Artificial intelligence-based time series database monitoring method, device, equipment and medium

Abstract

The application provides a time series database monitoring method and device based on artificial intelligence, electronic equipment and storage medium. The time series database monitoring method based on artificial intelligence comprises the following steps: storing collected time series data into a time series database to obtain a time series data set; dividing storage nodes of the conventional time series data set and the high-precision time series data set respectively to obtain conventional monitoring data nodes and high-precision monitoring data nodes; setting node capacity thresholds for the conventional monitoring data nodes and the high-precision monitoring data nodes respectively; calculating data amount growth rates of the conventional monitoring data nodes and the high-precision monitoring data nodes respectively according to a preset period to obtain node predicted data amounts; and optimizing and adjusting the time series database based on the node capacity thresholds and the node predicted data amounts. The application can dynamically adjust the storage nodes of the conventional monitoring data and the high-precision monitoring data, thereby improving the storage efficiency of the time series database.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a time-series database monitoring method, apparatus, electronic device, and storage medium based on artificial intelligence. Background Technology

[0002] In recent years, time-series databases have developed rapidly and are widely used in data storage in fields such as surveillance and the Internet of Things. Typically, time-series databases allow users to set different retention periods for data and automatically clean up expired data.

[0003] In conventional monitoring, the data volume grows steadily, and expansion can usually be achieved based on data volume, capacity, and expansion thresholds. However, high-precision monitoring is characterized by high-frequency data acquisition, which brings challenges to data storage in terms of cost and performance. Therefore, how to dynamically adjust the mixed storage of conventional and high-precision monitoring data to improve the storage efficiency of time-series databases is an urgent technical problem to be solved. Summary of the Invention

[0004] In view of the above, it is necessary to propose an artificial intelligence-based time-series database monitoring method, device, electronic device and storage medium to solve the technical problem of how to improve the storage efficiency of time-series databases.

[0005] This application provides a time-series database monitoring method based on artificial intelligence, the method comprising:

[0006] The collected time-series data is stored in a time-series database to obtain a time-series dataset, which includes a regular time-series dataset and a high-precision time-series dataset.

[0007] The storage nodes of the conventional time-series dataset and the high-precision time-series dataset are divided to obtain conventional monitoring data nodes and high-precision monitoring data nodes respectively;

[0008] Set node capacity thresholds for the conventional monitoring data nodes and the high-precision monitoring data nodes respectively. The node capacity thresholds include node shrinkage thresholds and node expansion thresholds.

[0009] The predicted data volume of the nodes is obtained by calculating the data volume growth rate of the conventional monitoring data nodes and the high-precision monitoring data nodes according to a preset period. The predicted data volume of the nodes includes the predicted data volume of the conventional nodes and the predicted data volume of the high-precision nodes.

[0010] The time-series database is optimized and adjusted based on the node capacity threshold and the node predicted data volume.

[0011] In some embodiments, storing the acquired time-series data in a time-series database to obtain a time-series dataset, the time-series dataset including a regular time-series dataset and a high-precision time-series dataset, including:

[0012] The memory of the time-series database is divided into a regular storage database and a high-precision storage database;

[0013] All collected time-series data are divided to obtain an initial regular dataset and an initial high-precision dataset;

[0014] The initial regular dataset is stored in the regular storage database to obtain a regular time series dataset;

[0015] The initial high-precision dataset is stored in the high-precision storage database to obtain a high-precision time-series dataset;

[0016] A time series dataset is constructed based on the conventional time series dataset and the high-precision time series dataset.

[0017] In some embodiments, the step of partitioning the storage nodes of the conventional time-series dataset and the high-precision time-series dataset to obtain conventional monitoring data nodes and high-precision monitoring data nodes includes:

[0018] The storage nodes of the conventional storage database and the high-precision storage database are respectively divided into two categories: resident nodes and elastic nodes;

[0019] The conventional time-series dataset and the high-precision time-series dataset are respectively divided to obtain a conventional classification dataset and a high-precision classification dataset;

[0020] Based on the resident nodes and elastic nodes of the conventional storage database, the conventional classification dataset is classified and stored to obtain conventional resident data nodes and conventional elastic data nodes. The conventional resident data nodes and the conventional elastic data nodes constitute conventional monitoring data nodes.

[0021] Based on the resident nodes and elastic nodes of the high-precision storage database, the high-precision classification dataset is classified and stored to obtain high-precision resident data nodes and high-precision elastic data nodes. The high-precision resident data nodes and the high-precision elastic data nodes constitute high-precision monitoring data nodes.

[0022] In some embodiments, the step of partitioning the conventional time-series dataset and the high-precision time-series dataset to obtain a conventional classification dataset and a high-precision classification dataset includes:

[0023] The conventional time series dataset and the high-precision time series dataset are divided according to the 1σ criterion;

[0024] All time-series data in the regular time-series dataset that conform to the 1σ criterion are taken as the regular resident dataset, and all time-series data in the regular time-series dataset that do not conform to the 1σ criterion are taken as the regular elastic dataset. The regular resident dataset and the regular elastic dataset constitute the regular classification dataset.

[0025] All time-series data in the high-precision time-series dataset that conform to the 1σ criterion are taken as the high-precision resident dataset, and all time-series data in the high-precision time-series dataset that do not conform to the 1σ criterion are taken as the high-precision elastic dataset. The high-precision resident dataset and the high-precision elastic dataset constitute the high-precision classification dataset.

[0026] In some embodiments, setting node capacity thresholds for the conventional monitoring data nodes and the high-precision monitoring data nodes respectively, wherein the node capacity thresholds include node shrinkage thresholds and node expansion thresholds, including:

[0027] Set the node capacity for the conventional monitoring data nodes and the high-precision monitoring data nodes respectively;

[0028] Based on the node capacity, the node capacity thresholds for the conventional monitoring data node and the high-precision monitoring data node are obtained respectively. The node capacity thresholds include node shrinkage thresholds and node expansion thresholds.

