An intelligent detection method, device, equipment and storage medium based on electric energy meter

Abstract

The present invention relates to the field of intelligent power management, and discloses an intelligent detection method, device, equipment and storage medium based on an electric energy meter. The method comprises: obtaining key data to be tested of each electric energy meter, generating a detection matrix according to the key data to be tested of the electric energy meter, obtaining an electric quantity detection matrix, calculating a first parameter according to the electric quantity detection matrix, obtaining an electric quantity difference, calculating a second parameter according to the electric quantity detection matrix, obtaining a charging coefficient, calculating a third parameter according to the electric quantity detection matrix, obtaining an electric quantity sufficient cycle, calculating a fourth parameter according to the electric quantity detection matrix, obtaining an electric quantity fluctuation coefficient, and judging whether the electric energy meter is abnormal based on an abnormal detection function according to the electric quantity difference, the charging coefficient, the electric quantity sufficient cycle and the electric quantity fluctuation coefficient. The method can realize abnormal detection of the electric energy meter.

Description

Technical Field

[0001] The present invention relates to the field of intelligent electricity management, and in particular to an intelligent detection method, device, equipment and storage medium based on an electric energy meter. Background Art

[0002] In the field of intelligent power management, the widespread application of intelligent energy meters and collection systems has greatly improved the real-time collection, analysis and management capabilities of user power consumption information. Traditional meter reading methods have gradually been replaced by intelligent meter reading technology. Intelligent meter reading systems achieve real-time monitoring and prediction of user power consumption behavior through remote communication and data management. This technological innovation not only significantly improves power supply reliability and user satisfaction, but also provides important support for the rapid development of the power Internet of Things. However, with the increasing demand for smart grid data management, how to accurately identify faults in the operation of energy meters or abnormal power consumption of users has become the focus and difficulty of technology development at this stage.

[0003] In the existing technology, various faults or power consumption anomalies may occur in the operation of the energy meter, including data collection errors, abnormal power consumption and other problems. The occurrence of these faults and anomalies not only affects the accuracy of power consumption data, but also may have a negative impact on the stability of the power supply system. In particular, in traditional technologies, there is a lack of comprehensive detection and evaluation methods for the operating status of the energy meter. Most of the existing abnormality detection methods rely on data analysis and manual intervention of subsequent concentrators, resulting in slow response speed and low efficiency. In addition, there is a lack of refined analysis methods for the detection and processing of abnormal electricity, and it is impossible to effectively distinguish normal fluctuations from real faults or abnormal power consumption behaviors. This technical defect seriously restricts the actual application effect of the intelligent meter reading system.

[0004] In summary, the existing technology cannot achieve comprehensive anomaly detection of meter data, and it is difficult to detect meter failures or power consumption anomalies in a timely manner, which affects the stability of the system and user experience. Summary of the invention

[0005] The present invention provides an intelligent detection method, device, equipment and storage medium based on an electric energy meter to realize abnormality detection of the electric energy meter.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides an intelligent detection method based on an electric energy meter, comprising:

[0007] Obtain key data to be tested for each electric energy meter; wherein the key data to be tested includes: starting power, ending power and detection duration;

[0008] Generate a detection matrix according to the starting power, the ending power and the detection duration to obtain a power detection matrix;

[0009] Performing a first parameter calculation according to the power detection matrix to obtain a power difference;

[0010] Calculating a second parameter according to the power detection matrix to obtain a charging coefficient;

[0011] Calculating a third parameter according to the power detection matrix to obtain a sufficient power cycle;

[0012] Calculating a fourth parameter according to the power detection matrix to obtain a power consumption fluctuation coefficient;

[0013] According to the power difference, the charging coefficient, the power sufficient period and the power consumption fluctuation coefficient, based on the abnormality detection function, it is determined whether the electric energy meter is abnormal.

[0014] Preferably, a detection matrix is ​​generated according to the starting power, the ending power and the detection duration to obtain a power detection matrix, including:

[0015] According to the starting power, the ending power and the detection duration, time sorting is performed to obtain detection data entries;

[0016] According to the detection data items, the detection data items are numbered and arranged to obtain a power detection matrix;

[0017] Wherein, the power detection matrix is:

[0018] ,

[0019] in, is the power detection matrix, For the The energy meter is The end power of the measurement time period, For the The energy meter is The starting power of the measurement time period, For the The energy meter is The length of the measurement period, is the total number of time periods, Less than .

[0020] Preferably, performing a first parameter calculation according to the power detection matrix to obtain a power difference includes:

[0021] The power difference is calculated using the following formula:

[0022] ,

[0023] in, For the The difference in electricity consumption between the two electric energy meters. For the The energy reading of each energy meter at the end of the measurement cycle, For the The energy reading of an energy meter at the beginning of the measurement cycle.

