A remote control method and system for an intelligent electric energy meter based on an internet of things

Abstract

The present invention provides a remote control method and system for smart meters based on the Internet of Things. This method quantifies relevant equipment data and electricity consumption characteristic data to obtain a feature vector matrix, then groups the feature vector matrix to obtain refined equipment groups. Combined with real-time collected operational data, the method analyzes the smart meters to obtain a more accurate status assessment report. Users can then issue group control commands based on this report to remotely control the corresponding control targets within the equipment groups. This enables users to implement more accurate remote control, achieving precise grouping and efficient group control of large-scale smart meters, and improving power grid dispatching capabilities.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more particularly to a method and system for remote control of smart energy meters based on the Internet of Things. Background Technology

[0002] With the deepening of global energy transition and smart city construction, intelligent management of power systems has become a key infrastructure for ensuring energy security and improving electricity efficiency. As a crucial sensing node at the end of the power system, the large-scale deployment and refined management of smart meters directly impact the operational efficiency and service quality of the entire power grid. Current meter management methods primarily rely on traditional single-point control models. When dealing with large-scale meter clusters, a simple broadcast control approach is often employed, failing to implement precise management based on different user types and electricity consumption characteristics, thus leading to issues with accurate remote control. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a remote control method and system for smart energy meters based on the Internet of Things, thereby improving the accuracy of remote control of power systems.

[0004] To achieve the above objectives, embodiments of the present invention provide a remote control method for smart energy meters based on the Internet of Things, comprising:

[0005] Acquire device-related data and electricity consumption characteristic data of smart meters within a preset range, and perform quantitative analysis on the device-related data and electricity consumption characteristic data to obtain a feature vector matrix;

[0006] The smart energy meters are grouped according to the feature vector matrix to generate multiple device groups and corresponding group identifiers;

[0007] The system collects real-time operating data from the smart energy meter and generates a status assessment report for the device group based on the operating data. The status assessment report is used to determine whether the smart energy meter meets the conditions for remote control execution.

[0008] Receive group control commands sent by users based on the status assessment report, parse the group control commands, and obtain the control target and target group identifier;

[0009] The target group identifier is compared with the group identifier to obtain the target group corresponding to the control target from multiple device groups. A corresponding control instruction is generated based on the target group and the control instruction and sent to the control target.

[0010] For example, quantitative analysis is performed on the device-related data and the electricity consumption characteristic data to obtain a feature vector matrix, including:

[0011] The device-related data and the electricity consumption characteristic data are denoised and formatted to obtain a standard dataset;

[0012] The feature vector matrix is ​​obtained by performing multi-dimensional quantitative analysis on the standard dataset.

[0013] When the dimension value in the feature vector matrix exceeds a preset threshold, the dimension data corresponding to the dimension value is matched and analyzed with the historical dimension data in the preset database to determine whether the smart energy meter corresponding to the dimension data is abnormal and to mark the abnormal mode, thereby obtaining the marked feature vector matrix.

[0014] For example, the smart energy meters are grouped according to the feature vector matrix to generate multiple device groups and corresponding group identifiers, including:

[0015] The similarity value is obtained by performing pairwise calculations on the electricity consumption feature vectors in the feature vector matrix; the target device identifiers whose similarity values ​​exceed a first preset value are recorded in the candidate group list; the coordinate data corresponding to the device identifiers in the candidate group list are obtained, and the coordinate data is calculated pairwise to obtain the distance value; the target device identifiers whose distance values ​​are less than a second preset value are assigned to the same group identifier to obtain the device group.

[0016] For example, grouping the smart energy meters according to the feature vector matrix to generate multiple device groups and corresponding group identifiers further includes:

[0017] Statistical analysis is performed on the power demand of all devices in the device group; devices whose power demand exceeds the limit are grouped according to the power consumption characteristic data or the coordinate data.

[0018] For example, the operating data of the smart energy meter is collected in real time, and a status assessment report corresponding to the device group is generated based on the operating data. The status assessment report is used to determine whether the smart energy meter meets the conditions for remote control execution, including:

[0019] The operating data of the smart meters is collected in real time according to the group identifier. The operating data is classified and processed to obtain a group parameter set. The operating data includes operating parameters, communication status, and load status. The operating parameters and communication status are compared. When any value of the operating parameters or communication status exceeds a preset parameter value, the corresponding group is marked as an abnormal category, resulting in an abnormal group. The load status of the smart meters in the abnormal group is checked. When the check result is uneven load, the smart meters in the abnormal group are regrouped to obtain a load distribution result. A status assessment report is generated based on the load distribution result. The abnormal data in the status assessment report is compared according to rules to determine whether the remote control execution conditions are met.

[0020] For example, comparing the target group identifier with the group identifier to obtain the target group corresponding to the control target from multiple device groups, generating a corresponding control instruction based on the target group and the control instruction, and sending it to the control target includes:

[0021] Extract the set of devices that match the control target from the group set to obtain the device address list; compare the group identifiers in the device address list with the target group identifiers one by one to remove mismatched group identifiers to obtain the address set; when the smart energy meter corresponding to the address set meets the requirements of the operation type, generate operation parameters corresponding to the operation type and execution time; generate the control command according to the operation parameters.

[0022] Exemplarily, the method further includes:

[0023] When a device malfunctions in the target group, a safety factor for executing the control command is calculated. Based on the safety factor, it is determined whether to suspend the issuance of the control command, resulting in an executable queue. The executable queue is then time-optimized according to the processing capacity of the devices in the target group, resulting in an optimized queue. The control command is then sent to the target group according to the optimized queue, and response data is monitored. If no response data is received or abnormal data is received within a preset time, an exception handling procedure is triggered, and the identifier of the abnormal device is recorded.

