Green power consumption and storage data processing system based on block chain

Abstract

The invention, which relates to the technical field of the data processing system, discloses a block chain-based green power consumption and storage data processing system comprising an edge preprocessing module, a block chain evidence storage verification module, an energy storage health management module, a multi-scene prediction module, a cross-chain authority management module and a federal cooperative processing module. The modules realize data interaction through a distributed node network; and the block chain evidence storage verification module comprises a lightweight consensus unit and a data uplink unit. According to the method, the edge node is screened as the verification node through the lightweight consensus unit of the block chain evidence storage verification module, only the data of the adjacent edge node is limited to be verified, and the efficient verification and credible evidence storage of the power data are realized in combination with the standardized packaging and distributed storage of the data uplink unit. The power data uploading efficiency and the storage security can be improved, so that the problems of heavy node verification burden and non-uniform data storage formats in traditional block chain evidence storage can be solved.

Description

Technical Field

[0001] This invention relates to the field of data processing system technology, and in particular to a blockchain-based green electricity consumption and storage data processing system. Background Technology

[0002] The power consumption and storage data processing system is a core tool of the power system, used to manage power consumption and energy storage operation data throughout its entire lifecycle. It has functions of data acquisition, cleaning and integration, analysis and mining, and visualization, and can support power dispatch, user power consumption optimization, and energy storage maintenance. Ultimately, it can improve grid stability, reduce energy waste, and help the consumption of new energy sources.

[0003] Currently, power consumption and storage data processing systems suffer from problems such as low data verification efficiency, heavy node verification burden, and inconsistent data storage formats. This leads to slow power data upload to the blockchain, poor security, and affects the accuracy of power grid dispatch, the effectiveness of user power consumption optimization, and the scientific nature of energy storage maintenance. It also restricts the improvement of new energy consumption efficiency and power grid stability.

[0004] Therefore, a blockchain-based green electricity consumption and storage data processing system is proposed to address the aforementioned issues. Summary of the Invention

[0005] The main objective of this invention is to provide a blockchain-based green electricity consumption and storage data processing system to address the problems mentioned in the background above.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: a blockchain-based green electricity consumption and storage data processing system, including an edge preprocessing module, a blockchain evidence verification module, an energy storage health management module, a multi-scenario prediction module, a cross-chain permission management module, and a federated collaborative processing module, wherein each module realizes data interaction through a distributed node network; The edge preprocessing module is used to preprocess the raw power data generated by the green power terminal, including a data cleaning unit and a data compression unit; The blockchain evidence storage and verification module is used to verify and distribute the preprocessed power data, and generate green power traceability certificates, including a lightweight consensus unit and a data on-chain unit. The energy storage health management module is used to monitor the health status and residual value assessment of green power energy storage equipment, and to provide data support for energy storage asset accounting. It includes a health data acquisition unit and a residual value calculation unit. The multi-scenario prediction module is used to dynamically predict green power output and electricity load, including a scenario recognition unit and a prediction model switching unit. The cross-chain permission management module is used to realize permission control and traceability of green power cross-chain data, including a dynamic permission control unit and a cross-chain traceability unit; The federated collaborative processing module is used to realize collaborative analysis of encrypted green power data from multiple entities, including a local model training unit and a global model aggregation unit.

[0007] Preferably, the data cleaning unit of the edge preprocessing module includes a power data receiving subunit and an outlier removal subunit; The power data receiving subunit is used to receive the raw voltage and current data output by the green power terminal. The outlier removal subunit uses a sliding window algorithm to identify and remove instantaneous fault data that exceeds the normal range. The data compression unit uses differential coding technology to compress the volume of the cleaned power data to obtain preprocessed data.

[0008] Preferably, the lightweight consensus unit of the blockchain evidence storage and verification module selects edge nodes from the blockchain network as verification nodes, and limits each verification node to verifying only the power data output by its adjacent edge nodes; The data on-chain unit includes a data encapsulation subunit and an on-chain storage subunit; The data encapsulation subunit encapsulates the preprocessed power data in a standardized format, and the on-chain storage subunit uploads the encapsulated data to the blockchain network for distributed storage.

[0009] Preferably, the health data acquisition unit of the energy storage health management module includes a sensor group and a data encryption subunit; The sensor group is used to collect indicator data of the energy storage device, and the data encryption subunit uses an encryption algorithm to encrypt the collected indicator data before uploading it to the blockchain. The residual value calculation unit includes a coefficient determination subunit and a calculation subunit; The coefficient determination subunit determines the health coefficient, usage duration coefficient, and environmental adaptability coefficient of the energy storage device, and the calculation subunit calculates the residual value of the energy storage device based on the determined coefficients.

[0010] Preferably, the sensor group includes a voltage sensor, a temperature sensor, a current sensor, and a capacity detection sensor; The voltage sensor is used to collect the output voltage data of the energy storage device, the temperature sensor is used to collect the internal temperature data of the energy storage device, the current sensor is used to collect the charging and discharging current data of the energy storage device, and the capacity detection sensor is used to collect the remaining capacity data of the energy storage device to calculate the capacity decay rate.

[0011] Preferably, the scene recognition unit of the multi-scene prediction module includes a scene data receiving subunit and a scene determination subunit; The scenario data receiving subunit receives historical green power load data, temperature and humidity data, meteorological early warning data, and regional power consumption event information. The scenario determination subunit determines the current green power prediction scenario based on the received data. The prediction model switching unit includes a model invocation subunit and a model calibration subunit; The model invocation subunit invokes the corresponding prediction model according to the determined scenario, and the model calibration subunit periodically compares the prediction data with the actual data to calibrate the parameters of the prediction model.

[0012] Preferably, the dynamic permission control unit of the cross-chain permission management module includes a permission division subunit and a permission management subunit; The permission division subunit divides data access permissions into three levels: viewing permission, usage permission, and modification permission. The permission management subunit assigns corresponding permissions based on user type and revoks the assigned permissions when preset conditions are triggered. The cross-chain traceability unit includes an identifier generation subunit and a traceability query subunit; The identifier generation subunit generates a unique identifier for each cross-chain data, which includes the source chain ID, original data hash, cross-chain time, receiving chain ID, and usage record. The traceability query subunit provides an identifier query function to obtain the data flow path.

