Full stack type password service method and system

Abstract

The invention discloses a full-stack password service method and system. The method comprises the following operation steps: constructing a monitoring network platform according to a micro-service architecture design; collecting monitoring data by using the monitoring network platform to obtain monitoring network data; performing preliminary fault diagnosis by monitoring network data in combination with a machine learning algorithm, and outputting a network data diagnosis result; secret key storage management and control are carried out by using a hardware security manager and a secret key management service, fault monitoring is carried out on the secret key in the storage management and control by using a trusted execution environment, and a monitoring fault diagnosis result is output; and establishing a multi-dimensional correlation analysis model according to a network data diagnosis result and the monitoring fault diagnosis result in combination with a complex event processing engine.

Description

Technical Field

[0001] The present invention relates to the field of cryptographic services, and in particular to a full-stack cryptographic service method and system. Background Art

[0002] The full-stack cryptographic service architecture system has the following technical advantages, for example: centralized equipment management realizes the unified scheduling and management of cryptographic computing resources and cryptographic equipment resources, and provides unified and compliant cryptographic computing and key management services for business applications. Intelligent operation and maintenance provides advanced real-time intelligent supervision capabilities, realizing intelligent operation and maintenance and supervision of services, businesses, logs, middleware monitoring, server monitoring, etc.

[0003] However, after research, it was found that there may be the following major technical difficulties in the process of implementing the above technologies: Achieving unified management and automated operation and maintenance across layers: It is necessary to solve the data and operations between the IaaS, PaaS and SaaS layers. Coordination problems between layers are prone to occur, and the resources and services of each layer are also difficult to be uniformly managed and scheduled. It is necessary to establish a complete monitoring and alarm system, and combine it with the automated operation and maintenance mechanism to realize fault diagnosis.

[0004] Therefore, in order to realize the above technical solutions, in-depth research and innovation are needed in system architecture design, data processing, security management and control, and intelligent analysis to solve the above technical difficulties. Summary of the invention

[0005] The purpose of the present invention is to provide a full-stack cryptographic service method and system to solve the above-mentioned technical problems pointed out in the prior art.

[0006] The present invention provides a full-stack cryptographic service method, comprising the following steps:

[0007] Design and construct a monitoring network platform according to the microservice architecture; use the monitoring network platform to collect monitoring data to obtain monitoring network data; perform preliminary fault diagnosis through the monitoring network data combined with a machine learning algorithm, and output network data diagnosis results;

[0008] Use the hardware security manager and key management service to manage key storage, and use the trusted execution environment to monitor the key failures in storage management and output the monitoring fault diagnosis results;

[0009] A multi-dimensional correlation analysis model is established based on the network data diagnosis results and the monitoring fault diagnosis results in combination with a complex event processing engine.

[0010] Preferably, a monitoring network platform is designed and constructed according to the microservice architecture; the monitoring network platform is used to collect monitoring data to obtain monitoring network data; preliminary fault diagnosis is performed through the monitoring network data combined with a machine learning algorithm, and the network data diagnosis result is output. The specific operation steps are as follows:

[0011] Build microservice architecture through layered services of cloud architecture;

[0012] The layered services of the cloud architecture include: IaaS, PaaS, and SaaS;

[0013] Decouple each layer of services in the cloud architecture according to the microservice architecture to build various layered microservices; the various layered microservices are interactively connected through standardized API interfaces;

[0014] Collect information indicators of each layered microservice, and build a unified monitoring platform through the information indicators of each layered microservice and monitoring tools;

[0015] The unified monitoring platform is used to collect monitoring data on the information indicators of each layered microservice to obtain monitoring network data of each layered microservice; a preliminary fault diagnosis is performed on the monitoring network data through a machine learning algorithm, and a network data diagnosis result is output.

[0016] Preferably, the various layered microservices include: IaaS layer microservices, PaaS layer microservices, and SaaS layer microservices;

[0017] Preferably, the various layered microservices include: the IaaS layer microservices include network fault storage information; the PaaS layer microservices include message queues and service registration information; and the SaaS layer microservices include cryptographic service application information.