[0024] Preferably, a second parameter calculation is performed according to the power detection matrix to obtain a charging coefficient, including:

[0025] The charging factor is calculated by the following formula:

[0026] ,

[0027] in, For the The charging factor of an energy meter, For the The energy meter is The end power of the measurement time period, For the The energy meter is The starting power of the measurement time period, For the The energy meter is The length of the measurement period, is the total number of time periods.

[0028] Preferably, performing a third parameter calculation according to the power detection matrix to obtain a sufficient power cycle includes:

[0029] The battery life cycle is calculated using the following formula:

[0030] ,

[0031] in, For the The sufficient power cycle of each energy meter, For the The energy meter is The end power of the measurement time period, For the The energy meter is The starting power of the measurement time period, For the The energy meter is The length of the measurement period, is the total number of time periods.

[0032] Preferably, a fourth parameter calculation is performed according to the power detection matrix to obtain a power consumption fluctuation coefficient, including:

[0033] The power consumption fluctuation coefficient is calculated by the following formula:

[0034] ,

[0035] in, For the The power consumption fluctuation coefficient of each electric energy meter, For the The energy meter is The end power of the measurement time period, For the The energy meter is The starting power of the measurement time period, For the The energy meter is The length of the measurement period, is the total number of time periods.

[0036] Preferably, judging whether the electric energy meter is abnormal based on the abnormality detection function according to the electric energy difference, the charging coefficient, the sufficient electric energy cycle and the electric energy fluctuation coefficient includes:

[0037] When the output value of the abnormality detection function is greater than or equal to a predetermined threshold, the electric energy meter is determined to be abnormal;

[0038] When the output value of the abnormality detection function is less than a predetermined threshold, the electric energy meter is determined to be normal;

[0039] Wherein, the anomaly detection function is:

[0040] ,

[0041] In the formula, is the anomaly detection function, For the The charging factor of an energy meter, For the The difference in electricity consumption between the two electric energy meters. For the The sufficient power cycle of each energy meter, For the The power consumption fluctuation coefficient of each electric energy meter, , , and is the weight coefficient.

[0042] In a second aspect, the present invention provides an intelligent detection device based on an electric energy meter, comprising:

[0043] A data acquisition module is used to acquire key data to be tested for each electric energy meter; wherein the key data to be tested includes: starting power, ending power and detection duration;

[0044] A detection matrix generation module, used to generate a detection matrix according to the starting power, the ending power and the detection duration to obtain a power detection matrix;

[0045] A first parameter calculation module, used to perform a first parameter calculation according to the power detection matrix to obtain a power difference;

[0046] A second parameter calculation module, used to perform second parameter calculation according to the power detection matrix to obtain a charging coefficient;

[0047] A third parameter calculation module, used to calculate a third parameter according to the power detection matrix to obtain a sufficient power cycle;

[0048] A fourth parameter calculation module, used to perform fourth parameter calculation according to the power detection matrix to obtain a power consumption fluctuation coefficient;

[0049] The abnormality determination module is used to determine whether the electric energy meter is abnormal based on the abnormality detection function according to the power difference, the charging coefficient, the power sufficient period and the power consumption fluctuation coefficient.

[0050] In a third aspect, the present invention further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the intelligent detection method based on the electric energy meter as described above is implemented.

[0051] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned intelligent detection methods based on electric energy meters.

[0052] Compared with the prior art, the present invention has the following beneficial effects: the method obtains the key data to be tested of each electric energy meter, generates a detection matrix by using the key data to be tested, obtains the power detection matrix, calculates the power difference, charging coefficient, power sufficient cycle and fluctuation coefficient through the power detection matrix, and uses the abnormality detection function to determine whether the electric energy meter is abnormal. The method can realize abnormality detection of the electric energy meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic flow chart of an intelligent detection method based on an electric energy meter provided in the first embodiment of the present invention;

[0054] Figure 2 It is a schematic diagram of an intelligent detection method device based on an electric energy meter provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0055] 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.

[0056] Reference Figure 1 The first embodiment of the present invention provides an intelligent detection method based on an electric energy meter, comprising the following steps:

[0057] S11, obtaining key data to be tested for each electric energy meter;

[0058] S12, generating a detection matrix according to the starting power, the ending power and the detection duration to obtain a power detection matrix;

[0059] S13, performing a first parameter calculation according to the power detection matrix to obtain a power difference;

[0060] S14, performing a second parameter calculation according to the power detection matrix to obtain a charging coefficient;

[0061] S15, calculating a third parameter according to the power detection matrix to obtain a sufficient power cycle;

[0062] S16, performing a fourth parameter calculation according to the power detection matrix to obtain a power consumption fluctuation coefficient;

[0063] S17, judging whether the electric energy meter is abnormal based on the abnormality detection function according to the electric energy difference, the charging coefficient, the sufficient electric energy cycle and the electric energy fluctuation coefficient.

