Power detection method and device

Abstract

The invention is suitable for the technical field of electric power detection, and provides an electric power detection method and device, and the method comprises the steps: obtaining first historical electric power data and second historical electric power data of a plurality of electric energy meters in a previous detection period and a current detection period; extracting a regular sudden change value and a time interval corresponding to the regular sudden change value from the second historical power data; and when the current sub-cycle is in the abrupt change time interval, the system dynamically adjusts the initial prediction power data according to the regular abrupt change value. And the finally obtained target prediction power data is used for planning and scheduling power generation equipment, so that the power system can perform intelligent management according to the power consumption condition of the user, thereby optimizing the power resource configuration and improving the power supply efficiency. In conclusion, according to the scheme, through accurate data analysis and a dynamic adjustment mechanism, the accuracy of power consumption prediction of the power consumer is remarkably improved, and the stability of a power system and the reliability of power supply are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of power detection, and in particular relates to a power detection method and device. Background Art

[0002] The electric energy meter can measure the electricity consumption of the power users and output the measurement results in the form of statistics and pulses. The electric energy meter transmits power data to the power monitoring system according to the specified pulse output frequency and data transmission format, so that the power monitoring system can count and bill the power users' electricity consumption. Generally, the power monitoring system obtains power data according to a certain time period. In a cycle, the electric energy meter continuously monitors the power consumption of the power users and transmits the monitored power data to the power monitoring system. The power monitoring system can count these power data to calculate the total power consumption of the power users in the cycle, and record the total power consumption in the power user file.

[0003] With the development of the power Internet of Things technology, based on the power consumption of power users monitored by the energy meter, it is also possible to predict the future power consumption of power users to assist the power system in planning and scheduling. For example, in the power system, the power user's power consumption data for the previous year can be obtained based on the power user's power consumption in the previous year, and then the power consumption of the power user can be predicted, and the predicted power consumption can be used as the power user's power consumption data for the next year. For another example, in the power system, the power user's power consumption data for the next year can be predicted based on the prediction model in the power Internet of Things and the power user's power consumption data for the previous year monitored by the energy meter.

[0004] In the above method, although the electricity consumption of power users monitored by the power meter can be used to predict the future electricity consumption of power users, the abnormal electricity consumption of power users is not taken into account in the prediction process. The electricity consumption data of power users predicted under abnormal electricity consumption conditions may not be consistent with the actual electricity consumption of power users. For example, due to seasonal changes or holiday demand, the electricity demand of power users changes, resulting in a sudden increase in electricity consumption of power users in a period of time. If the sudden increase in electricity consumption of power users occurs after the time point when the power users have finished using electricity, the power system will be insufficient when planning and scheduling the predicted electricity consumption.

[0005] Therefore, how to predict the power consumption of power users based on the power consumption of power users monitored by the power meter and combined with the abnormal power consumption of power users, so as to ensure the reliability of power supply of the power system has become an urgent problem to be solved. Summary of the invention

[0006] In view of this, an embodiment of the present invention provides a power detection method and device to solve the technical problem of how to predict the power consumption of power users based on the power consumption of power users monitored by the power meter and in combination with abnormal power consumption of power users.

[0007] A first aspect of an embodiment of the present invention provides a power detection method, the power detection method comprising:

[0008] Acquire a plurality of first historical power data detected by a plurality of electric energy meters in a plurality of first historical sub-cycles in a previous detection cycle and a plurality of second historical power data detected by a plurality of second historical sub-cycles in a current detection cycle; wherein the previous detection cycle includes a plurality of first historical sub-cycles, and the current detection cycle includes a plurality of second historical sub-cycles;

[0009] Calculating initial predicted power data of a current sub-period according to a plurality of the first historical power data and a plurality of the second historical power data;

[0010] Extracting regular mutation values ​​and mutation time intervals from the plurality of second historical power data; the regular mutation value refers to the second historical power data greater than the delimiting value, and the mutation time interval refers to the time interval in which the regular mutation value is located;

[0011] If the current sub-cycle is in the mutation time interval, adjusting the initial predicted power data according to the regular mutation value to obtain target predicted power data;

[0012] If the current sub-cycle is not in the mutation time interval, the initial predicted power data is used as the target predicted power data; the target predicted power data is used for planning and scheduling power generation equipment.

