Energy aggregation management method and system, computer equipment, readable storage medium and program product

Abstract

The invention relates to an energy aggregation management method and system, computer equipment, a readable storage medium and a program product. The method comprises the following steps: generating an energy device identifier matched with each energy device and a sensor identifier matched with each sensor; performing data acquisition on each energy device to obtain device real-time data, and performing data acquisition on each sensor to obtain energy real-time data; associating the equipment real-time data with an energy equipment identifier matched with the energy equipment so as to associate the energy real-time data with a sensor identifier matched with the sensor; and managing the associated equipment real-time data and energy real-time data through the energy equipment identifier and the sensor identifier. By adopting the method, the reliability of data transmission can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of energy management systems, and in particular to a method, system, computer device, readable storage medium and program product for energy aggregation management. Background Art

[0002] With the development of the global economy, energy demand continues to grow, and higher requirements are placed on efficient energy management and optimized scheduling. The rapid development of renewable energy such as solar energy and wind energy requires energy management systems to be able to adapt to diverse energy inputs and outputs. At present, in order to optimize energy management and load scheduling, researchers have proposed many solutions, such as a prediction architecture based on artificial neural networks, which is used to integrate low-cost and low-carbon renewable energy into the power grid to reduce carbon emissions and user energy costs. As more and more energy systems are connected to distribution substations, especially new energy equipment, users have higher and higher requirements for the stability of electricity use. As new energy equipment in distribution substations is connected to the power network, how to ensure the reliability of data transmission is an important issue that needs to be considered. Summary of the invention

[0003] Based on this, it is necessary to provide a method, system, computer device, readable storage medium and program product for energy aggregation management that can improve the reliability of data transmission in response to the above technical problems.

[0004] In a first aspect, the present application provides a method for energy aggregation management, comprising:

[0005] generating an energy device identifier that matches each energy device, and a sensor identifier that matches each sensor;

[0006] Collect data from each energy device to obtain device data, and collect data from each sensor to obtain real-time energy data;

[0007] Associating the device data with the energy device identification matched by the energy device to associate the energy real-time data with the sensor identification matched by the sensor;

[0008] Through energy equipment identification and sensor identification, the associated equipment data and energy real-time data are managed.

[0009] In one embodiment, data collection is performed on each energy device to obtain device data, including:

[0010] Collect data from each energy device to obtain initial device data;

[0011] Use edge computing technology to process the initial device data to obtain device data;

[0012] Data is collected from each sensor to obtain real-time energy data, including:

[0013] Collect data from each sensor to obtain initial energy real-time data;

[0014] Use edge computing technology to process the initial energy real-time data to obtain energy real-time data;

[0015] Among them, edge computing technology includes at least one of the following: data filtering technology, real-time data analysis technology, data compression technology and security processing technology.

[0016] In one embodiment, the method for energy aggregation management further includes:

[0017] Monitor the operating status of each energy device separately;

[0018] Real-time analysis of energy consumption is performed for each operating state to obtain energy analysis results. The energy analysis results are used to provide maintenance strategies and fault warnings for energy equipment. The energy analysis results are obtained by using machine learning algorithms to predict and optimize energy consumption for each operating state.

[0019] In one embodiment, the method for energy aggregation management further includes:

[0020] Displaying a user interaction interface for providing interactive display functions for users to query energy usage data, users to receive system notifications, and users to participate in energy management decision-making;

[0021] The user interaction interface supports interface access from multiple platforms and allows third-party service integration through a configured application programming interface. The multiple platforms include at least a web platform and a mobile device.

[0022] In one embodiment, the method for energy aggregation management further includes:

[0023] Determine the grid operator system from the existing grid operator systems by performing access assessment on each existing grid operator system; wherein the access assessment includes at least compatibility assessment, security assessment, performance assessment and compliance assessment;

[0024] Connect with the system interface of the power grid operator system to realize data sharing and operation coordination with the power grid operator system; the data shared with the power grid operator system is shared and coordinated after data sharing priority evaluation.

[0025] In one embodiment, each energy device has a different energy storage type;

[0026] Energy aggregation management methods also include:

[0027] Collect information on energy devices of different energy storage types to obtain performance data and operating status of each energy device;

[0028] Perform forecast analysis on the performance data and operating status of each energy device to obtain the status analysis results of each energy device;

[0029] Adjust the operating parameters of each energy device based on the analysis results of each state.

[0030] In a second aspect, the present application also provides an energy aggregation management system, including:

[0031] An identification registration and management module, used to generate an energy device identifier that matches each energy device, and a sensor identifier that matches each sensor;

[0032] A data acquisition module is used to collect data from each energy device to obtain device data, and to collect data from each sensor to obtain real-time energy data;

[0033] The identification registration and management module is also used to associate the device data with the energy device identification that matches the energy device, so as to associate the energy real-time data with the sensor identification that matches the sensor;

[0034] Data transmission module, used to transmit equipment data and real-time energy data to the data processing center;

[0035] The data processing center is used to manage the associated equipment data and energy real-time data through energy equipment identification and sensor identification.

[0036] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] generating an energy device identifier that matches each energy device, and a sensor identifier that matches each sensor;

[0038] Collect data from each energy device to obtain device data, and collect data from each sensor to obtain real-time energy data;

[0039] Associating the device data with the energy device identification matched by the energy device to associate the energy real-time data with the sensor identification matched by the sensor;

[0040] Through energy equipment identification and sensor identification, the associated equipment data and energy real-time data are managed.

[0041] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0042] generating an energy device identifier that matches each energy device, and a sensor identifier that matches each sensor;

[0043] Collect data from each energy device to obtain device data, and collect data from each sensor to obtain real-time energy data;

[0044] Associating the device data with the energy device identification matched by the energy device to associate the energy real-time data with the sensor identification matched by the sensor;

[0045] Through energy equipment identification and sensor identification, the associated equipment data and energy real-time data are managed.

[0046] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0047] generating an energy device identifier that matches each energy device, and a sensor identifier that matches each sensor;

[0048] Collect data from each energy device to obtain device data, and collect data from each sensor to obtain real-time energy data;

[0049] Associating the device data with the energy device identification matched by the energy device to associate the energy real-time data with the sensor identification matched by the sensor;

[0050] Through energy equipment identification and sensor identification, the associated equipment data and energy real-time data are managed.

