Elastic calculation method and device for electric energy metering data and medium

Abstract

The invention discloses an elastic calculation method and device for electric energy metering data and a medium. The method comprises the steps that an increase and decrease instruction is sent to a cloud computing server; adjusting the size of resources occupied by elastic computing according to the increase and decrease instruction to obtain a plurality of adjusted cloud computing server instances; adjusting the plurality of cloud computing server instances to obtain a cloud computing server instance set; and calculating the metering data through the cloud computing server instance set to obtain a calculation result. According to the elastic calculation method and device for the electric energy metering data and the medium, the increase and decrease instruction is sent to the cloud computing server according to the increase and decrease situation of the business requirements, and the stability and performance of the service can be ensured; and the cloud computing server instance is adjusted again according to the operation state and the index data of the operation system, so that service requirements can be further met, resource waste can be avoided, and the problem that high service requirements and computing resource waste during service strategy change are difficult to reduce can be solved.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to a method, device and medium for elastically calculating electric energy metering data. Background Art

[0002] With the development of cloud computing technology, container cloud has become more and more important in cloud computing because it can provide lightweight virtual solutions. Kubernetes (K8S), as the mainstream container cloud orchestration engine, is mainly responsible for: image management and distribution, container instance startup, management and status monitoring, service discovery, load balancing, expansion, release and upgrade between multiple service instances, etc. In K8S, service capabilities can be expanded in both horizontal and vertical directions. Horizontal expansion supports adding new service instances to improve service capabilities, while vertical expansion improves service capabilities by adding computing resources (CPU, memory) of existing service instances. With the gradual deepening of container cloud applications, people find that horizontal expansion is more convenient and direct than vertical expansion. Therefore, K8S provides a horizontal automatic scaling mechanism (HPA), also known as elastic scaling. In a cloud environment, the cost of resource usage is usually proportional to the number and usage time of resources. Through automatic scaling, the horizontal automatic scaling mechanism helps to increase resources when needed and reduce resources when not needed, thereby achieving cost-effectiveness.

[0003] However, the existing container shrinking mechanism only uses computing resources as the criterion, and the timing of shrinking is not accurately judged. Due to the inaccurate judgment of shrinking timing, the container may be shrunk prematurely when it still needs to process requests or tasks, thereby wasting the allocated computing resources. Summary of the invention

[0004] The present invention provides a method, device and medium for elastically calculating electric energy metering data, so as to solve the problem that it is difficult to reduce the waste of computing resources when high business demands and business strategies are changed.

[0005] In order to solve the above problems, the present invention provides a flexible calculation method for electric energy metering data, comprising:

[0006] Business needs for obtaining metering data;

[0007] Controlling the elastic scaling system to send increase and decrease instructions to the cloud computing server according to the increase and decrease of the business demand;

[0008] Controlling the cloud computing server to adjust the size of resources occupied by elastic computing according to the increase / decrease instruction to obtain a plurality of adjusted cloud computing server instances;

[0009] Adjusting the plurality of cloud computing server instances according to the operating status and indicator data of the elastic computing system to obtain a cloud computing server instance set;

[0010] The metering data is calculated by the cloud computing server instance set to obtain a calculation result.

[0011] By identifying the business needs of metering data, the present invention can send increase and decrease instructions to the cloud computing server in the first time, so that the cloud computing server can adjust the size of resources occupied by elastic computing according to the increase or decrease of business needs, so as to meet the computing needs, ensure the stability and performance of the service, and maximize the utilization of resources; and after the initial adjustment, several cloud computing server instances are adjusted according to the operating status and indicator data of the cloud computing server and the elastic scaling system, which can further consider the actual operation problems, avoid the business load exceeding the processing capacity of the current instance set or the resource utilization rate is too low, ensure that the system responds to the changes in business needs in time, so that the final cloud computing server instance set can meet the computing needs and maximize the resource utilization rate.

[0012] Compared with the prior art, the present invention sends increase and decrease instructions to cloud computing servers according to the increase and decrease of business needs, and can adjust the number of cloud computing server instances in time to ensure the stability and performance of the service; the cloud computing server instances are readjusted according to the operating status and indicator data of the operating system, which can further ensure that while meeting business needs, resource waste is avoided, thereby solving the problem of difficulty in reducing computing resource waste when high business needs and business policy changes occur.

[0013] As a preferred solution, according to the increase / decrease instruction, the elastic scaling system sends an increase / decrease instruction to the cloud computing server according to the increase / decrease amount of the business demand, specifically:

[0014] If the business demand is an increase, controlling the elastic scaling system to send an increase instruction to the cloud computing server;

[0015] If the business demand is decreasing, the elastic scaling system is controlled to send a decrease instruction to the cloud computing server.

[0016] In this preferred solution, when business demand grows, the elastic scaling system can identify this growth trend and automatically trigger the elastic scaling mechanism to send growth instructions to the cloud computing server; in this way, the cloud computing server can quickly increase resources to meet the growing business needs and ensure the stability and performance of the service; when business demand decreases, the system can also automatically detect and send a decrease instruction to the cloud computing server; this method can avoid waste of resources, reduce unnecessary cost expenditures, and by flexibly adjusting the allocation of resources, the system can utilize resources more efficiently and maximize resource utilization.

[0017] As a preferred solution, according to the increase / decrease instruction, the cloud computing server is allowed to adjust the resource size occupied by the elastic computing to obtain several adjusted cloud computing server instances, specifically:

[0018] If the increase / decrease instruction is an increase instruction, controlling the cloud computing server to increase the number of cloud computing server instances to obtain the adjusted number of cloud computing server instances;

[0019] If the increase / decrease instruction is a decrease instruction, the cloud computing server is controlled to reduce the number of the cloud computing server instances to obtain the adjusted cloud computing server instances.

[0020] In this preferred solution, when business demand grows, the system's processing capacity can be improved by increasing the number of cloud computing server instances to ensure high availability and performance of the service, which helps to cope with sudden traffic growth or computing needs and avoid system overload or response delays; when business demand decreases, reducing the number of cloud computing server instances can avoid waste of resources.

[0021] As a preferred solution, the plurality of cloud computing server instances are adjusted according to the operating status and indicator data of the cloud computing server and the elastic scaling system to obtain a cloud computing server instance set, specifically:

[0022] According to the running status of the cloud computing server, the plurality of cloud computing server instances are adjusted by elastic self-healing to obtain a first cloud computing server instance set;

[0023] According to the alarm status of the indicator data in the elastic scaling system, scaling adjustment is performed on the first cloud computing server instance set to obtain the cloud computing server instance set.