[0064] In step S11, key data to be tested of each electric energy meter is obtained.

[0065] It is worth noting that in step S11, the process of obtaining the key data to be tested for each electric energy meter needs to be strictly executed to ensure the accuracy and reliability of the data. First, the system establishes a stable communication connection with the electric energy meter to be tested. This is achieved through supported communication interfaces such as RS485, GPRS or wireless communication modules. After the connection is established, the system will send a read instruction to ensure that the required readings can be correctly obtained from the electric energy meter. During the communication establishment process, the system will monitor the signal stability to avoid interruptions or noise interference to ensure the accuracy and integrity of data transmission.

[0066] After successfully establishing communication, the system first reads the starting power of the energy meter, which is the initial reading of the energy meter at the beginning of the detection cycle. To ensure that the collected data is accurate, the system obtains the value from the energy meter in real time and stores it in the specified data storage unit. During the storage process, the system performs a data verification operation to verify whether the read starting power is consistent with the data reported by the energy meter. This verification mechanism can effectively prevent errors caused by data transmission or device anomalies. If any inconsistency or data error is detected, the system will resend the request and re-read the starting power until the correct data is obtained.

[0067] After completing the acquisition of the initial power consumption, the system will set the detection duration, that is, the time period for recording the power consumption data of the energy meter. This time period can be set to a fixed time unit, such as seconds, minutes or hours, according to the requirements of the measurement task. After setting, the system will start the timing device to accurately record the time to ensure the consistency of the measurement cycle. The precise control of the detection duration is crucial for the subsequent power calculation. The system will avoid errors through a high-precision timer and monitor the entire cycle to ensure accurate timing.

[0068] At the end of the detection cycle, the system will send another instruction to read the end power of the energy meter. This is the reading of the energy meter at the end of the detection cycle, which is used to calculate the actual power consumption of the energy meter during the cycle. The system will obtain the end power data from the energy meter in real time and store it immediately. Similar to the acquisition of the starting power, the storage of the ending power also requires data verification. The system will verify the data multiple times to ensure that there is no interference or data loss during the collection and transmission process. After the data verification is completed, the system will store the starting power, ending power and detection time in the database, providing a data basis for the subsequent power detection matrix generation and analysis.

[0069] The entire process requires the system to strictly follow the operating specifications to avoid data omissions, reading errors, or inaccurate timing. By accurately obtaining the starting power, ending power, and detection duration, the system can provide reliable measurement data to support subsequent power consumption analysis and power anomaly detection.

[0070] In step S12, a detection matrix is ​​generated according to the starting power, the ending power and the detection duration to obtain a power detection matrix; including:

[0071] According to the starting power, the ending power and the detection duration, time sorting is performed to obtain detection data entries;

[0072] According to the detection data items, the detection data items are numbered and arranged to obtain a power detection matrix;

[0073] Wherein, the power detection matrix is:

[0074] ,

[0075] in, is the power detection matrix, For the The energy meter is The end power of the measurement time period, For the The energy meter is The starting power of the measurement time period, For the The energy meter is The length of the measurement period, is the total number of time periods, Less than .

[0076] It is worth noting that in the step of generating the power detection matrix, all data are first time-sorted according to the obtained starting power, ending power and detection duration. The purpose of this step is to ensure that the data is accurately arranged in the order of acquisition for subsequent calculation and analysis. Specifically, time sorting involves sorting all readings within the measurement cycle of each electric energy meter one by one. The starting power represents the meter reading at the beginning of the measurement, the ending power reflects the meter status at the end of the measurement cycle, and the detection duration records the time span of the measurement process. After the sorting is completed, the time order relationship of the data will be clearly guaranteed, thus laying the foundation for the next step of operation.

[0077] Next, the system will number and arrange the time-sorted detection data entries. The purpose of numbering is to ensure that each data entry can uniquely correspond to a specific electric energy meter and its measurement time. The data of each electric energy meter will have corresponding starting power, ending power and detection duration at different measurement time points. These data will form a systematic entry structure after being numbered. The numbering process ensures that data entries in different time periods are not confused, and can also quickly index and access the required data in subsequent calculations.

[0078] After completing the numbering arrangement, the system will generate a power detection matrix based on the sorted and numbered data. Each row of this matrix represents the power consumption data of a specific power meter in a measurement period, including the starting power, ending power and detection duration. The energy meter is The power consumption at the time is , the end power is , the detection time is The structure of the matrix neatly arranges this data so that each column corresponds to a different measurement period, and each row reflects the power consumption of an energy meter in a specific period. Through this structured arrangement, the matrix can accurately characterize the changes in the energy consumption of the energy meter during the measurement cycle.

