Collaborative service method and system based on internet of things

Abstract

This invention discloses a collaborative service method and system based on the Internet of Things (IoT), relating to the field of energy IoT technology. The method includes: S1: building a blockchain platform; S2: device interconnection, connecting various energy devices via IoT; S3: intelligent scheduling; S4: user participation and interaction, establishing a user interface; S5: monitoring and early warning, constructing a real-time monitoring system; and S6: service evaluation and continuous improvement, establishing a service evaluation index system. This invention constructs a blockchain collaborative platform and a multi-level early warning system, effectively realizing the integration and real-time monitoring of energy-related data, ensuring the efficiency and transparency of information flow, while optimizing energy flow and reducing energy waste. Furthermore, user participation and interaction, through the establishment of a user interface, enhance user engagement, enabling users to view their energy usage in real time and provide feedback on scheduling decisions, thereby improving energy utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of energy Internet of Things (IoT) technology, and in particular to a collaborative service method and system based on IoT. Background Technology

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

[0003] Energy can also be divided into renewable and non-renewable energy. When existing energy is used, it is generally combined with the Internet of Things, using advanced sensors, controls and software applications to connect hundreds of millions of devices, machines and systems at the energy production end, energy transmission end and energy consumption end. However, the existing energy network system control methods are relatively simple and difficult to adapt to the requirements of large-scale energy utilization. Furthermore, due to the lack of coordination in 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 this invention is to provide a collaborative service method and system based on the Internet of Things (IoT) to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a collaborative service method based on the Internet of Things, comprising the following steps:

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

[0007] S2: Device interconnection, connecting energy devices through the Internet of Things to facilitate information flow and real-time communication;

[0008] S3: Intelligent scheduling, which adjusts energy production and consumption and energy flow according to real-time supply and demand conditions;

[0009] S4: User interaction is implemented to create a user interface, allowing users to view energy usage in real time and participate in scheduling decisions;

[0010] S5: Monitoring and early warning, building a real-time monitoring system, tracking key indicators in energy production and consumption, setting early warning thresholds, and promptly detecting and responding 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 optimize based on the evaluation results.

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

[0013] S11: Requirements Analysis. Analyze user needs and business processes, identify the data types that need to be recorded and managed on the blockchain, and select the appropriate blockchain type.

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

[0015] S13: Data integration, standardizing energy-related data and storing and managing it in a unified manner;

[0016] S14: User identity management, establish a user authentication system and permission management mechanism, and assign different data access and operation permissions according to user roles.

[0017] Preferably, the device interconnection in step S2 includes the following steps:

[0018] S21: Equipment identification and classification, uniquely identifying all connected energy devices and classifying them according to device type, function and communication protocol;

[0019] S22: Select a communication protocol, choose a protocol suitable for the energy equipment, and unify the interface standards of the equipment;

[0020] S23: Device access and network configuration, conduct access tests for each type of device, and design a suitable network topology based on device distribution;

[0021] S24: Data acquisition and monitoring, real-time acquisition of equipment operating status and energy consumption data, uploading to the collaborative service platform, and analysis of the uploaded data through a data quality detection mechanism;

[0022] S25: Edge computing, deploying edge computing nodes near the device to perform preliminary data processing and analysis;

[0023] S26: Secure communication, encrypting data during transmission.

[0024] Preferably, in step S25, edge computing uses a sliding window mean filter to process the data.

[0025]

[0026] in, This represents real-time data from the device, where N is the window size. The value is the filtered value, and t is the time point in the data stream.

[0027] Preferably, the monitoring and early warning in step S5 includes the following steps:

[0028] S51: Key Indicator Identification: Identify key indicators related to energy production and consumption;

[0029] S52: Real-time data acquisition and analysis, which collects data from various devices in real time and detects energy consumption and abnormal situations that occur in production in real time;

[0030] S53: Based on the risk assessment results, adopt corresponding risk prevention and control measures.

