Energy consumption analysis method and system based on microgrid resource physical digital space model

Abstract

The invention discloses an energy consumption analysis method and system based on a micro-grid resource physical digital space model, and the method comprises the steps: obtaining first monitoring data of distributed energy equipment and second monitoring data of electric equipment in a micro-grid, and fusing the data to construct the micro-grid resource physical digital space model; constructing a distributed energy transaction platform based on the block chain based on the model; an energy consumption traceability function is integrated in the transaction platform, an energy consumption traceability tree model is constructed, then the energy consumption contribution degree of each distributed energy device and the energy consumption responsibility sharing proportion of each electric device are calculated, and an energy consumption traceability report is generated and is linked for evidence storage; by constructing the energy consumption traceability tree model, fine traceability and responsibility sharing of micro-grid energy flow can be realized, accurate data support is provided for energy consumption analysis and management, and transparency and fairness of micro-grid energy management are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power energy consumption analysis, and more specifically, to an energy consumption analysis method and system based on a physical digital space model of microgrid resources. Background Art

[0002] With the advancement of the construction of new power systems, microgrids, as an autonomous power supply and utilization system that integrates distributed renewable energy, energy storage systems and flexible loads, have increasingly complex internal energy flows, which puts higher demands on refined energy consumption management and fair trading mechanisms.

[0003] The Chinese patent with the authorization announcement number CN205231853U discloses an Internet of Things-based power consumption analysis system, which consists of a power consumption analyzer and a power consumption monitor, wherein the power consumption analyzer includes a wireless communication module, an energy consumption analyzer, an energy-saving potential analyzer and an economic analyzer, and the power consumption monitor includes an energy-consuming equipment monitor, a power quality monitor, a wireless communication module, a microprocessor and a power supply. The system can monitor energy-consuming equipment and power quality in real time, and perform energy consumption analysis, energy-saving potential analysis and economic analysis. However, the technical solution mainly focuses on enterprise-level power consumption analysis, targeting traditional centralized power systems, lacks consideration of the complex relationship between distributed energy and equipment within the microgrid, and is difficult to achieve refined management of various devices within the microgrid. In addition, the technical solution does not utilize digital twin technology, cannot achieve real-time mapping and interaction between the physical world and the digital world, is difficult to perform effective predictive maintenance and optimization control, and cannot reflect the relationship between independent devices within the microgrid, so it cannot support energy trading based on the physical digital space model of microgrid resources.

[0004] The Chinese patent with publication number CN119010335A discloses a distributed microgrid control method, system, device and storage medium. The method includes: performing topological analysis on the physical structure and energy flow path of the distributed microgrid, establishing a microgrid architecture including a control center and multiple control nodes; deploying acquisition and transmission equipment for the microgrid architecture, establishing a communication network, and introducing a security mechanism; the control center distributes the data collected by the acquisition and transmission equipment to the target control node based on the communication network and the security mechanism, monitors the energy consumption of the microgrid architecture in real time, and dynamically adjusts the working status of each part of the control microgrid architecture. The system includes a demand analysis module, an architecture design module, a communication system design module, and an energy management and optimization algorithm design module. The technical solution has the characteristics of strong controllability and comprehensive analysis. However, the method mainly focuses on the control problem of the microgrid, lacks consideration of the energy trading link, and does not involve the energy consumption traceability problem in the transaction process, and cannot motivate the enthusiasm of the participants in the microgrid. In addition, the technical solution relies on a centralized control center, has a single point failure risk, and the security and transparency of the data are difficult to guarantee.

[0005] In summary, the existing technology is unable to achieve refined management and traceability of energy transactions within the microgrid, especially it is unable to accurately calculate the energy consumption contribution of each distributed energy device and the energy consumption responsibility sharing ratio of each electrical device, making it difficult to reflect fairness; the existing technology lacks consideration of the complex relationship between distributed energy and devices within the microgrid, making it difficult to achieve refined management of each device within the microgrid. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides an energy consumption analysis method and system based on the physical digital space model of microgrid resources. First, a digital twin model of the microgrid is constructed, and then a blockchain distributed energy trading platform is built based on the model, and the energy consumption traceability function is integrated. By constructing an energy consumption traceability tree model, the energy transaction process is traced in a refined manner, and the energy consumption contribution and responsibility sharing ratio of each participant are calculated. Finally, an energy consumption traceability report is generated and stored on the chain, thereby improving the transparency, fairness and traceability of microgrid energy management.

[0007] To achieve the above object, the present invention provides the following technical solutions: An energy consumption analysis method based on a physical digital space model of microgrid resources includes: Acquire first monitoring data of distributed energy equipment and second monitoring data of power-consuming equipment in the microgrid, fuse the first monitoring data and the second monitoring data, and construct a physical digital space model of microgrid resources; Based on the physical digital space model of microgrid resources, a distributed energy trading platform based on blockchain is constructed; Integrate the energy consumption traceability function into the distributed energy trading platform and build an energy consumption traceability tree model. According to the energy consumption traceability tree model, calculate the energy consumption contribution of each distributed energy device and the energy consumption responsibility sharing ratio of each power-consuming device, generate an energy consumption traceability report, and store it on the chain.

[0008] Furthermore, the first monitoring data includes energy equipment ID and energy equipment location information; The second monitoring data includes the electrical equipment ID and the electrical equipment location information; The construction of the microgrid resource physical digital space model includes: According to the energy equipment ID and the power equipment ID, the first monitoring data and the second monitoring data are matched to form equipment panoramic data; According to the location information of energy equipment and power equipment, establish the association relationship between equipment nodes to form equipment association data; Based on the equipment panoramic data and equipment association data, a physical digital space model of microgrid resources is constructed. Distributed energy equipment and power-consuming equipment are device nodes in the physical digital space model of microgrid resources.

[0009] Furthermore, the construction of a distributed energy trading platform based on blockchain based on the physical digital space model of microgrid resources includes: Uploading the first monitoring data and the second monitoring data in the physical digital space model of the microgrid resources to the chain; Design blockchain-based energy trading smart contracts and deploy them to the blockchain network; Based on the first monitoring data and the second monitoring data, determine whether the energy trading smart contract is triggered. If triggered, energy trading based on the physical digital space model of microgrid resources is carried out according to the deployed energy trading smart contract; if not triggered, continue to monitor the operating status of the microgrid and wait for the triggering conditions to be met.

[0010] Furthermore, the design of the energy transaction smart contract based on blockchain includes: Design energy trading algorithms and convert them into smart contract codes; the energy trading algorithms include transaction matching algorithms, pricing algorithms and settlement algorithms; Set the trigger conditions for the transaction contract and deploy the smart contract code to the blockchain network.

[0011] Furthermore, the design method of the energy trading algorithm includes: Determine the electricity sales demand information based on the first monitoring data; determine the electricity purchase demand information based on the second monitoring data; Calculate the matching degree of supply and demand based on the electricity sales demand information and the electricity purchase demand information; Based on the supply and demand matching degree and equipment association data, a supply and demand matching queue is generated to automatically match the top-ranked supply and demand parties.

[0012] Further, the calculating of the supply-demand matching degree based on the electricity sales demand information and the electricity purchase demand information includes: calculating the energy type matching degree, the electricity quantity matching degree, the price matching degree and the time window matching degree according to the electricity sales demand information and the electricity purchase demand information; calculating the supply-demand matching degree according to the energy type matching degree, the electricity quantity matching degree, the price matching degree and the time window matching degree; The setting of trigger conditions for transaction contracts includes: setting trigger conditions based on transaction time windows, setting trigger conditions based on energy balance status, setting trigger conditions based on market electricity price fluctuations, and setting trigger conditions based on major events.

[0013] Furthermore, the energy transaction based on the physical digital space model of microgrid resources includes: Distributed energy devices transmit electricity sales demand information to energy trading smart contracts through their device nodes in the physical digital space model of microgrid resources; The power-consuming equipment transmits the power purchase demand information to the energy trading smart contract through its device node in the physical digital space model of the microgrid resources; The energy trading smart contract deployed in the blockchain network automatically matches the received electricity sales demand information and electricity purchase demand information according to the preset energy trading algorithm to obtain the matching result; The energy trading smart contract automatically executes energy transactions and settles according to the matching results, records each energy transaction, and generates blockchain records.

[0014] Furthermore, the energy consumption traceability function is integrated into the distributed energy trading platform to construct an energy consumption traceability tree model, including: Extract the first energy consumption data of each energy transaction based on blockchain records; Access the physical digital spatial model of microgrid resources and extract the second energy consumption data related to each transaction; The first energy consumption data and the second energy consumption data are fused and analyzed to construct an energy consumption traceability tree model.

[0015] Furthermore, the energy consumption traceability tree model is constructed including: Based on the first energy consumption data recorded in the blockchain, a directed graph of energy transactions is constructed; The second energy consumption data is attached as attribute data to the corresponding node of the energy transaction directed graph; Obtain time series data, embed the time series data in the energy transaction directed graph, and form a spatiotemporal energy consumption traceability graph; Based on the spatiotemporal energy consumption traceability diagram, typical transaction patterns are extracted and an energy consumption traceability tree model is constructed.

[0016] An energy consumption analysis system based on a physical digital space model of microgrid resources is used to implement the above-mentioned energy consumption analysis method based on a physical digital space model of microgrid resources. The system includes: A digital model building module: used to obtain first monitoring data of distributed energy equipment and second monitoring data of power consumption equipment in the microgrid, integrate the first monitoring data and the second monitoring data, and build a physical digital space model of microgrid resources; Energy trading platform construction module: Based on the physical digital space model of microgrid resources, a distributed energy trading platform based on blockchain is constructed; Energy consumption analysis module: used to integrate the energy consumption traceability function in the distributed energy trading platform and build an energy consumption traceability tree model; according to the energy consumption traceability tree model, calculate the energy consumption contribution of each distributed energy device and the energy consumption responsibility sharing ratio of each power-consuming device, generate an energy consumption traceability report, and store it on the chain.

[0017] Compared with the prior art, the present invention has the following beneficial effects: By constructing a physical digital space model of microgrid resources, comprehensive, real-time monitoring and digital modeling of distributed energy equipment and power equipment in the microgrid are achieved. This digital twin model can intuitively display the operating status of the microgrid, energy flow, and interaction between devices, thereby greatly improving the transparency and observability of microgrid operation and laying the foundation for refined management. The distributed energy trading platform based on blockchain uses smart contracts to automatically perform transaction matching, settlement and recording, improves the efficiency of energy trading and reduces transaction costs. At the same time, the decentralization, tamper-proof and transparency of blockchain technology ensure the fairness and justice of the transaction process and enhance the trust of both parties to the transaction. By integrating the energy consumption traceability function and constructing an energy consumption traceability tree model, the ins and outs of each energy transaction can be traced, and the energy consumption contribution of each distributed energy device and the energy consumption responsibility sharing ratio of each power device can be accurately calculated. This refined energy consumption analysis method provides a scientific basis for optimizing energy allocation, formulating reasonable energy prices and incentive mechanisms, and promotes fair and reasonable energy responsibility sharing. Through the energy trading platform and energy consumption traceability mechanism, the production and use of distributed energy, especially clean energy, can be encouraged. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a principle flow chart of the energy consumption analysis method based on the physical digital space model of microgrid resources in the present invention; Figure 2 A flow chart of a method for constructing a microgrid resource physical digital space model in an energy consumption analysis method based on a microgrid resource physical digital space model of the present invention; Figure 3 A flowchart of a method for designing a blockchain-based energy transaction smart contract in the energy consumption analysis method based on the microgrid resource physical digital space model of the present invention; Figure 4 A flow chart of a method for designing a transaction matching algorithm in an energy consumption analysis method based on a physical digital space model of microgrid resources of the present invention; Figure 5 A method flow chart of setting a transaction contract trigger condition in the energy consumption analysis method based on a microgrid resource physical digital space model of the present invention; Figure 6 A flow chart of a method for determining whether to trigger an energy trading smart contract in an energy consumption analysis method based on a microgrid resource physical digital space model of the present invention; Figure 7 A flow chart of a method for carrying out energy trading based on a microgrid resource physical digital space model in an energy consumption analysis method based on a microgrid resource physical digital space model of the present invention; Figure 8 A flow chart of a method for constructing an energy consumption tracing tree model in an energy consumption analysis method based on a microgrid resource physical digital space model of the present invention; Fig. 9 It is a functional module diagram of the energy consumption analysis system based on the physical digital space model of microgrid resources in the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0021] Application scenarios of the present invention: The energy consumption analysis method based on the physical digital space model of microgrid resources described in the present invention can be applied to intelligent microgrid systems that include a variety of distributed energy equipment (such as photovoltaic, wind power, energy storage, etc.) and various types of power loads, such as industrial parks, commercial buildings, communities / residences, etc.

