New energy charging digital ecological base system

Abstract

The invention discloses a new energy charging digital ecological base system which comprises a charging pile module used for charging an electric vehicle and monitoring charging power and an equipment state; the environment sensing module is used for collecting environment data in the charging process and transmitting the environment data to the system; the data collection and transmission module is used for collecting equipment data in real time and transmitting the equipment data to the cloud platform for storage and analysis; the big data platform module is used for processing and analyzing equipment data and generating a charging scheduling and load prediction optimization scheme; the scheduling optimization module is used for optimizing the charging power and time period of the charging pile according to the data analysis result; the block chain encryption and data storage module is used for encrypting and storing the charging data; and the risk assessment and protection suggestion module is used for assessing risks in the charging process and providing protection suggestions. According to the invention, big data analysis, intelligent scheduling and block chain technologies are utilized, the charging process is optimized, the efficiency is improved, the power grid load is balanced, and the data security is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging systems, and in particular to a new energy charging digital ecological base system. Background Art

[0002] With the rapid development of the global electric vehicle market, the issue of electric vehicle charging has become a focus of attention. Charging piles, as the core facilities in the charging process of electric vehicles, play a vital role. The popularization and construction of charging piles have greatly promoted the use of electric vehicles, but there are many technical challenges in the charging process, especially in the management of charging piles, optimization of the charging process, balancing of grid loads, data security and charging efficiency. At present, although some mature charging systems have been applied to the market, they still face some problems that need to be solved.

[0003] First, the existing charging system is insufficient in monitoring and managing the charging process. Although some smart charging piles already have basic monitoring functions, these systems can usually only monitor the basic status of the charging piles, such as charging power, battery status, etc., and do not comprehensively monitor and manage environmental factors during the charging process, interactions between charging piles, and dynamic changes in grid load. Therefore, the charging power of the charging pile is often difficult to dynamically adjust according to the real-time grid load and demand, resulting in waste of resources of the charging pile and the grid during the charging process, as well as unbalanced grid load problems.

[0004] Secondly, the existing charging pile management system lacks sufficient intelligence in the scheduling and optimization of the charging process. In some traditional charging pile systems, the charging power and charging period of the charging pile are adjusted based on fixed rules or manual settings, and fail to be dynamically optimized based on real-time grid load data and charging demand. This causes large fluctuations in grid load during the charging process, making it difficult to achieve optimal utilization of charging pile resources, and charging efficiency cannot be maximized. In addition, due to the lack of efficient scheduling optimization, the grid load may be overloaded during peak hours, leading to grid stability issues.

[0005] In terms of data security and privacy protection during the charging process, existing technologies also have certain defects. Although some charging pile systems have begun to use cloud platforms for data storage and management, most of this data is stored in a traditional way, lacking effective encryption protection and privacy protection measures. If the payment settlement information, device management information and charging data involved in the charging process are not effectively encrypted and stored, there may be risks of data tampering, leakage and attack. In addition, traditional charging systems usually cannot provide data transparency and immutability, which brings potential security risks to users and operators.

[0006] In addition, although some charging systems have started to apply big data and Internet of Things technologies, the existing technologies still have limitations in data processing, analysis, and decision support. The current systems mainly rely on a single data source or rules for charging scheduling and management, lacking the comprehensive processing ability for multi-dimensional and multi-source data. This makes the scheduling of charging piles and the optimization of grid load lack a global perspective, and fails to fully utilize real-time environmental data, battery status, and user behavior patterns and other information for dynamic adjustment and optimization. Therefore, the existing charging pile management systems lack the comprehensive analysis ability for multiple factors and are difficult to make more intelligent and accurate charging strategies.

[0007] Therefore, how to provide a new energy charging digital ecological base system is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0008] An object of the present invention is to provide a new energy charging digital ecological base system. The present invention makes full use of Internet of Things technology, big data analysis, deep learning algorithms, and blockchain technology, and details key technologies such as charging pile management, charging process optimization, grid load balancing, and data security deposit. By dynamically adjusting the charging power and time period of the charging pile, combined with real-time grid load data and charging demands, it ensures the efficiency of the charging process and the stability of the grid; at the same time, through blockchain technology, it encrypts and deposits the payment settlement, equipment management, and data during the charging process to ensure the security, transparency, and immutability of the data. The present invention has the advantages of optimizing charging efficiency, improving grid load management ability, enhancing data security, and improving the operation efficiency and system management level of charging stations.

[0009] A new energy charging digital ecological base system according to an embodiment of the present invention includes:

[0010] A charging pile module, configured to provide charging for electric vehicles and monitor the charging power and equipment status;

[0011] An environmental perception module, configured to collect environmental data during the charging process and transmit it to the system;

[0012] A data collection and transmission module, configured to collect device data in real time and transmit it to the cloud platform for storage and analysis;

[0013] A big data platform module, configured to process and analyze device data and generate an optimized charging scheduling and load forecasting plan;

[0014] A scheduling optimization module, configured to optimize the charging power and time period of the charging pile according to the data analysis results;

[0015] A blockchain encryption and data deposit module, configured to encrypt and deposit charging data;

[0016] A risk assessment and protection advice module for assessing risks during charging and providing protection advice.

