Internet-based collaborative service method and system

Abstract

The invention discloses an Internet-based collaborative service method and system, and relates to the technical field of energy Internet, and the method comprises the steps: S1, building a block chain platform, S2, carrying out equipment interconnection, and connecting various types of energy equipment through the Internet, S3, carrying out intelligent scheduling, and developing an intelligent scheduling algorithm, S4, carrying out user participation and interaction, and building a user interface, and S5, carrying out monitoring and early warning, and constructing a real-time monitoring system. And S6, performing service evaluation and continuous improvement, and establishing a service evaluation index system. According to the invention, a block chain collaboration platform, an intelligent scheduling algorithm and a multi-stage early warning system are constructed, integration and real-time monitoring of energy related data are effectively realized, high efficiency and transparency of information circulation are ensured, energy flow direction is optimized, energy waste is reduced, and in addition, user participation and interaction establish a user interface, so that user experience is improved. Therefore, the sense of participation of the user is enhanced, the user can check the energy use condition in real time and give feedback to the scheduling decision, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy internet, and in particular to an internet-based collaborative service method and system thereof. Background Art

[0002] Energy is a resource that can provide energy. The energy here usually refers to thermal energy, electrical energy, light energy, mechanical energy, chemical energy, etc. Different types of energy exist in various forms in nature. For example, fossil energy such as coal, oil, and natural gas contain chemical energy, water can generate mechanical energy through flow, and solar energy exists in the form of light energy in solar radiation. These energies play a vital role in the development of human society. They provide power and support for our production and life.

[0003] Energy can also be divided into renewable energy and non-renewable energy. Existing energy is generally combined with the Internet when in use, using advanced sensors, controls and software applications to connect hundreds of millions of devices, machines and systems at the energy production, energy transmission and energy consumption ends. However, the existing energy network system control methods are relatively simple and difficult to adapt to the requirements of large-scale energy utilization. In addition, due to the lack of coordination of the energy network system, the energy utilization rate is low, resulting in a large amount of energy waste. Summary of the Invention

[0004] The purpose of the present invention is to provide an Internet-based collaborative service method and system thereof to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: an Internet-based collaborative service method, comprising the following steps:

[0006] S1: Build a blockchain platform and create a blockchain-based collaborative service platform to integrate energy-related data;

[0007] S2: Device interconnection, connecting various energy devices through the Internet to carry out information flow and real-time communication;

[0008] S3: Intelligent Scheduling: Develop intelligent scheduling algorithms to automatically adjust energy production and consumption based on real-time supply and demand conditions, optimizing energy flows;

[0009] S4: User participation and interaction, establishing a user interface so that users can view energy usage in real time and participate in scheduling decisions;

[0010] S5: Monitoring and early warning: Build a real-time monitoring system to track key indicators in energy production and consumption, set early warning thresholds, and promptly detect and respond to abnormal situations;

[0011] S6: Service evaluation and continuous improvement: Establish a service evaluation indicator system, conduct regular evaluations of the effectiveness of energy collaborative services, and continuously optimize algorithms and strategies based on the evaluation results.

[0012] Preferably, building the blockchain platform in step S1 includes the following steps:

[0013] S11: Demand Analysis and Design: Analyze user needs and business processes, clarify the types of data that need to be recorded and managed on the blockchain, and select the appropriate blockchain type;

[0014] S12: Develop blockchain infrastructure, set up nodes of the blockchain network and develop smart contracts;

[0015] S13: Data integration: standardize energy-related data and store and manage them in a unified manner;

[0016] S14: Security protection, using hash algorithm encryption technology to protect user data and transaction information;

[0017] S15: User identity management, establish a user authentication system and permission management mechanism, and assign different data access and operation permissions according to user roles.

[0018] Preferably, the device interconnection in step S2 includes the following steps:

[0019] S21: Device identification and classification: uniquely identify all connected energy devices and classify them according to device type, function and communication protocol;

[0020] S22: Select the communication protocol, choose the protocol that best suits the energy equipment, and unify the interface standards of the equipment;

[0021] S23: Device access and network configuration: perform access tests on each type of device and design a network topology based on device distribution;

[0022] S24: Data collection and monitoring: real-time acquisition of equipment operating status, energy consumption data, and other information, which is uploaded to the collaborative service platform and analyzed through a data quality detection mechanism;

[0023] S25: Edge computing: deploy edge computing nodes near the device to perform preliminary data processing and analysis;

[0024] S26: Secure communication, encrypting data during transmission.