[0022] Example 1

[0023] See also Figure 1As shown, this embodiment provides an energy consumption analysis method based on a physical digital space model of microgrid resources, including: Step S1000, obtaining first monitoring data of distributed energy equipment and second monitoring data of power consumption equipment in a microgrid, integrating the first monitoring data and the second monitoring data, and constructing a physical digital space model of microgrid resources; Furthermore, step S1000 includes: Step S1100, real-time monitoring of distributed energy equipment in the microgrid to obtain first monitoring data; the first monitoring data includes energy equipment ID, equipment operation parameters, energy equipment energy consumption data, energy equipment environmental parameters and energy equipment location information; Specifically, the purpose of step S1100 is to comprehensively and in real time grasp the operating status and energy consumption of distributed energy equipment in the microgrid, and lay a data foundation for building a physical digital space model of microgrid resources. Distributed energy equipment usually refers to small power generation units located on the user side, such as photovoltaic power generation systems, small wind turbines, micro gas turbines, fuel cells, etc. These devices have the characteristics of being close to the load, clean and environmentally friendly, and are an important part of building a smart microgrid. Step S1100 performs multi-dimensional and multi-granular real-time monitoring of each distributed energy device.

[0024] Energy device ID refers to the unique identifier of each distributed energy device, such as device number, serial number, etc., which is used to distinguish different devices. Equipment operating parameters refer to various parameters during the operation of the equipment, such as power generation, speed, voltage, current, etc. These parameters reflect the real-time working status of the equipment. Intelligent sensors are installed on each distributed energy device to collect equipment operating parameters. For example, for photovoltaic power generation systems, sensors for measuring the output voltage, current, and power of photovoltaic modules, as well as sensors for measuring the surface temperature, inclination, and azimuth of photovoltaic panels need to be installed. If the photovoltaic system has a maximum power point tracking (MPPT) function, it is also necessary to monitor the input and output voltage, current, and power of the MPPT. For small wind turbines, sensors for measuring wind turbine speed, output voltage, current, and power, as well as sensors for measuring wind direction and wind speed need to be installed. If the wind turbine has a yaw system, it is also necessary to monitor parameters such as the yaw angle.

[0025] Energy equipment energy consumption data refers to the amount of power generated, self-consumed power, and online power of the equipment over a period of time. These data reflect the energy production and consumption of the equipment. Smart meters are installed at the grid connection point of each distributed energy device to collect power generation, self-consumed power, and online power. Power generation refers to the total electric energy produced by distributed energy equipment over a period of time. Self-consumed power refers to the part of the electric energy produced by distributed energy equipment that is directly consumed by local loads. Online power refers to the part of the electric energy produced by distributed energy equipment that is transmitted to the power grid, which can participate in energy trading. Energy equipment environmental parameters refer to the temperature, humidity, wind speed, radiation intensity, etc. of the environment where the equipment is located. These parameters will affect the operating efficiency and power generation of the equipment. Environmental monitoring equipment is installed near each distributed energy device to collect environmental parameters of the environment where the equipment is located, including temperature, humidity, wind speed, wind direction, radiation intensity, etc. Ambient temperature and humidity will affect the operating efficiency of distributed energy equipment. For example, high temperature will reduce the power generation efficiency of photovoltaic modules, and high humidity may affect the insulation performance of electrical equipment. For wind turbines, wind speed and wind direction are the main factors affecting their power generation.

[0026] Energy equipment location information refers to the geographical location information of the equipment, such as longitude, latitude, altitude, etc. This information is essential for analyzing the spatial relationship between equipment and the energy flow path. Through GPS or other positioning technologies, the geographical location information of each distributed energy device, including longitude, latitude, altitude, etc., is obtained. This information is essential for building a physical digital spatial model of microgrid resources and can be used to analyze the spatial relationship between equipment and the energy flow path. Through wireless or wired communication methods, the collected data is transmitted to the data center in real time for storage and processing.

[0027] Through multi-dimensional and multi-granular real-time monitoring of distributed energy equipment, the operating status, energy consumption and environmental parameters of each device can be fully understood, providing data support for subsequent analysis and optimization. Detailed monitoring data lays a solid foundation for building an accurate physical digital space model of microgrid resources, enabling the digital space model to more realistically reflect the state of the microgrid in the physical world. The combination of operating parameters and environmental parameters can be used for fault diagnosis and predictive maintenance, reducing downtime and improving system reliability. For example, by monitoring the temperature and output power of photovoltaic panels, it can be determined whether there is dust shielding or hot spot effect, and timely cleaning or maintenance can be performed.

[0028] Step S1200, real-time monitoring of the electric equipment in the microgrid to obtain second monitoring data; the second monitoring data includes the electric equipment ID, equipment operation status data, electric equipment energy consumption data, equipment load data, electric equipment location information and electric equipment environmental parameters; Specifically, smart sensors are installed on electrical equipment to collect data on the equipment's operating status, including power-on / off status, working mode, load rate, etc. Smart sensors are a new type of sensor that integrates information collection, data processing, and wireless communication functions. They can sense the equipment's operating status in real time and transmit the collected data to the monitoring center via a wireless network. By monitoring the power-on / off status of the equipment, the equipment's usage period and frequency can be understood; by monitoring the working mode, the equipment's operating conditions and energy efficiency level can be understood; by monitoring the load rate, the equipment's utilization efficiency and energy-saving space can be evaluated.

[0029] Smart meters are used to collect energy consumption data of electrical equipment, including power consumption, peak and valley power consumption, power factor, etc. Smart meters are a new type of electric energy meter that integrates metering, monitoring and two-way communication. They can record the power consumption of electrical equipment in detail and support remote meter reading and demand-side management. Power consumption data reflects the total power consumption of the equipment over a period of time; peak and valley power data reflects the power consumption characteristics of the equipment at different times, which helps to formulate peak power consumption strategies; power factor data reflects the reactive power of the equipment and is an important indicator for evaluating power supply quality and equipment health.

[0030] The demand response management system collects load data of power equipment, including load forecast values, load dispatch plans, etc. Demand response is a demand-side management method that guides users to change their electricity consumption behavior through price signals or incentives. It can alleviate the contradiction between supply and demand during peak hours of the power grid and improve energy efficiency. Load forecasting is the basis of demand response. Through big data analysis and machine learning algorithms, the load change trend of power equipment in the future can be accurately predicted; load dispatching is the core of demand response. According to the load forecast results and the dispatch requirements of the power grid, the optimal load control strategy is formulated to guide users to use electricity during the low electricity price period and reduce unnecessary electricity consumption during peak hours.

[0031] By monitoring the operating status, energy consumption and load of power-consuming equipment in real time, we can accurately grasp the power demand and energy-saving potential on the user side, and provide a decision-making basis for optimizing energy trading and energy consumption management. For example, an industrial park installs smart sensors and smart meters on major production equipment to collect detailed equipment operation and energy consumption data; then analyzes the overall load characteristics of the park through the demand response management system and predicts the load curve for the next day; according to the load forecast results, adjusts the production plan to avoid the peak period of the power grid, and achieves "peak shaving and valley filling" in the park through staggered production and equipment scheduling, which can save more than 20% of electricity bills each year. It can be seen that real-time monitoring of power equipment operation data is a key link in realizing refined energy management and demand response, and is of great significance for reducing energy costs and improving power grid efficiency.

[0032] Step S1300: integrating the first monitoring data and the second monitoring data to construct a physical digital space model of microgrid resources.

[0033] The Cyber-Physical System (CPS) is an intelligent system that closely integrates the physical world with the digital world. Through a large number of sensors and controllers, it realizes real-time perception, dynamic control and deep integration of physical objects. In the microgrid scenario, distributed energy equipment and power equipment are physical components of the CPS system, while the first monitoring data and the second monitoring data are digital descriptions of these physical components. By integrating monitoring data from different sources and dimensions, a CPS model that fully reflects the actual operating status of the microgrid can be constructed in the digital space, that is, the physical digital space model of microgrid resources.

[0034] Furthermore, if Figure 2 As shown, step S1300 includes: Step S1310, matching the first monitoring data and the second monitoring data according to the energy device ID and the power device ID to form device panoramic data; Step S1320, establishing association relationships between device nodes based on energy device location information and power device location information to form device association data; Step S1330, based on the equipment panoramic data and equipment association data, a physical digital space model of microgrid resources is constructed, and distributed energy equipment and power consumption equipment are device nodes in the physical digital space model of microgrid resources.

[0035] Specifically, equipment panoramic data refers to the aggregation of different monitoring data of the same equipment to form a complete, multi-dimensional equipment portrait. Since the first monitoring data and the second monitoring data come from the energy side and the user side respectively, they need to be matched and fused according to the unique equipment identifier (such as equipment ID). By cleaning, denoising and standardizing the monitoring data, the data quality is improved to ensure the consistency and reliability of the data. On the one hand, equipment panoramic data includes the static attributes of the equipment, such as equipment type, rated parameters, manufacturer, etc.; on the other hand, it includes the dynamic attributes of the equipment, such as the real-time operating status of the equipment, historical energy consumption data, health level, etc. Based on equipment panoramic data, the operating laws and energy efficiency characteristics of the equipment can be deeply analyzed, and data support can be provided for applications such as intelligent scheduling and predictive maintenance.

[0036] Device-associated data reflects the direction of energy flow and the properties of electrical connections in a microgrid. A microgrid is a small power system composed of distributed energy, energy storage devices, loads, and related control and protection equipment. Its physical structure has obvious network characteristics. By analyzing the location information of energy equipment and power-consuming equipment (such as longitude and latitude coordinates, substations, etc.), the topological relationship between device nodes can be established in the digital space to form a virtual "microgrid". In this virtual grid, distributed energy nodes serve as power sources, power-consuming equipment nodes serve as loads, and electric energy flows from source nodes to load nodes. At the same time, the physical connections between different devices, such as transformers and lines, will also be abstracted as logical links between nodes. The value of device-associated data lies in that it provides a networked perspective for subsequent energy optimization, fault location, and other analyses, making the overall operating status of the microgrid clear at a glance. For example, when a branch fails, it can quickly determine which device nodes will be affected and take emergency measures; in another example, in an energy trading scenario, the optimal path for electric energy from the source to the end point can be calculated and used as a reference for transaction pricing.

[0037] The physical digital space model of microgrid resources realizes the deep integration of microgrid physical resources and digital resources through the digital twin of physical equipment. The model uses the panoramic data of equipment as nodes and the associated data of equipment as edges, abstracts the key physical equipment of the microgrid into digital twins, and then organizes these twins into a digital microgrid that is isomorphic to the physical microgrid. Each digital twin is a high-simulation mapping of the physical equipment. It receives the monitoring data sent back by the physical equipment in real time, and dynamically simulates the behavior characteristics of the equipment according to the embedded mechanism model and data model. The digital twins interact with each other through data flow and energy flow, and together form a "living" microgrid system. The advantage of the physical digital space model of microgrid resources is that it provides a digital means for realizing the full life cycle management of the microgrid. Before operation, the microgrid can be fully simulated and verified in the digital space to optimize the design scheme; during operation, the safety and stability level of the microgrid can be dynamically evaluated based on real-time monitoring data to assist energy scheduling decisions; after operation, the health degradation process of the equipment can be traced through the digital twin and an equipment maintenance plan can be formulated.

[0038] In summary, the physical digital space model of microgrid resources integrates multi-source heterogeneous equipment monitoring data and introduces cutting-edge technologies such as digital twins and graph models to build a dynamic mapping relationship between the physical entity of the microgrid and the digital virtual entity, so that the perception, analysis, decision-making, and control processes of the microgrid are all completed in the digital space, thereby maximizing the flexibility, efficiency, and intelligence of the microgrid. This model can accurately depict the flow trajectory of energy in the microgrid and optimize the coordination of source-grid-load-storage.

[0039] Step S2000, building a distributed energy trading platform based on blockchain based on the physical digital space model of microgrid resources; Furthermore, step S2000 includes: Step S2100, uploading the first monitoring data and the second monitoring data in the physical digital space model of the microgrid resources to the chain; Furthermore, step S2100 includes: Step S2110, select the alliance chain as the underlying architecture of the blockchain, set each entity in the microgrid as an alliance member, and allocate nodes to the alliance members; Step S2120, formatting the first monitoring data and the second monitoring data into transaction events on the blockchain, and uploading them to the blockchain node; In step S2130, each blockchain node verifies and confirms the transaction event through a consensus mechanism.

[0040] Specifically, step S2100 of uploading monitoring data to the blockchain refers to using blockchain technology to store the real-time operating data of distributed energy equipment and power equipment in the blockchain network in an encrypted and tamper-proof manner to achieve secure data sharing and trusted exchange. Blockchain is a decentralized distributed ledger technology that achieves data consistency among multiple participating nodes through cryptographic principles and consensus mechanisms, ensuring the authenticity and non-tamperability of the data. Storing the first monitoring data (energy equipment ID, equipment operating parameters, energy equipment energy consumption data, energy equipment environmental parameters, and energy equipment location information) and the second monitoring data (power equipment ID, equipment operating status data, power equipment energy consumption data, equipment load data, power equipment location information, and power equipment environmental parameters) in the physical digital space model of microgrid resources on the chain can provide a reliable data basis for subsequent energy transactions and energy consumption analysis.

