A high-availability method and device for virtualization and cloudization of resources of a measurement and control system

Abstract

The application discloses a kind of high availability method and device of resource virtualization cloudification of measurement and control system, first according to measurement and control task, start data cache container, receive real-time signal data packet, then apply cache queue for real-time signal data packet in data cache container, real-time signal data packet is sequentially written in cache queue and handled, when detecting that container is abnormal, unhandled real-time signal data packet is written in persistent data volume and is stored, until after troubleshooting, unhandled real-time signal data packet is accessed by mounting directory.The application stores unhandled real-time signal data packet in cache queue by persistent data volume, so that real-time signal data packet is recovered by packet serial number when algorithm is reconstructed, so that the continuity of signal character stream analysis is not affected by the time delay generated by platform abnormal reconstruction, the traceability of real-time data is realized, and the high availability of cloud measurement and control system is further improved.

Description

Technical Field

[0001] This invention relates to the fields of aerospace telemetry and control systems and container cloud, and more specifically, to a highly available method and apparatus for virtualizing and cloudifying telemetry and control system resources. Background Technology

[0002] Telemetry, tracking, and command (TT&C) system resource virtualization and cloudification is a novel general-purpose aerospace TT&C method built upon hardware resource virtualization and cloudification technology. It enables functions such as ranging, velocity measurement, angle measurement, remote control, telemetry, and data transmission of multiple TT&C signals. Its TT&C functions are achieved by multiple algorithm modules running through complex interface relationships within cloud-based lightweight container resources. These containers coordinate their operation under cluster control to complete TT&C tasks, offering greater flexibility and speed compared to traditional TT&C systems. Due to the high-speed flight of the target, the rapidly changing distance, speed, and altitude require the TT&C system to process large amounts of information in real time within a very short period. Otherwise, problems such as temporary data loss of TT&C signals and algorithm modules, unstable functional states, or mission deployment failures may occur. Therefore, the TT&C system must complete signal acquisition and information processing within a specified time period (usually on the millisecond level).

[0003] To address the high real-time processing requirements of telemetry, tracking, and command (TT&C) tasks, existing cloud-based resource technologies improve system disaster recovery through container restart migration or primary / backup deployment. Specifically, this is achieved through the following measures: ① Health checks are performed on containers running TT&C algorithm modules using heartbeats and probes. The kubelet component of the container cloud platform periodically obtains the status of all processes (containers) through the Docker daemon; if an anomaly is detected, the process is migrated or restarted. ② Multiple independent container clusters are deployed in the container cloud TT&C system to prevent all clusters from failing simultaneously, thus improving the overall system disaster recovery. However, given the characteristics of real-time continuous TT&C signal character streams, the latency (typically on the order of seconds) introduced by measure ① during container migration and restart exceeds the signal processing time, causing the loss of some signal stream data and intermediate temporary variables generated by the algorithm modules, thereby affecting the stability of the loop during signal analysis. Measure ② results in system resource redundancy, which, especially for the complex TT&C tasks of a large number of future on-orbit spacecraft, will consume significant hardware and software resources and cannot fundamentally solve the problem of TT&C function continuity under system resource virtualization and cloudification.

[0004] Therefore, it is necessary to ensure the highly reliable transmission and processing of signals through highly available methods and devices for virtualizing and cloudifying the resources of the measurement and control system. That is, when a container running a measurement and control function algorithm module fails, the problem of interruption of real-time measurement and control signals caused by failure delay and the loss of temporary variables in the algorithm module should be solved to maintain the business continuity and stability of the measurement and control system. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a highly available method and apparatus for virtualizing and cloudifying measurement and control system resources. This method can improve the high availability of virtualizing and cloudifying measurement and control system resources through data backtracking and achieve consistent processing of real-time continuous signals in the measurement and control system.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] In a first aspect, this invention proposes a highly available method for virtualizing and cloudifying measurement and control system resources, applied to a container cloud platform, comprising:

[0008] The data buffer container is activated according to the telemetry and control task to receive real-time signal data packets;

[0009] In the data cache container, a cache queue is requested for the real-time signal data packets, and the real-time signal data packets are written into the cache queue for processing in sequence;

[0010] When a container anomaly is detected, the unprocessed real-time signal data packets in the cache queue are written to the persistent data volume for storage.

