Energy-saving management system data processing method, device, equipment and medium

Abstract

The invention provides an energy-saving management system data processing method and device, equipment and a medium, and relates to the technical field of data processing. The method comprises the steps that energy-saving data of the energy-saving management system are obtained and stored in a data lake deployed by storage equipment according to different data types, the energy-saving data stored in the data lake are marked, the data lake comprises a plurality of data pools, each data pool comprises a plurality of data storage tanks, and each data storage tank comprises a plurality of data storage tanks; and each storage tank in the plurality of data storage tanks corresponds to a set energy-saving management system, associating the energy-saving data by taking the Istio as a load engine and combining the marks corresponding to the energy-saving data, and querying the energy-saving data by taking the Istio as the load engine. Through the method provided by the invention, the energy-saving data query is more efficient, the energy-saving data query process is simplified through classified storage and marking of the energy-saving data, and efficient query performance optimization is realized based on Istio load balancing.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a data processing method, device, equipment and medium for energy-saving management system. Background Art

[0002] With the rapid development of computer technology and information technology, data centers, as the core infrastructure supporting the above industries, have been widely used and developed rapidly. Since the servers in the data center rely on electricity to drive and operate, in order to ensure that the data center can provide services uninterruptedly throughout the year, many data centers have adopted measures such as dual power supply to improve the reliability of power supply.

[0003] However, the significant increase in power consumption of data centers has brought tremendous pressure on energy supply. Faced with a large number of data centers that have been built and put into use, a comprehensive transformation of the data centers is not only costly but also difficult to implement. Therefore, it is necessary to deploy energy-saving management systems based on existing data centers to achieve effective savings and optimization of data center energy consumption.

[0004] When managing and optimizing energy consumption for data centers, it is necessary to obtain energy-saving data from energy-saving management systems in a timely manner. However, the existing methods for collecting and processing energy-saving data usually store the energy-saving data first and then analyze it later. Although this traditional data processing method can ensure the security and integrity of the data, it is not flexible and efficient enough when facing a rapidly changing data environment and an increasing amount of data. Especially in operation and maintenance scenarios, the scale of network services is large, the application relationships are complex, and there are many levels of dependence, making it particularly difficult to troubleshoot problems. In addition, when retrieving energy-saving data, traditional retrieval models mainly rely on manual fitting of sorting formulas and determine the best parameter combination through continuous experiments. This method is not only time-consuming and labor-intensive, but also has poor timeliness, and is difficult to adapt to the needs of data centers for real-time or near-real-time data analysis.

[0005] Therefore, how to effectively pre-process the collected energy-saving data and build an efficient retrieval mechanism based on this has become a key technical problem that needs to be solved urgently. Summary of the invention

[0006] The present invention provides an energy-saving management system data processing method, device, equipment and medium, which are used to pre-process the collected energy-saving data and build an efficient retrieval mechanism to perform efficient data processing and analysis.

[0007] In a first aspect, the present application provides a data processing method for an energy-saving management system, which is applied to a data center. The method includes: obtaining energy-saving data of an energy-saving management system of at least one data center, storing the energy-saving data in a data lake deployed by a storage device according to different data types, and marking the energy-saving data stored in the data lake, wherein the mark includes the data source of the energy-saving data, the data type of the energy-saving data, and the initial storage location of the energy-saving data. The data lake includes multiple data pools for storing different types of data, each data pool includes multiple data storage tanks, each of the multiple data storage tanks corresponds to a set energy-saving management system, based on Istio as a load engine and combined with the marks corresponding to the energy-saving data, the energy-saving data in the data storage tanks of each data pool are associated, and Istio is used as the load engine to query the energy-saving data.

[0008] The method provided by the present invention makes energy-saving data query more efficient. The consistency and integrity of energy-saving data are ensured through classified storage and marking of energy-saving data, which simplifies the energy-saving data query process. Based on Istio's load balancing, efficient query performance optimization and resource utilization are achieved.

[0009] As a possible implementation method, the method also includes: obtaining metadata corresponding to energy-saving data of an energy-saving management system of at least one data center, the metadata including descriptions of data records, indexes, key values, and relationships between different data attributes, and using the metadata to mark the energy-saving data stored in the data lake.

[0010] By tagging data with metadata, the consistency and integrity of the data can be ensured. Data in different data pools and storage tanks can be associated through metadata.

[0011] As a possible implementation, the method further includes: associating each data storage tank with metadata and energy-saving data, and locating the energy-saving data in the data lake through metadata when querying the energy-saving data.

[0012] Through the index and key value information in the metadata, fast data query and retrieval can be achieved, which improves the efficiency of data query and reduces query time.

[0013] As a possible implementation, before storing the energy-saving data in a data lake deployed in a storage device according to different data types, the method also includes: identifying initial energy-saving data that has not changed within a predetermined time interval in the energy-saving data of an energy-saving management system of at least one data center, and storing the initial energy-saving data in an initial data pool in the data lake.

[0014] The initial energy-saving data does not need to be backed up frequently. Storing it separately in the initial data pool can simplify the backup strategy and reduce backup time and resource consumption. When energy-saving data is lost or damaged, the unchanged data can be quickly restored from the initial data pool, shortening the recovery time and improving the availability of energy-saving data.

[0015] As a possible implementation, the data lake includes an analog signal data pool, a text type data pool, and an application data pool. The analog signal data pool is used to store data related to continuously changing physical quantities such as temperature, humidity, current, and voltage in the energy-saving data of the energy-saving management system of at least one data center. The text type data pool is used to store data related to documents, reports, and configuration files in the energy-saving data of the energy-saving management system of at least one data center. The application data pool is used to store data related to application running status, performance statistics, and application logs in the energy-saving data of the energy-saving management system of at least one data center.

[0016] By dividing the data lake into analog signal data pool, text type data pool and application data pool, data can be better managed and organized, and energy-saving data query efficiency can be improved.