[0051] The above-mentioned energy aggregation management method, system, computer device, readable storage medium and program product generate energy device identifiers that match each energy device and sensor identifiers that match each sensor, then collect data from each energy device to obtain device data, and collect data from each sensor to obtain energy real-time data, and associate the device data with the energy device identifier that matches the energy device, so as to associate the energy real-time data with the sensor identifier that matches the sensor, thereby managing the associated device data and energy real-time data through the energy device identifier and the sensor identifier. By collecting real-time data from energy devices and sensors, real-time data collection and monitoring of the power grid are achieved to improve the stability and reliability of the power grid. And by providing unique identifiers for energy devices and sensors, efficient aggregation and sharing of real-time data across regions is achieved, thereby performing more reliable data transmission and processing in a stable and reliable power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0053] FIG1 is an application environment diagram of a method for energy aggregation management in one embodiment;

[0054] Figure 2 A schematic flow chart of a method for energy aggregation management in one embodiment;

[0055] Figure 3 It is a flowchart of the steps of collecting data from each energy device to obtain device data, and collecting data from each sensor to obtain real-time energy data in one embodiment;

[0056] Figure 4 A schematic flow chart of a method for energy aggregation management in another embodiment;

[0057] Figure 5 A schematic flow chart of a method for energy aggregation management in yet another embodiment;

[0058] Figure 6 A schematic diagram of a flow chart of a method for energy aggregation management in yet another embodiment;

[0059] Figure 7 A schematic diagram of a flow chart of a method for energy aggregation management in another embodiment;

[0060] Figure 8 It is a flowchart of a method for processing energy aggregation management in an energy aggregation management system in one embodiment;

[0061] Fig. 9 A connection block diagram of an energy aggregation management system in one embodiment;

[0062] FIG. 10 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0064] The energy aggregation management method provided in the embodiment of the present application can be applied in the application environment shown in FIG1 . The terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0065] Specifically, the energy aggregation management method provided in the embodiment of the present application is applied to server 104 as an example for explanation. Server 104 generates an energy device identifier that matches each energy device, and a sensor identifier that matches each sensor, and collects data from each energy device to obtain device data, and collects data from each sensor to obtain energy real-time data, and associates the device data with the energy device identifier that matches the energy device, so as to associate the energy real-time data with the sensor identifier that matches the sensor, and then manages the associated device data and energy real-time data through the energy device identifier and the sensor identifier.

[0066] Among them, terminal 102 is industrial equipment and product equipment in the industrial field. The aforementioned industrial equipment refers to machinery and devices used for industrial activities such as production, processing, transportation, storage or measurement. Therefore, industrial equipment can be large and complex system equipment, or small and special tools. Industrial equipment can be any of the following equipment, and is not limited to the following example equipment: production equipment, power equipment and conveying equipment. Production equipment is used to manufacture products, such as machine tools, injection molding machines, assembly lines, welding robots, etc. Power equipment is used to provide electricity and other types of electrical energy, such as generators, transformers, motors, etc. Conveying equipment is used for the handling and transportation of materials, such as conveyor belts, cranes, forklifts, etc. The energy equipment in the embodiment of the present application is the aforementioned power equipment for providing electricity and other types of electrical energy, such as power.

[0067] Secondly, product equipment refers to various equipment and devices related to electric energy. Product equipment can be any of the following equipment, and is not limited to the following examples: power generation equipment, energy storage equipment, smart meters and sensors. The aforementioned power generation equipment is a key equipment for energy production, such as wind turbines, solar photovoltaic panels, small gas turbines, etc. The aforementioned energy storage equipment is used to store and regulate energy supply, such as battery energy storage systems, pumped storage power stations, compressed air energy storage equipment, etc. The aforementioned smart meters: equipment used to monitor and record energy consumption in real time, such as smart meters, water meters, heat meters, etc. And sensors provide necessary data input for the energy management system, that is, sensors are devices used to measure parameters such as temperature, pressure, flow, etc.

[0068] In the embodiment of the present application, the server 104 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. In the embodiment of the present application, when the server 104 is a distributed system composed of multiple physical servers, the server 104 can constitute the energy aggregation management system provided in the embodiment of the present application.

[0069] In an exemplary embodiment, Figure 2 As shown, a method for energy aggregation management is provided. This embodiment uses the method applied to the server 104 as an example. It can be understood that the method can also include an energy aggregation management system of the terminal 102 and the server 104, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps 202 to 206. Among them:

[0070] Step 202: Generate an energy device identifier that matches each energy device, and a sensor identifier that matches each sensor.

[0071] The energy device identifier is an identifier used to uniquely identify the energy device, and similarly, the sensor identifier is an identifier used to uniquely identify the sensor. The aforementioned identifier may be a Universally Unique Identifier (UUID) or other identification codes, which are not limited here.

[0072] Specifically, before data collection, the server generates an energy device identifier that matches each energy device, and a sensor identifier that matches each sensor. The server stores and manages the energy device identifier and the sensor identifier through a distributed database system to ensure the consistency and scalability of the energy device identifier and the sensor identifier.

[0073] Optionally, the server may also store and manage the energy device identifier and the sensor identifier through blockchain technology to enhance the security and non-tamperability of the energy device identifier and the sensor identifier.

[0074] Step 204: collect data from each energy device to obtain device data, and collect data from each sensor to obtain real-time energy data.

[0075] Among them, equipment data is data used to support the management and optimization of energy equipment, and energy real-time data is data obtained by real-time collection of energy equipment by sensors. The aforementioned equipment data may include but is not limited to: basic information of energy equipment, technical parameters of energy equipment, installation configuration information of energy equipment, maintenance and repair records for energy equipment, and operation data of energy equipment. And energy real-time data includes but is not limited to temperature, pressure, flow, energy consumption, etc.