[0024] For example, the device processing capability includes real-time bandwidth detection data and network status parameters. Based on the device processing capability, the executable queue is time-optimized to obtain an optimized queue, including:

[0025] The system acquires real-time bandwidth detection data of the target group under the current network environment and calculates network utilization based on the bandwidth detection data. When the network utilization exceeds a preset congestion threshold, a network load report carrying the network status parameters is generated, and a load warning mechanism is triggered. Based on the network load report, the processing speed of each device in the target group is quantitatively rated to obtain a device performance profile, which is used to rank the processing capabilities of devices. Based on the device performance profile, the executable queue is divided into multiple sub-batches to obtain a batch scheduling scheme. The executable queue is then sent with control commands to each control target batch in the target group at predetermined time intervals according to the batch scheduling scheme to obtain an optimized queue.

[0026] Exemplarily, the method further includes:

[0027] The system identifies whether multiple commands received by the same energy meter device conflict with each other. When a conflict exists, it obtains the command priority and timestamp information corresponding to the conflicting command. The conflicting commands are sorted according to the command priority and timestamp information to obtain a valid command and send a conflict resolution solution to the energy meter device.

[0028] To achieve the above objectives, embodiments of the present invention also provide a remote control system for smart energy meters based on the Internet of Things, comprising:

[0029] The device attribute acquisition module is used to acquire device-related data and electricity consumption characteristic data of smart meters within a preset range, and to perform quantitative analysis on the device-related data and the electricity consumption characteristic data to obtain a feature vector matrix;

[0030] The grouping module is used to group the smart energy meters according to the feature vector matrix, and generate multiple device groups and corresponding group identifiers;

[0031] The monitoring module is used to collect the operating data of the smart energy meter in real time, and generate a status assessment report corresponding to the device group based on the operating data. The status assessment report is used to determine whether the smart energy meter meets the remote control execution conditions.

[0032] The instruction parsing module is used to receive group control instructions sent by the user based on the status assessment report, parse the group control instructions, and obtain the control target and target group identifier;

[0033] The instruction scheduling module is used to compare the target group identifier with the group identifier to obtain the target group corresponding to the control target from multiple device groups, generate a corresponding control instruction based on the target group and the control instruction, and send it to the control target.

[0034] The IoT-based remote control method and system for smart meters provided in this invention quantifies device-related data and electricity consumption characteristic data to obtain a feature vector matrix, then groups the feature vector matrix to obtain refined device groups. Combined with real-time collected operational data, the system analyzes the smart meters to obtain a more accurate status assessment report. Users can then issue group control commands based on this report to remotely control the corresponding control targets within the device groups. This enables users to implement more accurate remote control, achieving precise grouping and efficient group control of large-scale smart meters, and improving power grid dispatching capabilities. Attached Figure Description

[0035] Figure 1 This is a flowchart of an embodiment of the Internet of Things-based remote control method for smart energy meters according to the present invention.

[0036] Figure 2 This is a schematic diagram of the program modules of a second embodiment of the IoT-based smart energy meter remote control system of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0038] Example 1

[0039] See Figure 1 This document illustrates a flowchart of the steps in a remote control method for a smart energy meter based on the Internet of Things (IoT) according to Embodiment 1 of the present invention. It is understood that the flowchart in this embodiment is not intended to limit the order of execution steps. The following description uses computer device 2 as the execution subject. Specifically, it follows.

[0040] Step S100: Obtain device-related data and electricity consumption characteristic data of smart meters within a preset range, and perform quantitative analysis on the device-related data and electricity consumption characteristic data to obtain a feature vector matrix.

[0041] This embodiment utilizes an Internet of Things (IoT) infrastructure to implement the method. A device attribute acquisition module obtains device-related data and electricity consumption characteristic data associated with the smart meter. Device-related data includes geographic location information, user type identifiers, and device hardware parameters, while electricity consumption characteristic data includes historical electricity consumption data. Data cleaning tools are used to denoise and standardize the format of the device-related data and electricity consumption characteristic data, resulting in a standardized basic dataset. Based on this standardized basic dataset, feature extraction tools are used to perform multi-dimensional quantitative analysis of the electricity consumption characteristic data, decomposing features based on dimensions such as peak electricity consumption periods, load variation amplitude, and electricity consumption periodicity, resulting in data containing multiple feature vector matrices.

[0042] In this embodiment, step S100 further includes:

[0043] The equipment-related data and electricity consumption characteristic data are denoised and formatted to obtain a standard dataset. The standard dataset is then subjected to multi-dimensional quantitative analysis to obtain a feature vector matrix. When the dimension values ​​in the feature vector matrix exceed a preset threshold, the dimension data corresponding to the dimension values ​​is matched and analyzed with historical dimension data in a preset database to determine whether the smart energy meter corresponding to the dimension data is abnormal and to mark the abnormal mode, thus obtaining the marked feature vector matrix.

[0044] In this embodiment, when acquiring data from smart meters via the device attribute acquisition module, it can be understood as follows: A city's power company manages the electricity consumption data of 100,000 households. The device attribute acquisition module extracts geographical location information, user type identifiers, historical electricity consumption data from the past year, and device hardware parameters from each smart meter daily. The user type identifier can be a residential or commercial user, and the device hardware parameters can be the meter model and installation year. This data may contain noise, such as missing data from some meters due to signal problems, or inconsistent data formats, such as different timestamp formats. Data cleaning tools can fill in missing values ​​with historical averages and standardize the time format to year-month-day, ensuring data standardization, forming a basic dataset, improving data quality, and facilitating analysis.