[0017] As a possible implementation method, the energy-saving management system specifically includes an air-conditioning energy-saving management module, a wind-heat transfer energy-saving management module, a data center equipment energy-saving management module and a power distribution system energy-saving management module. The air-conditioning energy-saving management module is used to obtain the power consumption of the air conditioner in the data center. The wind-heat transfer energy-saving management module is used to obtain the sensor data of the temperature sensor and the humidity sensor in the data center. The data center equipment energy-saving management module is used to obtain the temperature of the processor in the data center and the load energy consumption data of the server in the data center. The power distribution system energy-saving management module is used to obtain the energy consumption data of the power distribution system in the data center.

[0018] As a possible implementation method, Istio is used as a load engine to query energy-saving data, specifically including: receiving a query request, and determining the range of energy-saving data to be searched according to the keywords carried in the query request, and pre-marking the keywords, and sending the keywords and their pre-marking and other parameters to the Istio engine, so that after receiving the query request, the Istio engine can pre-read data from the data lake according to the keywords carried in the query request and the pre-marks corresponding to the keywords, and send the energy-saving data with the same mark and pre-mark to a cache group composed of multiple cache threads, and match the query request with the low-load thread in the cache group through the Istio engine, and use the low-load thread to execute the query request to obtain the query result.

[0019] By pre-reading data and storing it in the cache group, query response time can be reduced, and cache threads can directly obtain data from the cache without accessing the data lake every time, thereby improving query efficiency. The Istio engine distributes query requests to cache threads with low loads through a load balancing mechanism, avoiding overload of a single cache thread and improving overall performance and stability.

[0020] As a possible implementation method, the method also includes: if no query result is found in the cache group, traversing all energy-saving data in the data lake through the Istio engine for query.

[0021] If the required query result is not found in the cache group, the Istio engine can traverse all energy-saving data in the data lake to ensure the completeness and accuracy of the query. In this way, the required data can still be obtained, thus avoiding data omissions and ensuring access to all energy-saving data in the data center.

[0022] As a possible implementation method, the energy-saving data stored in the data lake is marked, specifically including: marking the energy-saving data stored in the data lake for less than a set time as new data, and marking the energy-saving data with a query number greater than a set number as high-frequency query data.

[0023] For energy-saving data marked as new data, the Istio engine performs fast filtering and processing. By quickly filtering and processing these new data, the main needs of users can be met and the query response speed can be improved.

[0024] As a possible implementation, the query request is matched with a thread with a lower load in the cache group through the Istio engine, specifically including: after receiving the query request, the energy-saving data is preferentially queried from at least one energy-saving data with a high-frequency query data tag. By preferentially querying high-frequency query data, the query response time can be significantly reduced.

[0025] In a second aspect, the present application provides an energy-saving management system data processing device, comprising:

[0026] A collection module, used to obtain energy-saving data of an energy-saving management system of at least one data center;

[0027] A classification storage module is used to store energy-saving data in a data lake deployed by a storage device according to different data types, and to mark the energy-saving data stored in the data lake. The mark includes the data source of the energy-saving data, the data type of the energy-saving data, and the initial storage location of the energy-saving data. The data lake includes multiple data pools for storing different types of data, each data pool includes multiple data storage tanks, and each storage tank in the multiple data storage tanks corresponds to a set energy-saving management system;

[0028] The tagging module is used to associate the energy-saving data in the data storage tanks of each data pool based on Istio as the load engine and the tags corresponding to the energy-saving data;

[0029] The query module is used to query energy-saving data using Istio as the load engine.

[0030] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any one of the energy-saving management system data processing methods in the first aspect is implemented.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, any energy-saving management system data processing method in the first aspect is implemented.

[0032] In a fifth aspect, an embodiment of the present application further provides a computer program product, including a computer program, which is executed by a processor to implement any energy-saving management system data processing method as described in the first aspect above.

[0033] The technical effects brought about by any one of the implementation methods in the second to fifth aspects can refer to the technical effects brought about by the corresponding implementation method in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flowchart of the steps of the energy-saving management system data processing method provided in this application;

[0035] Figure 2 It is a structural diagram of the energy-saving management system;

[0036] Figure 3 A storage device Figure 1 ;

[0037] Figure 4 A storage device Figure 2 ;

[0038] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0039] Figure 6 A schematic diagram of the structure of a data processing device for an energy-saving management system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The specific operating method in the method embodiment can also be applied to the device embodiment or the system embodiment. It should be noted that in the description of the present application, "at least one" refers to one or more, wherein multiple refers to two or more. In view of this, "multiple" can also be understood as "at least two" in the embodiment of the present application. "And / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / ", unless otherwise specified, generally indicates that the related objects before and after are in an "or" relationship. In addition, it should be understood that in the description of the present application, words such as "first" and "second" are only used to distinguish the purpose of description, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order.

[0041] Below, some of the terms involved in the embodiments of the present application are explained to facilitate easy understanding by those skilled in the art.

[0042] (1) Istio is a service mesh technology used to manage and connect various services in a microservice architecture.

[0043] (2) Metadata refers to "data about data". It provides descriptions, definitions or explanations of other data. Metadata can help understand, manage and use data.

[0044] (3) Power Usage Effectiveness (PUE) is an important indicator for measuring the energy efficiency of a data center. It is defined as the ratio of the total energy consumption of a data center to the energy consumption of IT equipment. The closer the PUE value is to 1.0, the higher the energy efficiency of the data center, that is, the less energy is consumed by non-IT equipment (such as cooling systems, lighting, etc.).

[0045] With the rapid development of computer technology, the power consumption of data centers is increasing. The servers in data centers are completely dependent on electricity to operate. In addition, data centers have very high requirements for power quality and usually use a dual-circuit power supply system to ensure uninterrupted operation throughout the year. Due to this year-round operation demand and the promotion of informatization, more and more data centers are being built, and they are gradually developing in the direction of large-scale and high-density, which has led to a continuous increase in the power consumption of data centers in my country.