[0076] Secondly, the basic information of the energy equipment at least includes but is not limited to: the equipment model of the energy equipment, the manufacturer of the energy equipment, the production date of the energy equipment, and the serial number of the energy equipment. The technical parameters of the energy equipment at least include but are not limited to: the rated power of the energy equipment, the voltage level of the energy equipment, and the capacity of the energy equipment. The installation configuration information of the energy equipment at least includes but is not limited to: the installation location of the energy equipment, the configuration parameters of the energy equipment, the connection method of the energy equipment, etc. The maintenance and repair records for the energy equipment at least include but are not limited to: the maintenance history of the energy equipment, the maintenance records, the replacement parts information of the energy equipment, etc. And the operation data of the energy equipment may include but is not limited to the real-time operation data of the energy equipment or the historical operation data of the energy equipment. The operation data may be the energy consumption of the energy equipment, the efficiency of the energy equipment and the status indication of the energy equipment, etc., which are generated by the energy equipment during operation.

[0077] Specifically, the server collects equipment data for each energy device, that is, obtains the operating data of the energy equipment through real-time data collection and historical data acquisition, and performs multi-dimensional equipment data collection on the basic information, technical parameters, installation configuration information and maintenance and repair records of each energy device, thereby obtaining the equipment data of each energy device.

[0078] Secondly, the server collects real-time data for each sensor, and can obtain real-time energy data for each sensor for energy equipment. For example, the real-time operation data of wind turbines can be collected in real time through sensors and smart meters. The real-time operation data of wind turbines includes power output, wind speed, temperature, etc.

[0079] Step 206 , associating the device data with the energy device identifier that matches the energy device, so as to associate the energy real-time data with the sensor identifier that matches the sensor.

[0080] Specifically, for each energy device, the server associates the energy device identifier matched to the energy device with the device data obtained by performing data collection on the energy device. For example, energy device identifier B1 is configured for energy device A1, energy device identifier B2 is configured for energy device A2, and energy device identifier B3 is configured for energy device A3. If data collection is performed on energy device A1 to obtain device data C1, data collection is performed on energy device A2 to obtain device data C2, and data collection is performed on energy device A3 to obtain device data C3, then the energy device identifier B1 can be associated with the device data C1, the energy device identifier B2 can be associated with the device data C2, and the energy device identifier B3 can be associated with the device data C3.

[0081] Similarly, for each sensor, the server associates the sensor identifier matched to the sensor and the real-time energy data obtained by collecting data from the sensor. The specific association method is similar to the above and will not be repeated here.

[0082] Optionally, the server stores and manages the association relationship between the data and the identifier through a distributed database system to ensure the consistency and scalability of the association relationship between the data and the identifier.

[0083] Optionally, the server may also store and manage the association between the aforementioned data and the identification through blockchain technology to enhance the security and non-tamperability of the association between the aforementioned data and the identification.

[0084] Step 208: managing the associated equipment data and energy real-time data through the energy equipment identification and sensor identification.

[0085] Specifically, since in the aforementioned steps the device data is associated with the energy device identifier that matches the energy device, so as to associate the energy real-time data with the sensor identifier that matches the sensor, the device data and energy real-time data respectively associated with the energy device identifier and the sensor identifier can be determined based on the aforementioned association relationship through the energy device identifier and the sensor identifier, thereby managing the associated device data and energy real-time data.

[0086] Optionally, the management of the associated equipment data and energy real-time data may include data analysis, status monitoring, maintenance scheduling, fault warning and report generation. The aforementioned data analysis may be: combining the equipment data of the energy equipment, using a data analysis tool to conduct an in-depth analysis of the collected energy real-time data to obtain data analysis results, and the aforementioned data analysis results include but are not limited to: power output trend, efficiency analysis, fault prediction, etc.

[0087] Secondly, status monitoring can monitor the operating status of energy equipment in real time. When the operating status of the energy equipment determines that the energy equipment has an abnormality, such as: the power output of the energy equipment suddenly drops, or the temperature of the energy equipment is too high, an early warning can be issued for the abnormal energy equipment.

[0088] Maintenance scheduling can automatically generate maintenance plans and reminders for energy equipment based on the maintenance and repair records for energy equipment in the equipment data, ensuring that energy equipment can be maintained regularly and reducing the risk of failure of energy equipment.

[0089] Fault warning can use machine learning algorithms to analyze the real-time operation data and historical operation data of energy equipment in equipment data, predict possible fault problems of energy equipment, and issue warnings for energy equipment before the fault occurs so that timely measures can be taken.

[0090] Report generation can generate equipment operation reports and maintenance reports for energy equipment on a regular basis. The operation reports can include power output reports and efficiency reports, while the maintenance reports can include maintenance history, etc., so as to adjust the equipment operation mode based on the operation reports and maintenance reports.

[0091] It should be understood that the corresponding examples in the embodiments of the present application are used to understand the present solution, but should not be understood as specific limitations of the present solution.

[0092] In the above energy aggregation management method, an energy device identifier that matches each energy device and a sensor identifier that matches each sensor are generated, and then data is collected in each energy device to obtain device data, and data is collected in each sensor to obtain energy real-time data, and the device data is associated with the energy device identifier that matches the energy device, so as to associate the energy real-time data with the sensor identifier that matches the sensor, thereby managing the associated device data and energy real-time data through the energy device identifier and the sensor identifier. By collecting real-time data from energy devices and sensors, real-time data collection and monitoring of the power grid are achieved to improve the stability and reliability of the power grid. And by providing unique identifiers for energy devices and sensors, efficient aggregation and sharing of real-time data across regions is achieved, so that more reliable data transmission and processing can be performed in a stable and reliable power grid.

[0093] In an exemplary embodiment, Figure 3 As shown, step 204 includes steps 302 to 308 .

[0094] in:

[0095] Data collection is performed on each energy device to obtain real-time data of the device, including:

[0096] Step 302: collect data from each energy device to obtain initial device data.

[0097] Specifically, the server collects equipment data for each energy device, that is, obtains the operation data of the energy device through real-time data collection and historical data acquisition, and collects multi-dimensional equipment data for each energy device's basic information, technical parameters, installation configuration information, and maintenance and repair records, thereby obtaining the initial equipment data of each energy device. The specific collection method is similar to that described in the previous embodiment and will not be repeated here.

[0098] Step 304: Use edge computing technology to process the initial device data to obtain real-time device data.

