CN116737560A - Intelligent training system based on intelligent guide control - Google Patents
Intelligent training system based on intelligent guide control Download PDFInfo
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Abstract
The invention discloses an intelligent training system based on intelligent guide control, which consists of a testing management and control layer facing a cloud environment and a testing scheduling layer based on a heterogeneous cloud environment. Aiming at the problems of multiple types of test resources, large scale of test environment, complex test process and frequent test interaction faced by basic software cloud test, the invention researches the test control technology facing the complex environment, and forms a universal test control process and control instructions by analyzing test control scenes so as to meet the control demands of different resources and complex flows; on the basis, resource monitoring events are combed and designed around the testing process for different testing resources and different control granularity, so that state, flow, progress and abnormal multi-angle monitoring are realized, and the basic software testing in a complex environment can be ensured to complete the preset testing process in order according to the requirement.
Description
Technical Field
The invention belongs to the field of cloud environments, and particularly relates to an intelligent training system based on intelligent guide control.
Background
With the gradual improvement of the capability of autonomous basic software, the construction of next-generation military information infrastructure by adopting domestic software and hardware becomes a necessary requirement; in the development and use process of the system, the system complexity is continuously improved and the systemization degree is continuously enhanced, so that the testing cost and the expenditure of the basic software are rapidly improved, the testing scene is more complex, the testing scale is obviously expanded, and the method and the technology have higher requirements on the existing testing method and technology. Along with the complexity of the test scene and the diversity of the tested system, the construction of the test environment becomes more and more difficult, and the time and cost spent on the construction of the test environment are more and more great;
due to the high-speed development of cloud computing, three novel service modes of SaaS, paaS and IaS are used, and convenience is provided for constructing a virtual and reliable test environment. By utilizing calculation, storage and network resources in a cloud environment, a heterogeneous autonomous cloud management platform is utilized to construct a large-scale test basic resource pool, so that the full-flow control of the test on the cloud is realized, and an efficient and elastic supporting environment is provided for the basic software test.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides an intelligent training system based on intelligent guide control, which aims at the problems of control of a test execution flow and scheduling of test resources in a cloud environment as a research object, and ensures that a test process can be executed according to expectations by distributed control of a complex test environment, thereby solving the problems of high management cost and weaker automation degree of a large-scale complex test environment.
In order to achieve the above purpose, the present invention provides the following technical solutions:
intelligent training system based on intelligent control, the system is by cloud environment-oriented test management and control layer and heterogeneous cloud environment-based test scheduling layer constitution, cloud environment-oriented test management and control layer includes:
the integrated test tube control scene construction module is characterized in that test resources built in the integrated test tube control scene construction module comprise a service simulation unit, an acquisition unit, a data processing unit, an analysis display unit, a virtual machine, a container and a physical machine, and meanwhile, the integrated test tube control scene construction module adopts a test management and control technology based on distributed test middleware to provide quick access integration and interconnection capacity for the test resources and meet unified integration and on-demand scheduling of a test environment;
the full life cycle test tube control module is used for realizing cloud environment-oriented test control, wherein the test tube control needs to be provided with an infrastructure as a basic support, the infrastructure provides computing resources, data storage resources and network communication resources for the test environment, a test execution environment is planned based on the infrastructure and test tasks, a test control technology oriented to the cloud environment takes test planning as input, and test resources in the cloud environment are controlled according to a test planning flow;
the complex test environment sensing is used for grasping the test condition at any time, dynamically adjusting, managing and controlling the test environment, and monitoring various tasks, test resources and infrastructures in the test environment in real time, wherein the main monitoring content comprises test completion progress, running state, test activity, key events and abnormal alarm information;
the self-adaptive test scheduling management module utilizes an abnormal event detection algorithm to automatically capture abnormal events, on the basis, the self-adaptive test scheduling management module realizes the automatic acquisition of the influence relation between test resources and events, and provides self-adaptive scheduling management and control methods with different levels and different dimensions from the aspects of intelligent configuration of test parameters and dynamic scheduling of the test resources, so that the high availability of a test environment and the online execution of expected test contents are ensured under the condition of abnormal events.