[0029] In some embodiments, the step of calculating the data growth rate of the conventional monitoring data nodes and the high-precision monitoring data nodes according to a preset period to obtain the node predicted data volume, wherein the node predicted data volume includes the conventional node predicted data volume and the high-precision node predicted data volume, including:

[0030] The data volume growth rate of the conventional monitoring data node and the high-precision monitoring data node in each preset period is calculated to obtain the conventional growth rate set and the high-precision growth rate set;

[0031] The predicted data volume of the regular monitoring data nodes is obtained based on the set of regular growth rates and preset weights.

[0032] The high-precision node prediction data volume of the high-precision monitoring data node is obtained based on the high-precision growth rate set and the preset weight.

[0033] In some embodiments, optimizing the time-series database based on the node capacity threshold and the node prediction data volume includes:

[0034] Compare the node capacity threshold with the node predicted data volume;

[0035] If the predicted data volume of the node exceeds the node expansion threshold, then the storage nodes in the database will be expanded.

[0036] If the predicted data volume of the node is less than the node scaling-down threshold, then the storage node in the database will be scaled down.

[0037] This application embodiment also provides an artificial intelligence-based time-series database monitoring device, the device comprising:

[0038] The acquisition unit is used to store the collected time-series data into a time-series database to obtain a time-series dataset, which includes a regular time-series dataset and a high-precision time-series dataset.

[0039] A partitioning unit is used to partition the storage nodes of the conventional time-series dataset and the high-precision time-series dataset to obtain conventional monitoring data nodes and high-precision monitoring data nodes, respectively.

[0040] The setting unit is used to set node capacity thresholds for the conventional monitoring data nodes and the high-precision monitoring data nodes respectively. The node capacity thresholds include node shrinkage thresholds and node expansion thresholds.

[0041] The calculation unit is used to calculate the data growth rate of the conventional monitoring data node and the high-precision monitoring data node according to a preset period to obtain the node prediction data volume, wherein the node prediction data volume includes the conventional node prediction data volume and the high-precision node prediction data volume.

[0042] An optimization unit is used to optimize and adjust the time-series database based on the node capacity threshold and the node predicted data volume.

[0043] This application embodiment also provides an electronic device, the electronic device comprising:

[0044] Memory, storing at least one instruction;

[0045] The processor executes the instructions stored in the memory to implement the artificial intelligence-based time-series database monitoring method.

[0046] This application also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the artificial intelligence-based time-series database monitoring method.

[0047] This application classifies and stores the collected time-series data into two categories: conventional time-series data and high-precision time-series data. By calculating the predicted data volume of conventional nodes and the predicted data volume of high-precision nodes, the storage nodes of each category of data are dynamically adjusted, thereby effectively improving the storage efficiency of the time-series database. Attached Figure Description

[0048] Figure 1 This is a flowchart of a preferred embodiment of the artificial intelligence-based time-series database monitoring method involved in this application.

[0049] Figure 2 This is a functional block diagram of a preferred embodiment of the artificial intelligence-based time-series database monitoring device involved in this application.

[0050] Figure 3 This is a schematic diagram of the structure of an electronic device that is a preferred embodiment of the time-series database monitoring method based on artificial intelligence involved in this application. Detailed Implementation

[0051] To better understand the purpose, features, and advantages of this application, a detailed description of the application is provided below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of this application can be combined with each other. Numerous specific details are set forth in the following description to provide a thorough understanding of this application; the described embodiments are only a part of the embodiments of this application, and not all of them.

[0052] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0054] This application provides an artificial intelligence-based time-series database monitoring method, which can be applied to one or more electronic devices. An electronic device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0055] Electronic devices can be any electronic product that allows human-computer interaction with a customer, such as personal computers, tablets, smartphones, personal digital assistants (PDAs), game consoles, interactive network television (IPTV), smart wearable devices, etc.

[0056] Electronic devices may also include network devices and / or client devices. The network devices include, but are not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0057] The networks in which electronic devices are located include, but are not limited to, the Internet, wide area networks, metropolitan area networks, local area networks, and virtual private networks (VPNs).

[0058] like Figure 1 The diagram shown is a flowchart of a preferred embodiment of the time-series database monitoring method based on artificial intelligence according to this application. The order of the steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements.

[0059] S10, the collected time series data is stored in a time series database to obtain a time series dataset, which includes a regular time series dataset and a high-precision time series dataset.

[0060] In an optional embodiment, the step of storing the collected time-series data in a time-series database to obtain a time-series dataset, the time-series dataset including a regular time-series dataset and a high-precision time-series dataset, including:

[0061] S101, divide the memory of the time series database to obtain a regular storage database and a high-precision storage database;

[0062] S102, Divide all the collected time-series data to obtain an initial regular dataset and an initial high-precision dataset;

[0063] S103, the initial regular dataset is stored in the regular storage database to obtain a regular time series dataset;

[0064] S104, store the initial high-precision dataset into the high-precision storage database to obtain a high-precision time-series dataset;

[0065] S105, a time series dataset is constructed based on the conventional time series dataset and the high-precision time series dataset.

[0066] In this optional embodiment, routine monitoring of data refers to monitoring in the conventional sense, characterized by low index collection frequency (e.g., data is collected once every 1 minute or 5 minutes), wide collection range, and long data retention period; high-precision monitoring of data refers to monitoring of high-frequency data collection, characterized by high index collection frequency (e.g., data is collected once per second), narrow collection range, and short data retention period.

[0067] In this optional embodiment, staff can pre-divide the time-series data to be monitored according to the different monitoring methods, and use all data belonging to the conventional monitoring method as the initial conventional dataset, and all data belonging to the high-precision monitoring method as the initial high-precision dataset.

