Green smart energy application management service platform

Abstract

The invention relates to the field of energy utilization, in particular to a green smart energy application management service platform which comprises a data acquisition module, a data storage module, a data service module, a data mining module, a smart control module and a visual management module. The data acquisition module is used for acquiring initial data of the energy industry chain early warning system; the data service module is used for carrying out classified storage on the initial data to obtain result information and carrying out visualization processing on the result information; the data mining module is used for matching a preset data mining component according to the result information; the data acquisition module comprises a sensor and a detection device; real-time data acquisition and recording are carried out on the green smart energy through a sensor and a monitoring device; the system has the functions of real-time monitoring, data analysis, decision support and operation optimization, and provides a scientific and efficient solution for management and decision in the field of renewable energy sources.

Description

Technical Field

[0001] The present invention relates to the field of energy utilization, and specifically to a green intelligent energy application management service platform. Background Art

[0002] Existing green intelligent energy lacks a good service platform in application, resulting in poor connection between users and merchants, inability to upgrade services, and being unfavorable for energy consumption savings.

[0003] Therefore, a green intelligent energy application management service platform is proposed to solve the above problems. Summary of the Invention

[0004] In view of this, embodiments of the present invention hope to provide a green intelligent energy application management service platform to solve or alleviate the technical problems existing in the prior art, and provide at least one beneficial option for the above technical problems.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A green intelligent energy application management service platform, the service platform includes:

[0007] A data acquisition module, a data storage module, a data service module, a data mining module, an intelligent control module, and a visualization management module;

[0008] The data acquisition module is used to obtain the initial data of the energy industry chain warning system;

[0009] The data service module is used to classify and store the initial data to obtain result information, and perform visualization processing on the result information;

[0010] The data mining module is used to match a preset data mining component according to the result information.

[0011] As a further aspect of the present invention: The data acquisition module includes sensors and detection devices; real-time data of green intelligent energy is collected and recorded through sensors and monitoring devices; in the following aspects: water flow and water level, for hydropower stations, parameters such as water flow velocity and water level height are collected to realize the monitoring and optimization of hydropower generation;

[0012] Tides and ocean waves, for tidal power generation and wave power generation, parameters such as tides, ocean wave height and frequency are collected to realize the monitoring and optimization of tidal and wave power generation;

[0013] Solar radiation, for solar power generation, parameters such as solar radiation and solar altitude angle are collected to realize the monitoring and optimization of solar power generation;

[0014] Wind power. For wind power generation, real-time data parameters such as wind speed, wind force, and frequency are collected to monitor, manage, and optimize power generation.

[0015] As a further solution of the present invention: The data obtained by the data acquisition module is uploaded and saved through the data storage module. At the same time, a cloud database is established, and a corresponding data model is established;

[0016] Data loss can be prevented, and at the same time, data comparison and analysis are facilitated;

[0017] The data is de-duplicated and compressed to reduce data storage space, improve data transmission efficiency, and improve data quality and accuracy.

[0018] As a further solution of the present invention: The data mining module performs big data comparison processing and analysis on the data uploaded to the cloud database. Through data mining technology, potential and valuable information is extracted from the data, and future renewable energy power generation, energy consumption, and energy acquisition are predicted based on the data, etc., to provide certain data reference and support for decision-making solutions;

[0019] Calculate and plan the potential problems and optimization solutions of green and intelligent energy.

[0020] As a further solution of the present invention: The solution obtained by the data analysis module is efficiently executed and implemented through the intelligent control module; it is divided into a scheduling system, a monitoring system, and a maintenance system;

[0021] Through the scheduling system, targeted intelligent real-time scheduling is performed on energy demand and energy supply situations, and the energy supply and power generation of renewable energy are adjusted in real time to improve energy utilization efficiency;

[0022] Through the monitoring system, renewable energy is monitored, allocated, and warned, energy waste is reduced, remote monitoring of renewable energy facilities is realized, the efficiency and convenience of monitoring are improved, and at the same time, the monitoring cost is reduced. At the same time, problems are discovered in time and rapid response and efficient processing are achieved;

[0023] Through the maintenance system, the operation of renewable energy facilities is maintained, including fault diagnosis and preventive maintenance, etc., to improve the reliability and availability of the facilities and reduce facility hidden dangers.