[0017] Optionally, the modules are implemented by the following methods:

[0018] S1. Connect the charging pile, sensors, and battery management system through Internet of Things technology, collect the charging pile status, battery data, and environmental data, generate multi-dimensional data, and transmit the multi-dimensional data to the cloud platform;

[0019] S2. Use the collected multi-dimensional data to perform real-time prediction of the grid load, and dynamically dispatch the grid load according to the prediction results, and adjust the charging power and charging time period of the charging pile;

[0020] S3. Based on the collected multi-dimensional data, perform spatio-temporal prediction of the charging demand through a spatio-temporal graph convolutional network, and combine the grid load prediction results to generate a charging scheduling plan;

[0021] S4. Apply the generated charging demand prediction results and grid load scheduling results, optimize the charging pile distribution, charging path, and power distribution through a multi-objective optimization model to generate a final charging scheduling plan;

[0022] S5. Combine the final charging scheduling plan, and use blockchain technology to encrypt the payment settlement, device management, and data evidence storage during the charging process;

[0023] S6. Use the grid load scheduling results and charging demand prediction results to perform self-learning and dynamic scheduling on the charging pile, and adjust the charging strategy according to the real-time data and prediction results;

[0024] S7. Continuously monitor the charging process, dynamically adjust the charging strategy, and at the same time analyze through a big data platform to optimize the operation of the charging station and system management.

[0025] Optionally, the said S2 includes the following specific steps:

[0026] S21. Based on the collected multi-dimensional data, construct a grid load prediction model. The grid load prediction model uses a deep reinforcement learning algorithm to train historical load data to predict the future grid load change situation, where the historical load data includes the charging pile utilization rate, battery charging amount, surrounding power load, and environmental factors:

[0027]

[0028] Wherein, is the grid load at the prediction time t, is the actual grid load, P i,t$P_{i,t}$ is the charging power of charging pile $i$ at time $t$, $N$ is the number of charging piles, $\lambda$ is the regularization coefficient, $T$ is the time step, $\min$ is the minimization operation, and $P$ is the optimization objective function;

[0029] S22. Based on the optimized power grid load forecasting results by the deep reinforcement learning algorithm, combined with the current charging demand, dynamically adjust the power grid load distribution plan through the fuzzy adaptive control algorithm:

[0030]

[0031] Among them, $L$ i,t is the scheduled load of charging pile $i$ at time $t$, $D_{i,t}$ is the actual demand load of charging pile $i$ at time $t$, $\hat{L}$ is the power grid load forecasting result, $\Delta P$ j,t is the change in the charging power of charging pile $j$ at time $t$, $\gamma$ is the load adjustment coefficient, and $\alpha$ and $\beta$ are adjustment coefficients;

[0032] S23. Through the generated load distribution plan, adopt the reinforcement learning algorithm to dynamically adjust the charging power and charging time period of each charging pile:

[0033]

[0034] Among them, $K$ i,t is the charging power of charging pile $i$ at time $t$, $\delta$ i,t is the load deviation of charging pile $i$, $E$ t is the power grid load at time $t$, $E$ max is the maximum power grid load;

[0035] S24. According to the charging power adjustment result, generate a power grid load scheduling plan, and the scheduling plan is dynamically adjusted according to the real-time load of the charging pile and the change of the power grid load;

[0036] S25. During the charging process, monitor the power grid load and the status of the charging pile in real time, and perform dynamic scheduling according to the power grid load scheduling plan.

[0037] Optionally, the said S3 includes the following specific steps:

[0038] S31. Based on the collected charging pile status data, battery data and environmental data, construct a spatio-temporal graph convolutional network model to perform spatio-temporal prediction on the charging demand, and the charging demand includes the utilization rate, load demand, and battery charging status of each charging pile:

[0039] $H$ t $=\sigma(A\cdot H$ t-1 $\cdot W + X$ t )

[0040] Among them, $H$ tis the node feature representation at time t, A is the adjacency matrix of the graph, W is the convolution kernel matrix, and X t is the input feature matrix at time t, σ is the activation function, and H t-1 is the node feature representation at time t-1;

[0041] S32. Based on the generated spatio-temporal demand prediction results, combined with the power grid load prediction results, calculate the load prediction value of each charging pile by the weighted average method:

[0042]

[0043] Among them, is the load prediction value of charging pile i at time t, δ and ε are the weighting coefficients, is the power grid load prediction result, is the demand load of charging pile i;

[0044] S33. According to the load prediction results, apply a multi-objective optimization algorithm to optimize the scheduling strategy of the charging piles, and the optimization objectives are charging efficiency and resource utilization rate:

[0045]

[0046] Among them, is the actual load of charging pile i at time t, and K j,t is the charging power of charging pile j at time t, is the maximum charging power of charging pile j, λ is the regularization coefficient, N is the number of charging piles, M is the number of charging pile scheduling, min is the minimization operation, and F is the optimization function of charging efficiency and resource utilization rate;

[0047] S34. Based on the optimization results, generate a charging scheduling plan and make real-time adjustments. The charging scheduling plan considers the following factors:

[0048]

[0049] Among them, C i,t is the charging power of charging pile i at time t, S i is the maximum charging power of charging pile i, D i is the equipment status data of charging pile i, and E t is the current load of the power grid;

[0050] S35. According to the generated charging scheduling plan, monitor the charging status of the charging piles in real time, adjust the charging strategy, and optimize the charging efficiency.