[0025] Preferably, the edge computing in step S25 uses sliding window mean filtering to process data:

[0026]

[0027] Among them, x i is the real-time data of the device, N is the window size, is the filtered value, and t is the time point of the data stream.

[0028] Preferably, the intelligent scheduling in step S3 includes the following steps:

[0029] S31: Data collection and analysis: collecting real-time key information on energy production, consumption, and market prices from interconnected devices and blockchain platforms, and performing cleaning and standardization;

[0030] S32: Establish a demand forecast model, using historical energy consumption data and trend analysis to establish a demand forecast model, and use a time series forecasting algorithm to perform demand forecasting;

[0031] S33: Scheduling algorithm development, designing different scheduling models for different energy types, and using the alternating direction multiplier method to develop energy production and consumption scheduling plans;

[0032] S34: Real-time adjustment: Dynamically adjust the scheduling strategy through the automation system based on real-time supply and demand conditions and market changes;

[0033] S35: Feedback and optimization: real-time monitoring of the effects of scheduling execution, and adjustment and optimization of the scheduling algorithm based on the monitoring results.

[0034] Preferably, the user participation and interaction in step S4 includes the following steps:

[0035] S41: User classification: classify different types of users to understand their needs and preferences in energy use;

[0036] S42: User interface design, design different user interfaces according to different types of users;

[0037] S43: Real-time information display, developing visualization tools to display key information such as users' energy usage, real-time prices, supply and demand status in real time to help users make decisions;

[0038] S44: Develop interactive features to allow users to provide suggestions and feedback on scheduling plans;

[0039] S45: Personalized services: Based on the user's historical energy usage data and preferences, a personalized energy usage recommendation system is developed to recommend optimized energy usage strategies and energy-saving measures;

[0040] S46: User feedback: Design a feedback collection mechanism so that users can evaluate and provide feedback on the system’s usage experience and scheduling plan.

[0041] Preferably, the monitoring and early warning in step S5 includes the following steps:

[0042] S51: Identification of key indicators, identifying key indicators related to energy production and consumption;

[0043] S52: Real-time data collection and analysis: collects data from various devices in real time and applies the isolation forest algorithm to detect abnormalities in energy consumption and production in real time;

[0044] S53: Develop corresponding risk prevention and control strategies and measures based on risk assessment results;

[0045] S54: Early warning design: set reasonable early warning thresholds based on historical data and industry standards, and design a three-level early warning system with different response measures for different levels of abnormal situations;

[0046] S55: Subsequent optimization processing, regular evaluation and improvement of warning thresholds and the three-level warning system, and improvement and optimization of corresponding measures based on actual conditions and newly emerging abnormal situations.

[0047] Preferably, the three-level warning system formula in step S54 is:

[0048]

[0049] Among them, x is the real-time monitoring value, μ is the mean of historical data, σ is the standard deviation of historical data, Level 1 refers to the first-level warning, Level 2 refers to the second-level warning, and Level 3 refers to the third-level warning.

[0050] Preferably, the service evaluation and continuous improvement in step S6 includes the following steps:

[0051] S61: Determine the evaluation objectives and the specific content and indicators of the energy collaborative services to be evaluated;

[0052] S62: Establish multiple user feedback channels, organize and categorize collected user feedback, and identify common user concerns and needs;

[0053] S63: Evaluation and Optimization: Regularly write service evaluation reports and formulate specific improvement plans based on evaluation results and user feedback, clearly defining improvement goals, measures, and timelines.

[0054] S64: Regularly re-evaluate the optimized service results to verify the effectiveness of improvement measures.

[0055] The present invention also provides an Internet-based collaborative service system, comprising:

[0056] The blockchain platform module creates a blockchain-based collaborative service platform to integrate energy-related data;

[0057] The device interconnection module connects various energy devices through the Internet to carry out information flow and real-time communication;

[0058] Intelligent scheduling module, develops intelligent scheduling algorithms to automatically adjust energy production and consumption based on real-time supply and demand conditions and optimize energy flows;

[0059] User participation and interaction module, which establishes a user interface so that users can view energy usage in real time and participate in scheduling decisions;

[0060] Monitoring and early warning module: build a real-time monitoring system to track key indicators in energy production and consumption, set early warning thresholds, and promptly detect and report abnormal situations;

[0061] The service evaluation and continuous improvement module establishes a service evaluation indicator system, conducts regular evaluations of the effectiveness of energy collaborative services, and continuously optimizes algorithms and strategies based on the evaluation results.

