Pipeline detection system based on combination of browser and mobile terminal server architecture

Abstract

The application discloses a pipeline detection system based on combination of a browser and a mobile terminal server architecture, relates to the field of network pipeline transmission monitoring, and utilizes a browser kernel loading rate analysis model, intrusion detection and mobile terminal interactive data transmission technology to transmit network pipeline transmission operation monitoring intrusion detection and mobile terminal interactive data, network pipeline transmission monitoring intrusion detection and mobile terminal interactive data, medical worker operation intrusion detection and mobile terminal interactive data, network pipeline transmission state intrusion detection and mobile terminal interactive data and high-precision browser kernel flow effective intrusion detection and mobile terminal interactive data obtained by a network pipeline transmission intrusion detection and mobile terminal interactive data management module to a server security neural network neuron adjustment module through the browser kernel loading rate analysis model, and to a network pipeline transmission intrusion virus type detection module through the browser kernel loading rate analysis model, so that the network pipeline transmission monitoring network pipeline transmission timeliness and reliability are effectively improved, and the network pipeline transmission intrusion detection level is improved.

Description

Technical Field

[0001] This invention relates to the field of network pipeline intrusion detection, and more particularly to a pipeline detection system based on a combination of browser and mobile server architecture. Background Technology

[0002] With the continuous development of network technology, network security has become a critical issue. Existing solutions involve using the NIDS (Network Intrusion Detection System) to monitor network ports. This system detects data within each network segment and uses all data from the detected segments as training samples to train a DNWN (Deep Neural Networks) model. The DNN model architecture includes an input layer, hidden layers, and an output layer, with each layer consisting of multiple neurons. The DNN model is then used to identify whether the data is abnormal.

[0003] As the number of layers and neurons in each layer of a DNN model increases, the features of the training samples learned by the DNN model also increase. The DNN model is prone to learning redundant sign bits of the training samples, resulting in higher and higher redundancy of the DNN, causing overfitting and reducing the accuracy of the DNN model. Therefore, the accuracy of existing technologies in identifying network intrusion data is not high. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a pipeline detection system based on a combination of browser and mobile server architecture.

[0005] This invention is achieved through the following technical solution:

[0006] A pipeline detection system based on a browser and mobile server architecture includes: a network pipeline transmission intrusion detection and mobile interaction data management module, which manages in real time the network pipeline transmission monitoring intrusion detection and mobile interaction data requiring intrusion detection and control, monitors the network pipeline transmission status intrusion detection and mobile interaction data, and collects valid intrusion detection and mobile interaction data from browser kernel flow, and organizes it into an intrusion detection and mobile interaction dataset, which is then sent to the different network pipeline transmission browser loading rate evaluation modules; the different network pipeline transmission browser loading rate evaluation modules are equipped with different browser kernel loading rate analysis models, and the different browser kernel loading rate analysis models are compared... An association algorithm is provided. The intrusion detection and mobile interaction dataset is transmitted to the browser kernel load rate analysis model. The intrusion detection and mobile interaction dataset is then transmitted via the association algorithm, and subsequently sent to the server security neural network neuron adjustment module via the browser kernel load rate analysis model. The server security neural network neuron adjustment module includes a network pipeline transmission information table containing network pipeline transmission load rate intrusion detection and mobile interaction data. The server security neural network neuron adjustment module monitors the intrusion detection and mobile interaction dataset and its network pipeline transmission... Loading rate intrusion detection and mobile interaction data are compared, normalized, and cleaned to obtain different categories of network pipeline transmission intrusion detection and mobile interaction data. This network pipeline transmission intrusion detection and mobile interaction data is then transmitted to the browser kernel loading rate analysis model via the association algorithm, and finally passed to the network pipeline transmission intrusion virus type detection module. The network pipeline transmission intrusion virus type detection module includes a network pipeline transmission intrusion detection interface and a network pipeline transmission speed monitoring interface. These interfaces are used to receive network... The system transmits intrusion detection and mobile interaction data through network pipelines. Based on the category of the intrusion detection and mobile interaction data, it visualizes the corresponding browser kernel evaluation instructions. The browser kernel evaluation instructions are transmitted to the browser kernel loading rate analysis model through the network pipeline intrusion virus type detection module, and then transmitted to the browser kernel loading rate analysis model through the correlation algorithm. The browser kernel loading rate analysis model transmits the relevant real-time monitoring intrusion detection and mobile interaction data to the network pipeline intrusion detection and mobile interaction data management module. The visualization of the network pipeline intrusion detection and mobile interaction data is completed through the network pipeline intrusion detection interface.

