A control method and system for integrating an intelligent electric meter with real-time monitoring of a power system

Abstract

The present invention relates to the technical fields of power systems and smart grids, and discloses a control method and system for the integration of smart meters and real-time monitoring of power systems, including: collecting the operation data of the power system in real time through smart meters and transmitting it to edge computing devices for preprocessing; encrypting the preprocessed operation data of the power system and storing it through blockchain technology; the central control platform obtains the data stored in the blockchain for real-time monitoring; according to the real-time monitoring results, the central control platform dynamically adjusts the network structure of the power system. By combining smart meters with edge computing and blockchain technology, real-time collection, encrypted storage, and efficient processing of the operation data of the power system are achieved. Edge computing reduces data transmission latency and improves real-time response capabilities; blockchain ensures the security and immutability of data; the stability and efficiency of the power system are optimized.

Description

Technical Field

[0001] The present invention relates to the technical fields of power systems and smart grids, and specifically provides a control method and system for the integration of smart meters and real-time monitoring of power systems. Background Art

[0002] In recent years, with the continuous increase in global power demand and the continuous development of energy management technologies, the intelligence of power systems has become the key to improving energy utilization efficiency and ensuring power security. As an important development direction of power systems, smart grid technology aims to achieve efficient power dispatching and real-time monitoring through the comprehensive application of information technology, automation technology, communication technology, etc. Traditional power systems usually adopt a centralized monitoring mode. However, with the increasing requirements for the real-time performance, flexibility, and efficiency of power systems, distributed monitoring, data collection, and analysis based on smart meters have become an important part of the intelligence of power systems. As an important device connecting power users and power companies, smart meters can collect real-time operation data of power systems, such as current, voltage, power, load, etc., thereby providing data support for the real-time monitoring, load forecasting, and optimal dispatching of power systems.

[0003] However, the integration of existing smart meters and power systems still faces many challenges. First, the existing power data collection methods usually rely on a centralized management platform for data storage and analysis, resulting in relatively high data transmission delays and being unable to meet real-time requirements. Especially in large-scale and widely distributed power systems, the efficiency of real-time monitoring is greatly reduced. Second, the security issues during data transmission are still major challenges faced by the intelligence of power systems. Existing technologies often face risks of being tampered with, leaked, or lost during data storage and transmission, seriously affecting the stability and reliability of power systems. Third, the adjustment of the existing power system network structure mostly relies on manual intervention and rule setting, lacking a flexible, real-time data-based adaptive adjustment mechanism, resulting in the power system not being able to respond quickly enough to emergencies and load changes, reducing the dynamic stability of the system. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: data transmission delay, security, and network structure adaptive adjustment problems in the real-time monitoring of power systems. Through the combination of smart meters, edge computing, and blockchain technology, efficient, real-time, and secure data processing and dynamic control are achieved.

[0006] To solve the above technical problems, the present invention provides the following technical solution: A control method for the integration of a smart meter and real-time monitoring of a power system, including:

[0007] The operating data of the power system is collected in real time through smart meters and transmitted to edge computing devices for preprocessing;

[0008] The preprocessed operating data of the power system is encrypted and stored through blockchain technology;

[0009] The central control platform obtains the data stored in the blockchain for real-time monitoring;

[0010] According to the real-time monitoring results, the central control platform dynamically adjusts the network structure of the power system.

[0011] As a preferred solution of the control method for the integration of real-time monitoring of smart meters and power systems according to the present invention, wherein: the operating data of the power system includes voltage data, current data, power data, frequency data, energy data, power quality data, temperature and environmental monitoring data, equipment status and fault data, communication data, time synchronization data, and user behavior and electricity consumption pattern data;

[0012] The preprocessing includes cleaning and filtering the collected operating data of the power system by edge computing devices to remove noise and outliers; compressing the operating data of the power system to reduce the data volume; and extracting the peak load and load trend of the power system.