[0079] The generation process of the power detection matrix is ​​crucial. It not only provides a clear power consumption data structure, but also provides a reliable basis for subsequent calculation and analysis. When generating the matrix, the system needs to ensure the integrity and consistency of the data. All starting power, ending power and detection duration must be verified to confirm that there are no errors or abnormal data. For example, if it is found that the data of a certain power meter does not match the expected reading, the system needs to re-collect or correct the data. After the matrix is ​​constructed, the data will be saved according to a specific storage structure to provide support for subsequent power consumption analysis, parameter calculation and anomaly detection.

[0080] In the specific implementation, the system adopts a multi-level data verification mechanism to ensure that the generation process of the power detection matrix is ​​not affected by interference and errors. For example, in the process of data sorting and numbering, the system needs to avoid overlapping and misordering of data entries. After the matrix is ​​generated, data integrity verification is required to ensure that the power consumption data of all power meters are accurately recorded during the measurement cycle. Through these steps, the final generated power detection matrix can fully and accurately reflect the power consumption of each power meter, providing reliable data support for subsequent operations.

[0081] In step S13, a first parameter calculation is performed according to the power detection matrix to obtain a power difference; including:

[0082] The power difference is calculated using the following formula:

[0083] ,

[0084] For the The difference in electricity consumption between the two electric energy meters. For the The energy reading of each energy meter at the end of the measurement cycle, For the The energy reading of an energy meter at the beginning of the measurement cycle.

[0085] It is worth noting that in the step of calculating the power difference, the purpose is to accurately measure the power consumption changes of each power meter within a specified measurement period, and provide basic data for subsequent power consumption analysis and anomaly detection. Specifically, the process determines the power difference by calculating the absolute difference between the end power and the start power. Here, the end power Indicates The energy reading of each energy meter at the end of the measurement cycle, the starting energy Indicates the electricity reading of the same energy meter at the beginning of the measurement cycle. In order to obtain accurate electricity change data, it is necessary to ensure that these two values ​​are accurate, complete and verified during the measurement cycle.

[0086] First, the system reads the final power at the end of the measurement cycle from the energy meter through a stable communication connection This data needs to be read and verified by the system in the same way as the starting power, to ensure that the value truly reflects the current state of the meter. Any abnormal reading caused by communication interruption, transmission error or external interference will be monitored and marked by the system, and the data will be required to be retrieved. After reading the ending power, the system will compare it with the starting power in the same cycle. Matching is performed to ensure that they belong to the same measurement cycle and energy meter unit.

[0087] Next, the system calculates and To ensure that the value always reflects the absolute change in power rather than the difference in direction, the difference is expressed in absolute value, i.e. This calculation ensures that the system can accurately record the net change value regardless of whether the power increases or decreases. The absolute value processing method can eliminate negative values, so that the power difference is reflected only in the form of positive values, which is convenient for subsequent analysis and processing. This step can also avoid potential errors caused by negative values, thereby ensuring the reliability of the power calculation results.

[0088] After the calculation is completed, the system will Stored in a designated database for subsequent analysis and processing. To ensure data integrity and consistency, the system verifies all calculation results, including checking whether the data in the calculation process is consistent with the initial collection value, and whether there are omissions or errors. The stored power difference is associated with the number and time period of the energy meter to ensure rapid indexing and access in subsequent use. This data is the core data source for energy meter power consumption pattern analysis and anomaly detection.

[0089] It is worth noting that the entire calculation process must be carried out strictly in accordance with the predetermined measurement cycle to avoid affecting the calculation results due to inaccurate time cycles or data interference. Each step needs to ensure the stability of the communication connection, the accuracy of data reading and the precision of calculation to provide high-quality data support. Through the above steps, the system can accurately obtain the power changes of each electric energy meter within the specified cycle, providing a reliable data basis for subsequent analysis and power management decisions.

[0090] In step S14, a second parameter calculation is performed according to the power detection matrix to obtain a charging coefficient; including:

[0091] The charging factor is calculated by the following formula:

[0092] ,

[0093] in, For the The charging factor of an energy meter, For the The energy meter is The end power of the measurement time period, For the The energy meter is The starting power of the measurement time period, For the The energy meter is The length of the measurement period, is the total number of time periods.

[0094] It is worth noting that in the step of calculating the charging coefficient, the purpose is to calculate a comprehensive indicator by analyzing the changes in the amount of electricity in the energy meter in different time periods to reflect the charging status of the energy meter in a specified period. The calculation is based on the relevant data of the power detection matrix, including the starting power , Termination power and the duration of each time period By summarizing the changes in power consumption over multiple time periods, the charging characteristics of the energy meter can be accurately calculated, further supporting power consumption analysis and anomaly detection.