[0051] Optionally, the S4 includes the following specific steps:

[0052] S41. Based on the generated charging demand prediction results and the power grid load scheduling plan, use a multi-objective optimization algorithm to optimize the distribution of charging piles, the charging path, and the power allocation:

[0053]

[0054] Among them, U is the optimization objective function for the distribution of charging piles, the charging path, and the power allocation. is the load prediction value of charging pile i at time t. is the actual load of charging pile i at time t, and C i,t is the charging power of charging pile i at time t. is the maximum charging power of charging pile i, N is the number of charging piles, α is the adjustment coefficient, and min represents the minimization operation.

[0055] S42. Determine the optimal distribution location and charging path selection of the charging piles through the optimization results, and precisely schedule the charging power and charging time period of the charging piles:

[0056]

[0057] Among them, is the adjusted load demand of charging pile i at time t, γ is the adjustment coefficient, and M is the number of charging pile schedules.

[0058] S43. According to the adjustment results, adjust the scheduling strategy of the charging piles in real time, generate the final charging scheduling plan, and optimize the charging efficiency and power grid load management.

[0059] S44. During the charging process, monitor the status of the charging piles in real time and perform dynamic scheduling according to the charging scheduling plan.

[0060] Optionally, the above-mentioned S5 includes the following specific steps:

[0061] S51. Based on the generated final charging scheduling plan, use blockchain technology to encrypt the payment settlement, equipment management, and data storage evidence during the charging process. The encryption process includes generating encrypted data blocks related to the charging process:

[0062] DataBlock t = Hash(T t ||C t ||D t ||P t );

[0063] Among them, DataBlock t is the encrypted data block at time t, T t is the charging time at time t, C t is the charging power at time t, Dt is the status information of the charging pile device, P t is the payment settlement information at time t, and Hash is the encryption hash function;

[0064] S52. Utilize the smart contract function in the blockchain to synchronize payment settlement, device management, and data storage evidence during the charging process among different charging piles:

[0065] SmartContract(C t ,P t ) = Verify(C t ,P t ) -> Store(DataBlock t );

[0066] Among them, SmartContract(C t ,P t ) is the execution function, Verify(C t ,P t ) is the verification function, and Store(DataBlock t ) is the storage operation;

[0067] S53. Through the smart contract, add the generated encrypted data block to the blockchain ledger. Each block in the blockchain ledger contains the hash value of the previous block, the timestamp, and the data of this block, forming an immutable historical record:

[0068] Blockchain t = Hash(Blockchain t-1 ||DataBlock t );

[0069] Among them, Blockchain t is the blockchain ledger at time t, and Blockchain t-1 is the hash value of the previous block;

[0070] S54. Through the blockchain data storage result, conduct real-time auditing on all data during the charging process:

[0071] Audit(T t ,C t ,D t ,P t ) = Retrieve(Blockchain t ) -> Verify(DataBlock t );

[0072] Among them, Audit(T t,C t ,D t ,P t ) To audit and verify the data during the charging process, Retrieve(Blockchain t ) is an operation for retrieving blockchain data, Verify(DataBlock t ) is a data verification operation;

[0073] S55. During the charging process, encrypt and store the charging data in real time, and store and query the data based on blockchain technology:

[0074] Query(T t ,C t ,D t ,P t ) = Verify(Retrieve(Blockchain t ));

[0075] Among them, Query(T t ,C t ,D t ,P t ) is to verify and obtain the key information during the charging process by querying the data on the blockchain. Verify is a data verification operation, and Retrieve is an operation for retrieving blockchain data.

[0076] Optionally, the S7 includes the following specific steps:

[0077] S71. Continuously monitor the charging process through a big data platform, and collect the charging power, device status, battery status, and grid load data of the charging pile in real time:

[0078] D t = (C i,t ,E t ,T t ,P t );

[0079] Among them, D t is the charging process data at time t, C i,t is the charging power of charging pile i at time t, E t is the grid load at time t, T t is the charging duration, and P t is the payment settlement information;

[0080] S72. Based on the collected data, adjust the charging strategy in real time, and adjust the charging power and charging time period of the charging pile:

[0081]

[0082] Among them, α and β are adjustment coefficients, is the adjusted charging power of charging pile i at time t, and δ i,t is the load deviation, and S i is the maximum charging power;

[0083] S73. Analyze the operation efficiency of charging piles using a big data platform and generate an operation report for the charging station:

[0084]

[0085] Among them, Efficiency is the operation efficiency of the charging station, and N is the number of charging piles in the charging station;

[0086] S74. Optimize the resource allocation and management of the charging station based on the generated operation report:

[0087]

[0088] Among them, R optimal is the optimal resource allocation of the charging station;

[0089] S75. During the charging process, monitor the status of the charging pile in real time and make dynamic adjustments according to the optimization results.

[0090] The beneficial effects of the present invention are:

[0091] By introducing a big data platform and blockchain technology, the present invention significantly improves the management and optimization level of the electric vehicle charging system. First, through the combination of Internet of Things technology and environmental perception module, the present invention can collect key data such as charging piles, charging power, battery status, and grid load in real time, and perform centralized processing and analysis through the big data platform. Compared with the prior art, the present invention realizes the dynamic scheduling optimization of charging piles, and can automatically adjust the charging power and charging time period according to the real-time grid load and charging demand, thus effectively avoiding the problems of unbalanced grid load and waste of charging pile resources. This not only improves the charging efficiency, but also ensures the stability of the grid and avoids the risk of grid overload during peak hours.