[0013] Preferably, the local model training unit of the federated collaborative processing module includes a local encrypted data acquisition subunit and a sub-model training subunit; The local encrypted data acquisition subunit acquires locally stored encrypted green electricity data, and the sub-model training subunit uses the acquired encrypted data to train a green electricity prediction sub-model locally.

[0014] Preferably, the global model aggregation unit of the federated collaborative processing module includes a parameter receiving subunit, a model aggregation subunit, and a contribution evaluation subunit; The parameter receiving sub-unit receives the sub-model parameters uploaded by each subject; The model aggregation subunit performs weighted aggregation of the received sub-model parameters according to the proportion of each subject's data volume to generate a global prediction model.

[0015] Preferably, the contribution evaluation subunit calculates the data contribution of each subject based on the impact of the uploaded sub-model parameters on the accuracy of the global prediction model, and uploads the contribution results to the blockchain.

[0016] The present invention has the following beneficial effects: 1. This invention selects edge nodes as verification nodes through the lightweight consensus unit of the blockchain evidence storage and verification module, limiting the verification to data of adjacent edge nodes. Combined with the standardized encapsulation and distributed storage of the data on-chain unit, it realizes efficient verification and reliable evidence storage of power data. Compared with existing technologies, it can improve the efficiency of power data on-chain and storage security. Therefore, it can solve the problems of heavy node verification burden and inconsistent data storage formats in traditional blockchain evidence storage.

[0017] 2. This invention trains sub-models locally through the local model training unit of the federated collaborative processing module, and the global model aggregation unit aggregates sub-model parameters according to the proportion of data volume and evaluates the contribution of each subject. This achieves collaborative optimization of models under the protection of multi-subject data privacy. Compared with the prior art, it can improve the security and model accuracy of multi-subject collaborative analysis. Therefore, it can solve the problems of high privacy leakage risk and unbalanced collaborative model optimization when sharing data among multiple subjects.

[0018] 3. This invention establishes an edge preprocessing module, a blockchain evidence verification module, an energy storage health management module, a multi-scenario prediction module, a cross-chain permission management module, and a federated collaborative processing module. These modules interact through a distributed node network, enabling closed-loop management of green power data from acquisition and preprocessing to storage, analysis, prediction, and cross-entity collaboration. Compared to existing technologies, this improves the coherence and integrity of green power data processing, thus solving the problems of fragmented green power data processing and low data flow efficiency in existing technologies. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall system architecture of the present invention; Figure 2 This is a schematic diagram of the edge preprocessing module architecture of the present invention; Figure 3 This is a schematic diagram of the blockchain evidence storage and verification module architecture of the present invention; Figure 4 This is a schematic diagram of the energy storage health management module architecture of the present invention; Figure 5 This is a schematic diagram of the multi-scene prediction module architecture of the present invention; Figure 6 This is a schematic diagram of the cross-chain permission management module architecture of the present invention; Figure 7 This is a schematic diagram of the federal collaborative processing module architecture of the present invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0021] Example 1, please refer to Figure 1 and Figure 2 As shown: A blockchain-based green electricity consumption and storage data processing system includes an edge preprocessing module, a blockchain evidence verification module, an energy storage health management module, a multi-scenario prediction module, a cross-chain permission management module, and a federated collaborative processing module. Each module achieves data interaction through a distributed node network. The edge preprocessing module is used to preprocess the raw power data generated by the green power terminal, including a data cleaning unit and a data compression unit; The data cleaning unit of the edge preprocessing module includes a power data receiving subunit and an outlier removal subunit; The power data receiving subunit is used to receive the raw voltage and current data output by the green power terminal. The outlier removal subunit uses a sliding window algorithm to identify and remove instantaneous fault data that exceeds the normal range. The data compression unit uses differential coding technology to compress the volume of the cleaned power data to obtain preprocessed data.

[0022] Furthermore, the power data receiving subunit can communicate with various types of green power terminals, including but not limited to photovoltaic power generation terminals, wind power generation terminals, and hydropower generation terminals. The power data receiving subunit receives voltage and current data output by the green power terminals in real time through communication protocols. The voltage data acquisition range covers 0V to 1000V, and the current data acquisition range covers 0A to 500A, which can meet the data source requirements of green power terminals with different power levels.

[0023] During the data reception process, the power data receiving subunit will also perform preliminary format standardization on the received data, converting the non-standardized data output by different terminals into a binary data format that is uniformly recognized by the system, providing a unified data foundation for the subsequent outlier removal subunit. The outlier removal subunit uses a sliding window algorithm to identify and remove abnormal data. By dynamically constructing a data window, it performs real-time analysis on the power data within the window, thereby accurately identifying instantaneous fault data that exceeds the normal range.

[0024] The outlier removal subunit first sets the normal fluctuation range of voltage and current data based on the historical operating data of the green power terminal and industry standard parameters. The normal voltage range is set to ±5% of the rated output voltage, and the normal current range is set to ±8% of the rated output current. The sliding window slides on the real-time received power data stream at 1-second intervals, and the length of each window is set to include 5 consecutive sampling data points.

[0025] During each window's sliding process, the outlier removal sub-unit calculates the average and standard deviation of the data within the window. Data within the window that deviates from the average by more than three times the standard deviation is marked as suspected outlier data. At the same time, if a data point exceeds the preset normal fluctuation range of voltage or current, it will also be marked as suspected outlier data.

[0026] For flagged suspected abnormal data, the outlier removal subunit will further verify the data by combining the data change trends within one adjacent window before and after the flagged data. The adjacent windows have no time overlap to ensure clear verification logic. If the data before and after the flagged suspected abnormal data are both within the normal range and the data changes are consistent with the normal operation rules of the green power terminal, then the data is determined to be instantaneous fault data and is removed. If there are also abnormal fluctuations in the data before and after the flagged data, then it is determined that there may be a device fault. In this case, the data will be retained and a fault warning signal will be sent to the system operation and maintenance terminal to remind the staff to check the equipment.

[0027] The dual verification mechanism of the outlier removal subunit can effectively remove instantaneous fault data caused by factors such as electromagnetic interference and instantaneous sensor errors, and avoid accidentally deleting abnormal data caused by equipment failure, thus ensuring the accuracy and reliability of data cleaning. The data compression unit uses differential coding technology to compress data. Compared with traditional compression technologies such as Huffman coding and LZW coding, differential coding technology has higher compression efficiency when processing continuously changing power data, and the decompression process is simple and can quickly restore the original data.

