A method and device for online adaptive calibration of electricity consumption information collection

Abstract

The present invention relates to the technical field of smart grids, and discloses an online adaptive calibration method for electricity consumption information collection, comprising the following steps: real-time monitoring of the communication status and data transmission quality between an electric meter and an electricity consumption information collection terminal; based on monitoring data, automatically analyzing abnormal readings of the electric meter or the error range of the collection terminal; dynamically generating calibration instructions according to the analysis results, and sending them to a device to be calibrated via wireless communication; after receiving the calibration instructions, the device to be calibrated executes an internal calibration procedure to adjust measurement or collection parameters; after completing the calibration, automatically feeding back the calibration results to a monitoring center to form a closed-loop feedback mechanism; the present invention can realize online, real-time, adaptive calibration of electricity consumption information collection without human intervention, thereby improving the accuracy and reliability of data collection; realizes online adaptive calibration of electricity consumption information collection, thereby improving the accuracy of data collection and the intelligent level of power grid management.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to an online adaptive calibration method and device for collecting electricity consumption information. Background Art

[0002] With the rapid development of smart grids, electricity meters and electricity information collection terminals are important devices for obtaining grid data. Their measurement accuracy and data transmission reliability directly affect the operation efficiency and management level of the grid. However, traditional calibration methods mostly rely on manual on-site operations, which have problems such as long calibration cycle, high cost, and poor real-time performance. Therefore, it is particularly important to develop a method and device that can calibrate electricity meters and collection terminals online, automatically, and in real time. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In view of the shortcomings of the prior art, the present invention provides an online adaptive calibration method and device for electricity consumption information collection, which has the advantages of being able to automatically calibrate electricity consumption information collection without human supervision, effectively improving the accuracy of data collection and the level of intelligence of power grid management, and solving the problems of low efficiency, high labor cost and poor real-time performance existing in traditional calibration methods.

[0005] (II) Technical solution

[0006] In order to achieve the purpose of enabling the above-mentioned electric meter and collection terminal to automatically calibrate in an unattended manner, effectively improving the accuracy of data collection and the intelligent level of power grid management, the present invention provides the following technical solutions: an online adaptive calibration method for electricity consumption information collection, comprising the following steps:

[0007] Step S1: real-time monitoring of the communication status and data transmission quality between the electric meter and the electric consumption information collection terminal;

[0008] Step S2: Pre-set N groups of benchmark values ​​for the four-dimensional variables through cluster analysis Q , Q The variables include current, voltage, power factor, and frequency, and the instantaneous power reference values ​​corresponding to N four-dimensional variable reference value groups are set accordingly. A ;

[0009] Step S3: Based on the actual monitoring of power parameter data P , P For a set of variables ， The measured values ​​of the power consumption information collection terminal, including current, voltage, power factor, and frequency, are calculated separately P The Euclidean distance from N sets of benchmark four-dimensional parameter sets d (P,Q) , the formula is as follows: ; For the N calculated Euclidean distances , take the minimum value Corresponding instantaneous power reference value A , is determined as the instantaneous power value under the current measurement parameters, and the calculation formula is: ;

[0010] Step S4: Calculate the actual power value measured by the electric energy meter , reference value , calculate the deviation , the calculation formula is: ;

[0011] Step S5: Dynamically generate calibration instructions based on analysis results and sent to the device to be calibrated via wireless communication;

[0012] Step S6: After receiving the calibration instruction, the device to be calibrated executes an internal calibration procedure to adjust the instantaneous power parameter value;

[0013] Step S7: After the calibration is completed, the calibration result is automatically fed back to the monitoring center to form a closed-loop feedback mechanism.

[0014] Preferably, step S1 also includes evaluating the communication network status to determine whether communication parameters need to be adjusted to optimize data transmission quality.

[0015] Preferably, in step S2, a machine learning algorithm is used to analyze abnormal data to improve the accuracy and efficiency of error identification.