[0045] In this embodiment, features are extracted from the standardized base dataset, and then the extracted features are subjected to multi-dimensional quantitative analysis using feature extraction tools. For example, for data from 1000 households in a certain community, the peak electricity consumption periods, load variation, and electricity consumption periodicity are analyzed. For instance, electricity consumption is significantly higher between 18:00 and 20:00 each day than at other times; a user's electricity consumption suddenly increases from 1 kWh to 10 kWh in one day; and electricity consumption is higher on Mondays. Through feature decomposition, a feature vector matrix data containing multi-dimensional features such as peak values, load variations, and periodicity is generated. This multi-dimensional analysis helps to accurately characterize user electricity consumption behavior, thereby providing a basis for anomaly detection.

[0046] In this embodiment, during the anomaly detection phase, if a value in a certain dimension of the feature matrix exceeds a preset threshold, such as a user's load fluctuation exceeding three times the average, a data comparison tool is used to match and analyze it against historical records in a preset database. Assuming the user has not experienced similar fluctuations in the past year and has no recent record of purchasing large appliances, this is marked as an abnormal electricity usage pattern, ultimately generating a feature vector matrix with anomaly marking. This process effectively identifies potential electricity usage anomalies, such as electricity theft or equipment malfunction, ensuring the safe operation of the power grid.

[0047] In this embodiment, clustering tools are used to group users based on the feature vector matrix after anomaly labeling. Assuming 100,000 users are divided into three categories: low-consumption residential users, peak-consumption residential users, and commercial users, typical features are extracted for each group. For example, the average daily electricity consumption of the low-consumption group is 2 kWh, and the peak-consumption group is concentrated in the evening. This forms a categorized electricity consumption behavior dataset and a corresponding feature vector matrix. This grouping and pattern summarization helps power companies develop differentiated management strategies for different user groups, such as peak-shifting electricity plans, improving resource allocation efficiency. This embodiment forms a complete technology chain from data collection to anomaly detection to behavior classification, which not only improves the accuracy of data analysis but also provides a scientific basis for power management, significantly reducing operational risks and optimizing service quality.

[0048] Step S101: Group the smart energy meters according to the feature vector matrix to generate multiple device groups and corresponding group identifiers.

[0049] In this embodiment, the feature vector matrices are compared pairwise using a cosine similarity calculation tool. If the similarity exceeds a first preset threshold of 0.8, the similar device identifiers are recorded in the candidate group list. The latitude and longitude coordinates of each device in the candidate group list are obtained, and the geographical distance between devices is measured using the Euclidean distance calculation formula. If the geographical distance between two devices is less than a second preset threshold of 5 kilometers, they are assigned to the same group identifier. The power demand of the smart meters within the device group is statistically analyzed using a load balancing calculation tool. If the total power demand of the devices in a certain device group exceeds the regional power grid capacity limit, the group is further subdivided. A network topology mapping tool can be used to establish an association between the subdivided device groups and the corresponding distribution transformers to obtain the final device group configuration table with the same control strategy requirements.

[0050] In this embodiment, step S101 further includes:

[0051] The similarity value is obtained by performing pairwise calculations on the electricity consumption feature vectors in the feature vector matrix; the similarity values ​​of target devices that exceed the first preset value are recorded in the candidate group list; the coordinate data corresponding to the device identifiers in the candidate group list are obtained, and the coordinate data is calculated pairwise to obtain the distance value; the target device identifiers with distance values ​​less than the second preset value are assigned to the same group identifier to obtain the device group.

[0052] In this embodiment, when comparing the electricity consumption characteristics between devices based on feature vector matrices, a cosine similarity calculation tool can be used to measure the similarity of the electricity consumption behaviors of two smart meters. Assuming a city power company manages 50,000 meters, the feature vector matrix for each meter includes information such as peak electricity consumption periods and load changes. The system compares these vectors pairwise. If the similarity between two smart meters exceeds a first preset threshold of 0.8, their device numbers are recorded in the candidate grouping list, quickly filtering out devices with similar electricity consumption patterns and providing a basis for subsequent grouping.

[0053] After obtaining the latitude and longitude coordinates of smart meters in the candidate group list, the system measures the geographical distance between the devices using the Euclidean distance formula. For example, if two meters have a similarity of 0.85 in their electricity consumption characteristics, but one is located in the north of the city and the other in the south, 20 kilometers apart (exceeding a preset second threshold of 5 kilometers), they will not be assigned to the same group. Conversely, if two smart meters are only 3 kilometers apart, such as within the same residential area, they will be grouped together. This geographical constraint helps ensure the rationality of the grouping and facilitates regional management.

[0054] In this embodiment, step S101 further includes:

[0055] Perform statistical analysis on the power demand of all devices in the equipment group; group devices whose power demand exceeds the limit value according to power consumption characteristic data or coordinate data.

[0056] In this embodiment, a load balancing calculation tool is used to statistically analyze the power demand of devices within a group. Assuming a group contains 100 meters with a total power demand of 5000 kilowatts, while the regional power grid capacity is limited to 4000 kilowatts, exceeding the limit, the system will initiate a secondary subdivision process. This further divides the group into two subgroups based on peak electricity consumption periods or geographical location, ensuring that the power demand of each subgroup remains within a controllable range and effectively avoiding the risk of power grid overload.