[0028] First, the data compression unit acquires the voltage and current time series data after data cleaning. The data series is sampled at 1-second intervals and includes the voltage and current values ​​at each sampling time. During compression, the data compression unit selects the first data in the data series as the reference value and then calculates the difference between each subsequent data and the previous data, i.e., the differential difference.

[0029] Since the voltage and current data output by the green power terminal show a continuous and stable trend under normal operating conditions, the difference between adjacent data is small. Therefore, the values ​​in the differential difference sequence are mostly in a small range. Compared with the original data sequence, the data distribution of the differential difference sequence is more concentrated, which can effectively reduce the number of bits of data storage.

[0030] After obtaining the difference sequence, the data compression unit optimizes the encoding of the difference. Based on the size range of the difference, a variable-length encoding method is used to encode it. For difference values ​​with smaller absolute values, a shorter encoding length is used, and for difference values ​​with larger absolute values, a longer encoding length is used, thereby further reducing the overall size of the data.

[0031] Example 2, please refer to Figure 3 As shown: A blockchain-based green electricity consumption and storage data processing system. The blockchain evidence verification module is used to verify and distribute the pre-processed electricity data to generate green electricity traceability certificates, including a lightweight consensus unit and a data on-chain unit. The lightweight consensus unit of the blockchain evidence storage and verification module selects edge nodes from the blockchain network as verification nodes, and limits each verification node to verifying only the power data output by its adjacent edge nodes. The data on-chain unit includes a data encapsulation subunit and an on-chain storage subunit; The data encapsulation subunit encapsulates the preprocessed power data in a standardized format, and the on-chain storage subunit uploads the encapsulated data to the blockchain network for distributed storage.

[0032] Furthermore, the lightweight consensus unit first selects edge nodes from the blockchain network as verification nodes. The selection process follows specific evaluation indicators to ensure that the selected verification nodes have reliable verification capabilities. The specific selection indicators include the node's hardware performance, network connection stability, historical verification accuracy, and the distance between the node's geographical location and the green power terminal. Hardware performance requires the node to have at least 4 CPU cores, 8GB of memory, and 100GB of available storage space to ensure the smooth operation of the data verification process. Network connection stability requires the node's average network latency to be no more than 50 milliseconds and the packet loss rate to be less than 1% to avoid network problems affecting verification efficiency. The historical verification accuracy must be higher than 98% to ensure that the node can accurately identify data anomalies. The distance between the node's geographical location and the green power terminal is no more than 50 kilometers to reduce latency and loss during data transmission.

[0033] Through multi-dimensional evaluation, qualified edge nodes are selected from the blockchain network to form a verification node pool. The size of the verification node pool is dynamically adjusted according to the number of green power terminals. One verification node is configured for every 10 green power terminals to ensure that verification resources can be allocated reasonably.

[0034] After the verification node selection is completed, the lightweight consensus unit will limit each verification node to verify only the power data output by adjacent edge nodes, and build a regional verification network. The determination of adjacent edge nodes is based on the topology of the nodes in the blockchain network. The blockchain network is divided into multiple sub-networks according to geographical regions. Edge nodes in each sub-network are adjacent nodes. The division of sub-networks is based on administrative divisions, and the coverage of each sub-network is controlled within 100 square kilometers to ensure that the data transmission distance between adjacent nodes is short.

[0035] After receiving preprocessed power data transmitted from neighboring edge nodes, each verification node verifies the data from three dimensions: data integrity, data consistency, and data authenticity. Data integrity verification compares the byte length of the data with a preset standard length. If they match, the data is considered complete; otherwise, retransmission is required. Data consistency verification compares the data with the historical data transmitted by neighboring nodes. If the data change is within a preset threshold (voltage change threshold ±3%, current change threshold ±5%), the data is considered consistent. Data authenticity verification verifies the digital signature of the data. Each edge node attaches a unique digital signature when transmitting data. The verification node reads the digital signature and matches it with the node's public key. If the match is successful, the data is considered authentic.

[0036] Once the verification node completes the verification, it will generate a verification result and feed it back to the adjacent edge nodes. At the same time, the verification result will be synchronized to the shared ledger of the blockchain network. If a piece of data passes the verification of 3 or more adjacent verification nodes, it is determined that the data has passed the consensus verification and can enter the subsequent data on-chain stage.

[0037] The data encapsulation subunit in the data on-chain unit is responsible for standardizing and encapsulating the preprocessed power data to solve the problem of inconsistent data formats output by different edge nodes, ensuring that the data can be transmitted and stored normally in the blockchain network. The encapsulation process follows a preset standardized data format, which includes three parts: data header, data body, and data tail.

[0038] The data header contains data identification information, including the green power terminal number, data collection time, and data type. The green power terminal number uses a 16-bit binary code to ensure that each terminal has a unique identifier. The data collection time is accurate to the millisecond level and uses the UTC time format to avoid time recording confusion due to time zone differences. The data type is distinguished by a 2-bit binary code, with 01 representing voltage data and 10 representing current data.

[0039] The main data body contains specific power data values, with voltage data accurate to 0.01V and current data accurate to 0.01A. It is stored in a floating-point data format to ensure that the data accuracy meets the requirements of subsequent applications. The data tail contains data verification information, which uses a cyclic redundancy check algorithm to generate a 16-bit check code to detect whether data errors occur during data storage and transmission.

[0040] After the data format is encapsulated, the data encapsulation subunit encrypts the encapsulated data using a symmetric encryption algorithm. Each data upload unit is equipped with a dedicated encryption key. The encrypted data can effectively prevent it from being stolen or tampered with during transmission, thus ensuring data security.

[0041] The on-chain storage sub-unit is responsible for uploading the encapsulated and encrypted power data to the blockchain network for distributed storage. First, the encapsulated data is sharded and divided into multiple data shards according to the size of the data. The size of each data shard is set to 1MB to avoid storage efficiency reduction due to excessively large single data.

[0042] After data sharding is completed, the on-chain storage subunit uses distributed hash table technology to distribute each data shard to different nodes in the blockchain network for storage. Each data shard will be stored on 5 different nodes to achieve multi-replica storage of data.