[0092] Secondly, the present invention encrypts and stores the data during the charging process through blockchain technology, ensuring the security, transparency, and immutability of the payment settlement, equipment management, and charging data of the charging pile. This innovative measure effectively solves the problems of poor data security and privacy leakage in the traditional charging system, provides a reliable data storage and traceability function, and makes the charging process safer and more reliable. At the same time, the application of blockchain technology also improves the trust of users and operators in charging data, ensuring the integrity of data during transmission and storage.

[0093] In addition, the present invention introduces a risk assessment and protection recommendation module. By performing multi-dimensional real-time analysis on various data during the charging process, it can promptly identify potential risks and generate protection recommendations. This module not only effectively avoids problems such as grid overload and equipment failures but also provides precise scheduling strategies for charging piles, further enhancing the safety of the charging process and the utilization rate of charging piles.

[0094] By combining big data analysis, deep learning algorithms, and blockchain technology, the present invention has achieved remarkable beneficial effects in aspects such as charging pile management, grid load optimization, charging efficiency improvement, and data security guarantee. Compared with the prior art, the system of the present invention is more intelligent, dynamic, and has higher safety and reliability. It can better adapt to the rapidly growing charging demand of electric vehicles, promote the intelligent development of charging infrastructure, and provide strong support for the future operation of charging stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0096] Figure 1 is a flowchart of a new energy charging digital ecological base system proposed by the present invention;

[0097] Figure 2 is a flowchart of the encrypted storage and evidence preservation of charging process data of a new energy charging digital ecological base system proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0098] Now, the present invention will be further described in detail with reference to the drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0099] Refer to Figure 1-2 , a new energy charging digital ecological base system, comprising:

[0100] A charging pile module for providing charging for electric vehicles and monitoring the charging power and equipment status;

[0101] An environment perception module for collecting environmental data during the charging process and transmitting it to the system;

[0102] A data collection and transmission module for real-time collecting device data and transmitting it to the cloud platform for storage and analysis;

[0103] A big data platform module for processing and analyzing device data and generating optimized charging scheduling and load prediction solutions;

[0104] A scheduling optimization module for optimizing the charging power and time period of charging piles according to the data analysis results;

[0105] A blockchain encryption and data storage and certification module for encrypting and storing charging data;

[0106] A risk assessment and protection advice module for assessing risks during the charging process and providing protection advice.

[0107] In this embodiment, the modules are implemented through the following methods:

[0108] S1. Connect the charging pile, sensor and battery management system through the Internet of Things technology, collect the charging pile status, battery data and environmental data, generate multi-dimensional data, and transmit the multi-dimensional data to the cloud platform;

[0109] S2. Use the collected multi-dimensional data to make a real-time prediction of the power grid load, and dynamically dispatch the power grid load according to the prediction results, and adjust the charging power and charging time period of the charging pile;

[0110] S3. Based on the collected multi-dimensional data, perform spatio-temporal prediction of the charging demand through a spatio-temporal graph convolutional network, and combine the power grid load prediction results to generate a charging scheduling plan;

[0111] S4. Apply the generated charging demand prediction results and power grid load dispatch results, optimize the charging pile distribution, charging path and power distribution through a multi-objective optimization model, and generate the final charging scheduling plan;

[0112] S5. Combine the final charging scheduling plan, and use blockchain technology to encrypt the payment settlement, equipment management and data storage and certification during the charging process;

[0113] S6. Use the power grid load dispatch results and charging demand prediction results to perform self-learning and dynamic scheduling on the charging pile, and adjust the charging strategy according to the real-time data and prediction results;

[0114] S7. Continuously monitor the charging process, dynamically adjust the charging strategy, and at the same time analyze through the big data platform to optimize the operation of the charging station and system management.

[0115] In this embodiment, the S2 includes the following specific steps:

[0116] S21. Based on the collected multi-dimensional data, construct a power grid load prediction model. The power grid load prediction model uses a deep reinforcement learning algorithm to train historical load data to predict the future power grid load change situation, where the historical load data includes the charging pile utilization rate, battery charging amount, surrounding power load and environmental factors:

[0117]

[0118] Among them, is the grid load at prediction time t, is the actual grid load, P i,t is the charging power of charging pile i at time t, N is the number of charging piles, λ is the regularization coefficient, T is the time step, min is the minimization operation, and P is the optimization objective function;

[0119] S22. Based on the grid load prediction result optimized by the deep reinforcement learning algorithm, combined with the current charging demand, dynamically adjust the grid load distribution scheme through the fuzzy adaptive control algorithm:

[0120]

[0121] Among them, L i,t is the scheduled load of charging pile i at time t, is the actual demand load of charging pile i at time t, is the grid load prediction result, ΔP j,t is the change in the charging power of charging pile j at time t, γ is the load adjustment coefficient, and α and β are adjustment coefficients;

[0122] S23. Through the generated load distribution scheme, use the reinforcement learning algorithm to dynamically adjust the charging power and charging time period of each charging pile:

[0123]

[0124] Among them, K i,t is the charging power of charging pile i at time t, δ i,t is the load deviation of charging pile i, E t is the grid load at time t, E max is the maximum grid load;

[0125] S24. According to the charging power adjustment result, generate a grid load scheduling scheme, and the scheduling scheme is dynamically adjusted according to the real-time load of the charging pile and the change of the grid load;

[0126] S25. During the charging process, monitor the grid load and the status of the charging pile in real time, and perform dynamic scheduling according to the grid load scheduling scheme.