[0057] In this embodiment, during the generation of the final device group configuration table, the network topology mapping tool associates the subdivided device sets with their corresponding distribution transformers. Assuming 50 meters in a sub-device group are distributed on the same street, the system will bind them to the nearest transformer, forming a configuration table with consistent control strategies. This facilitates the power company in developing unified scheduling strategies for specific transformer areas, improving management efficiency. Through the close integration of each step, from feature similarity comparison to geographical location constraints, and then to power demand balancing and topology association, a complete device grouping process is formed. This not only accurately divides device groups but also provides reliable support for power grid resource allocation and control strategy formulation, significantly improving the operational stability of the power system.

[0058] Step S102: Collect the operating data of the smart energy meter in real time, and generate a status assessment report corresponding to the device group based on the operating data. The status assessment report is used to determine whether the smart energy meter meets the conditions for remote control execution.

[0059] In this embodiment, a data acquisition tool obtains operational data from the energy meters within each device group based on the group identifier. This operational data includes operational parameters, communication status, and load conditions. A parameter processing tool categorizes the operational data to obtain a processed group parameter set. For this processed set, a data verification tool compares each operational parameter and communication status with preset parameter values. If the operational parameters and communication status of at least one smart meter exceed a preset threshold range, the group is marked as an abnormal group, and the scope of groups requiring processing is determined. Information about the marked abnormal groups is obtained, and a load check tool performs a detailed check of their load conditions. If the load distribution is unbalanced, a load adjustment tool redistributes the devices within the group, resulting in an updated load distribution. Based on the updated load distribution, a report generation tool creates a group-level status assessment document. The abnormal content in the assessment document is compared against conditions. If the execution criteria for remote control are met, an adjustment command is sent to the devices within the group via a command issuance tool to determine if the control objective has been achieved.

[0060] In this embodiment, step S102 further includes:

[0061] The system collects real-time operational data from smart meters based on group identifiers. This data is then categorized to obtain a set of group parameters, including operational parameters, communication status, and load conditions. The operational parameters and communication status are compared; if any value exceeds a preset parameter, the corresponding group is marked as an anomaly, resulting in an anomaly group. The load conditions of the smart meters in the anomaly group are then checked. If uneven load is found, the smart meters in the anomaly group are regrouped to obtain a load distribution result. A status assessment report is generated based on the load distribution result, and the abnormal data in the report is compared against rules to determine whether remote control execution conditions are met.

[0062] In this embodiment, when using data acquisition tools to obtain operating parameters, communication status, and load conditions from smart meters in various device groups, a scenario can be set up where a city power company manages smart meters in multiple areas, with each group containing dozens to hundreds of devices. The data acquisition tools periodically extract key information from the device groups, such as voltage values, current values, communication signal strength, and real-time load data. For example, if a group contains 80 meters, and one meter displays a voltage of 250 volts, exceeding the normal range of 220-240 volts, the data acquisition tools will record this and upload it to the system, providing a basis for subsequent classification and processing.

[0063] In this embodiment, the collected data is categorized using a parameter processing tool, classifying operating parameters, communication status, and load conditions to form clear group parameters. For example, if the communication status data for a certain group shows that the signal strength of 10 devices is below a preset value of 60%, these are separately marked as communication anomalies. Load conditions are categorized into high, medium, and low levels for easier subsequent analysis and to help quickly locate problematic devices.

[0064] In this embodiment, when using a data verification tool to compare operating parameters and communication status with preset parameter values, if it is found that any operating parameter or communication status in a group exceeds the preset parameter value, such as communication interruption exceeding 5 minutes or load exceeding 120% of the rated value, the group is marked as an abnormal group. For example, if a group containing 50 electricity meters has 3 devices with communication interruptions, the entire group is marked as a high-priority area for further investigation.

[0065] In this embodiment, when the load check tool performs a detailed check of the load situation, if an uneven load distribution is found within a certain device group—for example, the total load is 3000 kilowatts, but 20 devices are carrying 80% of the load—the load adjustment tool will regroup the device groups, prioritizing the distribution of high-load devices to other groups to achieve a more balanced load distribution. For example, after adjustment, the average load of each smart meter device may be reduced to 60% of a reasonable range.

[0066] In this embodiment, a report generation tool records detailed information such as the load status and number of abnormal devices for each group, creating a status assessment document. If, after load balancing in a group, two devices still exhibit abnormal parameters, the status assessment document will list them as requiring intervention. If remote control criteria are met, such as the abnormality lasting more than one hour, the command delivery tool sends adjustment commands to the relevant devices, such as reducing the load or restarting the communication module. The tool also monitors in real time whether the control objectives have been achieved, such as the load recovering to below 90% of the normal range, effectively improving the operational stability of smart meter devices.

[0067] Step S103: Receive the group control command sent by the user based on the status assessment report, parse the group control command, and obtain the control target and target group identifier.

[0068] In this embodiment, an instruction parsing tool is used to structurally decompose the control target, execution time, and operation type in the group control instruction, obtaining the decomposed target field, time field, and type field to ensure the completeness of the instruction content. The target field includes the control target and the target group identifier. When using the instruction parsing tool to structurally decompose the group control instruction, a complex control instruction is broken down into more easily processed field information, ensuring the completeness of the instruction content, facilitating subsequent system identification and processing, and avoiding execution deviations due to instruction ambiguity. Assume an instruction contains a control target of a group of electricity meters in a certain area, an execution time of 2 PM on the same day, and an operation type of load adjustment.