[0068] In this optional embodiment, since the collected time-series data needs to be divided into two types—regular time-series data and high-precision time-series data—based on different data monitoring types, the memory of the time-series database can be partitioned. However, it is not necessary to partition the memory of the time-series database in a fixed proportion; two data tables with different field names can be created. For example, the initial regular dataset can be stored in a data table with the field name "Regular Monitoring Data," and the initial high-precision dataset can be stored in a data table with the field name "High-Precision Monitoring Data."

[0069] In this optional embodiment, the memory space in the time-series database that stores all data tables with the field name "regular monitoring data" is used as the regular storage database, and the memory space that stores all data tables with the field name "high-precision monitoring data" is used as the high-precision storage database.

[0070] In this optional embodiment, the initial regular dataset stored in the regular storage database is used as a regular time series dataset, the initial high-precision data stored in the high-precision storage database is used as a high-precision time series dataset, and the regular time series dataset and the high-precision time series dataset are used as a time series dataset.

[0071] In this way, the collected time-series data can be classified and stored in the time-series database according to different data monitoring methods, providing data support for subsequent adjustments to the database storage space.

[0072] S11, the storage nodes of the conventional time-series dataset and the high-precision time-series dataset are divided to obtain conventional monitoring data nodes and high-precision monitoring data nodes respectively.

[0073] In an optional embodiment, the step of partitioning the storage nodes of the conventional time-series dataset and the high-precision time-series dataset to obtain conventional monitoring data nodes and high-precision monitoring data nodes respectively includes:

[0074] S111, the storage nodes of the conventional storage database and the high-precision storage database are respectively divided into two categories: resident nodes and elastic nodes;

[0075] S112, the conventional time series dataset and the high-precision time series dataset are divided into a conventional classification dataset and a high-precision classification dataset, respectively;

[0076] S113, Based on the resident nodes and elastic nodes of the conventional storage database, the conventional classification dataset is classified and stored to obtain conventional resident data nodes and conventional elastic data nodes. The conventional resident data nodes and the conventional elastic data nodes constitute conventional monitoring data nodes.

[0077] S114, Based on the resident nodes and elastic nodes of the high-precision storage database, the high-precision classification dataset is classified and stored to obtain high-precision resident data nodes and high-precision elastic data nodes. The high-precision resident data nodes and the high-precision elastic data nodes constitute high-precision monitoring data nodes.

[0078] In this optional embodiment, a time-series database may contain multiple storage nodes, and each storage node may include multiple data tables. For example, a time-series database may be distributed across multiple different servers, and the storage space corresponding to each server may serve as a storage node of the time-series database.

[0079] In this optional embodiment, both the conventional storage database and the high-precision storage database include two types of storage nodes: resident nodes and elastic nodes. A resident node is one that runs continuously with a constant capacity, while an elastic node dynamically increases or decreases based on the relationship between the amount of stored data and the node's capacity.

[0080] In this optional embodiment, the conventional time-series dataset and the high-precision time-series dataset can be divided according to the 1σ criterion and the storage time of the time-series data in the database, respectively. Specifically, the average values ​​μ1 and μ2 and the standard deviations σ1 and σ2 of all time-series data in both the conventional and high-precision time-series datasets are calculated.

[0081] In this optional embodiment, all time-series data concentrated in the range of (μ1-σ1, μ1+σ1) are regarded as regular resident datasets, and all time-series data in the regular time-series dataset that are not in the range of (μ1-σ1, μ1+σ1) are regarded as regular elastic datasets. The regular resident dataset and the regular elastic dataset constitute a regular classification dataset.

[0082] In this optional embodiment, all time-series data concentrated in the range of (μ2-σ2, μ2+σ2) are regarded as high-precision resident datasets, and all time-series data in the high-precision time-series dataset that are not in the range of (μ2-σ2, μ2+σ2) are regarded as high-precision elastic datasets. The high-precision resident dataset and the high-precision elastic dataset constitute a high-precision classification dataset.

[0083] In this optional embodiment, a regular resident data node is used to store the regular resident dataset, and a regular elastic data node is used to store the regular elastic dataset. The regular resident data node and the regular elastic data node constitute a regular monitoring data node.

[0084] In this optional embodiment, a high-precision resident data node is used to store the high-precision resident dataset, and a high-precision elastic data node is used to store the high-precision elastic dataset. The high-precision resident data node and the high-precision elastic data node constitute a high-precision monitoring data node.

[0085] Thus, by dividing the storage nodes of the time series dataset, the time series data in the time series dataset can be further classified and stored according to the storage time of the time series dataset, which makes it easier to effectively distinguish between long-term and short-term time series data.

[0086] S12, set node capacity thresholds for the conventional monitoring data nodes and the high-precision monitoring data nodes respectively. The node capacity thresholds include node shrinkage thresholds and node expansion thresholds.

[0087] In an optional embodiment, setting node capacity thresholds for the conventional monitoring data nodes and the high-precision monitoring data nodes respectively, the node capacity thresholds including node shrinkage thresholds and node expansion thresholds, including:

[0088] S121, set the node capacity for the conventional monitoring data node and the high-precision monitoring data node respectively;

[0089] S122, based on the node capacity, obtain the node capacity thresholds for the conventional monitoring data node and the high-precision monitoring data node respectively, wherein the node capacity thresholds include node shrinkage thresholds and node expansion thresholds.

[0090] In this optional embodiment, corresponding node capacities can be set for all storage nodes included in the conventional monitoring data nodes and the high-precision monitoring data nodes. For example, for resident storage nodes, the corresponding storage capacity is 500M, and for elastic storage nodes, the corresponding storage capacity is 300M.

[0091] In this optional embodiment, node capacity thresholds for the regular monitoring data nodes and the high-precision monitoring data nodes can be obtained based on the node capacity. Since resident storage nodes exist for a long time and their storage capacity does not change, there is no need to set node capacity thresholds for resident storage nodes.