[0011] During troubleshooting, the unprocessed real-time signal data packets are accessed via the mounted directory.

[0012] Optionally, the step of activating the data buffer container and receiving real-time signal data packets according to the measurement and control task includes:

[0013] Allocate computing nodes according to the task requirements of the measurement and control mission;

[0014] The computing node pulls the image from the image library and starts the data cache container and the measurement and control function container. The measurement and control function container is used to process signals on a sampling point basis.

[0015] Receive real-time signal data packets.

[0016] Optionally, the step of requesting a cache queue for the real-time signal data packets from the data cache container and sequentially writing the real-time signal data packets into the cache queue for processing includes:

[0017] Request a cache queue for the real-time signal data packets from the data cache container, and set up read and write terminals for the cache queue;

[0018] The real-time signal data packets are sequentially written into the buffer queue through the read / write terminal;

[0019] Read the real-time signal data packet from the cache queue and send the real-time signal data packet to the measurement and control function algorithm container;

[0020] The measurement and control function algorithm container completes the processing of the real-time signal data packets and returns the processing results.

[0021] Optionally, the step of processing the real-time signal data packet and returning the processing result through the measurement and control function algorithm container includes:

[0022] The algorithm module obtains temporary data by accumulating the sampling points of the real-time signal data packet through the measurement and control function algorithm container.

[0023] The algorithm module performs loop accumulation or locking judgment based on temporary data;

[0024] Delete the processed real-time signal data packets and return the processing result.

[0025] Optionally, before the step of writing unprocessed real-time signal packets in the cache queue to a persistent data volume for storage, the method further includes:

[0026] Declare storage resource specifications for data caching containers by using persistent volume declarations;

[0027] Allocate a corresponding persistent data volume to the persistent volume declaration according to the storage resource specifications.

[0028] Secondly, this invention also proposes a high-availability device for virtualizing and cloudifying measurement and control system resources, applied to a container cloud platform, comprising:

[0029] The receiving module is used to activate the data buffer container according to the measurement and control task and receive real-time signal data packets;

[0030] The signal processing algorithm module is used to request a cache queue for the real-time signal data packets in the data cache container, and to write the real-time signal data packets into the cache queue for processing in sequence.

[0031] The data caching algorithm module is used to write unprocessed real-time signal data packets in the cache queue into a persistent data volume for storage when a container anomaly is detected.

[0032] The access module is used to access the unprocessed real-time signal data packets via the mounted directory during troubleshooting.

[0033] Thirdly, the present invention also proposes a computer device, the computer device including a processor and a memory, the memory storing a computer program, the computer program being loaded and executed by the processor to implement a high availability method for virtualization and cloudification of measurement and control system resources as described in any of the first aspects.

[0034] Fourthly, the present invention also proposes a computer-readable storage medium storing a computer program, which is loaded and executed by a processor to implement a high-availability method for virtualizing and cloudifying measurement and control system resources as described in any of the first aspects.

[0035] The main solution of the present invention and its various further alternative solutions described above can be freely combined to form multiple solutions, all of which are solutions that can be adopted and are claimed by the present invention; furthermore, the (non-conflicting alternatives) can also be freely combined with each other and with other alternatives. Those skilled in the art, after understanding the solution of the present invention, will realize from the prior art and common general knowledge that there are many combinations, all of which are technical solutions to be protected by the present invention, and will not be exhaustively listed here.

[0036] This invention discloses a highly available method and apparatus for virtualizing and cloudifying resources in a measurement and control system. First, a data cache container is started according to the measurement and control task to receive real-time signal data packets. Then, a cache queue is requested for the real-time signal data packets in the data cache container, and the real-time signal data packets are sequentially written into the cache queue for processing. When a container anomaly is detected, unprocessed real-time signal data packets are written to a persistent data volume for storage. After the fault is resolved, the unprocessed real-time signal data packets are accessed through the mounted directory. This invention uses a persistent data volume to store unprocessed real-time signal data packets in the cache queue, allowing the real-time signal data packets to be recovered using packet sequence numbers during algorithm reconstruction. This ensures that the latency caused by platform anomaly reconstruction does not affect the continuity of signal character stream parsing, achieving real-time data traceability and thus improving the high availability of the cloud measurement and control system. Attached Figure Description

[0037] Figure 1 This diagram illustrates a task deployment process provided by an embodiment of the present invention.