[0099] Among them, edge computing technology includes at least one of the following: data filtering technology, real-time data analysis technology, data compression technology, and security processing technology. And edge computing technology can also include local decision-making technology. Specifically, the server uses edge computing technology to process the initial device data to obtain real-time device data.

[0100] Since edge computing technology is a distributed computing paradigm, it processes computing, storage, and network services close to the data source rather than in a remote data center or cloud server. Edge computing technology can be used to process the collected data after data collection, which can reduce the delay of data transmission.

[0101] The following is a detailed introduction to data filtering technology, real-time data analysis technology, data compression technology, security processing technology, and local decision-making technology in edge computing technology:

[0102] 1. Data filtering technology.

[0103] Data filtering technology includes at least denoising technology and aggregation technology. Among them, denoising technology takes into account that the collected initial device data may contain noise. At this time, an algorithm can be applied to remove the noise in the initial device data, and only the denoised initial device data is cleaned. Aggregation technology can aggregate continuous data points, such as calculating the average or sum of different data in the initial device data over a period of time, to reduce the amount of data transmitted.

[0104] 2. Real-time data analysis technology.

[0105] Real-time data analysis technology includes at least anomaly detection technology and predictive analysis technology. Among them, anomaly detection technology can detect abnormal conditions in real time for energy equipment, such as excessive temperature of energy equipment or abnormal speed of energy equipment, so as to immediately handle or send an alarm. Predictive analysis technology can use machine learning models to predict the performance or failure of energy equipment and perform maintenance or adjustments on energy equipment in advance.

[0106] 3. Data compression technology.

[0107] Data compression technology includes at least format conversion technology and compression algorithm. Among them, format conversion technology can convert the initial device data into a more compact format to reduce the size of the transmitted data. The compression algorithm is to apply data compression technology to reduce the size of the initial device data and speed up the transmission speed. It can be seen that through data filtering and compression, the amount of data to be transmitted is reduced, thereby reducing the transmission time.

[0108] 4. Safe handling technology.

[0109] Security processing technology includes at least encryption technology and access control technology. Among them, encryption technology can encrypt the initial device data before transmission to ensure the security of data transmission. Access control technology can ensure that only authorized entities can access the initial device data by implementing access control.

[0110] 5. Local decision-making technology.

[0111] Local decision-making technologies include at least automated control and response optimization. Automated control can make control decisions directly locally based on the results of edge computing, such as adjusting the operating parameters of energy equipment. Response optimization can quickly respond to environmental changes or user interactions without waiting for system instructions.

[0112] Based on this, data is collected from each sensor to obtain real-time energy data, including:

[0113] Step 306: collect data from each sensor to obtain initial energy real-time data.

[0114] Specifically, the server collects real-time data for each sensor to obtain real-time energy data for each energy device. For example, the real-time operation data of the wind turbine is collected in real time through sensors and smart meters. The real-time operation data of the wind turbine includes power output, wind speed, temperature, etc. The specific collection method is similar to that described in the previous embodiment and will not be repeated here.

[0115] Step 308: Use edge computing technology to process the initial energy real-time data to obtain energy real-time data.

[0116] Specifically, edge computing technology is used to process the initial energy real-time data to obtain energy real-time data. This is similar to the method described in the method of using edge computing technology to process the initial device data, and will not be repeated here.

[0117] In this embodiment, by filtering and compressing data, the amount of data to be transmitted is reduced, thereby reducing the transmission time of data transmission. Secondly, since data processing occurs after data collection, the distance that data is transmitted between multiple servers is reduced, further reducing the transmission time of data transmission, thereby improving the efficiency of data processing. Since the data processing burden of the data processing center is reduced through data processing, the data processing center can handle other tasks more efficiently. In addition, encryption and security processing are performed before data transmission, reducing the risk of data being intercepted or tampered with during transmission, thereby ensuring the reliability and security of data processing.

[0118] In an exemplary embodiment, Figure 4 As shown, the method for energy aggregation management also includes steps 402 to 404. Among them:

[0119] Step 402, monitoring the operating status of each energy device.

[0120] Specifically, the server monitors each energy device by developing a user-friendly monitoring dashboard and integrating real-time data analysis and report generation tools to obtain the respective operating status of each energy device.

[0121] Step 404: Perform real-time analysis of energy consumption for each operating state to obtain energy analysis results.

[0122] Among them, the energy analysis results are used to provide maintenance strategies for energy equipment and fault warnings for energy equipment; the energy analysis results are obtained by using machine learning algorithms to predict and optimize energy consumption for each operating state. Specifically, that is, through the aforementioned monitoring instrument integrated with real-time data analysis and report generation tools, the real-time data analysis tool is used to perform real-time analysis of energy consumption for each operating state, and the energy analysis results are obtained through the report generation tool. The real-time data analysis tool can use machine learning algorithms to predict and optimize energy consumption for each operating state, and determine the obtained energy consumption prediction results and energy consumption optimization results as energy analysis results.

[0123] In this embodiment, by monitoring the operating status of energy equipment, analyzing energy consumption in real time, and providing equipment maintenance and fault warnings, the energy equipment can be repaired when problems occur, that is, the energy equipment can be maintained or adjusted in advance to ensure the normal operation of the energy equipment, thereby improving the reliability of data collection and processing.

[0124] In an exemplary embodiment, Figure 5 As shown, the method for energy aggregation management also includes step 502. Among them:

[0125] Step 502 , displaying a user interaction interface for providing interactive display functions for users to query energy usage data, receive system notifications, and participate in energy management decision-making.

[0126] Among them, the user interaction interface supports multi-platform interface access, and allows third-party service integration through the configured application programming interface, and the multi-platform includes at least a web platform and a mobile device. Specifically, the developer can use the user interaction interface to display the corresponding interactive display function according to the user's query needs, so that the user can query energy usage data, receive system notifications, and participate in energy management decisions through the user interaction interface. And the developer specifically involves a more intuitive and clear user interaction interface, and the user interaction interface can support multi-platform interface access on a web platform (Web) and a mobile device. The aforementioned multi-platform interface access adopts the design and development of applications or websites that can adapt to different devices and operating systems. The following is an introduction to the technology for implementing multi-platform interface access:

[0127] 1. Responsive web design: Use fluid grid layouts and scalable images to ensure that web pages can automatically resize to fit different screen sizes.