[0092] In this optional embodiment, a corresponding node capacity threshold is set only for elastic storage nodes. For example, 80% of the node capacity is used as the node expansion threshold, and 20% of the node capacity is used as the node reduction threshold. When the amount of time-series data stored in an elastic storage node exceeds 80% of the node capacity, a new elastic storage node can be added to store the excess time-series data. When the amount of time-series data stored in an elastic storage node is less than 20% of the node capacity, the storage node can be released, and the time-series data stored in the released storage node can be migrated to other storage nodes with available storage space.

[0093] In this way, by using the node capacity threshold, the amount of time-series data stored can be dynamically adjusted based on the node capacity of each storage node, preventing storage overload or ineffective utilization of storage space and improving the storage efficiency of the time-series database.

[0094] S13, calculate the data growth rate of the conventional monitoring data node and the high-precision monitoring data node according to the preset period to obtain the node prediction data volume, the node prediction data volume includes the conventional node prediction data volume and the high-precision node prediction data volume.

[0095] In an optional embodiment, the step of calculating the data growth rate of the conventional monitoring data nodes and the high-precision monitoring data nodes according to a preset period to obtain the node predicted data volume, wherein the node predicted data volume includes the conventional node predicted data volume and the high-precision node predicted data volume, including:

[0096] S131, calculate the data volume growth rate of the conventional monitoring data node and the high-precision monitoring data node in each preset period to obtain the conventional growth rate set and the high-precision growth rate set;

[0097] S132, Based on the set of regular growth rates and preset weights, obtain the predicted data volume of the regular monitoring data nodes;

[0098] S133, based on the high-precision growth rate set and the preset weight, obtain the high-precision node prediction data volume of the high-precision monitoring data node.

[0099] In this optional embodiment, the conventional monitoring data nodes and high-precision monitoring data nodes can be periodically detected according to a preset period, and the predicted data volume of the nodes can be obtained by calculating the data volume growth rate of the conventional monitoring data nodes and high-precision monitoring data nodes within the time period of the detection period. For example, the preset period can be 1 hour.

[0100] In this optional embodiment, the amount of data in each preset period can be counted, and the ratio of the amount of data in each preset period to the amount of data in the previous preset period can be used as the data growth rate corresponding to the current preset period. Preset weights can be assigned to the data growth rates of the three preset periods closest to the current preset period to predict the amount of data in the next preset period.

[0101] In an optional embodiment, since time-series data has strong continuity over time, the preset weights assigned to the data growth rates within the three most recent preset periods are 1 / 2, 1 / 3, and 1 / 6, respectively. That is, the closer to the next preset period, the greater the weight of the corresponding data growth rate. Specifically, the data growth rates of the regular monitoring data nodes within the three most recent preset periods are defined as the regular growth rate set, and the data growth rates of the high-precision monitoring data nodes within the three most recent preset periods are defined as the high-precision growth rate set.

[0102] For example, the data growth rates corresponding to the three consecutive preset periods of 6-7, 7-8, and 8-9 are 1.2, 1.5, and 1.8, respectively. Then, the data growth rate in the next preset period of 9-10 can be calculated as 1.8×1 / 2+1.5×1 / 3+1.2×1 / 6=1.6.

[0103] In this optional embodiment, the product of the calculated data growth rate and the data volume of the conventional monitoring data node and the high-precision monitoring data node corresponding to the start time of the next preset period can be used as the predicted data volume of the conventional node and the predicted data volume of the high-precision node, respectively.

[0104] For example, if the data volume of a regular monitoring data node at 9 o'clock is 1000 and the corresponding data volume growth rate is 1.6, then the predicted data volume of the regular node for the next preset period can be calculated to be 1600. The calculation process of the predicted data volume of the high-precision node is the same as the calculation process of the predicted data volume of the regular node.

[0105] In this way, the amount of predicted data for regular nodes and the amount of predicted data for high-precision nodes can be calculated based on the growth rate of the time series data within each preset period, which makes it easier to optimize and adjust the database in advance.

[0106] S14, optimize and adjust the time series database based on the node capacity threshold and the node predicted data volume.

[0107] In an optional embodiment, optimizing the time-series database based on the node capacity threshold and the node prediction data volume includes:

[0108] S141, compare the node capacity threshold with the node predicted data volume;

[0109] S142, if the predicted data volume of the node is greater than the node expansion threshold, then the storage nodes in the database are expanded.

[0110] S143, if the predicted data volume of the node is less than the node scaling-down threshold, then the storage node in the database is scaled down.

[0111] In this optional embodiment, the node capacity threshold includes a node shrinkage threshold and a node expansion threshold, and the node prediction data volume includes a regular node prediction data volume and a high-precision node prediction data volume. Therefore, it is necessary to compare the regular node prediction data volume and the high-precision node prediction data volume with their corresponding node capacity thresholds.

[0112] In this optional embodiment, if the amount of predicted data from the regular nodes or the amount of predicted data from the high-precision nodes is greater than the node expansion threshold, then it is necessary to add regular elastic data nodes or high-precision elastic data nodes to expand the time-series database.

[0113] In this optional embodiment, if the amount of predicted data for the regular node or the amount of predicted data for the high-precision node is less than the node scaling-down threshold, then the regular elastic data node or the high-precision elastic data node needs to be released accordingly to achieve scaling down of the time-series database.

[0114] In this optional embodiment, since time-series data have corresponding storage times in the time-series database, when the time-series data reaches the storage time, it is necessary to immediately clear the time-series data and calculate the storage time of the remaining time-series data in real time, so as to dynamically adjust the storage space of each storage node, such as migrating time-series data that conforms to the 1σ criterion in the elastic storage node to the corresponding resident storage node.

[0115] Thus, by comparing the node capacity threshold and the node predicted data volume, the time-series database can be dynamically optimized and adjusted, thereby effectively improving the storage efficiency of the time-series database.