[0007] In this solution, browser kernel load rate analysis models are used for intrusion detection and mobile interaction data transmission technology. In areas with poor network coverage, different browser kernel load rate analysis models are transmitted through different network pipelines composed of correlation algorithms. The browser load rate evaluation module serves as the data transmission link for intrusion detection and mobile interaction. The network pipeline transmission intrusion detection and mobile interaction data management module acquires monitoring intrusion detection and mobile interaction data, network pipeline transmission monitoring intrusion detection and mobile interaction data, medical worker operation intrusion detection and mobile interaction data, network pipeline transmission status intrusion detection and mobile interaction data, and high-precision browser kernel flow effective intrusion detection and mobile interaction data. These are then processed by the browser kernel load rate analysis model. The data is transmitted via a correlation algorithm to the server's security neural network neuron adjustment module. After normalization and cleaning, it generates network pipeline transmission intrusion detection and mobile terminal interaction data. This data is then sent to the network pipeline transmission intrusion virus type detection module via a browser kernel loading rate analysis model. This enables the monitoring and visualization of network pipeline transmission intrusion detection and mobile terminal interaction data, eliminating limitations on the range of intrusion detection and mobile terminal interaction data transmission. It also allows for monitoring different types of network pipeline transmission, effectively improving the timeliness and reliability of network pipeline transmission monitoring, enhancing the technological level of network pipeline transmission monitoring, and improving the level of network pipeline transmission intrusion detection.

[0008] Furthermore, the server security neural network neuron adjustment module includes a network pipeline transmission category judgment system. This system sets a corresponding model based on the network pipeline transmission loading rate intrusion detection and mobile terminal interaction data from the network pipeline transmission information table. The server security neural network neuron adjustment module screens, normalizes, and cleans the intrusion detection and mobile terminal interaction dataset, and imports the processed intrusion detection and mobile terminal interaction data into the preset model. By adjusting and optimizing the processed intrusion detection and mobile terminal interaction data and the model, network pipeline transmission intrusion detection and mobile terminal interaction data of the corresponding category are generated and transmitted to the network pipeline transmission intrusion virus type detection module through the browser kernel loading rate analysis model.

[0009] In this solution, a network pipeline transmission category judgment system is established to classify and determine the network pipeline transmission category. The accuracy of the classification of network pipeline transmission categories is improved by comparing the network pipeline transmission loading rate intrusion detection with mobile terminal interaction data. Classified network pipeline transmission alerts can reduce economic losses caused by disasters and casualties among medical workers.

[0010] Furthermore, the network pipeline transmission intrusion detection and mobile interaction data management module includes a network pipeline transmission monitoring sensor, a network pipeline transmission intrusion detection and mobile interaction data collection module, a network pipeline transmission module, and a browser kernel loading rate analysis model intrusion detection and mobile interaction data transmission module. Different input terminals of the network pipeline transmission monitoring sensor convert the managed intrusion detection and mobile interaction data into different electrical signals. Different output terminals of the network pipeline transmission monitoring sensor are connected to the network pipeline transmission intrusion detection and mobile interaction data collection module. The network pipeline transmission intrusion detection and mobile interaction data collection module converts the electrical signals into various types of monitored intrusion detection and mobile interaction data. The network pipeline transmission module aggregates these various types of monitored intrusion detection and mobile interaction data and transmits them to the browser kernel loading rate analysis model intrusion detection and mobile interaction data transmission module. The browser kernel loading rate analysis model intrusion detection and mobile interaction data transmission module and the browser kernel loading rate analysis model engage in bidirectional intrusion detection and mobile interaction data transmission. The browser kernel loading rate analysis model intrusion detection and mobile interaction data transmission module transmits the aggregated various types of monitored intrusion detection and mobile interaction data to the browser kernel loading rate analysis model.