[0018] Preferably, a hardware security manager and a key management service are used to perform key storage management and control, and a trusted execution environment is used to monitor the key failures in the storage management and control, and output the monitoring fault diagnosis results. The specific operation steps are as follows:

[0019] Arrange a hardware security manager in the SaaS layer microservice, wherein the hardware security manager is used for key generation and key storage;

[0020] Using key management services to manage the keys of the hardware security manager throughout their life cycle;

[0021] Monitor faults of keys under full life cycle control in a trusted execution environment and output monitoring fault diagnosis results.

[0022] Preferably, a multi-dimensional correlation analysis model is established based on the network data diagnosis result and the monitoring fault diagnosis result in combination with a complex event processing engine. The specific operation steps are as follows:

[0023] Further collecting and storing the network data diagnosis results and the monitoring fault diagnosis results to obtain network fault storage information;

[0024] Collect historical network fault storage information, and use a complex event processing engine to establish a multi-dimensional correlation analysis model for the historical network fault storage information.

[0025] Preferably, historical network fault storage information is collected, and a multi-dimensional correlation analysis model is established for the historical network fault storage information using a complex event processing engine. The specific operation steps are as follows:

[0026] Collecting historical network fault storage information, preprocessing the historical network fault storage information, and obtaining preprocessed historical network fault storage information;

[0027] Analyzing the pre-processed historical network fault storage information to obtain event patterns;

[0028] The event pattern is defined by a complex event processing engine, and the relationship between events within the event pattern is judged to obtain an event logic combination;

[0029] By combining the event logic and the complex event processing engine to perform multi-dimensional correlation analysis, a multi-dimensional correlation analysis model is obtained.

[0030] Preferably, the event pattern includes event quantity, event type, event attribute, and event timestamp.

[0031] Accordingly, the present invention also proposes a full-stack cryptographic service system, including: a microservice module, a key module, and a positioning module;

[0032] The microservice module is used to design and build a monitoring network platform according to the microservice architecture; use the monitoring network platform to collect monitoring network data to obtain monitoring network data; and perform fault diagnosis through the monitoring network data combined with a machine learning algorithm;

[0033] The key module is used to use the hardware security manager and key management service to perform key storage management and control, and use the trusted execution environment to protect and monitor the keys in storage management and control;

[0034] The positioning module is used to establish a multi-dimensional correlation analysis model based on the monitoring network data and key storage management and control combined with a complex event processing engine; and locate the fault diagnosis through the multi-dimensional correlation analysis model.

[0035] Compared with the prior art, the embodiments of the present invention have at least the following technical advantages:

[0036] From the analysis of the above-mentioned full-stack cryptographic service method and system provided by the present invention, it can be known that the method constructs a microservice architecture through the layered services of the cloud architecture during specific application. The microservice architecture is divided into three layers of microservices: IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service). Through each layered microservice, a separate business function can be performed to reduce the coupling degree generated during system operation; and at the same time, each layered microservice is interconnected through a standardized API interface, so that each layered microservice can flexibly interact with data and perform unified operation scheduling; information indicators of each layered microservice are collected to determine the monitoring target, and at the same time, a monitoring tool and the determined monitoring target are selected to build a unified monitoring platform, and the unified monitoring platform can uniformly monitor each layered microservice, collect monitoring network data, and detect the network data diagnosis results of the monitoring network data through a machine learning algorithm;

[0037] Furthermore, by deploying a hardware security manager in the SaaS layer microservice in the layered microservice, the hardware security manager will generate and store the keys; at the same time, the keys generated and stored by the key management service are generated, distributed, rotated, and destroyed throughout the entire life cycle management and control, and the keys in the whole life cycle management and control process need to be monitored for faults in the trusted execution environment, so as to obtain monitoring fault diagnosis results;