[0016] Preferably, the calibration instruction in step S5 includes precise adjustment parameters for specific error types, and supports compatible calibration of multiple types of electricity meters and acquisition terminals.

[0017] Preferably, a threshold value may be set , when the deviation lasts for m cycles Greater than threshold , it can be determined that the electric energy meter body is in failure.

[0018] A calibration device for implementing the above-mentioned online adaptive calibration method for electricity consumption information collection comprises a shell, a plurality of reinforcing ribs are arranged on the upper end of the shell, a fan is arranged in the middle of the upper end of the shell, a plurality of heat dissipation ports are provided on the side wall of the shell, a display screen, a start button for starting the device, an indicator light for displaying the normal operation of the device and a warning light for displaying the abnormality of the device are provided on one side of the shell adjacent to the heat dissipation port, the display screen is provided with fixed protrusions on both sides, the fixed protrusions are provided with mounting holes for fixing the display screen, at least one photoelectric sensor is arranged on the upper end of the display screen, an adjustment button, an audio interface and a network cable interface are arranged on the other side of the device, a circuit board is installed in the shell, a monitoring module, an analysis module, an instruction generation module, a communication module and a calibration execution module are installed on the circuit board, the modules work together to realize the online calibration function, the network cable interface is electrically connected to the circuit board, and the four corners of the circuit board are provided with fixing holes for fixing the circuit board in the shell.

[0019] Preferably, the monitoring module further comprises a network communication quality detection unit for evaluating the communication network status in real time.

[0020] Preferably, the analysis module has a built-in machine learning algorithm model for automatically analyzing data anomalies and determining error types.

[0021] Preferably, it further comprises a data storage unit for storing historical calibration data, and the communication module supports a variety of wireless communication protocols.

[0022] (III) Beneficial effects

[0023] Compared with the prior art, the present invention provides an online adaptive calibration method and device for collecting electricity consumption information, which has the following beneficial effects:

[0024] The online adaptive calibration method and device for electricity consumption information collection can improve the accuracy of the electric energy meter's judgment of current, voltage, power factor, frequency and other parameters through the calibration algorithm, and monitor and calibrate the electric energy meter's measurement value in real time, thereby improving the measurement accuracy and making the electric energy meter's application range wider and more accurate. In a power grid disturbance environment, the reaction speed of the electric energy meter is improved, and the measurement value is more accurate. At the same time, the online, real-time, and adaptive calibration of the electric energy meter and the electricity consumption information collection terminal can be realized without manual intervention, thereby improving the accuracy and reliability of data collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a block diagram of the steps of the online adaptive calibration method for collecting electricity consumption information of the present invention;

[0026] Figure 2 It is a block diagram of the module structure of the device of the present invention;

[0027] Figure 3 It is a front schematic diagram of the overall structure of the device of the present invention;

[0028] Figure 4 It is a schematic diagram of the overall structure of the device of the present invention;

[0029] Figure 5 This is a schematic diagram of the display screen structure of the present invention;

[0030] Figure 6 It is a schematic diagram of the circuit board structure of the present invention.

[0031] In the figure: 1. Shell; 11. Reinforcement ribs; 12. Heat dissipation vent; 13. Adjustment button; 14. Audio interface; 15. Network cable interface; 16. Start button; 17. Indicator light; 18. Warning light; 2. Fan; 3. Display screen; 31. Fixing protrusion; 32. Mounting hole; 33. Photoelectric sensor; 4. Circuit board; 41. Communication module; 42. Data storage unit; 43. Calibration execution module; 44. Analysis module; 45. Instruction generation module; 46. Network communication quality detection unit; 47. Fixing hole. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] like Figure 1 As shown, an online adaptive calibration method for collecting electricity consumption information includes the following steps:

[0034] Step S1: monitor the communication status and data transmission quality between the electric meter and the electricity consumption information collection terminal in real time; use the built-in monitoring module of the device to monitor the communication status between the electric meter and the collection terminal in real time, including signal strength, connection stability, etc.; at the same time, monitor the data transmission quality, such as key indicators such as data transmission integrity, timeliness, and packet loss rate.