[0062] Technical effects and advantages of the present invention:

[0063] (1) The present invention constructs a blockchain collaborative platform, an intelligent scheduling algorithm, and a multi-level early warning system, which effectively realizes the integration and real-time monitoring of energy-related data, ensures the efficiency and transparency of information flow, and automatically adjusts energy production and consumption according to real-time supply and demand conditions, thereby optimizing energy flow and reducing energy waste. In addition, user participation and interaction enhance user participation by establishing a user interface, allowing users to view their energy usage in real time and provide feedback on scheduling decisions, thereby improving energy utilization efficiency, adapting to the requirements of large-scale energy utilization, and greatly improving energy utilization, thereby reducing a large amount of energy waste;

[0064] (2) The present invention establishes a sound risk management and continuous improvement mechanism through monitoring, early warning and service evaluation. The real-time monitoring system can promptly detect and report abnormal situations, ensure the stability and security of the energy network, regularly evaluate the effectiveness of energy collaborative services, and promote the continuous optimization of algorithms and strategies based on user feedback. This not only improves the utilization rate of renewable energy, but also promotes the efficient use of non-renewable energy, helps to build a more intelligent and green energy management system, and promotes sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a flow chart of the Internet-based collaborative service method of the present invention.

[0066] Figure 2 Build a blockchain platform flow chart for this invention.

[0067] Figure 3 This is a flow chart of the device interconnection of the present invention.

[0068] Figure 4 This is the intelligent scheduling flow chart of the present invention.

[0069] Figure 5 This is a flowchart of user participation and interaction in the present invention.

[0070] Figure 6 This is a monitoring and early warning flow chart of the present invention.

[0071] Figure 7 Flowchart for evaluation and continuous improvement of services for this invention.

[0072] Figure 8 This is a block diagram of the Internet-based collaborative service system of the present invention. DETAILED DESCRIPTION

[0073] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0074] The present invention provides Figure 1-8 The collaborative service method based on the Internet includes the following steps:

[0075] S1: Build a blockchain platform and create a blockchain-based collaborative service platform to integrate energy-related data;

[0076] S2: Device interconnection, connecting various energy devices through the Internet to carry out information flow and real-time communication;

[0077] S3: Intelligent Scheduling: Develop intelligent scheduling algorithms to automatically adjust energy production and consumption based on real-time supply and demand conditions, optimizing energy flows;

[0078] S4: User participation and interaction, establishing a user interface so that users can view energy usage in real time and participate in scheduling decisions;

[0079] S5: Monitoring and early warning: Build a real-time monitoring system to track key indicators in energy production and consumption, set early warning thresholds, and promptly detect and respond to abnormal situations;

[0080] S6: Service evaluation and continuous improvement: Establish a service evaluation indicator system, conduct regular evaluations of the effectiveness of energy collaborative services, and continuously optimize algorithms and strategies based on the evaluation results.

[0081] By building a blockchain collaborative platform, intelligent scheduling algorithms and multi-level early warning systems, the integration and real-time monitoring of energy-related data are effectively realized, ensuring the efficiency and transparency of information flow. At the same time, energy production and consumption are automatically adjusted according to real-time supply and demand conditions, thereby optimizing energy flow and reducing energy waste. In addition, user participation and interaction enhance user participation by establishing a user interface, allowing users to view their energy usage in real time and provide feedback on scheduling decisions, thereby improving energy utilization efficiency and adapting to the requirements of large-scale energy utilization, greatly improving energy utilization, and reducing a large amount of energy waste. Through monitoring, early warning and service evaluation, a sound risk management and continuous improvement mechanism has been established. The real-time monitoring system can promptly detect and report abnormal situations, ensure the stability and security of the energy network, regularly evaluate the effectiveness of energy collaborative services, and combine user feedback to promote the continuous optimization of algorithms and strategies. It not only improves the utilization rate of renewable energy, but also promotes the efficient use of non-renewable energy, which helps to build a more intelligent and green energy management system and promote sustainable development.

[0082] Building the blockchain platform in step S1 includes the following steps:

[0083] S11: Demand Analysis and Design: Analyze user needs and business processes, clarify the types of data that need to be recorded and managed on the blockchain, and select the appropriate blockchain type. Choosing the appropriate blockchain type (such as public chain, consortium chain, or private chain) can maximize the adaptation to business scenarios and improve the platform's operating efficiency and security.