[0013] As a preferred solution of the control method for the integration of real-time monitoring of smart meters and power systems according to the present invention, wherein: encrypting the preprocessed operating data of the power system includes encrypting the preprocessed data through the AES encryption algorithm;

[0014] Adding additional information to the encrypted operating data of the power system;

[0015] The additional information includes a timestamp, data type, and the ID of the edge computing device;

[0016] Encapsulating the encrypted operating data of the power system and the additional information to form a data packet;

[0017] Storing and managing the encryption key of the operating data of the power system through an HSM hardware security module;

[0018] Storing through blockchain technology includes encapsulating the data packet into a transaction unit, defining an upload rule in the Quorum blockchain network through a smart contract, and triggering an upload operation when the conditions are met;

[0019] The upload rule is to upload data every hour;

[0020] After the transaction unit is triggered by the smart contract, it is sent to the verification nodes in the Quorum blockchain network for verification;

[0021] The verification node checks the validity of the transaction unit through additional information;

[0022] After more than 3 / 4 of the verification nodes agree that the transaction unit is valid, the transaction is finally confirmed and the transaction unit is written into the Quorum blockchain network.

[0023] As a preferred solution of the control method for the integration of the smart meter and the real-time monitoring of the power system according to the present invention, wherein: the data stored in the blockchain obtained by the central control platform includes that the central control platform queries the operation data of the power system stored on the blockchain by calling a smart contract and preprocesses the operation data of the power system stored on the blockchain;

[0024] Based on the preprocessed operation data of the power system, the steady-state operation characteristics, instantaneous change characteristics and historical data characteristics of the power system are extracted to form a feature set.

[0025] As a preferred solution of the control method for the integration of the smart meter and the real-time monitoring of the power system according to the present invention, wherein: the central control platform obtains the data stored in the blockchain and performs real-time monitoring and status analysis, including training an initial random forest model using historical power system data;

[0026] Each tree uses the Bootstrap sampling method to randomly select a feature subset for training;

[0027] Each tree uses the CART algorithm to construct each tree, selects the optimal feature for splitting, calculates the Gini index, and stops until the maximum tree depth is reached. The formula is expressed as:

[0028] ;

[0029] wherein, represents the left subset, represents the number of samples in the right subset; represents the left subset, represents the right subset; represents the total number of samples of the current node; represents the Gini index of the left subset, represents the Gini index of the right subset;

[0030] For each tree, evaluate the contribution of each leaf node to the overall prediction result, and judge the contribution by calculating the error weight of the node. The formula is expressed as:

[0031] ;

[0032] wherein, represents the node 's contribution to the overall prediction, Indicates the prediction result of the random forest, Indicates a node The predicted value of, Is the node The number of samples;

[0033] When a certain leaf node The contribution of, Is less than a certain threshold Perform pruning operation, delete the node from the tree and update the tree structure, which is expressed by the formula:

[0034] ;

[0035] Among them, Indicates the pruning operation, Indicates the minimum threshold of the contribution, Indicates from the tree Delete the node ;

[0036] After the pruning operation, the depth of the tree will decrease; the adjustment of the tree depth is achieved through the dynamically changing depth factor Which is expressed by the formula:

[0037] ;

[0038] Among them, Indicates the current depth of the tree, Indicates the reduction in depth during the pruning process, Indicates the minimum depth limit;

[0039] Regularly monitor the prediction error of the tree and evaluate it through the incremental error function ;

[0040] ;

[0041] Among them, Indicates the Error increment of the tree, Indicates the number of samples, Indicates the true value, Indicates the model prediction value;

[0042] When Trigger the retraining of the tree;

[0043] Repeat the training process to generate a trained random forest model.