[0038] Figure 2 A schematic diagram illustrating the generation of temporary data provided in an embodiment of the present invention is shown.

[0039] Figure 3 This diagram illustrates a high availability method for virtualization and cloudification of measurement and control system resources according to an embodiment of the present invention.

[0040] Figure 4 This diagram illustrates a scenario of container malfunction provided by an embodiment of the present invention. Detailed Implementation

[0041] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0042] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] This invention addresses the issues of preventing packet loss of real-time continuous signals and intermediate temporary variable data in algorithm modules, as well as ensuring uninterrupted measurement and control services in virtualized and cloud-based measurement and control systems. It proposes a high-availability method for virtualized and cloud-based measurement and control system resources. This method improves the high availability of virtualized and cloud-based measurement and control system resources through data backtracking, achieving consistent processing of real-time continuous signals within the measurement and control system. The following section uses downlink signal processing with spread spectrum functionality as an example, and provides a detailed explanation of the technical solution of this invention in conjunction with the accompanying drawings.

[0044] The high-availability method of virtualizing and cloudifying measurement and control system resources is applied to the container cloud platform of the measurement and control system, including the following steps:

[0045] S110. Start the data buffer container according to the measurement and control task and receive real-time signal data packets.

[0046] Real-time signal data packets consist of a packet header, a data field, and a data packet checksum. The packet header includes the packet sequence number, which is used to count data packets. The number of sampling points in the data field can be set to 2000. Each sampling point in the data packet can be processed sequentially through signal processing algorithms.

[0047] Real-time signal data can be packetized according to the sampling frequency of the radio frequency front end to obtain real-time signal data packets, and the signal processing algorithm modules transmit signals in the form of data packets.

[0048] Optionally, computing nodes are allocated according to the task requirements of the measurement and control mission. The computing nodes pull up images from the image library and start the data cache container and the measurement and control function container. The measurement and control function container is used to process signals on a sampling point basis and receive real-time signal data packets.

[0049] Temporary data includes the output values ​​of the accumulator, NCO controller, phase detector, and filter in the code ring container. The temporary data varies depending on the measurement and control function of the algorithm module.

[0050] In addition, the measurement and control tasks need to be deployed before the container cloud platform can request a cache queue for real-time signal data packets. Please refer to [link / reference needed]. Figure 1 , Figure 1 This diagram illustrates a task deployment process provided by an embodiment of the present invention. When all 2000 sampling points in the Nth packet have completed the capture, PN code ring, and carrier ring container algorithm calculations of the measurement and control function Pod in Node1, the signal of the (N-1)th packet and its intermediate temporary variables are deleted from the buffer queue by locating the packet sequence number, and the signal of the Nth packet and the output result of the algorithm module container are sent to Node2.

[0051] Based on task requirements, the control node (Master) allocates computing resources (Nodes) from the resource pool.

[0052] The compute node pulls the image from the image repository and starts the container.

[0053] The container includes a data buffer container and a measurement and control function container. The measurement and control function container is used for signal processing or data processing, while the buffer container is used for data buffering. After the measurement and control task is deployed, the digital radio frequency front-end equipment receives real-time measurement and control signals. The real-time measurement and control signals are distributed to the computing nodes in units of data packets after being buffered by the data buffer container, and then processed by the measurement and control function container.

[0054] S120. Request a cache queue for real-time signal data packets in the data cache container, and write the real-time signal data packets into the cache queue for processing in sequence.

[0055] Optionally, request a buffer queue for real-time signal packets from the data buffer container and set up read / write endpoints for the buffer queue;

[0056] Real-time signal data packets are sequentially written into the buffer queue via the read / write terminal;

[0057] Read real-time signal data packets from the cache queue and send them to the measurement and control function algorithm container;

[0058] The measurement and control function algorithm container completes the processing of real-time signal data packets and returns the processing results.