[0095] First, the system reads the power detection matrix and the power meter All relevant data items, including the starting power in each time period and termination power , Indicates different time period numbers. In order to calculate the overall power change of the energy meter, the system will calculate the power difference in each time period in turn. The difference represents the amount of electricity changed in the energy meter during the period. A positive value indicates the charging process or an increase in electricity consumption, while a negative value indicates electricity consumption. During the calculation process, all the differences will be accumulated one by one to obtain the total amount of electricity changed in the energy meter during the entire measurement period. This step ensures the accuracy and comprehensiveness of the electricity change.

[0096] Next, the system normalizes the total amount of power changes. To do this, the total duration of all time periods needs to be calculated. The total duration is calculated by dividing the detection time of each time period by the detection time of each time period. The system ensures that the duration data of all time periods are accurate to avoid any data omission or miscalculation. The calculation result of the total duration reflects the cumulative measurement time of the energy meter during the entire measurement cycle, and is used as the normalized denominator to balance the impact of different power differences.

[0097] Finally, the charging factor The calculation formula is: In this formula, the numerator is the total power change of the energy meter in all time periods, and the denominator is the total duration of all time periods. It can accurately reflect the charging status and power consumption characteristics of the energy meter in different time periods. A larger charging factor indicates a higher power change, which is related to the specific usage pattern or power load of the energy meter. After the system calculates the charging factor, it will store and mark the result for further power consumption analysis and anomaly detection.

[0098] The entire process requires the system to strictly follow the data reading and calculation specifications to ensure that the power data for each time period is accurate. Any data deviation or calculation error may affect the accuracy of the charging coefficient, so the system will perform multiple checks before, during, and after the calculation to ensure the accuracy and consistency of all data and calculation results. Through this precise calculation process, the system can provide a more reliable charging status analysis for the electricity consumption behavior of the energy meter.

[0099] In step S15, a third parameter is calculated according to the power detection matrix to obtain a sufficient power cycle; including:

[0100] The battery life cycle is calculated using the following formula:

[0101] ,

[0102] in, For the The sufficient power cycle of each energy meter, For the The energy meter is The end power of the measurement time period, For the The energy meter is The starting power of the measurement time period, For the The energy meter is The length of the measurement period, is the total number of time periods.

[0103] It is worth noting that in the step of calculating the sufficient power cycle, the purpose is to generate a comprehensive indicator reflecting the charging and power consumption behavior of the meter during the measurement period by analyzing in detail the power changes of the energy meter in different time periods and the corresponding time lengths. This calculation process is intended to accurately characterize the power consumption characteristics of the energy meter, thereby supporting the system's accurate analysis and anomaly detection of power consumption behavior. The specific calculation formula is: .in, It is the cycle of sufficient power. It is The energy meter is The end power of the measurement time period, For the The energy meter is The starting power of the measurement time period, For the The energy meter is The length of the measurement period, is the total number of time periods.

[0104] First, the system extracts the All measurement data related to each energy meter. This includes the starting amount of electricity in each time period and termination power , and the measurement duration of each time period After the data is extracted, the system will conduct a preliminary verification of the data to ensure that they are accurate and consistent with the actual readings of the electricity meter. If anomalies or inconsistencies are found during the data verification, the system will re-acquire the data to ensure that the basic data of all calculation steps is reliable.

[0105] Next, the system will calculate the power difference for each time period. , which indicates the change in the amount of electricity in the meter during that time period. The absolute value of the difference in electricity is used to reflect the change in net electricity. Regardless of whether the electricity is increasing or decreasing, this can always truly represent the fluctuation of the electricity in the meter during that period. In this way, the system can effectively eliminate the calculation error caused by directional differences and ensure that the representation of electricity changes is accurate.

[0106] After calculating the power difference in each time period, the system will perform weighted processing on the power difference. Specifically, the power difference will be multiplied by a correction factor, which is .here, is the duration of the current time period, and is the total duration of all time periods. The existence of the correction factor allows the changes in electricity consumption in different time periods to be fairly reflected. In particular, for measurements over longer time periods, the correction factor will increase its weight, thereby better describing the changes in electricity consumption during that period. Conversely, for shorter time periods, the correction factor has less impact, ensuring that data from different time periods can be adjusted according to their actual contributions. The correction factor is calculated to balance the impact of different time lengths in the total calculation, thereby avoiding data distortion or deviation.

[0107] The system will accumulate the weighted power differences of all time periods to obtain the total weighted power change. This step ensures that the power changes in all time periods are fully and accurately considered throughout the measurement cycle. The accumulated result represents the total power change of the energy meter during the entire measurement cycle, combined with the weight corrections for different time periods.