[0043] The selection of nodes is based on their remaining storage space and network connection status. Nodes with more than 1GB of remaining storage space and stable network connection are given priority to ensure that the data can be stored stably for a long time. After the data is stored in shards, the on-chain storage sub-unit will record the storage location information of each data shard and generate a data index table. The data index table contains information such as data shard number, storage node address, and storage time. The storage location of each data shard can be quickly located through the data index table, which is convenient for subsequent data query and retrieval.

[0044] The on-chain storage sub-unit periodically performs integrity checks on the stored data, sending a data verification request to the storage node every 24 hours. The storage node returns a verification code for the data. The on-chain storage sub-unit compares the returned verification code with the original verification code. If they match, the data storage is deemed complete. If they do not match, a data recovery mechanism is initiated, retrieving the data from other nodes that store the data shard and re-storing it to ensure that the data is not lost due to node failure.

[0045] Example 3, please refer to Figure 4 As shown: A blockchain-based green electricity consumption and storage data processing system, the energy storage health management module is used to monitor the health status and residual value assessment of green electricity storage equipment, providing data support for energy storage asset accounting, including a health data acquisition unit and a residual value calculation unit; The health data acquisition unit of the energy storage health management module includes a sensor group and a data encryption subunit; The sensor array is used to collect indicator data of the energy storage device. The data encryption subunit uses an encryption algorithm to encrypt the collected indicator data before uploading it to the blockchain. The residual value calculation unit includes a coefficient determination subunit and a calculation subunit; The coefficient determination subunit determines the health coefficient, usage duration coefficient, and environmental adaptability coefficient of the energy storage device, and the calculation subunit calculates the residual value of the energy storage device based on the determined coefficients.

[0046] The sensor group includes a voltage sensor, a temperature sensor, a current sensor, and a capacity detection sensor; Voltage sensors are used to collect output voltage data of energy storage devices, temperature sensors are used to collect internal temperature data of energy storage devices, current sensors are used to collect charging and discharging current data of energy storage devices, and capacity detection sensors are used to collect remaining capacity data of energy storage devices to calculate capacity decay rate.

[0047] Furthermore, the sensor group in the health data acquisition unit includes four types: voltage sensor, temperature sensor, current sensor, and capacity detection sensor. The voltage sensor is installed at the positive and negative output terminals of the energy storage device and adopts high-precision DC voltage sensing technology. The acquisition range covers 0V to 1500V, and the sampling frequency is set to 1 time / second. It can capture the dynamic changes of the output voltage of the energy storage device in real time. When the voltage fluctuation exceeds ±2% of the rated voltage, an early warning signal is triggered in time.

[0048] The temperature sensors are distributed and installed in key parts of the energy storage device, such as the battery module, heat dissipation system, and control module. They use high-precision thermocouple sensing technology, with a measurement range of -20℃ to 80℃ and a measurement accuracy controlled within ±0.5℃. The sampling frequency is set to once every 30 seconds to monitor the temperature changes of various parts of the device in real time, so as to avoid performance degradation or safety accidents caused by local overheating.

[0049] The current sensor is installed in the charging and discharging circuit of the energy storage device. It adopts Hall current sensing technology and has a sampling range of -500A to 500A. The negative sign represents the discharge current and the positive sign represents the charging current. The sampling frequency is set to 1 time / second. It can accurately record the current changes during the charging and discharging process of the device and provide data for analyzing the charging and discharging efficiency of the device and the battery wear.

[0050] The capacity detection sensor is integrated into the battery management system of the energy storage device. It adopts a detection technology that combines constant current discharge method and AC impedance method. It performs a comprehensive detection of the remaining capacity of the energy storage device every 24 hours, and performs a rapid detection after each charge and discharge cycle. The detection accuracy is controlled within ±1%. By comparing the initial rated capacity of the device with the current remaining capacity, the capacity decay rate is calculated, which directly reflects the degree of battery aging.

[0051] The data encryption subunit adopts a hybrid encryption method that combines asymmetric and symmetric encryption algorithms. First, the collected raw indicator data is encrypted using a symmetric encryption algorithm to generate encrypted data blocks. The symmetric encryption key is randomly generated by the subunit and is automatically updated once every 24 hours to ensure key security.

[0052] Subsequently, an asymmetric encryption algorithm is used to encrypt the symmetric encryption key to generate key ciphertext. The public key used for encryption is stored in the shared nodes of the blockchain network, while the private key used for decryption is stored by the dedicated key management unit of the energy storage health management module. The private key is stored in hardware encryption to prevent leakage.

[0053] After encrypting the data and key, the data encryption subunit packages the encrypted data block and key ciphertext into a data transmission packet, which is then uploaded to the blockchain evidence verification module via a secure communication protocol. The blockchain evidence verification module verifies the data and stores it in the blockchain, thus achieving secure data storage and traceability.

[0054] The coefficient determination subunit in the residual value calculation unit is responsible for determining the health coefficient, usage time coefficient, and environmental adaptability coefficient of the energy storage equipment. These three coefficients reflect the value loss of the equipment from three dimensions: the equipment's own condition, usage time, and external environment. The value of each coefficient ranges from 0 to 1. The closer the coefficient value is to 1, the smaller the value loss of the equipment and the higher the residual value.

[0055] The health coefficient is mainly calculated based on the equipment index data collected by the sensor group. The calculation process comprehensively considers four indicators: capacity decay rate, voltage stability, temperature fluctuation range, and charge and discharge efficiency. Among them, the capacity decay rate accounts for 40% of the weight. When the capacity decay rate is ≤10%, the corresponding health coefficient score is 1.0. When the capacity decay rate is between 10% and 20%, the score decreases linearly to 0.8 as the decay rate increases. When the capacity decay rate exceeds 20%, the score decreases linearly to 0.5 as the decay rate increases.

[0056] Voltage stability accounts for 20% of the weight. It is calculated by taking the proportion of the number of times the voltage fluctuation exceeds the normal range in the past 72 hours out of the total number of samples. When the proportion is ≤5%, the score is 1.0. When the proportion is between 5% and 10%, the score decreases linearly to 0.8. When the proportion exceeds 10%, the score decreases linearly to 0.6.