[0041] Consortium Blockchain is a semi-decentralized blockchain form, between a fully open public chain and a fully closed private chain. In a consortium chain, the joining of nodes requires the permission and authorization of consortium members, so the consortium chain takes into account both decentralization and controllability. Choosing the consortium chain as the underlying architecture of the energy blockchain can achieve mutual trust and collaboration among different stakeholders while ensuring data privacy. In the microgrid scenario, microgrid operators, distributed energy owners, power sales companies, power grid companies, etc. can be used as consortium members, and blockchain nodes are assigned to each member. Nodes are the basic units of the consortium chain network, with functions such as ledger storage, smart contract execution, and transaction verification. By reasonably setting node permissions and consensus rules, an efficient, reliable, and scalable energy blockchain network can be formed. The consortium chain architecture can achieve trusted data sharing and transaction matching while meeting the privacy protection needs of all parties in the microgrid. For example, distributed energy owners and users can authorize microgrid operators to view some of their own data, but not to disclose it to power grid companies and power sales companies; while power grid companies can obtain aggregated data provided by microgrid operators, but cannot obtain the privacy data of individual users. By flexibly configuring node permissions, a multi-level, differentiated data sharing mechanism can be built on the basis of a trusted alliance chain to effectively balance the relationship between data utilization and privacy protection.

[0042] The basic data unit of blockchain is "transaction". Each transaction will be recorded in a block. Multiple blocks are connected in chronological order, so it is called "blockchain". Formatting the monitoring data in the physical digital space model of microgrid resources into blockchain transactions means encoding and encapsulating these data in the data mode of blockchain network to generate standardized transaction events. For example, the monitoring data of a group of energy equipment can be packaged into a transaction, and the transaction timestamp, transaction content, transaction ID and other information can be written into the transaction body; the accumulated data over a period of time can also be used as a transaction to reduce the frequency of data on the chain. The generated transaction event is broadcast to each node through the P2P network of the blockchain, waiting for verification and confirmation by the nodes in the network.

[0043] Consensus Mechanism is one of the core technologies of blockchain, which is used to ensure that each node in the distributed network reaches a consensus on the verification results of the transaction. Through the consensus mechanism, malicious nodes in the network can be resisted, and security issues such as data tampering and double payment can be prevented. In the energy blockchain, consensus algorithms suitable for alliance chains can be adopted, such as PBFT (Practical Byzantine Fault Tolerance) and RAFT. Such consensus algorithms can achieve fast, efficient and secure transaction confirmation through multiple rounds of voting and message transmission. Each blockchain node verifies the received transaction events by executing the consensus protocol, and checks the legitimacy, integrity and consistency of the transaction. The verified transactions are packaged into new blocks and linked to the existing blockchain, forming an unalterable and traceable data chain. Transactions that fail to pass the verification are discarded and not written into the blockchain.

[0044] The use of blockchain technology for data chaining and consensus verification can significantly improve the security and reliability of microgrid energy data. In physical space, the geographical locations of distributed energy equipment and power equipment are relatively dispersed, and data collection, transmission and storage face risks such as network failure, equipment failure, and data loss. By chaining data, the distributed storage advantages of blockchain can be used to store data copies on multiple nodes to avoid data loss caused by single point failures; the anti-tampering mechanism of blockchain can be used to ensure the authenticity and integrity of data after chaining, providing reliable data support for energy transactions; the traceability of blockchain can be used to record the entire process of data generation, circulation, and transaction, so as to achieve auditability and traceability of energy consumption data. Blockchain enables the effective integration and protection of dispersed physical space data in digital space, consolidating the data foundation of the energy Internet.

[0045] Step S2200, designing a blockchain-based energy transaction smart contract and deploying it to the blockchain network; the energy transaction smart contract includes a power purchase contract and a power sales contract; Furthermore, if Figure 3 As shown, step S2200 includes: Step S2210, designing an energy trading algorithm and converting the energy trading algorithm into a smart contract code; the energy trading algorithm includes a transaction matching algorithm, a pricing algorithm, and a settlement algorithm; Further, step S2210 includes: Step S2211, designing a transaction matching algorithm; Specifically, transaction matching is a key link in energy trading. Its purpose is to automatically match the best transaction objects between energy supply and demand parties and realize the rapid matching of transaction intention and transaction price. Through the real-time collection and analysis of energy supply and demand information, the transaction matching algorithm can accurately characterize the intention and demands of both parties to the transaction, and calculate the supply and demand matching degree accordingly. The supply and demand matching degree is a quantitative indicator to measure the consistency of the intentions of both parties to the transaction, which comprehensively considers the matching degree of multiple dimensions such as energy type, transaction volume, transaction price, and transaction time. After calculating the supply and demand matching degree, the transaction matching algorithm prioritizes the transaction application according to the matching degree. The higher the matching degree, the higher the priority of the transaction, thus forming a supply and demand matching queue. The transaction matching algorithm automatically matches the supply and demand parties in the matching queue from high to low priority until the transaction is reached or the matching termination condition is reached.

[0046] Furthermore, if Figure 4 As shown, step S2211 includes: Step S22111, determining electricity sales demand information based on the first monitoring data; determining electricity purchase demand information based on the second monitoring data; Specifically, the electricity sales demand information is the electricity sales application submitted by distributed energy (such as photovoltaic power stations, energy storage power stations, etc.), including energy equipment ID, saleable electricity, expected price, tradable time window, etc. This information mainly comes from the first monitoring data, that is, the real-time monitoring data of distributed energy equipment. By analyzing the power generation power, energy storage capacity, self-energy consumption and other data of distributed energy equipment, its saleable electricity and trading willingness can be predicted. For example, when the power generation of a photovoltaic power station continues to be higher than its own load, it indicates that there is a demand for electricity sales; and when the state of charge (SOC) of the energy storage power station is close to full capacity, it may also trigger the demand for electricity sales. At the same time, the willingness of distributed energy to sell electricity is also affected by factors such as electricity price policies, subsidy mechanisms, and market conditions. Therefore, it is necessary to combine equipment operating parameters and energy equipment energy consumption data to dynamically determine the electricity sales demand information.

[0047] The power purchase demand information is the power purchase application submitted by power-consuming equipment (such as industrial parks, commercial buildings, residential communities, etc.), including the power equipment ID, the amount of power to be purchased, the acceptable price, the trading time window, etc. This information mainly comes from the second monitoring data, that is, the real-time monitoring data of power-consuming equipment. By analyzing the operating status, load forecast, electricity budget and other data of power-consuming equipment, its power purchase demand and trading willingness can be predicted. For example, when the production load of the industrial park continues to grow and it is predicted that the electricity price will rise in the future, the power purchase demand may be triggered in advance to lock in a relatively low power purchase cost; and when the power load of the commercial building shows obvious peak characteristics and the electricity budget is tight, it may choose to purchase electricity during the low electricity price period to avoid the cost expenditure during the peak period. Therefore, it is necessary to comprehensively integrate the equipment operating status data, the energy consumption data of the power-consuming equipment and the equipment load data to accurately characterize and predict the power purchase demand information.

[0048] Step S22112, calculating the supply-demand matching degree based on the electricity sales demand information and the electricity purchase demand information; The calculation of the supply and demand matching degree includes: calculating the energy type matching degree, the electricity matching degree, the price matching degree and the time window matching degree according to the electricity sales demand information and the electricity purchase demand information; calculating the supply and demand matching degree according to the energy type matching degree, the electricity matching degree, the price matching degree and the time window matching degree.

[0049] Specifically, the calculation of supply-demand matching requires a multi-dimensional comparative analysis of electricity sales demand information and electricity purchase demand information, which includes the following steps: 1) Calculate the matching degree of energy types. Compare the energy types in the electricity sales demand information (such as photovoltaic, wind power, hydropower, etc.) with the electricity types in the electricity purchase demand information (such as general industry and commerce, large industry, agriculture, etc.), and calculate the adaptability of the energy types. For example, photovoltaic power generation is more suitable for electricity types with a high proportion of daytime load, while wind power is more suitable for electricity types with a high proportion of nighttime load.

[0050] 2) Calculate the degree of electricity matching. Compare the saleable electricity in the electricity sales demand information with the required electricity purchase in the electricity purchase demand information to calculate the degree of electricity matching. If the sales amount is equal to the purchase amount, the matching degree is the highest; if the sales amount is greater than the purchase amount, the remaining electricity needs to be matched twice; if the sales amount is less than the purchase amount, the electricity gap needs to be supplemented from other electricity sellers.

[0051] 3) Calculate the price matching degree. Compare the expected selling price in the electricity sales demand information with the acceptable price in the electricity purchase demand information to calculate the price matching degree. Use an appropriate price matching model, consider factors such as electricity price policy, market average price, and bargaining space, and form a dynamic price matching curve. The faster the curve converges, the higher the price matching degree.

[0052] 4) Calculate the time window matching degree. Compare the tradable time windows in the electricity sales demand information and the electricity purchase demand information, and calculate the overlap degree of the time windows. The higher the time window overlap degree, the higher the matching degree of supply and demand in the time dimension. It is necessary to consider factors such as the start time, duration, transaction frequency, and emergencies of the time window.

[0053] 5) Calculate the matching degree of supply and demand. After calculating the matching degree of energy type, power, price and time window, the weighted average or geometric average method is used to form the matching degree index of supply and demand. The matching degree index quantifies the degree of fit between the power sales demand and the power purchase demand in multiple dimensions, providing an intuitive reference for transaction matching.

[0054] By calculating and comparing the matching degree of supply and demand, the transaction matching algorithm can quickly lock in the best transaction object from the massive electricity sales and purchase demands, and achieve efficient and accurate energy transaction matching. Compared with manual matching, algorithm matching can achieve global optimization in a larger range, significantly improving transaction efficiency and success rate. On the one hand, the supply and demand parties can reach a transaction intention more quickly, reducing repeated bargaining and waiting time, and improving energy consumption and utilization efficiency; on the other hand, the grid dispatching can balance energy surplus and shortage more finely, reduce resource waste problems such as wind and solar abandonment, and improve energy utilization efficiency. In addition, by improving the matching algorithm and bargaining mechanism, it is also possible to achieve dynamic optimization of transaction profits on the basis of transaction conclusion, so that all parties to the transaction can reasonably share the dividends of market-based pricing and stimulate the endogenous motivation of market players to participate in energy transactions.

[0055] Step S22113, based on the supply and demand matching degree and the equipment association data, a supply and demand matching queue is generated to automatically match the top-ranked supply and demand parties.

[0056] Specifically, according to the degree of supply and demand matching, the electricity sales demand and electricity purchase demand are arranged in order from high to low to form a supply and demand matching priority queue. The electricity sellers and electricity buyers at the front of the queue have priority in obtaining automatic matching and transaction execution opportunities. However, due to the transmission loss of electric energy in physical space, directly sorting by matching degree may lead to long-distance transmission of electricity across regions, increasing unnecessary network losses. Therefore, when generating a supply and demand matching queue, the distance factor reflected by the device association data also needs to be considered. The device association data contains the location coordinate information of energy equipment and power equipment, as well as the topological connection relationship between the equipment. By analyzing the association data, the electrical distance between the electricity seller and the electricity buyer can be calculated, and the matching queue can be optimized.

[0057] The optimized matching rule is: on the premise of satisfying the matching degree of supply and demand, transactions with the same matching degree will give priority to matching the electricity seller and electricity buyer with the closest electrical distance. In this way, "proximity balance" can be achieved in physical space, minimizing the loss of power transmission. After the optimized supply and demand matching queue is generated, the transaction matching algorithm starts from the head of the queue and automatically matches the supply and demand parties in order from high to low priority. When the match is successful, the transaction request is sent to the smart contract in a timely manner, and the contract automatically executes the subsequent transaction settlement and energy scheduling. When the match fails (such as the power cannot be met, the price cannot be agreed upon, the time window cannot be aligned, etc.), the transaction matching algorithm continues to match the suboptimal transactions in the queue until the supply and demand are balanced or the market clearing time limit is reached.

[0058] The optimized matching rules are innovative and practical in the field of energy trading. Traditional power trading and matching mechanisms mainly focus on the matching of transaction prices and electricity, and rarely consider the efficiency of electricity transmission in the physical power grid. This mechanism is acceptable in large-scale power markets across regions and provinces, but in microgrid scenarios dominated by distributed energy, it is easy to cause the physical transmission volume to deviate from the logical transaction volume. By integrating device-related data and innovatively introducing electrical distance as a matching constraint, the microgrid's dependence on the external power grid can be minimized and the microgrid's own supply and demand balancing ability can be improved. At the same time, local matching and local trading can maximize the advantages of local consumption of distributed energy, reduce the loss of electricity in the links of boosting, transmission, and stepping down, and improve the overall energy utilization efficiency of the microgrid. In addition, by shortening the transmission radius of electricity, it is helpful to reduce the flow pressure of the distribution network and delay the update and iteration cycle of distribution facilities, thereby reducing the construction and operation and maintenance costs of the power grid and improving the economy and sustainability of the microgrid.