[0069] Step S104: Compare the target group identifier with the group identifier to obtain the target group corresponding to the control target from multiple device groups, generate the corresponding control command based on the target group and control command, and send it to the control target.

[0070] In this embodiment, step S104 further includes:

[0071] Extract the set of devices that match the control target from the group set to obtain the device address list; compare the group identifiers in the device address list with the target group identifiers one by one to remove mismatched group identifiers to obtain the address set; when the smart energy meter corresponding to the address set meets the requirements of the operation type, generate operation parameters corresponding to the operation type and execution time; generate control commands based on the operation parameters.

[0072] In this embodiment, a group identifier comparison tool is used to extract a set of devices matching the control target from a pre-established device database, resulting in a device address list. The device groups are stored in the device database. If at least one address in the device address list does not match the target field, an address verification tool is used to check the target field against the list one by one, obtaining a verified address set to determine if it meets the requirements of the operation type. A parameter configuration tool is used to generate operation parameters corresponding to the operation type and execution time, resulting in precise control instructions containing the device address list and operation parameters, confirming that the instruction content meets the requirements.

[0073] Upon receiving the instruction, the system uses a group identifier comparison tool to extract the target device group from the device group database. Based on the group identifier in the target field, such as the device group numbered A-001, it quickly filters out the corresponding 50 devices, forming a device address list. This ensures the accuracy of the instruction's coverage and lays the foundation for subsequent operations.

[0074] The parameter configuration tool automatically generates specific control parameters based on the operation type and execution time. Assuming the operation type is load adjustment and the execution time is 2 PM, the system will configure parameters to reduce the load to 80% of the rated value for each device in the verified address set, and generate precise control instructions containing a list of device addresses and operation parameters. This ensures that the instructions meet actual needs and avoids abnormal device operation due to improper parameters.

[0075] The final generated precise control commands undergo multi-dimensional verification to determine if their content meets the requirements. Assuming the command contains the addresses of 50 devices and their corresponding load adjustment parameters, the verification checks whether the load adjustment parameters are within safe limits and whether the time field matches the current system time. Comprehensive verification ensures the executability of the commands, providing a reliable guarantee for subsequent remote control.

[0076] The aforementioned steps are interconnected, forming a complete control command generation chain from command breakdown to parameter configuration. Assuming a power management system processes hundreds of group control commands daily, this structured processing method can significantly improve command accuracy and execution efficiency. This approach ensures stable equipment operation while also providing technical support for large-scale equipment management.

[0077] The method in this embodiment also includes:

[0078] When a device malfunctions in the target group, a safety factor for executing control commands is calculated. Based on the safety factor, it is determined whether to suspend the issuance of control commands, resulting in an executable queue. The executable queue is then time-optimized according to the processing capacity of the target group's devices, resulting in an optimized queue. Control commands are sent to the target group based on the optimized queue, and response data is monitored. If no response data is received within a preset time or abnormal data is received, an exception handling procedure is triggered, and the identifier of the abnormal device is recorded.

[0079] In this embodiment, a device status monitoring tool performs real-time status scanning and monitoring of all devices within the target group. If a device times out or returns an error status code, the controlled target is marked as an abnormal device and a fault timestamp is recorded, resulting in a device anomaly list containing the abnormal device identifier and fault type. Based on the device anomaly list, a fault classifier is used to rate the severity of the fault type for each abnormal device. An abnormal device quantity statistics tool is used to calculate the proportion of abnormal devices to the total number of devices in the group, resulting in a risk parameter combination that includes fault severity and anomaly proportion. For the risk parameter combination, a safety factor calculator is used to perform weighted calculations based on the anomaly proportion weight and fault severity weight. If the safety factor is lower than a preset safety execution threshold, an instruction pause signal is generated, resulting in a risk assessment report containing safety assessment results and execution suggestions. Based on the risk assessment report, an instruction queue manager is used to filter the original group control instructions. An abnormal device address is removed using a device address filtering tool, and the instruction sequence of normal devices is reorganized to obtain an executable instruction queue that has undergone safety verification and contains only normal device addresses.

[0080] In this embodiment, control commands are sent to the target device group via a command transmission protocol. These commands carry the device identification code and operation parameters of the controlled target. Response data returned by the target device group is received; this response data includes device status information and an execution result identifier. If the response data is returned within a preset timeout threshold and its format conforms to the confirmation information standard, the device response is considered normal. If the response data exceeds a preset time window or contains an error code, the corresponding exception handling procedure is triggered: the corresponding device number is recorded in the failed device identifier list; based on the device information in the failed device identifier list, an error code parser is used to classify the exception data, resulting in an exception handling result containing the fault type and handling method; and a device fault file is generated based on the exception handling result, containing the device identification code, fault occurrence timestamp, and error type classification.

[0081] In this embodiment, control commands are sent to the target device group via a command transmission protocol. These control commands include specific device identification codes and operating parameters, such as the unique number of a certain electricity meter and the electricity data upload task to be performed. The control commands are packaged using a specific communication protocol to ensure data integrity during transmission. Assuming commands are sent to 100 devices, each device's identification code corresponds one-to-one with a record in the database, and the operating parameters vary depending on the device type.

[0082] After receiving response data from the target device group, the system waits for the group to return a data packet containing status information and an execution result identifier after sending the command. For example, if a power meter receives a command to upload data and returns data showing that the device is operating normally and the task has been completed, the system will record this result. If a device fails to return data within the preset 5-second timeout threshold, or if the returned data format does not meet the standard (e.g., missing key fields), the system will classify it as a response anomaly and add the device number to the list of failed device identifiers.