[0024] This preferred solution greatly simplifies the work of operation and maintenance personnel through elastic self-healing and automatic scaling adjustment mechanisms. There is no need to manually handle server failures or adjust resource configurations. Instead, these tasks can be completed automatically by the system. This approach not only reduces operation and maintenance costs, but also reduces the possibility of human errors and improves work efficiency.

[0025] As a preferred solution, according to the running state of the cloud computing server, the plurality of cloud computing server instances are adjusted by elastic self-healing to obtain a first cloud computing server instance set, specifically:

[0026] Acquire a second set of cloud computing server instances in a non-operating state from among the plurality of cloud computing server instances;

[0027] releasing the second cloud computing server instance set, and replacing the second cloud computing server instance set by creating a new third cloud computing server instance set;

[0028] The first cloud computing server instance set is composed of the third cloud computing server instance set and the server instances in running state among the plurality of cloud computing server instances.

[0029] In this preferred solution, releasing the second cloud computing server instance set means that these instances no longer occupy the computing resources of the cloud computing platform, which helps to free up resources from workloads that are no longer needed and avoid waste of resources; replacing the original second cloud computing server instance set by creating a new third cloud computing server instance set means that the new instance set adopts a more efficient configuration and can ensure the availability of the third cloud computing server instance set. Such replacement helps to improve the performance, reliability and security of the entire system.

[0030] As a preferred solution, according to the alarm status of the indicator data in the elastic scaling system, scaling adjustment is performed on the first cloud computing server instance set to obtain the cloud computing server instance set, specifically:

[0031] Collecting statistics on the operating data of the index data in the elastic scaling system; wherein the index data includes CPU usage, memory usage, intranet outbound traffic and intranet inbound traffic;

[0032] If the operating data reaches a preset alarm condition, the number of instances of the first cloud computing server instance set is dynamically adjusted according to the operating data to obtain the cloud computing server instance set.

[0033] This preferred solution can identify changes in the current business load through real-time monitoring of operating data. Once the operating data reaches the preset alarm condition, it means that the current business load has exceeded the processing capacity of the current set of instances or the resource utilization is too low, resulting in resource waste. At this time, dynamically adjusting the number of instances based on the operating data can ensure that the system responds to changes in business needs in a timely manner and avoids service performance degradation or resource waste.

[0034] The present invention also provides an elastic computing device for electric energy metering data, which is applicable to an elastic computing system, wherein the elastic computing system includes a cloud computing server and an elastic scaling system; the elastic computing device includes a demand module, a first control module, a second control module, an adjustment module and a computing module;

[0035] Wherein, the demand module is used to obtain the business demand of metering data;

[0036] The first control module is used to control the elastic scaling system to send an increase or decrease instruction to the cloud computing server according to the increase or decrease amount of the business demand;

[0037] The second control module is used to control the cloud computing server to adjust the resource size occupied by the elastic computing according to the increase / decrease instruction to obtain a plurality of adjusted cloud computing server instances;

[0038] The adjustment module is used to adjust the plurality of cloud computing server instances according to the operating status and indicator data of the elastic computing system to obtain a cloud computing server instance set;

[0039] The calculation module is used to calculate the metering data through the cloud computing server instance set to obtain a calculation result.

[0040] As a preferred solution, the first control module includes an increasing unit and a decreasing unit;

[0041] Wherein, the growth unit is used to control the elastic scaling system to send a growth instruction to the cloud computing server if the business demand is growth;

[0042] The decreasing unit is used to control the elastic scaling system to send a decreasing instruction to the cloud computing server if the business demand is decreasing.

[0043] As a preferred solution, the second control module includes an increasing unit and a decreasing unit;

[0044] The adding unit is used to control the cloud computing server to increase the number of cloud computing server instances if the increase / decrease instruction is a growth instruction, so as to obtain the adjusted number of cloud computing server instances;

[0045] The reducing unit is used to control the cloud computing server to reduce the number of the cloud computing server instances if the increase / decrease instruction is a decrease instruction, so as to obtain the adjusted cloud computing server instances.

[0046] As a preferred solution, the adjustment module includes an adjustment unit and an alarm unit;

[0047] The adjustment unit is used to adjust the plurality of cloud computing server instances in an elastic self-healing manner according to the running state of the cloud computing server to obtain a first cloud computing server instance set;

[0048] The alarm unit is used to perform scaling adjustment on the first cloud computing server instance set according to the alarm status of the indicator data in the elastic scaling system to obtain the cloud computing server instance set.

[0049] As a preferred solution, the adjustment unit includes an operation subunit, a replacement subunit and a configuration subunit;

[0050] The running subunit is used to obtain a set of second cloud computing server instances that are not running from among the plurality of cloud computing server instances;

[0051] The replacement subunit is used to release the second cloud computing server instance set and replace the second cloud computing server instance set by creating a new third cloud computing server instance set;

[0052] The constituting subunit is used to constitute the first cloud computing server instance set by the third cloud computing server instance set and the server instances in the plurality of cloud computing server instances that are in running state.

[0053] As a preferred solution, the alarm unit includes a statistical subunit and a quantity subunit;

[0054] The statistical subunit is used to count the operating data of the index data in the elastic scaling system; wherein the index data includes CPU usage, memory usage, intranet outbound traffic and intranet inbound traffic;

[0055] The quantity subunit is used to dynamically adjust the number of instances of the first cloud computing server instance set according to the operation data to obtain the cloud computing server instance set if the operation data reaches a preset alarm condition.

[0056] The present invention also provides a storage medium, on which a computer program is stored. The computer program is called and executed by a computer to implement the elastic calculation method for electric energy metering data as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flowchart of a method for elastic calculation of electric energy metering data provided by an embodiment of the present invention;

[0058] Figure 2 It is a schematic diagram of the composition of the elastic computing system provided by an embodiment of the present invention;

[0059] Figure 3 It is a schematic diagram of the terminal structure of the hardware operating environment provided by an embodiment of the present invention;

[0060] Figure 4 is a schematic diagram of the structure of an elastic computing system provided by an embodiment of the present invention;

[0061] Figure 5 It is a structural schematic diagram of a flexible calculation device for electric energy metering data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] In the description of the present application, it should be understood that the terms "first", "second" and "third" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first", "second" and "third" may explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, "several" means two or more.