[0031] Preferably, step S53 includes:

[0032] S531: Early warning level graded response rule configuration, pre-configure graded response rules: Level 1 early warning triggers system recording and prompts; Level 2 early warning triggers alarm notifications and fine-tuning of operating parameters; Level 3 early warning triggers emergency shutdown, backup power switching or remote dispatch instructions;

[0033] S532: When the monitored value triggers a level 2 or higher warning, a control command is generated based on the warning type and the device association, and then sent to the relevant energy equipment for execution via the Internet of Things;

[0034] S533: Manual intervention channel activated. When a Level 3 warning is triggered or the anomaly is not eliminated after control execution, the manual intervention channel is activated to send warning details and handling suggestions to the preset maintenance personnel, and to grant remote control permissions for manual intervention.

[0035] S534: Tracking and verifying the effectiveness of the response. After the implementation of prevention and control measures, continuously track the changes in key indicators. If the indicators recover to a safe range within a preset time, the warning is lifted and the response log is recorded. If the indicators continue to deteriorate or do not respond, the warning level is upgraded or the emergency plan is triggered.

[0036] S535: Emergency response plan linkage. When a major anomaly occurs and a Level 3 warning continues, the system will activate the emergency response plan, including switching on backup power, tiered load shedding, and starting emergency power, and will simultaneously notify relevant management personnel.

[0037] Preferably, in step S532, when the monitored value triggers a level two or higher warning, a control command is generated based on the warning type and device association, and then sent to the relevant energy equipment via the Internet of Things for execution, including:

[0038] The control command library is constructed by establishing a standardized control command library in advance for different warning types, equipment types and warning levels, and clarifying the operation object, command parameters and expected execution effect of each command;

[0039] Command matching and generation: When a level 2 or higher warning is triggered, the system matches the corresponding control command template from the command library based on the current warning type, level and associated device information, and fills the command parameters with real-time data to generate a specific executable command.

[0040] Instruction priority sorting: When multiple control instructions are generated simultaneously, they are prioritized according to their urgency and relevance to ensure that critical instructions are executed first.

[0041] Command issuance and confirmation: Commands are issued to the target device via the Internet of Things protocol, and the device is waited for an execution confirmation signal. If no confirmation is received within the time limit, the command is resent or an exception handling process is triggered.

[0042] Command execution effect verification: After the command is issued, the status changes of the relevant equipment are continuously monitored to verify whether the command has achieved the expected effect. If the execution fails or the effect is not up to standard, the command retry or upgraded handling measures are triggered.

[0043] Preferably, the formula for the three-level early warning system in step S54 is:

[0044]

[0045] in, For real-time monitoring values, The average of historical data. The standard deviation of historical data is used. Level 1 refers to the first level of warning, Level 2 refers to the second level of warning, and Level 3 refers to the third level of warning.

[0046] Preferably, the service evaluation and continuous improvement in step S6 includes the following steps:

[0047] S61: Determine the assessment objectives and the specific content and indicators of the energy synergy services that need to be assessed;

[0048] S62: Establish multiple user feedback channels, organize and classify the collected user feedback, and identify the issues and needs that users are generally concerned about;

[0049] S63: Evaluation and optimization. Regularly write service evaluation reports, and develop specific improvement plans based on evaluation results and user feedback, clarifying improvement goals, measures and timelines;

[0050] S64: Regularly re-evaluate the effectiveness of the optimized service to verify the effectiveness of the improvement measures.

[0051] This invention also provides a collaborative service system based on the Internet of Things, comprising:

[0052] The blockchain platform module creates a blockchain-based collaborative service platform that integrates energy data.

[0053] The device interconnection module connects energy devices via the Internet of Things (IoT) to facilitate information flow and real-time communication.

[0054] The intelligent scheduling module adjusts energy production and consumption as well as energy flow based on real-time supply and demand conditions;

[0055] The user participation and interaction module establishes a user interface, allowing users to view energy usage in real time and participate in dispatching decisions;

[0056] The monitoring and early warning module builds a real-time monitoring system to track key indicators in energy production and consumption, sets early warning thresholds, and promptly detects and responds to anomalies.