[0038] Furthermore, the network data diagnosis results and the monitoring fault diagnosis results are further deeply collected and stored to obtain more detailed network fault storage information; and the historical network fault storage information is collected and combined with the complex event processing (CEP) engine to construct a multi-dimensional correlation analysis model. Through the multi-dimensional correlation analysis model, the network data diagnosis results of the monitoring network data and the monitoring fault diagnosis results of the key storage management (i.e., the network fault storage information) can be more finely and accurately diagnosed. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is an overall flow chart of a full-stack cryptographic service method according to Embodiment 1;

[0040] Figure 2 A microservice flow chart for constructing a full-stack cryptographic service method according to Embodiment 1;

[0041] Figure 3 A key storage management and control flow chart of a full-stack cryptographic service method according to Embodiment 1;

[0042] Figure 4A flowchart of a fault diagnosis and location method of a full-stack cryptographic service method according to Embodiment 1;

[0043] Figure 5 A flowchart of a multi-dimensional correlation analysis model of a full-stack cryptographic service method according to Embodiment 1;

[0044] Figure 6 This is a flow chart of a full-stack cryptographic service system of Example 2.

[0045] Label: microservice module 10, key module 20, positioning module 30. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The present invention is further described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0048] The present invention provides a full-stack cryptographic service method, and the specific operation steps are as follows:

[0049] S10: Design and construct a monitoring network platform according to the microservice architecture; use the monitoring network platform to collect monitoring data to obtain monitoring network data; perform preliminary fault diagnosis through the monitoring network data combined with a machine learning algorithm, and output a network data diagnosis result;

[0050] It should be noted that by adopting the microservice architecture design, the functions of each layer (item) of the microservice architecture design are decoupled, and data interaction and operation scheduling are carried out through standardized API interfaces. Middleware technologies such as application message queues and service registration and discovery are used to implement cross-level event-driven and asynchronous processing mechanisms; at the same time, unified management and scheduling of microservices at all layers are also achieved.

[0051] A unified monitoring platform is built through microservice architecture design, using time series database, cache and other technical means to quickly collect and analyze monitoring indicators at each layer.

[0052] And collect monitoring network data, and perform preliminary fault diagnosis based on the collected monitoring network data combined with machine learning algorithms.

[0053] S20: Use the hardware security manager and key management service to perform key storage management and control, and use the trusted execution environment to monitor the key failure in the storage management and control, and output the monitoring fault diagnosis result;

[0054] It should be noted that the hardware security manager is a manager specifically designed to securely store and manage encryption keys. It provides a higher level of security protection than software because the keys are stored in a physically isolated environment and are difficult to be accessed by unauthorized persons. The hardware security manager also provides hardware acceleration for encryption operations on the keys, further enhancing security.

[0055] Key Management Service (KMS) is a centralized key management system used to manage the entire life cycle of keys, including generation, distribution, rotation, and destruction; KMS provides a set of automated tools and services to make key management more efficient and auditable.

[0056] A Trusted Execution Environment (TEE) is a secure isolation environment provided at the hardware level to protect sensitive data and computing processes from unauthorized access and tampering; TEE is usually implemented through a combination of hardware and software, providing a higher level of security protection than the operating system.

[0057] In the process of storage management and control through the hardware security manager and key management service (that is, storage management and control includes the secure generation, distribution, rotation and destruction of keys), there is a risk of unauthorized access and tampering. Therefore, the storage management and control of keys is monitored in a trusted execution environment to prevent security threats.

[0058] S30: Establishing a multi-dimensional correlation analysis model based on the network data diagnosis result and the monitoring fault diagnosis result in combination with a complex event processing engine;

[0059] It should be noted that the network data diagnosis results and monitoring fault diagnosis results obtained in step S10 and step S20 are combined with the complex event processing engine to establish a multi-dimensional correlation analysis model, and the multi-dimensional correlation analysis model is used to identify anomalies in the monitoring network data and key storage management, and the specific abnormal fault location is located after identification.