[0011] Preferably, the server security neural network neuron adjustment module, the network pipeline transmission intrusion virus type detection module, and the network pipeline transmission intrusion detection and mobile terminal interaction data management module all interact with the browser kernel loading rate analysis model for bidirectional intrusion detection and mobile terminal interaction data transmission. The server security neural network neuron adjustment module is also used to receive browser kernel evaluation instructions from the network pipeline transmission intrusion virus type detection module and send these instructions to the network pipeline transmission intrusion detection and mobile terminal interaction data management module to obtain specific monitoring intrusion detection and mobile terminal interaction data. Because the intrusion detection and mobile terminal interaction data transmission process involves a large volume of data and requires selective management, in areas and periods prone to network pipeline transmission, a large amount of monitoring intrusion detection and mobile terminal interaction data managed by the network pipeline transmission intrusion detection and mobile terminal interaction data management module is actively acquired to pre-process detailed normalization and cleaning of areas prone to network pipeline transmission.

[0012] Based on the above system, the present invention further improves the pipeline inspection system based on a combination of browser and mobile server architecture. The remote service of this system includes the following steps:

[0013] The network pipeline transmission intrusion detection and mobile terminal interaction data management module acquires network pipeline transmission monitoring intrusion detection and mobile terminal interaction data, network pipeline transmission status intrusion detection and mobile terminal interaction data, and browser kernel flow effective intrusion detection and mobile terminal interaction data that require intrusion detection and control in real time management, and organizes them into an intrusion detection and mobile terminal interaction dataset and sends it to the browser kernel loading rate analysis model.

[0014] The browser kernel loading rate analysis model transmits the intrusion detection and mobile interaction dataset through an association algorithm, and sends the intrusion detection and mobile interaction dataset to the server security neural network neuron adjustment module;

[0015] After normalization and cleaning, the server security neural network neuron adjustment module generates network pipeline transmission intrusion detection and mobile terminal interaction data, and sends the network pipeline transmission intrusion detection and mobile terminal interaction data to the browser kernel loading rate analysis model.

[0016] A network pipeline transmission category judgment system and a network pipeline transmission information table are established in the server security neural network neuron adjustment module.

[0017] The network pipeline transmission information table stores network pipeline transmission loading rate intrusion detection and mobile terminal interaction data. A corresponding model is set in the network pipeline transmission category judgment system. The model corresponds to different categories of network pipeline transmission loading rate intrusion detection and mobile terminal interaction data.

[0018] The server security neural network neuron adjustment module will import the intrusion detection and mobile interaction dataset for screening, normalization, and cleaning, and then import the processed intrusion detection and mobile interaction data into the preset model. By adjusting and optimizing the processed intrusion detection and mobile interaction data and the model, it will determine whether there is server paralysis and generate network pipelines to transmit intrusion detection and mobile interaction data of the corresponding category.

[0019] When a server failure occurs, the corresponding category of network pipeline intrusion detection and mobile terminal interaction data will be transmitted to the network pipeline intrusion virus type detection module through the browser kernel loading rate analysis model.

[0020] The browser kernel loading rate analysis model transmits network pipeline intrusion detection and mobile terminal interaction data through an association algorithm, and sends the network pipeline intrusion detection and mobile terminal interaction data to the network pipeline intrusion virus type detection module.

[0021] The network pipeline transmission intrusion virus type detection module completes the visualization of network pipeline transmission intrusion detection and mobile terminal interactive data through the network pipeline transmission intrusion detection interface and the network pipeline transmission network speed monitoring interface.

[0022] The network pipeline intrusion virus type detection module visualizes browser kernel evaluation commands based on network pipeline intrusion detection and mobile terminal interaction data, and sends them to the network pipeline intrusion detection and mobile terminal interaction data management module through the browser kernel loading rate analysis model to retrieve relevant real-time monitoring intrusion detection and mobile terminal interaction data.