[0128] 2. Development based on cross-platform frameworks: Using frameworks such as React Native, Flutter or Xamarin, you can write code once and then run it on multiple platforms, including iOS, Android, Web, etc.

[0129] In this embodiment, by providing a user interaction interface, users can query energy usage data, receive system notifications, and participate in energy management decisions, so that users can manage energy according to their actual needs. They can also understand the energy situation more intuitively and accurately through the user interaction interface, making the management and push of energy data more efficient and accurate.

[0130] In an exemplary embodiment, Figure 6 As shown, the method for energy aggregation management also includes steps 602 to 604. Among them:

[0131] Step 602, determining a grid operator system from among the existing grid operator systems by performing access assessment on each existing grid operator system; wherein the access assessment includes at least compatibility assessment, security assessment, performance assessment, and compliance assessment.

[0132] Among them, access assessment includes at least compatibility assessment, security assessment, performance assessment and compliance assessment. The following is an introduction to the above access assessment technologies:

[0133] 1. Compatibility assessment: Assess the compatibility of existing grid operator systems to ensure seamless integration with the integrated energy management system.

[0134] 2. Security assessment: Ensure that data sharing and operational collaboration are carried out under the premise of security, and evaluate the security of data transmission and storage.

[0135] 3. Performance evaluation: Evaluate the processing capacity and response time of the operator's system to ensure that system performance is not affected by data sharing.

[0136] 4. Compliance assessment: Ensure that data sharing and operational collaboration comply with relevant laws, regulations and industry standards.

[0137] Specifically, by conducting compatibility assessment, security assessment, performance assessment and compliance assessment on each existing grid operator system, a grid operator system is determined from the existing grid operator systems based on the obtained assessment results. That is, the determined grid operator system can be seamlessly connected with the integrated energy management system, and the data sharing and operation coordination between the systems are carried out under the premise of safety, and the system performance will not be affected by data sharing, and it complies with relevant laws, regulations and industry standards.

[0138] Step 604, connecting with the system interface of the power grid operator system to realize data sharing and operation coordination with the power grid operator system; wherein, the data shared with the power grid operator system is shared and coordinated after data sharing priority evaluation.

[0139] Among them, the data shared with the power grid operator system is shared and coordinated after data sharing priority assessment, and the data sharing priority assessment at least includes data sensitivity assessment, data importance assessment, data update frequency assessment and data access control assessment. The technology of data sharing priority assessment is introduced below: Data sensitivity assessment: used to determine which data is sensitive and requires a higher level of protection. Data importance assessment: used to determine the priority of data sharing based on the impact of data on business operations. Data update frequency assessment: used to determine the frequency and time point of data synchronization based on the frequency of data updates. Data access control assessment: used to evaluate the access rights of different operators and users to data to ensure the reasonable sharing of data.

[0140] Specifically, after the server determines the grid operator system from the existing grid operator systems, it needs to share data and coordinate operations, that is, it needs to connect with the system interface of the grid operator system to achieve data sharing and operation coordination with the grid operator system. The following introduces the technical methods for achieving the system interface connection with the grid operator system:

[0141] 1. Interface development: Develop interfaces compatible with grid operator systems, including APIs, database connections, etc.

[0142] 2. Data mapping: Map the data structure of the integrated energy management system with the data structure of the grid operator system to ensure data consistency and accuracy.

[0143] 3. Data synchronization mechanism: Implement data synchronization mechanism, including real-time synchronization, scheduled synchronization, etc., to ensure timely update of data.

[0144] 4. Security measures: Implement security measures such as data encryption, access control, and audit logs to protect data security.

[0145] 5. Testing and verification: Conduct testing and verification of system docking to ensure the accuracy and stability of data sharing and operational collaboration.

[0146] 6. Monitoring and maintenance: Establish a monitoring mechanism to monitor the status and performance of system docking in real time and deal with possible problems in a timely manner.

[0147] In this embodiment, the reliability of the access operator is guaranteed through compatibility assessment, security assessment, performance assessment and compliance assessment, and the data sharing priority is evaluated during the process of data sharing and operation coordination to ensure that data interaction between systems can be more efficient and accurate, thereby improving the efficiency and reliability of energy data management.

[0148] In an exemplary embodiment, Figure 7 As shown, each energy device has a different energy storage type, and the energy storage type includes at least wind power, solar energy, and energy storage, etc. Therefore, the energy devices of the corresponding energy storage type are specifically wind turbines, solar photovoltaic panels, and energy storage systems.

[0149] Based on this, the energy aggregation management method further includes steps 702 to 706. Among them:

[0150] Step 702, collect information for energy devices of different energy storage types to obtain performance data and operating status of each energy device. Specifically, collect real-time information for energy devices of different energy storage types to collect performance data and operating status of energy devices of different energy storage types in real time.

[0151] Step 704 , predictively analyze the performance data and operating status of each energy device to obtain a status analysis result of each energy device.

[0152] Specifically, energy complementation and optimization are carried out through the performance data and operating status obtained through real-time information collection. The main thing is to conduct predictive analysis on the performance data and operating status of each energy device to obtain the status analysis results of each energy device. The aforementioned status analysis results are used for intelligent scheduling.

[0153] Step 706: Adjust the operating parameters of each energy device based on the status analysis results.

[0154] Specifically, according to the real-time changes in energy supply and demand, the operating parameters of each energy device are adjusted based on the results of each state analysis to achieve the optimal allocation and utilization of energy, while predicting potential energy demand and supply fluctuations, optimizing energy production and consumption, reducing waste, and improving overall energy efficiency. In addition, the platform can also perform fault warnings and preventive maintenance to ensure stable operation of equipment and further enhance the reliability and efficiency of the energy system.

[0155] In this embodiment, by collecting and analyzing information for energy devices of different energy storage types respectively, and adjusting the operating parameters of each energy device based on the analysis results of each state, the optimal allocation and utilization of energy is achieved, so as to optimize energy production and consumption, reduce waste, and improve overall energy efficiency.