[0059] The container cloud platform requests a cache queue for real-time signal data packets in the data cache container to realize the data caching function between containers. In order to provide real-time signal caching support for the measurement and control function algorithm container on the compute node, the compute node receives measurement and control signal data packets through the bus interface. The container cloud platform sets up read and write terminals for the requested cache queue, so that real-time signal data packets can be written into the cache queue sequentially through the read and write terminals. The container cloud measurement and control system obtains the packet sequence number of each real-time signal data packet in the cache queue through the dequeue pointer.

[0060] The circular pointer controls the size of the real-time signal data packets written to the buffer queue. When the signal stream composed of real-time signal data packets enters the computing node through the bus, the enqueue pointer controls the speed at which the real-time signal data packets are written to the buffer queue. The distribution container reads data packets from the buffer queue and distributes them to the measurement and control function algorithm container. The measurement and control function algorithm container of the computing node completes signal processing and data processing, writes the processed data packets and processing results to the next computing node, and deletes the processed data packets from the buffer queue by the packet sequence number.

[0061] Optionally, temporary data for the algorithm module can be obtained by accumulating the sampling points of the real-time signal data packets through the measurement and control function algorithm container;

[0062] The algorithm module performs loop accumulation or locking judgment based on temporary data;

[0063] Delete the processed real-time signal data packets and return the processing result.

[0064] Please refer to Figure 2 , Figure 2 This diagram illustrates the generation of temporary data provided in an embodiment of the present invention. Taking the code loop measurement and control function algorithm as an example, the code loop phase detector obtains the code phase error based on the code autocorrelation. After the code phase error is filtered, the code loop NCO controls the generation rate to perform loop accumulation. The code loop locking state is determined by the code loop locking determination unit.

[0065] S120-a. Perform real-virtual separation on each sampling point to obtain the real part signal (I) and the imaginary part signal (Q).

[0066] S120-b: Using a carrier loop, the real and imaginary signals are frequency-biased to obtain the frequency-biased real and imaginary signals, respectively. Then, the frequency-biased real and imaginary signals are correlated with the ahead, prompt, first delay, and second delay codes generated by the PN code generator to obtain the real correlation signal: I ahead I prompt I delay I delays Related signals to the imaginary part: Q ahead Q prompt Q delay Q delays .

[0067] Among them, the lead code, instant code, first lag code and second lag code are controlled by the code ring NCO and generated by the PN code generator. The lead code, instant code and first lag code are 0.5 code widths apart, while the second lag code is 1.5 code widths apart from the instant code. The pseudo code generation rate is controlled by an independent pseudo code NCO controller.

[0068] S120-c, the real part correlation signal and the imaginary part correlation signal are accumulated and calculated by a correlation accumulator to obtain: I ahead_sum I prompt_sum I delay_sum I delays_sum Q ahead_sum Q prompt_sum Q delay_sum Q delays_sum The correlation accumulation signal of the lead code and the first lag code is used for the code loop phase detector, and the correlation accumulation signal of the instant code and the second lag code is used for code loop lock determination.

[0069] S120-d: Repeat step ac for each sampling point. When the number of sampling points processed by the code loop equals 2000 (the number of sampling points in one packet), write the intermediate temporary variable data into the buffer queue. The intermediate temporary variable data includes: the output value of the relevant accumulator and the output value of the pseudocode NCO controller. Since these values ​​are used by the code loop algorithm module for subsequent sampling points, they are represented as "packet number, accumulator count, I". ahead_sum I prompt_sum I delay_sum I delays_sum Q ahead_sum Q prompt_sum Q delay_sum Q delays_sum The format is written into the algorithm cache queue.

[0070] S130. When a container anomaly is detected, unprocessed real-time signal data packets in the cache queue are written to a persistent data volume for storage.

[0071] Signal processing is also performed through sampling points. During this process, intermediate temporary variables are generated for subsequent sampling point processing. When the sampling point count equals the number of sampling points in one packet, the intermediate temporary variables are written into the buffer queue.