[0057] The service evaluation and continuous improvement module establishes a service evaluation indicator system to regularly evaluate the effectiveness of energy collaborative services and optimize them based on the evaluation results.

[0058] The technical effects and advantages of this invention are as follows:

[0059] (1) The present invention constructs a blockchain collaborative platform and a multi-level early warning system, which effectively realizes the integration and real-time monitoring of energy-related data, ensuring the efficiency and transparency of information flow. At the same time, it adjusts energy production and consumption according to real-time supply and demand, thereby optimizing energy flow and reducing energy waste. In addition, user participation and interaction enhance the user's sense of participation by establishing a user interface, enabling users to view their energy usage in real time and provide feedback on scheduling decisions, thereby improving the efficiency of energy utilization, adapting to the requirements of large-scale energy utilization, greatly improving the energy utilization rate, and thus reducing a large amount of energy waste.

[0060] (2) This invention establishes a sound risk management and continuous improvement mechanism through monitoring, early warning and service evaluation. The real-time monitoring system can detect and report abnormal situations in a timely manner, ensuring the stability and security of the energy network. It regularly evaluates the effect of energy synergy services and promotes the continuous optimization of the method by combining user feedback. 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. Attached Figure Description

[0061] Figure 1 This is a flowchart of the collaborative service method based on the Internet of Things of the present invention. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] This invention provides, for example Figure 1 The illustrated IoT-based collaborative service method includes the following steps:

[0064] S1: Build a blockchain platform and create a blockchain-based collaborative service platform to integrate energy data;

[0065] S2: Device interconnection, connecting energy devices through the Internet of Things to facilitate information flow and real-time communication;

[0066] S3: Intelligent scheduling, which adjusts energy production and consumption and energy flow according to real-time supply and demand conditions;

[0067] S4: User interaction is implemented to create a user interface, allowing users to view energy usage in real time and participate in scheduling decisions;

[0068] S5: Monitoring and early warning, building a real-time monitoring system, tracking key indicators in energy production and consumption, setting early warning thresholds, and promptly detecting and responding to abnormal situations;

[0069] S6: Service evaluation and continuous improvement. Establish a service evaluation indicator system, conduct regular evaluations of the effectiveness of energy collaborative services, and optimize based on the evaluation results.

[0070] By constructing a blockchain collaborative platform and a multi-level early warning system, the integration and real-time monitoring of energy-related data have been effectively achieved, ensuring the efficiency and transparency of information flow. Simultaneously, energy production and consumption are adjusted based on real-time supply and demand, thereby optimizing energy flow and reducing energy waste. Furthermore, user participation and interaction are enhanced through the establishment of a user interface, allowing users to view their energy usage in real time and provide feedback on scheduling decisions, thus improving energy utilization efficiency and adapting to the requirements of large-scale energy use. This significantly increases energy utilization rates and reduces substantial energy waste. Moreover, 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 anomalies, ensuring the stability and security of the energy network. Regular evaluation of the effectiveness of energy collaborative services, combined with user feedback, drives continuous optimization of methods. This not only improves the utilization rate of renewable energy but also promotes the efficient use of non-renewable energy, contributing to the construction of a more intelligent and green energy management system and promoting sustainable development.

[0071] Step S1, setting up the blockchain platform, includes the following steps:

[0072] S11: Requirements Analysis. Analyze user needs and business processes, identify 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.

[0073] S12: Develop blockchain infrastructure, set up nodes in the blockchain network and develop smart contracts. In the energy sector, smart contracts can handle energy transactions, settlements and other business according to preset rules, reduce human intervention, improve the transparency and fairness of transactions, and reduce operating costs and the risk of human error.