[0023] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0024] This invention is a pipeline detection system based on a browser and mobile server architecture. It transmits network pipeline transmission intrusion detection and mobile interaction data, including network pipeline transmission operation monitoring intrusion detection and mobile interaction data, network pipeline transmission monitoring intrusion detection and mobile interaction data, medical worker operation intrusion detection and mobile interaction data, network pipeline transmission status intrusion detection and mobile interaction data, and high-precision browser kernel flow effective intrusion detection and mobile interaction data, to a server security neural network neuron adjustment module via a browser kernel load rate analysis model. After normalization and cleaning, it generates network pipeline transmission intrusion detection and mobile interaction data, which is then sent to a network pipeline transmission intrusion virus type detection module via the browser kernel load rate analysis model. This enables the transmission and visualization of network pipeline transmission intrusion detection and mobile interaction data, removing limitations on the range of intrusion detection and mobile interaction data transmission for network pipeline transmission monitoring. It also enables monitoring of different network pipeline transmissions, effectively improving the timeliness and reliability of network pipeline transmission monitoring, enhancing the technological level of network pipeline transmission monitoring, and improving the accuracy of network pipeline transmission intrusion detection. Attached Figure Description

[0025] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0026] Figure 1 This is a functional diagram of a pipeline inspection system module based on a browser and mobile server architecture in an embodiment of the present invention.

[0027] Figure 2 This is a flowchart illustrating the operation of a pipeline inspection system based on a browser and mobile server architecture, as described in this embodiment of the invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are only for explaining this invention and are not intended to limit this invention.

[0029] Example

[0030] like Figure 1As shown, this invention is a pipeline detection system based on a browser and mobile server architecture, comprising: a network pipeline transmission intrusion detection and mobile interaction data management module, which manages in real time the network pipeline transmission monitoring intrusion detection and mobile interaction data requiring intrusion detection and control, monitors the network pipeline transmission status intrusion detection and mobile interaction data, and the browser kernel flow valid intrusion detection and mobile interaction data, and organizes it into an intrusion detection and mobile interaction dataset, which is then sent to different network pipeline transmission browser loading rate evaluation modules; the different network pipeline transmission browser loading rate evaluation modules are equipped with different browser kernel loading rate analysis models, and an association algorithm is set between the different browser kernel loading rate analysis models. The intrusion detection and mobile interaction dataset is transmitted to the browser kernel loading rate analysis model, and then transmitted through the association algorithm, and then through the browser kernel. The loading rate analysis model sends the intrusion detection and mobile interaction dataset to the server security neural network neuron adjustment module. This module contains a network pipeline transmission information table with loading rate intrusion detection and mobile interaction data. The module compares, normalizes, and cleans the intrusion detection and mobile interaction data from the intrusion detection and mobile interaction dataset with the network pipeline loading rate intrusion detection and mobile interaction data, obtaining different categories of network pipeline intrusion detection and mobile interaction data. This data is then transmitted to the browser kernel loading rate analysis model via an association algorithm and finally to the network pipeline intrusion virus type detection module.The network pipeline intrusion virus type detection module includes a network pipeline intrusion detection interface and a network pipeline speed monitoring interface. These interfaces receive data from the interaction between the network pipeline intrusion detection module and the mobile terminal, and visualize corresponding browser kernel evaluation instructions based on the category of this data. These instructions are then transmitted through the network pipeline intrusion virus type detection module to the browser kernel load rate analysis model, and further processed by an association algorithm. Finally, the browser kernel load rate analysis model transmits these instructions to the network pipeline intrusion detection and mobile terminal interaction data management module to retrieve relevant data. Real-time monitoring of intrusion detection and mobile interaction data is implemented. This data is visualized through a network pipeline intrusion detection interface and sent to the network pipeline intrusion virus type detection module via a browser kernel loading rate analysis model. This enables the monitoring and visualization of intrusion detection and mobile interaction data within the network pipeline, eliminating limitations on the data transmission range. It supports different network pipeline monitoring methods, effectively improving the timeliness and reliability of network pipeline monitoring, enhancing the technological level of network pipeline monitoring, and improving network pipeline transmission capabilities.