[0024] As a further solution of the present invention: The collected data is presented in the form of charts and graphics and texts through the visualization management module, which is convenient for management personnel to compare, consult, and make decisions;

[0025] The operation data of renewable energy is presented in a multi-dimensional form so that decision-makers can more comprehensively understand the operation situation;

[0026] And share it with experts, scholars and others in related fields to improve the accuracy and scientific nature of decision-making;

[0027] At the same time, it allows users to browse and analyze data independently, improving users' awareness and participation in renewable energy.

[0028] As a further solution of the present invention: the data service module includes a data fusion sub-module, and the data fusion sub-module includes: a decision support degree calculation unit, which is used to obtain corresponding dimension data according to the decision attributes of the initial data, and calculate the support degree value of the initial data for the early warning decision;

[0029] An OWA operator weight vector calculation unit, which is used to determine a measurement operator according to a preset fuzzy semantic quantization criterion;

[0030] A data conversion and sorting unit, which is used to perform data fusion according to the support degree value and the measurement operator to obtain a decision value.

[0031] As a further solution of the present invention: the data mining module includes a relevance analysis component, a time series analysis component and a representation learning component;

[0032] The relevance analysis component includes frequent early warning index combination mining and early warning cycle pattern mining;

[0033] The time series analysis component, which includes time series relationship mining and time series prediction;

[0034] The representation learning component, which includes feature extraction, feature learning and high-dimensional feature dimensionality reduction.

[0035] As a further solution of the present invention: it further includes a multi-energy device digital twin complex application dependency sorting strategy using depth-first search to construct a hierarchical set and a path set for planning and operation optimization applications;

[0036] According to the position of the recognition calculation task in the hierarchical set, use the multi-energy device digital twin calculation task scheduling and management strategy of the greedy algorithm to comprehensively measure its priority by identifying the position and weight of each calculation task;

[0037] Based on the priority of the calculation task, obtain the calling and execution order of each calculation task;

[0038] Call and execute the calculation task at the head of the scheduling list, cache its data result, and remove it from the scheduling list until the scheduling list is empty, clear the cached data, and complete the multi-energy system digital twin application management and scheduling.

[0039] As a further solution of the present invention: The relationships between the computing tasks include information interaction, sequential dependence, and logical triggering.

[0040] Due to the above technical solutions adopted in the embodiments of the present invention, it has at least one of the following advantages:

[0041] Real-time data collection and monitoring: Real-time data of parameters such as water flow, water level, tides, waves, solar radiation, and wind power of green and intelligent energy are collected and recorded through sensors and monitoring devices to achieve real-time monitoring and optimization of the energy generation process.

[0042] Data fusion and decision support: The data service module classifies and stores the initial data through the data fusion sub-module, and calculates the dimensional data and support degree values of the decision attributes to provide support and reference for decision-making solutions.

[0043] Data mining and prediction: The data mining module uses big data comparison processing and analysis techniques to extract valuable information from the data uploaded to the cloud database, and predicts future renewable energy power generation, energy consumption, energy acquisition, etc. based on the data to provide data support for decision-making solutions.

[0044] Data storage and management: The data storage module uploads, saves, and deduplicates and compresses the collected data, establishes a cloud database and constructs a corresponding data model to prevent data loss, facilitate data comparison and analysis, and improve data quality and accuracy.

[0045] Visualization display and user participation: The collected data is presented in the form of charts and graphics through the visualization management module, which is convenient for management personnel to consult and make decisions, and allows users to browse and analyze the data independently, improving users' awareness and participation in renewable energy.

[0046] Intelligent scheduling and monitoring system: The solutions obtained by the data analysis module are efficiently executed and implemented through the intelligent control module, including the scheduling system, monitoring system, and maintenance system, to achieve intelligent real-time scheduling of energy demand and energy supply conditions, reduce energy waste, and improve the reliability and availability of facilities.