[0074] S13: Data integration involves standardizing energy-related data and storing and managing it uniformly. In the energy industry, standardized data supports more accurate energy forecasting, optimizes energy allocation, and improves energy efficiency. Simultaneously, unified storage management facilitates data backup and recovery, enhancing data security and stability.

[0075] S14: User identity management. Establish a user identity verification system and permission management mechanism. Assign different data access and operation permissions according to user roles. Through reasonable permission allocation, it can be ensured that all parties can carry out business activities efficiently under the premise of compliance, while maintaining the security and stability of the platform.

[0076] The device interconnection in step S2 includes the following steps:

[0077] S21: Equipment identification and classification. All connected energy devices are uniquely identified and classified according to device type, function and communication protocol, which facilitates accurate management and allows for quick device location, laying a good foundation for subsequent operations.

[0078] S22: Select a communication protocol. Choose the protocol most suitable for the energy equipment, unify the interface standards of the equipment, ensure smooth communication between devices, reduce compatibility issues, and improve the stability of data transmission.

[0079] S23: Device access and network configuration. Perform access tests on each type of device, and design a suitable network topology based on device distribution to ensure smooth network access, optimize network performance, and improve overall operating efficiency.

[0080] S24: Data acquisition and monitoring, real-time acquisition of equipment operating status and energy consumption data, uploading to the collaborative service platform, and analysis of the uploaded data through a data quality detection mechanism, real-time acquisition of equipment information and uploading to the platform, combined with data quality detection, to provide accurate basis for decision-making and improve the accuracy of energy management;

[0081] S25: Edge computing. Deploy edge computing nodes near the device to perform preliminary data processing and analysis. Deploying edge computing nodes to process data in advance reduces data transmission volume and latency, and improves response speed and system real-time performance.

[0082] S26: Secure communication, encrypting data during transmission to effectively prevent information leakage and malicious attacks, ensuring data security and maintaining stable system operation.

[0083] In step S25, edge computing uses a sliding window mean filter to process the data.

[0084]

[0085] in, This represents real-time data from the device, where N is the window size. The filtered value is t, where t is the time point of the data stream. Using a sliding window mean filter to process the data can effectively smooth the data, reduce random noise interference, make the data more reflective of the true 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.

[0086] Intelligent scheduling in step S3 includes the following steps:

[0087] S31: Data collection and analysis, collecting real-time key information on energy production, consumption and market prices from connected devices and blockchain platforms, and cleaning and standardizing the data to ensure accuracy and standardization, providing solid and reliable data support for subsequent scheduling decisions;

[0088] S32: By utilizing historical energy consumption data and trend analysis, and conducting demand forecasting, the dynamics of energy demand can be grasped in advance, making dispatching more forward-looking and targeted.

[0089] S33: Develop different energy production and consumption scheduling schemes for different energy types, optimize energy allocation, improve energy utilization efficiency, and reduce production and consumption costs;

[0090] S34: Real-time adjustment. Based on real-time supply and demand conditions and market changes, the dispatch strategy is dynamically adjusted. The dispatch is adjusted according to real-time conditions to quickly respond to supply and demand and market changes, maintain the balance of energy supply and demand, and improve dispatch flexibility.

[0091] S35: Feedback and optimization. Real-time monitoring of the effects of scheduling execution, and adjustment and optimization based on monitoring results, so as to continuously improve the scheduling scheme and enhance the accuracy and effectiveness of energy scheduling.

[0092] Step S4, user participation and interaction, includes the following steps:

[0093] S41: User classification. Classify users into different types to understand their needs and preferences, lay the foundation for providing personalized services, and enhance users' sense of identification with the energy system.

[0094] S42: User interface design. Design different user interfaces according to different types of users, design exclusive interfaces for different users, improve the ease of operation and user experience, and make it easier for users to interact with the energy system.

[0095] S43: Real-time information display. Develop visualization tools to display users' energy usage, real-time prices, supply and demand status and other key information in real time. Visualize key energy information to help users keep abreast of the situation, make rational energy use decisions, and improve the rationality of energy use.