[0084] S12: Develop blockchain infrastructure, set up nodes in the blockchain network, and develop smart contracts. In the energy sector, smart contracts can automatically handle energy transactions, settlements, and other operations based on preset rules, reducing manual intervention, improving transaction transparency and fairness, and reducing operating costs and the risk of human error.

[0085] S13: Data integration: Standardize, store, and manage energy-related data in a unified manner. In the energy industry, standardized data can support more accurate energy forecasting, optimize energy allocation, and improve energy efficiency. At the same time, unified storage management also facilitates data backup and recovery, enhancing data security and stability.

[0086] S14: Security protection. Hash algorithm encryption technology is used to protect user data and transaction information. In energy trading, encryption technology can protect user privacy and transaction confidentiality, prevent data leakage and malicious attacks, and safeguard users' legitimate rights and interests. At the same time, a safe and reliable trading environment can also enhance user trust in the platform and promote its widespread application and development.

[0087] S15: User identity management, establish a user authentication system and permission management mechanism, and assign different data access and operation permissions according to user roles. Through reasonable permission allocation, it can ensure that all parties can carry out business activities efficiently under the premise of compliance, while maintaining the security and stability of the platform.

[0088] The device interconnection in step S2 includes the following steps:

[0089] S21: Device identification and classification: uniquely identify all connected energy devices and classify them according to device type, function, and communication protocol to facilitate accurate management and quickly locate devices, laying a good foundation for subsequent operations;

[0090] S22: Select the communication protocol that best suits the energy equipment and unify the interface standards of the equipment to ensure smooth communication between devices, reduce compatibility issues, and improve data transmission stability;

[0091] S23: Device access and network configuration: perform access tests on each type of device and design a network topology based on device distribution to ensure smooth device access, optimize network performance, and improve overall operational efficiency.

[0092] S24: Data collection and monitoring: Real-time acquisition of equipment operating status, energy consumption data, and other information, which is uploaded to the collaborative service platform. The uploaded data is analyzed through a data quality detection mechanism. Real-time collection of equipment information is uploaded to the platform, and combined with data quality detection, this provides an accurate basis for decision-making and improves the accuracy of energy management.

[0093] S25: Edge computing: deploy edge computing nodes near the equipment to perform preliminary data processing and analysis, reduce data transmission volume and delay, and improve response speed and system real-time performance;

[0094] S26: Secure communication, encrypting data during transmission to effectively prevent information leakage and malicious attacks, ensure data security, and maintain stable system operation.

[0095] In step S25, edge computing uses sliding window mean filtering to process data:

[0096]

[0097] Among them, x i is the real-time data of the device, N is the window size, is the filtered value, t is the time point of the data stream, and the sliding window mean filter is used to process the data, which can effectively smooth the data, reduce random noise interference, make the data more reflective of the real trend, reduce data fluctuations, improve data quality, and make subsequent analysis and decision-making based on this data more accurate and reliable, thereby enhancing the stability and effectiveness of the system.

[0098] The intelligent scheduling in step S3 includes the following steps:

[0099] S31: Data collection and analysis: Collecting real-time key information on energy production, consumption, and market prices from interconnected devices and blockchain platforms, and performing cleansing and standardization to ensure data accuracy and provide solid and reliable data support for subsequent scheduling decisions;

[0100] S32: Establish a demand forecasting model. Utilize historical energy consumption data and trend analysis to establish a demand forecasting model. Use a time series forecasting algorithm to forecast demand. Using historical data to build a model and forecast demand can help you understand energy demand dynamics in advance, making scheduling more forward-looking and targeted.

[0101] S33: Scheduling algorithm development, designing different scheduling models for different energy types, and using the alternating direction multiplier method to develop energy production and consumption scheduling plans, customizing scheduling models and plans for different energy sources, optimizing energy distribution, improving energy utilization efficiency, and reducing production and consumption costs;

[0102] S34: Real-time adjustment: Based on real-time supply and demand conditions and market changes, the dispatch strategy is dynamically adjusted through the automation system. The dispatch is automatically adjusted according to real-time conditions, quickly responding to supply and demand and market changes, maintaining energy supply and demand balance, and improving dispatch flexibility.