[0044] As a preferred solution of the control method for the integration of the intelligent electricity meter and the real-time monitoring of the power system according to the present invention, wherein: the central control platform obtains the data stored in the blockchain, and the real-time monitoring further includes inputting the preprocessed operation data of the power system into the random forest model, calculating the contribution of each leaf node to the overall prediction result, and the real-time monitoring is expressed by the formula:

[0045] ;

[0046] ;

[0047] wherein, represents the contribution of node to the overall prediction; represents the predicted value of node for the input sample , represents the final predicted value of the random forest model, represents the number of samples of node , represents the information entropy measure of node for sample ;

[0048] The value range of

[0049] is from 0 to positive infinity, indicating the contribution size of the node to the prediction; The value range of is

[0050] , indicating the uncertainty of the sample,

[0051] ;

[0052] wherein, represents the weight of the th tree, defined as:

[0053] ;

[0054] wherein, represents the prediction error increment of the th tree, defined as:

[0055] ;

[0056] wherein, represents the final predicted value of the power load, represents the predicted value of the th tree, represents the total number of decision trees, represents the weight of the th tree, represents the predicted error increment of the th tree, represents the number of samples, represents the true load value, represents the th tree's predicted value for the

[0057] The value range of

[0058] is from 0 to 1;

[0059] A preferred solution of a control method for the integration of an intelligent meter and real-time monitoring of a power system, wherein:

[0060] An edge computing module, which collects the operation data of the power system in real time through an intelligent meter and transmits it to an edge computing device for preprocessing;

[0061] A blockchain module, which encrypts the preprocessed operation data of the power system and stores it through blockchain technology;

[0062] A monitoring module, the central control platform obtains the data stored in the blockchain for real-time monitoring;

[0063] An adjustment module, according to the real-time monitoring results, the central control platform dynamically adjusts the network structure of the power system.

[0064] A computer device, including: a memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the method described in any one of the present inventions are implemented.

[0065] A computer-readable storage medium, on which a computer program is stored, characterized in that: when the computer program is executed by a processor, the steps of the method described in any one of the present inventions are implemented.

[0066] Advantages of the present invention: The control method for integrating an intelligent electricity meter with real-time monitoring of a power system provided by the present invention combines the intelligent electricity meter with edge computing and blockchain technology to achieve real-time collection, encrypted storage, and efficient processing of power system operation data. Edge computing reduces data transmission latency and improves real-time response capabilities; blockchain ensures data security and immutability; the central control platform optimizes the stability and efficiency of the power system through real-time monitoring and dynamic adjustment of the network structure, enhancing the overall system's security, reliability, and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0068] Figure 1 It is the overall flowchart of a control method for integrating an intelligent electricity meter with real-time monitoring of a power system provided in the first embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0069] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention with reference to the drawings of the specification. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0070] Embodiment 1, referring to Figure 1 , which is an embodiment of the present invention, provides a control method for integrating an intelligent electricity meter with real-time monitoring of a power system, including:

[0071] S1: Real-time collect the operation data of the power system through the intelligent electricity meter and transmit it to the edge computing device for preprocessing.

[0072] The operation data of the power system includes voltage data, current data, power data, frequency data, energy data, power quality data, temperature and environmental monitoring data, equipment status and fault data, communication data, time synchronization data, and user behavior and electricity consumption pattern data.

[0073] The preprocessing includes cleaning and filtering the collected operation data of the power system through the edge computing device to remove noise and outliers; compressing the operation data of the power system to reduce the data volume; and extracting the peak load and load trend of the power system.

[0074] Furthermore, the edge computing device cleans, filters, compresses, and extracts features from the collected power system operation data, aiming to improve data processing efficiency and accuracy. Data cleaning and filtering can remove noise and outliers, ensure the quality of the input data, and avoid interference with subsequent analysis; data compression helps reduce the amount of data, optimize transmission efficiency, and reduce bandwidth pressure; while extracting peak load and load trends can mine key features of the power system from massive data, facilitating real-time monitoring and prediction, and improving the operation stability and response ability of the power system. Through this series of preprocessing operations, it is possible to ensure that the subsequent analysis model obtains high-quality and concise data input, thereby achieving more efficient power load forecasting and real-time monitoring.

[0075] S2: Encrypt the preprocessed operation data of the power system and store it through blockchain technology.

[0076] Encrypting the preprocessed operation data of the power system includes encrypting the preprocessed data through the AES encryption algorithm.

[0077] Add additional information to the encrypted operation data of the power system.