[0064] An elastic calculation method for electric energy metering data provided in an embodiment of the present invention is mainly used to accurately judge the timing of shrinking computing resources, thereby adjusting the size of resources occupied by elastic computing of cloud computing servers to reduce the waste of computing resources during high business demand and business policy changes.

[0065] Embodiment 1:

[0066] See also Figure 1 The embodiment of the present invention provides an elastic calculation method for electric energy metering data, which is applicable to an elastic calculation system, wherein the elastic calculation system includes a cloud computing server and an elastic scaling system; the elastic calculation method includes S1 to S5, and the specific implementation steps are as follows:

[0067] S1. Business needs for obtaining metering data.

[0068] In step S1 of the embodiment of the present invention, S1 includes S1.1 to S1.2, specifically:

[0069] S1.1, constructing an elastic computing system according to business load data, resource utilization data, performance monitoring data, cost-effectiveness data, user feedback data and compliance data; wherein the elastic computing system includes one or more cloud computing servers 10 providing elastic computing, and an elastic scaling system 20, and the elastic scaling system 20 includes an elastic expansion unit and an elastic contraction unit;

[0070] In addition, the elastic computing system also includes a monitoring module 30, a mirroring module 40, an encryption module 50, a container management module 60 and a resource orchestration module 70;

[0071] Among them, the elastic expansion unit is used to increase the cloud computing server instances of the cloud computing server when the business demand increases;

[0072] Elastic shrinking unit, used to release low-level resources when business demand decreases;

[0073] Monitoring module 30, used to monitor indicators such as CPU, memory and hard disk of the cloud computing server;

[0074] The image module 40 is used to create an entire image template for the system disk and data disk of the cloud host; and the created image contains the user's business data;

[0075] The encryption module 50 is used to encrypt the cloud host disk using the national secret algorithm, and to encrypt the data in the cloud disk and the data transmitted between the cloud disk and the cloud computing server instance;

[0076] The container management module 60 is used to adjust the corresponding container resources when adjusting the resource size occupied by the elastic computing of the cloud computing server;

[0077] The resource orchestration module 70 is used to create and configure resources and perform automated deployment and operation and maintenance.

[0078] To apply the embodiments of the present invention, please refer to Figure 2 , Figure 2 It is a schematic diagram of the composition of the elastic computing system provided by an embodiment of the present invention, showing the components of the elastic computing system.

[0079] To apply the embodiments of the present invention, please refer to Figure 3 , Figure 3 100 is a schematic diagram of the terminal structure of the hardware operating environment provided by an embodiment of the present invention. The terminal may include: a processor 1001 (such as a CPU), a communication bus 1002, a user interface 1003, a network interface 1004 and a memory 1005;

[0080] The processor 1001 is used to call the data calculation program stored in the memory 1005;

[0081] The communication bus 1002 is used to realize the connection and communication between the above components;

[0082] The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and optionally the user interface 1003 may also include a standard wired interface and a wireless interface; and the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client.

[0083] The network interface 1004 may include a standard wired interface and a wireless interface (such as a WI-FI interface); and the network interface 1004 is mainly used to connect to a background server and perform data communication with the background server.

[0084] The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory; and the memory 1005 may also be a storage device independent of the processor 1001;

[0085] Optionally, the terminal may also include a camera, RF (Radio Frequency) circuit, sensor, audio circuit, WiFi module, etc. Among them, sensors include light sensors, motion sensors and other sensors; specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display screen according to the brightness of the ambient light, and the proximity sensor may turn off the display screen and / or backlight when the mobile terminal is moved to the ear; as a type of motion sensor, the gravity acceleration sensor may detect the magnitude of acceleration in each direction (generally three axes), and the magnitude and direction of gravity when stationary, which may be used for applications that identify the posture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; of course, the mobile terminal may also be equipped with other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., which will not be described in detail here.

[0086] S1.2. Business needs for obtaining metering data.

[0087] It should be noted that the elastic calculation method of electric energy metering data provided in this embodiment is completed in the terminal through the collaborative execution between the processor 1001, the communication bus 1002, the user interface 1003, the network interface 1004 and the memory 1005;

[0088] Furthermore, the terminal of the embodiment of the present invention may be a PC, or may be a smart phone, a tablet computer, an e-book reader, an MP3 (Moving Picture Experts Group Audio Layer III) player, an MP4 (Moving Picture Experts Group Audio Layer IV) player, a portable computer, or other portable terminal devices with a display function.

[0089] To apply the embodiments of the present invention, please refer to Figure 4 , Figure 4 It is a schematic diagram of the structure of the elastic computing system provided by an embodiment of the present invention, showing a structure in which several cloud computing servers 10 and an elastic scaling system 20 perform data interaction.

[0090] S2. Control the elastic scaling system to send increase or decrease instructions to the cloud computing server according to the increase or decrease of business needs.

[0091] Step S2 of the embodiment of the present invention is specifically:

[0092] If the business demand is growth, the elastic scaling system 20 is controlled to send a growth instruction to the cloud computing server 10;

[0093] If the business demand is decreasing, the elastic scaling system 20 is controlled to send a decrease instruction to the cloud computing server 10 .

[0094] In this embodiment S2, when business demand increases, the elastic scaling system can identify this growth trend and automatically trigger the elastic scaling mechanism to send growth instructions to the cloud computing server; in this way, the cloud computing server can quickly increase resources to meet the growing business needs and ensure the stability and performance of the service; when business demand decreases, the system can also automatically detect and send a decrease instruction to the cloud computing server; this method can avoid waste of resources, reduce unnecessary cost expenditures, and by flexibly adjusting the allocation of resources, the system can utilize resources more efficiently and maximize resource utilization.

[0095] S3. Control the cloud computing server to adjust the size of resources occupied by the elastic computing according to the increase or decrease instruction, and obtain a number of adjusted cloud computing server instances.

[0096] Step S3 of the embodiment of the present invention is specifically:

[0097] If the increase / decrease instruction is a growth instruction, the cloud computing server is controlled by the elastic expansion unit to increase the number of cloud computing server instances to obtain a number of adjusted cloud computing server instances;

[0098] If the increase / decrease instruction is a decrease instruction, the cloud computing server is controlled by the elastic contraction unit to reduce the number of cloud computing server instances to obtain a number of adjusted cloud computing server instances;

[0099] According to the adjusted cloud computing server instances, the container management module 60 is controlled to adjust the corresponding container resources; wherein, the container management module 60 supports the lifecycle management of enterprise-level Kubernetes containerized applications. Moreover, the container management module 60 can simplify the operation and maintenance work such as cluster construction and expansion, and integrate virtualization, storage, network and security capabilities, which is the best containerized application operating environment in the cloud. In addition, the container management module 60 supports the lifecycle management of enterprise-level containerized applications, and can efficiently run containerized applications in the cloud, realizing automated, intelligent, simple and efficient application management and operation and maintenance.

