Self-adaptive adjustment high-speed connection method and device

Abstract

The invention discloses a self-adaptive adjustment high-speed connection method and device, and relates to the field of data link calling. The method comprises the steps of obtaining instruction data; the instruction data comprises calling frequency, response time and response data magnitude; inputting the instruction data into a data prediction model, and outputting prediction calling data; the data prediction model is obtained by training by adopting a machine learning method based on historical log data; the historical log data comprises historical instruction data and corresponding label data; the label data comprises a response data link and fault data; the prediction calling data is used for carrying out fault early warning and carrying out adjustment planning on a link so as to carry out connection transmission of the data. According to the invention, stable transmission of information data can be realized.

Description

Technical Field

[0001] The present application relates to the field of data link calling, and in particular to a high-speed connection method and device with adaptive adjustment. Background Art

[0002] With the rapid development of existing information technology, various application data are being generated rapidly. With the development of application business, the original device types or data types do not meet the original business needs, and the interactive needs of various different protocol device data are growing. At present, there are many types of data, and the degree of electronicization is getting higher and higher. There are many types of IoT devices used by everyone in daily life, and different types of devices have different methods, which greatly increases the difficulty and cost of device docking.

[0003] And the relevant data is transmitted quickly and securely to the service terminal to realize the needs of data research, aggregation and processing, but the massive and messy data information is of no practical significance.

[0004] Due to the different docking methods of different types of devices, data processing and transmission forms are diverse, and data cannot be efficiently and quickly aggregated to the platform server, resulting in low data transmission efficiency. In addition, due to the different locations of terminal devices, the platform needs to manage multiple link data. When one of the link data fails, it needs to be manually repaired, affecting data transmission efficiency. Therefore, how to achieve stable transmission of data information is crucial. Summary of the invention

[0005] The purpose of this application is to provide a high-speed connection method and device with adaptive adjustment, which can realize stable transmission of information data.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides an adaptively regulated high-speed connection method, the adaptively regulated high-speed connection method comprising:

[0008] Obtain instruction data; the instruction data includes: call frequency, response time and response data magnitude;

[0009] The instruction data is input into a data prediction model, and predicted call data is output; the data prediction model is trained based on historical log data using a machine learning method; the historical log data includes: historical instruction data and corresponding label data; the label data includes: response data link and fault data; the predicted call data is used for fault warning and link adjustment planning to connect and transmit data.

[0010] Optionally, the obtaining instruction data specifically includes:

[0011] Get initial instruction data;

[0012] The initial instruction data is encrypted using the SM4 national secret encryption algorithm to obtain encrypted instruction data;

[0013] The encrypted instruction data is subjected to format compression processing to obtain the instruction data.

[0014] Optionally, the method for determining the data prediction model specifically includes:

[0015] Get historical log data;

[0016] The historical log data is preprocessed using a linear regression algorithm to obtain processed data; the preprocessing includes: normalization processing and deduplication processing;

[0017] Build a neural network;

[0018] Inputting the processed data into the neural network, training the parameters of the neural network, and obtaining a trained neural network;

[0019] The trained neural network is determined as the data prediction model.

[0020] Optionally, the adaptively adjusted high-speed connection method further includes:

[0021] When a fault warning occurs, based on the predicted link in the predicted call data, a diversion and replacement process is performed in an addressing manner to obtain a replacement link to achieve data transmission.

[0022] In a second aspect, the present application provides an adaptively adjustable high-speed connection device, the adaptively adjustable high-speed connection device comprising: a data processing module, an adaptive adjustment module, and a calling data module implemented by the adaptively adjustable high-speed connection method described above;

[0023] The data processing module is connected to the calling data module; the calling data module is connected to the adaptive adjustment module;

[0024] The data processing module is used to perform information adaptation on the platform bottom layer data based on the protocol, and encrypt the obtained adaptation data to obtain instruction data;

[0025] The calling data module is used to:

[0026] Acquire the instruction data;

[0027] Inputting the instruction data into a data prediction model and outputting prediction call data;

[0028] The adaptive adjustment module is used to perform fault warning according to the predicted call data, and to adjust and plan the link according to the predicted call data.

