path. At the same time, combined with the guarantee of security detection, the data is prevented from being threatened during the transmission process. This method that comprehensively considers transmission efficiency and security can significantly improve the overall performance of the system and ensure the stable transmission of data between the Web and the cloud.

[0025] In one embodiment, the path analysis module includes: a data detection module, a delay calculation module and a weight calculation module: A data detection module is used to send a detection data packet to the web end and the cloud end respectively to obtain a first detection time and a second detection time, and the web end and the cloud end receive the detection data packet and send the detection data packet to each other to obtain a first verification time and a second verification time; A delay calculation module is used to calculate the link delay according to the first detection time, the second detection time, the first verification time and the second verification time, The weight calculation module is used to obtain the minimum remaining bandwidth of the target path, calculate the path delay according to the link delay of each link in the target path, and calculate the target path weight according to the minimum remaining bandwidth and the path delay.

[0026] In one implementation, by sending detection data packets to the Web end and the cloud end respectively and obtaining the first detection time and the second detection time, and at the same time allowing the Web end and the cloud end to send detection data packets to each other to obtain the first verification time and the second verification time, the two-way transmission time of the network link can be accurately measured. The technical effect of this process is that the delay data of the network link can be obtained in real time, providing accurate timing information for subsequent link delay calculations, thereby ensuring the reliability of link performance evaluation.

[0027] In one implementation, link delay is calculated based on the first detection time, the second detection time, and the verification time, which can accurately quantify the network delay between the Web end and the cloud. This technical effect is to provide key performance indicators for path selection, help identify potential bottlenecks in the network, and thus optimize the data transmission path and reduce transmission delay. For example: the first detection time and the second detection time , first verification time and the second verification time , link delay: , where LY represents the link delay, Indicates the first detection time, Indicates the second detection time, Indicates the first verification time, Indicates the second verification time.

[0028] In one implementation, the minimum remaining bandwidth of the target path is obtained, and the path delay is calculated based on the delay of each link, which can comprehensively evaluate the transmission capacity and performance of the path. The minimum remaining bandwidth reflects the available bandwidth resources of the path, while the path delay integrates the delay of all links. The effect of this technology is to provide comprehensive performance parameters for the calculation of path weights, ensuring that path selection not only considers delay, but also takes into account the availability of bandwidth resources. For example: Path delay: , where JY represents path delay, LY represents link delay, j represents j links; path weight: , where W represents the path weight, DK represents the minimum residual bandwidth, and JY represents the path delay.

[0029] In one implementation, the target path weight is calculated by combining the minimum remaining bandwidth and path delay to comprehensively evaluate the transmission performance of the path. The path weight reflects the comprehensive advantages of the path in terms of delay and bandwidth. The higher the weight, the better the transmission performance of the path. The effect of this technology is to provide a quantitative standard for the selection of transmission paths, making the path screening process more scientific and reasonable.

[0030] In one implementation, the transmission paths are screened according to the target path weights to obtain a transmission path set, and finally determine the transmission strategy. The technical effect of this process is that the optimal transmission path can be selected from multiple candidate paths, thereby achieving efficient and reliable data transmission. By screening the path set, it is possible to ensure that the transmission path that best suits the current network status is found in the multi-path selection, thereby improving the overall transmission efficiency and reliability.

[0031] In one embodiment, the path analysis module further includes a policy optimization module; the policy optimization module is used to obtain an expert data set and a historical log set, and input the path weight, link bandwidth, and historical log set into a preset model to obtain a transmission strategy; the policy optimization module is used to perform the following steps, which specifically include: Step 1: Divide the expert data set into expert data subsets according to preset rules, and determine the expert strategy based on the expert data subsets; Step 2: Initialize the strategy to obtain the target strategy, and set the target strategy as the optimal solution of the reward function; Step 3: Save the dynamic distribution of the target strategy , update the reward function by online projected gradient method; Step 4: determine the corresponding historical log according to the target strategy, calculate the service quality evaluation value according to the link bandwidth, packet loss rate and historical logs, and determine the transmission strategy according to the service quality evaluation value.