[0100] It should be noted that the cloud computing server 10 is a virtual computing environment, including the most basic computing components such as CPU and memory, and is the actual operating entity presented by the cloud computing server 10;

[0101] The cloud computing server instance is the core concept of the cloud computing server 10. A series of operations on the cloud computing server instance can be completed through the cloud computing server management console;

[0102] Cloud host IP management supports the creation of cloud hosts by specifying IP addresses for cloud computing servers 10, making it convenient for operation and maintenance personnel to conduct coordinated management of IPs. It supports the configuration of IPv6 / IPv4 dual-stack networks, and cloud computing server instances can automatically obtain IPv6 addresses for intranet communication.

[0103] In this embodiment S3, when business demand increases, the system's processing capacity can be improved by increasing the number of cloud computing server instances to ensure high availability and performance of the service, which helps to cope with sudden traffic growth or computing needs and avoid system overload or response delays; when business demand decreases, reducing the number of cloud computing server instances can avoid waste of resources.

[0104] S4. Adjust several cloud computing server instances according to the operating status and indicator data of the elastic computing system to obtain a cloud computing server instance set.

[0105] In step S4 of the embodiment of the present invention, S4 includes S4.1 to S4.2, specifically:

[0106] S4.1. Obtain a set of second cloud computing server instances in a non-operating state from among the plurality of cloud computing server instances, and define the set of second cloud computing server instances as being in an unhealthy state; wherein data transmitted between the instances is completed through the encryption module 50;

[0107] Release the second cloud computing server instance set, and control the mirroring module 40 and the resource orchestration module 70 to replace the second cloud computing server instance set by creating a new third cloud computing server instance set;

[0108] The first cloud computing server instance set is formed by the third cloud computing server instance set and the server instances in the running state among the plurality of cloud computing server instances;

[0109] Among them, the encryption module 50 supports the national secret algorithm encryption of the cloud host disk, and can encrypt the data in the cloud disk, the data transmitted between the cloud disk and the instance, and all snapshots created by the cloud disk to ensure the security of the data; at the same time, it supports users to remotely access the cloud host through VNC, supports users to set the cloud host VNC password (non-system password), and can perform VNC password authentication when the user accesses by VNC, to ensure the terminal user's secure access to the cloud host, and provide official product documentation or product function screenshots.

[0110] The image module 40 supports the creation of a whole-machine image template for the system disk and data disk of the cloud host. The created image contains the user's business data and can be used to quickly release a new elastic cloud host containing the user's business data.

[0111] The resource orchestration module 70 is a service that simplifies the management of cloud computing resources. The template specification defined by the resource orchestration module 70 compiles the resource stack template, and defines the required cloud computing resources, the dependencies between resources, etc. in the template. The orchestration engine of the resource orchestration module 70 will automatically complete the creation and configuration of all resources according to the template, and realize automated deployment and operation and maintenance;

[0112] The template of the resource orchestration module 70 is a readable and easy-to-write text file. You can directly edit the JSON format text, or control the template version through version control tools such as SVN and Git to achieve the purpose of controlling the infrastructure version. The orchestration capabilities of the resource orchestration module 70 can be integrated with applications through APIs, SDKs, etc. to achieve infrastructure as code;

[0113] The template of the resource orchestration module 70 is also a standardized way of delivering resources and applications. If it is an independent software vendor (ISV), the overall system and solution including cloud resources and applications can be delivered through the resource orchestration service template;

[0114] The resource orchestration service manages a group of cloud resources in a unified manner through a resource stack. Operations such as creation, deletion, and re-creation of cloud resources can all be completed in units of resource stacks.

[0115] In this embodiment S4.1, releasing the second cloud computing server instance set means that these instances no longer occupy the computing resources of the cloud computing platform, which helps to free up resources from workloads that are no longer needed and avoid waste of resources. Replacing the original second cloud computing server instance set with a new third cloud computing server instance set means that the new instance set adopts a more efficient configuration and can ensure the availability of the third cloud computing server instance set. Such replacement helps to improve the performance, reliability and security of the entire system.

[0116] S4.2. Monitor and collect statistics on the operating data of the index data in the elastic scaling system in real time through the monitoring module 30; the index data includes but is not limited to CPU usage, memory usage, intranet outbound traffic and intranet inbound traffic; the monitoring module 30 also supports monitoring the CPU, memory and number of open files of each process in the cloud host system, providing users with system-level, active and fine-grained monitoring services;

[0117] If the operation data reaches a preset alarm condition, an alarm is triggered, and the number of instances of the first cloud computing server instance set is dynamically adjusted according to the operation data to obtain a cloud computing server instance set;

[0118] Furthermore, the elastic scaling system 20 supports specifying configuration information of the cloud computing server instance to be scaled, including but not limited to instance specifications, image types, system disks, data disks, and custom data;

[0119] The elastic scaling system 20 supports elastic expansion of cloud computing server instances according to the configured scaling rules, and can automatically add cloud computing server instances to the backend server group of the load balancing instance and the access whitelist of the database instance;

[0120] The elastic scaling system 20 supports users to execute scaling rules on a regular basis, thereby automatically expanding or shrinking computing resources to meet business needs while controlling costs.

[0121] This embodiment S4.2 can identify changes in the current business load through real-time monitoring of the operating data. Once the operating data reaches the preset alarm condition, it means that the current business load has exceeded the processing capacity of the current instance set or the resource utilization rate is too low, resulting in resource waste. At this time, dynamically adjusting the number of instances according to the operating data can ensure that the system responds to changes in business needs in a timely manner and avoids service performance degradation or resource waste.

[0122] Overall, this embodiment S4 greatly simplifies the work of operation and maintenance personnel through elastic self-healing and automatic scaling adjustment mechanism. There is no need to manually handle server failures or adjust resource configurations. Instead, these tasks can be completed automatically by relying on the system. This method not only reduces operation and maintenance costs, but also reduces the possibility of human errors and improves work efficiency.

[0123] S5. Calculate the metering data through the cloud computing server instance set to obtain calculation results.