[0103] S35: Feedback and optimization, real-time monitoring of the effects of scheduling execution, and based on the monitoring results, adjustment and optimization of the scheduling algorithm, monitoring the scheduling effect and optimizing the algorithm, so that the scheduling plan is continuously improved, and the accuracy and effectiveness of energy scheduling are continuously improved.

[0104] User participation and interaction in step S4 includes the following steps:

[0105] S41: User classification: Categorize different types of users to understand their needs and preferences for energy use. This helps lay the foundation for providing personalized services and enhance users' sense of identity with the energy system.

[0106] S42: User interface design: Design different user interfaces according to different types of users, design exclusive interfaces for different users, improve operation convenience and user experience, and make it easier for users to interact with the energy system;

[0107] S43: Real-time information display: Develop visualization tools to display key information such as users' energy usage, real-time prices, supply and demand status in real time to help users make decisions. Visual display of key energy information helps users grasp the situation in a timely manner, make reasonable energy use decisions, and improve the rationality of energy use.

[0108] S44: Develop interactive functions to allow users to provide suggestions and feedback on scheduling plans. This interactive function allows users to participate in scheduling, enhances user participation, makes scheduling plans more in line with user needs, and improves user satisfaction.

[0109] S45: Personalized services: Based on users' historical energy usage data and preferences, a personalized energy usage recommendation system is developed to recommend optimized energy usage strategies and energy-saving measures. Based on user data, personalized recommendations are provided to guide users to optimize energy usage, achieve energy conservation and consumption reduction, and improve energy efficiency.

[0110] S46: User feedback. Design a feedback collection mechanism so that users can evaluate and provide feedback on the system's usage experience and scheduling plans. Establish a feedback mechanism to collect user opinions, facilitate timely improvements to the system and scheduling plans, and continuously improve user experience.

[0111] The monitoring and early warning in step S5 includes the following steps:

[0112] S51: Key indicator identification, identifying key indicators related to energy production and consumption, accurately identifying key energy indicators, and providing clear goals and directions for subsequent data collection, analysis, monitoring and early warning;

[0113] S52: Real-time data collection and analysis: This system collects data from various devices in real time and applies the isolation forest algorithm to detect energy consumption and production anomalies in real time. By collecting and analyzing data and detecting anomalies in real time, it can promptly identify energy production and consumption issues, buying time for risk prevention and control.

[0114] S53: Develop corresponding risk prevention and control strategies and measures based on the risk assessment results. Develop prevention and control strategies based on the assessment results to address energy risks in a targeted manner and ensure the safe and stable operation of the energy system.

[0115] S54: Early warning design: Set reasonable early warning thresholds based on historical data and industry standards, and design a three-level early warning system. Set different response measures for different levels of abnormal situations. Setting reasonable thresholds and three-level early warnings can respond to abnormalities in a graded manner, quickly handle problems, and reduce the impact of energy accidents;

[0116] S55: Subsequent optimization processing, regular evaluation and improvement of warning thresholds and the three-level warning system, improvement and optimization of corresponding measures based on actual conditions and newly emerging abnormal situations, regular evaluation and improvement of the warning system to adapt it to new situations, and continuous improvement of the accuracy and effectiveness of monitoring and warning.

[0117] The formula of the three-level early warning system in step S54 is:

[0118]

[0119] Here, x is the real-time monitoring value, μ is the mean of historical data, and σ is the standard deviation of historical data. Level 1 refers to a level 1 warning, Level 2 refers to a level 2 warning, and Level 3 refers to a level 3 warning. Level 1 is a lighter warning, while Level 3 is a more severe warning. For example, Level 1 automatic adjustment and Level 3 emergency load shedding can be used. Multi-level warnings can accurately match risk levels, avoid over-response, reduce the probability of major accidents, optimize resource allocation, and reduce operation and maintenance costs.

[0120] The service evaluation and continuous improvement in step S6 includes the following steps:

[0121] S61: Determine the evaluation objectives, determine the specific content and indicators of the energy synergy services to be evaluated, clarify the evaluation content and indicators, provide a clear direction for the service evaluation, and make the evaluation more targeted and operational;

[0122] S62: Establish multiple user feedback channels, organize and categorize collected user feedback, identify common user concerns and needs, collect and categorize user feedback through multiple channels, accurately grasp user needs and issues, and provide a strong basis for service improvement;

[0123] S63: Evaluation and Optimization: Regularly write service evaluation reports. Based on the evaluation results and user feedback, formulate specific improvement plans with clear improvement goals, measures, and timelines. Regularly evaluate and formulate improvement plans based on the results and feedback to ensure that service optimization has a clear framework and that improvement work proceeds in an orderly manner.