[0108] Finally, the system divides the weighted total power change by the total duration of all time periods. , get the sufficient power cycle The normalization process in this step is to unify the measurement results of different electric energy meters or different measurement cycles to ensure the comparability and consistency of the calculation results. It can reflect the electricity consumption behavior of the electric energy meter during the overall measurement period, including the frequency and intensity of its electricity consumption changes, and provide important reference data for system analysis and detection.

[0109] The entire calculation process requires the system to strictly follow the operating steps and data processing specifications to ensure the accuracy of all calculation steps. Any deviation in the calculation will affect the final calculation result of the sufficient power cycle. Therefore, the system will perform multiple checks at each stage of data extraction, weighted calculation and normalization to ensure the accuracy of the data and the reliability of the results. After the calculation is completed, the system will The values ​​are stored and marked as indicators related to the corresponding energy meter and measurement period, which is convenient for quick retrieval and use in subsequent power consumption analysis and abnormality detection. Through this process, the system can comprehensively and accurately reflect the power consumption characteristics of the energy meter, providing solid data support for further analysis.

[0110] In step S16, a fourth parameter calculation is performed according to the power detection matrix to obtain a power consumption fluctuation coefficient; including:

[0111] The power consumption fluctuation coefficient is calculated by the following formula:

[0112] ,

[0113] in, For the The power consumption fluctuation coefficient of each electric energy meter, For the The energy meter is The end power of the measurement time period, For the The energy meter is The starting power of the measurement time period, For the The energy meter is The length of the measurement period, is the total number of time periods.

[0114] It is worth noting that in the step of calculating the power consumption fluctuation coefficient, the system aims to quantify the power consumption change amplitude and volatility of the electric energy meter in different time periods, thereby reflecting its power consumption stability and change characteristics in different time periods. Through this indicator, the system can more accurately analyze the power consumption characteristics of the electric energy meter and the fluctuation of the power load, providing support for further power management and abnormal detection.

[0115] First, the system extracts all The measurement data related to each electric energy meter. Specifically including the starting power in each time period , Termination power and the corresponding measurement duration These data are the basis for calculating the power consumption fluctuation coefficient and must be systematically verified and confirmed to ensure that they are accurate, complete, and can truly reflect the status of the power meter in each measurement cycle.

[0116] Next, the system calculates the power difference in each time period, expressed as , to reflect the net power consumption change of the energy meter during this period. This power difference eliminates the influence of the change direction through absolute value calculation, so that whether the power consumption increases or decreases, it can be uniformly expressed as the magnitude of power change. In order to more accurately describe the power consumption fluctuation of the energy meter, the power difference will be multiplied by a correction factor. The correction factor is in the form of ,in is the measurement duration of the current time period, and Indicates the total duration of all time periods. The purpose of this correction factor is to increase the weight of the impact of time length on power fluctuations. In particular, longer measurement periods have a greater impact on overall power fluctuations, so this impact is strengthened through square operations. Although the impact of short-term measurements is smaller, it will also be reflected in the overall calculation. Through such corrections, the power consumption fluctuations of the energy meter in different time periods can be more accurately described.

[0117] The system then squares the weighted power differences for all time periods and adds them up. The purpose of the square operation is to emphasize larger fluctuations so that larger power fluctuations are significantly amplified in the overall calculation, thereby more clearly showing the instability of the power meter during the measurement period. After the accumulation is completed, the system normalizes the result and divides it by the total length of all time periods. to ensure comparability and consistency of results.

[0118] Finally, the power consumption fluctuation coefficient is obtained through the square root operation to map the result back to an intuitive magnitude. In this way, the system can accurately evaluate the degree of power consumption fluctuation of the energy meter throughout the measurement cycle through this coefficient. A larger power consumption fluctuation coefficient indicates that the power consumption behavior of the energy meter has a larger variation and instability, which is related to load changes, power consumption anomalies, etc. After the system completes the calculation, it will store the results and associate them with the corresponding energy meter number and measurement cycle for quick access for subsequent analysis and management operations. The entire process requires precise calculation and data processing to ensure that the power consumption fluctuation coefficient reflects the true power consumption fluctuation characteristics of the energy meter.

[0119] In step S17, judging whether the electric energy meter is abnormal based on the abnormality detection function according to the electric energy difference, the charging coefficient, the electric energy sufficient cycle and the electric energy fluctuation coefficient; including:

[0120] When the output value of the abnormality detection function is greater than or equal to a predetermined threshold, the electric energy meter is determined to be abnormal;

[0121] When the output value of the abnormality detection function is less than a predetermined threshold, the electric energy meter is determined to be normal;

[0122] Wherein, the anomaly detection function is:

[0123] ,

[0124] In the formula, is the anomaly detection function, For the The charging factor of an energy meter, For the The difference in electricity consumption between the two electric energy meters. For the The sufficient power cycle of each energy meter, For the The power consumption fluctuation coefficient of each electric energy meter, , , and is the weight coefficient.