[0035] Step S2: Pre-set N groups of benchmark values ​​for the four-dimensional variables through cluster analysis Q , Q The variables include current, voltage, power factor, and frequency, and the instantaneous power reference values ​​corresponding to N four-dimensional variable reference value groups are set accordingly. A， The QA associated values ​​of a single-item electric energy meter are set according to the meter range as shown in the following table:

[0036] ;

[0037] Step S3: Based on the actual monitoring of power parameter data P , P For a set of variables ， The measured values ​​of the power consumption information collection terminal, including current, voltage, power factor, and frequency, are calculated separately. For example, if the measured values ​​are (5.2, 218.7, 0.84, 50.1), P The Euclidean distance from N sets of benchmark four-dimensional parameter sets d(P,Q) , the formula is as follows: ;

[0038] The N Euclidean distances calculated for for

[0039]

[0040] Take the minimum value Corresponding instantaneous power reference value A , is determined as the instantaneous power value under the current measurement parameters, and the calculation formula is: ;

[0041] Therefore, the instantaneous power value under the current measurement parameters is , corresponding to the reference instantaneous power It is 880kw.

[0042] At this time, read the power value displayed on the energy meter , calculate the deviation , the calculation formula is: ;

[0043] Step S5: According to the analysis results, If the value is unacceptable, calibration instructions are generated dynamically and sent to the device to be calibrated via wireless communication;

[0044] Step S6: After receiving the calibration instruction, the device to be calibrated executes an internal calibration procedure to adjust the instantaneous power parameter value;

[0045] Step S7: After the calibration is completed, the calibration result is automatically fed back to the monitoring center to form a closed-loop feedback mechanism.

[0046] First, in step S1, the communication status and data transmission quality between the electric meter and the acquisition terminal are monitored in real time through the built-in monitoring module of the device. This is the basis for ensuring that the calibration process can be carried out accurately. At the same time, considering the impact of the communication network status on data transmission, the communication network status can also be evaluated so that the communication parameters can be adjusted when necessary to optimize the data transmission quality.

[0047] Next, in step S2, N groups of reference values ​​Q of four-dimensional variables are set by cluster analysis. These variables include current, voltage, power factor and frequency. Each group of reference values ​​corresponds to an instantaneous power reference value A. This is to calculate the deviation between the actual measurement value and the reference value and perform calibration accordingly.

[0048] In step S3, based on the actually monitored electric energy parameter data P (including the measured values ​​of current, voltage, power factor and frequency), the Euclidean distance d(P,Q) between P and N groups of reference four-dimensional parameter groups is calculated. By comparing these distances, the reference value group closest to P is found, and the corresponding instantaneous power reference value A is determined as the instantaneous power value under the current measurement parameters.

[0049] Then, in step S4, the actual power value measured by the electric energy meter is calculated and compared with the instantaneous power reference value A determined in step S3 to calculate the deviation. This deviation reflects the accuracy of the electric energy meter measurement and is the basis for subsequent calibration.

[0050] In step S5, according to the analysis result of step S4, a calibration instruction is dynamically generated and sent to the device to be calibrated via wireless communication. The calibration instruction contains the information required to adjust the instantaneous power parameter value.

[0051] In step S6, after receiving the calibration instruction, the device to be calibrated executes an internal calibration procedure to adjust the instantaneous power parameter value. In this way, the measurement accuracy of the electric energy meter is improved.

[0052] Finally, in step S7, after the calibration is completed, the device to be calibrated automatically feeds back the calibration result to the monitoring center. In this way, a closed-loop feedback mechanism is formed, which can monitor the calibration effect in real time and calibrate again when necessary.