[0156] The energy aggregation management system provided in the embodiments of the present application is described in detail below. Figure 8 As shown, in this embodiment, the modules in the energy aggregation management system perform energy aggregation management specifically through the following steps 802 to 810. Among them:

[0157] Step 802: The identification registration and management module is used to generate an energy device identifier that matches each energy device and a sensor identifier that matches each sensor.

[0158] Specifically, before data collection, the identification registration and management module generates an energy device identifier that matches each energy device, and a sensor identifier that matches each sensor. The identification registration and management module specifically stores and manages the energy device identifier and the sensor identifier through a distributed database system to ensure the consistency and scalability of the energy device identifier and the sensor identifier.

[0159] Optionally, the identification registration and management module can also store and manage the energy device identifier and the sensor identifier through blockchain technology to enhance the security and non-tamperability of the energy device identifier and the sensor identifier.

[0160] Step 804: a data collection module is used to collect data from each energy device to obtain real-time data of the device, and to collect data from each sensor to obtain real-time energy data.

[0161] Specifically, the data acquisition module collects equipment data for each energy device, that is, obtains the operating data of the energy equipment through real-time data collection and historical data acquisition, and performs multi-dimensional equipment data collection on the basic information, technical parameters, installation configuration information and maintenance and repair records of each energy device, so as to obtain the equipment data of each energy device.

[0162] Secondly, the data collection module collects real-time data for each sensor, and can obtain real-time energy data for each sensor for energy equipment. For example, the real-time operation data of wind turbines is collected in real time through sensors and smart meters. The real-time operation data of wind turbines includes power output, wind speed, temperature, etc.

[0163] Optionally, the data acquisition module can also collect data from each energy device to obtain initial device data, and then use edge computing technology to process the initial device data to obtain real-time device data. Since edge computing technology is a distributed computing paradigm, edge computing technology processes computing, storage, and network services close to the data source, rather than in a remote data center or cloud server. Edge computing technology can be used to process the collected data after data collection, which can reduce the delay in data transmission. The detailed introduction of data filtering technology, real-time data analysis technology, data compression technology, security processing technology, and local decision-making technology in edge computing technology is similar to the aforementioned embodiment and will not be repeated here.

[0164] Similarly, the data acquisition module can also perform real-time data collection for each sensor, so as to obtain the real-time energy data of each sensor for the energy equipment, and then use the edge computing technology to process the initial real-time energy data to obtain the real-time energy data.

[0165] Step 806, the identification registration and management module is further used to associate the device real-time data with the energy device identification that matches the energy device, so as to associate the energy real-time data with the sensor identification that matches the sensor.

[0166] Specifically, the identification registration and management module also associates, for each energy device, the energy device identification matched to the energy device and the device data obtained by collecting data from the energy device. Similarly, for each sensor, the identification registration and management module associates, for each sensor, the sensor identification matched to the sensor and the energy real-time data obtained by collecting data from the sensor. The specific association method is similar to the above and will not be repeated here.

[0167] Optionally, the identification registration and management module stores and manages the association relationship between data and identification through a distributed database system to ensure the consistency and scalability of the association relationship between data and identification.

[0168] Optionally, the identification registration and management module can also store and manage the association relationship between the aforementioned data and the identification through blockchain technology to enhance the security and non-tamperability of the association relationship between the aforementioned data and the identification.

[0169] Step 808: a data transmission module is used to transmit the real-time data of the equipment and the real-time data of the energy to a data processing center.

[0170] Specifically, through the data transmission module, the real-time data of the equipment and the real-time data of the energy are transmitted to the data processing center, so as to ensure the security and efficiency of the data transmission between the data acquisition module and the data processing center. And the data transmission module specifically uses the Internet of Things protocols such as the Message Queuing Telemetry Transport Protocol (MQTT) and the Constrained Application Protocol (CoAP) as communication protocols, and uses encryption technologies such as the Transport Layer Security (TLS) / Secure Socket Layer (SSL) to ensure the security and integrity of the data transmission between the data transmission module and the data processing center. That is, the data transmission module encrypts the real-time data of the equipment and the real-time data of the energy through encryption technologies such as TLS / SSL, and then transmits the encrypted real-time data of the equipment and the real-time data of the energy to the data processing center.

[0171] Optionally, the data transmission module can also improve the transmission efficiency by data compression, that is, the data transmission module compresses the real-time data of the equipment and the real-time data of the energy, and then transmits the compressed real-time data of the equipment and the real-time data of the energy to the data processing center.

[0172] Optionally, the data transmission module can also improve the transmission efficiency by optimizing the transmission path. The following is a detailed description of the implementation method of optimizing the transmission path:

[0173] 1. Routing algorithm optimization: Routing algorithms include shortest path algorithms and load balancing. The shortest path algorithm uses algorithms such as Dijkstra or Bellman-Ford to find the shortest path from source to destination to reduce transmission delay. Load balancing dynamically adjusts routing to distribute data traffic to multiple paths to avoid overloading a single path.

[0174] 2. Use multi-path transmission: Multi-path transmission includes path diversity and link aggregation. Path diversity requires designing multiple paths in the network. When a path has a problem, it can automatically switch to another path to ensure the continuity of data transmission. Link aggregation is to bundle multiple physical links into one logical link to increase bandwidth and provide redundancy, thereby improving transmission reliability and speed.

[0175] 3. Network topology optimization: Network topology optimization includes simplifying the network structure and optimizing the network layout. Simplifying the network structure reduces the number of hops and complexity in the network, directly connects key nodes, and reduces the data transmission time in the network. Optimizing the network layout requires reasonable layout of network nodes and links according to data traffic and business needs to reduce the distance and delay of data transmission.

[0176] Step 810: The data processing center is used to manage the associated equipment real-time data and energy real-time data through the energy equipment identification and sensor identification.

[0177] Specifically, the data processing center can determine the device data and energy real-time data respectively associated with the energy device identification and the sensor identification based on the aforementioned association relationship through the energy device identification and the sensor identification, thereby managing the associated device data and energy real-time data.

[0178] Optionally, the management of the associated equipment data and energy real-time data may include data analysis, status monitoring, maintenance scheduling, fault warning and report generation. The aforementioned data analysis may be: combining the equipment data of the energy equipment, using a data analysis tool to conduct an in-depth analysis of the collected energy real-time data to obtain data analysis results, and the aforementioned data analysis results include but are not limited to: power output trend, efficiency analysis, fault prediction, etc.