[0046] For large-scale data centers that have already been built, it is obviously unrealistic to carry out a comprehensive system transformation. Therefore, it is very important to achieve energy saving in data centers and the timeliness of energy saving data retrieval and storage based on the existing system. Traditional retrieval models rely on manual fitting sorting formulas and continuous experiments to determine the best parameter combination. This method is not only time-consuming, but also difficult to adapt to the rapidly changing data environment. At the same time, the application of artificial intelligence algorithms also puts higher requirements on the timeliness of data analysis, which means that preprocessing of energy consumption data collection becomes particularly important.

[0047] Existing technologies usually store the collected data before analyzing it, which obviously cannot meet the needs of modern artificial intelligence analysis. The method of this application proposes a new solution to the problems of large-scale network services, complex application relationships, multiple levels of dependencies, and difficulty in troubleshooting in data center operation and maintenance scenarios. This method improves the timeliness of energy-saving data storage and query in data centers. Effective preprocessing is carried out in the process from data collection to storage, and Istio soft load balancing technology is introduced to replace traditional hard load balancing to solve the problems of function expansion and cost increase.

[0048] See also Figure 1 As shown, Figure 1 This is a flow chart of the steps of the energy-saving management system data processing method provided by the present application, the method comprising the following steps:

[0049] Step S101: acquiring energy-saving data of an energy-saving management system of at least one data center.

[0050] See also Figure 2 As shown, Figure 2 The energy-saving management system of the data center of the present invention mainly includes the following modules: air conditioning energy-saving management module 201, wind heat transfer energy-saving management module 202, data center equipment energy-saving management module 203, power distribution system energy-saving management module 204. Each module is responsible for collecting energy consumption data of a specific energy-saving management system, and performing comprehensive analysis and optimization through the control unit of the data center.

[0051] The air conditioning energy saving management module 201 obtains the power consumption data of the air conditioning system in the current period through program inspection, and records and analyzes these energy saving data. Its main goal is to reduce the power consumption of the air conditioning in the data center computer room, thereby effectively reducing the PUE value of the data center.

[0052] The air conditioning energy saving management module 201 collects the power consumption data of the air conditioning system regularly and records the relevant environmental conditions (such as temperature, humidity, etc.). Combined with historical data and real-time environmental conditions, the energy consumption pattern and efficiency of the air conditioning system are analyzed. According to the analysis results, the working mode of the air conditioning is dynamically adjusted. For example, the cooling mode is optimized, the set temperature is adjusted, the energy saving mode is enabled, etc. Exemplarily, by optimizing the working mode of the air conditioning system, the PUE value of the data center is reduced, thereby improving the energy utilization efficiency.

[0053] The wind heat energy saving management module 202 monitors the temperature and humidity in the data center through temperature and humidity sensors, and records the working status of related equipment such as chillers, humidifiers, lighting equipment, auxiliary equipment, etc. Its main function is to optimize the cooling effect of the data center and reduce unnecessary energy consumption.

[0054] Specifically, the wind heat energy saving management module 202 monitors the temperature and humidity in the data center in real time through temperature and humidity sensors, and records the working status of chillers, humidifiers, lighting equipment, auxiliary equipment, etc., and combines historical data and real-time monitoring data to analyze the cooling demand and equipment operation efficiency of the data center. The working mode of the cooling equipment is dynamically adjusted according to the sensor data to ensure that energy is saved to the maximum extent while meeting the cooling demand. For example, the outlet water temperature of the chiller is automatically adjusted, and the start and stop of the humidifier is controlled.

[0055] The data center equipment energy-saving management module 203 is used to obtain the processor temperature of IT equipment in the data center and the server load energy consumption data in the data center through program inspection, and to monitor and analyze these data in real time. Its function is to reduce the energy consumption of IT equipment and servers in the data center, especially to reduce the processor temperature of IT equipment and the load of servers, thereby reducing the PUE value of the data center. The specific implementation method is that the wind heat energy-saving management module 202 automatically adjusts the working status of IT equipment and servers based on real-time data and historical energy consumption trends, such as dynamically allocating computing resources, optimizing task scheduling, etc.

[0056] The power distribution system energy-saving management module 204 is used to obtain the energy consumption data of the power distribution system through program inspection and analyze it in combination with historical data. Its purpose is to reduce the energy consumption of the uninterruptible power supply (UPS) and the power distribution unit (PDU) and improve the overall efficiency of the power distribution system. The wind heat transfer energy-saving management module 202 optimizes the power distribution path and load distribution through intelligent algorithms, thereby reducing unnecessary power loss.

[0057] Through the comprehensive application of the above four modules, data centers can achieve more efficient energy management and optimization. Each module ensures that data centers can meet business needs while minimizing energy consumption and improving energy efficiency, thereby reducing PUE values. This not only helps reduce operating costs, but also enables data centers to develop in a green and sustainable direction.

[0058] It should be noted that the energy-saving management system in the data center provided in this application may include but is not limited to the above-mentioned modules. Technical personnel in this field can add or reduce various energy-saving management modules according to the specific situation of the data center, and no further details will be given here.

[0059] For the above four modules (air conditioning energy saving management module 201, wind heat transfer energy saving management module 202, data center equipment energy saving management module 203 and power distribution system energy saving management module 204), the energy saving data may include multiple types. The following are the specific energy saving data that each module may collect:

[0060] The specific energy-saving data of the air-conditioning energy-saving management module 201 may include:

[0061] Power consumption data: power consumption of the air conditioning system in the current period, temperature data: real-time temperature in the computer room (unit: °C), humidity data: real-time humidity in the computer room (unit: RH), working mode: the current working mode of the air conditioning system (for example, cooling mode, energy-saving mode, etc.), historical energy consumption data: energy consumption records of the air conditioning system in the past period of time, environmental conditions: external ambient temperature, humidity, etc.