[0124] S64: Regularly re-evaluate the optimized service results, verify the effectiveness of improvement measures, continuously promote the continuous improvement and enhancement of energy collaborative services, regularly review the optimization results, verify the effectiveness of measures, form a closed-loop improvement mechanism, and continuously improve the quality of energy collaborative services.

[0125] The present invention also provides an Internet-based collaborative service system, comprising:

[0126] The blockchain platform module creates a blockchain-based collaborative service platform to integrate energy-related data;

[0127] The device interconnection module connects various energy devices through the Internet to conduct information flow and real-time communication, ensuring data security and non-tamperability, enabling efficient data sharing, and providing a solid data foundation for subsequent links. By connecting energy devices through the Internet to achieve information flow and communication, it breaks down information silos between devices and improves device collaboration and the overall operating efficiency of the energy system.

[0128] Intelligent scheduling module, develops intelligent scheduling algorithms, automatically adjusts energy production and consumption according to real-time supply and demand conditions, and optimizes energy flows;

[0129] The user participation and interaction module establishes a user interface, allowing users to view energy usage in real time and participate in scheduling decisions. It also develops algorithms to automatically adjust energy production and consumption and optimize energy flows based on supply and demand, thereby improving energy utilization, reducing costs, and ensuring a stable energy supply.

[0130] Monitoring and early warning module: build a real-time monitoring system to track key indicators in energy production and consumption, set early warning thresholds, and promptly detect and report abnormal situations. Build a monitoring system to track indicators and set early warning thresholds, so as to promptly detect abnormalities in energy production and consumption, quickly respond and handle them, and ensure the safety of the energy system;

[0131] The service evaluation and continuous improvement module establishes a service evaluation indicator system, regularly evaluates the effectiveness of energy collaborative services, and continuously optimizes algorithms and strategies based on the evaluation results. The establishment of an evaluation system to regularly evaluate service effects and optimize algorithm strategies can promote the continuous upgrading of energy collaborative services and improve the overall service quality.

[0132] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A collaborative service method based on the Internet, characterized in that: The following steps are involved: S1: Build a blockchain platform and create a blockchain-based collaborative service platform to integrate energy-related data; S2: Device interconnection, connecting various energy devices through the Internet to carry out information flow and real-time communication; S3: Intelligent Scheduling: Develop intelligent scheduling algorithms to automatically adjust energy production and consumption based on real-time supply and demand conditions, optimizing energy flows; S4: User participation and interaction, establishing a user interface so that users can view energy usage in real time and participate in scheduling decisions; S5: Monitoring and early warning: Build a real-time monitoring system to track key indicators in energy production and consumption, set early warning thresholds, and promptly detect and respond to abnormal situations; S6: Service evaluation and continuous improvement: Establish a service evaluation indicator system, conduct regular evaluations of the effectiveness of energy collaborative services, and continuously optimize algorithms and strategies based on the evaluation results.

2. The collaborative service method based on the Internet according to claim 1, characterized in that: Building the blockchain platform in step S1 includes the following steps: S11: Demand Analysis and Design: Analyze user needs and business processes, clarify the types of data that need to be recorded and managed on the blockchain, and select the appropriate blockchain type; S12: Develop blockchain infrastructure, set up nodes of the blockchain network and develop smart contracts; S13: Data integration: standardize energy-related data and store and manage them in a unified manner; S14: Security protection, using hash algorithm encryption technology to protect user data and transaction information; S15: User identity management, establish a user authentication system and permission management mechanism, and assign different data access and operation permissions according to user roles.

3. The collaborative service method based on the Internet according to claim 1, characterized in that: The device interconnection in step S2 includes the following steps: S21: Device identification and classification: uniquely identify all connected energy devices and classify them according to device type, function and communication protocol; S22: Select the communication protocol, choose the protocol that best suits the energy equipment, and unify the interface standards of the equipment; S23: Device access and network configuration: perform access tests on each type of device and design a network topology based on device distribution; S24: Data collection and monitoring: real-time acquisition of equipment operating status, energy consumption data, and other information, which is uploaded to the collaborative service platform and analyzed through a data quality detection mechanism; S25: Edge computing: deploy edge computing nodes near the device to perform preliminary data processing and analysis; S26: Secure communication, encrypting data during transmission.