[0124] Step S5 of the embodiment of the present invention is specifically as follows:

[0125] The metering data is calculated through a set of cloud computing server instances to obtain calculation results.

[0126] It should be noted that the elastic computing system provided in the embodiment of the present invention can also realize multiple functions, and the corresponding functions only need to be equipped with corresponding modules;

[0127] For example, it can be applied to virtual load balancing, distribution strategy, hot migration, downtime migration, data backup, immediate security group effect, anti-ARP spoofing and VNC support, etc.

[0128] The elastic computing system supports the deployment of third-party virtual load balancing and firewalls, and provides deployment (such as F5 load balancing and firewalls) to meet the needs of some customers who rely on traditional hardware models for application deployment;

[0129] The elastic computing system supports the scheduling of cloud hosts according to the physical topology of host machines, racks, and network switches, improving the reliability of services. The distribution strategy on the console supports two options: strict distribution and maximum distribution;

[0130] The elastic computing system supports hot migration of cloud hosts. Operation and maintenance personnel can manually specify the bandwidth limit of the migration task to reduce the interference of migration traffic on normal business. For active hot migration of a single virtual machine, the network interruption time is less than 200ms.

[0131] The elastic computing system supports high availability of cloud hosts (downtime migration). When a physical node fails unexpectedly, the cloud host running on it can be restarted on other normal physical nodes to ensure business continuity.

[0132] The elastic computing system supports users to back up cloud host data through snapshots and restore cloud computing server data through snapshots. 64 snapshots can be created for each disk.

[0133] Security group rules take effect immediately after modification. Links that were already connected before the rule modification but do not meet the updated security rules can be blocked immediately.

[0134] Overall, this embodiment has the following beneficial effects:

[0135] By identifying the business needs of metering data, the present invention can send increase and decrease instructions to the cloud computing server in the first time, so that the cloud computing server can adjust the size of resources occupied by elastic computing according to the increase or decrease of business needs, so as to meet the computing needs, ensure the stability and performance of the service, and maximize the utilization of resources; and after the initial adjustment, several cloud computing server instances are adjusted according to the operating status and indicator data of the cloud computing server and the elastic scaling system, which can further consider the actual operation problems, avoid the business load exceeding the processing capacity of the current instance set or the resource utilization rate is too low, ensure that the system responds to the changes in business needs in time, so that the final cloud computing server instance set can meet the computing needs and maximize the resource utilization rate.

[0136] In addition, an elastic computing system for electric energy metering data proposed in an embodiment of the present invention determines whether business needs and / or business policies are adjusted, and when business needs and business policies are adjusted, adjusts the size of resources occupied by elastic computing of the cloud computing server; and then performs elastic computing through one or more cloud computing servers, which can greatly reduce the waste of computing resources when high business needs and business policy changes occur.

[0137] Embodiment 2:

[0138] See also Figure 5 , an embodiment of the present invention provides an elastic computing device for electric energy metering data, which is applicable to an elastic computing system, wherein the elastic computing system includes a cloud computing server and an elastic scaling system; the elastic computing device includes a demand module 100, a first control module 200, a second control module 300, an adjustment module 400 and a computing module 500;

[0139] Wherein, the demand module 100 is used to obtain the business demand of metering data;

[0140] The first control module 200 is used to control the elastic scaling system to increase or decrease the amount according to business needs and send increase or decrease instructions to the cloud computing server;

[0141] The second control module 300 is used to control the cloud computing server to adjust the resource size occupied by the elastic computing according to the increase or decrease instruction, so as to obtain a plurality of adjusted cloud computing server instances;

[0142] An adjustment module 400 is used to adjust a number of cloud computing server instances according to the operating status and indicator data of the elastic computing system to obtain a cloud computing server instance set;

[0143] The calculation module 500 is used to calculate the metering data through the cloud computing server instance set to obtain the calculation result.

[0144] In one embodiment, the demand module 100 includes a system construction unit and a data acquisition unit, specifically:

[0145] The system construction unit is used to construct an elastic computing system according to business load data, resource utilization data, performance monitoring data, cost-effectiveness data, user feedback data and compliance data; wherein the elastic computing system includes one or more cloud computing servers 10 providing elastic computing, and an elastic scaling system 20, and the elastic scaling system 20 includes an elastic expansion unit and an elastic contraction unit;

[0146] In addition, the elastic computing system also includes a monitoring module 30, a mirroring module 40, an encryption module 50, a container management module 60 and a resource orchestration module 70;

[0147] Among them, the elastic expansion unit is used to increase the cloud computing server instances of the cloud computing server when the business demand increases;

[0148] Elastic shrinking unit, used to release low-level resources when business demand decreases;

[0149] Monitoring module 30, used to monitor indicators such as CPU, memory and hard disk of the cloud computing server;

[0150] The image module 40 is used to create an entire image template for the system disk and data disk of the cloud host; and the created image contains the user's business data;

[0151] The encryption module 50 is used to encrypt the cloud host disk using the national secret algorithm, and to encrypt the data in the cloud disk and the data transmitted between the cloud disk and the cloud computing server instance;

[0152] The container management module 60 is used to adjust the corresponding container resources when adjusting the resource size occupied by the elastic computing of the cloud computing server;

[0153] The resource orchestration module 70 is used to create and configure resources and perform automated deployment and operation and maintenance.

[0154] To apply the embodiments of the present invention, please refer to Figure 2 , Figure 2 It is a schematic diagram of the composition of the elastic computing system provided by an embodiment of the present invention, showing the components of the elastic computing system.

[0155] To apply the embodiments of the present invention, please refer to Figure 3 , Figure 3100 is a schematic diagram of the terminal structure of the hardware operating environment provided by an embodiment of the present invention. The terminal may include: a processor 1001 (such as a CPU), a communication bus 1002, a user interface 1003, a network interface 1004 and a memory 1005;

[0156] The processor 1001 is used to call the data calculation program stored in the memory 1005;

[0157] The communication bus 1002 is used to realize the connection and communication between the above components;

[0158] The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and optionally the user interface 1003 may also include a standard wired interface and a wireless interface; and the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client.

[0159] The network interface 1004 may include a standard wired interface and a wireless interface (such as a WI-FI interface); and the network interface 1004 is mainly used to connect to a background server and perform data communication with the background server.