[0179] Secondly, status monitoring can monitor the operating status of energy equipment in real time. When the operating status of the energy equipment determines that the energy equipment has an abnormality, such as: the power output of the energy equipment suddenly drops, or the temperature of the energy equipment is too high, an early warning can be issued for the abnormal energy equipment.

[0180] Maintenance scheduling can automatically generate maintenance plans and reminders for energy equipment based on the maintenance and repair records for energy equipment in the equipment data, ensuring that energy equipment can be maintained regularly and reducing the risk of failure of energy equipment.

[0181] Fault warning can use machine learning algorithms to analyze the real-time operation data and historical operation data of energy equipment in equipment data, predict possible fault problems of energy equipment, and issue warnings for energy equipment before the fault occurs so that timely measures can be taken.

[0182] Report generation can generate equipment operation reports and maintenance reports for energy equipment on a regular basis. The operation reports can include power output reports and efficiency reports, while the maintenance reports can include maintenance history, etc., so as to adjust the equipment operation mode based on the operation reports and maintenance reports.

[0183] In an optional embodiment, the energy aggregation management system further includes an energy monitoring and management module. The energy monitoring and management module is used to monitor the operating status of each energy device and perform real-time analysis of energy consumption for each operating status to obtain energy analysis results.

[0184] That is, the energy monitoring and management module develops a user-friendly monitoring dashboard and integrates real-time data analysis and report generation tools to monitor each energy device to obtain the operating status of each energy device. Then, the real-time data analysis and report generation tools are integrated through the aforementioned monitoring instrument, and the real-time data analysis tool is used to perform real-time analysis of energy consumption for each operating state, and the energy analysis results are obtained through the report generation tool. The real-time data analysis tool can use machine learning algorithms to predict and optimize energy consumption for each operating state, and determine the obtained energy consumption prediction results and energy consumption optimization results as energy analysis results.

[0185] In an optional embodiment, the energy aggregation management system further includes a user interaction and service module. The user interaction and service module is used to display a user interaction interface for providing interactive display functions for users to query energy usage data, receive system notifications, and participate in energy management decisions.

[0186] That is, the user interaction and service module is a user interaction interface that developers use to display corresponding interactive functions according to user query needs, so that users can query energy usage data, receive system notifications, and participate in energy management decisions through the user interaction interface. And the developer specifically involves a more intuitive and clear user interaction interface, and the user interaction interface can support multi-platform interface access on web platforms (Web) and mobile devices. The aforementioned multi-platform interface access adopts the design and development of applications or websites that can adapt to different devices and operating systems.

[0187] In an optional embodiment, the energy aggregation management system also includes a smart grid integration module. The smart grid integration module is used to determine the grid operator system from the existing grid operator systems by performing access assessment on each existing grid operator system; wherein the access assessment includes at least compatibility assessment, security assessment, performance assessment and compliance assessment. Then, the smart grid integration module is also used to connect with the system interface of the grid operator system to achieve data sharing and operational coordination with the grid operator system; wherein, the data shared with the grid operator system is shared and coordinated after data sharing priority assessment.

[0188] That is, the smart grid integration module conducts compatibility assessment, security assessment, performance assessment and compliance assessment on each existing grid operator system, and determines the grid operator system from the existing grid operator systems based on the obtained assessment results. That is, the determined grid operator system can be seamlessly connected with the integrated energy management system, and the data sharing and operation coordination between the systems are carried out under the premise of safety, and the system performance will not be affected by data sharing, and it complies with relevant laws, regulations and industry standards. Based on this, after the smart grid integration module determines the grid operator system from the existing grid operator systems, it needs to carry out data sharing and operation coordination, that is, it needs to be connected with the system interface of the grid operator system to realize data sharing and operation coordination with the grid operator system.

[0189] In an optional embodiment, the energy aggregation management system further includes an integrated energy station management module. The integrated energy station management module is used to collect information on energy devices of different energy storage types, obtain performance data and operating status of each energy device, perform predictive analysis on the performance data and operating status of each energy device, and obtain status analysis results of each energy device; and adjust the operating parameters of each energy device based on the status analysis results.

[0190] That is, the integrated energy station management module collects real-time information for energy devices of different energy storage types, so as to collect the performance data and operating status of energy devices of different energy storage types in real time. Based on this, the integrated energy station management module uses the performance data and operating status obtained through real-time information collection to carry out energy complementation and optimization, mainly to predict and analyze the performance data and operating status of each energy device, and obtain the status analysis results of each energy device, and the aforementioned status analysis results are used for intelligent scheduling. Therefore, the integrated energy station management module can adjust the operating parameters of each energy device based on the results of each status analysis according to the real-time changes in energy supply and demand, realize the optimal allocation and utilization of energy, and predict potential energy demand and supply fluctuations, optimize energy production and consumption, reduce waste, and improve overall energy efficiency. In addition, the platform can also perform fault warning and preventive maintenance to ensure the stable operation of equipment and further enhance the reliability and efficiency of the energy system.

[0191] It should be understood that the corresponding examples in the embodiments of the present application are used to understand the present solution, but should not be understood as specific limitations of the present solution.

[0192] In the above energy aggregation management system, real-time data collection and monitoring of the power grid are realized by real-time data collection of energy equipment and sensors to improve the stability and reliability of the power grid. And by providing unique identifiers for energy equipment and sensors, efficient aggregation and sharing of real-time data across regions are realized, so as to achieve more reliable data transmission and processing in a stable and reliable power grid.

[0193] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0194] Based on the same inventive concept, the embodiment of the present application also provides an energy aggregation management system for implementing the energy aggregation management method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more energy aggregation management system embodiments provided below can refer to the limitations of the energy aggregation management method above, and will not be repeated here.