[0078] The additional information includes a timestamp, data type, and the ID of the edge computing device.

[0079] Package the encrypted operation data of the power system with the additional information to form a data packet.

[0080] Store and manage the encryption key of the operation data of the power system through the HSM hardware security module.

[0081] Storing through blockchain technology includes encapsulating the data packet into a transaction unit, defining an upload rule in the Quorum blockchain network through a smart contract, and triggering an upload operation when the conditions are met.

[0082] The upload rule is to upload data every hour.

[0083] After the transaction unit is triggered by the smart contract, it is sent to the verification node in the Quorum blockchain network for verification.

[0084] The verification node checks the validity of the transaction unit through the additional information.

[0085] After more than 3 / 4 of the verification nodes agree that the transaction unit is valid, the transaction is finally confirmed, and the transaction unit is written into the Quorum blockchain network.

[0086] Furthermore, through encryption, additional information encapsulation, and blockchain storage technology, the security, integrity, and immutability of the operation data of the power system are ensured. The data is encrypted using the AES encryption algorithm, and additional information such as timestamps, data types, and edge computing device IDs is added to the data to improve the traceability and verifiability of the data. At the same time, the HSM hardware security module is used to manage the encryption keys to ensure the secure storage and management of the keys. Through the mechanism of the Quorum blockchain network and smart contracts, the data upload process is strictly verified and confirmed, ensuring the secure transmission and storage of each piece of data in the network, preventing data loss or tampering, and enhancing the trust and transparency of the operation data of the power system.

[0087] S3: The central control platform obtains the data stored in the blockchain and conducts real-time monitoring.

[0088] The central control platform queries the operation data of the power system stored on the blockchain by invoking smart contracts and preprocesses the operation data of the power system stored on the blockchain.

[0089] Based on the preprocessed operation data of the power system, the steady-state operation characteristics, instantaneous change characteristics, and historical data characteristics of the power system are extracted to form a feature set.

[0090] The initial random forest model is trained using historical power system data.

[0091] For each tree, the Bootstrap sampling method is used to randomly select a feature subset for training.

[0092] For each tree, the CART algorithm is used to construct each tree, select the optimal feature for splitting, calculate the Gini index, and stop until the maximum tree depth is reached. The formula is expressed as:

[0093] ;

[0094] where represents the left subset, represents the number of samples in the right subset; represents the left subset, represents the right subset; represents the total number of samples at the current node; represents the Gini index of the left subset, represents the Gini index of the right subset.

[0095] For each tree, evaluate the contribution of each leaf node to the overall prediction result, and judge the contribution by calculating the error weight of the node. The formula is expressed as:

[0096] ;

[0097] where Represents a node Contribution to the overall prediction Represents the prediction result of the random forest Represents a node The predicted value of Is the node The number of samples

[0098] When a certain leaf node The contribution of Is less than a certain threshold Perform pruning operation, delete the node from the tree and update the tree structure, which is expressed by the formula:

[0099] ;

[0100] Wherein, Represents the pruning operation Represents the minimum threshold of the contribution Represents from the tree Delete the node .

[0101] After the pruning operation, the depth of the tree will decrease; the adjustment of the tree depth is achieved through the dynamically changing depth factor Which is expressed by the formula:

[0102] ;

[0103] Wherein, Represents the current depth of the tree Represents the reduction in depth during the pruning process Represents the minimum depth limit

[0104] Regularly monitor the prediction error of the tree and evaluate it through the incremental error function For evaluation

[0105] ;

[0106] Wherein, Represents the Error increment of the Represents the number of samples Represents the true value Represents the model predicted value

[0107] When Trigger the retraining of the tree

[0108] Repeat the training process to generate a trained random forest model

[0109] Input the preprocessed operation data of the power system into the random forest model, calculate the contribution of each leaf node to the overall prediction result, and conduct real-time monitoring. The formula is expressed as:

[0110] ;

[0111] ;

[0112] Among them, represents the contribution of node to the overall prediction; represents the predicted value of node for the input sample , represents the final predicted value of the random forest model, represents the number of samples of node , represents the information entropy measure of node for sample .