[0083] When determining whether a smart meter device is responding normally, it is filtered according to preset rules. For example, if 10 smart meters fail to return data within the timeout threshold, and 5 of them return data containing error codes, the device numbers of these 10 smart meters are recorded. This filtering mechanism can quickly locate the problematic device, providing a basis for subsequent processing. Based on the list of failed device identifiers, an error code parser is used for classification, and the 5 devices that returned error codes are analyzed in depth. Assuming the error codes are divided into two categories—communication interruption and internal device failure—the parser categorizes 3 as communication problems and 2 as hardware failures, generating corresponding processing suggestions, such as re-establishing the connection or arranging maintenance. This classification method helps to accurately pinpoint the root cause of the problem.

[0084] When generating equipment fault files based on the anomaly handling results, a detailed record is created for each faulty device. For example, the file for a smart energy meter shows its identification code as E123, the fault occurrence timestamp as 10:30 AM on October 10, 2023, and the error type as communication interruption. These file records provide crucial reference for subsequent equipment management and maintenance, ensuring that problems can be tracked and resolved promptly.

[0085] The process described in this embodiment can also be optimized by incorporating historical data. For example, when generating a fault device profile, fault records of similar smart meters from the past month might be referenced. If communication interruptions are found to occur frequently in specific areas, it could be inferred that insufficient network coverage is the cause. This analytical approach can provide additional clues for system optimization.

[0086] The method in this embodiment also includes:

[0087] The system acquires real-time bandwidth detection data of the target group under the current network environment and calculates network utilization based on the bandwidth detection data. When the network utilization exceeds a preset congestion threshold, a network load report carrying network status parameters is generated and a load warning mechanism is triggered. Based on the network load report, the processing speed of each device in the target group is quantitatively rated to obtain a device performance profile, which is used to rank the devices' processing capabilities. Based on the device performance profile, the executable queue is divided into multiple sub-batches to obtain a batch scheduling scheme. The executable queue is then sent with control commands to each control target batch in the target group at predetermined time intervals according to the batch scheduling scheme to obtain an optimized queue.

[0088] In this embodiment, a network monitor performs real-time bandwidth detection of the current network environment. Specifically, within the network environment of a city power management system, the network monitor samples the upload and download speeds every few seconds. If the network utilization rate reaches 85%, while the preset congestion threshold is 80%, a load warning mechanism is immediately triggered, generating a detailed network load report containing parameters such as the current bandwidth usage percentage and peak traffic times. This real-time detection method can quickly capture changes in network status, providing a basis for subsequent decision-making.

[0089] Based on network load reports, a device performance evaluator is used to quantitatively rate the processing speed of each device in the target device group, analyzing the response speed and data processing capability of each electricity meter individually. Assuming there are 100 devices in the group, with 60 having fast processing speeds, 30 having medium speeds, and 10 having slow speeds, a device performance profile is generated, ranking the devices from highest to lowest processing capability. This rating method helps to identify which devices can prioritize processing commands and which require delayed processing.

[0090] A batch divider is used to split the command queue into multiple sub-batches based on network capacity and congestion levels indicated in network load reports. For example, the command queue can be divided into three sub-batches: the first batch contains 30 of the fastest-processing devices, scheduled for transmission during the early morning hours when network traffic is low; the second and third batches contain 40 and 30 smart meters respectively, scheduled for later times. This batch scheduling scheme can rationally allocate network resources and avoid further network congestion caused by sending a large number of commands at once. According to the batch scheduling scheme, a command distributor sends control commands to each batch of devices sequentially at predetermined time intervals, strictly adhering to the time points set in the scheduling scheme, such as 1:00 AM, 2:00 AM, and 3:00 AM, sending commands to the three batches respectively. The command execution sequence is rearranged according to timing optimization principles to obtain an optimized queue, ensuring that each device can receive commands under optimal network conditions. This time-sharing distribution mechanism effectively alleviates network pressure while ensuring the orderly transmission of commands.

[0091] The batch scheduling described in this embodiment can also prioritize devices with strong processing capabilities to receive instructions as early as possible, based on the order in the device performance profile. For example, if a high-performance energy meter is assigned to the first batch, its strong processing capability can ensure smooth instruction execution even with slight network fluctuations. This flexible arrangement further enhances the overall stability of instruction delivery.

[0092] The method in this embodiment also includes:

[0093] The system identifies whether multiple commands received by the same energy meter device conflict with each other. When a conflict exists, it obtains the command priority and timestamp information corresponding to the conflicting command. The conflicting commands are sorted according to their priority and timestamp information to obtain the valid command and send a conflict resolution solution to the energy meter device.

[0094] In this embodiment, multiple control commands from the same device are received. Each control command carries an operation type, timestamp, and priority weight. An instruction parser extracts the operation type and priority weight from the control commands. If the operation type involves the same hardware resource, the control command is marked as a conflict command, and conflict detection data is generated. Based on the conflict detection data, a priority comparison algorithm is used to sort the conflict commands. If the priority weights are the same, the execution priority is determined according to the order of the timestamps, resulting in a sorted list containing the execution order. A conflict resolution processor processes the sorted list to generate a conflict resolution solution, which includes a valid instruction identifier and delayed execution parameters. The conflict resolution solution is sent to the target device, and an instruction status tracker receives the conflict resolution confirmation information returned by the device and updates the instruction execution status.