[0195] In an exemplary embodiment, Fig. 9 As shown, an energy aggregation management system is provided, including: an identification registration and management module 902, a data acquisition module 904, a data transmission module 906 and a data processing center 908, wherein:

[0196] The identification registration and management module 902 is used to generate an energy device identifier that matches each energy device, and a sensor identifier that matches each sensor;

[0197] The data collection module 904 is used to collect data from each energy device to obtain real-time data of the device, and collect data from each sensor to obtain real-time data of energy;

[0198] The identification registration and management module 902 is further used to associate the device real-time data with the energy device identification that matches the energy device, so as to associate the energy real-time data with the sensor identification that matches the sensor;

[0199] Data transmission module 906, used to transmit the real-time data of equipment and real-time data of energy to the data processing center;

[0200] The data processing center 908 is used to manage the associated equipment real-time data and energy real-time data through energy equipment identification and sensor identification.

[0201] In one embodiment, the data acquisition module is specifically used to collect data from each energy device to obtain initial device data; use edge computing technology to process the initial device data to obtain device real-time data; collect data from each sensor to obtain initial energy real-time data; use edge computing technology to process the initial energy real-time data to obtain energy real-time data; wherein the edge computing technology includes at least one of the following: data filtering technology, real-time data analysis technology, data compression technology, and security processing technology.

[0202] In one embodiment, the energy aggregation management system further includes an energy monitoring and management module;

[0203] The energy monitoring and management module is used to monitor the operating status of each energy device; perform real-time analysis of energy consumption for each operating status to obtain energy analysis results; the energy analysis results are used to provide maintenance strategies for energy equipment and fault warnings for energy equipment; the energy analysis results are obtained by using machine learning algorithms to predict and optimize energy consumption for each operating status.

[0204] In one embodiment, the energy aggregation management system further includes a user interaction and service module;

[0205] The user interaction and service module is used to display a user interaction interface for providing interactive display functions for users to query energy usage data, receive system notifications, and participate in energy management decisions; wherein the user interaction interface supports interface access on multiple platforms and allows third-party service integration through the configured application programming interface, and the multiple platforms include at least web platforms and mobile devices.

[0206] In one embodiment, the energy aggregation management system further includes a smart grid integration module;

[0207] A smart grid integration module is used to determine a grid operator system from among the existing grid operator systems by performing access assessment on each existing grid operator system; wherein the access assessment includes at least compatibility assessment, security assessment, performance assessment and compliance assessment; and to connect with the system interface of the grid operator system to achieve data sharing and operational coordination with the grid operator system; wherein the data shared with the grid operator system is shared and coordinated after data sharing priority assessment.

[0208] In one embodiment, each energy device has a different energy device type;

[0209] The energy aggregation management system also includes an integrated energy station management module;

[0210] The integrated energy station management module is used to collect information on energy equipment of different types to obtain the performance data and operating status of each energy equipment; to perform predictive analysis on the performance data and operating status of each energy equipment to obtain the status analysis results of each energy equipment; and to adjust the operating parameters of each energy equipment based on the status analysis results.

[0211] Each module in the above energy aggregation management system can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0212] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG10. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to the embodiment of the present application, such as real-time device data and real-time energy data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for energy aggregation management is implemented.

[0213] Those skilled in the art will understand that the structure shown in FIG. 10 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0214] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0215] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0216] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0217] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0218] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0219] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0220] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for energy aggregation management, characterized in that: The method comprises: generating an energy device identifier that matches each energy device, and a sensor identifier that matches each sensor; Collecting data from each of the energy devices to obtain device data, and collecting data from each of the sensors to obtain real-time energy data; Associating the device data with an energy device identifier that matches the energy device, so as to associate the energy real-time data with a sensor identifier that matches the sensor; The associated equipment data and energy real-time data are managed through the energy equipment identification and the sensor identification.

2. The method according to claim 1, characterized in that The data acquisition for each energy device to obtain device data includes: Collecting data from each of the energy devices to obtain initial device data; Processing the initial device data using edge computing technology to obtain device data; The data collection for each sensor to obtain real-time energy data includes: Collect data from each of the sensors to obtain initial energy real-time data; Using edge computing technology to process the initial energy real-time data to obtain energy real-time data; Among them, the edge computing technology includes at least one of the following: data filtering technology, real-time data analysis technology, data compression technology and security processing technology.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: Monitoring the operating status of each of the energy devices; Real-time analysis of energy consumption is performed for each of the operating states to obtain energy analysis results; wherein the energy analysis results are used to provide maintenance strategies for the energy equipment and fault warnings for the energy equipment; the energy analysis results are obtained by using machine learning algorithms to predict and optimize energy consumption for each of the operating states.

4. The method according to claim 1 or 2, characterized in that: The method further comprises: Displaying a user interaction interface for providing interactive display functions for users to query energy usage data, users to receive system notifications, and users to participate in energy management decision-making; The user interaction interface supports interface access from multiple platforms and allows third-party service integration through a configured application programming interface, wherein the multiple platforms include at least a web platform and a mobile device.

5. The method according to claim 1 or 2, characterized in that: The method further comprises: Determine the grid operator system from the existing grid operator systems by performing access assessment on each existing grid operator system; wherein the access assessment includes at least compatibility assessment, security assessment, performance assessment and compliance assessment; Connecting to the system interface of the power grid operator system to achieve data sharing and operation coordination with the power grid operator system; wherein the data shared with the power grid operator system is shared and coordinated after data sharing priority evaluation.

6. The method according to claim 1 or 2, characterized in that: Each of the energy devices has a different energy storage type; The method further comprises: Collect information on energy devices of different energy storage types to obtain performance data and operating status of each energy device; Performing predictive analysis on the performance data and operating status of each of the energy devices to obtain status analysis results of each of the energy devices; The operating parameters of each of the energy devices are adjusted based on the status analysis results.

7. An energy aggregation management system, characterized in that: The system comprises: An identification registration and management module, used to generate an energy device identifier that matches each energy device, and a sensor identifier that matches each sensor; A data acquisition module, used to acquire data from each of the energy devices to obtain device data, and to acquire data from each of the sensors to obtain real-time energy data; The identification registration and management module is further used to associate the device data with the energy device identification matched by the energy device, so as to associate the energy real-time data with the sensor identification matched by the sensor; A data transmission module, used to transmit the device data and the energy real-time data to a data processing center; The data processing center is used to manage the associated equipment data and energy real-time data through the energy equipment identification and the sensor identification.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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