[0047] Powerful data mining components: The data mining module includes components for correlation analysis, time series analysis, and representation learning, which can extract information about early warning index combinations, periodic patterns, time series relationships, etc. from the data to provide support for problem identification and optimization solutions of green and intelligent energy.

[0048] Digital twin technology for multi-energy devices: Applying depth-first search and greedy algorithms, hierarchical sets and path sets for planning and operation optimization applications are constructed, the priority of computing tasks is identified and scheduled for management to ensure the efficient execution and scheduling of digital twin applications of multi-energy systems.

[0049] In summary, the green intelligent energy application management service platform combines technologies such as sensor technology, data fusion analysis, big data mining, and intelligent scheduling, and has the functions of real-time monitoring, data analysis, decision support, and operation optimization, providing a scientific and efficient solution for the management and decision-making in the field of renewable energy.

[0050] The above summary is only for the purpose of the specification and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0052] Figure 1 It is a schematic structural diagram of a green intelligent energy application management service platform proposed by the present invention.

[0053] Figure 2 It is an implementation method of a green intelligent energy application management service platform proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] In the following, only some exemplary embodiments are briefly described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present invention. Therefore, the drawings and the description are considered to be exemplary in nature rather than restrictive.

[0055] It should be noted that terms such as "first", "second", "symmetric", "array", etc. are only used for the purpose of distinguishing descriptions and position descriptions, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first", "symmetric", etc. can explicitly or implicitly include one or more of such features; similarly, when certain features are not limited in quantity by words such as "two", "three", etc., it should be noted that such features also belong to explicitly or implicitly including one or more feature quantities.

[0056] As Figure 1 shown, for a green intelligent energy application management service platform of the present invention, the service platform includes:

[0057] Data acquisition module, data storage module, data service module, data mining module, intelligent control module and visualization management module.

[0058] The data acquisition module is used to obtain the initial data of the energy industry chain warning system;

[0059] The data acquisition module includes sensors and detection devices;

[0060] Real-time data collection and recording of green intelligent energy are carried out through sensors and monitoring devices;

[0061] Such as the following aspects: water flow and water level. For hydropower stations, parameters such as water flow velocity and water level height are collected to achieve the monitoring and optimization of hydropower generation;

[0062] Tides and ocean waves. For tidal power generation and wave power generation, parameters such as tides, ocean wave height and frequency are collected to achieve the monitoring and optimization of tidal and wave power generation;

[0063] Solar radiation. For solar power generation, parameters such as solar radiation and solar altitude angle are collected to achieve the monitoring and optimization of solar power generation;

[0064] Wind power. For wind power generation, real-time data parameters such as wind speed, wind force and frequency are collected to achieve the monitoring, management and optimization of power generation.

[0065] The data service module is used to classify and store the initial data to obtain result information and perform visualization processing on the result information;

[0066] The data service module includes a data fusion sub-module. The data fusion sub-module includes: a decision support degree calculation unit, which is used to obtain corresponding dimension data according to the decision attributes of the initial data and calculate the support degree value of the initial data for the warning decision;

[0067] The OWA operator weight vector calculation unit is used to determine the measurement operator according to the preset fuzzy semantic quantization criterion;

[0068] The data conversion and sorting unit is used to perform data fusion according to the support degree value and the measurement operator to obtain a decision value.

[0069] The data mining module is used to match preset data mining components according to the result information;

[0070] The data mining module processes and analyzes the data uploaded to the cloud database through big data comparison. Through data mining technology, it extracts potential and valuable information from the data, and predicts future renewable energy power generation, energy consumption, and energy acquisition based on the data, providing certain data reference and support for decision-making solutions;

[0071] Calculate and plan the potential problems and optimization solutions of green and intelligent energy.

[0072] The data storage module uploads and saves the data obtained by the data acquisition module, and simultaneously establishes a cloud database and corresponding data models;

[0073] It can prevent data loss and facilitate data comparison and analysis;

[0074] Deduplicate and compress the data to reduce data storage space, improve data transmission efficiency, and enhance data quality and accuracy.