[0116] Please see Figure 2 , Figure 2 This is a functional block diagram of a preferred embodiment of the AI-based time-series database monitoring device of this application. The AI-based time-series database monitoring device 11 includes an acquisition unit 110, a partitioning unit 111, a setting unit 112, a calculation unit 113, and an optimization unit 114. The module / unit referred to in this application refers to a series of computer-readable instruction segments that can be executed by the processor 13 and perform a fixed function, and are stored in the memory 12. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0117] In an optional embodiment, the obtaining unit 110 is used to store the collected time series data into a time series database to obtain a time series dataset, the time series dataset including a regular time series dataset and a high-precision time series dataset.

[0118] In an optional embodiment, the step of storing the collected time-series data in a time-series database to obtain a time-series dataset, the time-series dataset including a regular time-series dataset and a high-precision time-series dataset, including:

[0119] The memory of the time-series database is divided into a regular storage database and a high-precision storage database;

[0120] All collected time-series data are divided to obtain an initial regular dataset and an initial high-precision dataset;

[0121] The initial regular dataset is stored in the regular storage database to obtain a regular time series dataset;

[0122] The initial high-precision dataset is stored in the high-precision storage database to obtain a high-precision time-series dataset;

[0123] A time series dataset is constructed based on the conventional time series dataset and the high-precision time series dataset.

[0124] In this optional embodiment, routine monitoring of data refers to monitoring in the conventional sense, characterized by low index collection frequency (e.g., data is collected once every 1 minute or 5 minutes), wide collection range, and long data retention period; high-precision monitoring of data refers to monitoring of high-frequency data collection, characterized by high index collection frequency (e.g., data is collected once per second), narrow collection range, and short data retention period.

[0125] In this optional embodiment, staff can pre-divide the time-series data to be monitored according to the different monitoring methods, and use all data belonging to the conventional monitoring method as the initial conventional dataset, and all data belonging to the high-precision monitoring method as the initial high-precision dataset.

[0126] In this optional embodiment, since the collected time-series data needs to be divided into two types—regular time-series data and high-precision time-series data—based on different data monitoring types, the memory of the time-series database can be partitioned. However, it is not necessary to partition the memory of the time-series database in a fixed proportion; two data tables with different field names can be created. For example, the initial regular dataset can be stored in a data table with the field name "Regular Monitoring Data," and the initial high-precision dataset can be stored in a data table with the field name "High-Precision Monitoring Data."

[0127] In this optional embodiment, the memory space in the time-series database that stores all data tables with the field name "regular monitoring data" is used as the regular storage database, and the memory space that stores all data tables with the field name "high-precision monitoring data" is used as the high-precision storage database.

[0128] In this optional embodiment, the initial regular dataset stored in the regular storage database is used as a regular time series dataset, the initial high-precision data stored in the high-precision storage database is used as a high-precision time series dataset, and the regular time series dataset and the high-precision time series dataset are used as a time series dataset.

[0129] In an optional embodiment, the partitioning unit 111 is used to partition the storage nodes of the conventional time-series dataset and the high-precision time-series dataset to obtain conventional monitoring data nodes and high-precision monitoring data nodes, respectively.

[0130] In an optional embodiment, the step of partitioning the storage nodes of the conventional time-series dataset and the high-precision time-series dataset to obtain conventional monitoring data nodes and high-precision monitoring data nodes respectively includes:

[0131] The storage nodes of the conventional storage database and the high-precision storage database are respectively divided into two categories: resident nodes and elastic nodes;

[0132] The conventional time-series dataset and the high-precision time-series dataset are respectively divided to obtain a conventional classification dataset and a high-precision classification dataset;

[0133] Based on the resident nodes and elastic nodes of the conventional storage database, the conventional classification dataset is classified and stored to obtain conventional resident data nodes and conventional elastic data nodes. The conventional resident data nodes and the conventional elastic data nodes constitute conventional monitoring data nodes.

[0134] Based on the resident nodes and elastic nodes of the high-precision storage database, the high-precision classification dataset is classified and stored to obtain high-precision resident data nodes and high-precision elastic data nodes. The high-precision resident data nodes and the high-precision elastic data nodes constitute high-precision monitoring data nodes.

[0135] In this optional embodiment, a time-series database may contain multiple storage nodes, and each storage node may include multiple data tables. For example, a time-series database may be distributed across multiple different servers, and the storage space corresponding to each server may serve as a storage node of the time-series database.

[0136] In this optional embodiment, both the conventional storage database and the high-precision storage database include two types of storage nodes: resident nodes and elastic nodes. A resident node is one that runs continuously with a constant capacity, while an elastic node dynamically increases or decreases based on the relationship between the amount of stored data and the node's capacity.

[0137] In this optional embodiment, the conventional time-series dataset and the high-precision time-series dataset can be divided according to the 1σ criterion and the storage time of the time-series data in the database, respectively. Specifically, the average values ​​μ1 and μ2 and the standard deviations σ1 and σ2 of all time-series data in both the conventional and high-precision time-series datasets are calculated.

[0138] In this optional embodiment, all time-series data concentrated in the range of (μ1-σ1, μ1+σ1) are regarded as regular resident datasets, and all time-series data in the regular time-series dataset that are not in the range of (μ1-σ1, μ1+σ1) are regarded as regular elastic datasets. The regular resident dataset and the regular elastic dataset constitute a regular classification dataset.

[0139] In this optional embodiment, all time-series data concentrated in the range of (μ2-σ2, μ2+σ2) are regarded as high-precision resident datasets, and all time-series data in the high-precision time-series dataset that are not in the range of (μ2-σ2, μ2+σ2) are regarded as high-precision elastic datasets. The high-precision resident dataset and the high-precision elastic dataset constitute a high-precision classification dataset.

[0140] In this optional embodiment, a regular resident data node is used to store the regular resident dataset, and a regular elastic data node is used to store the regular elastic dataset. The regular resident data node and the regular elastic data node constitute a regular monitoring data node.

[0141] In this optional embodiment, a high-precision resident data node is used to store the high-precision resident dataset, and a high-precision elastic data node is used to store the high-precision elastic dataset. The high-precision resident data node and the high-precision elastic data node constitute a high-precision monitoring data node.