[0096] S44: Develop interactive features that allow users to provide suggestions and feedback on scheduling plans. These interactive features enable users to participate in scheduling, enhance user engagement, make scheduling plans more aligned with user needs, and improve user satisfaction.

[0097] S45: Personalized services. Based on users' historical energy consumption data and preferences, a personalized energy consumption suggestion system is developed to provide personalized suggestions based on user data, guide users to optimize energy consumption, achieve energy saving and consumption reduction, and improve energy efficiency.

[0098] S46: User feedback. Set up a feedback collection mechanism so that users can evaluate and provide feedback on the system's user experience and scheduling schemes. Establish a feedback mechanism to collect user opinions to facilitate timely improvements to the system and scheduling schemes and continuously enhance the user experience.

[0099] Step S5, monitoring and alerting, includes the following steps:

[0100] S51: Key indicator identification. Identify key indicators related to energy production and consumption, accurately identify key energy indicators, and provide clear goals and directions for subsequent data collection, analysis, monitoring and early warning.

[0101] S52: Real-time data acquisition and analysis, which collects data from various equipment in real time and detects energy consumption and abnormalities in production in real time. It can promptly identify energy production and consumption problems and buy time for risk prevention and control.

[0102] S53: Based on the risk assessment results, adopt corresponding risk prevention and control measures.

[0103] Step S53: Based on the risk assessment results, formulate corresponding risk prevention and control strategies and measures. Developing prevention and control strategies based on the assessment results, and addressing energy risks in a targeted manner to ensure the safe and stable operation of the energy system, includes:

[0104] S531: Early warning level graded response rule configuration, pre-configure graded response rules: Level 1 early warning triggers system recording and prompts; Level 2 early warning triggers alarm notifications and fine-tuning of operating parameters; Level 3 early warning triggers emergency shutdown, backup power switching or remote dispatch instructions;

[0105] S532: When the monitored value triggers a level 2 or higher warning, a control command is generated based on the warning type and the device association, and then sent to the relevant energy equipment for execution via the Internet of Things;

[0106] S533: Manual intervention channel activated. When a Level 3 warning is triggered or the anomaly is not eliminated after control execution, the manual intervention channel is activated to send warning details and handling suggestions to the preset maintenance personnel, and to grant remote control permissions for manual intervention.

[0107] S534: Tracking and verifying the effectiveness of the response. After the implementation of prevention and control measures, continuously track the changes in key indicators. If the indicators recover to a safe range within a preset time, the warning is lifted and the response log is recorded. If the indicators continue to deteriorate or do not respond, the warning level is upgraded or the emergency plan is triggered.

[0108] S535: Emergency response plan linkage. When a major anomaly occurs and a Level 3 warning continues, the system will activate the emergency response plan, including switching on backup power, tiered load shedding, and starting emergency power, and will simultaneously notify relevant management personnel.

[0109] Step S53: Based on the risk assessment results, corresponding risk control measures are adopted. Specifically, by pre-configuring tiered response rules, the system achieves a binding between early warning levels and response actions. When the monitored value triggers a level 2 or higher early warning, the system generates control commands based on the early warning type and sends them to relevant devices via the Internet of Things for execution. Simultaneously, a manual intervention channel is opened. If a level 3 early warning is triggered or control is ineffective, details are pushed to maintenance personnel and remote control permissions are granted. After the measures are implemented, the system continuously tracks changes in key indicators for closed-loop verification. If the indicators recover, the early warning is lifted and a log is recorded. If the situation continues to deteriorate, the early warning level is upgraded. In the event of a major anomaly, the emergency response plan is activated, and operations such as switching on backup energy and tiered load shedding are performed, while simultaneously notifying management personnel. This tiered response and closed-loop control mechanism can improve the accuracy and efficiency of risk handling. Through human-machine collaboration, it ensures effective intervention in complex anomalies. At the same time, by leveraging effect tracking and emergency response plan linkage, it maximizes the safe and stable operation of the energy system and provides data support for subsequent optimization through complete handling records.