[0062] For example, the energy-saving data may be: the power consumption of the air-conditioning system is 150 kWh, the temperature of the computer room is 22° C., the humidity is 50% RH, and the air-conditioning system is in energy-saving mode.

[0063] The specific energy-saving data of the wind heat transfer energy-saving management module 202 may include:

[0064] Temperature data: real-time temperature in the data center (unit: °C); humidity data: real-time humidity in the data center (unit: RH); chiller data: chiller outlet water temperature, return water temperature, flow rate, etc.; humidifier data: humidifier start and stop status, humidification amount, etc.; lighting data: lighting equipment switch status, energy consumption, etc.; auxiliary equipment data: working status and energy consumption of other auxiliary equipment (such as cooling fans, water pumps, etc.); historical energy consumption data: energy consumption records of the cooling system over the past period of time.

[0065] For example, the energy-saving data may be: the data center temperature is 23°C, the humidity is 48% RH, the chiller outlet water temperature is 15°C, the return water temperature is 20°C, the flow rate is 100L / min, the humidifier is in the off state, etc.

[0066] The specific energy-saving data of the data center equipment energy-saving management module 203 may include:

[0067] Processor temperature data: processor temperature of IT equipment (unit: °C), server load data: server load percentage (unit: %), energy consumption data: real-time energy consumption of IT equipment and servers (unit: W), historical energy consumption data: energy consumption records of IT equipment and servers over a period of time, task scheduling data: task scheduling status, including computing resource allocation, task priority, etc.

[0068] For example, the energy saving data may be: the processor temperature of server A is 60° C., the load is 70%, the energy consumption of server B is 300 W, and the task scheduling shows that server C is processing a high priority task.

[0069] The specific energy-saving data of the power distribution system energy-saving management module 204 may include: power consumption data: real-time energy consumption of the power distribution system (unit: KVA), uninterruptible power supply data: working status of the uninterruptible power supply, battery capacity, charging / discharging status, etc., working status of the power distribution unit, energy consumption of each interface, etc., historical energy consumption data: energy consumption records of the power distribution system in the past period of time, load balancing data: distribution path and load distribution status.

[0070] For example, the energy-saving data can be: the total energy consumption of the distribution system is 500KVA, the battery capacity of the uninterruptible power supply is 80% and is in charging state, the energy consumption of the device connected to interface 1 of the power distribution unit is 100W, the energy consumption of the device connected to interface 2 is 200W, and the distribution path shows that the main line load is 300KVA and the standby line load is 200KVA.

[0071] By collecting these energy-saving data, data centers can more effectively manage and optimize energy use, reduce operating costs, improve energy utilization efficiency, and reduce the PUE value of the entire data center.

[0072] Step S102: Store the energy-saving data in a data lake deployed by a storage device according to different data types, and mark the energy-saving data stored in the data lake. The mark includes the data source of the energy-saving data, the data type of the energy-saving data, and the initial storage location of the energy-saving data. The data lake includes multiple data pools for storing different types of data, each data pool includes multiple data storage tanks, and each of the multiple data storage tanks corresponds to a set energy-saving management system.

[0073] In order to effectively manage and analyze the energy-saving data collected from each energy-saving management module, these data can be stored in a centralized storage device for storing the above energy-saving data. In this application, the storage device storing the above energy-saving data is called a data lake. A data lake is a system or repository that stores a large amount of raw data, supports multiple data types and formats, and can perform efficient data processing and analysis.

[0074] See also Figure 3 As shown, Figure 3 A storage device Figure 1 ; Among them, the data lake 301 is a centralized repository for storing a large amount of raw data, including structured, semi-structured and unstructured data. It has high scalability and flexibility, supports multiple data formats and types, and supports efficient data processing and analysis.

[0075] There are multiple data pools 302 in the data lake 301, each of which is used to store a specific type of data. Each data pool 302 includes multiple data storage tanks 303, and each data storage tank 303 corresponds to a set energy-saving management system.

[0076] The data source of the data lake 301 is the energy-saving data collected from the air-conditioning energy-saving management module 201, the wind heat transfer energy-saving management module 202, the data center equipment energy-saving management module 203, and the power distribution system energy-saving management module 204. The data type can be an analog signal type, a text type, an application type, etc. According to the data type, the data is stored in the corresponding data pool 302 and the data storage tank 303.

[0077] In order to achieve efficient data management and analysis, the data stored in the data lake can also be labeled, which helps with real-time monitoring and trend analysis, and can also provide strong support for subsequent optimization and adjustment, thereby improving the overall energy utilization efficiency of the data center.

[0078] The data tag may include the following: Data source: Identifies which energy-saving management module the data comes from (e.g., air conditioning energy-saving management module 201, wind heat transfer energy-saving management module 202, etc.). Data type: Identifies the specific type of data (e.g., power consumption data, temperature data, humidity data, etc.). Initial storage location: Identifies the location where the data is initially stored, to facilitate subsequent data tracking and management.

[0079] The specific storage method is to first collect data, collect energy-saving data regularly through various energy-saving management modules, and transmit the energy-saving data to the data lake. Then, according to the data type, the data is allocated to the corresponding data pool. In each data pool, the energy-saving data is further stored in the corresponding data storage tank. In addition, when storing energy-saving data, each energy-saving data is marked, including the data source, data type and initial storage location.

[0080] Exemplarily, for the air conditioning energy saving management module 201, the data types of the energy saving data obtained include:

[0081] Analog signal type: temperature data, humidity data, power consumption data; Text type: air conditioning system working mode records, maintenance logs, etc.; Application type: air conditioning system control parameters, optimization algorithm execution records, etc.

[0082] The types of marking the energy-saving data acquired by the air-conditioning energy-saving management module 201 may be: data source, such as the air-conditioning energy-saving management module 201, data type, such as analog signal type, text type, application type.