[0142] In an optional embodiment, the setting unit 112 is used to set node capacity thresholds for the conventional monitoring data node and the high-precision monitoring data node respectively, wherein the node capacity thresholds include a node shrinkage threshold and a node expansion threshold.

[0143] In an optional embodiment, setting node capacity thresholds for the conventional monitoring data nodes and the high-precision monitoring data nodes respectively, the node capacity thresholds including node shrinkage thresholds and node expansion thresholds, including:

[0144] Set the node capacity for the conventional monitoring data nodes and the high-precision monitoring data nodes respectively;

[0145] Based on the node capacity, the node capacity thresholds for the conventional monitoring data node and the high-precision monitoring data node are obtained respectively. The node capacity thresholds include node shrinkage thresholds and node expansion thresholds.

[0146] In this optional embodiment, corresponding node capacities can be set for all storage nodes included in the conventional monitoring data nodes and the high-precision monitoring data nodes. For example, for resident storage nodes, the corresponding storage capacity is 500M, and for elastic storage nodes, the corresponding storage capacity is 300M.

[0147] In this optional embodiment, node capacity thresholds for the regular monitoring data nodes and the high-precision monitoring data nodes can be obtained based on the node capacity. Since resident storage nodes exist for a long time and their storage capacity does not change, there is no need to set node capacity thresholds for resident storage nodes.

[0148] In this optional embodiment, a corresponding node capacity threshold is set only for elastic storage nodes. For example, 80% of the node capacity is used as the node expansion threshold, and 20% of the node capacity is used as the node reduction threshold. When the amount of time-series data stored in an elastic storage node exceeds 80% of the node capacity, a new elastic storage node can be added to store the excess time-series data. When the amount of time-series data stored in an elastic storage node is less than 20% of the node capacity, the storage node can be released, and the time-series data stored in the released storage node can be migrated to other storage nodes with available storage space.

[0149] In an optional embodiment, the calculation unit 113 is used to calculate the data growth rate of the conventional monitoring data node and the high-precision monitoring data node according to a preset period to obtain the node prediction data volume, wherein the node prediction data volume includes the conventional node prediction data volume and the high-precision node prediction data volume.

[0150] In an optional embodiment, the step of calculating the data growth rate of the conventional monitoring data nodes and the high-precision monitoring data nodes according to a preset period to obtain the node predicted data volume, wherein the node predicted data volume includes the conventional node predicted data volume and the high-precision node predicted data volume, including:

[0151] The data volume growth rate of the conventional monitoring data node and the high-precision monitoring data node in each preset period is calculated to obtain the conventional growth rate set and the high-precision growth rate set;

[0152] The predicted data volume of the regular monitoring data nodes is obtained based on the set of regular growth rates and preset weights.

[0153] The high-precision node prediction data volume of the high-precision monitoring data node is obtained based on the high-precision growth rate set and the preset weight.

[0154] In this optional embodiment, the conventional monitoring data nodes and high-precision monitoring data nodes can be periodically detected according to a preset period, and the predicted data volume of the nodes can be obtained by calculating the data volume growth rate of the conventional monitoring data nodes and high-precision monitoring data nodes within the time period of the detection period. For example, the preset period can be 1 hour.

[0155] In this optional embodiment, the amount of data in each preset period can be counted, and the ratio of the amount of data in each preset period to the amount of data in the previous preset period can be used as the data growth rate corresponding to the current preset period. Preset weights can be assigned to the data growth rates of the three preset periods closest to the current preset period to predict the amount of data in the next preset period.

[0156] In an optional embodiment, since time-series data has strong continuity over time, the preset weights assigned to the data growth rates within the three most recent preset periods are 1 / 2, 1 / 3, and 1 / 6, respectively. That is, the closer to the next preset period, the greater the weight of the corresponding data growth rate. Specifically, the data growth rates of the regular monitoring data nodes within the three most recent preset periods are defined as the regular growth rate set, and the data growth rates of the high-precision monitoring data nodes within the three most recent preset periods are defined as the high-precision growth rate set.

[0157] For example, the data growth rates corresponding to the three consecutive preset periods of 6-7, 7-8, and 8-9 are 1.2, 1.5, and 1.8, respectively. Then, the data growth rate in the next preset period of 9-10 can be calculated as 1.8×1 / 2+1.5×1 / 3+1.2×1 / 6=1.6.

[0158] In this optional embodiment, the product of the calculated data growth rate and the data volume of the conventional monitoring data node and the high-precision monitoring data node corresponding to the start time of the next preset period can be used as the predicted data volume of the conventional node and the predicted data volume of the high-precision node, respectively.

[0159] For example, if the data volume of a regular monitoring data node at 9 o'clock is 1000 and the corresponding data volume growth rate is 1.6, then the predicted data volume of the regular node for the next preset period can be calculated to be 1600. The calculation process of the predicted data volume of the high-precision node is the same as the calculation process of the predicted data volume of the regular node.

[0160] In an optional embodiment, the optimization unit 114 is used to optimize and adjust the time-series database based on the node capacity threshold and the node predicted data volume.

[0161] In an optional embodiment, optimizing the time-series database based on the node capacity threshold and the node prediction data volume includes:

[0162] Compare the node capacity threshold with the node predicted data volume;

[0163] If the predicted data volume of the node exceeds the node expansion threshold, then the storage nodes in the database will be expanded.

[0164] If the predicted data volume of the node is less than the node scaling-down threshold, then the storage node in the database will be scaled down.

[0165] In this optional embodiment, the node capacity threshold includes a node shrinkage threshold and a node expansion threshold, and the node prediction data volume includes a regular node prediction data volume and a high-precision node prediction data volume. Therefore, it is necessary to compare the regular node prediction data volume and the high-precision node prediction data volume with their corresponding node capacity thresholds.