[0110] Step S532: When the monitored value triggers a level 2 or higher warning, a control command is generated based on the warning type and device association, and sent to the relevant energy equipment via the Internet of Things for execution, including:

[0111] The control command library is constructed by establishing a standardized control command library in advance for different warning types, equipment types and warning levels, and clarifying the operation object, command parameters and expected execution effect of each command;

[0112] Command matching and generation: When a level 2 or higher warning is triggered, the system matches the corresponding control command template from the command library based on the current warning type, level and associated device information, and fills the command parameters with real-time data to generate a specific executable command.

[0113] Instruction priority sorting: When multiple control instructions are generated simultaneously, they are prioritized according to their urgency and relevance to ensure that critical instructions are executed first.

[0114] Command issuance and confirmation: Commands are issued to the target device via the Internet of Things protocol, and the device is waited for an execution confirmation signal. If no confirmation is received within the time limit, the command is resent or an exception handling process is triggered.

[0115] Command execution effect verification: After the command is issued, the status changes of the relevant equipment are continuously monitored to verify whether the command has achieved the expected effect. If the execution fails or the effect is not up to standard, the command retry or upgraded handling measures are triggered.

[0116] By pre-establishing a standardized control command library, the corresponding operation objects, parameters, and expected effects for different warning types, equipment types, and levels are clearly defined. When the system detects a level 2 or higher warning, it matches the corresponding command template from the command library based on the current warning type, level, and associated equipment information, and generates specific executable commands by combining real-time data. A command priority sorting mechanism is also introduced to ensure that critical commands are executed first based on urgency and relevance. The commands are then sent to the target device via IoT protocol and await execution confirmation signals. If no confirmation is received within the time limit, the commands are automatically resent or an exception handling process is triggered. Finally, the status changes of related equipment are continuously monitored after command execution to verify the commands. Whether the expected results are achieved is determined. If the execution fails or the results are not satisfactory, the command is retried or the handling measures are upgraded. The standardized construction of the command library ensures the standardization and consistency of control operations, avoiding the arbitrariness and errors that may occur in manual operation. The automatic matching and generation of commands greatly improves the response speed and accuracy. Priority sorting ensures that the most critical control needs in complex operating conditions are met first. The issuance confirmation mechanism effectively prevents command loss or execution failure. The execution effect verification forms a complete control closed loop, ensuring that every control action produces actual results, thereby significantly improving the reliability and automation level of risk handling and ensuring the safe and stable operation of the energy system.

[0117] The formula for the three-level early warning system in step S531 is:

[0118]

[0119] in, For real-time monitoring values, The average of historical data. The standard deviation of historical data is used. Level 1 refers to the first level of warning, Level 2 refers to the second level of warning, and Level 3 refers to the third level of warning. Level 1 is less severe, and Level 3 is more severe. For example, Level 1 is for adjustment and Level 3 is for emergency load shedding. Multi-level warning can accurately match the risk level, avoid over-response, reduce the probability of major accidents, optimize resource allocation, and reduce operation and maintenance costs.

[0120] Step S6, service evaluation and continuous improvement, includes the following steps:

[0121] S61: Define the assessment objectives, identify the specific content and indicators of the energy synergy services to be assessed, clarify the assessment content and indicators, provide a clear direction for service assessment, and make the assessment more targeted and operable.

[0122] S62: Establish multiple user feedback channels, organize and classify the collected user feedback, identify the issues and needs that users are generally concerned about, collect and classify user feedback through multiple channels, accurately grasp user needs and problems, and provide a strong basis for service improvement;

[0123] S63: Evaluation and optimization. Regularly write service evaluation reports, formulate specific improvement plans based on evaluation results and user feedback, clarify improvement goals, measures and timelines, conduct regular evaluations and formulate improvement plans based on results and feedback, so that service optimization is systematic and the improvement work is carried out in an orderly manner.