[0127] In this embodiment, the said S3 includes the following specific steps:

[0128] S31. Based on the collected charging pile status data, battery data and environmental data, construct a spatio-temporal graph convolutional network model to perform spatio-temporal prediction on the charging demand. The charging demand includes the utilization rate, load demand and battery charging status of each charging pile:

[0129] H t = σ(A·Ht-1 ·W + X t );

[0130] Among them, H t is the node feature representation at time t, A is the adjacency matrix of the graph, W is the convolution kernel matrix, X t is the input feature matrix at time t, σ is the activation function, H t-1 is the node feature representation at time t - 1;

[0131] S32. Based on the generated spatio-temporal demand prediction results, combined with the power grid load prediction results, calculate the load prediction value of each charging pile by the weighted average method:

[0132]

[0133] Among them, is the load prediction value of charging pile i at time t, δ and ε are the weighting coefficients, is the power grid load prediction result, is the demand load of charging pile i;

[0134] S33. According to the load prediction results, apply the multi-objective optimization algorithm to optimize the scheduling strategy of the charging piles, and the optimization objectives are charging efficiency and resource utilization rate:

[0135]

[0136] Among them, is the actual load of charging pile i at time t, K j,t is the charging power of charging pile j at time t, is the maximum charging power of charging pile j, λ is the regularization coefficient, N is the number of charging piles, M is the number of charging pile scheduling, min is the minimization operation, and F is the optimization function of charging efficiency and resource utilization rate;

[0137] S34. Based on the optimization results, generate a charging scheduling plan and make real-time adjustments. The charging scheduling plan considers the following factors:

[0138]

[0139] Among them, C i,t is the charging power of charging pile i at time t, S i is the maximum charging power of charging pile i, D i is the device status data of charging pile i, E t is the current load of the power grid;

[0140] S35. According to the generated charging scheduling plan, monitor the charging status of the charging piles in real time, adjust the charging strategy, and optimize the charging efficiency.

[0141] In this embodiment, S4 includes the following specific steps:

[0142] S41. Based on the generated charging demand prediction results and the grid load scheduling plan, use a multi-objective optimization algorithm to optimize the distribution of charging piles, the charging path, and the power allocation:

[0143]

[0144] Among them, U is the optimization objective function for the distribution of charging piles, the charging path, and the power allocation, is the load prediction value of charging pile i at time t, is the actual load of charging pile i at time t, C i,t is the charging power of charging pile i at time t, is the maximum charging power of charging pile i, N is the number of charging piles, α is the adjustment coefficient, and min represents the minimization operation;

[0145] S42. Based on the optimization results, determine the optimal distribution location and charging path selection of the charging piles, and precisely schedule the charging power and charging time period of the charging piles:

[0146]

[0147] Among them, is the adjusted load demand of charging pile i at time t, γ is the adjustment coefficient, and M is the number of charging piles scheduled;

[0148] S43. According to the adjustment results, adjust the scheduling strategy of the charging piles in real time, generate the final charging scheduling plan, and optimize the charging efficiency and grid load management;

[0149] S44. During the charging process, monitor the status of the charging piles in real time and perform dynamic scheduling according to the charging scheduling plan.

[0150] In this embodiment, S5 includes the following specific steps:

[0151] S51. Based on the generated final charging scheduling plan, use blockchain technology to encrypt the payment settlement, equipment management, and data archiving during the charging process. The encryption process includes generating encrypted data blocks related to the charging process:

[0152] DataBlock t = Hash(T t ||C t ||D t ||P t );

[0153] Among them, DataBlock tThe encrypted data block at time t, T t The charging time at time t, C t The charging power at time t, D t The status information of the charging pile device, P t The payment settlement information at time t, Hash is the encryption hash function;

[0154] S52. Utilize the smart contract function in the blockchain to synchronize payment settlement, device management, and data deposit during the charging process among different charging piles:

[0155] SmartContract(C t ,P t ) = Verify(C t ,P t ) → Store(DataBlock t );

[0156] Among them, SmartContract(C t ,P t ) is the execution function, Verify(C t ,P t ) is the verification function, Store(DataBlock t ) is the storage operation;

[0157] S53. Through the smart contract, add the generated encrypted data block to the blockchain ledger. Each block in the blockchain ledger contains the hash value of the previous block, the timestamp, and the data of this block, forming an immutable historical record:

[0158] Blockchain t = Hash(Blockchain t-1 || DataBlock t );

[0159] Among them, Blockchain t is the blockchain ledger at time t, Blockchain t-1 is the hash value of the previous block;

[0160] S54. Through the blockchain data storage result, conduct real-time auditing on all data during the charging process:

[0161] Audit(T t ,C t ,D t ,P t ) = Retrieve(Blockchain t ) → Verify(DataBlockt );

[0162] Among them, Audit(T t , C t , D t , P t ) is to audit and verify the data during the charging process, Retrieve(Blockchain t ) is a blockchain data retrieval operation, and Verify(DataBlock t ) is a data verification operation;

[0163] S55. During the charging process, encrypt and store the charging data in real time, and store and query the data based on blockchain technology:

[0164] Query(T t , C t , D t , P t ) = Verify(Retrieve(Blockchain t ));

[0165] Among them, Query(T t , C t , D t , P t ) is to verify and obtain the key information during the charging process by querying the data on the blockchain. Verify is a data verification operation, and Retrieve is a blockchain data retrieval operation.