[0075] Through the visualization management module, the collected data is presented in the form of charts and graphics, facilitating managers to conduct comparison, review, and decision-making;

[0076] Present the operation data of renewable energy in a multi-dimensional form so that decision-makers can more comprehensively understand the operation situation;

[0077] And share it with experts, scholars, etc. in related fields to improve the accuracy and scientific nature of decision-making;

[0078] At the same time, it allows users to browse and analyze data independently, enhancing users' awareness and participation in renewable energy.

[0079] Such as Figure 2 As shown, in an application management service platform for green and intelligent energy of the present invention, the solutions obtained by the data analysis module are efficiently executed and implemented through the intelligent control module; it is divided into a scheduling system, a monitoring system, and a maintenance system;

[0080] Through the scheduling system, targeted intelligent real-time scheduling is performed on energy demand and energy supply situations, and the energy supply and power generation of renewable energy are adjusted in real time to improve energy utilization efficiency;

[0081] Through the monitoring system, renewable energy is monitored, allocated, and warned, reducing energy waste, realizing remote monitoring of renewable energy facilities, improving the efficiency and convenience of monitoring, while reducing monitoring costs, and timely discovering problems and achieving rapid response and efficient handling;

[0082] Maintenance of the operation of renewable energy facilities through a maintenance system, including fault diagnosis and preventive maintenance, etc., to improve the reliability and availability of the facilities and reduce potential hazards of the facilities.

[0083] In one embodiment, the data mining module includes a correlation analysis component, a time series analysis component, and a representation learning component;

[0084] The correlation analysis component includes frequent warning index combination mining and warning cycle pattern mining;

[0085] The time series analysis component, the time series analysis component includes time series relationship mining and time series prediction;

[0086] The representation learning component, the representation learning component includes feature extraction, feature learning, and high-dimensional feature dimensionality reduction.

[0087] In one embodiment, a green and intelligent energy application management service platform of the present invention further includes a multi-energy device digital twin complex application dependency sorting strategy using depth-first search to construct a hierarchical set and a path set for planning and operation optimization applications;

[0088] According to the position of the recognition calculation task in the hierarchical set, a multi-energy device digital twin calculation task scheduling management strategy using the greedy algorithm comprehensively measures its priority by identifying the position and weight of each calculation task;

[0089] Based on the priority of the calculation task, the sequence of calling and execution of each calculation task is obtained;

[0090] Call and execute the calculation task at the head of the scheduling list, cache its data result, and remove it from the scheduling list until the scheduling list is empty, clear the cached data, and complete the multi-energy system digital twin application management scheduling.

[0091] The relationships between the calculation tasks include information interaction, forward and backward dependencies, and logical triggers.

[0092] In one embodiment, the data acquisition module: real-time data acquisition and recording of green and intelligent energy through sensors and detection devices, including parameters such as water flow and water level, tides and waves, solar radiation, and wind power, etc. These data are used for monitoring and optimization of energy generation methods such as hydropower, tidal power, solar power generation, and wind power generation.

[0093] The data service module: classifies and stores the initial data, and performs calculation and visualization processing of the result information, including a data fusion sub-module, obtaining dimensional data through a decision support degree calculation unit, and calculating the support degree value of the initial data for the warning decision; the OWA operator weight vector calculation unit determines the metric operator; the data conversion and sorting unit performs data fusion to obtain a decision value.

[0094] Data mining module: According to the result information, match the preset data mining components, perform big data comparison processing and analysis on the data uploaded to the cloud database, use data mining technology to extract potential and valuable information, and predict future renewable energy power generation, energy consumption, and energy acquisition based on the data, etc., provide data reference and support for decision-making plans, and plan potential problems and optimization plans for green and intelligent energy.

[0095] Data storage module: Upload and save the data obtained by the data acquisition module, establish a cloud database and corresponding data models. At the same time, deduplicate and compress the data to reduce storage space and improve transmission efficiency, and improve data quality and accuracy.

[0096] Visualization management module: Display the collected data in the form of charts and pictures and texts, which is convenient for management personnel to compare, consult and make decisions, display the operation data of renewable energy in multiple dimensions, help decision-makers comprehensively understand the operation situation, share data with experts and scholars in related fields, etc., improve the accuracy and scientificity of decision-making. Users can browse and analyze the data independently to improve their awareness and participation in renewable energy.