[0059] Step S2212, designing pricing algorithm and settlement algorithm; Specifically, pricing algorithms and settlement algorithms are the core components for building energy trading smart contracts, which are used to determine energy trading prices and process transaction settlements, respectively.

[0060] When designing a pricing algorithm, it is necessary to comprehensively consider factors such as energy supply and demand conditions, power grid electricity price policies, and market competition patterns to form a dynamic and real-time pricing mechanism. A feasible pricing algorithm is a bilateral bidding mechanism based on the market clearing principle. Under this mechanism, the electricity seller and the electricity buyer submit their own quotations and electricity quantities respectively. The trading system sorts and matches the buying and selling declarations according to the principles of price priority and time priority, forms a supply and demand intersection, and determines a unified clearing price. The clearing price refers to the equilibrium price that can enable the largest transaction volume in the market to be traded. The market clearing mechanism can effectively concentrate market supply and demand information, discover market prices, and improve market efficiency. At the same time, pricing mechanisms such as peak and valley electricity prices and time-of-use electricity prices can also be designed to guide users to use electricity at off-peak times and smooth the load curve.

[0061] When designing a settlement algorithm, it is necessary to follow the principles of fairness, transparency, and trustworthiness to ensure that the funds and electricity of the parties to the transaction are accurately allocated. A feasible settlement algorithm is automated settlement based on smart contracts. When the transaction is successfully matched, the transaction results are written into the power purchase contract and the power sales contract, and the contract automatically triggers the transfer of funds and electricity. The payment funds of the power buyer are automatically transferred to the power seller's account through a third-party payment channel or e-wallet; the power sold by the power seller is automatically transferred to the power buyer through an energy router or distribution network. At the same time, the trading system also needs to design a reconciliation mechanism for funds and electricity to ensure that the transaction results are consistent with the contract execution. If there is a mismatch in funds or electricity, it is necessary to start the exception handling procedure in time to trace and correct the transaction.

[0062] The adoption of market-based pricing mechanisms and automated settlement mechanisms can significantly improve the efficiency and credibility of energy transactions. Traditional energy transactions adopt a "one-to-one" negotiated pricing model. The transaction price lacks a unified standard and market benchmark, and is easily affected by factors such as information asymmetry and bargaining power, resulting in low pricing efficiency. After the introduction of the market clearing mechanism, it is possible to match supply and demand preferences to the maximum extent, achieve dynamic optimization of transaction volume and transaction prices, and allow the market to play a decisive role in resource allocation. At the same time, the use of smart contracts instead of manual settlement can reduce the time cost of transaction reconciliation and clearing, and improve capital turnover efficiency. Placing the settlement process on the blockchain can achieve transparency and traceability of the entire transaction process, allowing the parties to the transaction to form a game relationship of mutual checks and balances and mutual trust, effectively reducing the risk of default and credit risk.

[0063] Step S2213, converting the transaction matching algorithm, pricing algorithm and settlement algorithm into smart contract code.

[0064] Specifically, in order to automatically execute transaction matching, pricing and settlement algorithms in the blockchain network, these algorithms need to be converted into smart contract codes. Smart contracts are self-executing codes based on blockchain, which are triggered by events and automatically perform corresponding operations according to preset conditions. In the energy trading scenario, a series of modular and parameterized smart contracts can be designed for different transaction stages and transaction events, such as matching declaration contracts, pricing bidding contracts, fund settlement contracts, and electricity transfer contracts.

[0065] When designing the contract code, it is necessary to clarify the roles, responsibilities and interaction processes of the transaction parties, and map them to the state variables, function methods and event processing logic of the contract. For example, in the matching declaration contract, it is necessary to define the quotation and electricity composition of the electricity seller and the electricity buyer, and design functions such as quotation sorting and electricity matching; in the pricing bidding contract, it is necessary to design operations such as inquiry, quotation, withdrawal, and transaction, and embed the market clearing algorithm; in the settlement contract, it is necessary to connect the blockchain wallet and energy router, call the transfer interface and electricity collection interface, and design the balance reconciliation and dispute arbitration clauses. At the same time, it is also necessary to strictly test the security, availability, and concurrency of the contract to ensure the smooth operation of the contract code in extreme scenarios such as high-frequency trading, large settlement, and abnormal quotations.

[0066] Embedding transaction rules and business logic in the form of code into the blockchain is an innovative solution to the pain points of energy trading. The traditional energy trading process relies on centralized transaction matching, pricing and settlement systems, which are prone to single point failures, data tampering and other risks, and the system upgrades and rule adjustments are not timely, making it difficult to adapt to the rapidly changing requirements of the energy market. By moving the entire transaction process onto the chain through smart contracts, the transaction agreement can be automatically executed, reducing human intervention and operational errors. The anti-tampering and traceability characteristics of the blockchain can ensure the objectivity and fairness of transaction data and transaction rules, making energy trading more standardized. With the help of the distributed architecture of the blockchain, the dynamic expansion and elastic upgrade of the transaction system can be achieved, and the personalized needs of the energy market and trading entities can be quickly responded to.

[0067] Step S2220, set the transaction contract trigger conditions and deploy the smart contract code to the blockchain network.

[0068] Specifically, setting the trigger conditions for transaction contracts means clarifying the circumstances under which the energy transaction process is initiated and automatically triggering the execution of smart contracts. Trigger conditions can be set based on multiple dimensions such as energy balance status, market price fluctuations, equipment maintenance plans, etc., to form a comprehensive transaction trigger mechanism. When the trigger conditions are met, the smart contract code is automatically deployed to the blockchain network, and each node competes for the execution right to reach a transaction result through a consensus mechanism. Deploying smart contract code to the blockchain can ensure that the transaction rules are verified and trusted by all parties, allowing energy transactions to be conducted in a fair, just and open environment.

[0069] Furthermore, if Figure 5 As shown, step S2220 includes: Step S2221, setting a trigger condition based on a transaction time window; Further, step S2221 includes: Step S22211, setting the energy trading opening time period; Step S22212, automatically triggering the energy trading smart contract during the energy trading open time period.

[0070] Specifically, the trading time window refers to the time period during which energy trading is allowed. The open time period for energy trading can be preset according to the grid dispatching needs, market trading habits, user energy consumption characteristics, etc.; the setting of the open time period for energy trading needs to take into account multiple factors. In general, the time period with low grid load and high output of new energy can be selected as the trading window, such as 9:00-11:00 and 14:00-16:00 every day. Opening transactions during the low load period can encourage distributed power sources to participate in grid peak regulation and alleviate the problem of wind and solar abandonment; opening transactions during the period of new energy enrichment can guide distributed power sources to be consumed nearby and reduce long-distance transmission losses. Of course, the setting of the trading time window also needs to consider factors such as the trading frequency, trading volume, and trading varieties of the power market, as well as the connection and coordination with the conventional power market and the inter-provincial and inter-regional market, and design differentiated trading windows. Once the time enters the open time period for energy trading, the smart contract automatically enters the "running" state. The electricity purchaser and seller can submit transaction applications and quoted electricity quantities to the smart contract. After receiving the transaction application, the smart contract immediately starts the transaction matching algorithm to match the received electricity purchase and sales applications until the transaction is reached or the transaction period ends. During the open time period of energy trading, the smart contract keeps running and responds to new transaction requests at any time. During non-trading periods, the smart contract enters a "dormant" state, closes the external interface, and no longer accepts new transaction applications, and wakes up again when the next trading window arrives. By setting a window trigger mechanism, energy transactions can be carried out automatically and intelligently at a specific time, reducing transaction time and labor costs.

[0071] Step S2222, setting a trigger condition based on the energy balance state; Further, step S2222 includes: Step S22221, calculating the energy profit and loss value according to the power generation power of the distributed energy equipment and the power consumption power of the power consumption equipment; Step S22222: When the energy profit or loss value exceeds the first threshold, the electricity sales contract is triggered; when the energy profit or loss value is lower than the second threshold, the electricity purchase contract is triggered.

[0072] Specifically, in actual power grid operation, there is often an imbalance between power supply and demand, which manifests as an oversupply or undersupply of power. The imbalance between supply and demand will have a significant impact on key indicators such as the voltage and frequency of the power grid, threatening the safe and stable operation of the power grid. Therefore, it is necessary to adjust the power grid operation mode in a timely manner according to the energy balance state, trigger energy transactions, and guide the flexible interaction of sources, grids, loads, and storage.

[0073] By comparing and analyzing the real-time power generation of distributed energy equipment and the actual power consumption of power equipment in the physical digital space model of microgrid resources, the energy profit and loss value of the microgrid in different periods can be calculated. When the power generation is greater than the power consumption, the energy profit and loss value is positive, indicating that the microgrid is in a state of power surplus and needs to release the surplus power through power sales; when the power generation is less than the power consumption, the energy profit and loss value is negative, indicating that the microgrid is in a state of power shortage and needs to supplement the power gap through power purchase. By analyzing the energy profit and loss trend, the power supply and demand situation of the microgrid in the future period can be accurately predicted, and preparations for power dispatch can be made in advance.

[0074] Setting a threshold trigger line for the energy surplus and loss value can realize the automation of grid energy regulation. For example, set the first threshold (such as 20%) and the second threshold (such as -10%) of the energy surplus and loss value. When the energy surplus ratio exceeds 20%, it indicates that the microgrid has sufficient power generation output and needs to sell electricity to the outside. At this time, the power sales contract is automatically triggered to provide power support to other microgrids or large power grids that are short of electricity; and when the energy shortage ratio exceeds 10%, it indicates that the microgrid has a high electricity demand and needs to purchase electricity from the outside. At this time, the power purchase contract is automatically triggered to purchase electricity from neighboring microgrids or large power grids to ensure the power supply quality of users. Through the threshold trigger mechanism, human intervention in grid dispatch can be reduced, and the timeliness and effectiveness of energy and electricity allocation can be improved.

[0075] Step S2223, setting a trigger condition based on market electricity price fluctuations; The triggering conditions based on market electricity price fluctuations are: When the grid electricity price is lower than the first electricity price threshold, the power purchase contract is triggered; when the grid electricity price is higher than the second electricity price threshold, the power sales contract is triggered.

[0076] Specifically, in an open electricity market environment, electricity prices are an important factor affecting the willingness of market players to purchase and sell electricity. When the market electricity price fluctuates violently, price signals can be used to guide users to use electricity rationally and promote the balance of electricity supply and demand. Therefore, it is necessary to set up a transaction trigger mechanism based on market electricity price fluctuations. When the power grid electricity price is lower than a certain electricity price threshold (such as 0.3 yuan / kWh), it indicates that the current electricity price is at a stage low point and the cost of purchasing electricity is relatively low. At this time, the power purchase contract can be triggered to encourage users to increase electricity consumption or shave peaks and fill valleys, which is equivalent to "giving profits" to users; and when the power grid electricity price is higher than a certain electricity price threshold (such as 0.6 yuan / kWh), it indicates that the current cost of purchasing electricity is relatively high and the power grid has peak-time electricity purchase pressure. At this time, the power sales contract can be triggered to encourage distributed power sources to generate more electricity and connect to the grid, which can "reduce the burden" on the power grid. Through the price trigger mechanism, the enthusiasm of market players to participate in grid regulation can be mobilized to form a power demand-side response based on market mechanisms.

[0077] Step S2224, setting trigger conditions based on major events.

[0078] Specifically, during the operation of the power grid, some major events are often encountered, such as power grid maintenance, equipment failure, extreme weather, etc. These events often have a great impact on the power supply capacity and load trend of the power grid. Therefore, it is necessary to adjust the energy trading strategy in time according to the impact and duration of major events to improve the resilience and flexibility of the power grid. For example, when it is known that a typhoon will land in the next week, the power purchase contract can be triggered in advance to increase the energy storage capacity of the microgrid and improve the disaster prevention and disaster resistance capabilities; when the routine maintenance of the power grid is about to begin, the power sales contract can be triggered in advance to sell the distributed power that may be idle during the maintenance period to reduce power waste. Through the major event triggering mechanism, energy trading can be used to enhance the emergency regulation capability of the power grid and maximize the flexible advantages of distributed energy.

[0079] By setting up all-round energy trading trigger conditions, it is possible to achieve real-time dynamic matching between distributed energy and grid demand, maximize the regulation potential of distributed energy, and improve the economy and safety of grid operation. Traditional energy trading models often adopt the "day-ahead + day-intraday" trading sequence, with low trading frequency and trading flexibility, and often miss the best time window for grid regulation. After adopting a trigger mechanism based on time, energy balance, electricity price fluctuations, and major events, energy trading can be continuously optimized in both time and space dimensions, making distributed energy a powerful tool for flexible grid regulation. At the same time, clearly writing the transaction trigger conditions into smart contracts can reduce subjective judgment and human intervention in the triggering process, return energy trading to the physical essence of electricity, and improve the accuracy and controllability of grid regulation. In addition, with the help of the automatic execution and supervision functions of smart contracts, it can ensure that the trigger rules are fairly executed, avoid abuse of grid dispatching rights, and safeguard the legitimate rights and interests of all parties.