[0031] The expression for the browser kernel loading rate analysis model is:

[0032] y(s)=β[U qw V hj (s)+Z kl (M(σ))]+B po

[0033] Where y(s) represents the storage function for comprehensive information analysis of loading rate data, β represents the storage coefficient matrix for analysis of loading rate data, and U qw V represents the time distribution of data transmission at the loading rate. hj (s) represents the model-calculated value of the loading rate data, Z kl M(σ) represents the error range of the loading rate information, and B represents the analysis function for abnormal loading rate data. po Indicates storage error;

[0034] The dynamic control information set for the loading rate data is expressed as follows:

[0035] F(n)=ζ·R(n)(1-N)+y(s)·R(n)

[0036] Where F(n) represents the dynamic control information set of loading rate data, ζ represents the parameter to be adjusted to control the loading rate data, R(n) represents the duration of loading rate data transmission, N represents the control factor, which is a constant, and X represents the change matrix of loading rate data between different time periods.

[0037] As a preferred embodiment of the above, the server security neural network neuron adjustment module includes a network pipeline transmission category judgment system. This system sets a corresponding model based on the network pipeline transmission loading rate intrusion detection and mobile terminal interaction data from the network pipeline transmission information table. The server security neural network neuron adjustment module screens, normalizes, and cleans the intrusion detection and mobile terminal interaction dataset, and imports the processed data into the preset model. By adjusting and optimizing the processed data and model, corresponding categories of network pipeline transmission intrusion detection and mobile terminal interaction data are generated and transmitted to the network pipeline transmission intrusion virus type detection module via the browser kernel loading rate analysis model. Through multi-level network pipeline transmission alerts, economic losses and casualties among medical workers are reduced.

[0038] As a preferred embodiment of the above, the network pipeline transmission intrusion detection and mobile interaction data management module includes a network pipeline transmission monitoring sensor, a network pipeline transmission intrusion detection and mobile interaction data collection module, a network pipeline transmission module, and a browser kernel loading rate analysis model intrusion detection and mobile interaction data transmission module. The input terminals of different network pipeline transmission monitoring sensors convert the managed intrusion detection and mobile interaction data into different electrical signals. The output terminals of different network pipeline transmission monitoring sensors are connected to the network pipeline transmission intrusion detection and mobile interaction data collection module. The network pipeline transmission intrusion detection and mobile interaction data collection module converts the electrical signals into various types of monitored intrusion detection and mobile interaction data. The network pipeline transmission module aggregates these various monitored intrusion detection and mobile interaction data and transmits them to the browser kernel loading rate analysis model intrusion detection and mobile interaction data transmission module. The browser kernel loading rate analysis model intrusion detection and mobile interaction data transmission module and the browser kernel loading rate analysis model engage in bidirectional intrusion detection and mobile interaction data transmission. The browser kernel loading rate analysis model intrusion detection and mobile interaction data transmission module transmits the aggregated various monitored intrusion detection and mobile interaction data to the browser kernel loading rate analysis model.

[0039] As a preferred embodiment of the above, such as Figure 1As shown, the server security neural network neuron adjustment module, the network pipeline intrusion virus type detection module, and the network pipeline intrusion detection and mobile terminal interaction data management module all interact with the browser kernel loading rate analysis model for bidirectional intrusion detection and mobile terminal interaction data transmission. The server security neural network neuron adjustment module also receives browser kernel evaluation instructions from the network pipeline intrusion virus type detection module and sends these instructions to the network pipeline intrusion detection and mobile terminal interaction data management module to obtain specific monitoring intrusion detection and mobile terminal interaction data. Due to the large volume of intrusion detection and mobile terminal interaction data during transmission, there is a problem of selective management of this data. Therefore, in areas and periods prone to network pipeline transmission, a large amount of monitoring intrusion detection and mobile terminal interaction data managed by the network pipeline intrusion detection and mobile terminal interaction data management module is actively acquired to pre-process detailed normalization and cleaning of areas prone to network pipeline transmission.