4. The collaborative service method based on the Internet according to claim 3, characterized in that: In step S25, edge computing uses sliding window mean filtering to process data: Among them, x i is the real-time data of the device, N is the window size, is the filtered value, and t is the time point of the data stream.

5. The collaborative service method based on the Internet according to claim 1, characterized in that: The intelligent scheduling in step S3 includes the following steps: S31: Data collection and analysis: collecting real-time key information on energy production, consumption, and market prices from interconnected devices and blockchain platforms, and performing cleaning and standardization; S32: Establish a demand forecast model, using historical energy consumption data and trend analysis to establish a demand forecast model, and use a time series forecasting algorithm to perform demand forecasting; S33: Scheduling algorithm development, designing different scheduling models for different energy types, and using the alternating direction multiplier method to develop energy production and consumption scheduling plans; S34: Real-time adjustment: Dynamically adjust the scheduling strategy through the automation system based on real-time supply and demand conditions and market changes; S35: Feedback and optimization: real-time monitoring of the effects of scheduling execution, and adjustment and optimization of the scheduling algorithm based on the monitoring results.

6. The collaborative service method based on the Internet according to claim 1, characterized in that: The user participation and interaction in step S4 includes the following steps: S41: User classification: classify different types of users to understand their needs and preferences in energy use; S42: User interface design, design different user interfaces according to different types of users; S43: Real-time information display, developing visualization tools to display key information such as users' energy usage, real-time prices, supply and demand status in real time to help users make decisions; S44: Develop interactive features to allow users to provide suggestions and feedback on scheduling plans; S45: Personalized services: Based on the user's historical energy usage data and preferences, a personalized energy usage recommendation system is developed to recommend optimized energy usage strategies and energy-saving measures; S46: User feedback: Design a feedback collection mechanism so that users can evaluate and provide feedback on the system’s usage experience and scheduling plan.

7. The collaborative service method based on the Internet according to claim 1, characterized in that: The monitoring and early warning in step S5 includes the following steps: S51: Identification of key indicators, identifying key indicators related to energy production and consumption; S52: Real-time data collection and analysis: collects data from various devices in real time and applies the isolation forest algorithm to detect abnormalities in energy consumption and production in real time; S53: Develop corresponding risk prevention and control strategies and measures based on risk assessment results; S54: Early warning design: set reasonable early warning thresholds based on historical data and industry standards, and design a three-level early warning system with different response measures for different levels of abnormal situations; S55: Subsequent optimization processing, regular evaluation and improvement of warning thresholds and the three-level warning system, and improvement and optimization of corresponding measures based on actual conditions and newly emerging abnormal situations.

8. The Internet-based collaborative service method according to claim 7, characterized in that: The formula of the three-level early warning system in step S54 is: Among them, x is the real-time monitoring value, μ is the mean of historical data, σ is the standard deviation of historical data, Level 1 refers to the first-level warning, Level 2 refers to the second-level warning, and Level 3 refers to the third-level warning.

9. The collaborative service method based on the Internet according to claim 1, characterized in that: The service evaluation and continuous improvement in step S6 includes the following steps: S61: Determine the evaluation objectives and the specific content and indicators of the energy collaborative services to be evaluated; S62: Establish multiple user feedback channels, organize and categorize collected user feedback, and identify common user concerns and needs; S63: Evaluation and Optimization: Regularly write service evaluation reports and formulate specific improvement plans based on evaluation results and user feedback, clearly defining improvement goals, measures, and timelines. S64: Regularly re-evaluate the optimized service results to verify the effectiveness of improvement measures.

10. An Internet-based collaborative service system, characterized in that: include: The blockchain platform module creates a blockchain-based collaborative service platform to integrate energy-related data; The device interconnection module connects various energy devices through the Internet to carry out information flow and real-time communication; Intelligent scheduling module, develops intelligent scheduling algorithms, automatically adjusts energy production and consumption according to real-time supply and demand conditions, and optimizes energy flows; User participation and interaction module, which establishes a user interface so that users can view energy usage in real time and participate in scheduling decisions; Monitoring and early warning module: build a real-time monitoring system to track key indicators in energy production and consumption, set early warning thresholds, and promptly detect and report abnormal situations; The service evaluation and continuous improvement module establishes a service evaluation indicator system, conducts regular evaluations of the effectiveness of energy collaborative services, and continuously optimizes algorithms and strategies based on the evaluation results.

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