[0080] Step S2300, based on the first monitoring data and the second monitoring data, determine whether the energy trading smart contract is triggered. If triggered, carry out energy trading based on the physical digital space model of microgrid resources according to the deployed energy trading smart contract; if not triggered, continue to monitor the operating status of the microgrid and wait for the triggering conditions to be met.

[0081] Further, step S2300 includes: Step S2310, judging whether to trigger the energy trading smart contract according to the first monitoring data, the second monitoring data and the preset trading contract triggering condition; Specifically, the triggering of energy trading smart contracts is a complex decision-making process based on multi-source data fusion analysis. The triggering judgment needs to comprehensively consider the real-time operating status of the energy equipment end and the power equipment end, as well as the market signals and dispatch instructions of the external power grid. The first monitoring data reflects the power generation capacity and output willingness of distributed energy equipment, which is the key basis for triggering power sales contracts; the second monitoring data reflects the load demand and power consumption willingness of power equipment, which is the key basis for triggering power purchase contracts; the preset transaction contract trigger conditions provide a "rule library" for triggering transactions, and clarify the specific rules and thresholds for triggering transactions in different scenarios. By comparing and logically judging the three types of data in real time, the current energy supply and demand situation and trading opportunities can be automatically identified, thereby triggering the corresponding power purchase or sales contracts.

[0082] By integrating multi-source heterogeneous data to trigger energy transactions, more comprehensive, accurate and fast transaction decisions can be made. Compared with single data triggering, multi-source data fusion can conduct a three-dimensional analysis of the energy transaction situation from multiple perspectives such as the supply side, demand side, and market side, overcome the problems of data dispersion and information asymmetry, and reduce the blind spots and misjudgments of triggering decisions. For example, triggering a power sales contract based only on the power generation data of distributed energy may ignore the actual electricity demand on the user side, resulting in a "virtualization" of transactions; while triggering a power purchase contract based only on the load data of power-consuming equipment may not take into account the overall supply and demand balance and transaction price of the power grid, resulting in an "information island". By organically integrating the data on the energy side, the power consumption side, and the market side, and making trigger judgments based on this, it is technically possible to achieve the "supply and demand coordination" and "source-grid-load-storage" integration of energy transaction triggering, thereby leveraging the systematic optimization of the entire energy transaction process.

[0083] Furthermore, if Figure 6 As shown, step S2310 includes: Step S2311, obtaining real time, and determining whether the triggering condition based on the transaction time window is met; Step S2312, obtaining the power generation power of the distributed energy equipment in the first monitoring data and the power consumption power of the power consumption equipment in the second monitoring data, calculating the real-time energy profit and loss value, and determining whether the trigger condition based on the energy balance state is met; Step S2313, obtaining the real-time power grid electricity price, and determining whether the triggering conditions based on the market electricity price fluctuation are met; Step S2314, obtaining major event information and determining whether a trigger condition based on a major event is met; Step S2315: If any of the conditions in the above steps S2311-S2314 is met, it is determined that the energy trading smart contract is triggered.

[0084] Specifically, the trading time window trigger condition is a trading rule based on the time dimension. By pre-setting the permitted time periods for energy trading (such as 9:00-11:00 and 14:00-16:00 every day), the current time is automatically obtained during the specified trading window period to determine whether it is within the permitted trading period. If the current time belongs to the trading period and meets other necessary trading conditions (such as power grid safety verification, power threshold, etc.), the energy trading smart contract is automatically triggered, allowing market entities to submit trading applications; if the current time does not belong to the trading period, or does not meet other trading conditions, the trading contract will not be triggered for the time being, and the trading application will not be accepted. By window-based management of energy trading time, while ensuring the dispatching control of the power grid, a time window can be reserved for distributed energy to participate in cross-regional and cross-provincial transactions.

[0085] Transaction triggering based on energy balance status is a commonly used triggering method. Real-time acquisition of distributed energy equipment power data and power consumption data of power consumption equipment, and energy profit and loss comparison analysis. When energy generation is greater than power consumption, the energy profit and loss value is positive, indicating that energy supply is in excess, and the distributed energy power sales contract can be triggered at this time; conversely, when power consumption is greater than power generation, the energy profit and loss value is negative, indicating that energy supply is insufficient, and the distributed energy power purchase contract can be triggered at this time. By setting reasonable trigger thresholds (such as 20% and -10%) for energy profit and loss values, and conducting real-time monitoring and threshold comparison, the timing of energy transaction triggering can be automatically determined, and distributed energy can be guided to flexibly participate in the real-time balance of the power grid.

[0086] The economic efficiency of grid operation is an important consideration for energy trading. Access to external power markets, obtain real-time grid price signals, such as the Beijing-Tianjin-Tangshan power grid’s electricity price during the same period or the inter-regional electricity price in neighboring provinces, and judge market trading opportunities accordingly. When the grid electricity price is lower than a preset threshold (such as 0.25 yuan / kWh), a distributed energy power purchase contract can be triggered to encourage distributed energy to purchase electricity at a low price, thereby “reducing the burden” on the grid; when the grid electricity price is higher than a preset threshold (such as 1 yuan / kWh), a distributed energy power sales contract can be triggered to guide distributed energy to sell electricity at a high price, thereby “increasing supply” for the grid. By using electricity price fluctuations to trigger trading behaviors, the economic regulation capacity of distributed energy can be improved, and the peak-shifting complementarity between distributed energy and centralized power grids can be achieved.

[0087] In special periods and extreme events, it is often necessary to adjust the conventional energy trading operation mechanism. It is necessary to achieve data interconnection with business systems such as the power grid dispatching system and the meteorological system to obtain information on major events such as power grid maintenance plans, equipment failure information, and extreme weather warnings. Once a specific event is detected and the event has a significant impact on the energy operation of the microgrid, a special trading emergency plan can be initiated. For example, during power grid maintenance, distributed energy can be triggered to take on the power grid backup and suspend external output transactions; during extreme weather warnings, distributed energy additional transactions can be triggered in advance to prepare for power grid peak regulation. By applying scenario-based event triggering mechanisms, we can accurately respond to complex and changing external environments and improve the flexibility and adaptability of energy trading.

[0088] The judgment of energy trading trigger is not a "rigid superposition" of multiple conditions, but follows the principles of "priority response" and "flexible triggering". Priorities and weights are set for various trigger conditions. For situations where multiple trigger conditions are met at the same time, the conditions are sorted and triggered according to their importance. For example, the priority of major event trigger conditions is the highest. Once a major event occurs, the event emergency trading plan must be immediately activated regardless of whether other trading conditions are met; while conventional trigger conditions (such as time windows, energy balance, market electricity prices, etc.) follow the flexible rule of "triggering when one is met", that is, if any conventional trigger condition is met, it can be determined to trigger the energy trading smart contract without meeting all conditions at the same time. By reasonably setting the priority of conditions and triggering rules, it is possible to respond quickly while taking into account the overall coordination and improve the level of refinement of energy trading triggering.

[0089] The trigger judgment of energy trading smart contracts is a key link with both availability, reliability and flexibility. By comprehensively sorting out and finely setting the trigger conditions and realizing "integrated" real-time trigger judgment, smart contracts can truly become "neurons" connecting the physical energy network and the digital trading system, and realize the deep coordination of the power operation layer and the value transaction layer. From the perspective of availability, the trigger conditions can cover more than 80% of typical trading scenarios, and the conditions are set with a unified caliber and clear logic, which is easy for market entities to understand and trust, which is conducive to improving the practicality and universality of trading contracts; from the perspective of reliability, by integrating multi-source data for cross-validation and embedding fault diagnosis and abnormal analysis algorithms, it can avoid false triggering or missed triggering caused by data quality problems, communication interruptions and other reasons, and ensure the high reliability of trading contract triggering; from the perspective of flexibility, by reserving emergency channels such as event triggering and manual triggering, it can cope with complex and changeable energy system conditions, achieve "rigidity and flexibility", and organically combine rule triggering and elastic triggering.

[0090] Step S2320: If the energy trading smart contract is triggered, energy trading based on the physical digital space model of microgrid resources is carried out.

[0091] Specifically, once the energy trading smart contract is determined to be triggered, the transaction enters the automatic execution stage of the smart contract. Based on the device mapping relationship in the physical digital space model of microgrid resources, distributed energy devices and power-consuming devices can interact directly with the energy trading contract through their own digital identities (i.e., digital twin nodes). Distributed energy devices determine the willingness to sell electricity and the amount of electricity they trade based on their real-time power generation data, and pass the power selling demand to the power selling contract through the corresponding digital nodes; power-consuming devices determine the willingness to purchase electricity and the amount of electricity they purchase based on their actual power consumption data, and pass the power purchasing demand to the power purchasing contract through the corresponding digital nodes. After the contract receives the power selling demand and the power purchasing demand, it starts the intelligent matching program, and quickly locks in the best trading object among the numerous power purchasing and selling demands according to the preset matching rules and optimization algorithms to form a trading matching plan. Once the transaction is successfully matched, the contract will automatically execute the subsequent transaction settlement, power scheduling and other processes, and synchronously record the transaction results in the blockchain ledger. The entire transaction matching process is autonomously executed by the energy trading algorithm, and the full process closed loop from intention matching to power settlement can be completed within milliseconds.

[0092] Furthermore, if Figure 7 As shown, step S2320 includes: Step S2321, the distributed energy device transmits the electricity sales demand information to the energy transaction smart contract through its device node in the physical digital space model of the microgrid resource; Step S2322, the power-consuming device transmits the power purchase demand information to the energy trading smart contract through its device node in the physical digital space model of the microgrid resources; Step S2323, the energy transaction smart contract deployed in the blockchain network automatically matches the received electricity sales demand information and electricity purchase demand information according to a preset energy transaction algorithm to obtain a matching result; Step S2324: The energy transaction smart contract automatically executes energy transactions and performs settlements based on the matching results, records each energy transaction, and generates blockchain records.

[0093] Specifically, distributed energy devices are the source of energy transactions, and their willingness and ability to sell electricity directly determine the basic plate of transactions. In order to enable distributed energy devices to perceive and respond to energy trading opportunities, it is necessary to establish a mapping connection between physical devices and digital systems through digital twin technology. The device nodes in the physical digital space model of microgrid resources play the role of "incarnation" of distributed energy devices in the digital space, allowing distributed energy devices to cross the "dimensional gap" and freely transfer their status data, control instructions and other information between the physical domain and the information domain. When distributed energy devices have a certain willingness and ability to sell electricity (such as when photovoltaic power generation continues to be higher than self-consumption electricity), they initiate a power sale request to the energy transaction smart contract through their own digital nodes, and submit key transaction elements including energy device ID, saleable electricity, expected price, and transaction time window. Through the "automatic perception + active request" method, human intervention can be minimized to the greatest extent, allowing distributed energy to truly become an "autonomous subject" in the trading market.

[0094] Similar to distributed energy equipment, power-consuming equipment also needs to obtain the ability to perceive transaction information and initiate transaction demands through digital mapping in the physical digital space model of microgrid resources. Once the power-consuming equipment detects that the local power supply cannot meet the power load demand, or needs to carry out power purchase transactions according to the period electricity price, demand response and other mechanisms, it will initiate a power purchase request to the energy transaction smart contract through its own digital node, and submit key transaction elements including the power-consuming equipment ID, planned power purchase quantity, acceptable power price range, and trading period. These power purchase demand judgment logics are mainly based on the real-time analysis of the equipment operation status data (such as equipment on / off status, load power, etc.) in the second monitoring data, the energy consumption data of the power-consuming equipment (such as daily power consumption, peak power, etc.), and the equipment load data (such as load forecast curve, demand response strategy, etc.). It can be seen that the power purchase transaction demand of the power-consuming equipment not only takes into account its own actual power consumption, but also integrates external signals such as grid dispatching instructions and demand response events, which can realize more accurate, real-time and flexible demand-side transactions. The combination of "self-perception + other regulation" is conducive to improving the intelligence level and transaction efficiency of demand-side response.