Preferably, the process of performing real-time monitoring on various tasks, test resources and infrastructure in the test environment includes:
monitoring parameter settings, status monitoring, process monitoring, progress monitoring, and event monitoring. The monitoring parameter setting mainly comprises the steps of configuring relevant parameter information of a monitoring object and monitoring information; flow monitoring constructs a data flow view by acquiring current entity information from the joint test middleware; progress monitoring evaluates the current progress by obtaining the desired and physical running states from the joint test middleware; event monitoring is mainly achieved by acquiring event record information from joint test middleware.
Preferably, the abnormal event detection algorithm specifically comprises:
1) Abnormal event detection
And actively checking the states of all the reference resources to obtain abnormal events by adopting an active probing mode. For the first reporting mode, the abnormal event receiving is completed through the distributed test platform;
2) Abnormal event classification and processing method
According to the test platform architecture provided by the subject, the subject divides the abnormal event into three types of capturing infrastructure, namely service layer abnormality, test platform, namely service layer abnormality and test service, namely service layer abnormality, and positions the abnormality into 5 stages according to the influence of the abnormality, wherein the lower the stage is, the higher the degree of importance is;
3) Dynamic allocation of resources
Dynamic allocation of resources refers to the need to dynamically allocate virtual machines, virtual containers, or network resources that are capable of carrying the original traffic.
Preferably, the testing scheduling layer based on heterogeneous cloud environment includes:
and a scheduling module: the method is responsible for managing scheduling information, sends a scheduling request according to scheduling configuration, and does not bear service codes; the dispatching system is decoupled from the task, so that the availability and stability of the system are improved, and meanwhile, the performance of the dispatching system is not limited by a task module; visual, simple and dynamic management scheduling information is supported, including task creation, updating, deletion, GLUE development and task alarming, all of which are effective in real time, monitoring scheduling results and execution logs are supported, and executors Failover are supported;
the execution module: is responsible for receiving the scheduling request and executing task logic; the task module is focused on the execution operation of the task, so that the development and maintenance are simpler and more efficient; and receiving an execution request, a termination request and a log request of the scheduling module.
Preferably, the scheduling module processes tasks sequentially from high to low according to the priority of different process instances prior to the priority of the same process instance prior to the priority of tasks in the same process prior to the task submitting sequence in the same process;
the method comprises the following steps: and analyzing the priority according to the task instance, storing the task priority task id information of the process instance priority process instance id in a task queue, and obtaining the task which needs to be executed with priority through character string comparison when the task id information is obtained from the task queue.
Preferably, the execution module distributes the test task which is arranged by the model to cloud hosts of different designated heterogeneous processors according to a scheduling flow, a multi-task execution service is deployed on the cloud hosts, the multi-task execution service is a task runner, and the execution state and the execution result of the test task are fed back to a task scheduling system.
Preferably, the TaskRunner is deployed on a designated cloud host, and provides a user HTTP interface for remotely executing tasks, each task uses JobId as a unique identifier, the executed task can be an independent process or a shell script, the task process provides standard command line interface parameters for controlling execution logic of the task, and the process parameters must include JobId;
the state and result of task execution are sent to TaskRunner, taskRunner by an HTTPPOST interface or a websocket interface, after the state or result information sent by the task is received, the state or result information is broadcast to all clients connected to a TaskRunner through websocket, and state or result transfer is achieved.
Preferably, the cloud environment-oriented test control layer comprises the following specific workflow:
1) A test plan design stage, which is used as an input of test tube control to generate a test plan to be integrated and controlled by test;
2) In the test environment construction stage, firstly, loading a test scheme from a test scheme library, sending infrastructure requirements of test resources related in the test scheme to a cloud platform management system, and utilizing a cloud platform to open containers/virtual machines to finish the establishment of the infrastructure in the test environment;
3) In the test tube control and dispatch stage, each test resource receives a control command and a dispatch instruction from a test tube control platform and reports state information and collected index information to the test tube control platform in real time so as to realize state monitoring and index display of a test process and grasp the execution condition of the test;
4) In the test result analysis stage, the test control platform sends a result feedback instruction to each test node through the middleware agent, and after each test node uploads the respective offline result to a designated position through the middleware agent, the test control platform sends a test environment withdrawal command to each test node to complete withdrawal and cleaning of the test environment, so that the test environment is restored to an initial state, and other tests are prevented from being influenced in the test iteration process.