[0166] In this embodiment, S7 includes the following specific steps:

[0167] S71. Continuously monitor the charging process through the big data platform, and collect the charging power, device status, battery status, and grid load data of the charging pile in real time:

[0168] D t = (C i,t , E t , T t , P t );

[0169] Among them, D t is the charging process data at time t, C i,t is the charging power of charging pile i at time t, E t is the grid load at time t, T t is the charging duration, and P t is the payment settlement information;

[0170] S72. Based on the collected data, adjust the charging strategy in real time, and adjust the charging power and charging time period of the charging pile:

[0171]

[0172] where α and β are adjustment coefficients, is the adjusted charging power of charging pile i at time t, and δ i,t is the load deviation, and S i is the maximum charging power;

[0173] S73. Analyze the operation efficiency of the charging piles using a big data platform and generate an operation report for the charging station:

[0174]

[0175] where Efficiency is the operation efficiency of the charging station and N is the number of charging piles in the charging station;

[0176] S74. Based on the generated operation report, optimize the resource allocation and management of the charging station:

[0177]

[0178] where R optimal is the optimal resource allocation of the charging station;

[0179] S75. During the charging process, monitor the status of the charging piles in real time and make dynamic adjustments according to the optimization results.

[0180] Example 1:

[0181] To verify the feasibility of the present invention in implementation, the present invention is applied to a new energy charging network in a large city. This city has multiple public charging stations, covering the main urban areas and transportation hubs. Each charging station is equipped with multiple charging piles to support simultaneous charging of multiple electric vehicles. The charging piles are connected to multiple systems such as the battery management system, the power grid, and the charging pile management system. Real-time data is collected through Internet of Things technology and managed and optimized through a cloud platform. However, the existing charging systems face several challenges: First, the charging demands of the charging piles vary greatly at different time periods, resulting in uneven power grid loads. Especially during peak periods, the power grid often overloads. Second, the data management of the charging process of the charging piles is insufficient, lacking effective protection for payment settlement, device status, and charging data, and the data is prone to being tampered with or lost. Third, there is a lack of an intelligent scheduling mechanism. The optimization of the charging power and time period of the charging piles fails to fully consider the power grid load and the charging demands of electric vehicles, resulting in low charging efficiency and power grid utilization rate.

[0182] To effectively solve the above problems, the new energy charging digital ecological base system of the present invention has been applied in the urban charging network. The system connects various devices such as charging piles, environmental sensors, and battery management systems through Internet of Things technology, collects data in real time and uploads it to the cloud platform. The cloud platform uses big data analysis algorithms to analyze data such as charging demand, grid load, and battery status in real time, and dynamically adjusts the charging power and charging time period of the charging piles according to the charging demand and the prediction results of the grid load. In addition, the system uses blockchain technology to encrypt and store evidence for payment settlement, device management, and charging data during the charging process to ensure the security and immutability of the data.

[0183] The system adjusts the charging power and charging time period of the charging piles by analyzing data such as grid load, battery status, and user charging demand in real time. For example, during periods of low grid load, the system will increase the charging power of some charging piles to improve charging efficiency; during periods of high grid load, the system will automatically reduce the charging power and balance the distribution of charging piles to avoid grid overload. The system adjusts the load demand of the charging station by monitoring the grid load situation in real time according to the distribution of the charging piles and the adjustment results of the charging power, ensuring the stability of the grid operation. During peak hours, the system will automatically reduce the charging power of some charging piles to prevent the grid from being overloaded and avoid grid failures. Each payment settlement, device status, and charging data during the charging process will be encrypted through blockchain technology to generate immutable data blocks. Each data block contains key information such as timestamp, charging power, and payment amount, and is chained to the previous data block. In this way, the data security during the charging process is ensured, and data traceability can be carried out at any time. The system continuously monitors the data during the charging process, conducts risk assessments, timely identifies potential risks such as equipment failures and grid overloads, and generates corresponding protection suggestions. For example, when the system detects that the battery is charging too fast or the charging pile device status is abnormal, the system will immediately issue a warning and give relevant handling suggestions through the platform.

[0184] Table 1 Data on the operation of the charging station and the optimization of the grid load

[0185]

[0186]

[0187] As can be seen from Table 1, after the implementation of the intelligent charging system of the present invention, the overall operation efficiency of the charging station and the grid load balance have been significantly improved. Specifically, the table shows the changes in charging efficiency, grid load, and equipment failure handling of the central city charging station, the southern area charging station, and the western area charging station before and after the implementation.

[0188] First, the charging efficiency after implementation has been significantly improved. For example, the average charging duration at the central city charging station before implementation was 60 minutes, while after implementation, this duration was shortened to 45 minutes, and the charging efficiency increased by 25%. Similarly, the charging efficiencies of the southern area charging station and the western area charging station have also been improved, reaching 20.7% and 22.6% respectively. This shows that through the optimization of the charging process and intelligent scheduling function of the present invention, the charging power and charging time period of the charging piles have been more effectively adjusted, avoiding excessive fluctuations in the grid load, thus greatly improving the charging efficiency.

[0189] Secondly, the management of the grid load has been optimized. Before implementation, the grid load of the central city charging station was 80%, while after implementation, the grid load decreased to 65%. Similar trends of grid load reduction also occurred at the southern area charging station and the western area charging station, decreasing from 75% to 68% and from 82% to 70% respectively. This change indicates that after implementing charging scheduling and load balancing optimization, the cooperation between the charging piles and the grid has become closer, avoiding the risk of overloading the grid, thus enhancing the stability of the grid.