[0083] For the wind heat transfer energy-saving management module 202, the data types of the energy-saving data obtained include: analog signal types, such as temperature data, humidity data, chiller outlet water temperature, humidifier start and stop status; text types, such as cooling system maintenance logs, operation records, etc.; application types, such as cooling system control parameters, optimization algorithm execution records, etc.

[0084] The types of marking the energy-saving data acquired by the wind heat transfer energy-saving management module 202 may be: data source, such as the wind heat transfer energy-saving management module 202, data type, such as analog signal type, text type, application type.

[0085] For the data center equipment energy saving management module 203, the data types of energy saving data obtained include: analog signal type, such as processor temperature data, server load data, energy consumption data. Text type, such as log files and configuration files of data center equipment. Application type, such as task scheduling records, computing resource allocation data, etc. The types of marking the energy saving data of the data center equipment energy saving management module 203 can be: data source, such as data center equipment energy saving management module 203. Data type, such as analog signal type, text type, application type.

[0086] For the power distribution system energy-saving management module 204, the data types of the energy-saving data obtained include: analog signal type, such as power consumption data, uninterruptible power supply battery capacity, and energy consumption of the interface connection device of the power distribution unit. Text type: maintenance log and operation record of the power distribution system. Application type, such as control parameters of the power distribution system, execution record of the optimization algorithm, etc. The type of marking the energy-saving data of the power distribution system energy-saving management module 204 can be, such as data source: power distribution system energy-saving management module 204. Data type, such as analog signal type, text type, application type.

[0087] As a possible implementation method, metadata corresponding to energy-saving data of the energy-saving management system of at least one data center is obtained, and the metadata includes descriptions of data records, indexes, key values, and relationships between different data attributes; the energy-saving data stored in the data lake is marked using metadata. Each data storage tank is associated with the energy-saving data through metadata; when querying energy-saving data, the energy-saving data is located in the data lake through metadata.

[0088] Metadata is a description of data records, indexes, key values, and the relationships between different data attributes. Data records are the specific content and format of data, indexes are used to quickly find and access data structures, key values ​​are keys that uniquely identify each piece of data, and relationships are described as associations between different data attributes, such as the relationship between temperature data and humidity data.

[0089] When storing energy-saving data, use metadata to mark each piece of data. These tags can include information such as the source of the data, data type, and initial storage location. The metadata is stored together with the energy-saving data and marked in the data lake. This makes it easier to identify and locate the data in subsequent data processing and queries. The related data in different data storage tanks are associated through the relationship description in the metadata. For example, the temperature data in the air-conditioning energy-saving management system can be associated with the temperature data in the wind heat transfer energy-saving management system through metadata. When querying energy-saving data, the index and key value information in the metadata can be used to quickly locate and retrieve related data in the data lake.

[0090] By marking data with metadata, the consistency and integrity of the data can be ensured. Data in different data pools and storage tanks can be associated through metadata. The index and key value information in the metadata can be used to quickly query and retrieve data. This greatly improves the efficiency of data query and reduces query time.

[0091] Through the relationship description in metadata, specialized analysis can be performed on different types of data. For example, analog signal data, text data, and application data can be analyzed separately, improving the accuracy and efficiency of data analysis.

[0092] See also Figure 4 As shown, Figure 4 A storage device Figure 2 The data lake includes an analog signal data pool 401, a text type data pool 402 and an application data pool 403. The analog signal data pool 401 is used to store data related to continuously changing physical quantities such as temperature, humidity, current, and voltage in the energy-saving data of the energy-saving management system of at least one data center. The text type data pool 402 is used to store data related to documents, reports, and configuration files in the energy-saving data of the energy-saving management system of at least one data center. The application data pool 403 is used to store data related to application running status, performance statistics, and application logs in the energy-saving data of the energy-saving management system of at least one data center.

[0093] Storing different types of data in different data pools helps to better organize and manage data. For example, analog signal data, text data, and application data each have their own specific formats and uses, and separate storage can avoid data mixing. By storing data in categories, data query and retrieval can be performed faster. For example, if temperature and humidity data need to be analyzed, the analog signal data pool 401 can be accessed directly without traversing the entire data lake. Different types of data may have different query requirements. For example, analog signal data is usually data collected at a high frequency and requires efficient read and write performance; while text data may be a configuration file or report that is updated at a low frequency and has lower requirements for read and write performance. By storing in separate pools, optimization can be performed for different types of performance requirements. Different data pools can be load balanced independently to ensure that each data pool can operate efficiently and the performance of other data pools will not be affected by the high load of a certain data pool.

[0094] In addition, different access permissions can be set for different data pools to improve the security of energy-saving data. For example, sensitive application logs can be stored in the strictly controlled application data pool 403, while public documents can be stored in the text type data pool 402. Data isolation between different data pools can also prevent data leakage or misoperation and improve the overall security of the system.

[0095] Specialized analysis can be performed for different types of energy-saving data. For example, time series analysis tools can be used to process analog signal data, text analysis tools can be used to process documents and reports, and application performance monitoring tools can be used to process application data. Different data pools use different data processing technologies and tools to simplify the energy-saving data processing process and improve processing efficiency.

[0096] Continue reading Figure 4 As shown, before storing the energy-saving data in the data lake deployed in the storage device according to different data types, the method also includes: identifying initial energy-saving data that has not changed within a predetermined time interval in the energy-saving data of the energy-saving management system of at least one data center, and storing the initial energy-saving data in an initial data pool 404 in the data lake.

[0097] Initial energy-saving data that has not changed within a predetermined time interval usually does not need to be frequently updated. Storing it separately in the initial data pool 404 can avoid these data occupying the space of the main data pool, thereby saving storage resources. By reducing the storage of redundant data, storage costs can be reduced.

[0098] Initial energy-saving data usually does not need to be frequently processed and analyzed. Storing it separately can reduce the processing burden of the data pool and improve the overall efficiency of energy-saving data processing. When needed, these unchanged data can be quickly obtained from the initial data pool 404 without searching in a large amount of dynamic data.