[0060] Specifically, in step S10, a monitoring network platform is designed and constructed according to the microservice architecture; monitoring data is collected using the monitoring network platform to obtain monitoring network data; preliminary fault diagnosis is performed through the monitoring network data combined with a machine learning algorithm, and a network data diagnosis result is output. The specific operation steps are as follows:

[0061] S11: Build microservice architecture through layered services of cloud architecture;

[0062] The layered services of the cloud architecture include: IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service);

[0063] Decouple each layer of services in the cloud architecture according to the microservice architecture to build various layered microservices; the various layered microservices are interactively connected through standardized API interfaces;

[0064] It should be noted that the present invention is based on the layered design of cloud architecture: according to the different capability encapsulation, access mode and business-oriented degree, it is divided into three layers: IaaS, PaaS and SaaS; Crypto Cloud provides systematic operation management and operation support capabilities, which is convenient for users to carry out unified operation and management;

[0065] The functions of each layer of services, including IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service), are constructed into a microservice architecture. Each layer of services is constructed into a separate layered microservice through the microservice architecture. Each layered microservice focuses on a single business function, reducing the coupling between systems and improving the scalability and maintainability of the system.

[0066] And through API interface standardization, the standards of API interface are defined, so that each layered microservice can exchange data and schedule operations through a unified interface, realizing loose coupling and flexible combination of services.

[0067] S12: The various layered microservices include: IaaS layer microservices, PaaS layer microservices, and SaaS layer microservices;

[0068] The IaaS layer microservice includes network fault storage information; the PaaS layer microservice includes message queue and service registration information; the SaaS layer microservice includes cryptographic service application information;

[0069] It should be noted that each layered microservice contains different information. In the subsequent process, the information contained is used to build a unified monitoring platform, and the network information of each layered microservice is monitored through the unified monitoring platform. For example, when a user registers for a password service, the user's password service registration information will be processed asynchronously by each layered microservice through the message queue, reducing the user's waiting time and event-driven load.

[0070] S13: Collect information indicators of each layered microservice, and build a unified monitoring platform through the information indicators of each layered microservice and monitoring tools;

[0071] It should be noted that to build a unified monitoring platform: first, determine the monitoring target, collect information indicators of each layered microservice (information indicators include network fault storage information, message queues, service registration information, and password service application information), and determine them as monitoring targets; and set monitoring rules through monitoring tools (that is, monitoring tools are existing monitoring tools such as Prometheus, StatsD, etc.) (that is, the collection frequency of information indicators, and the threshold of information indicators. When the collected information indicators exceed the set threshold, the alarm system is punished, so as to reduce the high load when collecting information indicators and ensure that the subsequent unified monitoring platform can collect information normally). Through the monitoring targets (that is, information indicators) of each layered microservice and monitoring tools, a unified monitoring platform is built;

[0072] S14: Collect monitoring data on the information indicators of each layered microservice through the unified monitoring platform to obtain monitoring network data of each layered microservice; perform preliminary fault diagnosis on the monitoring network data through a machine learning algorithm, and output a network data diagnosis result;

[0073] It should be noted that machine learning algorithms can automatically process large amounts of monitoring data and quickly identify potential fault problems, thereby reducing the need for human intervention and improving the accuracy and efficiency of fault diagnosis.

[0074] The monitoring network data is preprocessed (that is, the preprocessing includes removing noise, outliers, duplicate data, etc., and converting the data into a unified format). After the monitoring network data is preprocessed, the key features related to the fault are extracted (such as: data resources cannot be managed in a unified manner, and load occurs, resulting in faults). The machine learning algorithm is trained to form a diagnostic model, which is integrated with a unified monitoring platform through the intelligent mechanism of the model to achieve real-time fault diagnosis of each layered microservice.