[0097] Intelligent control module: Efficiently execute and implement the solutions obtained by the data analysis module, which is divided into a scheduling system, a monitoring system, and a maintenance system. The scheduling system conducts intelligent real-time scheduling according to energy demand and energy supply situations, and makes real-time adjustments to the energy supply and power generation of renewable energy to improve energy utilization efficiency. The monitoring system monitors, allocates, and gives early warnings to renewable energy, reduces energy waste, realizes remote monitoring, improves monitoring efficiency and convenience, and reduces monitoring costs. The maintenance system conducts fault diagnosis and preventive maintenance on renewable energy facilities to improve the reliability and availability of the facilities and reduce facility hidden dangers.

[0098] In addition, in one embodiment, the data mining module includes a correlation analysis component, a time series analysis component, and a representation learning component. The correlation analysis component is used to mine frequent warning index combinations and warning cycle patterns. The time series analysis component is used to mine time series relationships and time series predictions. The representation learning component is used for feature extraction, feature learning, and high-dimensional feature dimensionality reduction.

[0099] In addition, the green intelligent energy application management service platform of the present invention further includes a multi-energy device digital twin complex application dependency sorting strategy using depth-first search to construct a hierarchical set and a path set for planning and operation optimization applications. By identifying the position of a computing task in the hierarchical set, a multi-energy device digital twin computing task scheduling and management strategy using the greedy algorithm is employed. Considering the position and weight of the task comprehensively, the priority of each computing task is determined, and the order of calling and execution is arranged. The computing tasks are executed sequentially through the scheduling list, and the data results are cached until the scheduling list is empty, at which time the cached data is cleared to complete the scheduling and management of the multi-energy system digital twin application.

[0100] Among the above technical points, the relationships between computing tasks are also involved, including information interaction, forward and backward dependencies, and logical triggers. These relationships are of great significance for realizing the management and decision-making of green intelligent energy applications.

[0101] The technical process of this solution: A green intelligent energy application management service platform includes a data acquisition module, a data storage module, a data service module, a data mining module, an intelligent control module, and a visualization management module. Among them, the data acquisition module collects relevant data of green intelligent energy in real time through sensors and detection devices, such as water flow and water level, tides and waves, solar radiation, and wind power. The data service module classifies and stores the collected data, and obtains decision values through data fusion, sorting, etc. The data mining module uses data mining technology to extract valuable information from the data and predict future energy power generation, consumption, and acquisition, etc. The data storage module uploads and saves the collected data, and establishes a cloud database and corresponding data models. The visualization management module displays the collected data in the form of charts and texts, facilitating management personnel to consult and make decisions, and sharing with experts, scholars, etc. The intelligent control module performs efficient execution and implementation based on the data analysis results, including a scheduling system, a monitoring system, and a maintenance system. The scheduling system schedules the energy demand and supply situation in real time to improve energy utilization efficiency. The monitoring system monitors, allocates, and warns of renewable energy to reduce energy waste. The maintenance system performs fault diagnosis and preventive maintenance on facilities to improve the reliability and availability of the facilities. In specific implementation, the data mining module includes a correlation analysis component, a time series analysis component, and a representation learning component. In addition, the platform also applies strategies such as depth-first search and greedy algorithm to construct planning and operation optimization applications and realize the digital twin application management scheduling of the multi-energy system.

[0102] In this process, first, the data acquisition module collects and records real-time data of green smart energy through sensors and detection devices. According to different energy types, such as hydropower, tidal power, solar power, and wind power, it collects corresponding parameter data, such as water flow velocity, water level height, tides and wave heights, solar radiation and solar altitude angle, wind speed, wind force, and frequency, etc.

[0103] The data service module is used to classify and store the collected initial data, and perform calculation and visualization processing of the result information. The data service module includes a data fusion sub-module, which includes a decision support degree calculation unit, an OWA operator weight vector calculation unit, and a data conversion and sorting unit. The data fusion sub-module obtains dimensional data according to decision attributes, calculates the support degree value of the initial data for the early warning decision, and then determines the measurement operator according to the preset fuzzy semantic quantization criterion to perform data fusion and obtain the decision value.