[0095] In this embodiment, a single electricity meter may receive multiple control commands, which may involve different operational requirements. For example, an electricity meter may receive two commands within a short period: one requesting the upload of current electricity data, and the other requesting the adjustment of device operating parameters. Both commands require access to the same hardware resource, necessitating proper management and processing to avoid resource conflicts. An command parser extracts the operation type and priority weight from each command. Assuming the command to upload electricity data has a priority weight of 8 and a timestamp of October 10, 2023, at 10:00:00, while the command to adjust operating parameters has a priority weight of 5 and a timestamp of October 10, 2023, at 10:00:02, the parser identifies that both commands involve the same hardware resource, thus marking them as conflicting commands and generating corresponding conflict detection data to provide a basis for subsequent processing. A priority comparison algorithm can be used to sort the conflicting commands, determining the execution order based on their priority weights. Using the above example, upload data instructions with a priority weight of 8 will be prioritized, while parameter adjustment instructions with a weight of 5 will be prioritized. If the weights are the same, the timestamp is further considered, and instructions with earlier timestamps will be executed first. This sorting mechanism ensures that important tasks are processed first.

[0096] In this embodiment, the conflict resolution processor generates conflict solutions based on the sorted list. For example, in the conflict solutions, higher-priority upload data instructions are marked as valid instructions and executed immediately, while lower-priority adjustment parameter instructions are set to be executed with a delay of 10 seconds. The implementation of conflict solutions effectively avoids resource contention while ensuring the orderly execution of instructions. When sending conflict solutions to the control target, i.e., the smart meter device, and tracking its status, the conflict solutions are transmitted to the smart meter device, and the confirmation information returned by the smart meter device is received through the instruction status tracker. Assuming the smart meter device returns information showing that the upload data instruction has been successfully executed, while the adjustment parameter instruction is waiting to be executed, the instruction execution status is updated accordingly to ensure that subsequent operations can be adjusted and optimized based on the latest status.

[0097] This embodiment can also be optimized by combining historical command execution records. Suppose that a certain electricity meter device is found to have frequent command conflict issues in the past week, and most of them are conflicts between data upload and parameter adjustment. Priority weights or delay parameters can be adjusted in advance before future commands are issued, thereby reducing the possibility of conflicts and further improving the efficiency of command management.

[0098] Example 2

[0099] Please continue reading. Figure 2This diagram illustrates a program module schematic of a second embodiment of the IoT-based smart meter remote control system of the present invention. In this embodiment, the IoT-based smart meter remote control system 20 may include or be divided into one or more program modules. One or more program modules are stored in a storage medium and executed by one or more processors to complete the present invention and implement the aforementioned IoT-based smart meter remote control method. The program module referred to in this embodiment of the invention refers to a series of computer program instruction segments capable of performing specific functions, which are more suitable than the program itself for describing the execution process of the IoT-based smart meter remote control system 20 in the storage medium. The following description will specifically introduce the functions of each program module in this embodiment:

[0100] The device attribute acquisition module 200 is used to acquire device-related data and electricity consumption characteristic data of smart meters within a preset range, and to perform quantitative analysis on the device-related data and electricity consumption characteristic data to obtain a feature vector matrix.

[0101] The grouping module 201 is used to group smart energy meters according to the feature vector matrix, and generate multiple device groups and corresponding group identifiers.

[0102] The monitoring module 202 is used to collect the operating data of the smart energy meter in real time and generate a status assessment report corresponding to the device group based on the operating data. The status assessment report is used to determine whether the smart energy meter meets the conditions for remote control execution.

[0103] The instruction parsing module 203 is used to receive group control instructions sent by users based on status assessment reports, parse the group control instructions, and obtain the control target and target group identifier.

[0104] The instruction scheduling module 204 is used to compare the target group identifier with the group identifier to obtain the target group corresponding to the control target from multiple device groups, generate the corresponding control instruction according to the target group and the control instruction, and send it to the control target.

[0105] The IoT-based remote control system for smart meters provided in this invention quantifies device-related data and electricity consumption characteristic data to obtain a feature vector matrix. This feature vector matrix is ​​then grouped to obtain refined device groups. Real-time operational data is then analyzed to generate a more accurate status assessment report. Users can issue group control commands based on this report to remotely control the corresponding device groups, enabling more accurate remote control. This achieves precise grouping and efficient group control of large-scale smart meters, improving grid dispatching capabilities.

[0106] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0107] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0108] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A remote control method for smart energy meters based on the Internet of Things, characterized in that, include: Acquire device-related data and electricity consumption characteristic data of smart meters within a preset range, and perform quantitative analysis on the device-related data and electricity consumption characteristic data to obtain a feature vector matrix; The smart energy meters are grouped according to the feature vector matrix to generate multiple device groups and corresponding group identifiers; The system collects real-time operating data from the smart energy meter and generates a status assessment report for the device group based on the operating data. The status assessment report is used to determine whether the smart energy meter meets the conditions for remote control execution. Receive group control commands sent by users based on the status assessment report, parse the group control commands, and obtain the control target and target group identifier; The target group identifier is compared with the group identifier to obtain the target group corresponding to the control target from multiple device groups. A corresponding control instruction is generated based on the target group and the group control instruction and sent to the control target. The method further includes: When a device malfunctions in the target group, a safety factor for executing the control command is calculated, and a decision is made based on the safety factor to whether to suspend the issuance of the control command, thus obtaining an executable queue. The executable queue is time-optimized based on the device processing capacity of the target group to obtain an optimized queue. The control command is sent to the target group according to the optimized queue, and the response data is monitored. If no response data is received within the preset time or abnormal data is received, the abnormal handling procedure is triggered and the abnormal device identifier is recorded. The device processing capability includes real-time bandwidth detection data and network status parameters. Based on the device processing capability, the executable queue is time-optimized to obtain an optimized queue, including: Obtain real-time bandwidth detection data of the target group under the current network environment, and calculate network utilization using the bandwidth detection data; When the network utilization exceeds a preset congestion threshold, a network load report carrying the network status parameters is generated and a load warning mechanism is triggered. Based on the network load report, the processing speed of each device in the target group is quantitatively rated to obtain a device performance profile, which is used to rank the device processing capabilities. Based on the device performance profile, the executable queue is divided into multiple sub-batches to obtain a batch scheduling scheme. The executable queue is sent with control commands to each control target batch in the target group at predetermined time intervals according to the batch scheduling scheme, thereby obtaining an optimized queue.