[0032] In one implementation, the strategy is initialized to obtain the target strategy, and the target strategy is the optimal solution of the reward function; that is, the strategy is initialized to the optimal solution of the reward function, providing a reasonable starting point for subsequent optimization. This step can speed up the convergence of the algorithm and avoid inefficient training caused by starting from a random strategy. Strategy: In reinforcement learning, a strategy refers to the rule for selecting actions under a given state. Strategies can be deterministic (fixed actions) or random (action probability distribution). Reward function: It is used to measure the immediate benefits of taking a specific action under a specific state. The reward function is the core of reinforcement learning and is used to guide the behavior of the agent. Expert datasets refer to high-quality data generated by experienced operators or optimization algorithms to guide learning algorithms. In Web and cloud data transmission, expert datasets include historical transmission paths, link performance indicators (such as delay, packet loss rate, bandwidth utilization), and corresponding transmission quality evaluation results. These data reflect the optimal transmission strategy under different network conditions. The expert dataset consists of the union of expert data subsets ,in, represents the expert dataset, represents the expert data subset; the reward function is , the dynamic distribution is , the reward function is updated by Update, where Represents expert strategy.

[0033] In one implementation, save the dynamic distribution: save the dynamic distribution under the current strategy to facilitate comparison and adjustment with the expert strategy in subsequent steps. This step ensures that the algorithm can track changes in the strategy in real time and provide a basis for updating the reward function. Professional term explanation: Dynamic distribution: represents the state distribution of the system under the current strategy. In reinforcement learning, the dynamic distribution reflects the transition probability of the state during the execution of the strategy. Policy refers to the rule for selecting actions in a given state. In Web and cloud data transmission, the policy can be a rule for selecting the optimal transmission path based on the current network status (such as link delay, bandwidth utilization). Through imitation learning, the algorithm continuously optimizes the policy so that it can dynamically adjust the transmission path according to the real-time network status.

[0034] In one implementation, the reward function is updated through the online projected gradient method to make it closer to the expert strategy. This step is the core of the algorithm. By continuously adjusting the reward function and optimizing the strategy, it gradually approaches the optimal solution. The online projected gradient method is an optimization algorithm that dynamically adjusts parameters by calculating the gradient and projecting it into the feasible domain. In imitation learning, it is used to update the reward function to approximate the expert strategy.

[0035] In one implementation, the service quality evaluation value is calculated based on parameters such as link bandwidth and packet loss rate to evaluate the performance of the current strategy. This step provides quantitative indicators for subsequent policy updates to ensure that the optimization direction meets actual needs. Service quality: a transmission performance indicator that measures the network path, including latency, packet loss rate, jitter, throughput, etc. Link bandwidth: the transmission capacity of a network link, usually measured in bits per second (bps). Bandwidth directly affects the speed of data transmission. Packet loss rate: the proportion of data packets lost during network transmission, which is a key indicator for measuring network reliability.

[0036] In one implementation, the strategy is gradually optimized through multiple iterations to achieve better performance in multi-objective optimization problems. The iterative process ensures that the algorithm can gradually approach the optimal solution while adapting to dynamically changing environments. Multi-objective optimization: Optimization problems that optimize multiple objectives simultaneously usually require trade-offs between different objectives. Number of iterations: The number of loops executed by the algorithm determines the accuracy and computational cost of the optimization. The transmission strategy is determined based on the service quality evaluation value. Through multi-objective optimization (such as minimizing latency, maximizing bandwidth utilization, and reducing packet loss rate), the algorithm can find a set of optimal paths that meet multi-objective requirements in a dynamic network environment. These paths can not only improve the efficiency and reliability of data transmission, but also adapt to changes in network status.