[0190] In terms of equipment failure handling, the charging system after implementation has significantly improved its fault warning and handling capabilities. The central city charging station experienced 5 equipment failure handling operations before implementation, while only 5 failures occurred after implementation. The number of failure handling operations at the southern area and western area charging stations has also decreased, to 3 times and 6 times respectively. This shows that the risk assessment and protection advice module of the present invention effectively prevents the occurrence of equipment failures and improves the operational reliability of the charging station.

[0191] In summary, the new energy charging digital ecological base system of the present invention significantly improves the operational efficiency of the charging station and the stability of the grid, reduces the equipment failure rate, and provides a more efficient, safe, and reliable solution for electric vehicle charging by optimizing the charging pile scheduling, balancing the grid load, improving the charging efficiency, and providing reliable guarantees in terms of data security.

[0192] The above is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A new energy charging digital ecological base system, characterized in that: include: Charging pile module, used to provide electric vehicle charging and monitor charging power and device status; Environmental perception module, used to collect environmental data during charging and transmit it to the system; Data collection and transmission module, used to collect device data in real time and transmit it to the cloud platform for storage and analysis; Big data platform module, used to process and analyze equipment data and generate charging scheduling and load forecasting optimization solutions; Scheduling optimization module, used to optimize the charging power and time period of charging piles based on data analysis results; Blockchain encryption and data storage module, used to encrypt and store charging data; The risk assessment and protection suggestion module is used to assess the risks during the charging process and provide protection suggestions.

2. A new energy charging digital ecological base system according to claim 1, characterized in that: The modules are implemented in the following ways: S1. Connect charging piles, sensors and battery management systems through the Internet of Things technology, collect charging pile status, battery data and environmental data, generate multi-dimensional data, and transmit the multi-dimensional data to the cloud platform; S2. Use the collected multi-dimensional data to make real-time predictions of the grid load, and dynamically dispatch the grid load according to the prediction results to adjust the charging power and charging time of the charging piles; S3. Based on the collected multi-dimensional data, the spatiotemporal graph convolutional network is used to predict the charging demand in time and space, and the charging scheduling plan is generated in combination with the grid load prediction results; S4. Apply the generated charging demand forecast results and grid load dispatch results to optimize the charging pile distribution, charging path and power distribution through a multi-objective optimization model to generate a final charging dispatch plan; S5. Combined with the final charging scheduling plan, the payment settlement, equipment management and data storage in the charging process are encrypted through blockchain technology; S6. Use the grid load dispatch results and charging demand forecast results to conduct self-learning and dynamic dispatch of charging piles, and adjust the charging strategy according to real-time data and forecast results; S7. Continuously monitor the charging process, dynamically adjust the charging strategy, and analyze through the big data platform to optimize charging station operations and system management.

3. A new energy charging digital ecological base system according to claim 2, characterized in that: The S2 specifically includes: S21. Based on the collected multi-dimensional data, a power grid load prediction model is constructed. The power grid load prediction model uses a deep reinforcement learning algorithm to train historical load data to predict future power grid load changes. The historical load data includes the usage rate of charging piles, battery charge, surrounding power load and environmental factors: in, To predict the grid load at time t, is the actual grid load, P i,t is the charging power of charging pile i at time t, N is the number of charging piles, λ is the regularization coefficient, T is the time step, min is the minimization operation, and P is the optimization objective function; S22. Based on the grid load forecast results optimized by the deep reinforcement learning algorithm and combined with the current charging demand, the grid load distribution plan is dynamically adjusted through the fuzzy adaptive control algorithm: Among them, L i,t is the dispatching load of charging pile i at time t, is the actual demand load of charging pile i at time t, is the grid load forecast result, ΔP j,t is the charging power change of charging pile j at time t, γ is the load adjustment coefficient, α and β are the adjustment coefficients; S23. Using the generated load distribution plan, a reinforcement learning algorithm is used to dynamically adjust the charging power and charging time of each charging pile: Among them, K i,t is the charging power of charging pile i at time t, δ i,t is the load deviation of charging pile i, E t is the grid load at time t, E max is the maximum load of the power grid; S24. Generate a grid load dispatching plan based on the charging power adjustment result. The dispatching plan is dynamically adjusted according to the changes in the real-time load of the charging pile and the grid load; S25. During the charging process, the grid load and the status of the charging piles are monitored in real time, and dynamic scheduling is performed according to the grid load scheduling plan.