[0029] Optionally, the data processing module includes: a device adaptation module and an encryption transmission module;

[0030] The device adaptation module is connected to the encryption transmission module;

[0031] The device adaptation module is used to perform information adaptation on the platform bottom layer data based on the protocol to obtain the adaptation data;

[0032] The encryption transmission module is used to encrypt the initial instruction data using the SM4 national secret encryption algorithm to obtain encrypted instruction data; the initial instruction data includes: adaptation data;

[0033] The encryption transmission module is also used to perform format compression processing on the encrypted instruction data to obtain the instruction data.

[0034] Optionally, the adaptively adjustable high-speed connection device further comprises: a channel linking module;

[0035] The channel linking module is connected to the device adapting module and the encryption transmission module respectively.

[0036] Optionally, the device adaptation module includes: a device connection module, a protocol adaptation module, and a data mapping module;

[0037] The device connection module is connected to the protocol adaptation module; the protocol adaptation module is connected to the data mapping module; the data mapping module is also connected to the channel link module;

[0038] The device connection module is used to receive platform bottom layer data;

[0039] The protocol adaptation module is used to perform information adaptation on the underlying data of the platform based on the protocol to obtain adapted data;

[0040] The data mapping module is used to transmit the adaptation data to the encryption transmission module in the form of http protocol.

[0041] Optionally, the protocols include: SSH protocol, Telnet protocol, HTTP protocol, SFTP protocol, CORBA protocol, SSL protocol, SOAP protocol and Socket protocol.

[0042] Optionally, the channel link module uses https protocol to interactively connect with the device adaptation module; the channel link module is connected with the encrypted transmission module based on an ssl encrypted tunnel.

[0043] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0044] The present application provides a high-speed connection method and device with adaptive adjustment, which inputs the acquired instruction data into the data prediction model, outputs the predicted call data, and then performs fault warning based on the predicted call data, and adjusts the link planning to connect and transmit data. By predicting based on the overall platform call chain, that is, based on the data prediction model, the overall platform connection and joint debugging are adjusted in real time and automatically to ensure smooth and high-speed interaction between business applications and devices, without the need for additional manual processing, reducing the untimeliness of manual operations, and ensuring the overall high-speed and stable operation of the business. As a result, the present application can achieve stable transmission of information data. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A schematic diagram of a high-speed connection method for adaptive adjustment;

[0047] Figure 2 Schematic diagram of the overall technical concept of adaptively adjusted high-speed connection. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0050] Example 1

[0051] like Figure 1 As shown, the present application provides a high-speed connection method with adaptive adjustment, the method comprising:

[0052] Step 100: Obtain instruction data, which includes call frequency, response time, and response data magnitude.

[0053] Among them, obtaining instruction data specifically includes:

[0054] Acquire initial instruction data; encrypt the initial instruction data using the SM4 national secret encryption algorithm to obtain encrypted instruction data; perform format compression on the encrypted instruction data to obtain instruction data.

[0055] Step 200: Input the instruction data into the data prediction model and output the predicted call data. The data prediction model is trained based on historical log data using machine learning methods; the historical log data includes: historical instruction data and corresponding label data; the label data includes: response data link and fault data; the predicted call data is used for fault warning and link adjustment planning to connect and transmit data.

[0056] The method for determining the data prediction model specifically includes:

[0057] Get historical log data.

[0058] A linear regression algorithm is used to preprocess historical log data to obtain processed data; the preprocessing includes: normalization processing and deduplication processing.

[0059] Construct a neural network; input the processed data into the neural network, train the parameters of the neural network, and obtain a trained neural network.

[0060] The trained neural network is determined as a data prediction model.

[0061] As an optional implementation manner, the adaptively adjusted high-speed connection method further includes:

[0062] When a fault warning occurs, based on the predicted link in the prediction call data, an addressing method is used to perform diversion and replacement processing to obtain a replacement link to achieve data transmission.

[0063] Example 2

[0064] An embodiment of the present application provides an adaptively adjustable high-speed connection device, which includes: a data processing module, an adaptive adjustment module, and a calling data module implemented by the adaptively adjustable high-speed connection method in Example 1.