[0095] The automatic matching of electricity sales demand and electricity purchase demand is the core function of the energy trading smart contract. After the contract receives two types of transaction requests, it starts the intelligent matching program to quickly analyze and accurately match the transaction requirements. Intelligent matching is not a simple "queue matching", but a global optimization of transaction requests at different times, prices and quantities that fully considers multiple factors such as the preferences of the transaction subject, transaction timing and network status. The core of the matching engine is the energy trading algorithm, which comprehensively uses a variety of theoretical tools and optimization models such as energy routing, market clearing, and game optimization. Based on the panoramic data of the equipment, the trading algorithm preliminarily establishes a group of potential best trading objects by comparing the transaction subject's willingness price range, transaction volume scale, transaction time window and other information; on this basis, combined with the network topology constraints reflected by the device-related data, the transaction path with the shortest energy transmission distance and the smallest network loss is further selected to form the global optimal transaction combination plan. Through "willing matching + physical optimization", the economic efficiency of energy trading can be locked, while taking into account the transaction quality and network loss level, achieving multi-objective balance. The matching result not only includes the identity information and transaction volume of both parties, but also the network power flow plan for implementing the transaction, which can be directly used to guide the real-time regulation of the distribution network.

[0096] Energy trading contracts must not only complete the automatic matching of transaction needs, but also perform closed-loop execution of transaction results. The "execution" here mainly includes three aspects: First, the physical scheduling of energy. The contract converts the optimized network flow plan into control instructions, and directly sends them to the distributed energy equipment and power-consuming equipment related to the transaction through physical interfaces such as energy routers, adjusts the output power and load power of the equipment, and guides the electric energy to be transmitted from the power generation equipment to the power-consuming equipment at the agreed time and power, so as to complete the delivery of goods. Second, the automatic settlement of funds. According to the electronic wallet information reserved by the transaction parties, the contract automatically completes the transfer of transaction funds between the buyer and the seller through the smart payment interface, and simultaneously performs supporting operations such as tax payment and electricity fee payment, so as to minimize the "flight time" of funds and ensure the safety of funds of all parties to the transaction. Third, the on-chain storage of information. The contract automatically records the detailed information of the transaction execution (such as the transaction subject, transaction power, transaction price, execution time, flow plan, invoicing information, etc.) on the chain, and completes cross-chain data sharing, so as to achieve transparent and traceable information throughout the process. Through the trinity of "physics + capital + information", we can truly realize the closed-loop automation of energy trading physics and value flow, and improve the timeliness, coordination and credibility of energy trading.

[0097] Energy trading smart contracts are responsible for implementing transaction intentions, optimizing transaction combinations, and executing transaction results. They are the key "engine" for realizing the digitalization and automation of energy transactions. By embedding energy physical models, contracts can realize online synchronous simulation of energy transactions and distribution networks, greatly improving the security and executability of transaction combination schemes; by applying intelligent matching algorithms, contracts can quickly lock in the global optimal transaction scheme and balance market efficiency and system benefits; by directly connecting with energy routers, smart meters, and smart payment systems, contracts can complete the entire transaction process such as energy scheduling, fee settlement, and information recording within milliseconds, maximizing the compression of the time and space span of transactions. In addition, solidifying the energy transaction process into smart contract code and deploying it on the chain is conducive to strengthening the transparency and certainty of transaction rules. When the transaction subject has objections to the transaction results, the contract code logic can be traced for arbitration to effectively protect the legitimate rights and interests of all parties to the transaction. It can be foreseen that with the continuous enrichment of energy transaction scenarios and the continuous increase in transaction frequency, energy transaction contracts will inevitably evolve from single function execution to comprehensive intelligent decision-making, becoming the commanding heights for leveraging the innovation of energy Internet transaction paradigm.

[0098] Step S3000, integrate the energy consumption traceability function into the distributed energy trading platform, generate an energy consumption traceability report, and form an energy consumption traceability certificate on the blockchain.

[0099] Furthermore, step S3000 includes: Step S3100, extracting the first energy consumption data of each energy transaction according to the blockchain record; Specifically, blockchain records contain rich and difficult-to-tamper transaction data. By "decoding" blockchain records, the key elements of each energy transaction can be extracted from them, and a structured first energy consumption data set can be formed. The extracted content mainly includes: transaction power data, which reflects the physical flow of energy from the power generation side to the power consumption side; transaction amount data, which reflects the transfer of economic value in the process of energy flow; transaction time series, which reflects the timeline and dynamic process of energy transactions; transaction subject information, which clearly identifies the electricity seller and electricity buyer in each transaction. These elements together constitute the basic portrait of energy transactions, but the physical mechanism and efficiency characteristics of energy flow cannot be fully portrayed from the first energy consumption data alone. Therefore, it is necessary to introduce more dimensional data and superimpose and integrate them with the first energy consumption data to truly gain insight into the energy consumption laws behind energy transactions.

[0100] The advantages of blockchain data "on-chain" are obvious, but to realize the value of data, "off-chain" applications are also needed. By extracting fields related to energy consumption analysis from blockchain records in a targeted manner and performing pre-processing operations such as data cleaning and normalization off-chain, high-quality blockchain "raw materials" can be provided for subsequent energy consumption traceability analysis, while also protecting the privacy and security of blockchain data to the greatest extent. The deep integration of energy transaction data and blockchain technology is an innovative idea to promote the sharing and openness of energy data and improve the collaborative efficiency of energy systems. It is of great significance to the construction of a modern energy system that is ubiquitous, peer-to-peer, and flexible and interactive.

[0101] Step S3200, accessing the physical digital space model of microgrid resources to extract second energy consumption data related to each transaction; Specifically, after completing the extraction of the first energy consumption data, it is also necessary to combine the physical perspective of energy consumption analysis to supplement and extract more comprehensive and three-dimensional energy consumption data from the physical digital space model of microgrid resources to form the second energy consumption data set. The second energy consumption data set mainly includes: real-time energy consumption data of equipment related to each transaction, which reflects the energy consumption status of energy equipment during the transaction period; equipment energy efficiency data, which quantifies the energy conversion efficiency of energy equipment under different load levels; load curve data, which depicts the dynamic distribution characteristics of energy demand in the time domain. These data are directly derived from the real-time monitoring of physical equipment, which makes up for the blind spot of energy consumption insight of transaction data. During the access process, firstly, according to the transaction subject ID, the source point equipment and terminal equipment involved in the transaction are locked in the equipment panoramic information library; then, based on the equipment operation monitoring data and equipment nameplate information, the energy consumption, energy efficiency and load data during the transaction period are screened out; finally, these data are organized and mapped according to the equipment ID and timestamp to form a structured equipment-level energy consumption portrait as the second energy consumption data set.

[0102] By integrating blockchain transaction data and physical equipment monitoring data, a panoramic digital portrait of energy flow can be constructed from both macro and micro levels, so that energy consumption analysis is no longer a "black box detection", but is based on observable and reliable data, maximizing the accuracy and interpretability of energy consumption analysis. In addition, with the increasing maturity of the energy and power Internet of Things, massive multi-source heterogeneous data is continuously collected, and the cost of data collection, transmission, storage and computing is continuously reduced. The spatiotemporal granularity and quality of energy consumption data will be significantly improved, which will inevitably give rise to more new applications for energy consumption management.

[0103] Step S3300, performing a fusion analysis on the first energy consumption data and the second energy consumption data to construct an energy consumption traceability tree model; Specifically, the energy consumption traceability tree model is an innovative energy consumption analysis paradigm. It takes transactions as the entry point, integrates blockchain data and energy consumption physical data, and traces and depicts the energy flow trajectory from the two dimensions of time and space. The model construction process can be divided into four steps: first, based on the first energy consumption data on the blockchain, the original energy transaction directed graph is constructed; second, the second energy consumption data extracted from the physical digital space model of microgrid resources is used as the attribute data of nodes and edges, and attached to the directed graph to form a multi-attribute, heterogeneous complex network; third, the transaction time series data is mapped to the nodes and edges of the graph to form a spatiotemporal energy consumption traceability graph with a time dimension; finally, the frequently co-occurring transaction patterns are extracted from the spatiotemporal energy consumption traceability graph to construct an energy consumption traceability tree. From a mathematical point of view, the energy consumption traceability tree is an N-ary tree structure. The hierarchy of the tree reflects the progressive relationship of energy flow from the supply side to the demand side. The nodes of the tree are the key carriers of the flow process, and the edges of the tree carry the key attributes of the flow direction, efficiency, time variation, etc. From a physical perspective, the energy consumption traceability tree is a microcosm of the coordinated operation of source-grid-load-storage in a specific area. It reveals the loss mechanism and efficiency bottleneck of energy in the production, transmission, consumption and other links, and provides a data method for carrying out energy consumption management and control throughout the entire life cycle.

[0104] Furthermore, if Figure 8 As shown, step S3300 includes: Step S3310, constructing an energy transaction directed graph based on the first energy consumption data in the blockchain record; The energy trading directed graph has distributed energy device nodes and power consumption device nodes participating in energy trading as vertices, energy trading as directed edges, the direction of the directed edges represents the direction of energy flow, and the weight of the edges is the transaction power.

[0105] Step S3320, attaching the second energy consumption data as attribute data to the corresponding node of the energy transaction directed graph; Step S3330, obtaining time series data, embedding the time series data in the energy transaction directed graph, and forming a spatiotemporal energy consumption traceability graph; Step S3340, based on the spatiotemporal energy consumption traceability diagram, extract typical transaction patterns and construct an energy consumption traceability tree model.

[0106] Assume that there are m distributed energy equipment nodes, n power equipment nodes, and c energy trading nodes in the microgrid; The energy consumption tracing tree model G is: Where V is a node set: , arrive For distributed energy equipment nodes, arrive For power consumption equipment nodes, arrive is an energy trading node. E is a directed edge set: ; Represents a directed edge from the i-th distributed energy device node to the j-th power device node.

[0107] Specifically, the energy transaction directed graph is the basic network for energy consumption analysis. There are two types of nodes in the graph: one is the distributed energy equipment nodes participating in energy transactions, representing the electricity sales entities in the transaction; the other is the electricity consumption equipment nodes participating in energy transactions, representing the electricity purchase entities in the transaction. The directed edge connecting the nodes represents an actual energy transaction. The direction of the directed edge points from the electricity sales node to the electricity purchase node, representing the actual flow direction of energy; the weight of the directed edge is the transaction power, reflecting the scale of energy flow. These key element data are directly derived from the first energy consumption data of the blockchain. By globally mapping and topologically reconstructing many distributed transaction records, an associated network of energy transactions is established, which can intuitively display the transaction scope, transaction objects, transaction scale and other elements of distributed energy, and preliminarily reveal the spatial structure of energy flow. However, the energy transaction directed graph only reflects the "skeleton" of energy flow, lacks the characterization of the energy consumption characteristics of the equipment itself.

[0108] Embedding the second energy consumption data into the directed graph of energy transactions can enrich the semantic information of the graph and improve the analytical generalization ability of the graph. According to the device ID index in the second energy consumption data set, the real-time energy consumption, energy efficiency, load and other data can be accurately mapped to the corresponding source node and terminal node in the graph. These data, as the attribute data of the node, give each node in the graph network a distinct energy consumption "portrait". For example, the attributes carried by distributed photovoltaic nodes may include information such as photovoltaic panel power generation efficiency, inverter efficiency, and grid-connected load matching; the attributes carried by industrial park power consumption nodes may include information such as park power consumption, load peak-valley difference, and energy-saving potential. Embedding these device-level energy consumption portraits into the network can more finely compare and analyze the energy consumption differences of different trading entities, and can also deduce the local transmission effect of energy consumption in space in combination with the network topology. Through the fusion modeling of attribute data and graph data, the "breadth" of energy consumption analysis has been greatly expanded, and the multi-dimensional deconstruction of the energy flow process has been achieved.

[0109] Energy transactions and energy flows are not isolated static events, but a process of dynamic evolution on a continuous timeline. In order to fully characterize the spatiotemporal evolution of energy consumption, it is necessary to introduce time series data into the existing graph model. Time series data mainly comes from the transaction timestamp field in the blockchain record, which reflects the order in which transactions occur. Time series data can be mapped to time attributes on nodes and edges to form a spatiotemporal complex network with a time dimension. For example, at time t1, nodes A and B have a transaction, forming an edge; at time t2, nodes A and C have another new transaction, forming another edge. As time goes by, the edges will continue to increase, and the network will continue to evolve. At the same time, the energy consumption attribute value of the node itself also changes dynamically over time. By embedding time series data into the graph, we can not only analyze the snapshot of energy flow at a certain moment, but also analyze the longitudinal progressive law of flow on the time scale, introduce the new degree of freedom of "time" in energy consumption analysis, and realize true spatiotemporal integrated traceability. For example, according to the edge slices at different times, several subgraphs under time windows can be formed, which can intuitively show the stage characteristics of energy flow in the time dimension.