4. A new energy charging digital ecological base system according to claim 2, characterized in that: The S3 specifically includes: S31. Based on the collected charging pile status data, battery data and environmental data, a spatiotemporal graph convolutional network model is constructed to perform spatiotemporal prediction of charging demand, which includes the utilization rate of each charging pile, load demand and battery charging status: H t =σ(A·H t-1 ·W+X t ); Among them, H t is the node feature representation at time t, A is the adjacency matrix of the graph, W is the convolution kernel matrix, X t is the input feature matrix at time t, σ is the activation function, H t-1 is the node feature representation at time t-1; S32. Based on the generated spatiotemporal demand forecast results and combined with the grid load forecast results, the load forecast value of each charging pile is calculated by weighted average method: in, is the load forecast value of charging pile i at time t, δ and ε are weighting coefficients, is the grid load forecast result, is the demand load of charging pile i; S33. Based on the load forecast results, a multi-objective optimization algorithm is applied to optimize the scheduling strategy of charging piles, with the optimization objectives being charging efficiency and resource utilization: in, is the actual load of charging pile i at time t, K j,t is the charging power of charging pile j at time t, is the maximum charging power of charging pile j, λ is the regularization coefficient, N is the number of charging piles, M is the number of charging piles to be scheduled, min is the minimization operation, and F is the optimization function of charging efficiency and resource utilization; S34. Based on the optimization results, a charging scheduling plan is generated and adjusted in real time. The charging scheduling plan takes the following factors into consideration: Among them, C i,t is the charging power of charging pile i at time t, S i is the maximum charging power of charging pile i, D i is the device status data of charging pile i, E t is the current load of the power grid; S35. According to the generated charging scheduling plan, the charging status of the charging pile is monitored in real time, the charging strategy is adjusted, and the charging efficiency is optimized.

5. A new energy charging digital ecological base system according to claim 2, characterized in that: The S4 specifically includes: S41. Based on the generated charging demand forecast results and the grid load dispatching plan, a multi-objective optimization algorithm is used to optimize the charging pile distribution, charging path and power distribution: Among them, U is the optimization objective function of charging pile distribution, charging path and power distribution, is the load forecast value of charging pile i at time t, is the actual load of charging pile i at time t, C i,t is the charging power of charging pile i at time t, is the maximum charging power of charging pile i, N is the number of charging piles, α is the adjustment coefficient, and min is the minimization operation; S42. Determine the optimal distribution location and charging path selection of charging piles through the optimization results, and accurately schedule the charging power and charging period of the charging piles: in, is the adjusted load demand of charging pile i at time t, γ is the adjustment coefficient, and M is the number of charging piles to be scheduled; S43. According to the adjustment results, the scheduling strategy of the charging pile is adjusted in real time to generate the final charging scheduling plan to optimize the charging efficiency and grid load management; S44. During the charging process, the status of the charging pile is monitored in real time, and dynamic scheduling is performed according to the charging scheduling plan.

6. A new energy charging digital ecological base system according to claim 2, characterized in that: The S5 specifically includes: S51. Based on the generated final charging scheduling plan, blockchain technology is used to encrypt payment settlement, equipment management and data storage in the charging process. The encryption process includes generating encrypted data blocks related to the charging process: DataBlock t =Hash(T t ||C t ||D t ||P t ); Among them, DataBlock t is the encrypted data block at time t, T t is the charging time at time t, C t is the charging power at time t, D t is the status information of the charging pile equipment, P t is the payment settlement information at time t, Hash is the encrypted hash function; S52. Using the smart contract function in the blockchain, payment settlement, equipment management and data storage during the charging process are synchronized between different charging piles: SmartContract(C t ,P t )=Verify(C t ,P t )→Store(DataBlock t ); Among them, SmartContract (C t ,P t ) is the execution function, Verify(C t ,P t ) is the verification function, Store(DataBlock t ) is a storage operation; S53. Through the smart contract, the generated encrypted data block is added to the blockchain ledger. Each block in the blockchain ledger contains the hash value, timestamp and data of the previous block, forming an unalterable historical record: Blockchain t =Hash(Blockchain t-1 ||DataBlock t ); Among them, Blockchain t is the blockchain ledger at time t, t-1 is the hash value of the previous block; S54. Through the blockchain data storage results, all data in the charging process are audited in real time: Audit(T t ,C t ,D t ,P t )=Retrieve(Blockchain t )→Verify(DataBlock t ); Among them, Audit(T t ,C t ,D t ,P t ) To audit and verify the data during the charging process, Retrieve (Blockchain t ) is the blockchain data retrieval operation, Verify(DataBlock t ) is a data verification operation; S55. During the charging process, the charging data is encrypted and stored in real time, and the data is stored and queried based on blockchain technology: Query(T t ,C t ,D t ,P t )=Verify(Retrieve(Blockchain t )); Among them, Query(T t ,C t ,D t ,P t ) is to verify and obtain key information of the charging process by querying the data on the blockchain, Verify is a data verification operation, and Retrieve is a blockchain data retrieval operation.

7. A new energy charging digital ecological base system according to claim 2, characterized in that: The S7 specifically includes: S71. Continuously monitor the charging process through the big data platform, and collect the charging power, equipment status, battery status, and grid load data of the charging pile in real time: D t =(C i,t ,E t ,T t ,P t ); Among them, D t is the charging process data at time t, C i,t is the charging power of charging pile i at time t, E t is the grid load at time t, T t is the charging time, P t For payment settlement information; S72. Based on the collected data, the charging strategy is adjusted in real time, and the charging power and charging period of the charging pile are adjusted: Among them, α and β are adjustment coefficients, is the adjusted charging power of charging pile i at time t, δ i,t is the load deviation, S i is the maximum charging power; S73. Use the big data platform to analyze the operating efficiency of charging piles and generate operating reports of charging stations: Among them, Efficiency is the operating efficiency of the charging station, and N is the number of charging piles in the charging station; S74. Optimize resource allocation and management of charging stations based on the generated operation report: Among them, R optimal Optimal resource allocation for charging stations; S75. During the charging process, the status of the charging pile is monitored in real time, and dynamic adjustments are made according to the optimization results.

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