[0057] The temperature fluctuation range accounts for 20% of the weight. The difference between the highest and lowest temperatures of each part of the equipment in the past 72 hours is calculated. When the difference is ≤10℃, the score is 1.0. When the difference is between 10℃ and 20℃, the score decreases linearly to 0.8. When the difference exceeds 20℃, the score decreases linearly to 0.6.

[0058] The charge / discharge efficiency accounts for 20% of the weight. When the charge / discharge efficiency is ≥90%, the score is 1.0. When the charge / discharge efficiency is between 80% and 90%, the score decreases linearly to 0.8. When the charge / discharge efficiency is below 80%, the score decreases linearly to 0.6. The scores of the four indicators are weighted and summed according to their weights to obtain the health coefficient.

[0059] The usage duration coefficient is determined based on the ratio of the usage time of the energy storage device to its designed service life. The environmental adaptability coefficient is determined based on four indicators of the operating environment of the energy storage device: temperature and humidity, dust concentration, and vibration intensity. Each indicator has a weight of 33.33%.

[0060] Regarding temperature, the score is 1.0 when the ambient temperature is between 15℃ and 25℃, 0.8 when the temperature is between 5℃ and 15℃ or between 25℃ and 35℃, and 0.6 when the temperature is below 5℃ or above 35℃.

[0061] Regarding humidity, the score is 1.0 when the relative humidity is between 40% and 60%, 0.8 when the humidity is between 20% and 40% or between 60% and 80%, and 0.6 when the humidity is below 20% or above 80%.

[0062] Regarding dust concentration, the ambient dust concentration is ≤0.1mg / m³. 3 At that time, the score was 1.0, and the dust concentration was 0.1 mg / m³. 3 Up to 0.5 mg / m 3 When the score is between 0.8 and 0.5 mg / m³, the dust concentration exceeds 0.5 mg / m³. 3 At that time, the score was 0.6.

[0063] Regarding vibration intensity, the score is 1.0 when the environmental vibration acceleration is ≤0.1g, 0.8 when the vibration acceleration is between 0.1g and 0.5g, and 0.6 when the vibration acceleration exceeds 0.5g. The scores of the four indicators are weighted and summed to obtain the environmental adaptability coefficient.

[0064] The calculation sub-unit determines the health coefficient, usage duration coefficient, and environmental adaptability coefficient based on the coefficients determined by the sub-unit, and calculates the current residual value of the equipment in combination with the initial purchase cost of the energy storage equipment.

[0065] After obtaining the initial purchase cost of the equipment, a residual value calculation model is constructed. The calculation formula is: Equipment residual value = initial purchase cost × health coefficient × usage duration coefficient × environmental adaptability coefficient.

[0066] During the calculation process, the calculation subunit retrieves the three coefficient values ​​uploaded by the coefficient determination subunit and the initial purchase cost data from the blockchain, automatically substitutes them into the formula for calculation, and the calculation result is accurate to two decimal places. At the same time, the calculation subunit packages the residual value calculation results, the coefficient values ​​on which the calculation is based, and the initial purchase cost data into a residual value assessment report, and uploads it to the blockchain evidence storage and verification module for evidence storage. Users can retrieve and view the assessment report at any time through the blockchain query interface, realizing the transparency and traceability of the residual value assessment process.

[0067] Example 4, please refer to Figure 5 As shown: A blockchain-based green electricity consumption and storage data processing system, with a multi-scenario prediction module for dynamically predicting green electricity output and electricity load, including a scenario identification unit and a prediction model switching unit; The scene recognition unit of the multi-scene prediction module includes a scene data receiving subunit and a scene determination subunit; The scenario data receiving subunit receives historical green power load data, temperature and humidity data, meteorological early warning data, and regional power consumption event information. The scenario determination subunit determines the current green power prediction scenario based on the received data. The prediction model switching unit includes a model invocation subunit and a model calibration subunit; The model invocation subunit invokes the corresponding prediction model based on the determined scenario, and the model calibration subunit periodically compares the prediction data with the actual data to calibrate the parameters of the prediction model.

[0068] Furthermore, the scene data receiving subunit receives four types of key data. The first type is the historical load data of regional green electricity over the past three years stored on the blockchain. The sampling interval is fifteen minutes, and the data has been deduplicated and smoothed to eliminate the impact of occasional fluctuations.

[0069] The second category is temperature and humidity data obtained from regional meteorological monitoring stations. The temperature range covers minus 30 degrees Celsius to 45 degrees Celsius, and the humidity range covers 0% to 100%. The sampling frequency is once per hour, and the data includes real-time values ​​and forecasts for the next 24 hours.

[0070] The third category is weather warning information pushed by meteorological departments, covering types such as rainstorms, strong winds, high temperatures and cold waves, including warning levels and warning durations. The warning levels are divided into four levels: blue, yellow, orange and red.

[0071] The fourth category is information on regional electricity events reported by regional power management departments and related units, including adjustments to production plans of large industrial enterprises, the holding of large-scale events in the region, and changes in residential electricity policies, along with explanations of the time period and load impact of the events.

[0072] The scenario determination subunit uses a hierarchical determination method to determine the current green electricity forecast scenario, which is divided into six core scenarios: regular workday scenario, regular rest day scenario, holiday scenario, extreme weather scenario, large-scale electricity consumption event scenario, and policy adjustment scenario. The determination process is divided into three levels. The first level is based on the date type to initially classify the scenario. If the date is a statutory holiday or a holiday including adjusted holidays, it is initially determined to be a holiday scenario. If it is a workday or rest day, it enters the second level of determination.

[0073] The second level combines meteorological warning data for judgment. If there is an orange or red meteorological warning and the warning duration covers more than 50% of the predicted period for the next 24 hours, it is judged as an extreme weather scenario. If there is only a blue or yellow warning or the warning duration is short, it will proceed to the third level of judgment.

[0074] The third level integrates regional electricity consumption event information and temperature and humidity data for judgment. If there is a large-scale electricity consumption event that is expected to affect the regional electricity load by more than 10%, it is judged as a large-scale electricity consumption event scenario. The load impact threshold is derived from the preset scenario judgment threshold library and is linked to the correction coefficient of the subsequent large-scale electricity consumption event prediction model. When the impact load range is 10% to 20%, the model correction coefficient is set to 1.1, and when the impact range exceeds 20%, the correction coefficient is set to 1.2.