Preferably, in the test environment construction stage, after the infrastructure is set up, the middleware agent on each test node automatically registers node information with the registration server, the test management and control platform acquires node information from the registration server, downloads required test resources from the resource library by using the middleware agent on each node, and the test resources comprise software services, scripts and configurations, and automatically starts software to complete the deployment of the test resources.
The invention has the technical effects and advantages that: compared with the prior art, the intelligent training system based on intelligent guide control aims at the problems of control of a test execution flow and scheduling of test resources in a cloud environment as a research object, and ensures that a test process can be executed according to expectations through distributed control of a complex test environment, so that the problems of high management cost and weaker automation degree of the large-scale and complex test environment are solved;
additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
FIG. 1 is a diagram of an intelligent training system architecture based on intelligent guidance in accordance with the present invention;
FIG. 2 is a schematic diagram of a typical usage scenario for test control in an integrated test control scenario construction module according to an embodiment of the present invention;
fig. 3 is a diagram of a test control structure facing a complex environment in a test control layer facing a cloud environment according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides an intelligent training system based on intelligent guidance as shown in fig. 1-3, which consists of a testing management layer facing cloud environment and a testing scheduling layer based on heterogeneous cloud environment.
The test management and control layer facing the cloud environment comprises:
the integrated test tube control scene construction module is characterized in that test resources built in the integrated test tube control scene construction module comprise a business simulation unit, an acquisition unit, a data processing unit, an analysis display unit, a virtual machine, a container and a physical machine, and meanwhile, the integrated test tube control scene construction module adopts a test management and control technology based on distributed test middleware, provides quick access integration and interconnection capacity for the test resources, and meets unified integration of test environments and scheduling according to requirements;
the full life cycle test tube control module is used for realizing cloud environment-oriented test control, wherein the test tube control needs to be provided with an infrastructure as a basic support, the infrastructure provides computing resources, data storage resources and network communication resources for the test environment, a test execution environment is planned based on the infrastructure and test tasks, a test planning is used as input for a cloud environment-oriented test control technology, and test resources in the cloud environment are controlled according to a test planning flow;
the complex test environment sensing is used for grasping the test condition at any time, dynamically adjusting, managing and controlling the test environment, and monitoring various tasks, test resources and infrastructures in the test environment in real time, wherein the main monitoring content comprises test completion progress, running state, test activity, key events and abnormal alarm information;
the self-adaptive test scheduling management module is used for automatically capturing the abnormal event by utilizing an abnormal event detection algorithm, automatically acquiring the influence relation between the test resource dependence and the event on the basis, and providing self-adaptive scheduling management and control methods with different levels and different dimensions from aspects of intelligent configuration of test parameters and dynamic scheduling of the test resource, so that high availability of a test environment and online execution of expected test contents are ensured under the condition of occurrence of the abnormal event.
Further, the abnormal event detection algorithm specifically includes:
1) Abnormal event detection
And actively checking the states of all the reference resources to obtain abnormal events by adopting an active probing mode. For the first reporting mode, the abnormal event receiving is completed through the distributed test platform;
2) Abnormal event classification and processing method
According to the test platform architecture provided by the subject, the subject divides the abnormal event into three types of capturing infrastructure, namely service layer abnormality, test platform, namely service layer abnormality and test service, namely service layer abnormality, and positions the abnormality into 5 stages according to the influence of the abnormality, wherein the lower the stage is, the higher the degree of importance is;
3) Dynamic allocation of resources
Dynamic allocation of resources refers to the need to dynamically allocate virtual machines, virtual containers, or network resources that are capable of carrying the original traffic.