[0072] Please refer to Figure 3 , Figure 3 This illustration shows a high availability diagram of a measurement and control system resource virtualization and cloudification according to an embodiment of the present invention. The container cloud measurement and control system uses lightweight and scalable containers as the running unit to achieve features such as application microservices, rapid application deployment, rapid application migration, application health checks, and rapid fault recovery, including the following steps:

[0073] S1. Declare storage resource specifications for a data cache container by declaring a persistent volume.

[0074] S2. Allocate and bind the corresponding persistent data volumes according to the storage resource specifications.

[0075] Among them, a container is a running instance created from the measurement and control function algorithm image. Containers share a Pod virtualization isolated computing and storage resources. Each Pod is configured with an independent lifecycle of persistent data volume (PV) for distributed data storage. Persistent volume claim (PVC) provides proxy requests for PV for Pod virtualization resource calls.

[0076] The container cloud platform creates Pods by calling API service interfaces. When creating a Pod, the PVC first defines the specifications of the storage resources, such as storage space size and access mode. Then, the data volume controller allocates and binds the PVCs corresponding to the PVs according to the specifications, and mounts the host machine's storage resource directory to the Pod directory, enabling the PV to provide real-time data storage services to the Pod through storage plugins.

[0077] First, configure the Network File System (NFS). Create a persistent data volume cluster and a persistent volume declaration cluster in the container cloud platform. PVs are used to store unprocessed real-time signal data packets and intermediate temporary variables of the algorithm module. Based on the real-time signal data packets stored in the data volume, establish mount points on NFS. Mount points are used to represent the directory of real-time signal data packets and are used for data sharing. Next, use the data volume controller to bind PVs and PVCs according to specifications to create different data cache containers. These containers use persistent volume declarations to receive real-time data packets.

[0078] Use a persistent volume (PVC) to attach a persistent data volume (PV) to store data. Configure a measurement and control function container and a real-time signal data packet cache container according to the measurement and control task. When measurement and control signals enter the system, they are first cached in the real-time signal data packet cache container before entering the algorithm module container. If the algorithm module fails and causes the container to malfunction, the unprocessed data packets in the cache queue are stored in the persistent data volume (PV). The system reconstructs a container with the same configuration as the corresponding node to process real-time data and cached data in the storage resource pool.

[0079] The virtualized cloud resources include virtualized cloud resource Pods and their persistent volume declarations for running measurement and control function algorithm modules, data volume controllers, storage resources, and storage plugins. Downlink signal demodulation methods mainly include acquisition, PN code loop, carrier loop, bit synchronization loop, and frame synchronization.

[0080] Specifically, when the API service detects an abnormal status in the monitoring and control function Pod, it will reallocate computing resources (Nodes) through the scheduler on the control node, referring to... Figure 4 , Figure 4 This diagram illustrates a scenario of container malfunction provided by an embodiment of the present invention.

[0081] For monitoring and control function Pods in abnormal states, the control node is configured with a compute node, and the monitoring and control function container on the compute node is responsible for processing the buffered data in the storage resource pool.

[0082] The new compute node pulls up the image from the image repository and deploys the container, then processes the real-time signals stored in the persistent data volume in sequence. The intermediate temporary variable cache data in the persistent data volume provides relevant computational support for the measurement and control function container, avoiding the circular computation of the algorithm.

[0083] The Kubelet component periodically reports the status information of the compute nodes to the API service in the control node. The API service is responsible for communication between the various functional modules of the cluster. Each functional module in the cluster stores information in etcd through the API service. etcd is a highly available key-value store system mainly used for sharing configuration and service discovery. When it is necessary to retrieve and manipulate this data, it is done through the REST interface provided by the API service, thereby realizing information exchange between the modules.

[0084] When the API service detects an abnormal status of the monitoring and control function Pod, it will reallocate the computing resource (Node) from the original computing resource (Node) to another computing resource (Node) through the scheduler of the control node.

[0085] In addition, to handle cached data caused by fault latency, master-slave nodes are configured for the control node. The master node's measurement and control function container is responsible for handling real-time signal streams, while the slave node's measurement and control function container is responsible for handling buffered data in the storage resource pool.