[0040] Furthermore, this invention improves the pipeline detection system based on a combination of browser and mobile server architecture, such as... Figure 2 As shown, it includes the following steps:

[0041] The network pipeline transmission intrusion detection and mobile interaction data management module acquires network pipeline transmission monitoring intrusion detection and mobile interaction data, monitoring network pipeline transmission status intrusion detection and mobile interaction data, and browser kernel flow effective intrusion detection and mobile interaction data that are required for real-time management of intrusion detection and control, and organizes them into an intrusion detection and mobile interaction dataset, which is then sent to the browser kernel loading rate analysis model.

[0042] The browser kernel loading rate analysis model transmits intrusion detection and mobile interaction datasets through a correlation algorithm, and sends the intrusion detection and mobile interaction datasets to the server security neural network neuron adjustment module;

[0043] After normalization and cleaning, the server security neural network neuron adjustment module generates network pipeline transmission intrusion detection and mobile terminal interaction data, and sends the network pipeline transmission intrusion detection and mobile terminal interaction data to the browser kernel loading rate analysis model.

[0044] In the server security neural network neuron adjustment module, a network pipeline transmission category judgment system and a network pipeline transmission information table are established.

[0045] The network pipeline transmission loading rate intrusion detection and mobile terminal interaction data are stored in the network pipeline transmission information table. The corresponding model is set in the network pipeline transmission category judgment system. The model corresponds to different categories of network pipeline transmission loading rate intrusion detection and mobile terminal interaction data.

[0046] The server security neural network neuron adjustment module will import the intrusion detection and mobile interaction dataset for screening, normalization, and cleaning, and then import the processed intrusion detection and mobile interaction data into the preset model. By adjusting and optimizing the processed intrusion detection and mobile interaction data and the model, it will determine whether there is server paralysis and generate network pipelines to transmit intrusion detection and mobile interaction data of the corresponding category.

[0047] When a server failure occurs, the corresponding category of network pipeline intrusion detection and mobile terminal interaction data will be transmitted to the network pipeline intrusion virus type detection module through the browser kernel loading rate analysis model.

[0048] The browser kernel loading rate analysis model transmits intrusion detection and mobile interaction data through a correlation algorithm, and sends the intrusion detection and mobile interaction data through the network pipeline to the network pipeline intrusion virus type detection module.

[0049] The network pipeline intrusion virus type detection module uses the network pipeline intrusion detection interface and the network pipeline network speed monitoring interface to visualize the data of network pipeline intrusion detection and mobile terminal interaction.

[0050] The network pipeline transmission intrusion virus type detection module includes a multi-level intrusion detection center and related medical worker APPs. It is used to receive network pipeline transmission intrusion detection and mobile terminal interaction data visualized by the server security neural network neuron adjustment module, and visualize the corresponding browser kernel evaluation instructions to the remote browser kernel evaluation and intrusion detection and mobile terminal interaction data center.

[0051] The network pipeline intrusion virus type detection module visualizes browser kernel evaluation commands based on network pipeline intrusion detection and mobile terminal interaction data. These commands are then sent to the network pipeline intrusion detection and mobile terminal interaction data management module via the browser kernel loading rate analysis model to retrieve relevant real-time monitoring intrusion detection and mobile terminal interaction data.

[0052] This invention utilizes a browser kernel load rate analysis model for intrusion detection and mobile interaction data transmission technology. It transmits network pipeline operation monitoring intrusion detection and mobile interaction data, medical worker operation intrusion detection and mobile interaction data, network pipeline status intrusion detection and mobile interaction data, and high-precision browser kernel flow effective intrusion detection and mobile interaction data obtained by the network pipeline intrusion detection and mobile interaction data management module to the server security neural network neuron adjustment module via the browser kernel load rate analysis model. After normalization and cleaning, it generates network pipeline intrusion detection and mobile interaction data, which is then sent to the network pipeline intrusion virus type detection module via the browser kernel load rate analysis model. This achieves network pipeline intrusion detection and mobile interaction data transmission and visualization, eliminating limitations on the range of intrusion detection and mobile interaction data transmission for network pipeline monitoring. It enables different network pipeline monitoring and transmission, effectively improving the timeliness and reliability of network pipeline monitoring and transmission, enhancing the technological level of network pipeline monitoring, and improving network pipeline transmission capabilities.