[0104] The data mining module matches the preset data mining components according to the result information, performs big data comparison processing and analysis on the data uploaded to the cloud database. Through data mining technology, it extracts potential and valuable information from the data, and predicts future renewable energy power generation, energy consumption, and energy acquisition, etc., to provide reference and support for the decision-making plan. At the same time, the data mining module also calculates and plans the potential problems and optimization plans of green smart energy.

[0105] The data storage module uploads and saves the data obtained by the data acquisition module, and establishes a cloud database and corresponding data models, which can prevent data loss and facilitate data comparison and analysis. At the same time, it performs data deduplication and compression to reduce storage space and improve transmission efficiency, and improve data quality and accuracy.

[0106] The visualization management module displays the collected data in the form of charts and graphics and texts, which is convenient for management personnel to compare, consult and make decisions. By presenting the operation data of renewable energy in multiple dimensions, it enables decision-makers to understand the operation situation more comprehensively, and share the data with experts, scholars and others in related fields to improve the accuracy and scientificity of decision-making. At the same time, users can also browse and analyze the data independently to improve their awareness and participation in renewable energy.

[0107] In the intelligent control module, efficient execution and implementation are carried out according to the solutions obtained by the data analysis module. The intelligent control module includes a scheduling system, a monitoring system, and a maintenance system. The scheduling system is used for intelligent real-time scheduling of energy demand and energy supply situations, making real-time adjustments to the energy supply and power generation of renewable energy, improving the energy utilization efficiency. The monitoring system monitors, allocates, and gives early warnings to renewable energy, reduces energy waste, realizes remote monitoring of facilities, improves the efficiency and convenience of monitoring, reduces monitoring costs, discovers problems in a timely manner, and achieves rapid response and efficient handling. The maintenance system conducts fault diagnosis and preventive maintenance on the operation of facilities, improves the reliability and availability of facilities, and reduces potential hazards.

[0108] In one embodiment, the data mining module includes a correlation analysis component, a time series analysis component, and a representation learning component. The correlation analysis component is used for frequent early warning index combination mining and early warning cycle pattern mining. The time series analysis component includes time series relationship mining and time series prediction. The representation learning component includes feature extraction, feature learning, and high-dimensional feature dimensionality reduction.

[0109] In addition, in one embodiment, the present invention also adopts a multi-energy device digital twin complex application dependency sorting strategy using depth-first search to construct a hierarchical set and a path set for planning and operation optimization applications. By identifying the position of a computing task in the hierarchical set, a multi-energy device digital twin computing task scheduling and management strategy using a greedy algorithm comprehensively measures its priority according to the position and weight of each computing task, determines the calling and execution order of each computing task, then calls and executes the computing tasks in the scheduling list, and caches their data results until the scheduling list is empty, completing the management and scheduling of the multi-energy system digital twin application. The relationships between computing tasks include information interaction, front-back dependency, and logical triggering.

[0110] In several embodiments provided in the present application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0111] In addition, each functional unit in various embodiments of the present application may be integrated into one processing unit, may exist physically alone for each unit, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. The above is only the implementation manner of the present application, and does not limit the patent scope of the present application accordingly. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A green smart energy application management service platform, characterized in that: The service platform includes: Data acquisition module, data storage module, data service module, data mining module, intelligent control module and visual management module; The data acquisition module is used to obtain initial data of the energy industry chain early warning system; The data service module is used to classify and store the initial data, obtain result information, and visualize the result information; The data mining module is used to match the preset data mining components according to the result information.

2. A green smart energy application management service platform according to claim 1, characterized in that: The data acquisition module includes sensors and detection devices; the sensors and monitoring devices are used to collect and record real-time data of green smart energy; such as the following aspects: water flow and water level, for hydropower stations, parameters such as water flow velocity and water level height are collected to achieve monitoring and optimization of hydropower generation; Tides and waves: for tidal power generation and wave power generation, parameters such as tides, wave heights and frequencies are collected to monitor and optimize tidal and wave power generation; Solar radiation: for solar power generation, collect parameters such as solar radiation and solar altitude angle to monitor and optimize solar power generation; Wind power: For wind power generation, real-time data parameters such as wind speed, wind force and frequency are collected to monitor, manage and optimize power generation.