[0113] The value range of

[0114] is from 0 to positive infinity, indicating the contribution size of the node to the prediction. The value range of is

[0115] , indicating the uncertainty of the sample,

[0116] , improving the accuracy of load prediction:

[0117] ;

[0118] Among them, represents the weight of the th tree, defined as:

[0119] ;

[0120] Among them, represents the prediction error increment of the th tree, defined as:

[0121] ;

[0122] Among them, represents the final predicted value of the power load, represents the predicted value of the th tree, represents the total number of decision trees, represents the weight of the th tree, represents the incremental prediction error of the th tree, represents the number of samples, represents the true load value, represents the th tree's predicted value for the th sample;

[0123] The value range of is from 0 to 1.

[0124] The value range of is the set of real numbers.

[0125] Furthermore, the steady-state operating characteristics of the system include, but are not limited to, the voltage values of lines or nodes, the voltage level distribution, the current voltage amplitude, the current amplitude, and the power grid frequency.

[0126] By training a random forest model using historical power system data and combining real-time monitoring data and load prediction results, the operating state and network structure of the power system are dynamically adjusted. Specifically, by preprocessing, feature extraction, and model training on the power system operation data stored on the blockchain, real-time load prediction and error weighted calculation are performed using the random forest model to ensure that the system can accurately predict power load demand and optimize power flow. In addition, by combining online pruning and error weighting mechanisms, the prediction accuracy can be effectively improved. At the same time, through the dynamic adjustment and real-time monitoring of the model, optimal control of the power system is achieved, reducing power losses and enhancing system efficiency and stability.

[0127] S4: According to the real-time monitoring results, the central control platform dynamically adjusts the network structure of the power system.

[0128] Reallocate the load through switching devices, especially when the load prediction results show that some lines have excessive loads, and transfer part of the load to the lines with lighter loads.

[0129] Based on the results of real-time monitoring and load prediction, the control platform can selectively disconnect unnecessary power lines or temporarily connect standby lines to reduce the risk of load overload and effectively reduce power losses during power transmission.

[0130] The control platform dynamically adjusts the current flow direction and voltage level in the power grid through topology optimization of the power grid, thereby achieving lower power transmission losses.

[0131] Furthermore, based on the real-time monitoring and load forecasting results, the central control platform can intelligently adjust the network structure of the power system to ensure a more balanced and efficient flow of electricity in the system. By redistributing loads, disconnecting unnecessary lines, connecting standby lines, and optimizing the topology, the platform can effectively avoid load overloading, reduce power transmission losses, and improve the stability and reliability of the power system. This not only reduces power transmission losses but also enhances the system's adaptability to sudden load changes, thus achieving the optimal allocation of power resources.

[0132] Embodiment 2 is an embodiment of the present invention, which provides a control system for the integration of an intelligent electricity meter and real-time monitoring of a power system, including:

[0133] An edge computing module that collects the operation data of the power system in real time through an intelligent electricity meter and transmits it to an edge computing device for preprocessing.

[0134] A blockchain module that encrypts the preprocessed operation data of the power system and stores it through blockchain technology.

[0135] A monitoring module that the central control platform obtains the data stored in the blockchain for real-time monitoring.

[0136] An adjustment module that the central control platform dynamically adjusts the network structure of the power system according to the real-time monitoring results.

[0137] Embodiment 3 is an embodiment of the present invention, which is different from the previous two embodiments in that:

[0138] If the said functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. And the aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0139] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0140] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing when necessary, and then stored in a computer memory.

[0141] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0142] Example 4, an embodiment of the present invention, provides a control method and system for the integration of real-time monitoring of an intelligent electricity meter and a power system. To verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0143] This embodiment aims to verify the effect of a control method for integrating an intelligent meter with real-time monitoring of a power system in load forecasting and power system optimization and adjustment. The experiment is carried out by deploying intelligent meters and edge computing devices in an actual power system, and combining blockchain technology for data storage and management to achieve real-time monitoring and dynamic adjustment of the power system. The power system used in the experiment includes 10 substations, 50 transmission lines, and 30 power load monitoring points, covering typical load demand situations in urban areas.