[0075] Specifically, in step S20, the hardware security manager and the key management service are used to perform key storage management and control, and a trusted execution environment is used to monitor the key in the storage management and control for faults, and the monitoring fault diagnosis results are output. The specific operation steps are as follows:

[0076] S21: deploying a hardware security manager in the SaaS layer microservice, wherein the hardware security manager is used for key generation and key storage;

[0077] It should be noted that since the SaaS layer microservice contains cryptographic service application information, the physical isolation and reinforcement of the key can be ensured by arranging a hardware security manager to store the key, thereby preventing unauthorized access and tampering;

[0078] The above keys may include private keys used for data encryption, identity authentication, digital signatures, etc.;

[0079] S22: Using a key management service to perform full life cycle management and control on the key of the hardware security manager;

[0080] It should be noted that the key management service is used to uniformly manage all the keys in the system; the key management service can automatically generate new keys (i.e. key generation) and securely distribute the keys to the corresponding systems or services when needed (i.e. key distribution); at the same time, the key management service can also rotate the keys regularly (i.e. key rotation) to reduce the risk of key cracking; when the keys are no longer needed, the key management service can also securely destroy the keys (i.e. key destruction) to prevent them from being abused (i.e. full life cycle management of secure key generation, distribution, rotation and destruction (i.e. full life cycle management is the comprehensive management and monitoring of all key activities in this cycle));

[0081] S23: Monitor the faults of the keys under full life cycle control in the trusted execution environment and output the monitoring fault diagnosis results;

[0082] It should be noted that the key storage management and control in the trusted execution environment can avoid unauthorized access or tampering during the key storage management process, prevent it from being leaked or tampered with, and protect the security of the process and intermediate results; at the same time, key security can be ensured, management efficiency can be improved, and compliance requirements can be met through monitoring;

[0083] In summary, this section describes how to monitor and ensure the compliance and security of key management through various means such as hardware security manager, key management service, and trusted execution environment; at the same time, it constitutes a comprehensive security system to ensure that keys are protected from unauthorized access and tampering during storage control.

[0084] Specifically, in step S30, a multi-dimensional correlation analysis model is established based on the network data diagnosis result and the monitoring fault diagnosis result in combination with a complex event processing engine. The specific operation steps are as follows:

[0085] S31: further collecting and storing the network data diagnosis result and the monitoring fault diagnosis result to obtain network fault storage information;

[0086] It should be noted that the network data diagnosis results and monitoring fault diagnosis results are further collected and stored (i.e., the network data diagnosis results of the monitoring network data and the monitoring fault diagnosis results of the key storage management are further collected to obtain the fault attributes in the deep network data and the fault attributes monitored in the storage, so that the event mode can be more easily analyzed in the subsequent step S331), and the deeper network data and keys are refined to obtain the network fault storage information;

[0087] S32: Collect historical network fault storage information, and use a complex event processing (CEP) engine to establish a multi-dimensional correlation analysis model for the historical network fault storage information;

[0088] It should be noted that the complex event processing (CEP) engine: processes data streams in real time, identifies abnormal events based on preset rules and algorithms, and enables rapid response and early warning;

[0089] Use historical network fault storage information to train a multi-dimensional correlation analysis model, conduct in-depth analysis of network fault storage information, automatically discover potential fault modes and root causes, and improve accuracy and efficiency.

[0090] Specifically, in step S32, historical network fault storage information is collected, and a multi-dimensional correlation analysis model is established for the historical network fault storage information using a complex event processing (CEP) engine. The specific operation steps are as follows:

[0091] S321: Collect historical network fault storage information, and pre-process the historical network fault storage information to obtain pre-processed historical network fault storage information;

[0092] Analyzing the pre-processed historical network fault storage information to obtain event patterns;

[0093] The event pattern includes event quantity, event type, event attributes, and event timestamp;

[0094] It should be noted that the historical network fault storage information is obtained through in-depth collection and storage of network data diagnosis results and monitoring fault diagnosis results, so the historical network fault storage information can more clearly analyze the fault event source (that is, event mode, event mode includes: event number, event type, event attribute, event timestamp information);

[0095] S322: defining the event pattern through a complex event processing engine, judging the relationship between events within the event pattern, and obtaining an event logic combination;

[0096] It should be noted that the complex event processing engine is responsible for processing complex events, and the event pattern is defined in order to understand the logical combination of events within the event pattern, whether there is any correlation, and whether there are complex events. Complex events may be composed of multiple simple events according to specific relationships, such as sequential relationships, aggregation relationships, hierarchical relationships, etc. (that is, the logical combination of events reflects the complex relationships such as correlation, sequentiality, aggregation or hierarchy between events).