2. The remote control method for smart energy meters based on the Internet of Things according to claim 1, characterized in that, The device-related data and the electricity consumption characteristic data are quantitatively analyzed to obtain an feature vector matrix, including: The device-related data and the electricity consumption characteristic data are denoised and formatted to obtain a standard dataset; The feature vector matrix is ​​obtained by performing multi-dimensional quantitative analysis on the standard dataset. When the dimension value in the feature vector matrix exceeds a preset threshold, the dimension data corresponding to the dimension value is matched and analyzed with the historical dimension data in the preset database to determine whether the smart energy meter corresponding to the dimension data is abnormal and to mark the abnormal mode, thereby obtaining the marked feature vector matrix.

3. The remote control method for smart energy meters based on the Internet of Things according to claim 1, characterized in that, The smart energy meters are grouped according to the feature vector matrix to generate multiple device groups and corresponding group identifiers, including: The similarity value is obtained by performing pairwise calculations on the electricity consumption feature vectors in the feature vector matrix. The identifiers of target devices whose similarity values ​​exceed a first preset value are recorded in the candidate group list; Obtain the coordinate data corresponding to the device identifiers in the candidate group list, calculate the distance value by pairwise calculation of the coordinate data; The target devices whose distance values ​​are less than the second preset value are assigned to the same group identifier to obtain the device group.

4. The remote control method for smart energy meters based on the Internet of Things according to claim 3, characterized in that, Grouping the smart energy meters according to the feature vector matrix to generate multiple device groups and corresponding group identifiers also includes: Statistical analysis of the power demand of all devices in the device group was performed. Devices whose power demand exceeds the limit are grouped according to the power consumption characteristic data or the coordinate data.

5. The remote control method for smart energy meters based on the Internet of Things according to claim 1, characterized in that, The system collects real-time operating data from the smart meters and generates a status assessment report for the device group based on the operating data. This status assessment report is used to determine whether the smart meters meet the conditions for remote control execution, including: The operating data of the smart energy meter is collected in real time according to the group identifier, and the operating data is classified and processed to obtain a group parameter set. The operating data includes operating parameters, communication status and load status. The operating parameters and the communication status are compared. When the comparison result shows that any value of the operating parameters and the communication status exceeds the preset parameter value, the corresponding group is marked as an abnormal category to obtain an abnormal group. The load status of the smart meters in the abnormal group is checked. When the check result is that the load is uneven, the smart meters in the abnormal group are regrouped to obtain the load distribution result. The status assessment report is generated based on the load distribution results, and the abnormal data in the status assessment report is compared with rules to determine whether the conditions for remote control execution are met.

6. The remote control method for smart energy meters based on the Internet of Things according to claim 1, characterized in that, The process includes comparing the target group identifier with the group identifier to obtain the target group corresponding to the control target from multiple device groups, generating a corresponding control command based on the target group and the control command, and sending it to the control target. Extract the set of devices that match the control target from the group set to obtain the device address list; The group identifiers in the device address list are compared one by one with the target group identifiers to remove mismatched group identifiers and obtain the address set. When the smart energy meter corresponding to the address set meets the requirements of the operation type, operation parameters corresponding to the operation type and execution time are generated. The control command is generated based on the operating parameters.

7. The remote control method for smart energy meters based on the Internet of Things according to claim 1, characterized in that, The method further includes: Identify whether there are operational conflicts among multiple commands received by the same electricity meter device; When an operation conflict exists, obtain the instruction priority and timestamp information corresponding to the conflicting instruction; The conflicting instructions are sorted according to the instruction priority and the timestamp information to obtain valid instructions and send conflict resolution solutions to the electricity meter device.

8. A remote control system for a smart energy meter based on the Internet of Things, characterized in that, For implementing the method as described in any one of claims 1-7, comprising: The device attribute acquisition module is used to acquire device-related data and electricity consumption characteristic data of smart meters within a preset range, and to perform quantitative analysis on the device-related data and the electricity consumption characteristic data to obtain a feature vector matrix; The grouping module is used to group the smart energy meters according to the feature vector matrix, and generate multiple device groups and corresponding group identifiers; The monitoring module is used to collect the operating data of the smart energy meter in real time, and generate a status assessment report corresponding to the device group based on the operating data. The status assessment report is used to determine whether the smart energy meter meets the remote control execution conditions. The instruction parsing module is used to receive group control instructions sent by the user based on the status assessment report, parse the group control instructions, and obtain the control target and target group identifier; The instruction scheduling module is used to compare the target group identifier with the group identifier to obtain the target group corresponding to the control target from multiple device groups, generate corresponding control instructions based on the target group and the group control instruction, and send them to the control target.

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