[0086] The compute nodes pull up image deployment containers from the image repository and process the signals stored in the persistent data volume in turn. The middleware cache data in the persistent data volume provides relevant computing support for the measurement and control function containers of the slave nodes, avoiding the circular operation of the algorithm.

[0087] In one possible embodiment, if the packet number of the cached signal is j, then the correlation accumulator cache data of the (j-1)th packet data is read from the persistent data volume, the code ring input is the first sampling point of the j-th packet data, and the real and imaginary parts are correlated with the carrier NCO of the (j-1)th packet and the pseudocode generated by the code ring NCO controller of the (j-1)th packet, respectively. The correlation accumulator performs accumulation operation based on the cache of the (j-1)th packet, and the result is input to the code ring path phase detector and the code ring lock determination unit. At this time, the code ring lock state of the j-th packet data is not affected.

[0088] S140. During troubleshooting, access unprocessed real-time signal data packets via the mounted directory.

[0089] After troubleshooting, the measurement and control function container needs to be restarted or migrated. Unprocessed real-time signal data packets can be accessed through the mounted directory, allowing for backtracking to unprocessed data via packet sequence number. Alternatively, data from the previous data loop can be read via packet sequence number, maintaining the continuity of the loop accumulator data. The container cloud platform performs the following steps:

[0090] Step 1: The container cloud platform includes the kubelet component, which is deployed on the platform's nodes. This allows the platform to detect whether containers providing monitoring and control services are malfunctioning. Specifically, the kubelet component uses a heartbeat mode to monitor the status of all services to determine if they are functioning correctly, and a probe mode to monitor the business software to determine if containers are running normally. A container providing services is considered malfunctioning only when either the service status or the container is malfunctioning, or both, is abnormal. Conversely, for a container to be functioning normally, both a normal service status and normal container operation must be met. If the container is functioning normally, processed data packets are deleted.

[0091] Step 2: Data cleanup of the cache queue caused by abnormalities in the measurement and control function container. When the amount of data in the cache queue exceeds the queue length, the data is stored in the real-time data storage resource pool, which is provided by the data volume PV for persistent storage. Other containers can access the stored data through the mounted directory.

[0092] Since the data volume controller assigns and binds the corresponding data volume PV according to the specifications, and mounts the host machine's storage resource directory to the Pod directory, the data volume PV can provide real-time data storage for the Pod through the storage plugin.

[0093] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0094] By storing unprocessed real-time signal data packets in a cache queue using persistent data volumes, the real-time signal data packets can be recovered by packet sequence number during algorithm reconstruction. This ensures that the latency caused by abnormal platform reconstruction does not affect the continuity of signal character stream parsing, while also achieving the traceability of real-time data, thereby improving the high availability of the cloud measurement and control system.

[0095] Furthermore, this invention also proposes a high-availability device for virtualizing and cloudifying measurement and control system resources, applied to a container cloud platform, to achieve the aforementioned high-availability method for virtualizing and cloudifying measurement and control system resources. The device includes virtualized cloud resources, data volume controllers, storage resources, storage plugins, etc., and further includes:

[0096] The receiving module is used to activate the data buffer container according to the measurement and control task and receive real-time signal data packets.

[0097] The signal processing algorithm module is used to request a buffer queue for real-time signal data packets in the data buffer container, and write the real-time signal data packets into the buffer queue for processing in sequence.

[0098] The data caching algorithm module is used to write unprocessed real-time signal data packets in the cache queue to a persistent data volume for storage when a container anomaly is detected.

[0099] The access module is used to access unprocessed real-time signal data packets via mounted directories during troubleshooting.

[0100] Furthermore, after the real-time signal enters the computing node of the measurement and control system, the device first passes through the data cache Pod and then enters the measurement and control function Pod to complete the signal processing. If the algorithm module fails and causes the container to malfunction, the real-time signal data packet in the cache queue and the intermediate temporary variable data of the measurement and control function container are stored in the data volume PV. The system is then reconstructed and a container with the same configuration as the corresponding node is used to process the real-time data and the cached data in the storage resource pool.