[0097] S323: Perform multi-dimensional correlation analysis through the event logic combination and the complex event processing engine to obtain a multi-dimensional correlation analysis model;

[0098] It should be noted that the complex event processing engine constructs a multi-dimensional correlation analysis model through the rules of correlation logic of event logic combination; the multi-dimensional correlation analysis model can reflect the complex relationship between events and is used to predict, diagnose or optimize the performance of network storage systems.

[0099] Embodiment 2

[0100] On the other hand, a full-stack cryptographic service system provided in the second embodiment includes: a microservice module 10, a key module 20, and a positioning module 30;

[0101] The microservice module 10 is used to design and construct a monitoring network platform according to the microservice architecture; use the monitoring network platform to collect monitoring network data to obtain monitoring network data; and perform fault diagnosis by combining the monitoring network data with a machine learning algorithm;

[0102] The key module 20 is used to use the hardware security manager and key management service to perform key storage management and control, and adopt a trusted execution environment to protect and monitor the keys in storage management and control;

[0103] The positioning module 30 is used to establish a multi-dimensional correlation analysis model based on the monitoring network data and key storage management and control combined with a complex event processing engine; and locate the fault diagnosis through the multi-dimensional correlation analysis model.

[0104] In summary, a full-stack cryptographic service method and system proposed in the example of the present invention builds a microservice architecture through layered services of a cloud architecture. The microservice architecture is divided into three layers of microservices: IaaS, PaaS, and SaaS. Each layered microservice can perform a separate business function to reduce the coupling degree generated during system operation; and at the same time, each layered microservice is interconnected through a standardized API interface, so that each layered microservice can flexibly interact with data and perform unified operation scheduling; information indicators of each layered microservice are collected to determine monitoring targets, and at the same time, monitoring tools and determined monitoring targets are selected to build a unified monitoring platform, which can uniformly monitor each layered microservice, collect monitoring network data, and detect network data diagnosis results of the monitoring network data through a machine learning algorithm;

[0105] Furthermore, by deploying a hardware security manager in the SaaS layer microservice in the layered microservice, the hardware security manager will generate and store the keys; at the same time, the keys generated and stored by the key management service are generated, distributed, rotated, and destroyed throughout the entire life cycle management and control, and the keys in the whole life cycle management and control process need to be monitored for faults in the trusted execution environment, so as to obtain monitoring fault diagnosis results;

[0106] Furthermore, the network data diagnosis results and the monitoring fault diagnosis results are further deeply collected and stored to obtain more detailed network fault storage information; and the historical network fault storage information is collected and combined with the complex event processing engine to build a multi-dimensional correlation analysis model. Through the multi-dimensional correlation analysis model, the network data diagnosis results of the monitoring network data and the monitoring fault diagnosis results of the key storage management can be more finely and accurately diagnosed.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. A person skilled in the art may modify the technical solutions described in the above embodiments, or replace part or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A full-stack cryptographic service method, characterized in that: The steps are as follows: Design and construct a monitoring network platform based on the microservice architecture; use the monitoring network platform to collect monitoring data to obtain monitoring network data; perform preliminary fault diagnosis based on the monitoring network data combined with a machine learning algorithm, and output network data diagnosis results; Use the hardware security manager and key management service to manage key storage, and use a trusted execution environment to monitor key failures in storage management and output monitoring fault diagnosis results; A multi-dimensional correlation analysis model is established based on the network data diagnosis results and the monitoring fault diagnosis results in combination with a complex event processing engine.