[0099] The initial energy-saving data is usually static, and storing it in a separate data pool can ensure the consistency and integrity of the data, avoiding data inconsistency problems caused by frequent updates. When the energy-saving data is lost or damaged, the unchanged data can be quickly restored from the initial data pool 404, shortening the recovery time and improving the availability of the energy-saving data.

[0100] By dividing the data lake into an analog signal data pool 401, a text type data pool 402 and an application data pool 403, and identifying and separately storing initial energy-saving data before storage, data can be better managed and organized, and the efficiency of data query and retrieval can be improved; performance optimization is performed for different types of data, load balancing is achieved, and overall performance is improved. In addition, targeted data analysis can be supported, data processing procedures can be simplified, processing efficiency can be improved, redundant data can be reduced, storage space can be saved, storage costs can be reduced, processing burden can be reduced, and consistency and integrity of energy-saving data can be maintained.

[0101] Moreover, dividing each data pool into multiple data storage tanks and making each storage tank correspond to a set energy-saving management system has the following benefits: each storage tank corresponds to the energy-saving management system of a specific data center, which can ensure that the data between different data centers are isolated from each other. Even if a data center has a problem, it will not affect the data of other data centers.

[0102] By isolating the data in each data center, data access rights can be better controlled. For example, only authorized personnel or systems can access the data in a specific storage tank, thereby improving data security. Each storage tank corresponds to a specific energy-saving management system, making data management and maintenance simpler and more intuitive. Administrators can more easily locate and process data related to a specific system. When troubleshooting is required, you can quickly locate the problem by viewing the energy-saving data in a specific storage tank without searching in multiple large data pools. Distributing data to different storage tanks can reduce the amount of data in a single storage tank, thereby improving read and write performance. Especially in high concurrency situations, the system's response speed can be significantly improved. By distributing data to different storage tanks, better load balancing can be achieved and a single storage tank can be prevented from becoming a bottleneck.

[0103] Since the data in each storage tank is specific to a specific data center, this ensures data consistency and integrity. With clear data center data tank division, it is easier to ensure data integrity and accuracy. The data in each storage tank has a clear source, which facilitates data query.

[0104] In addition, storage resources can be reasonably allocated according to the needs of different energy-saving management systems. For example, for energy-saving management systems with large data volumes, more storage space can be allocated; for energy-saving management systems with small data volumes, less storage space can be allocated. Through reasonable resource allocation, storage resources can be used more effectively and the overall storage cost can be reduced. At the same time, targeted data management and optimization can reduce unnecessary waste of resources.

[0105] Step S103, based on Istio as a load engine and in combination with the tags corresponding to the energy-saving data, the energy-saving data in the data storage tanks of each data pool are associated.

[0106] Deploy Istio service grid in the data center to provide unified service discovery, traffic management and security control for each energy-saving management system. Use Istio's intelligent routing function to route data from different energy-saving management modules to the data storage tank of the corresponding data pool. Istio can perform intelligent routing based on the tag information corresponding to the energy-saving data (such as data source, data type, etc.).

[0107] Through Istio's label and selector mechanism, related data in different data pools are associated based on data tag information. The Istio engine can identify and match these tags to achieve data association across data pools.

[0108] For example, in Istio, data association can be performed based on the tags corresponding to energy-saving data. In addition, a virtual service can be created for each data pool to define data routing rules. The virtual service can route data to the correct data storage tank based on the data tag information. By defining destination rules, ensure that data is correctly routed to the specified data storage tank.

[0109] Through the tagging and Istio labeling mechanism, data consistency and integrity can be ensured. Related data in different data pools can be associated through tagging information.

[0110] Istio's intelligent routing function can perform efficient routing based on data tag information, reduce unnecessary data transmission and processing, and improve overall performance. Istio can also implement soft load balancing to ensure that each data

[0111] Step S104: Use Istio as a load engine to query energy-saving data.

[0112] A query interface is opened in Istio to allow users or systems to query relevant data based on specific tag information. For example, users can obtain all data related to the "air conditioning energy saving management system" through the query interface. Through Istio's label and selector mechanism, relevant data from different data pools are aggregated to form a complete data view. For example, all data related to the air conditioning energy saving management system from the analog signal data pool 401, the text type data pool 402, and the application data pool 403 are associated and aggregated.

[0113] As a possible implementation method, Istio is used as a load engine to query energy-saving data, specifically including: receiving a query request, and determining the range of energy-saving data to be searched according to the keyword carried in the query request, and pre-marking the keyword; sending the keyword and its pre-marking and other parameters to the Istio engine, so that after receiving the query request, the Istio engine can pre-read data from the data lake according to the keyword carried in the query request and the pre-mark corresponding to the keyword, and send the energy-saving data with the same mark and pre-mark to a cache group composed of multiple cache threads, match the query request with the thread with low load in the cache group through the Istio engine, and use the thread with low load to execute the query request to obtain the query result.

[0114] The Istio engine receives query requests from users or applications. The query requests contain keywords, which are used to determine the range of energy-saving data to be found. Based on the keywords in the query requests, the system pre-marks these keywords. The pre-marking can include information such as the type, relevance, and priority of the keyword.

[0115] The keyword and its pre-marking parameters are sent to the Istio engine. The above parameters help the Istio engine understand the specific requirements of the query request. After receiving the query request, the Istio engine pre-reads data from the data lake according to the keywords carried in the query request and the pre-marking corresponding to the keywords. Istio will search for energy-saving data consistent with the pre-marking and send this data to the cache group composed of multiple cache threads.

[0116] The Istio engine matches query requests with threads with low load in the cache group. Through the load balancing mechanism, it ensures that query requests are assigned to cache threads with low current load. The query request is executed using cache threads with low load. The cache thread obtains the pre-read data from the cache group, and further processes and filters it to finally obtain the query results, and returns the query results to the user or application.