[0101] This invention provides a computer device that can implement the steps in any embodiment of the high availability method for virtualizing and cloudifying measurement and control system resources provided in this invention. Therefore, it can achieve the beneficial effects of the high availability method for virtualizing and cloudifying measurement and control system resources provided in this invention. For details, please refer to the previous embodiments, which will not be repeated here.

[0102] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. Therefore, embodiments of the present invention provide a storage medium storing multiple instructions that can be loaded by a processor to execute the steps of any embodiment of the high-availability method for virtualization and cloudification of measurement and control system resources provided by the present invention.

[0103] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0104] Since the instructions stored in the storage medium can execute the steps in any of the high availability methods for virtualization and cloudification of measurement and control system resources provided in the embodiments of the present invention, the beneficial effects that any of the high availability methods for virtualization and cloudification of measurement and control system resources provided in the embodiments of the present invention can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0105] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-availability method for virtualizing and cloudizing resources of a TT&C system, characterized in that, The application is applied to a container cloud platform, and comprises: allocating a computing node according to a task demand of a measurement and control task; pulling up an image from an image library through the computing node, starting a data cache container and a measurement and control function container, and using the measurement and control function container to process signals in units of sampling points; receiving real-time signal data packets; applying for a cache queue for the real-time signal data packets in the data cache container, and writing the real-time signal data packets into the cache queue in sequence for processing; when a container exception is detected, writing unprocessed real-time signal data packets in the cache queue into a persistent data volume for storage, and using the persistent data volume to store intermediate temporary variables in an algorithm module; when troubleshooting, accessing the unprocessed real-time signal data packets through a mounted directory.

2. The high availability method of measurement and control system resource virtualization cloudification of claim 1, wherein, The step of applying for the cache queue for the real-time signal data packets in the data cache container and writing the real-time signal data packets into the cache queue in sequence for processing comprises: applying for the cache queue for the real-time signal data packets in the data cache container, setting a read-write end for the cache queue, writing the real-time signal data packets into the cache queue in sequence through the read-write end, reading the real-time signal data packets from the cache queue, and sending the real-time signal data packets to a measurement and control function algorithm container; processing the real-time signal data packets through the measurement and control function algorithm container and returning a processing result. The step of processing the real-time signal data packets through the measurement and control function algorithm container and returning a processing result comprises: performing accumulation operation on sampling points of the real-time signal data packets through the measurement and control function algorithm container to obtain temporary data of an algorithm module; 3. The high availability method of measurement and control system resource virtualization cloudification of claim 2, wherein, performing loop accumulation or locking judgment according to the temporary data of the algorithm module; deleting the real-time signal data packets after processing and returning the processing result. Before the step of writing the unprocessed real-time signal data packets in the cache queue into the persistent data volume for storage, the method further comprises: declaring a storage resource specification for a data cache container through a persistent volume; 4. The high availability method of measurement and control system resource virtualization cloudification of claim 1, wherein, allocating a corresponding persistent data volume for the persistent volume declaration according to the storage resource specification. The application is applied to a container cloud platform, and comprises: allocating a computing node according to a task demand of a measurement and control task; 5. A high-availability device for virtualization and cloudization of resources of a TT&C system, characterized in that, pulling up an image from an image library through the computing node, starting a data cache container and a measurement and control function container, and using the measurement and control function container to process signals in units of sampling points; receiving real-time signal data packets; a signal processing algorithm module, configured to apply for a cache queue for the real-time signal data packets in the data cache container, and write the real-time signal data packets into the cache queue in sequence for processing; a data cache algorithm module, configured to, when a container exception is detected, write unprocessed real-time signal data packets in the cache queue into a persistent data volume for storage, and use the persistent data volume to store intermediate temporary variables in an algorithm module; an access module, configured to, when troubleshooting, access the unprocessed real-time signal data packets through a mounted directory. ​ ​ 6. A computer device, comprising: The computer device comprises a processor and a memory, and the memory stores a computer program, which is loaded and executed by the processor to implement the high-availability method for virtualization and cloudization of resources of a measurement and control system according to any one of claims 1-4.

7. A computer readable storage medium characterized in that, The storage medium stores a computer program, which is loaded and executed by the processor to implement the high-availability method for virtualization and cloudization of resources of a measurement and control system according to any one of claims 1-4.

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