Wherein, according to the level of the abnormal event, different processing methods are formulated, for 3-5 level abnormality, the abnormality can be processed by a method of adjusting test parameters, for example, when the deployment of test resources is partially successful (only part of test scripts are successfully deployed to a test tool), repeated deployment can be performed by adjusting test deployment retransmission parameters; for the level 2 exception, the problem can be solved by adjusting the test parameters, if the problem can not be solved by the parameters, the problem can be solved by a mode of reallocating resources; for the 1-level exception, the problem is directly solved by dynamically allocating resources;
as to be described, the above-mentioned dynamic allocation of resources can be implemented by means of resource copying, task migration and dynamic application of resources, and these technologies all have relevant technological bases in the research of cloud computing technologies, and the differences in this embodiment are shown in:
1) Marking the test data which are affected by dynamic allocation of resources according to the set time points in the dynamic allocation process, wherein the data can be invalid data in the subsequent data analysis process;
2) The dynamic allocation of resources needs to consider the special requirements of the test resources, such as higher requirements of the test index calculation tool on the migration cost and reliability of the resources, and strict requirements of partial fault injection tools on the network topology condition.
The specific technical scheme is described by using virtual machine dynamic allocation to test index calculation tool migration according to the variability, and the specific scheme is as follows:
the purpose of dynamic allocation of virtual machines is to meet the service operation needs and improve fault tolerance, but distributed copying of virtual machines improves the cost of network transmission. In order to balance the fault tolerance and transmission cost problems, the present subject proposes virtual machine allocation based on network distance. And among the nodes which also meet the test conditions, the node with the optimal network distance is preferentially selected through the network distance so as to achieve the rapid adjustment of the resources in the test execution.
The network distance is the distance of any physical host in the distributed test environment on the network topology, reflects whether two nodes are adjacent to each other and the bandwidth between the two nodes, and theoretically cannot be too large, because the time cost for transmitting data becomes large when the distance is too large, but the network distance cannot be too small, the distance is too small, the probability of simultaneous failure of the hosts is increased, and therefore virtual machine allocation needs to be carried out by comprehensively considering the two factors.
As to be described in this embodiment, a test scheduling layer based on a heterogeneous cloud environment includes:
and a scheduling module: the method is responsible for managing scheduling information, sends a scheduling request according to scheduling configuration, and does not bear service codes; the dispatching system is decoupled from the task, so that the availability and stability of the system are improved, and meanwhile, the performance of the dispatching system is not limited by a task module; visual, simple and dynamic management scheduling information is supported, including task creation, updating, deletion, GLUE development and task alarming, all of which are effective in real time, monitoring scheduling results and execution logs are supported, and executors Failover are supported;
the execution module: is responsible for receiving the scheduling request and executing task logic; the task module is focused on the execution operation of the task, so that the development and maintenance are simpler and more efficient; and receiving an execution request, a termination request and a log request of the scheduling module. The execution module distributes the test tasks which are arranged by the model to cloud hosts of different designated heterogeneous processors according to a scheduling flow, a multi-task execution service is deployed on the cloud hosts, the multi-task execution service is a task runner, and the execution state and the execution result of the test tasks are fed back to a task scheduling system.
The scheduling module processes tasks from high to low in sequence according to the priority of different process instances prior to the priority of the same process instance prior to the priority of tasks in the same process prior to the task submitting sequence in the same process;
the method comprises the following steps: and analyzing the priority according to the task instance, storing the task priority task id information of the process instance priority process instance id in a task queue, and obtaining the task which needs to be executed with priority through character string comparison when the task id information is obtained from the task queue. A step of
Further, the task runner is deployed on a designated cloud host, a user HTTP interface is provided for remotely executing tasks, each task uses Jobid as a unique identifier, the executed task can be an independent process or a shell script, the task process provides standard command line interface parameters for controlling the execution logic of the task, and the process parameters must contain Jobid;
the state and result of task execution are sent to TaskRunner, taskRunner by an HTTPPOST interface or a websocket interface, after the state or result information sent by the task is received, the state or result information is broadcast to all clients connected to a TaskRunner through websocket, and state or result transfer is achieved.