3. A green smart energy application management service platform according to claim 2, characterized in that: The data acquired by the data acquisition module is uploaded and saved through the data storage module, and a cloud database is established, and a corresponding data model is established; It can prevent data loss and facilitate data comparison and analysis; Deduplication and compression of data can reduce data storage space, improve data transmission efficiency, and improve data quality and accuracy.

4. A green smart energy application management service platform according to claim 3, characterized in that: The data mining module performs big data comparison processing and analysis on the data uploaded to the cloud database, extracts potential and valuable information from the data through data mining technology, and predicts future renewable energy power generation, energy consumption and energy acquisition based on the data, so as to provide certain data reference and support for decision-making plans; Calculate and plan potential problems and optimization solutions for green smart energy.

5. A green smart energy application management service platform according to claim 4, characterized in that: The solution obtained by the data analysis module is efficiently executed and implemented through the intelligent control module; it is divided into a scheduling system, a monitoring system and a maintenance system; Through the dispatching system, targeted intelligent real-time dispatching of energy demand and energy supply is carried out, and the energy supply and power generation of renewable energy are adjusted in real time to improve the utilization efficiency of energy; Through the monitoring system, renewable energy is monitored, allocated and warned, energy waste is reduced, remote monitoring of renewable energy facilities is achieved, monitoring efficiency and convenience are improved, monitoring costs are reduced, problems are discovered in a timely manner and rapid responses and efficient processing are achieved; By maintaining the operation of renewable energy facilities through maintenance systems, including fault diagnosis and preventive maintenance, the reliability and availability of facilities can be improved and hidden dangers of facilities can be reduced.

6. A green smart energy application management service platform according to claim 5, characterized in that: The visual management module displays the collected data in the form of charts and graphics, making it easier for managers to compare, review and make decisions; Presenting renewable energy operation data in a multi-dimensional form so that decision makers can have a more comprehensive understanding of the operation; And share with experts and scholars in related fields to improve the accuracy and scientificity of decision-making; At the same time, it allows users to browse and analyze data independently, increasing their awareness and participation in renewable energy.

7. A green smart energy application management service platform according to claim 6, characterized in that: The data service module includes a data fusion submodule, and the data fusion submodule includes: a decision support calculation unit, which is used to obtain corresponding dimension data according to the decision attributes of the initial data, and calculate the support value of the initial data for the early warning decision; An OWA operator weight vector calculation unit, used to determine a measurement operator according to a preset fuzzy semantic quantization criterion; The data conversion and sorting unit is used to perform data fusion according to the support value and the metric operator to obtain a decision value.

8. A green smart energy application management service platform according to claim 7, characterized in that: The data mining module includes a correlation analysis component, a time series analysis component and a representation learning component; The correlation analysis component includes frequent warning indicator combination mining and warning cycle pattern mining; The time series analysis component includes time series relationship mining and time series prediction; The representation learning component includes feature extraction, feature learning and high-dimensional feature dimensionality reduction.

9. A green smart energy application management service platform according to claim 8, characterized in that: It also includes strategies for combing complex application dependencies of digital twins of multiple energy devices using depth-first search, and building hierarchical sets and path sets for planning and running optimized applications; According to the position of the identified computing task in the hierarchical set, a multi-energy equipment digital twin computing task scheduling management strategy using a greedy algorithm is used to comprehensively measure the priority of each computing task by identifying its position and weight; Based on the priority of the computing tasks, the calling and execution order of each computing task is obtained; Call and execute the computing task at the top of the scheduling list, cache its data results, and clear it from the scheduling list until the scheduling list is empty, clear the cached data, and complete the multi-energy system digital twin application management scheduling.

10. A green smart energy application management service platform according to claim 9, characterized in that: The relationship between the computing tasks includes information interaction, context dependency and logic triggering.

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