[0110] The spatiotemporal energy consumption traceability diagram reveals the spatiotemporal panorama of energy flow, but the analysis complexity of the graph model is high, which is not conducive to intuitive understanding of the energy consumption law. It is necessary to further refine the key transaction mode on the basis of the graph model and construct a concise and generalized energy consumption traceability tree paradigm. The transaction mode is a node connection substructure that frequently co-occurs in the spatiotemporal graph, representing the energy consumption combination characteristics in the three dimensions of energy, time and space. The graph mining algorithm can be used to perform pattern recognition on the transaction path, transaction power, and transaction time distribution in the spatiotemporal energy consumption traceability diagram to screen out the key transaction modes that appear frequently in a statistical sense. These patterns often contain the inherent laws of energy flow and are of great value in describing the spatiotemporal distribution and conduction mechanism of energy consumption. After obtaining the key transaction mode, it can be organized into a tree topological structure. The root node of the tree is the source distributed energy device that initiates the transaction, the leaf node is the terminal power load device, and the middle layer node is the energy transaction event that connects the source and the load. Through the structured representation of key transaction patterns, a concise and semantically rich energy consumption traceability tree is formed. This tree model has obvious advantages in intuitiveness, explainability, and computability.

[0111] The energy consumption traceability tree model is a new energy consumption analysis method. Through the integration of graph data, attribute data, and time series data, a complete energy consumption spatiotemporal characterization framework is established, and on this basis, high-level semantic features are extracted to form a tree-shaped traceability structure with "explanatory power" for energy consumption data. Compared with traditional energy consumption statistical analysis, this model has significant advantages such as globality, correlation, evolution, and semantics, and can fully utilize the physical and data value potential of the energy system. From a global perspective, the tree model can cover the entire process data from energy generation to terminal energy consumption, revealing the energy flow law of each link of source, grid, load and storage; from a correlation perspective, the tree model takes into account both transaction data and physical attribute data, and describes the dynamic mapping relationship between energy flow and physical space; from an evolutionary perspective, the tree model maps time series features into a hierarchical structure, which is convenient for understanding the distribution and evolution characteristics of energy at different time scales; from a semantic perspective, the tree model extracts the association rules behind frequent trading behaviors, forming a highly generalized energy consumption combination model, which is convenient for knowledge-based interpretation and reasoning decision-making.

[0112] Step S3400: Calculate the energy consumption contribution and energy consumption responsibility sharing ratio of each transaction participant based on the energy consumption traceability tree model, generate an energy consumption traceability report, and store it on the chain.

[0113] Specifically, the energy consumption traceability tree model not only depicts the flow trajectory of energy in time and space, but also implies deep information such as efficiency loss and subject responsibility during the flow process. By quantitatively deconstructing and semantically mining the tree model, a comprehensive and multi-dimensional energy consumption analysis report can be formed, and the report can be stored on the chain using blockchain technology to form a credible energy digital certificate. The post-analysis process of the energy consumption traceability tree mainly includes four steps: first, statistically analyzing the key energy consumption data of each device node in the tree; second, calculating the energy consumption contribution of each source node; third, calculating the energy consumption responsibility sharing ratio of each terminal node; and fourth, estimating the power transmission loss of each transaction node. After obtaining the energy consumption analysis results of each node, a comprehensive energy consumption traceability report for the microgrid is formed, and the report hash is chained through a cryptographic algorithm to ensure the objectivity, fairness and non-tamperability of energy consumption information. The energy consumption traceability report can be used as a new type of energy digital certificate for energy efficiency assessment, carbon asset management, green finance and other fields, allowing energy consumption data to release application value. At the same time, through the blockchain evidence storage mechanism, all stakeholders can reach a consensus on the content of the report, accept social supervision, and establish a responsible energy concept.

[0114] Further, step S3400 includes Step S3410, traverse each node in the energy consumption traceability tree model, and count the cumulative electricity sales E of each distributed energy device node i , the cumulative amount of electricity purchased by each power-consuming device node L j and the power transmission loss H of each energy trading node k ; represents the cumulative electricity sales of the i-th distributed energy equipment node, ; represents the cumulative power purchase of the jth power consumption device node, ;H k represents the power transmission loss of the kth energy trading node, ; m is the total number of distributed energy device nodes, n is the total number of power consumption device nodes, and c is the total number of energy trading nodes; Specifically, the energy consumption traceability tree consists of three types of nodes: distributed energy equipment nodes, representing the source points in the microgrid, such as photovoltaic power stations, energy storage batteries, etc.; power equipment nodes, representing the end points in the microgrid, such as industrial parks, commercial buildings, etc.; energy trading nodes, representing the energy transfer events between the source point and the end point. The tree structure is the basis for the implementation of energy consumption responsibilities. It is necessary to use tree traversal algorithms, such as depth-first search, breadth-first search, etc., to visit the nodes of the tree one by one, and extract key energy consumption data according to the node type. For distributed energy equipment nodes, it is necessary to accumulate and sum the electricity sales of all previous transactions and count them as the cumulative electricity sales E. i , reflecting the total scale of its energy output; for power consumption equipment nodes, it is necessary to accumulate and sum the amount of electricity purchased in all previous transactions and calculate it as the cumulative amount of electricity purchased L j , reflecting the total scale of its energy consumption; for energy trading nodes, it is necessary to combine the spatial location and electrical topology of the two parties to evaluate the energy loss H generated during the power transmission process of the transaction k , reflecting the efficiency of energy transmission. These three types of data are the basis for energy consumption responsibility analysis. Among them, the cumulative electricity sales of distributed energy equipment nodes represent their historical contribution to the energy supply of microgrids; the cumulative electricity purchases of power equipment nodes represent the cumulative impact of their energy consumption behavior on microgrid energy consumption; the transmission loss of energy trading nodes reflects the degree of network flow optimization and energy allocation efficiency. By summarizing the tree structure data horizontally and vertically, the overall distribution and individual differences of energy flow can be portrayed.

[0115] Step S3420, calculating the energy consumption contribution of each distributed energy device to generate a first analysis result; The calculation of the energy consumption contribution of each distributed energy device includes: The cumulative electricity sales of each distributed energy device node are summed up to obtain the total energy supply of each distributed energy device node, and the cumulative electricity sales of the distributed energy device node E i Divided by the total energy supply, the energy consumption contribution of the i-th distributed energy device node is obtained.

[0116] Specifically, energy consumption contribution is a dimensionless indicator used to measure the contribution of a single distributed energy device to the total energy supply of a microgrid. The calculation method is to divide the cumulative electricity sales of the device by the sum of the cumulative electricity sales of all distributed energy devices to obtain a percentage value. The larger the index, the higher the proportion of green electricity provided by the device in the energy supply of the microgrid, and the greater the contribution to reducing fossil energy consumption and promoting energy conservation and emission reduction. The energy consumption contribution is objectively affected by factors such as the installed capacity of the equipment, power generation efficiency, and grid connection time, but in the long run, it mainly depends on the clean properties of the equipment itself and the level of operation and management. By comparing the energy consumption contribution of different devices horizontally, we can find out the "main force" and "new force" of energy supply, formulate targeted support policies, and optimize the energy structure configuration. For example, assuming that there are three distributed energy devices in microgrid A, and the cumulative electricity sales in one year are 1 million kWh, 800,000 kWh, and 500,000 kWh respectively, then the energy consumption contribution of the three is 43.5%, 34.8%, and 21.7% respectively. Based on this, it can be judged that the head power station with large installed capacity and advanced technology is the ballast of the microgrid's green energy. In the future, efforts should be made to improve its power generation efficiency and utilization hours to consolidate the dominant position of clean energy.

[0117] Step S3430, calculating the energy consumption responsibility sharing ratio of each electrical device and generating a second analysis result; The calculation of the energy consumption responsibility sharing ratio of each electrical equipment includes: The accumulated power purchase of each power consumption device node is summed up to obtain the total energy consumption of each power consumption device node. The accumulated power purchase of each power consumption device node L is used j Divide by the total energy consumption to get the energy responsibility sharing ratio of the jth power-consuming equipment node.

[0118] Specifically, the energy consumption responsibility sharing ratio is also a percentage indicator, which is used to measure the proportion of the electricity consumption behavior of a single power-consuming device in the total power consumption of the microgrid, and the degree of influence of its power consumption characteristics on the energy balance of the microgrid. The calculation method is to divide the cumulative power purchase of the device by the sum of the cumulative power purchase of all power-consuming devices. The higher the responsibility sharing ratio, on the one hand, it shows that the power consumption scale of the device is more prominent in the microgrid and the stronger its dependence on the power grid; on the other hand, it shows that the power consumption curve of the device may have greater volatility and uncertainty, which may induce peak load and frequency regulation pressure on the power grid. Therefore, when analyzing energy consumption responsibility, it is necessary to combine the scale of power consumption and the characteristics of the power consumption curve. For equipment with a large scale of power consumption but a relatively stable load curve, a certain preferential electricity price can be given to guide them to choose the time to use electricity and avoid peak loads; for equipment with a small scale of power consumption but a large fluctuation in the load curve, it can be required to bear the cost of spare capacity or share the power grid loss, so as to encourage them to actively optimize the internal power consumption arrangement. For example, the cumulative electricity consumption of the three parks A, B, and C in microgrid B is 2 million kWh, 1.2 million kWh, and 800,000 kWh respectively in one year, and Park C is a continuous chemical enterprise, which still consumes a lot of electricity at night. Based on this, it can be calculated that the electricity consumption responsibility sharing ratios of the three are 50%, 30%, and 20% respectively, and Park C bears the grid loss and standby costs caused by the peak-to-valley difference, so it bears greater electricity consumption responsibility under the same electricity consumption.

[0119] Step S3440, calculating the transmission loss of each energy trading node and generating a third analysis result; The calculation of the transmission loss shared by each energy trading node includes:

[0120] in: : The transaction power of the kth energy trading node, ; : The transaction power of the sth energy trading node, ; : The transmission loss of the kth energy trading node.

[0121] Specifically, during the transmission of electric energy in space, a certain amount of active power loss will be generated due to factors such as line resistance. The transmission loss is closely related to factors such as transmission distance, voltage level, and power flow load. The energy trading node records the spatial location information and electrical distance parameters of the two parties to the transaction, as well as the real-time power flow level when the transaction is reached. Therefore, it can be used as an important carrier for mapping and calculating transmission losses. For each energy trading node, its transmission loss can be estimated through the network power flow model, and the total transmission loss can be allocated among the trading nodes considering the proportion of the transaction power in the total power. The loss of power can be converted into electricity cost or line loss rate. By calculating the transmission loss allocated to each trading node, the economy and energy efficiency of each transaction can be evaluated. Transmission loss is an important factor affecting the efficiency of the energy system and is also the focus of optimization of the energy Internet. By analyzing the transmission loss data, the weak links of the power grid can be found and the grid structure can be optimized; the characteristics of line losses in different periods can also be summarized to achieve economic dispatch under time-of-use electricity prices. For example, in remote mountainous areas where the transmission lines are too long, energy routers can be used to guide transactions nearby to reduce long-distance transmission; during low-power periods, the transmission voltage level can be appropriately increased to reduce line current and power loss. The energy trading process is integrated with the physical network, and the definition of energy consumption responsibilities and power flow optimization are carried out simultaneously.

[0122] Step S3450, summarize the first analysis result, the second analysis result and the third analysis result, generate an energy consumption traceability report, and form an energy consumption traceability certificate on the blockchain.

[0123] Specifically, the energy consumption traceability report is a "physical examination report" of the microgrid operation, which presents the ins and outs of energy flow in a panoramic manner. On the one hand, the report includes a summary and evaluation of key indicators such as the total energy supply and demand of the microgrid, the structure of power purchase and sale, the trend of load changes, and the loss of power transmission and distribution, reflecting the overall energy efficiency level and energy conservation and emission reduction results of the microgrid; on the other hand, it includes a portrait analysis of the energy consumption contribution of key equipment and individual users, the proportion of energy consumption responsibility sharing, and the energy trading model, reflecting the participation distribution and influence distribution of energy consumption within the microgrid. The generation of energy consumption traceability reports can be embedded in various time scales of energy management. At the macro level, it can be released annually or quarterly for energy planning, power trading, and comprehensive energy efficiency assessment; at the micro level, it can be released monthly, daily, and hourly for equipment start-stop optimization, real-time demand response incentives, etc. After the report is released, it should be confirmed and stored through blockchain technology. Using cryptographic principles, the report content is hashed to obtain the digital "fingerprint" of the energy consumption report, and then the fingerprint information is written into the energy blockchain to form an energy consumption traceability certificate. By utilizing the immutable and traceable characteristics of blockchain, all parties can verify the authenticity of energy consumption reports at any time and accept social supervision. Energy consumption traceability certificates can be used as basic data assets in scenarios such as energy efficiency verification, carbon asset trading, and green bond issuance, providing a "trust anchor" for the transfer of energy value.