The test control layer facing the cloud environment is required to be said, and the specific workflow is as follows:
1) A test plan design stage, which is used as an input of test tube control to generate a test plan to be integrated and controlled by test;
2) In the test environment construction stage, firstly, loading a test scheme from a test scheme library, sending infrastructure requirements of test resources related in the test scheme to a cloud platform management system, and utilizing a cloud platform to open containers/virtual machines to finish the establishment of the infrastructure in the test environment; in the test environment construction stage, after the establishment of the infrastructure is completed, the middleware agent on each test node automatically registers node information to the registration server, the test management and control platform acquires the node information from the registration server, downloads required test resources from a resource library by utilizing the middleware agent on each node, and the test resources comprise software services, scripts and configurations, and automatically starts software to complete the deployment of the test resources;
3) In the test tube control and dispatch stage, each test resource receives a control command and a dispatch instruction from a test tube control platform and reports state information and collected index information to the test tube control platform in real time so as to realize state monitoring and index display of a test process and grasp the execution condition of the test;
4) In the test result analysis stage, the test control platform sends a result feedback instruction to each test node through the middleware agent, and after each test node uploads the respective offline result to a designated position through the middleware agent, the test control platform sends a test environment withdrawal command to each test node to complete withdrawal and cleaning of the test environment, so that the test environment is restored to an initial state, and other tests are prevented from being influenced in the test iteration process.
Finally, it should be noted that: the foregoing description of the preferred embodiments of the present invention should not be taken as limiting the invention, but it should be understood that it is obvious to those skilled in the art that the present invention can be modified or substituted for the specific embodiments shown and described, and that any modification, substitution and improvement made without departing from the spirit and principles of the present invention.
Claims (9)
1. Intelligent training system based on intelligent control, the system is by cloud environment-oriented test management and control layer and heterogeneous cloud environment-based test scheduling layer constitution, its characterized in that, cloud environment-oriented test management and control layer includes:
the integrated test tube control scene construction module is characterized in that test resources built in the integrated test tube control scene construction module comprise a service simulation unit, an acquisition unit, a data processing unit, an analysis display unit, a virtual machine, a container and a physical machine, and meanwhile, the integrated test tube control scene construction module adopts a test management and control technology based on distributed test middleware to provide quick access integration and interconnection capacity for the test resources and meet unified integration and on-demand scheduling of a test environment;
the full life cycle test tube control module is used for realizing cloud environment-oriented test control, wherein the test tube control needs to be provided with an infrastructure as a basic support, the infrastructure provides computing resources, data storage resources and network communication resources for the test environment, a test execution environment is planned based on the infrastructure and test tasks, a test control technology oriented to the cloud environment takes test planning as input, and test resources in the cloud environment are controlled according to a test planning flow;
the complex test environment sensing is used for grasping the test condition at any time, dynamically adjusting, managing and controlling the test environment, and monitoring various tasks, test resources and infrastructures in the test environment in real time, wherein the main monitoring content comprises test completion progress, running state, test activity, key events and abnormal alarm information;
the self-adaptive test scheduling management module utilizes an abnormal event detection algorithm to automatically capture abnormal events, on the basis, the self-adaptive test scheduling management module realizes the automatic acquisition of the influence relation between test resources and events, and provides self-adaptive scheduling management and control methods with different levels and different dimensions from the aspects of intelligent configuration of test parameters and dynamic scheduling of the test resources, so that the high availability of a test environment and the online execution of expected test contents are ensured under the condition of abnormal events.
2. The intelligent training system based on intelligent guidance according to claim 1, wherein: the real-time monitoring flow for various tasks, test resources and infrastructures in the test environment comprises the following steps:
monitoring parameter settings, status monitoring, process monitoring, progress monitoring, and event monitoring; the monitoring parameter setting mainly comprises the steps of configuring relevant parameter information of a monitoring object and monitoring information; flow monitoring constructs a data flow view by acquiring current entity information from the joint test middleware; progress monitoring evaluates current progress by acquiring the desired and physical operating states from the joint test middleware; event monitoring is mainly achieved by acquiring event record information from joint test middleware.
3. The intelligent training system based on intelligent guidance according to claim 1, wherein: the abnormal event detection algorithm specifically comprises the following steps:
1) Abnormal event detection
Actively checking the states of all the tested resources to obtain abnormal events by adopting an active probing mode; for the first reporting mode, the abnormal event receiving is completed through the distributed test platform;
2) Abnormal event classification and processing method
According to the test platform architecture provided by the subject, the subject divides the abnormal event into three types of capturing infrastructure, namely service layer abnormality, test platform, namely service layer abnormality and test service, namely service layer abnormality, and positions the abnormality into 5 stages according to the influence of the abnormality, wherein the lower the stage is, the higher the degree of importance is;
3) Dynamic allocation of resources
Dynamic allocation of resources refers to the need to dynamically allocate virtual machines, virtual containers, or network resources that are capable of carrying the original traffic.