[0053] The above method can realize online, real-time and adaptive calibration of electricity consumption information collection without human intervention, thereby improving the accuracy and reliability of data collection; the entire calibration process is unattended and automatic, greatly reducing labor costs and time costs; it can monitor the status of the meter and the collection terminal in real time, and immediately calibrate when an abnormality is found, thereby improving the timeliness and accuracy of calibration; it can dynamically generate calibration instructions according to specific error types, thereby realizing compatible calibration of different types of meters and collection terminals; errors are eliminated through calibration, thereby improving the accuracy and reliability of data collection; it provides more accurate data support for power grid management, thereby helping to improve the intelligence level and management efficiency of the power grid.

[0054] Furthermore, step S1 also includes an evaluation of the communication network status to determine whether it is necessary to adjust the communication parameters to optimize the data transmission quality; in particular, it also includes an evaluation of the communication network status, checking factors such as network delay, bandwidth utilization, signal interference, etc. to determine whether it is necessary to adjust the communication parameters to optimize the data transmission quality.

[0055] Furthermore, in step S2, a machine learning algorithm is used to analyze abnormal data to improve the accuracy and efficiency of error identification. First, real-time data of the electric meter and the acquisition terminal are collected, including readings, communication status, data transmission quality, etc. The collected data is preprocessed, including data cleaning (removing noise, outliers, etc.), data conversion (normalization, standardization, etc.) and data dimension reduction (reducing data dimensions and improving processing efficiency). Meaningful features are extracted from the preprocessed data, which can reflect abnormal information of the electric meter reading or the acquisition terminal status. The selection and extraction of features are crucial for subsequent analysis and identification, and need to be carefully designed according to specific application scenarios and data characteristics. Machine learning algorithms (such as decision trees, random forests, neural networks, etc.) are used to The extracted features are used for training to build an anomaly detection model. During the training process, known normal data and abnormal data are used to guide the learning and optimization of the model. The newly collected data is input into the trained model, and the model will evaluate the data based on the learned patterns and relationships. If the data is evaluated as abnormal (i.e., there is a significant difference from the normal pattern), the model will output the corresponding abnormal signal or alarm information. The anomaly detection results are fed back to the monitoring center or related systems for further processing and response. At the same time, the model is continuously optimized and adjusted based on the feedback results to improve its accuracy and efficiency. Machine learning algorithms can automatically learn from large amounts of data and identify complex patterns and relationships, thereby more accurately identifying abnormal meter readings or the error range of the acquisition terminal.

[0056] Through continuous iteration and optimization, the algorithm can gradually improve its recognition accuracy of abnormal data and reduce false alarms and missed alarms; compared with traditional manual analysis methods, machine learning algorithms can automatically process and analyze large amounts of data, significantly improving processing speed; the algorithm can monitor data in real time or near real time and respond immediately when anomalies are found, improving the efficiency of the entire calibration process; machine learning algorithms are adaptive and can automatically adjust and optimize their models as data increases and changes to adapt to different application scenarios and data characteristics; this enables the algorithm to better cope with complex and changing power grid environments and improve the robustness and stability of error identification.