[0144] All intelligent meters collect key operation data of the power system in real time, including voltage, current, power, frequency, equipment status, fault records, ambient temperature, and user behavior data. The collected raw data is updated every minute and preprocessed by edge computing devices. The preprocessing process includes data cleaning, removing noise and outliers, data compression to reduce data transmission volume, and extracting features such as peak load and load trend of the power system. This process realizes rapid response to real-time data and reasonable control of data volume, providing an efficient data basis for subsequent data storage and analysis.

[0145] The preprocessed data will be encrypted using the AES encryption algorithm to ensure high security during data transmission. Each data packet will be appended with additional information such as a timestamp, data type, and edge computing device ID, encapsulated into a transaction unit, and stored using the Quorum blockchain network. The data upload rule is triggered every hour, and the data is stored in the blockchain after passing the verification by the verification node. The upload and data verification processes are controlled by smart contracts to ensure data integrity and anti-tampering.

[0146] Based on historical power system data, the central control platform trains an initial model using the random forest algorithm. Each tree uses the Bootstrap sampling method to randomly select a subset of features for training, uses the CART algorithm to construct a decision tree and calculate the Gini index until the depth of the tree reaches the set value. During this process, the depth of the tree is dynamically adjusted through an error evaluation mechanism. The pruning operation of the tree is optimized based on the node contribution value and error weight to reduce unnecessary computational load and improve the accuracy of prediction.

[0147] Combined with online pruning and error weighting mechanisms for load forecasting, and combined with real-time monitoring data and prediction results, the central control platform dynamically adjusts the network structure of the power system. By redistributing the load, the load on some lines is avoided from being too high, reducing power loss. In addition, according to the real-time monitoring and load forecasting results, the control platform disconnects unnecessary power lines or reduces the risk of load overload by temporarily connecting standby lines to ensure the stable operation of the power system.

[0148] This test was conducted in an actual operating environment, and the collected data covered the operating states of the power system under different load conditions, as follows:

[0149] Power system operation data collection frequency: updated every minute. The data collected by the smart meter includes parameters such as voltage, current, power, frequency, and temperature.

[0150] Data volume: The number of data packets uploaded per hour is 300 (each data packet contains multi-dimensional data of multiple monitoring points).

[0151] Load prediction error: By comparing the load prediction results based on traditional methods (such as linear regression models) and the method of the present invention (random forest model), the error rates are respectively:

[0152] Traditional method: 10.2%

[0153] Method of the present invention: 6.4%

[0154] Power transmission loss: Before the optimization of the power system, the power transmission loss rate of the system was 4.5%. After real-time monitoring and load adjustment, the power transmission loss rate was reduced to 3.1%.

[0155] The model trained by the random forest algorithm can predict the power load more accurately than the traditional linear regression model, and the error rate is reduced by 3.8 percentage points. This shows that in the process of load prediction of the present invention, it can better capture the complexity and non-linear characteristics of the load changes in the power system, improving the prediction accuracy. The random forest model can effectively reduce the influence of local outliers on the overall prediction by introducing multiple decision trees and an error weighting mechanism, thus obtaining more stable and reliable prediction results. In the test, through the dynamic adjustment of the central control platform, the power transmission loss was reduced from 4.5% to 3.1%. This improvement is mainly due to the combination of real-time monitoring and dynamic load adjustment. In the traditional system, lines with excessive load often lead to increased power loss. However, the present invention greatly optimizes the operation efficiency of the power grid and reduces power loss through reasonable load distribution, timely access of standby lines, and disconnection of unnecessary lines.

[0156] The present invention uses blockchain technology to ensure the security and transparency of data, ensuring the reliability and immutability of the power system data. In addition, the preprocessing function of the edge computing device enables the power system to quickly respond to abnormal situations, improving the stability of the system. Through these technical means, the system can monitor and dynamically adjust the network structure in real time, effectively reducing the probability of faults and improving the reliability of the overall power system.