[0065] The data processing module is connected to the calling data module; the calling data module is connected to the adaptive adjustment module.

[0066] The data processing module is used to perform information adaptation on the underlying data of the platform based on the protocol, and encrypt the obtained adaptation data to obtain instruction data.

[0067] The calling data module is used to obtain instruction data; the calling data module is also used to input the instruction data into the data prediction model and output the predicted calling data.

[0068] The adaptive adjustment module is used to provide fault warning according to the predicted call data, and to adjust the link planning according to the predicted call data.

[0069] In one embodiment, the data processing module includes: a device adaptation module and an encryption transmission module.

[0070] The device adaptation module is connected to the encryption transmission module; the device adaptation module is used to perform information adaptation on the underlying data of the platform based on the protocol to obtain the adaptation data.

[0071] The encryption transmission module is used to encrypt the initial instruction data using the SM4 national secret encryption algorithm to obtain encrypted instruction data; the initial instruction data includes: adaptation data.

[0072] The encryption transmission module is also used to perform format compression processing on the encrypted instruction data to obtain instruction data.

[0073] The adaptively adjustable high-speed connection device also includes: a channel connection module; the channel connection module is connected to the device adaptation module and the encryption transmission module respectively.

[0074] The device adaptation module includes: a device connection module, a protocol adaptation module, and a data mapping module; the device connection module is connected to the protocol adaptation module; the protocol adaptation module is connected to the data mapping module; and the data mapping module is also connected to the channel connection module.

[0075] The device connection module is used to receive the underlying data of the platform.

[0076] The protocol adaptation module is used to adapt the platform's underlying data based on the protocol to obtain adapted data; the data mapping module is used to transmit the adapted data to the encrypted transmission module in the form of http protocol.

[0077] Among them, the protocols include: ssh protocol, telnet protocol, http protocol, sftp protocol, corba protocol, ssl protocol, soap protocol and socket protocol.

[0078] In one embodiment, the channel link module uses the https protocol to interactively connect with the device adaptation module; the channel link module is connected with the encrypted transmission module based on the ssl encrypted tunnel.

[0079] In practical applications, such as Figure 2The overall technical concept is shown in the figure. Specifically, the device adaptation module is the overall foundation. On this basis, the establishment of the underlying device capabilities and data interaction are realized through the channel link module and the high-speed encrypted transmission module. In the process of data interaction with the device, the platform data prediction model is built based on the existing data call interaction log data, and the data path of the existing link is predicted in real time according to the model data, so as to realize the adaptive adjustment capability of the high-speed connection capability of the data channel.

[0080] Specifically, the device adaptation module of the platform is mainly used to ensure the rapid access and adaptation of the platform's magnetic layer device module, and mainly includes a device connection module, a protocol adaptation module and a data mapping module.

[0081] The device adapter module can be compatible with various types of devices from various manufacturers. Among them, the protocol adapter module quickly adapts to various protocol devices such as ssh, telnet, http, sftp, corba, ssl, soap, socket, etc. by calling the platform underlying data of the device connection module, realizing one-click fast access to the device. At the same time, the data mapping module supports the provision of services to the outside world with a unified http protocol to ensure the adaptability and scalability between modules.

[0082] Channel link module: mainly responsible for establishing the actual data transmission channel. The lower layer is connected to the device adapter module. It initiates interaction with the device adapter module through the unified https protocol. After obtaining the actual instruction data, it is transmitted to the upper layer calling module, i.e. the encrypted transmission module, through the ssl encrypted tunnel. At the same time, the transmission channel is subjected to business data classification, device authorization control and other authority processing. Different users have different device permissions. After the administrator performs device authorization operations, relevant users can query and operate related devices. At the same time, in order to ensure the security of the underlying access devices and the full utilization of device resources, the platform relies on the existing redis components to calculate the number of device connections in real time, and supports instantaneous concurrent control and link control of channel links; ensures the availability of the underlying devices; at the same time, for the existing established channel links, it supports session reset reuse to improve data transmission efficiency.