[0057] Furthermore, the calibration instruction in step S5 contains precise adjustment parameters for specific error types, supporting compatible calibration of multiple types of meters and acquisition terminals; in step S2, the monitoring data of the meter and the acquisition terminal are deeply analyzed through intelligent analysis means such as machine learning algorithms to identify specific error types. These error types may include reading jumps, long-term deviations, communication delays, and other types; according to the identified error types, combined with the technical specifications and performance parameters of the equipment, a series of complex calculations and optimization algorithms are used to generate precise adjustment parameters for the error type. These parameters may include gain adjustment of the measurement circuit, modification of the sampling frequency, optimization of communication parameters, etc.; the calculated adjustment parameters are encapsulated into calibration instructions and encoded through specific communication protocols and formats. The calibration instruction contains key information such as the unique identification code of the device, the error type identification, and the adjustment parameter value; the calibration instruction is sent to the device to be calibrated through wireless communication (such as Zigbee, LoRa, NB-IoT, etc.). After receiving the calibration instruction, the device will automatically parse the instruction content and execute the corresponding internal calibration procedure. During the calibration process, the device will adjust and optimize its own measurement or acquisition parameters according to the adjustment parameters; after the calibration is completed, the device to be calibrated will automatically feedback the calibration results (including data comparison before and after calibration, calibration success / failure status, etc.) to the monitoring center. The monitoring center verifies and evaluates the feedback results to ensure the accuracy and effectiveness of the calibration; the adjustment parameters in the calibration instructions are accurately calculated according to the specific error type, and can be adjusted directly to the root cause of the error, thereby improving the accuracy and effectiveness of the calibration; supporting compatible calibration of multiple types of meters and acquisition terminals means that the same calibration system can be applied to meters and acquisition terminals of different models and specifications, reducing the types and number of calibration equipment and reducing costs; since the calibration instructions are dynamically generated, they can be adjusted according to the real-time monitored data and analysis results, so they can cope with various error types that may occur in different scenarios, improving the flexibility and adaptability of calibration; the calibration instructions can be automatically generated without human intervention and sent to the device to be calibrated through wireless communication, realizing the automation and intelligence of the calibration process and improving work efficiency.

[0058] In one embodiment of the present invention, a threshold value may be set , when the deviation lasts for m cycles Greater than threshold , it can be determined that the electric energy meter body is in failure; the determination mechanism includes the following steps:

[0059] Step 1: According to the accuracy requirements of the electric energy meter and the actual application scenario, set a reasonable deviation threshold. This threshold should be able to reflect the maximum deviation range that may occur in the normal working state of the electric energy meter.

[0060] Step 2: In each calibration cycle (which can be a fixed time interval, such as every day, every hour, etc.), calculate the deviation between the actual power value of the electric energy meter and the instantaneous power reference value according to the original calibration method.

[0061] Step 3: Record the deviation data of each calibration cycle to form a deviation data sequence.

[0062] Step 4: Traverse the deviation data sequence and check whether there are m consecutive cycles of deviations greater than the set threshold. It should be noted that the value of m should be reasonably set according to the actual application scenario and the performance of the electric energy meter to ensure that the failure of the electric energy meter can be discovered in time and misjudgment can be avoided.

[0063] Step 5: If the deviation is greater than the threshold for m consecutive cycles, it can be determined that the electric energy meter may be in a failed state. At this time, an alarm message should be sent to the monitoring center immediately, and appropriate measures (such as replacing the electric energy meter, further checking the cause of the fault, etc.) should be taken to solve the problem.

[0064] Step 6: After determining the status of the energy meter, the status information of the energy meter should be updated in time and fed back to the monitoring center. In this way, the monitoring center can grasp the operating status of the energy meter in real time and make corresponding processing; by adding this mechanism, the operating status of the energy meter can be monitored more accurately, and potential failure problems can be discovered and handled in time, thereby ensuring the accuracy and reliability of energy metering. At the same time, this also provides a more convenient and efficient way for the maintenance and management of the energy meter.