[0160] The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory; and the memory 1005 may also be a storage device independent of the processor 1001;

[0161] Optionally, the terminal may also include a camera, RF (Radio Frequency) circuit, sensor, audio circuit, WiFi module, etc. Among them, sensors include light sensors, motion sensors and other sensors; specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display screen according to the brightness of the ambient light, and the proximity sensor may turn off the display screen and / or backlight when the mobile terminal is moved to the ear; as a type of motion sensor, the gravity acceleration sensor may detect the magnitude of acceleration in each direction (generally three axes), and the magnitude and direction of gravity when stationary, which may be used for applications that identify the posture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; of course, the mobile terminal may also be equipped with other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., which will not be described in detail here.

[0162] The data acquisition unit is used to obtain the business needs of metering data.

[0163] It should be noted that the elastic calculation method of electric energy metering data provided in this embodiment is completed in the terminal through the collaborative execution between the processor 1001, the communication bus 1002, the user interface 1003, the network interface 1004 and the memory 1005;

[0164] Furthermore, the terminal of the embodiment of the present invention may be a PC, or may be a smart phone, a tablet computer, an e-book reader, an MP3 (Moving Picture Experts Group Audio Layer III) player, an MP4 (Moving Picture Experts Group Audio Layer IV) player, a portable computer, or other portable terminal devices with a display function.

[0165] To apply the embodiments of the present invention, please refer to Figure 4 , Figure 4 It is a schematic diagram of the structure of the elastic computing system provided by an embodiment of the present invention, showing a structure in which several cloud computing servers 10 and an elastic scaling system 20 perform data interaction.

[0166] In one embodiment, the first control module 200 includes an increase unit and a decrease unit;

[0167] The growth unit is used to control the elastic scaling system 20 to send a growth instruction to the cloud computing server 10 if the business demand is growth;

[0168] The decreasing unit is used to control the elastic scaling system 20 to send a decreasing instruction to the cloud computing server 10 if the business demand is decreasing.

[0169] In the first control module 200 of the present embodiment, when business demand increases, the elastic scaling system can identify this growth trend, and automatically trigger the elastic scaling mechanism to send growth instructions to the cloud computing server; in this way, the cloud computing server can quickly increase resources to meet the growing business demand and ensure the stability and performance of the service; when business demand decreases, the system can also automatically detect and send a decrease instruction to the cloud computing server; this method can avoid waste of resources, reduce unnecessary cost expenditure, and by flexibly adjusting the allocation of resources, the system can utilize resources more efficiently and maximize the utilization of resources.

[0170] In one embodiment, the second control module 300 includes an increasing unit and a decreasing unit;

[0171] The adding unit is used to control the cloud computing server to increase the number of cloud computing server instances through the elastic expansion unit if the increase / decrease instruction is a growth instruction, so as to obtain a plurality of adjusted cloud computing server instances;

[0172] a reducing unit, configured to control the cloud computing server to reduce the number of cloud computing server instances through the elastic contraction unit if the increase / decrease instruction is a decrease instruction, so as to obtain a plurality of adjusted cloud computing server instances;

[0173] According to the adjusted cloud computing server instances, the container management module 60 is controlled to adjust the corresponding container resources; wherein, the container management module 60 supports the lifecycle management of enterprise-level Kubernetes containerized applications. Moreover, the container management module 60 can simplify the operation and maintenance work such as cluster construction and expansion, and integrate virtualization, storage, network and security capabilities, which is the best containerized application operating environment in the cloud. In addition, the container management module 60 supports the lifecycle management of enterprise-level containerized applications, and can efficiently run containerized applications in the cloud, realizing automated, intelligent, simple and efficient application management and operation and maintenance.

[0174] It should be noted that the cloud computing server 10 is a virtual computing environment, including the most basic computing components such as CPU and memory, and is the actual operating entity presented by the cloud computing server 10;

[0175] The cloud computing server instance is the core concept of the cloud computing server 10. A series of operations on the cloud computing server instance can be completed through the cloud computing server management console;

[0176] Cloud host IP management supports the creation of cloud hosts by specifying IP addresses for cloud computing servers 10, making it convenient for operation and maintenance personnel to conduct coordinated management of IPs. It supports the configuration of IPv6 / IPv4 dual-stack networks, and cloud computing server instances can automatically obtain IPv6 addresses for intranet communication.

[0177] In the second control module 300 of this embodiment, when business demand increases, the system's processing capacity can be improved by increasing the number of cloud computing server instances to ensure high availability and performance of the service, which helps to cope with sudden traffic growth or computing needs and avoid system overload or response delays; when business demand decreases, reducing the number of cloud computing server instances can avoid wasting resources.

[0178] In one embodiment, the adjustment module 400 includes an operation subunit, a replacement subunit, a composition subunit, a statistics subunit and a quantity subunit, specifically:

[0179] The running subunit is used to obtain a set of second cloud computing server instances that are not running in a plurality of cloud computing server instances, and define the set of second cloud computing server instances as being in an unhealthy state; wherein data transmitted between instances is completed through the encryption module 50;

[0180] A replacement subunit, used to release the second cloud computing server instance set, and control the mirror module 40 and the resource orchestration module 70 to replace the second cloud computing server instance set by creating a new third cloud computing server instance set;

[0181] a constituting subunit, configured to constitute a first cloud computing server instance set from a third cloud computing server instance set and server instances in a running state among the plurality of cloud computing server instances;

[0182] Among them, the encryption module 50 supports the national secret algorithm encryption of the cloud host disk, and can encrypt the data in the cloud disk, the data transmitted between the cloud disk and the instance, and all snapshots created by the cloud disk to ensure the security of the data; at the same time, it supports users to remotely access the cloud host through VNC, supports users to set the cloud host VNC password (non-system password), and can perform VNC password authentication when the user accesses by VNC, to ensure the terminal user's secure access to the cloud host, and provide official product documentation or product function screenshots.

[0183] The image module 40 supports the creation of a whole-machine image template for the system disk and data disk of the cloud host. The created image contains the user's business data and can be used to quickly release a new elastic cloud host containing the user's business data.