[0037] In one embodiment, the strategy optimization module further includes: an evaluation value calculation module and a strategy determination module: An evaluation value calculation module is used to calculate a service quality evaluation value based on historical logs, cache packet transmission speed and path weight, and select a transmission path based on the service quality evaluation value; the historical logs include: average delay and maximum packet loss rate; A strategy determination module, configured to use the transmission path as a transmission strategy if the service quality evaluation value is greater than a preset threshold; Service quality calculation formula:

[0038] in, Represents the transmission quality, Represents the standard cache packet transmission speed, Represents the cache packet data transmission speed, represents the packet loss rate, represents the average delay, T represents the unit time, and W represents the path weight.

[0039] In one implementation, The cache packet transmission speed reflects the congestion of the transmission path. The faster the cache packet transmission speed, the lighter the congestion. is the average delay in the historical transmission log. The lower the average delay, the better the service quality. is the maximum packet loss rate in the historical transmission log. The lower the packet loss rate, the better the service quality. W is the weight of the transmission path. The higher the weight, the better the service quality, thus achieving efficient and reliable data transmission.

[0040] In one embodiment, the data transmission module further includes: a file cutting module and a data compression module: A file cutting module, used to determine the Euclidean space of the target file, and divide the Euclidean space into multiple data blocks according to a preset length; The data compression module is used to set marking points in any data block according to preset rules if there is an intersection between any data block and the Euclidean space, and combine all the marking points to obtain the target point cloud data.

[0041] In one implementation, the m-dimensional space where the point cloud is located is divided into data blocks (cubes) with a side length of α, in order to discretize the continuous point cloud data for subsequent processing. This division method simplifies complex spatial data into a regular grid structure, so that each point in the point cloud is assigned to a specific data block. The space is divided into regular grid points through the equivalence relation x∼y. This division method is similar to "slicing" the space, and each "slice" is a data block. Mathematical expression: , the value of i is: i=1, 2,…n. If the integer part of two points after dividing by α in each dimension is the same, they are considered to belong to the same data block.

[0042] In one implementation, a representative point x∗ is selected from each hypercube C that intersects with the point cloud X, thereby reducing the size of the point cloud. This step is the key to data simplification. By selecting representative points, the topological features of the original point cloud are retained while removing redundant information. If at least one point in a data block C belongs to the original point cloud X, then C is said to intersect with X, that is, this hypercube contains points in the point cloud. Marking points are set in the data block according to preset rules. The marking point x is determined by: determining the position of the data block C, using integer coordinates ( ) indicates that , the coordinate of the marker point x is , this point is usually the lower left vertex of the data block (or a fixed position) and does not necessarily belong to the original point cloud X.

[0043] In one implementation, all the marker points are combined to obtain the target point cloud data, and all the selected representative points x are combined into a new point cloud , this new point cloud The topological features of the original point cloud are retained, but on a smaller scale. When α increases, the volume of the data block increases, and each hypercube covers a wider range of space. Therefore, more points will be mapped to the same hypercube, and the number of representative points selected will eventually decrease. The result is a new point cloud The size of α decreases as α increases.

[0044] In one implementation, the size of the point cloud is reduced, the storage and computing costs are reduced, and the efficiency of subsequent processing is improved. Although the size of the point cloud is reduced, the topological structure of the original point cloud is retained by selecting representative points. By adjusting the size of α, the degree of simplification can be flexibly controlled according to actual needs.

[0045] In one embodiment, the data compression module further includes: a data conversion module, a model training module and a security detection module: The data conversion module is used to convert the target point cloud data into structured data and obtain the historical data set; the historical data set includes: historical training data set and historical verification data set; The model training module is used to input the historical training data set into the preset model for training to obtain the target model parameters, and update the model parameters of the preset model according to the target model parameters to obtain the target model; The safety detection module is used to input the historical verification data set into the target model for verification to obtain the pass rate. If the pass rate is greater than the preset pass rate, the model training is determined to be completed, and the structured data is input into the target model for safety detection. Otherwise, the target model is trained again.