[0065] like Figure 2-6As shown, a calibration device for realizing the above-mentioned online adaptive calibration method for electricity consumption information collection includes a shell 1, a plurality of reinforcing ribs 11 are arranged on the upper end of the shell 1, a fan 2 is arranged in the middle of the upper end of the shell 1, a plurality of heat dissipation ports 12 are provided on the side wall of the shell 1, a display screen 3, a start button 16 for starting the device, an indicator light 17 for displaying the normal operation of the device and a warning light 18 for displaying abnormalities of the device are arranged on one side of the shell 1 adjacent to the heat dissipation port 12, the display is provided with fixing protrusions 31 on both sides, the fixing protrusions 31 are provided with mounting holes 32 for fixing the display screen 3, at least one photoelectric sensor 33 is arranged on the upper end of the display screen 3, the other side of the device is provided with an adjustment button 13, an audio interface 14 and a network cable interface 15, a circuit board 4 is installed in the shell 1, and the circuit board 4 A monitoring module, an analysis module 44, an instruction generation module 45, a communication module 41 and a calibration execution module 43 are installed on the board 4. The modules work together to realize the online calibration function. The network cable interface 15 is electrically connected to the circuit board 4, and the four corners of the circuit board 4 are provided with fixing holes 47 for fixing the circuit board 4 in the shell 1; the reinforcing rib 11 design at the upper end of the shell 1 significantly enhances the overall structural strength of the shell 1, so that it can withstand greater external pressure, prevent deformation or damage caused by external force, and improve the durability and service life of the equipment; the reinforcing rib 11 also helps to improve the stability of the equipment, ensure that it can still operate stably in a complex environment, and reduce performance degradation caused by vibration or shaking; the fan 2 is combined with the heat dissipation port 12: the fan 2 arranged at the upper end of the shell 1 and the heat dissipation port 12 opened on the side wall form an efficient heat dissipation system. The fan 2 accelerates the air circulation inside the housing 1 by actively exhausting air, while the heat dissipation port 12 allows the heat to be quickly dissipated, effectively reducing the temperature inside the device and ensuring the normal operation of the circuit board 4 and electronic components; good heat dissipation performance helps to reduce the aging and failure of electronic components caused by high temperature, thereby extending the overall service life of the device; the reasonable layout of the display screen 3, the start button 16, the indicator light 17 and the warning light 18 enables the user to intuitively understand the operating status of the device. The display screen 3 is used to display detailed information and data, and the indicator light 17 and the warning light 18 quickly indicate the device status through color changes, which improves the convenience and efficiency of operation; the fixed protrusions 31 on both sides of the display screen 3 and the mounting holes 32 thereon are designed to make the installation of the display screen 3 more stable and reliable, reducing the risk of falling off or damage caused by vibration or collision; the monitoring module, analysis module 44, instruction generation module 45, communication module 41 and calibration execution module 43 integrated in the device realize the online calibration function through collaborative work.This function can calibrate and adjust the measurement accuracy of the device in real time, ensuring the accuracy and reliability of the data; the integration and application of multiple modules make the device more intelligent, and can automatically complete complex data processing and analysis tasks, reducing the possibility of manual intervention and errors; the device provides a network cable interface 15 to achieve a stable connection with the wired network. This provides users with more communication options and also improves the reliability and stability of data transmission; the setting of the audio interface 14 and the adjustment button 13 provides users with the convenience of audio output and parameter adjustment, enhancing the practicality and flexibility of the device.

[0066] The monitoring module collects key information such as the communication status and data transmission quality of the electric meter and the acquisition terminal in a regular or real-time manner, including signal strength, packet loss rate, response time, etc. The analysis module 44 has a built-in machine learning algorithm model to intelligently analyze the data collected by the monitoring module, identify abnormal meter readings or the error range of the acquisition terminal, and determine the error type; the instruction generation module 45 dynamically generates calibration instructions based on the results of the analysis module 44. The calibration instructions contain precise adjustment parameters for specific error types to ensure the accuracy and effectiveness of the calibration; the communication module 41 supports a variety of wireless communication protocols to ensure compatible communication with different types of electric meters and acquisition terminals. It sends the calibration instructions to the device to be calibrated and receives feedback on the calibration results; after receiving the calibration instructions from the communication module 41, the calibration execution module 43 executes the internal calibration procedure, adjusts the measurement or acquisition parameters, and completes the calibration process; the data storage unit 42 is used to store historical calibration data, calibration instructions, calibration results and other information to support data analysis and optimization of future calibration strategies.