[0075] If there is an adjustment to the residential electricity policy and the implementation time of the adjustment is within the predicted period, it is determined to be a policy adjustment scenario. If there is no such special event, and the temperature and humidity data are within the normal range of the same period in the region, the temperature fluctuation is within 5 degrees Celsius and the humidity fluctuation is within 10%, then it is finally determined to be a regular workday scenario or a regular rest day scenario based on the date type. The determination process relies on a preset scenario determination threshold library and sets quantitative standards for various determination conditions.

[0076] The model calling subunit of the prediction model switching unit has six built-in prediction models, each corresponding to one of the six scenarios. The first prediction model for regular workdays uses a long short-term memory network algorithm, which focuses on learning the fluctuation characteristics of the morning peak, noon off-peak, and evening peak of electricity load during workdays, as well as the linear law of photovoltaic output changing with sunshine. The model training data uses historical data of the same type of workdays over the past two years, with a prediction step size of fifteen minutes, and outputs the predicted load and output values ​​every fifteen minutes in the next twenty-four hours.

[0077] The second conventional rest day prediction model is based on the gradient boosting decision tree algorithm. Considering the characteristics of delayed peak load and smooth load fluctuation on rest days, the model strengthens the weight of afternoon and evening load data during training to improve the prediction accuracy for these periods.

[0078] The third holiday forecasting model uses a time-series decomposition algorithm to decompose holiday load into trend terms, seasonal terms, and residual terms, separating special electricity consumption patterns and regular fluctuations during holidays. The training data includes load and output records for all holidays over the past five years.

[0079] The fourth extreme weather prediction model is based on the support vector regression algorithm. It strengthens the weight of meteorological warning data in the input data. For example, it increases the air conditioning load prediction coefficient when issuing a high temperature warning and adjusts the wind power output prediction threshold when issuing a strong wind warning. It has a built-in extreme weather load correction formula and dynamically adjusts the prediction results according to the warning level. For example, it raises the basic load prediction value by 15% to 20% when issuing a red high temperature warning.

[0080] The fifth type of large-scale power consumption event prediction model adopts a hybrid architecture of basic model plus event correction. First, the basic load model is trained using historical data, and then the basic prediction results are corrected according to the power consumption scale and duration of the large-scale event. The correction coefficient is obtained by fitting historical impact data of similar events.

[0081] The sixth policy adjustment prediction model is based on logistic regression algorithm to analyze the guiding effect of policy changes on electricity consumption behavior. During the training process, load comparison data before and after policy implementation are introduced. After the model calling sub-unit receives the scenario judgment result, it completes the loading and initialization of the corresponding model within ten seconds, automatically retrieves historical data related to the scenario from the blockchain as model input and starts prediction calculation.

[0082] The model calibration subunit is responsible for periodically verifying and optimizing the accuracy of the prediction model to avoid increased prediction errors due to data drift or changes in the scenario. First, data comparison is performed. Every day at 2:00 AM when the electricity load is relatively stable, the subunit retrieves the prediction data output by the prediction model over the past 24 hours and compares it point by point with the actual electricity data of the same period stored in the blockchain. The output data in the actual electricity data comes from the records collected by the green power terminal, and the load data comes from the metering data of the regional power grid. After comparison, the prediction error is calculated. The error indicators include mean absolute error and root mean square error. The mean absolute error reflects the average deviation between the predicted value and the actual value, and the root mean square error reflects the degree of influence of extreme deviations.

[0083] Next, threshold determination is performed. The sub-unit has two preset error thresholds. When the mean absolute error does not exceed 3% and the root mean square error does not exceed 5%, the model accuracy is determined to meet the standard and no adjustment is needed. When the mean absolute error is between 3% and 5% or the root mean square error is between 5% and 8%, the model is determined to require light calibration. When the mean absolute error exceeds 5% or the root mean square error exceeds 8%, the model is determined to require heavy calibration.

[0084] Finally, parameter adjustments are made. During light calibration, the weight coefficients of the sub-unit model are fine-tuned. For example, in a regular weekday model, the weights of historical data during the morning rush hour are adjusted, with the adjustment range controlled within 5% to avoid excessive model fluctuations. During heavy calibration, the latest data from the past three months are selected for incremental training of the model, and the core parameters of the model are updated, such as the number of hidden layer nodes in the Long Short-Term Memory network model and the kernel function parameters in the Support Vector Regression model. After training, the model is tested to ensure that the mean absolute error and root mean square error fall back to the acceptable range after calibration.

[0085] Example 5, please refer to Figure 6As shown: A blockchain-based green electricity consumption and storage data processing system, the cross-chain permission management module is used to realize permission control and traceability of green electricity cross-chain data, including a dynamic permission control unit and a cross-chain traceability unit; The dynamic permission control unit of the cross-chain permission management module includes a permission division subunit and a permission management subunit; The permission division subunit divides data access permissions into three levels: viewing permission, usage permission, and modification permission. The permission management subunit assigns corresponding permissions based on user type and revokes the assigned permissions when preset conditions are triggered. The cross-chain traceability unit includes an identifier generation subunit and a traceability query subunit; The identifier generation subunit generates a unique identifier for each cross-chain data, including the source chain ID, original data hash, cross-chain time, receiving chain ID, and usage record. The traceability query subunit provides an identifier query function to obtain the data flow path.

[0086] Furthermore, the permission division subunit in the dynamic permission control unit divides data access permissions into three levels: viewing permission, usage permission, and modification permission. The scope of each level of permission is clear and does not overlap. Viewing permission only allows users to browse the basic content of cross-chain data, such as green power output statistics and electricity load summary information. Data cannot be downloaded or copied, and sensitive fields in the data, such as user privacy information and core device parameters, will be anonymized.

[0087] In addition to viewing permissions, usage permissions allow users to use cross-chain data for specific scenarios, such as importing cross-chain power data into the dispatch system to assist decision-making or for the preparation of carbon emission accounting reports, but the data usage process must be recorded and synchronized to the blockchain.

[0088] Modification permissions are only granted to system maintenance personnel and core management users. They are allowed to correct errors in cross-chain data, such as data entry deviations and format anomalies. Each modification requires a modification request explaining the reasons and basis for the modification. The modification can only be executed after the request is approved. Modification records will be kept throughout the process.