[0046] In one implementation, converting the target point cloud data into structured data is a key step to achieve efficient processing and analysis. Point cloud data is usually composed of a large number of disordered three-dimensional coordinate points, which is difficult to be directly used as the input of machine learning models. In order to convert it into structured data, the following methods can be used: Voxelization: Divide the point cloud data into a three-dimensional grid, and each grid unit (voxel) contains point information within a certain range, thereby converting the disordered point cloud into a regular three-dimensional array. Feature extraction: Extract the geometric features (such as curvature, normal) and statistical features (such as density, local variance) of the point cloud, and organize these features into a table form. Deep learning method: Use a three-dimensional convolutional neural network (3D CNN) or a point cloud processing network (such as PointNet) to encode the point cloud and generate a structured feature vector. Through these methods, point cloud data is converted into structured data, which can better adapt to subsequent model training and safety detection processes.

[0047] In one implementation, the preset model is a large language model: the preset model adopts a large language model (LLM). The above model has powerful natural language processing capabilities and context understanding capabilities, and can process complex text data and structured data. By inputting structured data into the large language model, its pre-trained parameters and powerful generalization capabilities can be used to quickly adapt to specific tasks, such as security detection. The effects of using a large language model include: Efficient feature learning: The large language model can automatically extract key features from the data and reduce the workload of manual feature engineering. Powerful context understanding: It can understand the complex relationships between data, so as to perform security detection more accurately. Quickly adapt to new tasks: Through fine-tuning or prompt engineering, the large language model can quickly adapt to new security detection tasks without training from scratch.

[0048] In one implementation, model training and verification: input the historical training data set into the preset model for training, obtain the target model parameters, and update the model parameters to obtain the target model. Use the historical verification data set to verify the target model and calculate the pass rate. Model optimization: Through training with historical data, the model can learn the patterns and rules in the data, thereby optimizing its own parameters and improving its ability to predict new data. Performance evaluation: Through testing of the verification data set, the performance of the model can be objectively evaluated to ensure its reliability and accuracy in practical applications. Iterative improvement: If the pass rate does not meet the preset standard, secondary training is performed to further optimize the model to ensure that its performance meets actual needs.

[0049] In one implementation, the security detection of three-dimensional data transmission is performed through a large language model. The structured data is input into the target model for security detection. By utilizing the powerful capabilities of the large language model, efficient detection of security risks in the three-dimensional data transmission process can be achieved. The benefits include: Accurate identification of abnormal behavior: The large language model can understand the context and pattern of the data, and accurately identify abnormal behavior in the transmission process, such as data tampering, data leakage, etc. Real-time security warning: By analyzing the transmission data in real time, potential security threats can be discovered in time, and warnings can be issued to reduce the occurrence of security incidents. Adaptive ability: The large language model can dynamically adjust the detection strategy according to the new data to adapt to the ever-changing network environment and security threats. Reduce false alarm rate: Using its powerful context understanding and feature extraction capabilities, it can reduce false alarms and improve the accuracy of security detection.

[0050] In one embodiment, the task execution module is also used to perform a risk assessment on the structured data to obtain a feedback report if the structured data is determined to be dangerous, and report the feedback report as a vulnerability.

[0051] In one implementation method, the detected unsafe data is quickly evaluated to determine the data type, scope of impact and potential risks; the unsafe data is isolated to prevent it from further spreading or affecting other systems, and the relevant network connections or access rights are blocked; the exploited security vulnerabilities are repaired according to feedback reports, and security policies and tools are updated, including but not limited to firewalls and intrusion detection systems; real-time monitoring and auditing of data flow are strengthened, and all access and operation behaviors are recorded to track and analyze potential security threats; the integrity of data backups is verified, and data is restored from backups when necessary to reduce losses caused by unsafe data; based on the results of event analysis, data security policies and protection measures are continuously optimized to prevent similar problems from occurring again.

[0052] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A web-based infrastructure programmatic modeling system, characterized in that: The system includes: a data modeling module, a path analysis module, a data transmission module and a task execution module: The data modeling module is used to obtain the data file on the web end, and perform programmatic modeling on the data file to obtain the target file; The path analysis module is used to perform congestion detection on the transmission path of the current cycle, and if the transmission path is determined to be congested, perform path optimization to obtain a transmission strategy; The data transmission module is used to perform spatial compression on the target file to obtain target point cloud data, convert the target point cloud data into structured data, and perform security detection on the structured data; The task execution module is used to perform data transmission according to the transmission strategy if the structured data is determined to be safe.