[0067] Furthermore, the monitoring module also includes a network communication quality detection unit 46 for real-time evaluation of the communication network status; using the built-in monitoring module of the device, the communication status and data transmission quality between the electric meter and the collection terminal are monitored in real time, including key indicators such as signal strength and packet loss rate.

[0068] Furthermore, the analysis module 44 has a built-in machine learning algorithm model for automatically analyzing data anomalies and determining error types. Based on monitoring data, a machine learning algorithm is used to automatically analyze meter reading anomalies or acquisition terminal error ranges, and accurately identify equipment that needs to be calibrated and error types.

[0069] Furthermore, it also includes a data storage unit 42 for storing historical calibration data, supporting data analysis and optimization of future calibration strategies. The device has a built-in data storage unit 42 for storing historical calibration data, supporting data analysis and optimization of future calibration strategies. At the same time, the monitoring center regularly generates calibration reports to provide data support for power grid management and optimization; the communication module 41 supports a variety of wireless communication protocols to ensure compatible communication with different types of meters and acquisition terminals. According to the analysis results, targeted calibration instructions are dynamically generated and sent to the device to be calibrated via wireless communication. The instructions contain precise adjustment parameters and support compatible calibration of multiple types of equipment; after the device to be calibrated executes the calibration instruction, it automatically feeds back the calibration results to the monitoring center to form a closed-loop feedback mechanism. Based on the feedback results, the monitoring center can further analyze the calibration effect and optimize the calibration strategy.

[0070] Working principle: The monitoring module collects the communication status and data transmission quality information of the electric meter and the acquisition terminal in real time or regularly; the analysis module 44 performs intelligent analysis on the collected data to identify the equipment and error type that need to be calibrated; the instruction generation module 45 generates a calibration instruction based on the analysis result and sends it to the equipment to be calibrated through the communication module 41; after receiving the calibration instruction, the equipment to be calibrated executes the internal calibration procedure, completes the calibration process, and feeds back the calibration result to the monitoring center; the monitoring center receives the calibration result feedback to form a closed-loop feedback mechanism. At the same time, the data storage unit 42 saves relevant data to support subsequent data analysis and calibration strategy optimization.

[0071] In summary, the online adaptive calibration method and device for electricity consumption information collection realizes online, automatic and real-time calibration of electricity consumption information collection, significantly shortens the calibration cycle, reduces manual intervention, and thus greatly reduces the manpower cost and time cost of calibration work; through the close combination of real-time monitoring, intelligent analysis and dynamic calibration, the present invention can accurately identify and correct abnormal meter readings and acquisition terminal errors, effectively improve the accuracy and reliability of data collection, and provide more accurate data support for power grid operation; the closed-loop feedback mechanism enables the calibration process to form a complete closed-loop system, and the monitoring center can grasp the calibration effect in real time, and continuously optimize the calibration strategy according to the feedback results, thereby enhancing the intelligence level of power grid management and improving the safety and stability of power grid operation; for power users, the problem of inaccurate electricity fee calculation caused by errors in meters or acquisition terminals is reduced, and user satisfaction and trust are improved; by improving the accuracy of data collection, it helps the power grid to grasp the power consumption more accurately, and provides strong support for implementing demand-side management, optimizing power resource allocation, and promoting energy conservation and emission reduction.