[0053] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood through the specific circumstances.

[0054] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A pipeline inspection system based on a browser and mobile server architecture, characterized in that, The system includes: The network pipeline transmission intrusion detection and mobile terminal interaction data management module is used to manage intrusion detection and control data of network pipeline transmission intrusion detection and mobile terminal interaction in real time, monitor network pipeline transmission status intrusion detection and mobile terminal interaction data and browser kernel flow valid intrusion detection and mobile terminal interaction data, and organize them into intrusion detection and mobile terminal interaction datasets and send them to different network pipeline transmission browser loading rate evaluation modules. The browser loading rate evaluation module for different network pipelines is equipped with different browser kernel loading rate analysis models. There is an association algorithm between the different browser kernel loading rate analysis models. The intrusion detection and mobile interaction dataset is transmitted to the browser kernel loading rate analysis model and transmitted through the association algorithm. Then, the intrusion detection and mobile interaction dataset is sent to the server security neural network neuron adjustment module through the browser kernel loading rate analysis model. The server security neural network neuron adjustment module includes a network pipeline transmission information table. This table contains network pipeline transmission loading rate intrusion detection and mobile terminal interaction data. The module compares, normalizes, and cleans the intrusion detection and mobile terminal interaction data from the intrusion detection and mobile terminal interaction dataset with the network pipeline transmission loading rate intrusion detection and mobile terminal interaction data to obtain different categories of network pipeline transmission intrusion detection and mobile terminal interaction data. This network pipeline transmission intrusion detection and mobile terminal interaction data is then transmitted to the browser kernel loading rate analysis model. The network pipeline transmission intrusion detection and mobile terminal interaction data is transmitted to the browser kernel loading rate analysis model via the association algorithm and then passed to the network pipeline transmission intrusion virus type detection module. The network pipeline intrusion virus type detection module includes a network pipeline intrusion detection interface and a network pipeline speed monitoring interface. These interfaces receive network pipeline intrusion detection and mobile terminal interaction data, and visualize corresponding browser kernel evaluation instructions based on the category of the data. The browser kernel evaluation instructions are transmitted through the network pipeline intrusion virus type detection module to the browser kernel loading rate analysis model, and then through the association algorithm. The browser kernel loading rate analysis model then transmits the data to the network pipeline intrusion detection and mobile terminal interaction data management module to retrieve relevant real-time monitoring intrusion detection and mobile terminal interaction data. The visualization of the network pipeline intrusion detection and mobile terminal interaction data is completed through the network pipeline intrusion detection interface.

2. The pipeline inspection system based on a browser and mobile server architecture as described in claim 1, characterized in that, The server security neural network neuron adjustment module includes a network pipeline transmission category judgment system. This system sets a corresponding model based on the network pipeline transmission loading rate intrusion detection and mobile terminal interaction data from the network pipeline transmission information table. The server security neural network neuron adjustment module screens, normalizes, and cleans the intrusion detection and mobile terminal interaction dataset, and imports the processed intrusion detection and mobile terminal interaction data into the preset model. By adjusting and optimizing the processed intrusion detection and mobile terminal interaction data and the model, network pipeline transmission intrusion detection and mobile terminal interaction data of the corresponding category is generated and transmitted to the network pipeline transmission intrusion virus type detection module through the browser kernel loading rate analysis model.

3. The pipeline inspection system based on a browser and mobile server architecture as described in claim 1, characterized in that, The network pipeline intrusion detection and mobile interaction data management module includes a network pipeline monitoring sensor, a network pipeline intrusion detection and mobile interaction data collection module, a network pipeline transmission module, and a browser kernel load rate analysis model intrusion detection and mobile interaction data transmission module. Different input terminals of the network pipeline monitoring sensor convert the managed intrusion detection and mobile interaction data into different electrical signals. The output terminals of different network pipeline monitoring sensors are connected to the network pipeline intrusion detection and mobile interaction data collection module. The network pipeline intrusion detection and mobile interaction data collection module converts the electrical signals into various types of monitored intrusion detection and mobile interaction data. The network pipeline transmission module aggregates these various monitored intrusion detection and mobile interaction data and transmits them to the browser kernel load rate analysis model intrusion detection and mobile interaction data transmission module. The browser kernel load rate analysis model intrusion detection and mobile interaction data transmission module and the browser kernel load rate analysis model engage in bidirectional intrusion detection and mobile interaction data transmission. The browser kernel load rate analysis model intrusion detection and mobile interaction data transmission module transmits the aggregated various monitored intrusion detection and mobile interaction data to the browser kernel load rate analysis model.