2. A web-based infrastructure programmatic modeling system according to claim 1, characterized in that: The path analysis module includes: a data detection module, a delay calculation module and a weight calculation module: The data detection module is used to send a detection data packet to the web end and the cloud end respectively to obtain a first detection time and a second detection time, and the web end and the cloud end receive the detection data packet and send the detection data packet to each other to obtain a first verification time and a second verification time; The delay calculation module is used to calculate the link delay according to the first detection time, the second detection time, the first verification time and the second verification time, The weight calculation module is used to obtain the minimum remaining bandwidth of the target path, calculate the path delay according to the link delay of each link in the target path, and calculate the target path weight according to the minimum remaining bandwidth and the path delay; The transmission paths are screened according to the target path weights to obtain a transmission path set, and a transmission strategy is determined according to the transmission path set.

3. A web-based infrastructure programmatic modeling system according to claim 1, characterized in that: The path analysis module also includes a strategy optimization module; The strategy optimization module is used to obtain an expert data set and a historical log set, and input the path weight, link bandwidth, and historical log set into a preset model to obtain a transmission strategy; The strategy optimization module is used to perform the following steps, which specifically include: Step 1: dividing the expert data set according to preset rules to obtain expert data subsets, and determining the expert strategy according to the expert data subsets; Step 2: Initialize the strategy to obtain the target strategy, and set the target strategy as the optimal solution of the reward function; Step 3: Save the dynamic distribution of the target strategy , update the reward function by online projected gradient method; Step 4: determine the corresponding historical log according to the target strategy, calculate the service quality evaluation value according to the link bandwidth, packet loss rate and historical log, and determine the transmission strategy according to the service quality evaluation value.

4. A web-based infrastructure programmatic modeling system according to claim 1, characterized in that: The strategy optimization module also includes: an evaluation value calculation module and a strategy determination module: The evaluation value calculation module is used to calculate the service quality evaluation value according to the historical log, the cache packet transmission speed and the path weight, and select the transmission path according to the service quality evaluation value; the historical log includes: average delay and maximum packet loss rate; The strategy determination module is used to use the transmission path as the transmission strategy if the service quality evaluation value is greater than a preset threshold; Service quality calculation formula: ， in, Represents the transmission quality, Represents the standard cache packet transmission speed, Represents the cache packet data transmission speed, represents the packet loss rate, represents the average delay, T represents the unit time, and W represents the path weight.

5. The web-based infrastructure programmatic modeling system according to claim 1, characterized in that: The data transmission module also includes: a file cutting module and a data compression module: The file cutting module is used to determine the Euclidean space of the target file, and divide the Euclidean space into multiple data blocks according to a preset length; The data compression module is used to set marking points in any data block according to preset rules if there is an intersection between any data block and the Euclidean space, and combine all marking points to obtain target point cloud data.

6. A web-based infrastructure programmatic modeling system according to claim 5, characterized in that: The data compression module also includes: a data conversion module, a model training module and a security detection module: The data conversion module is used to convert the target point cloud data into structured data and obtain a historical data set; the historical data set includes: a historical training data set and a historical verification data set; The model training module is used to input the historical training data set into a preset model for training to obtain target model parameters, and update the model parameters of the preset model according to the target model parameters to obtain the target model; The safety detection module is used to input the historical verification data set into the target model for verification to obtain the pass rate. If the pass rate is greater than the preset pass rate, the model training is determined to be completed, and the structured data is input into the target model for safety detection. Otherwise, the target model is trained again.

7. A web-based infrastructure programmatic modeling system according to claim 6, characterized in that: The task execution module is also used to perform risk assessment on the structured data to obtain a feedback report if the structured data is determined to be dangerous, and report the feedback report as a vulnerability.

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