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

[0125] This invention also provides a collaborative service system based on the Internet of Things, comprising:

[0126] The blockchain platform module creates a blockchain-based collaborative service platform that integrates energy data.

[0127] The device interconnection module connects energy devices via the Internet of Things (IoT) to facilitate information flow and real-time communication.

[0128] The intelligent scheduling module adjusts energy production and consumption as well as energy flow based on real-time supply and demand conditions;

[0129] The user participation and interaction module establishes a user interface, allowing users to view energy usage in real time and participate in dispatching decisions;

[0130] The monitoring and early warning module builds a real-time monitoring system to track key indicators in energy production and consumption, sets early warning thresholds, and promptly detects and responds to anomalies.

[0131] The service evaluation and continuous improvement module establishes a service evaluation indicator system to regularly evaluate the effectiveness of energy collaborative services and optimize them based on the evaluation results.

[0132] Finally, it should be noted that the above description 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 foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A collaborative service method based on the Internet of Things, characterized in that, Includes the following steps: S1: Build a blockchain platform and create a blockchain-based collaborative service platform to integrate energy data; S2: Device interconnection, connecting energy devices through the Internet of Things to facilitate information flow and real-time communication; S3: Intelligent scheduling, which adjusts energy production and consumption and energy flow according to real-time supply and demand conditions; S4: User interaction is implemented to create a user interface, allowing users to view energy usage in real time and participate in scheduling decisions; S5: Monitoring and early warning, building a real-time monitoring system, tracking key indicators in energy production and consumption, setting early warning thresholds, and promptly detecting and responding 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 optimize based on the evaluation results.

2. The collaborative service method based on the Internet of Things according to claim 1, characterized in that, The step S1 of building the blockchain platform includes the following steps: S11: Requirements Analysis. Analyze user needs and business processes, identify the data types that need to be recorded and managed on the blockchain, and select the appropriate blockchain type. S12: Develop blockchain infrastructure, set up nodes in the blockchain network, and develop smart contracts; S13: Data integration, standardizing energy-related data and storing and managing it in a unified manner; S14: 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 of Things according to claim 1, characterized in that, The device interconnection in step S2 includes the following steps: S21: Equipment identification and classification, uniquely identifying all connected energy devices and classifying them according to device type, function and communication protocol; S22: Select a communication protocol, choose a protocol suitable for the energy equipment, and unify the interface standards of the equipment; S23: Device access and network configuration, conduct access tests for each type of device, and design a suitable network topology based on device distribution; S24: Data acquisition and monitoring, real-time acquisition of equipment operating status and energy consumption data, uploading to the collaborative service platform, and analysis of the uploaded data through a data quality detection mechanism; S25: Edge computing, deploying 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 of Things according to claim 3, characterized in that, In step S25, edge computing uses a sliding window mean filter to process the data. in, This represents real-time data from the device, where N is the window size. The value is the filtered value, and t is the time point in the data stream.

5. The collaborative service method based on the Internet of Things according to claim 1, characterized in that, The monitoring and early warning process in step S5 includes the following steps: S51: Key Indicator Identification: Identify key indicators related to energy production and consumption; S52: Real-time data acquisition and analysis, which collects data from various devices in real time and detects energy consumption and abnormal situations that occur in production in real time; S53: Based on the risk assessment results, adopt corresponding risk prevention and control measures.