[0157] Experimental data show that when the load is too high, the system effectively avoids the increase of load overload and power transmission loss by dynamically adjusting the power lines (such as disconnecting unnecessary lines or connecting standby lines), ensuring the stable operation of the power system. The dynamic adjustment not only optimizes the structure of the power grid but also improves the system's ability to respond to emergencies.

[0158] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A control method for integrating smart meter and power system real-time monitoring, characterized in that: include: Collect power system operation data in real time through smart meters and transmit it to edge computing devices for preprocessing; Encrypt the pre-processed power system operation data and store it through blockchain technology; The central control platform obtains the data stored in the blockchain and conducts real-time monitoring; Based on the real-time monitoring results, the central control platform dynamically adjusts the network structure of the power system; The operation data of the power system includes voltage data, current data, power data, frequency data, energy data, power quality data, temperature and environmental monitoring data, equipment status and fault data, communication data, time synchronization data, and user behavior and power consumption pattern data; Preprocessing includes cleaning and filtering the collected power system operation data through edge computing devices to remove noise and outliers; Compress the power system operation data to reduce the data volume; Extract peak load and load trends of power systems; Encrypting the pre-processed operation data of the power system includes encrypting the pre-processed data by using an AES encryption algorithm; Adding additional information to the encrypted operation data of the power system; Additional information includes,timestamp, data type, and the ID of the edge computing device; Encapsulating the encrypted operation data of the power system and additional information to form a data packet; The encryption keys for the operation data of the power system are stored and managed through the HSM hardware security module; Storage through blockchain technology includes encapsulating data packets into transaction units, defining upload rules in the Quorum blockchain network through smart contracts, and triggering upload operations when conditions are met; The upload rule is to upload data every hour; After the transaction unit is triggered by the smart contract, it is sent to the verification node in the Quorum blockchain network for verification; The verification node checks the validity of the transaction unit through additional information; After more than 3 / 4 of the verification nodes agree that the transaction unit is valid, the transaction is finally confirmed and the transaction unit is written into the Quorum blockchain network.

2. The control method for integrating smart meter and power system real-time monitoring as claimed in claim 1, characterized in that: The central control platform obtains the data stored in the blockchain, including the central control platform inquiring the power system operation data stored in the blockchain by calling the smart contract, and preprocessing the power system operation data stored in the blockchain; Based on the preprocessed power system operation data, the steady-state operation characteristics, instantaneous change characteristics and historical data characteristics of the power system are extracted to form a feature set.

3. The control method for integrating smart meter and power system real-time monitoring as claimed in claim 2, characterized in that: The central control platform obtains data stored in the blockchain and conducts real-time monitoring and status analysis, including training the initial random forest model using historical power system data; Each tree uses the Bootstrap sampling method to randomly select a subset of features for training; Each tree is constructed using the CART algorithm, the optimal feature is selected for splitting, and the Gini index is calculated until the maximum tree depth is reached. The formula is expressed as: Among them, N L represents the left subset, N R represents the number of samples in the right subset; D L represents the left subset, D R represents the right subset; N represents the total number of samples of the current node; Gini(D L ) represents the Gini index of the left subset, Gini(D R ) represents the Gini index of the right subset; For each tree, evaluate the contribution of each leaf node to the overall prediction result, and judge the contribution by calculating the error weight of the node. The formula is expressed as: Among them, C j represents the contribution of node j to the overall prediction, represents the prediction result of random forest, f j (X ij ) represents the predicted value of node j, n j is the number of samples of node j; When the contribution of a leaf node j is C j Less than a certain threshold C min When , the pruning operation is performed to delete the node from the tree and update the tree structure. The formula is expressed as: in, represents the pruning operation, C min represents the minimum contribution threshold, Prune(T j ) represents the tree T j Delete node j; After the pruning operation, the depth of the tree will be reduced; the adjustment of the tree depth is achieved by dynamically changing the depth factor d adj The implementation formula is: d adj =max(d t -Δd,d min ) Among them, d t represents the depth of the current tree, Δd represents the reduction in depth during pruning, and d min Indicates the minimum depth limit; Periodically monitor the prediction error of the tree, using the incremental error function ΔE t Conduct assessments; Among them, ΔE t represents the error increment of the tth tree, n represents the number of samples, y i represents the true value, represents the model prediction value; When ΔE t >∈ max When , the tree retraining is triggered; Repeat the training process to generate a trained random forest model.