[0089] The permission management subunit assigns corresponding permissions based on user type and revoks assigned permissions when preset conditions are triggered. User types are divided into four categories: ordinary users, operation and maintenance personnel, management users, and third-party organizations. Ordinary users are mostly power users and are only assigned viewing permissions. Operation and maintenance personnel are responsible for data maintenance and are assigned viewing and modification permissions. Management users coordinate data management and are assigned all three types of permissions. Third-party organizations, such as carbon emission verification agencies, are assigned viewing or usage permissions according to cooperation needs.

[0090] When allocating permissions, user identity information and usage period are bound together. The usage period is set according to business needs. The permission period for ordinary users is usually 30 days, while the permission period for third-party organizations is consistent with the cooperation cycle and does not exceed 180 days. The preset conditions for revocation include three categories: permission expiration, user identity change, and termination of data usage scenario.

[0091] The identifier generation subunit in the cross-chain traceability unit generates a unique identifier for each piece of cross-chain data. The identifier includes five core pieces of information: source chain ID, original data hash, cross-chain time, receiving chain ID, and usage record. The source chain ID identifies the blockchain where the data was initially located, using an 8-bit character encoding. Each blockchain has a unique encoding. The original data hash is calculated from the original data before the cross-chain process using the SHA-256 algorithm, ensuring that the data has not been tampered with during the cross-chain process. The cross-chain time is accurate to the second, recording the specific moment when the data was transferred out of the source chain. The receiving chain ID identifies the target blockchain into which the data was transferred, and its encoding rules are consistent with those of the source chain ID. The usage record updates the data's usage in the receiving chain in real time, such as which users viewed it and in which scenarios it was used.

[0092] Once generated, the identifier is bound to cross-chain data and simultaneously stored in the source link's collection chain and the system's main chain, forming a triple backup to prevent identifier loss.

[0093] The source tracing query subunit provides an identifier query function. Users can obtain the data flow path by entering a unique identifier. During the query process, the source tracing query subunit will retrieve various information corresponding to the identifier from the main chain and display the data flow nodes in chronological order, including the time when the data is transferred out of the source chain, the time when it is transferred into the receiving chain, the users who processed each link, and the data usage scenarios. If an anomaly occurs during the data flow, such as a mismatch between the identifier and the data hash or a missing flow node, the subunit will issue an early warning and locate the abnormal link.

[0094] Example 6, please refer to Figure 7 As shown: A blockchain-based green electricity consumption and storage data processing system, the federated collaborative processing module is used to realize collaborative analysis of encrypted green electricity data from multiple entities, including a local model training unit and a global model aggregation unit.

[0095] The local model training unit of the federated collaborative processing module includes a local encrypted data acquisition subunit and a sub-model training subunit; The local encrypted data acquisition subunit acquires locally stored encrypted green electricity data, and the sub-model training subunit uses the acquired encrypted data to train the green electricity prediction sub-model locally.

[0096] The global model aggregation unit of the federated collaborative processing module includes a parameter receiving subunit, a model aggregation subunit, and a contribution evaluation subunit; The parameter receiving sub-unit receives the sub-model parameters uploaded by each subject; The model aggregation sub-unit performs weighted aggregation of the received sub-model parameters according to the proportion of each subject's data volume to generate a global prediction model.

[0097] The contribution assessment subunit calculates the data contribution of each subject based on the impact of the uploaded sub-model parameters on the accuracy of the global prediction model, and uploads the contribution results to the blockchain.

[0098] Furthermore, the local encrypted data acquisition subunit in the local model training unit is responsible for acquiring the encrypted green electricity data stored locally by each entity. It supports connection to the local databases of different entities such as photovoltaic power plants, wind farms, and regional power grids. The local encrypted data acquisition subunit adopts the secure multi-party computation technology in privacy computing and establishes a connection with the local database through a preset encrypted communication protocol. It can read the feature dimensions and data volume information of the encrypted data without decryption, thus preventing the data from being stolen during the reading process.

[0099] Before acquiring data, the identity and permissions of the subject are verified. Only subjects that have been certified by the cross-chain permission management module are allowed to participate in data calls. Each data acquisition operation generates an operation log, recording information such as acquisition time, data type, and data volume, and uploads it to the blockchain. The acquired encrypted data covers green power output data, equipment operation data, and electricity load data, with a data time span of no less than 6 months. The sampling interval is set according to the data type. The sampling interval for output data and load data is 15 minutes, and the sampling interval for equipment operation data is 1 hour, ensuring that the data has temporal continuity and integrity, and providing sufficient samples for sub-model training.

[0100] The sub-model training unit uses the acquired encrypted data to train the green electricity prediction sub-model locally. It adopts the local training framework in federated learning and supports a variety of algorithms such as linear regression, random forest, and long short-term memory network. Each entity can choose the appropriate algorithm according to its own data characteristics.

[0101] Before training, the encrypted data is preprocessed, including missing value imputation and outlier handling. Missing values ​​are imputed using the average of data from adjacent time points, and outliers are identified using the 3σ principle and replaced with the historical average of the current time period. The entire preprocessing process is completed locally without data transmission. The training process uses homomorphic encryption technology to directly encrypt the gradient parameters during model training, preventing the inference of the original data from the gradient. At the same time, it supports gradient calculation in the encrypted state, and parameter updates can be achieved without decryption, ensuring data privacy and security.

[0102] The parameter receiving subunit in the global model aggregation unit is responsible for receiving sub-model parameters uploaded by each subject. It adopts a distributed receiving architecture and supports receiving parameter upload requests from more than 100 subjects simultaneously. The upload link uses the TLS 1.3 encryption protocol to prevent parameters from being tampered with or intercepted during transmission. Before receiving, the integrity of the parameter ciphertext is verified by calculating the hash value of the parameter ciphertext and comparing it with the hash value uploaded by the subject. If they match, the parameter is accepted; otherwise, it is required to re-upload.

[0103] The received parameters are temporarily stored in an encrypted cache. The cache uses hardware encryption and is only accessible to the model aggregation subunit. The parameters are stored in the cache for no more than 24 hours and are deleted immediately after aggregation to avoid security risks caused by long-term parameter retention.