[0083] The platform uses the SM4 national secret encryption algorithm to implement data encryption, data compression, data retransmission and other measures to establish an efficient, secure and stable data transmission channel. At the same time, the data format can be compressed and formatted according to business needs. During the transmission process, the business data is not dropped or cached, and the data is destroyed immediately after transmission to ensure the privacy of user data. At the same time, the platform has the ability to adaptively adjust and automatically select the idle data channel in the existing data channel to achieve high-speed data transmission.

[0084] About the data prediction model: By collecting platform device instruction call logs regularly and cleaning up duplicate data in the stored log data, the cleaned log data is used as sample data for machine learning and building a platform data prediction model. For platform devices and platform upper-layer applications connected through high-speed connectors, the platform has established a real-time and complete set of security control log recording mechanisms, including application and device call frequency, response time, response data size, response data link, data failure cause, link failure cause and other log records, and records and archives the recorded log data. At the same time, for archived retention logs, the platform stores existing data logs, and performs data normalization and data deduplication processing on the log data through the linear regression algorithm. The final standardized logs are regularly input into the training model for machine learning, expanding the model sample data volume, improving the accuracy of the data prediction model judgment, and completing the construction of the platform call data performance model. Based on the trained platform call device performance model, the subsequent platform call peak is predicted in advance, and intelligent big data support is also provided for the subsequent platform sub-health prediction and adaptive adjustment.

[0085] The adaptive adjustment module is based on the existing call data performance model, and focuses on further analyzing and summarizing the overall platform call data when application node failures, device node failures, and link node failures occur, including data such as the existing call volume of instructions, concurrent device instructions, and device instruction response time, to achieve a sub-health detection mechanism for the overall business nodes of the platform from upper-level applications to lower-level devices, and to conduct preventive monitoring in advance for potential problems of platform business nodes; at the same time, based on the existing health strategy, when a business node is monitored to be in a sub-healthy state, an early warning will be issued, and the existing data business link of the platform will be automatically adjusted at the same time, and the node that is recently in an idle state of monitoring will be addressed for business diversion and replacement to ensure the stable transmission of existing data; at the same time, based on the call data performance model, the business busyness of the platform device link nodes is summarized and analyzed in real time, and idle data link channels are automatically planned to achieve efficient transmission of data services.

[0086] In order to achieve the adaptation support for the existing equipment capabilities, the platform bottom layer decouples the equipment docking module. Through the equipment adaptation module, it can quickly and dynamically adapt to various protocol devices such as ssh, telnet, http, sftp, corba, ssl, soap, socket, etc., to achieve one-click quick access to the device. At the same time, the equipment adaptation module supports the provision of services to the outside world with a unified http protocol to ensure the adaptability and scalability between modules.

[0087] Channel link module: For currently connected and adapted protocols, it supports the establishment of different data transmission channels. At the same time, for the established channel connections, in order to ensure the security of existing device connections, it supports instantaneous concurrent current limiting control to prevent the avalanche effect from causing catastrophic consequences for production equipment. In addition, for established connections, it supports connection channel reuse to reduce the time spent on connection establishment, improve the transmission efficiency of existing data channels, and improve the connection utilization rate of device resources.

[0088] Compared with existing connector technology, this application has the following advantages:

[0089] 1. Extensive device connection: Based on the deep decoupling of the underlying device docking, it supports fast adaptation and docking of multiple protocol devices such as SSH, Telnet, SSL, Socket, HTTP, CORBA, SOAP, etc., and performs business processing of device instruction issuance.

[0090] 2. Adaptive adjustment capability: Relying on data call data, predict the platform application call behavior, predict the business busyness of the existing data channel in advance, and automatically select the low-load data instruction channel.

[0091] 3. Automatic channel load adjustment: Relying on data prediction models and intelligent detection capabilities of service channels, when a business node in the overall data channel fails, it automatically addresses the nearest idle backup node to ensure smooth business operation.

[0092] 4. Sub-healthy node monitoring: Perform data aggregation and machine learning on historical node failure data to determine in advance whether the business load node is in a sub-healthy state and provide early warning of failures.