[0166] In this optional embodiment, if the amount of predicted data from the regular nodes or the amount of predicted data from the high-precision nodes is greater than the node expansion threshold, then it is necessary to add regular elastic data nodes or high-precision elastic data nodes to expand the time-series database.

[0167] In this optional embodiment, if the amount of predicted data for the regular node or the amount of predicted data for the high-precision node is less than the node scaling-down threshold, then the regular elastic data node or the high-precision elastic data node needs to be released accordingly to achieve scaling down of the time-series database.

[0168] In this optional embodiment, since time-series data have corresponding storage times in the time-series database, when the time-series data reaches the storage time, it is necessary to immediately clear the time-series data and calculate the storage time of the remaining time-series data in real time, so as to dynamically adjust the storage space of each storage node, such as migrating time-series data that conforms to the 1σ criterion in the elastic storage node to the corresponding resident storage node.

[0169] As can be seen from the above technical solutions, this application can classify and store the collected time-series data into two categories: conventional time-series data and high-precision time-series data. By calculating the predicted data volume of conventional nodes and the predicted data volume of high-precision nodes, the storage nodes of each category of data can be dynamically adjusted, thereby effectively improving the storage efficiency of the time-series database.

[0170] Please see Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 1 includes a memory 12 and a processor 13. The memory 12 is used to store computer-readable instructions, and the processor 13 executes the computer-readable instructions stored in the memory to implement the artificial intelligence-based time-series database monitoring method described in any of the above embodiments.

[0171] In an alternative embodiment, the electronic device 1 further includes a bus and a computer program stored in the memory 12 and executable on the processor 13, such as an artificial intelligence-based time-series database monitoring program.

[0172] Figure 3 Only an electronic device 1 with memory 12 and processor 13 is shown. It will be understood by those skilled in the art that... Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0173] Combination Figure 1 The memory 12 in the electronic device 1 stores a plurality of computer-readable instructions to implement an artificial intelligence-based time-series database monitoring method, and the processor 13 can execute the plurality of instructions to achieve:

[0174] The collected time-series data is stored in a time-series database to obtain a time-series dataset, which includes a regular time-series dataset and a high-precision time-series dataset.

[0175] The storage nodes of the conventional time-series dataset and the high-precision time-series dataset are divided to obtain conventional monitoring data nodes and high-precision monitoring data nodes respectively;

[0176] Set node capacity thresholds for the conventional monitoring data nodes and the high-precision monitoring data nodes respectively. The node capacity thresholds include node shrinkage thresholds and node expansion thresholds.

[0177] The predicted data volume of the nodes is obtained by calculating the data volume growth rate of the conventional monitoring data nodes and the high-precision monitoring data nodes according to a preset period. The predicted data volume of the nodes includes the predicted data volume of the conventional nodes and the predicted data volume of the high-precision nodes.

[0178] The time-series database is optimized and adjusted based on the node capacity threshold and the node predicted data volume.

[0179] Specifically, the processor 13's implementation method for the above instructions can be found in [reference needed]. Figure 1 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0180] Those skilled in the art will understand that the schematic diagram is merely an example of electronic device 1 and does not constitute a limitation on electronic device 1. Electronic device 1 can be a bus-type structure or a star-type structure. Electronic device 1 may also include more or fewer other hardware or software than shown in the diagram, or different component arrangements. For example, electronic device 1 may also include input / output devices, network access devices, etc.

[0181] It should be noted that electronic device 1 is only an example. Other existing or future electronic products that are suitable for this application should also be included within the scope of protection of this application and are incorporated herein by reference.

[0182] The memory 12 includes at least one type of readable storage medium, which can be non-volatile or volatile. The readable storage medium includes flash memory, portable hard drives, multimedia cards, card-type memory (e.g., SD or DX memory), magnetic storage, magnetic disks, optical disks, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 1, such as a portable hard drive of the electronic device 1. In other embodiments, the memory 12 can also be an external storage device of the electronic device 1, such as a plug-in portable hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the electronic device 1. The memory 12 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a time-series database monitoring program based on artificial intelligence, but also to temporarily store data that has been output or will be output.

[0183] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control unit of the electronic device 1, connecting various components of the electronic device 1 via various interfaces and lines. It executes programs or modules stored in the memory 12 (e.g., executing an AI-based time-series database monitoring program) and calls data stored in the memory 12 to perform various functions and process data in the electronic device 1.

[0184] The processor 13 executes the operating system of the electronic device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the various embodiments of the artificial intelligence-based time-series database monitoring method described above, for example... Figure 1 The steps are shown.

[0185] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to complete this application. The one or more modules / units may be a series of computer-readable instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into an acquisition unit 110, a partitioning unit 111, a setting unit 112, a calculation unit 113, and an optimization unit 114.

[0186] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute portions of the artificial intelligence-based time-series database monitoring method described in the various embodiments of this application.

[0187] If the modules / units integrated in electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0188] The computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory, and other memory.

[0189] Furthermore, the computer-readable storage medium may primarily include a stored program area and a stored data area, wherein the stored program area may store the operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of blockchain nodes, etc.

[0190] The blockchain referred to in this application is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0191] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, in... Figure 3 The symbol is represented by only one arrow, but this does not indicate that there is only one bus or one type of bus. The bus is configured to enable communication between the memory 12 and at least one processor 13, etc.

[0192] This application also provides a computer-readable storage medium (not shown), which stores computer-readable instructions. These computer-readable instructions are executed by a processor in an electronic device to implement the artificial intelligence-based time-series database monitoring method described in any of the above embodiments.