[0124] The post-analysis process of the energy consumption traceability tree model is a key link in deepening the digital application of the energy system. Through the fusion calculation of energy consumption data, the quantitative evaluation of energy consumption behavior, and the blockchain storage of energy consumption reports, a scientific and complete energy consumption responsibility definition mechanism has been established. Traditional power grids lack the fine accounting of carbon footprint and power consumption, and often share energy consumption responsibilities in a "flooding" manner according to the production and sales of electricity, which easily leads to insufficient motivation for energy-saving entities and "free riding" by high-consuming entities. After introducing the energy consumption traceability tree model, "precision drip irrigation" of energy consumption responsibility can be achieved. From a vertical perspective, the behavioral performance of each energy consumer can be objectively quantified and fairly evaluated; from a horizontal perspective, the contribution differences and efficiency gaps between different energy consumers are also clear at a glance. In terms of policy formulation, energy-saving incentives and demand response measures can be formulated according to local conditions, so that energy consumption data can truly become a "vane" for optimizing the power grid and a "measurement" for serving users. In terms of supervision and implementation, the energy consumption traceability certificate provides a data foundation for new energy consumption, energy efficiency labeling, energy rights trading and other means, allowing the market mechanism to play a decisive role in the allocation of energy resources. It can be foreseen that with the deepening of the digital transformation of the energy and power system, the innovative paradigm of the energy consumption traceability tree will inevitably move from microgrids to large power grids, awaken the endogenous motivation of participants in all links of the energy value chain with energy consumption data, promote the high integration of energy flow, information flow and value flow, and provide new ideas and new paths for building a clean, low-carbon, safe and efficient modern energy system.

[0125] Example 2 This embodiment provides an energy consumption analysis system based on the physical digital space model of microgrid resources on the basis of embodiment 1. Fig. 9 As shown, including: A digital model building module: used to obtain first monitoring data of distributed energy equipment and second monitoring data of power consumption equipment in the microgrid, integrate the first monitoring data and the second monitoring data, and build a physical digital space model of microgrid resources; Energy trading platform construction module: Based on the physical digital space model of microgrid resources, a distributed energy trading platform based on blockchain is constructed; Energy consumption analysis module: used to integrate the energy consumption traceability function in the distributed energy trading platform and build an energy consumption traceability tree model; according to the energy consumption traceability tree model, calculate the energy consumption contribution of each distributed energy device and the energy consumption responsibility sharing ratio of each power-consuming device, generate an energy consumption traceability report, and store it on the chain.

[0126] In the digital model construction module, the fusing of the first monitoring data and the second monitoring data to construct a physical digital space model of microgrid resources includes: Step S1310, matching the first monitoring data and the second monitoring data according to the energy device ID and the power device ID to form device panoramic data; Step S1320, establishing association relationships between device nodes based on energy device location information and power device location information to form device association data; Step S1330, based on the equipment panoramic data and equipment association data, a physical digital space model of microgrid resources is constructed, and distributed energy equipment and power consumption equipment are device nodes in the physical digital space model of microgrid resources.

[0127] In the energy trading platform construction module, the construction of a distributed energy trading platform based on blockchain includes: Step S2100, uploading the first monitoring data and the second monitoring data in the physical digital space model of the microgrid resources to the chain; Step S2200, designing a blockchain-based energy transaction smart contract and deploying it to the blockchain network; the energy transaction smart contract includes a power purchase contract and a power sales contract; Step S2300: determine whether to trigger the energy trading smart contract based on the first monitoring data and the second monitoring data; if triggered, carry out energy trading based on the physical digital space model of microgrid resources according to the deployed energy trading smart contract; if not triggered, continue to monitor the operation status of the microgrid and wait for the triggering condition to be met; The step S2100 includes: Step S2110, select the alliance chain as the underlying architecture of the blockchain, set each entity in the microgrid as an alliance member, and allocate nodes to the alliance members; Step S2120, formatting the first monitoring data and the second monitoring data into transaction events on the blockchain, and uploading them to the blockchain node; In step S2130, each blockchain node verifies and confirms the transaction event through a consensus mechanism.

[0128] The step S2200 includes: Step S2210, designing an energy trading algorithm and converting the energy trading algorithm into a smart contract code; the energy trading algorithm includes a transaction matching algorithm, a pricing algorithm, and a settlement algorithm; Step S2220, set the transaction contract trigger conditions and deploy the smart contract code to the blockchain network.

[0129] The step S2300 includes: Step S2310, judging whether to trigger the energy trading smart contract according to the first monitoring data, the second monitoring data and the preset trading contract triggering condition; Step S2320: If the energy trading smart contract is triggered, energy trading based on the physical digital space model of microgrid resources is carried out.

[0130] The step S2320 includes: Step S2321, the distributed energy device transmits the electricity sales demand information to the energy transaction smart contract through its device node in the physical digital space model of the microgrid resource; Step S2322, the power-consuming device transmits the power purchase demand information to the energy trading smart contract through its device node in the physical digital space model of the microgrid resources; Step S2323, the energy transaction smart contract deployed in the blockchain network automatically matches the received electricity sales demand information and electricity purchase demand information according to a preset energy transaction algorithm to obtain a matching result; Step S2324: The energy transaction smart contract automatically executes energy transactions and performs settlements based on the matching results, records each energy transaction, and generates blockchain records.

[0131] In the energy consumption analysis module, the construction of the energy consumption traceability tree model includes: Step S3310, constructing an energy transaction directed graph based on the first energy consumption data in the blockchain record; The energy trading directed graph has distributed energy device nodes and power consumption device nodes participating in energy trading as vertices, energy trading as directed edges, the direction of the directed edges represents the direction of energy flow, and the weight of the edges is the transaction power.

[0132] Step S3320, attaching the second energy consumption data as attribute data to the corresponding node of the energy transaction directed graph; Step S3330, obtaining time series data, embedding the time series data in the energy transaction directed graph, and forming a spatiotemporal energy consumption traceability graph; Step S3340, based on the spatiotemporal energy consumption traceability diagram, extract typical transaction patterns and construct an energy consumption traceability tree model.

[0133] In the energy consumption analysis module, the energy consumption contribution of each distributed energy device and the energy consumption responsibility sharing ratio of each power-consuming device are calculated to generate an energy consumption traceability report. The on-chain evidence includes: Step S3410, traverse each node in the energy consumption traceability tree model, and count the cumulative electricity sales E of each distributed energy device node i , the cumulative amount of electricity purchased by each power-consuming device node L j and the power transmission loss H of each energy trading node k ; represents the cumulative electricity sales of the i-th distributed energy equipment node, ; represents the cumulative power purchase of the jth power consumption device node, ;H k represents the power transmission loss of the kth energy trading node, ; m is the total number of distributed energy device nodes, n is the total number of power consumption device nodes, and c is the total number of energy trading nodes; Step S3420, calculating the energy consumption contribution of each distributed energy device to generate a first analysis result; Step S3430, calculating the energy consumption responsibility sharing ratio of each electrical device and generating a second analysis result; Step S3440, calculating the transmission loss of each energy trading node and generating a third analysis result; Step S3450, summarize the first analysis result, the second analysis result and the third analysis result, generate an energy consumption traceability report, and form an energy consumption traceability certificate on the blockchain.

[0134] The methods, systems, and devices of the present application may be implemented in many ways. For example, the methods, systems, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers recording media storing programs for executing the method according to the present application.

[0135] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0136] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An energy consumption analysis method based on a physical digital space model of microgrid resources, characterized in that: The method comprises: Acquire first monitoring data of distributed energy equipment and second monitoring data of power-consuming equipment in the microgrid, fuse the first monitoring data and the second monitoring data, and construct a physical digital space model of microgrid resources; Based on the physical digital space model of microgrid resources, a distributed energy trading platform based on blockchain is constructed; Integrate the energy consumption traceability function into the distributed energy trading platform and build an energy consumption traceability tree model. According to the energy consumption traceability tree model, calculate the energy consumption contribution of each distributed energy device and the energy consumption responsibility sharing ratio of each power-consuming device, generate an energy consumption traceability report, and store it on the chain.

2. The energy consumption analysis method based on the microgrid resource physical digital space model according to claim 1 is characterized in that: The first monitoring data includes energy equipment ID and energy equipment location information; The second monitoring data includes the electrical equipment ID and the electrical equipment location information; The construction of the microgrid resource physical digital space model includes: According to the energy equipment ID and the power equipment ID, the first monitoring data and the second monitoring data are matched to form equipment panoramic data; According to the location information of energy equipment and power equipment, establish the association relationship between equipment nodes to form equipment association data; Based on the equipment panoramic data and equipment association data, a physical digital space model of microgrid resources is constructed. Distributed energy equipment and power-consuming equipment are device nodes in the physical digital space model of microgrid resources.

3. The energy consumption analysis method based on the microgrid resource physical digital space model according to claim 2 is characterized in that: The distributed energy trading platform based on blockchain and based on the physical digital space model of microgrid resources includes: Uploading the first monitoring data and the second monitoring data in the physical digital space model of the microgrid resources to the chain; Design blockchain-based energy trading smart contracts and deploy them to the blockchain network; Based on the first monitoring data and the second monitoring data, determine whether the energy trading smart contract is triggered. If triggered, energy trading based on the physical digital space model of microgrid resources is carried out according to the deployed energy trading smart contract; if not triggered, continue to monitor the operating status of the microgrid and wait for the triggering conditions to be met.

4. The energy consumption analysis method based on the microgrid resource physical digital space model according to claim 3 is characterized in that: The energy transaction smart contract designed based on blockchain includes: Design energy trading algorithms and convert them into smart contract codes; the energy trading algorithms include transaction matching algorithms, pricing algorithms and settlement algorithms; Set the trigger conditions for the transaction contract and deploy the smart contract code to the blockchain network.

5. The energy consumption analysis method based on the microgrid resource physical digital space model according to claim 4 is characterized in that: The design method of the energy trading algorithm includes: Determine the electricity sales demand information based on the first monitoring data; determine the electricity purchase demand information based on the second monitoring data; Calculate the matching degree of supply and demand based on the electricity sales demand information and the electricity purchase demand information; Based on the supply and demand matching degree and equipment association data, a supply and demand matching queue is generated to automatically match the top-ranked supply and demand parties.

6. The energy consumption analysis method based on the microgrid resource physical digital space model according to claim 5 is characterized in that: The calculating of the supply-demand matching degree based on the electricity sales demand information and the electricity purchase demand information includes: calculating the energy type matching degree, the electricity quantity matching degree, the price matching degree and the time window matching degree according to the electricity sales demand information and the electricity purchase demand information; calculating the supply-demand matching degree according to the energy type matching degree, the electricity quantity matching degree, the price matching degree and the time window matching degree; The setting of trigger conditions for transaction contracts includes: setting trigger conditions based on transaction time windows, setting trigger conditions based on energy balance status, setting trigger conditions based on market electricity price fluctuations, and setting trigger conditions based on major events.

7. The energy consumption analysis method based on the microgrid resource physical digital space model according to claim 6 is characterized in that: The energy transaction based on the physical digital space model of microgrid resources includes: Distributed energy devices transmit electricity sales demand information to energy trading smart contracts through their device nodes in the physical digital space model of microgrid resources; The power-consuming equipment transmits the power purchase demand information to the energy trading smart contract through its device node in the physical digital space model of the microgrid resources; The energy trading smart contract deployed in the blockchain network automatically matches the received electricity sales demand information and electricity purchase demand information according to the preset energy trading algorithm to obtain the matching result; The energy trading smart contract automatically executes energy transactions and settles according to the matching results, records each energy transaction, and generates blockchain records.

8. The energy consumption analysis method based on the microgrid resource physical digital space model according to claim 7 is characterized in that: The energy consumption traceability function is integrated into the distributed energy trading platform to construct an energy consumption traceability tree model, including: Extract the first energy consumption data of each energy transaction based on blockchain records; Access the physical digital spatial model of microgrid resources and extract the second energy consumption data related to each transaction; The first energy consumption data and the second energy consumption data are fused and analyzed to construct an energy consumption traceability tree model.

9. The energy consumption analysis method based on the microgrid resource physical digital space model according to claim 8 is characterized in that: The energy consumption tracing tree model is constructed as follows: Based on the first energy consumption data recorded in the blockchain, a directed graph of energy transactions is constructed; The second energy consumption data is attached as attribute data to the corresponding node of the energy transaction directed graph; Obtain time series data, embed the time series data in the energy transaction directed graph, and form a spatiotemporal energy consumption traceability graph; Based on the spatiotemporal energy consumption traceability diagram, typical transaction patterns are extracted and an energy consumption traceability tree model is constructed.

10. An energy consumption analysis system based on a physical digital space model of microgrid resources, which is used to implement the energy consumption analysis method based on a physical digital space model of microgrid resources as described in any one of claims 1 to 9, characterized in that: The system comprises: A digital model building module: used to obtain first monitoring data of distributed energy equipment and second monitoring data of power consumption equipment in the microgrid, integrate the first monitoring data and the second monitoring data, and build a physical digital space model of microgrid resources; Energy trading platform construction module: Based on the physical digital space model of microgrid resources, a distributed energy trading platform based on blockchain is constructed; Energy consumption analysis module: used to integrate the energy consumption traceability function in the distributed energy trading platform and build an energy consumption traceability tree model; according to the energy consumption traceability tree model, calculate the energy consumption contribution of each distributed energy device and the energy consumption responsibility sharing ratio of each power-consuming device, generate an energy consumption traceability report, and store it on the chain.

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