4. The pipeline inspection system based on a browser and mobile server architecture as described in claim 1, characterized in that, The server security neural network neuron adjustment module, the network pipeline transmission intrusion virus type detection module, and the network pipeline transmission intrusion detection and mobile terminal interaction data management module all interact with the browser kernel loading rate analysis model for bidirectional intrusion detection and mobile terminal interaction data transmission. The server security neural network neuron adjustment module is also used to receive browser kernel evaluation instructions from the network pipeline transmission intrusion virus type detection module and send the browser kernel evaluation instructions to the network pipeline transmission intrusion detection and mobile terminal interaction data management module to obtain specific monitoring intrusion detection and mobile terminal interaction data.

5. The pipeline inspection system based on a browser and mobile server architecture according to any one of claims 1-4, characterized in that, The system's remote services include the following steps: The network pipeline transmission intrusion detection and mobile terminal interaction data management module acquires network pipeline transmission monitoring intrusion detection and mobile terminal interaction data, network pipeline transmission status intrusion detection and mobile terminal interaction data, and browser kernel flow effective intrusion detection and mobile terminal interaction data that require intrusion detection and control in real time management, and organizes them into an intrusion detection and mobile terminal interaction dataset and sends it to the browser kernel loading rate analysis model. The browser kernel loading rate analysis model transmits the intrusion detection and mobile interaction dataset through an association algorithm, and sends the intrusion detection and mobile interaction dataset to the server security neural network neuron adjustment module. After normalization and cleaning, the server security neural network neuron adjustment module generates network pipeline transmission intrusion detection and mobile terminal interaction data, and sends the network pipeline transmission intrusion detection and mobile terminal interaction data to the browser kernel loading rate analysis model. The browser kernel loading rate analysis model transmits network pipeline intrusion detection and mobile terminal interaction data through an association algorithm, and sends the network pipeline intrusion detection and mobile terminal interaction data to the network pipeline intrusion virus type detection module. The network pipeline transmission intrusion virus type detection module completes the visualization of network pipeline transmission intrusion detection and mobile terminal interactive data through the network pipeline transmission intrusion detection interface and the network pipeline transmission network speed monitoring interface. The network pipeline intrusion virus type detection module visualizes browser kernel evaluation instructions based on network pipeline intrusion detection and mobile terminal interaction data. The browser kernel evaluation instructions are then sent to the network pipeline intrusion detection and mobile terminal interaction data management module through the browser kernel loading rate analysis model to retrieve relevant real-time monitoring intrusion detection and mobile terminal interaction data.

6. The pipeline inspection system based on a browser and mobile server architecture as described in claim 5, characterized in that, The system also includes the following steps: A network pipeline transmission category judgment system and a network pipeline transmission information table are established in the server security neural network neuron adjustment module. The network pipeline transmission information table stores network pipeline transmission loading rate intrusion detection and mobile terminal interaction data. A corresponding model is set in the network pipeline transmission category judgment system. The model corresponds to different categories of network pipeline transmission loading rate intrusion detection and mobile terminal interaction data. The server security neural network neuron adjustment module will import the intrusion detection and mobile interaction dataset for screening, normalization, and cleaning, and then import the processed intrusion detection and mobile interaction data into the preset model. By adjusting and optimizing the processed intrusion detection and mobile interaction data and the model, it will determine whether there is server paralysis and generate network pipelines to transmit intrusion detection and mobile interaction data of the corresponding category. In the event of server failure, the corresponding category of network pipeline intrusion detection and mobile terminal interaction data will be transmitted to the network pipeline intrusion virus type detection module through the browser kernel loading rate analysis model.

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