4. The control method for integrating smart meter and power system real-time monitoring as claimed in claim 3 is characterized by: The central control platform obtains the data stored in the blockchain for real-time monitoring, and also inputs the pre-processed power system operation data into the random forest model, calculates the contribution of each leaf node to the overall prediction result, and performs real-time monitoring. j The formula is: Among them, C j represents the contribution of node j to the overall prediction; f j (X ij ) represents the response of node j to input sample X ij The predicted value of Represents the final prediction value of the random forest model, n j represents the number of samples of node j, H j (X ij ) represents the node j for sample X ij Information entropy measurement of C j The value range of is 0 to positive infinity, indicating the contribution of the node to the prediction; H j (X ij ) has a value range of [0, log(K)], which indicates the uncertainty of the sample, and K indicates the number of categories.

5. The control method for integrating smart meter and power system real-time monitoring as claimed in claim 4 is characterized by: The central control platform obtains the data stored in the blockchain for real-time monitoring and also combines online pruning and error weighting mechanisms to perform load forecasting. The formula is expressed as: Among them, w t represents the weight of the tth tree, defined as: Among them, ΔE t represents the prediction error increment of the t-th tree, defined as: in, represents the final predicted value of power load, represents the predicted value of the tth tree, T represents the total number of decision trees, and w t represents the weight of the tth tree, ΔE t represents the prediction error increment of the tth tree, n represents the number of samples, y i Indicates the true load value, Represents the predicted value of the t-th tree for the i-th sample; w t The value range of is 0 to 1; The range of is real numbers.

6. A control system integrating smart meter and power system real-time monitoring, characterized in that: The edge computing module collects the operation data of the power system in real time through smart meters and transmits it to the edge computing device for preprocessing; The blockchain module encrypts the pre-processed operation data of the power system and stores it through blockchain technology; Monitoring module: The central control platform obtains data stored in the blockchain and conducts real-time monitoring; Adjustment module: Based on the real-time monitoring results, the central control platform dynamically adjusts the network structure of the power system; The operation data of the power system includes voltage data, current data, power data, frequency data, energy data, power quality data, temperature and environmental monitoring data, equipment status and fault data, communication data, time synchronization data, and user behavior and power consumption pattern data; Preprocessing includes cleaning and filtering the collected power system operation data through edge computing devices to remove noise and outliers; Compress the power system operation data to reduce the data volume; Extract peak load and load trends of power systems; Encrypting the pre-processed operation data of the power system includes encrypting the pre-processed data by using an AES encryption algorithm; Adding additional information to the encrypted operation data of the power system; Additional information includes,timestamp, data type, and the ID of the edge computing device; Encapsulating the encrypted operation data of the power system and additional information to form a data packet; The encryption keys for the operation data of the power system are stored and managed through the HSM hardware security module; Storage through blockchain technology includes encapsulating data packets into transaction units, defining upload rules in the Quorum blockchain network through smart contracts, and triggering upload operations when conditions are met; The upload rule is to upload data every hour; After the transaction unit is triggered by the smart contract, it is sent to the verification node in the Quorum blockchain network for verification; The verification node checks the validity of the transaction unit through additional information; After more than 3 / 4 of the verification nodes agree that the transaction unit is valid, the transaction is finally confirmed and the transaction unit is written into the Quorum blockchain network.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the control method for integrating the real-time monitoring of the smart meter and the power system as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the control method for integrating the real-time monitoring of the smart meter and the power system as described in any one of claims 1 to 5 are implemented.

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