[0193] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0194] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0195] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0196] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in the specification may also be implemented by a single unit or device through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0197] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. An artificial intelligence-based time series database monitoring method, characterized by, The method includes: The collected time-series data is stored in a time-series database to obtain a time-series dataset, which includes a regular time-series dataset and a high-precision time-series dataset. The storage nodes of the conventional time-series dataset and the high-precision time-series dataset are partitioned to obtain conventional monitoring data nodes and high-precision monitoring data nodes, respectively, including: dividing the storage nodes of the conventional storage database and the high-precision storage database into resident nodes and elastic nodes; according to 1 The criteria and the storage time of the time-series data in the database are used to divide the regular time-series dataset and the high-precision time-series dataset into a regular classification dataset and a high-precision classification dataset, respectively. Based on the resident nodes and elastic nodes of the regular storage database, the regular classification dataset is classified and stored to obtain regular resident data nodes and regular elastic data nodes, which constitute the regular monitoring data nodes. Based on the resident nodes and elastic nodes of the high-precision storage database, the high-precision classification dataset is classified and stored to obtain high-precision resident data nodes and high-precision elastic data nodes, which constitute the high-precision monitoring data nodes. Set node capacity thresholds for the elastic nodes in the conventional monitoring data nodes and high-precision monitoring data nodes respectively. The node capacity thresholds include node shrinkage thresholds and node expansion thresholds. The predicted data volume of the nodes is obtained by calculating the data volume growth rate of the conventional monitoring data nodes and the high-precision monitoring data nodes according to a preset period. The predicted data volume of the nodes includes the predicted data volume of the conventional nodes and the predicted data volume of the high-precision nodes. The time-series database is optimized and adjusted based on the node capacity threshold and the node predicted data volume, including: if the node predicted data volume is greater than the node expansion threshold, the storage nodes in the database are expanded; if the node predicted data volume is less than the node shrinkage threshold, the storage nodes in the database are shrunk. Once the time-series data reaches its storage time, the time-series data that has reached its storage time will be cleared, and the storage space of each storage node will be dynamically adjusted based on the storage time of the remaining time-series data calculated in real time.

2. The time-series database monitoring method based on artificial intelligence as described in claim 1, characterized in that, The process involves storing the collected time-series data into a time-series database to obtain a time-series dataset. This time-series dataset includes both conventional and high-precision time-series datasets, including: The memory of the time-series database is divided into a regular storage database and a high-precision storage database; All collected time-series data are divided to obtain an initial regular dataset and an initial high-precision dataset; The initial regular dataset is stored in the regular storage database to obtain a regular time series dataset; The initial high-precision dataset is stored in the high-precision storage database to obtain a high-precision time-series dataset; A time series dataset is constructed based on the conventional time series dataset and the high-precision time series dataset.

3. The time-series database monitoring method based on artificial intelligence as described in claim 1, characterized in that, The step of dividing the conventional time-series dataset and the high-precision time-series dataset into a conventional classification dataset and a high-precision classification dataset respectively includes: According to 1 The criteria are used to divide the conventional time-series dataset and the high-precision time-series dataset into two categories, respectively. The conventional time series dataset contains elements that meet the condition 1. All time-series data according to the criteria are treated as a regular resident dataset, and data in the regular time-series dataset that does not conform to 1 are excluded. All time-series data of the criteria are used as a regular elastic dataset, and the regular resident dataset and the regular elastic dataset constitute a regular classification dataset; The high-precision time series dataset contains elements that meet the 1... All time-series data according to the criteria are used as a high-precision resident dataset, and data in the high-precision time-series dataset that does not conform to 1... All time-series data of the criteria are used as a high-precision elastic dataset. The high-precision resident dataset and the high-precision elastic dataset together constitute a high-precision classification dataset.

4. The time-series database monitoring method based on artificial intelligence as described in claim 1, characterized in that, The method involves setting node capacity thresholds for elastic nodes in both the conventional and high-precision monitoring data nodes. These node capacity thresholds include node shrinkage thresholds and node expansion thresholds, comprising: Set the node capacity for the conventional monitoring data nodes and the high-precision monitoring data nodes respectively; Based on the node capacity, the node capacity thresholds for the conventional monitoring data node and the high-precision monitoring data node are obtained respectively. The node capacity thresholds include node shrinkage thresholds and node expansion thresholds.

5. The time-series database monitoring method based on artificial intelligence as described in claim 1, characterized in that, The node predicted data volume is obtained by calculating the data volume growth rate of the conventional monitoring data nodes and the high-precision monitoring data nodes according to a preset period. The node predicted data volume includes the conventional node predicted data volume and the high-precision node predicted data volume, including: The data volume growth rate of the conventional monitoring data node and the high-precision monitoring data node in each preset period is calculated to obtain the conventional growth rate set and the high-precision growth rate set; The predicted data volume of the regular monitoring data nodes is obtained based on the set of regular growth rates and preset weights. The high-precision node prediction data volume of the high-precision monitoring data node is obtained based on the high-precision growth rate set and the preset weight.

6. A time-series database monitoring device based on artificial intelligence, characterized in that, The apparatus is used to implement the artificial intelligence-based time-series database monitoring method as described in any one of claims 1 to 5, the apparatus comprising: The acquisition unit is used to store the collected time-series data into a time-series database to obtain a time-series dataset, which includes a regular time-series dataset and a high-precision time-series dataset. A partitioning unit is used to partition the storage nodes of the conventional time-series dataset and the high-precision time-series dataset to obtain conventional monitoring data nodes and high-precision monitoring data nodes, respectively. The setting unit is used to set node capacity thresholds for elastic nodes in the conventional monitoring data nodes and high-precision monitoring data nodes respectively. The node capacity thresholds include node shrinkage thresholds and node expansion thresholds. The calculation unit is used to calculate the data growth rate of the conventional monitoring data node and the high-precision monitoring data node according to a preset period to obtain the node prediction data volume, wherein the node prediction data volume includes the conventional node prediction data volume and the high-precision node prediction data volume. An optimization unit is used to optimize and adjust the time-series database based on the node capacity threshold and the node predicted data volume.

7. An electronic device, characterized in that, The electronic device includes: Memory, which stores computer-readable instructions; and The processor executes computer-readable instructions stored in the memory to implement the artificial intelligence-based time-series database monitoring method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the artificial intelligence-based time-series database monitoring method as described in any one of claims 1 to 5.

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