[0093] 5. Carry out data format integration and data compression to improve data transmission efficiency; at the same time, use open source nginx components and openssl national encryption algorithm to encrypt the overall data channel to ensure data transmission security.

[0094] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0095] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A high-speed connection method with adaptive adjustment, It is characterized in that The adaptively regulated high-speed connection method comprises: Obtain instruction data; the instruction data includes: call frequency, response time and response data magnitude; The instruction data is input into a data prediction model, and predicted call data is output; the data prediction model is trained based on historical log data using a machine learning method; the historical log data includes: historical instruction data and corresponding label data; the label data includes: response data link and fault data; the predicted call data is used for fault warning and link adjustment planning to connect and transmit data.

2. The adaptive high-speed connection method according to claim 1, It is characterized in that The acquisition instruction data specifically includes: Get initial instruction data; The initial instruction data is encrypted using the SM4 national secret encryption algorithm to obtain encrypted instruction data; The encrypted instruction data is subjected to format compression processing to obtain the instruction data.

3. The adaptive high-speed connection method according to claim 1, It is characterized in that The method for determining the data prediction model specifically includes: Get historical log data; The historical log data is preprocessed using a linear regression algorithm to obtain processed data; the preprocessing includes: normalization processing and deduplication processing; Build a neural network; Inputting the processed data into the neural network, training the parameters of the neural network, and obtaining a trained neural network; The trained neural network is determined as the data prediction model.

4. The adaptive high-speed connection method according to claim 1, It is characterized in that The adaptively regulated high-speed connection method further includes: When a fault warning occurs, based on the predicted link in the predicted call data, a diversion and replacement process is performed in an addressing manner to obtain a replacement link to achieve data transmission.

5. A high-speed connection device with adaptive adjustment, It is characterized in that The adaptively regulated high-speed connection device comprises: a data processing module, an adaptively regulated module, and a calling data module implemented by the adaptively regulated high-speed connection method according to any one of claims 1 to 4; The data processing module is connected to the calling data module; the calling data module is connected to the adaptive adjustment module; The data processing module is used to perform information adaptation on the platform bottom layer data based on the protocol, and encrypt the obtained adaptation data to obtain instruction data; The calling data module is used to: Acquire the instruction data; Inputting the instruction data into a data prediction model and outputting prediction call data; The adaptive adjustment module is used to perform fault warning according to the predicted call data, and to adjust and plan the link according to the predicted call data.

6. The adaptive high-speed connection device according to claim 5, It is characterized in that The data processing module includes: a device adaptation module and an encryption transmission module; The device adaptation module is connected to the encryption transmission module; The device adaptation module is used to perform information adaptation on the platform bottom layer data based on the protocol to obtain the adaptation data; The encryption transmission module is used to encrypt the initial instruction data using the SM4 national encryption algorithm to obtain encrypted instruction data; the initial instruction data includes: adaptation data; The encryption transmission module is also used to perform format compression processing on the encrypted instruction data to obtain the instruction data.

7. The adaptive high-speed connection device according to claim 6, It is characterized in that The adaptively adjustable high-speed connection device further includes: a channel linking module; The channel linking module is connected to the device adapting module and the encryption transmission module respectively.

8. The adaptive high-speed connection device according to claim 7, It is characterized in that The device adaptation module includes: a device connection module, a protocol adaptation module, and a data mapping module; The device connection module is connected to the protocol adapter module; the protocol adapter module is connected to the data mapping module; the data mapping module is also connected to the channel link module; The device connection module is used to receive platform bottom layer data; The protocol adaptation module is used to perform information adaptation on the underlying data of the platform based on the protocol to obtain adapted data; The data mapping module is used to transmit the adaptation data to the encryption transmission module in the form of http protocol.

9. The adaptive high-speed connection device according to claim 6, It is characterized in that The protocols include: ssh protocol, telnet protocol, http protocol, sftp protocol, corba protocol, ssl protocol, soap protocol and socket protocol.

10. The self-adaptive high-speed connection device according to claim 7, It is characterized in that The channel link module uses the https protocol to interactively connect with the device adaptation module; the channel link module is connected with the encrypted transmission module based on an ssl encrypted tunnel.

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