[0117] By pre-reading data and storing it in the cache group, query response time can be reduced. The cache thread can directly obtain data from the cache without accessing the data lake every time, thereby improving query efficiency. The Istio engine distributes query requests to cache threads with low loads through the load balancing mechanism, avoiding the overload of a single cache thread and improving the overall performance and stability of the system.

[0118] Through pre-reading and caching mechanisms, computing and storage resources can be used more efficiently. Frequently accessed data can be pre-loaded into the cache, reducing the frequency of access to the underlying storage system and reducing resource consumption. Istio can dynamically adjust the number and allocation strategy of cache threads according to the current load situation to ensure the most reasonable use of resources.

[0119] As a possible implementation method, the method also includes: if no query result is found in the cache group, traversing all energy-saving data in the data lake through the Istio engine for query.

[0120] If the required query result is not found in the cache group, the Istio engine traverses all energy-saving data in the data lake for query to ensure the completeness and accuracy of the query. In this way, even if some data has not been cached or has been removed from the cache, users can still obtain the required data. This mechanism can avoid data omissions caused by incomplete caching and ensure that users can access all energy-saving data in the data center.

[0121] By continuing to traverse the data lake when no results are found in the cache group, the success rate of the query is improved. Even if some data is not in the cache, it can be retrieved from the data lake to meet the user's query needs.

[0122] As a possible implementation method, the energy-saving data stored in the data lake is marked, specifically including: marking the energy-saving data stored in the data lake for less than a set time as new data, and marking the energy-saving data with a query number greater than a set number as high-frequency query data.

[0123] The query request is matched with a thread with a lower load in the cache group through the Istio engine, specifically including: after receiving the query request, the energy-saving data is preferentially queried from at least one energy-saving data with a high-frequency query data tag.

[0124] Energy-saving data stored in the data lake for less than a set period of time (for example, 24 hours) is marked as new data. This data may be recently collected data with high timeliness and importance.

[0125] Energy-saving data that is queried more than a set number of times (for example, more than 100 times per day) is marked as high-frequency query data. These data are frequently accessed data with high usage frequency and importance.

[0126] The Istio engine matches query requests with threads with lower loads in the cache group. Through the load balancing mechanism, it ensures that query requests are assigned to cache threads with lower current loads.

[0127] After receiving the query request, the Istio engine will first query the energy-saving data from at least one energy-saving data with a high-frequency query data tag. If the keyword in the query request matches the high-frequency query data, the data is directly obtained from the cache to improve the query efficiency.

[0128] By prioritizing high-frequency query data, query response time can be significantly reduced. High-frequency query data has been pre-loaded into the cache and can be directly obtained from the cache without accessing the data lake every time.

[0129] The Istio engine quickly filters and processes energy-saving data marked as new data. These data are recently collected and have high timeliness and importance. By quickly filtering and processing these new data, the main needs of users can be met and the response speed of the system can be improved.

[0130] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0131] The electronic device includes at least one processor 501 and a memory 502 connected to the at least one processor 501. The specific connection medium between the processor 501 and the memory 502 is not limited in the embodiment of the present application. Figure 5 In the example, the processor 501 and the memory 502 are connected via a bus 503. The bus 503 is Figure 5 The connection between other components is shown by bold lines, and is not intended to be limiting. The bus 503 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. Alternatively, the processor 501 can also be called a controller, and there is no limitation on the name.

[0132] In an embodiment of the present application, the memory 502 stores instructions that can be executed by at least one processor 501. By executing the instructions stored in the memory 502, at least one processor 501 can execute an energy-saving management system data processing method described in the above embodiment.

[0133] Among them, the processor 501 is the control center of the device, and can use various interfaces and lines to connect the various parts of the entire control device. By running or executing instructions stored in the memory 502 and calling data stored in the memory 502, the various functions of the device and processing data, the device can be monitored as a whole.

[0134] In one possible design, the processor 501 may include one or more processing units, and the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a driver interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 501. In some embodiments, the processor 501 and the memory 502 may be implemented on the same chip, and in some embodiments, they may also be implemented separately on separate chips.

[0135] Processor 501 can be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of a model acquisition method disclosed in the embodiments of the present application can be directly embodied as a hardware processor execution, or a combination of hardware and software modules in the processor.

[0136] The memory 502 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 502 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 502 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 502 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0137] By programming the processor 501, the code corresponding to the model acquisition method described in the above embodiment can be fixed into the chip, so that the chip can execute the code when running. Figure 1 The embodiment shown is a data processing method for an energy-saving management system. How to design and program the processor 501 is a technology well known to those skilled in the art and will not be described in detail here.

[0138] It should be noted here that the above-mentioned electronic device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0139] Figure 6 A schematic diagram of the structure of the data processing device of the energy-saving management system provided in the embodiment of the present application, such as Figure 6 As shown, the device includes: a collection module 601, a classification storage module 602, a marking module 603, and a query module 604.

[0140] A collection module 601 is used to obtain energy-saving data of an energy-saving management system of at least one data center;

[0141] The classification storage module 602 is used to store the energy-saving data in the data lake deployed by the storage device according to different data types, and mark the energy-saving data stored in the data lake, and the mark includes the data source of the energy-saving data, the data type of the energy-saving data and the initial storage location of the energy-saving data. The data lake includes multiple data pools for storing different types of data, each data pool includes multiple data storage tanks, and each storage tank in the multiple data storage tanks corresponds to a set energy-saving management system;

[0142] The marking module 603 is used to associate the energy-saving data in the data storage tanks of each data pool based on Istio as a load engine and in combination with the tags corresponding to the energy-saving data;

[0143] The query module 604 is used to query energy-saving data using Istio as a load engine.

[0144] Optionally, the marking module 603 is further used for:

[0145] Obtain metadata corresponding to energy-saving data of an energy-saving management system of at least one data center, the metadata including descriptions of data records, indexes, key values, and relationships between different data attributes;

[0146] Use metadata to tag energy-saving data stored in the data lake.