[0184] The resource orchestration module 70 is a service that simplifies the management of cloud computing resources. The template specification defined by the resource orchestration module 70 compiles the resource stack template, and defines the required cloud computing resources, the dependencies between resources, etc. in the template. The orchestration engine of the resource orchestration module 70 will automatically complete the creation and configuration of all resources according to the template, and realize automated deployment and operation and maintenance;

[0185] The template of the resource orchestration module 70 is a readable and easy-to-write text file. You can directly edit the JSON format text, or control the template version through version control tools such as SVN and Git to achieve the purpose of controlling the infrastructure version. The orchestration capabilities of the resource orchestration module 70 can be integrated with applications through APIs, SDKs, etc. to achieve infrastructure as code;

[0186] The template of the resource orchestration module 70 is also a standardized way of delivering resources and applications. If it is an independent software vendor (ISV), the overall system and solution including cloud resources and applications can be delivered through the resource orchestration service template;

[0187] The resource orchestration service manages a group of cloud resources in a unified manner through a resource stack. Operations such as creation, deletion, and re-creation of cloud resources can all be completed in units of resource stacks.

[0188] In this embodiment, the running subunit, replacing subunit and constituting subunit release the second cloud computing server instance set, which means that these instances no longer occupy the computing resources of the cloud computing platform. This helps to free up resources from workloads that are no longer needed and avoid waste of resources. Replacing the original second cloud computing server instance set with a new third cloud computing server instance set means that the new instance set adopts a more efficient configuration and can ensure the availability of the third cloud computing server instance set. Such replacement helps to improve the performance, reliability and security of the entire system.

[0189] The statistics subunit is used to monitor and count the operating data of the index data in the elastic scaling system in real time through the monitoring module 30; the index data includes but is not limited to CPU usage, memory usage, intranet outbound traffic and intranet inbound traffic; the monitoring module 30 also supports monitoring the CPU, memory and number of open files of each process in the cloud host system, providing users with system-level, active and fine-grained monitoring services;

[0190] a quantity subunit, configured to trigger an alarm if the operation data reaches a preset alarm condition, and dynamically adjust the number of instances of the first cloud computing server instance set according to the operation data to obtain a cloud computing server instance set;

[0191] Furthermore, the elastic scaling system 20 supports specifying configuration information of the cloud computing server instance to be scaled, including but not limited to instance specifications, image types, system disks, data disks, and custom data;

[0192] The elastic scaling system 20 supports elastic expansion of cloud computing server instances according to the configured scaling rules, and can automatically add cloud computing server instances to the backend server group of the load balancing instance and the access whitelist of the database instance;

[0193] The elastic scaling system 20 supports users to execute scaling rules on a regular basis, thereby automatically expanding or shrinking computing resources to meet business needs while controlling costs.

[0194] The statistical subunit and quantity subunit of this embodiment can identify changes in the current business load through real-time monitoring of the operating data. Once the operating data reaches the preset alarm condition, it means that the current business load has exceeded the processing capacity of the current instance set or the resource utilization rate is too low, resulting in a waste of resources. At this time, dynamically adjusting the number of instances according to the operating data can ensure that the system responds to changes in business needs in a timely manner and avoids service performance degradation or resource waste.

[0195] Overall, the adjustment module 400 of this embodiment greatly simplifies the work of operation and maintenance personnel through elastic self-healing and automatic scaling adjustment mechanism. There is no need to manually handle server failures or adjust resource configurations. Instead, these tasks can be completed automatically by relying on the system. This method not only reduces operation and maintenance costs, but also reduces the possibility of human errors and improves work efficiency.

[0196] In one embodiment, the calculation module 500 is specifically:

[0197] The metering data is calculated through a set of cloud computing server instances to obtain calculation results.

[0198] It should be noted that the elastic computing system provided in the embodiment of the present invention can also realize multiple functions, and the corresponding functions only need to be equipped with corresponding modules;

[0199] For example, it can be applied to virtual load balancing, distribution strategy, hot migration, downtime migration, data backup, immediate security group effect, anti-ARP spoofing and VNC support, etc.

[0200] The elastic computing system supports the deployment of third-party virtual load balancing and firewalls, and provides deployment (such as F5 load balancing and firewalls) to meet the needs of some customers who rely on traditional hardware models for application deployment;

[0201] The elastic computing system supports the scheduling of cloud hosts according to the physical topology of host machines, racks, and network switches, improving the reliability of services. The distribution strategy on the console supports two options: strict distribution and maximum distribution;

[0202] The elastic computing system supports hot migration of cloud hosts. Operation and maintenance personnel can manually specify the bandwidth limit of the migration task to reduce the interference of migration traffic on normal business. For active hot migration of a single virtual machine, the network interruption time is less than 200ms.

[0203] The elastic computing system supports high availability of cloud hosts (downtime migration). When a physical node fails unexpectedly, the cloud host running on it can be restarted on other normal physical nodes to ensure business continuity.

[0204] The elastic computing system supports users to back up cloud host data through snapshots and restore cloud computing server data through snapshots. 64 snapshots can be created for each disk.

[0205] Security group rules take effect immediately after modification. Links that were already connected before the rule modification but do not meet the updated security rules can be blocked immediately.

[0206] Overall, this embodiment has the following beneficial effects:

[0207] By identifying the business needs of metering data, the present invention can send increase and decrease instructions to the cloud computing server in the first time, so that the cloud computing server can adjust the size of resources occupied by elastic computing according to the increase or decrease of business needs, so as to meet the computing needs, ensure the stability and performance of the service, and maximize the utilization of resources; and after the initial adjustment, several cloud computing server instances are adjusted according to the operating status and indicator data of the cloud computing server and the elastic scaling system, which can further consider the actual operation problems, avoid the business load exceeding the processing capacity of the current instance set or the resource utilization rate is too low, ensure that the system responds to the changes in business needs in time, so that the final cloud computing server instance set can meet the computing needs and maximize the resource utilization rate.

[0208] In addition, an elastic computing system for electric energy metering data proposed in an embodiment of the present invention determines whether business needs and / or business policies are adjusted, and when business needs and business policies are adjusted, adjusts the size of resources occupied by elastic computing of the cloud computing server; and then performs elastic computing through one or more cloud computing servers, which can greatly reduce the waste of computing resources when high business needs and business policy changes occur.

[0209] Embodiment three:

[0210] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the elastic calculation method of electric energy metering data;

[0211] Wherein, the elastic calculation method of electric energy metering data, if implemented in the form of a software functional unit and used as an independent product, can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0212] The above are preferred embodiments of the present invention. It should be noted that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for elastic calculation of electric energy metering data, characterized in that: Applicable to an elastic computing system, the elastic computing system includes a cloud computing server and an elastic scaling system, and the elastic computing method includes: Business needs for obtaining metering data; Controlling the elastic scaling system to send increase and decrease instructions to the cloud computing server according to the increase and decrease of the business demand; Controlling the cloud computing server to adjust the size of resources occupied by elastic computing according to the increase / decrease instruction to obtain a plurality of adjusted cloud computing server instances; Adjusting the plurality of cloud computing server instances according to the operating status and indicator data of the elastic computing system to obtain a cloud computing server instance set; The metering data is calculated by the cloud computing server instance set to obtain a calculation result.