2. A full-stack cryptographic service method according to claim 1, characterized in that: Design and build a monitoring network platform based on the microservice architecture; use the monitoring network platform to collect monitoring data to obtain monitoring network data; use the monitoring network data combined with machine learning algorithms to perform preliminary fault diagnosis and output network data diagnosis results. The specific steps are as follows: Build a microservice architecture through layered services of cloud architecture; The layered services of the cloud architecture include: IaaS, PaaS, and SaaS; Decouple each layer of cloud architecture services according to the microservice architecture to build various layered microservices; the various layered microservices are interactively connected through standardized API interfaces; Collect information indicators of each layered microservice, and build a unified monitoring platform through the information indicators of each layered microservice and monitoring tools; The unified monitoring platform collects monitoring data on the information indicators of each layered microservice to obtain monitoring network data of each layered microservice; a machine learning algorithm is used to perform preliminary fault diagnosis on the monitoring network data, and a network data diagnosis result is output.

3. A full-stack cryptographic service method according to claim 2, characterized in that: The various layered microservices include: IaaS layer microservices, PaaS layer microservices, and SaaS layer microservices.

4. A full-stack cryptographic service method according to claim 3, characterized in that: The various layered microservices include: the IaaS layer microservice contains network fault storage information; the PaaS layer microservice contains message queues and service registration information; and the SaaS layer microservice contains cryptographic service application information.

5. A full-stack cryptographic service method according to claim 4, characterized in that: Use the hardware security manager and key management service to manage key storage, and use the trusted execution environment to monitor key failures in storage management and output monitoring fault diagnosis results. The specific steps are as follows: Arrange a hardware security manager in the SaaS layer microservice, wherein the hardware security manager is used for key generation and key storage; Use key management services to manage the hardware security manager's keys throughout their lifecycle; Monitor faults of keys under full lifecycle control in a trusted execution environment and output monitoring fault diagnosis results.

6. A full-stack cryptographic service method according to claim 5, characterized in that: The multi-dimensional correlation analysis model is established based on the network data diagnosis results and the monitoring fault diagnosis results in combination with the complex event processing engine. The specific operation steps are as follows: Further collecting and storing the network data diagnosis results and the monitoring fault diagnosis results to obtain network fault storage information; Collect historical network fault storage information, and use a complex event processing engine to establish a multi-dimensional correlation analysis model for the historical network fault storage information.

7. A full-stack cryptographic service method according to claim 6, characterized in that: Collect historical network fault storage information and use the complex event processing engine to build a multi-dimensional correlation analysis model for the historical network fault storage information. The specific steps are as follows: Collecting historical network fault storage information, preprocessing the historical network fault storage information, and obtaining preprocessed historical network fault storage information; Analyzing the pre-processed historical network fault storage information to obtain event patterns; The event pattern is defined by a complex event processing engine, and the relationship between events within the event pattern is judged to obtain a logical combination of events; A multi-dimensional correlation analysis model is obtained by performing multi-dimensional correlation analysis through the event logic combination and the complex event processing engine.

8. A full-stack cryptographic service method according to claim 7, characterized in that: The event pattern includes event quantity, event type, event attributes, and event timestamp.

9. A full-stack cryptographic service system, characterized in that: include: Microservice module, key module, positioning module; The microservice module is used to build a monitoring network platform based on the microservice architecture design; Using the monitoring network platform to collect monitoring network data to obtain monitoring network data; Perform fault diagnosis by combining the monitoring network data with a machine learning algorithm; The key module is used to use the hardware security manager and key management service to manage key storage, and use the trusted execution environment to protect and monitor the keys in storage management; The positioning module is used to establish a multi-dimensional correlation analysis model based on the monitoring network data and key storage management combined with a complex event processing engine; and locate the fault diagnosis through the multi-dimensional correlation analysis model.

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