[0147] Optionally, the energy-saving management system data processing device further includes an association module 605, which is used to associate various data storage tanks through metadata and energy-saving data;

[0148] When querying energy-saving data, the energy-saving data is located in the data lake through metadata.

[0149] Optionally, the classification storage module 602 is also used to identify initial energy-saving data that has not changed within a predetermined time interval in the energy-saving data of the energy-saving management system of at least one data center; and store the initial energy-saving data in an initial data pool in the data lake.

[0150] Optionally, the query module 604 is further used to receive a query request, and determine the energy-saving data range to be searched according to a keyword carried in the query request, and pre-mark the keyword;

[0151] Send the keyword and its pre-mark and other parameters to the Istio engine, so that after receiving the query request, the Istio engine can pre-read data from the data lake according to the keyword carried in the query request and the pre-mark corresponding to the keyword, and send the energy-saving data with the same mark and pre-mark to the cache group composed of multiple cache threads;

[0152] The Istio engine matches the query request with the low-load thread in the cache group, and uses the low-load thread to execute the query request to obtain the query result.

[0153] An embodiment of the present application further provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute an energy-saving management system data processing method in the above embodiment.

[0154] An embodiment of the present application also provides a computer program product. When the computer program product is called by a computer, it enables the computer to execute an energy-saving management system data processing method in the above embodiment.

[0155] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0156] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 Flow or flows and / or boxes Figure 1 A device that performs the functions specified in a box or multiple boxes.

[0157] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 Flow or flows and / or boxes Figure 1 The functions specified in one or more boxes.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 Flow or flows and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0159] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A data processing method for an energy-saving management system, applied to a data center, characterized in that: The method comprises: Acquiring energy-saving data of an energy-saving management system of at least one of the data centers; The energy-saving data is stored in a data lake deployed by a storage device according to different data types, and the energy-saving data stored in the data lake is marked, wherein the mark includes a data source of the energy-saving data, a data type of the energy-saving data, and an initial storage location of the energy-saving data. The data lake includes a plurality of data pools for storing data of different types, each of the data pools includes a plurality of data storage tanks, and each of the plurality of data storage tanks corresponds to a set energy-saving management system; Based on Istio as a load engine and in combination with tags corresponding to the energy-saving data, the energy-saving data in the data storage tanks of each data pool are associated; The Istio is used as a load engine to query the energy-saving data.

2. The method according to claim 1, characterized in that: The method further comprises: Obtaining metadata corresponding to energy-saving data of at least one energy-saving management system of the data center, wherein the metadata includes descriptions of data records, indexes, key values, and relationships between different data attributes; The energy-saving data stored in the data lake is marked using the metadata.

3. The method according to claim 2, characterized in that The method further comprises: Each of the data storage tanks is associated with each other through the metadata and the energy-saving data; When querying the energy-saving data, the energy-saving data is located in the data lake through the metadata.

4. The method according to claim 1, characterized in that Before storing the energy-saving data in a data lake deployed by a storage device according to different data types, the method further includes: In the energy saving data of the energy saving management system of at least one of the data centers, identifying initial energy saving data that has not undergone any change within a predetermined time interval; The initial energy-saving data is stored in an initial data pool in the data lake.

5. The method according to claim 1, characterized in that The data lake includes an analog signal data pool, a text type data pool and an application data pool. The analog signal data pool is used to store data related to continuously changing physical quantities such as temperature, humidity, current, voltage, etc. in the energy-saving data of the energy-saving management system of at least one data center; the text type data pool is used to store data related to documents, reports, and configuration files in the energy-saving data of the energy-saving management system of at least one data center; and the application data pool is used to store data related to application running status, performance statistics, and application logs in the energy-saving data of the energy-saving management system of at least one data center.

6. The method according to claim 1, characterized in that The querying of the energy-saving data by using the Istio as a load engine specifically includes: Receive a query request, determine a range of energy-saving data to be searched according to a keyword carried in the query request, and pre-mark the keyword; Send the keyword and its pre-mark and other parameters to the Istio engine, so that after receiving the query request, the Istio engine can pre-read data from the data lake according to the keyword carried in the query request and the pre-mark corresponding to the keyword, and send the energy-saving data with a mark consistent with the pre-mark to a cache group composed of multiple cache threads; The query request is matched with a thread with low load in the cache group through the Istio engine, and the query request is executed using the thread with low load to obtain a query result.

7. The method according to claim 6, characterized in that The method further comprises: If no query result is found in the cache group, all energy-saving data in the data lake are traversed through the Istio engine for query.

8. The method according to claim 1, characterized in that The energy-saving data stored in the data lake is labeled, specifically including: The energy-saving data stored in the data lake for a period less than a set time is marked as new data, and the energy-saving data queried more than a set number of times is marked as high-frequency query data.

9. The method according to claim 8, characterized in that Matching the query request with a thread with a lower load in the cache group through the Istio engine specifically includes: After receiving the query request, energy-saving data is preferentially queried from at least one energy-saving data having the high-frequency query data tag.

10. A data processing device for an energy-saving management system, characterized in that: include: A collection module, used to obtain energy-saving data of an energy-saving management system of at least one of the data centers; A classification storage module, used to store the energy-saving data in a data lake deployed by a storage device according to different data types, and mark the energy-saving data stored in the data lake, wherein the mark includes a data source of the energy-saving data, a data type of the energy-saving data, and an initial storage location of the energy-saving data, wherein the data lake includes a plurality of data pools for storing data of different types, each of the data pools includes a plurality of data storage tanks, and each of the plurality of data storage tanks corresponds to a set energy-saving management system; A marking module, used to associate the energy-saving data in the data storage tank of each data pool based on Istio as a load engine and in combination with the tags corresponding to the energy-saving data; A query module is used to query the energy-saving data using the Istio as a load engine.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is used to cause the computer to execute the method according to any one of claims 1 to 9.

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