[0125] It is worth noting that in the abnormality detection process of the electric energy meter, the four weight coefficients in the abnormality detection function are , , and The setting of is the key to determine the accuracy of the test results. These weight coefficients are respectively , power difference , sufficient power cycle and power consumption fluctuation coefficient The degree of influence is adjusted to ensure that the effect of each parameter on the final test result meets the actual needs. The specific instructions are as follows:

[0126] First, the weight coefficient Adjust the charging factor The charging factor It is used to describe the charging or power consumption status of the energy meter during the entire measurement cycle, which reflects the overall power consumption trend. If the main goal of the detection is to monitor the long-term power consumption of the energy meter, such as whether there is a continuous overcharge or undercharge phenomenon, it is necessary to Set to a larger value to ensure that the charging coefficient has a larger weight in the detection results. This setting will make the system more sensitive to abnormal changes in long-term trends and help to promptly detect long-term abnormal conditions of the energy meter. If you are mainly concerned about short-term changes in electricity consumption and are not sensitive to overall trends, you can set The value of is set to a smaller value, thereby reducing the weight of the charging coefficient in the detection result so that it does not affect the accuracy of short-term anomaly detection.

[0127] Secondly, the weight coefficient Determine the power difference The weight of the battery. It is the core indicator to measure the change of electricity consumption of the energy meter during the measurement period. It reflects the fluctuation of electricity consumption of the energy meter. For electricity consumption scenarios with frequent load changes and significant fluctuations, Set to a larger value so that the power difference has a greater impact on the detection results. This weight setting allows the system to quickly identify large power usage changes and abnormal fluctuations, helping the system to promptly reflect existing power usage anomalies. If the power usage environment is relatively stable and the load changes are not obvious, you can The value of is set to a smaller value to avoid interference of normal minor power consumption fluctuations on the detection results.

[0128] Weight coefficient With sufficient power cycle The power sufficiency cycle describes the sufficiency and power consumption stability of the energy meter during the measurement cycle. For detection scenarios that require attention to the long-term power consumption trend of the energy meter, such as monitoring the long-term power shortage or over-power consumption of the energy meter, the Set the value of to a larger value, so that the period of sufficient power will account for a larger proportion in the detection. This helps the system more accurately reflect the abnormalities of long-term power consumption trends. On the contrary, if the focus is mainly on short-term changes and anomalies, The value of can be set to a smaller value to avoid the interference of long-term trends on short-term anomaly detection.

[0129] Finally, the weight coefficient Determine the power fluctuation coefficient The power consumption fluctuation coefficient describes the stability or fluctuation range of the power consumption of the energy meter in different time periods. If the power consumption scenario where the energy meter is located fluctuates greatly or frequently, the fluctuation situation needs to be closely monitored, then It should be set to a larger value to highlight the impact of the power consumption fluctuation coefficient on the test results, so as to accurately reflect load fluctuations or abnormal power consumption. The value of can be set to a smaller value to reduce over-sensitivity to small fluctuations and ensure that the detection results focus on more important anomalies.

[0130] The setting of weight coefficients needs to be adjusted in combination with specific power usage scenarios and detection requirements. The system will determine the most suitable weight value by analyzing historical power usage data and conducting on-site debugging to ensure the accuracy and pertinence of the detection results. The adjustment of weights directly affects the proportion of different parameters in the detection results, thereby determining the system's sensitivity to various power usage anomalies. Reasonable setting of each weight can ensure that the system can effectively identify the operating status of the energy meter in different environments, detect anomalies in a timely manner, and provide efficient and reliable power management support.

[0131] Reference Figure 2 The second embodiment of the present invention provides an intelligent detection device based on an electric energy meter, comprising:

[0132] A data acquisition module is used to acquire key data to be tested for each electric energy meter; wherein the key data to be tested includes: starting power, ending power and detection duration;

[0133] A detection matrix generation module, used to generate a detection matrix according to the starting power, the ending power and the detection duration to obtain a power detection matrix;

[0134] A first parameter calculation module, used to perform a first parameter calculation according to the power detection matrix to obtain a power difference;

[0135] A second parameter calculation module, used to perform second parameter calculation according to the power detection matrix to obtain a charging coefficient;

[0136] A third parameter calculation module, used to calculate a third parameter according to the power detection matrix to obtain a sufficient power cycle;

[0137] A fourth parameter calculation module, used to perform fourth parameter calculation according to the power detection matrix to obtain a power consumption fluctuation coefficient;

[0138] The abnormality determination module is used to determine whether the electric energy meter is abnormal based on the abnormality detection function according to the power difference, the charging coefficient, the power sufficient period and the power consumption fluctuation coefficient.