4. The intelligent training system based on intelligent guidance according to claim 1, wherein: the test scheduling layer based on the heterogeneous cloud environment comprises:
and a scheduling module: the method is responsible for managing scheduling information, sends a scheduling request according to scheduling configuration, and does not bear service codes; the dispatching system is decoupled from the task, so that the availability and stability of the system are improved, and meanwhile, the performance of the dispatching system is not limited by a task module; visual, simple and dynamic management scheduling information is supported, including task creation, updating, deletion, GLUE development and task alarming, all of which are effective in real time, monitoring scheduling results and execution logs are supported, and executors Failover are supported;
the execution module: is responsible for receiving the scheduling request and executing task logic; the task module is focused on the execution operation of the task, so that the development and maintenance are simpler and more efficient; and receiving an execution request, a termination request and a log request of the scheduling module.
5. The intelligent training system based on intelligent guidance of claim 4, wherein: the scheduling module processes tasks from high to low in sequence according to the priorities of different process instances, and the priority of the task in the same process is higher than the priority of the task in the same process;
the method comprises the following steps: and analyzing the priority according to the task instance, storing the task priority task id information of the process instance priority process instance id in a task queue, and obtaining the task which needs to be executed with priority through character string comparison when the task id information is obtained from the task queue.
6. The intelligent training system based on intelligent guidance of claim 4, wherein: and the execution module distributes the test tasks which are arranged by the model to cloud hosts of different designated heterogeneous processors according to a scheduling flow, a multi-task execution service is deployed on the cloud hosts, the multi-task execution service is a task runner, and the execution state and the execution result of the test tasks are fed back to a task scheduling system.
7. The intelligent training system based on intelligent guidance according to any one of claims 4-6, wherein: the task runner is deployed on a designated cloud host, a user HTTP interface is provided for remotely executing tasks, each task takes JobId as a unique identifier, the executed task can be an independent process or a shell script, the task process provides standard command line interface parameters for controlling the execution logic of the task, and the process parameters must contain JobId;
the state and result of task execution are sent to TaskRunner, taskRunner by an HTTPPOST interface or a websocket interface, after the state or result information sent by the task is received, the state or result information is broadcast to all clients connected to a TaskRunner through websocket, and state or result transfer is achieved.
8. The intelligent training system based on intelligent guidance according to claim 1, wherein: the cloud environment-oriented test control layer comprises the following specific workflow:
1) A test plan design stage, which is used as an input of test tube control to generate a test plan to be integrated and controlled by test;
2) In the test environment construction stage, firstly, loading a test scheme from a test scheme library, sending infrastructure requirements of test resources related in the test scheme to a cloud platform management system, and utilizing a cloud platform to open containers/virtual machines to finish the establishment of the infrastructure in the test environment;
3) In the test tube control and dispatch stage, each test resource receives a control command and a dispatch instruction from a test tube control platform and reports state information and collected index information to the test tube control platform in real time so as to realize state monitoring and index display of a test process and grasp the execution condition of the test;
4) In the test result analysis stage, the test control platform sends a result feedback instruction to each test node through the middleware agent, and after each test node uploads the respective offline result to a designated position through the middleware agent, the test control platform sends a test environment withdrawal command to each test node to complete withdrawal and cleaning of the test environment, so that the test environment is restored to an initial state, and other tests are prevented from being influenced in the test iteration process.
9. The intelligent training system based on intelligent guidance of claim 8, wherein: and in the test environment construction stage, after the establishment of the infrastructure is completed, the middleware agent on each test node automatically registers node information to the registration server, the test management and control platform acquires the node information from the registration server, downloads required test resources from the resource library by utilizing the middleware agent on each node, and the test resources comprise software services, scripts and configurations, and automatically starts software to complete the deployment of the test resources.
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