6. The collaborative service method based on the Internet of Things according to claim 5, characterized in that, Step S53, based on the risk assessment results, adopts corresponding risk control measures, including: S531: Early warning level graded response rule configuration, pre-configure graded response rules: Level 1 early warning triggers system recording and prompts; Level 2 early warning triggers alarm notifications and fine-tuning of operating parameters; Level 3 early warning triggers emergency shutdown, backup power switching or remote dispatch instructions; S532: When the monitored value triggers a level 2 or higher warning, a control command is generated based on the warning type and the device association, and then sent to the relevant energy equipment for execution via the Internet of Things; S533: Manual intervention channel activated. When a Level 3 warning is triggered or the anomaly is not eliminated after control execution, the manual intervention channel is activated to send warning details and handling suggestions to the preset maintenance personnel, and to grant remote control permissions for manual intervention. S534: Tracking and verifying the effectiveness of the response. After the implementation of prevention and control measures, continuously track the changes in key indicators. If the indicators recover to a safe range within a preset time, the warning is lifted and the response log is recorded. If the indicators continue to deteriorate or do not respond, the warning level is upgraded or the emergency plan is triggered. S535: Emergency response plan linkage. When a major anomaly occurs and a Level 3 warning continues, the system will activate the emergency response plan, including switching on backup power, tiered load shedding, and starting emergency power, and will simultaneously notify relevant management personnel.

7. A collaborative service method based on the Internet of Things according to claim 6, characterized in that, When the monitored value triggers a level 2 or higher warning, step S532 generates a control command based on the warning type and device association, and sends it to the relevant energy equipment via the Internet of Things for execution, including: The control command library is constructed by establishing a standardized control command library in advance for different warning types, equipment types and warning levels, and clarifying the operation object, command parameters and expected execution effect of each command; Command matching and generation: When a level 2 or higher warning is triggered, the system matches the corresponding control command template from the command library based on the current warning type, level and associated device information, and fills the command parameters with real-time data to generate a specific executable command. Instruction priority sorting: When multiple control instructions are generated simultaneously, they are prioritized according to their urgency and relevance to ensure that critical instructions are executed first. Command issuance and confirmation: Commands are issued to the target device via the Internet of Things protocol, and the device is waited for an execution confirmation signal. If no confirmation is received within the time limit, the command is resent or an exception handling process is triggered. Command execution effect verification: After the command is issued, the status changes of the relevant equipment are continuously monitored to verify whether the command has achieved the expected effect. If the execution fails or the effect is not up to standard, the command retry or upgraded handling measures are triggered.

8. A collaborative service method based on the Internet of Things according to claim 6, characterized in that, The formula for the three-level early warning system in step S531 is: in, For real-time monitoring values, The average of historical data. The standard deviation of historical data is used. Level 1 refers to the first level of warning, Level 2 refers to the second level of warning, and Level 3 refers to the third level of warning.

9. A collaborative service method based on the Internet of Things according to claim 1, characterized in that, The service evaluation and continuous improvement in step S6 includes the following steps: S61: Determine the assessment objectives and the specific content and indicators of the energy synergy services that need to be assessed; S62: Establish multiple user feedback channels, organize and classify the collected user feedback, and identify the issues and needs that users are generally concerned about; S63: Evaluation and optimization. Regularly write service evaluation reports, and develop specific improvement plans based on evaluation results and user feedback, clarifying improvement goals, measures and timelines; S64: Regularly re-evaluate the effectiveness of the optimized service to verify the effectiveness of the improvement measures.

10. A collaborative service system based on the Internet of Things, characterized in that, The IoT-based collaborative service system includes an IoT-based collaborative service method as described in any one of claims 1-9, specifically comprising: The blockchain platform module creates a blockchain-based collaborative service platform that integrates energy data. The device interconnection module connects energy devices via the Internet of Things (IoT) to facilitate information flow and real-time communication. The intelligent scheduling module adjusts energy production and consumption as well as energy flow based on real-time supply and demand conditions; The user participation and interaction module establishes a user interface, allowing users to view energy usage in real time and participate in dispatching decisions; The monitoring and early warning module builds a real-time monitoring system to track key indicators in energy production and consumption, sets early warning thresholds, and promptly detects and responds to anomalies. The service evaluation and continuous improvement module establishes a service evaluation indicator system to regularly evaluate the effectiveness of energy collaborative services and optimize them based on the evaluation results.

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