[0072] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0073] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An online adaptive calibration method for collecting electricity consumption information, characterized in that: The following steps are involved: Step S1: real-time monitoring of the communication status and data transmission quality between the electric meter and the electric consumption information collection terminal; Step S2: Pre-set N groups of benchmark values ​​for the four-dimensional variables through cluster analysis Q , Q The variables include current, voltage, power factor, and frequency, and the instantaneous power reference values ​​corresponding to N four-dimensional variable reference value groups are set accordingly. A ; Step S3: Based on the actual monitoring of electric energy parameter data P , P For a set of variables ， The measured values ​​of the power consumption information collection terminal, including current, voltage, power factor, and frequency, are calculated separately P The Euclidean distance from N sets of benchmark four-dimensional parameter sets d(P, Q） , the formula is as follows: ； The N Euclidean distances calculated for , take the minimum value Corresponding instantaneous power reference value A , is determined as the instantaneous power value under the current measurement parameters, and the calculation formula is: ； Step S4: Calculate the actual power value measured by the electric energy meter , reference value , calculate the deviation , the calculation formula is: ; Step S5: Dynamically generate calibration instructions based on analysis results , and sent to the device to be calibrated by wireless communication, wherein the device to be calibrated is an electric meter and an electric consumption information collection terminal; Step S6: After receiving the calibration instruction, the device to be calibrated executes an internal calibration procedure to adjust the instantaneous power parameter value; Step S7: After the calibration is completed, the calibration result is automatically fed back to the monitoring center to form a closed-loop feedback mechanism.

2. The method for online adaptive calibration of power consumption information collection according to claim 1, characterized in that: The step S1 also includes evaluating the communication network status to determine whether communication parameters need to be adjusted to optimize data transmission quality.

3. The method for online adaptive calibration of power consumption information collection according to claim 1, characterized in that: The step S2 includes using a machine learning algorithm to analyze abnormal data, identify abnormal meter readings or the error range of the electricity consumption information collection terminal, and determine the specific error type to improve the accuracy and efficiency of error identification.

4. The method for online adaptive calibration of power consumption information collection according to claim 3 is characterized in that: The calibration instruction in step S5 includes precise adjustment parameters for specific error types, and supports compatible calibration of multiple types of electricity meters and electricity consumption information collection terminals.

5. The method for online adaptive calibration of power consumption information collection according to claim 1, characterized in that: Configurable threshold , when the deviation lasts for m cycles Greater than threshold , it can be determined that the electric energy meter body is in failure.

6. A calibration device for implementing the online adaptive calibration method for electricity consumption information collection according to any one of claims 1 to 5, characterized in that: The device comprises a shell, wherein the upper end of the shell is provided with a plurality of reinforcing ribs, the middle part of the upper end of the shell is provided with a fan, the side wall of the shell is provided with a plurality of heat dissipation openings, one side of the shell adjacent to the heat dissipation opening is provided with a display screen, a start button for starting the device, an indicator light for displaying the normal operation of the device, and a warning light for displaying the abnormality of the device, both sides of the display screen are provided with fixing protrusions, the fixing protrusions are provided with mounting holes for fixing the display screen, the upper end of the display screen is provided with at least one photoelectric sensor, the other side of the device is provided with an adjustment button, an audio interface and a network cable interface, a circuit board is installed in the shell, a monitoring module, an analysis module, an instruction generation module, a communication module and a calibration execution module are installed on the circuit board, and each module works together to realize the online calibration function, the network cable interface is electrically connected to the circuit board, and the four corners of the circuit board are provided with fixing holes for fixing the circuit board in the shell; wherein the monitoring module also includes a network communication quality detection unit for real-time evaluation of the communication network status, the analysis module has a built-in machine learning algorithm model for automatically analyzing data anomalies and judging error types, and also includes a data storage unit for storing historical calibration data, and the communication module supports multiple wireless communication protocols; The monitoring module collects the communication status and data transmission quality information of the electric meter and the electricity consumption information collection terminal in real time or periodically; the analysis module performs intelligent analysis on the data collected by the monitoring module to identify the equipment that needs to be calibrated and the error type; the instruction generation module generates a calibration instruction according to the analysis result, and sends it to the equipment to be calibrated through the communication module; after receiving the calibration instruction, the equipment to be calibrated executes the internal calibration procedure, completes the calibration process, and feeds back the calibration result to the monitoring center; the monitoring center receives the calibration result feedback to form a closed-loop feedback mechanism.

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