[0104] The model aggregation subunit performs weighted aggregation of the received sub-model parameters according to the proportion of each subject's data volume to generate a global prediction model. The contribution evaluation subunit calculates the data contribution of each subject based on the impact of the uploaded sub-model parameters on the accuracy of the global prediction model. The contribution is evaluated using the accuracy improvement method. First, the accuracy of the temporary model generated after removing a subject's sub-model parameter is calculated, and then compared with the accuracy of the original global model. The larger the accuracy difference, the greater the contribution of that subject parameter to the global model.

[0105] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A blockchain-based green electricity consumption and storage data processing system, characterized in that, It includes an edge preprocessing module, a blockchain evidence storage and verification module, an energy storage health management module, a multi-scenario prediction module, a cross-chain permission management module, and a federated collaborative processing module. Each module achieves data interaction through a distributed node network. The edge preprocessing module is used to preprocess the raw power data generated by the green power terminal, including a data cleaning unit and a data compression unit; The blockchain evidence storage and verification module is used to verify and distribute the preprocessed power data to generate green power traceability certificates, including a lightweight consensus unit and a data on-chain unit. The energy storage health management module is used to monitor the health status and residual value assessment of green power energy storage equipment, and to provide data support for energy storage asset accounting. It includes a health data acquisition unit and a residual value calculation unit. The multi-scenario prediction module is used to dynamically predict green power output and electricity load, including a scenario recognition unit and a prediction model switching unit. The cross-chain permission management module is used to realize permission control and traceability of green power cross-chain data, including a dynamic permission control unit and a cross-chain traceability unit; The federated collaborative processing module is used to realize collaborative analysis of encrypted green power data from multiple entities, including a local model training unit and a global model aggregation unit.

2. The blockchain-based green electricity consumption and storage data processing system according to claim 1, characterized in that, The data cleaning unit of the edge preprocessing module includes a power data receiving subunit and an outlier removal subunit; The power data receiving subunit is used to receive the raw voltage and current data output by the green power terminal. The outlier removal subunit uses a sliding window algorithm to identify and remove instantaneous fault data that exceeds the normal range. The data compression unit uses differential coding technology to compress the volume of the cleaned power data to obtain preprocessed data.

3. The blockchain-based green electricity consumption and storage data processing system according to claim 1, characterized in that, The lightweight consensus unit of the blockchain evidence storage and verification module selects edge nodes from the blockchain network as verification nodes, and limits each verification node to verifying only the power data output by its adjacent edge nodes. The data on-chain unit includes a data encapsulation subunit and an on-chain storage subunit; The data encapsulation subunit encapsulates the preprocessed power data in a standardized format, and the on-chain storage subunit uploads the encapsulated data to the blockchain network for distributed storage.

4. The blockchain-based green electricity consumption and storage data processing system according to claim 1, characterized in that, The health data acquisition unit of the energy storage health management module includes a sensor group and a data encryption subunit; The sensor group is used to collect indicator data of the energy storage device, and the data encryption subunit uses an encryption algorithm to encrypt the collected indicator data before uploading it to the blockchain. The residual value calculation unit includes a coefficient determination subunit and a calculation subunit; The coefficient determination subunit determines the health coefficient, usage duration coefficient, and environmental adaptability coefficient of the energy storage device, and the calculation subunit calculates the residual value of the energy storage device based on the determined coefficients.

5. The blockchain-based green electricity consumption and storage data processing system according to claim 4, characterized in that, The sensor group includes a voltage sensor, a temperature sensor, a current sensor, and a capacity detection sensor; The voltage sensor is used to collect the output voltage data of the energy storage device, the temperature sensor is used to collect the internal temperature data of the energy storage device, the current sensor is used to collect the charging and discharging current data of the energy storage device, and the capacity detection sensor is used to collect the remaining capacity data of the energy storage device to calculate the capacity decay rate.

6. The blockchain-based green electricity consumption and storage data processing system according to claim 1, characterized in that, The scene recognition unit of the multi-scene prediction module includes a scene data receiving subunit and a scene determination subunit; The scenario data receiving subunit receives historical green power load data, temperature and humidity data, meteorological early warning data, and regional power consumption event information. The scenario determination subunit determines the current green power prediction scenario based on the received data. The prediction model switching unit includes a model invocation subunit and a model calibration subunit; The model invocation subunit invokes the corresponding prediction model according to the determined scenario, and the model calibration subunit periodically compares the prediction data with the actual data to calibrate the parameters of the prediction model.

7. The blockchain-based green electricity consumption and storage data processing system according to claim 1, characterized in that, The dynamic permission control unit of the cross-chain permission management module includes a permission division subunit and a permission management subunit; The permission division subunit divides data access permissions into three levels: viewing permission, usage permission, and modification permission. The permission management subunit assigns corresponding permissions based on user type and revoks the assigned permissions when preset conditions are triggered. The cross-chain traceability unit includes an identifier generation subunit and a traceability query subunit; The identifier generation subunit generates a unique identifier for each cross-chain data, which includes the source chain ID, original data hash, cross-chain time, receiving chain ID, and usage record. The traceability query subunit provides an identifier query function to obtain the data flow path.

8. The blockchain-based green electricity consumption and storage data processing system according to claim 1, characterized in that, The local model training unit of the federated collaborative processing module includes a local encrypted data acquisition subunit and a sub-model training subunit. The local encrypted data acquisition subunit acquires locally stored encrypted green electricity data, and the sub-model training subunit uses the acquired encrypted data to train a green electricity prediction sub-model locally.

9. The blockchain-based green electricity consumption and storage data processing system according to claim 1, characterized in that, The global model aggregation unit of the federated collaborative processing module includes a parameter receiving subunit, a model aggregation subunit, and a contribution evaluation subunit. The parameter receiving sub-unit receives the sub-model parameters uploaded by each subject; The model aggregation subunit performs weighted aggregation of the received sub-model parameters according to the proportion of each subject's data volume to generate a global prediction model.

10. The blockchain-based green electricity consumption and storage data processing system according to claim 9, characterized in that, The contribution evaluation subunit calculates the data contribution of each subject based on the impact of the uploaded sub-model parameters on the accuracy of the global prediction model, and uploads the contribution results to the blockchain.

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