[0139] It should be noted that the intelligent detection device based on an electric energy meter provided in an embodiment of the present invention is used to execute all the process steps of the intelligent detection method based on an electric energy meter in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be described in detail.

[0140] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned embodiments of the intelligent detection method based on the electric energy meter are implemented, for example Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the abnormality judgment module.

[0141] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0142] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0143] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0144] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0145] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0146] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0147] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent detection method based on electric energy meter, characterized in that: include: Obtain key data to be tested for each electric energy meter; wherein the key data to be tested includes: starting power, ending power and detection duration; Generate a detection matrix according to the starting power, the ending power and the detection duration to obtain a power detection matrix; Performing a first parameter calculation according to the power detection matrix to obtain a power difference; Calculating a second parameter according to the power detection matrix to obtain a charging coefficient; Calculating a third parameter according to the power detection matrix to obtain a sufficient power cycle; Calculating a fourth parameter according to the power detection matrix to obtain a power consumption fluctuation coefficient; According to the power difference, the charging coefficient, the power sufficient period and the power consumption fluctuation coefficient, based on an abnormality detection function, determining whether the electric energy meter is abnormal; Among them, the charging coefficient is a comprehensive indicator reflecting the charging status of the energy meter within a specified period; Wherein, the anomaly detection function is: ， In the formula, is the anomaly detection function, For the The charging factor of an energy meter, For the The difference in electricity consumption between the two electric energy meters. For the The sufficient power cycle of each energy meter, For the The power consumption fluctuation coefficient of each electric energy meter, , , and is the weight coefficient; The power difference is calculated by the following formula: ， in, For the The difference in electricity consumption between the two electric energy meters. For the The energy reading of each energy meter at the end of the measurement cycle, For the The energy reading of each energy meter at the beginning of the measurement cycle; Among them, the charging coefficient is calculated by the following formula: ， Among them, the sufficient power cycle is calculated by the following formula: ， Among them, the power consumption fluctuation coefficient is calculated by the following formula: ， in, For the The charging factor of an energy meter, For the The sufficient power cycle of each energy meter, For the The power consumption fluctuation coefficient of each electric energy meter, For the The energy meter is The end power of the measurement time period, For the The energy meter is The starting power of the measurement time period, For the The energy meter is The duration of the measurement period, is the total number of time periods.

2. The intelligent detection method based on electric energy meter according to claim 1 is characterized in that: The generating of a detection matrix according to the starting power, the ending power and the detection duration to obtain a power detection matrix includes: According to the starting power, the ending power and the detection duration, time sorting is performed to obtain detection data entries; According to the detection data items, the detection data items are numbered and arranged to obtain a power detection matrix; Wherein, the power detection matrix is: ， in, is the power detection matrix, For the The energy meter is The end power of the measurement time period, For the The energy meter is The starting power of the measurement time period, For the The energy meter is The duration of the measurement period, is the total number of time periods, Less than .

3. The intelligent detection method based on electric energy meter according to claim 1 is characterized in that: The determining whether the electric energy meter is abnormal based on the abnormality detection function according to the electric energy difference, the charging coefficient, the sufficient electric energy cycle and the electric energy fluctuation coefficient includes: When the output value of the abnormality detection function is greater than or equal to a predetermined threshold, the electric energy meter is determined to be abnormal; When the output value of the abnormality detection function is less than a predetermined threshold, the electric energy meter is determined to be normal.

4. An intelligent detection device based on an electric energy meter, used to implement the intelligent detection method based on an electric energy meter according to any one of claims 1 to 3, characterized in that: include: A data acquisition module is used to acquire key data to be tested for each electric energy meter; wherein the key data to be tested includes: starting power, ending power and detection duration; A detection matrix generation module, used to generate a detection matrix according to the starting power, the ending power and the detection duration to obtain a power detection matrix; A first parameter calculation module, used to perform a first parameter calculation according to the power detection matrix to obtain a power difference; A second parameter calculation module, used to perform second parameter calculation according to the power detection matrix to obtain a charging coefficient; A third parameter calculation module, used to calculate a third parameter according to the power detection matrix to obtain a sufficient power cycle; A fourth parameter calculation module, used to perform fourth parameter calculation according to the power detection matrix to obtain a power consumption fluctuation coefficient; The abnormality determination module is used to determine whether the electric energy meter is abnormal based on the abnormality detection function according to the power difference, the charging coefficient, the power sufficient period and the power consumption fluctuation coefficient.

5. An electronic device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the intelligent detection method based on the electric energy meter as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the intelligent detection method based on the electric energy meter according to any one of claims 1 to 3.

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