2. The method for elastically calculating electric energy metering data according to claim 1, characterized in that: According to the increase / decrease instruction, the elastic scaling system sends an increase / decrease instruction to the cloud computing server according to the increase / decrease amount of the business demand, specifically: If the business demand is an increase, controlling the elastic scaling system to send an increase instruction to the cloud computing server; If the business demand is decreasing, the elastic scaling system is controlled to send a decrease instruction to the cloud computing server.

3. The method for elastically calculating electric energy metering data according to claim 2, characterized in that: According to the increase / decrease instruction, the cloud computing server is made to adjust the resource size occupied by the elastic computing to obtain a plurality of adjusted cloud computing server instances, specifically: If the increase / decrease instruction is an increase instruction, controlling the cloud computing server to increase the number of cloud computing server instances to obtain the adjusted number of cloud computing server instances; If the increase / decrease instruction is a decrease instruction, the cloud computing server is controlled to reduce the number of the cloud computing server instances to obtain the adjusted cloud computing server instances.

4. The method for elastically calculating electric energy metering data according to claim 1, characterized in that: The plurality of cloud computing server instances are adjusted according to the operating status and indicator data of the cloud computing server and the elastic scaling system to obtain a cloud computing server instance set, specifically: According to the running status of the cloud computing server, the plurality of cloud computing server instances are adjusted by elastic self-healing to obtain a first cloud computing server instance set; According to the alarm status of the indicator data in the elastic scaling system, scaling adjustment is performed on the first cloud computing server instance set to obtain the cloud computing server instance set.

5. The method for elastically calculating electric energy metering data according to claim 4, characterized in that: According to the running state of the cloud computing server, the plurality of cloud computing server instances are adjusted in an elastic self-healing manner to obtain a first cloud computing server instance set, specifically: Acquire a second set of cloud computing server instances in a non-operating state from among the plurality of cloud computing server instances; releasing the second cloud computing server instance set, and replacing the second cloud computing server instance set by creating a new third cloud computing server instance set; The first cloud computing server instance set is composed of the third cloud computing server instance set and the server instances in running state among the plurality of cloud computing server instances.

6. The method for elastically calculating electric energy metering data according to claim 4, characterized in that: According to the alarm status of the indicator data in the elastic scaling system, scaling adjustment is performed on the first cloud computing server instance set to obtain the cloud computing server instance set, specifically: Collecting statistics on the operating data of the index data in the elastic scaling system; wherein the index data includes CPU usage, memory usage, intranet outbound traffic and intranet inbound traffic; If the operating data reaches a preset alarm condition, the number of instances of the first cloud computing server instance set is dynamically adjusted according to the operating data to obtain the cloud computing server instance set.

7. A flexible calculation device for electric energy metering data, characterized in that: Applicable to an elastic computing system, the elastic computing system includes a cloud computing server and an elastic scaling system; the elastic computing device includes a demand module, a first control module, a second control module, an adjustment module and a computing module; Wherein, the demand module is used to obtain the business demand of metering data; The first control module is used to control the elastic scaling system to send an increase or decrease instruction to the cloud computing server according to the increase or decrease amount of the business demand; The second control module is used to control the cloud computing server to adjust the resource size occupied by the elastic computing according to the increase / decrease instruction to obtain a plurality of adjusted cloud computing server instances; The adjustment module is used to adjust the plurality of cloud computing server instances according to the operating status and indicator data of the elastic computing system to obtain a cloud computing server instance set; The calculation module is used to calculate the metering data through the cloud computing server instance set to obtain a calculation result.

8. The flexible calculation device for electric energy metering data according to claim 7, characterized in that: The first control module includes an increase unit and a decrease unit; Wherein, the growth unit is used to control the elastic scaling system to send a growth instruction to the cloud computing server if the business demand is growth; The decreasing unit is used to control the elastic scaling system to send a decreasing instruction to the cloud computing server if the business demand is decreasing.

9. The flexible calculation device for electric energy metering data according to claim 8, characterized in that: The second control module includes an increasing unit and a decreasing unit; The adding unit is used to control the cloud computing server to increase the number of cloud computing server instances if the increase / decrease instruction is a growth instruction, so as to obtain the adjusted number of cloud computing server instances; The reducing unit is used to control the cloud computing server to reduce the number of the cloud computing server instances if the increase / decrease instruction is a decrease instruction, so as to obtain the adjusted cloud computing server instances.

10. The flexible calculation device for electric energy metering data according to claim 7, characterized in that: The adjustment module includes an adjustment unit and an alarm unit; The adjustment unit is used to adjust the plurality of cloud computing server instances in an elastic self-healing manner according to the running state of the cloud computing server to obtain a first cloud computing server instance set; The alarm unit is used to perform scaling adjustment on the first cloud computing server instance set according to the alarm status of the indicator data in the elastic scaling system to obtain the cloud computing server instance set.

11. The flexible calculation device for electric energy metering data according to claim 10, characterized in that: The adjustment unit includes an operation subunit, a replacement subunit and a configuration subunit; The running subunit is used to obtain a set of second cloud computing server instances that are not running from among the plurality of cloud computing server instances; The replacement subunit is used to release the second cloud computing server instance set and replace the second cloud computing server instance set by creating a new third cloud computing server instance set; The constituting subunit is used to constitute the first cloud computing server instance set by the third cloud computing server instance set and the server instances in the plurality of cloud computing server instances that are in running state.

12. The flexible calculation device for electric energy metering data according to claim 10, characterized in that: The alarm unit includes a statistical subunit and a quantity subunit; The statistical subunit is used to count the operating data of the index data in the elastic scaling system; wherein the index data includes CPU usage, memory usage, intranet outbound traffic and intranet inbound traffic; The quantity subunit is used to dynamically adjust the number of instances of the first cloud computing server instance set according to the operation data to obtain the cloud computing server instance set if the operation data reaches a preset alarm condition.

13. A storage medium, characterized in that: The storage medium stores a computer program, which is called and executed by a computer to implement the elastic calculation method for electric energy metering data as described in any one of claims 1 to 6 above.

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