[0013] Further, the step of calculating the initial predicted power data of the current sub-cycle according to the plurality of the first historical power data and the plurality of the second historical power data comprises:

[0014] If the number of the plurality of second historical sub-periods is less than a preset number, calculating the change rate of the plurality of second historical sub-periods;

[0015] If the change rate is less than the first threshold, taking an average value of the second historical power data corresponding to a plurality of the second historical sub-periods as the initial predicted power data;

[0016] If the change rate is not less than the first threshold, multiplying the second historical power data average value by the redundancy coefficient to obtain the initial predicted power data;

[0017] If the number of the plurality of second historical sub-periods is not less than a preset number, dividing the plurality of first historical sub-periods into a plurality of first historical sub-period sets, and dividing the plurality of second historical sub-periods into a plurality of second historical sub-period sets;

[0018] Matching a target first historical sub-period set and a target second historical sub-period set at the same period position;

[0019] Subtracting a first sum of the first historical power data in the target first historical sub-period set from a second sum of the second historical power data in the target second historical sub-period set to obtain a plurality of difference values;

[0020] If the number of the plurality of differences greater than the preset value is greater than a second threshold, the maximum value of the power data in the target first historical sub-period set and the target second historical sub-period set is used as the initial predicted power data;

[0021] If the number of the multiple differences that are greater than the preset value is not greater than the second threshold, the average value of the power data in the target second historical sub-period set is used as the initial predicted power data.

[0022] Furthermore, the step of extracting regular mutation values ​​and mutation time intervals from the plurality of second historical power data includes:

[0023] Acquire a plurality of second historical power data respectively collected by a plurality of electric energy meters;

[0024] Sort the plurality of second historical power data according to the numerical relationship between them, and divide the plurality of second historical power data into three equal parts from small to large, to obtain a first array to be screened, a second array to be screened, and a third array to be screened;

[0025] Calculate the mean of multiple data in the second array to be screened;

[0026] Multiplying the mean value by the redundancy coefficient to obtain a cutoff value;

[0027] Taking the second historical power data in the third array to be screened that is greater than the threshold value as the initial mutation value;

[0028] Determine a regular mutation value according to initial mutation values ​​corresponding to each of the plurality of electric energy meters;

[0029] The mutation time interval is determined according to the first time point at which the regular mutation value is located.

[0030] Furthermore, the step of determining the regular mutation value according to the initial mutation values ​​corresponding to each of the plurality of electric energy meters comprises:

[0031] Match the second time point corresponding to each initial mutation value;

[0032] Counting the number of initial mutation values ​​at each of the second time points in a plurality of electric energy meters;

[0033] If the number of the initial mutation values ​​is greater than a third threshold, confirming that the initial mutation value is the regular mutation value;

[0034] If the number of the initial mutation values ​​is not greater than the third threshold, the initial mutation value is confirmed to be an individual mutation value.

[0035] Furthermore, the step of using the second historical power data in the third array to be screened that is greater than the delimiting value as the initial mutation value includes:

[0036] Extracting second historical power data greater than the threshold value in the third array to be screened;

[0037] The median of a plurality of second historical power data that are greater than the boundary value is used as the initial mutation value.

[0038] Furthermore, if the current detection sub-cycle is in the mutation time interval, the step of adjusting the initial predicted power data according to the regular mutation value to obtain the target predicted power data includes:

[0039] If the initial predicted power data is greater than the regularity mutation value, the initial predicted power data is used as the target predicted power data;

[0040] If the initial predicted power data is not greater than the regular mutation value, then calculating the difference between the regular mutation value and the initial predicted power data;

[0041] Match the preset numerical interval in which the difference value is located, multiply the initial predicted power data by the adjustment coefficient corresponding to the preset numerical interval, and obtain the target predicted power data; wherein different preset numerical intervals correspond to different adjustment coefficients.

[0042] A second aspect of an embodiment of the present invention provides a power detection device, including:

[0043] An acquisition unit, used to acquire a plurality of first historical power data detected by a plurality of electric energy meters in a plurality of first historical sub-cycles in a previous detection cycle and a plurality of second historical power data detected by a plurality of second historical sub-cycles in a current detection cycle; wherein the previous detection cycle includes a plurality of first historical sub-cycles, and the current detection cycle includes a plurality of second historical sub-cycles;

[0044] a calculation unit, configured to calculate initial predicted power data of a current sub-period according to a plurality of the first historical power data and a plurality of the second historical power data;

[0045] An extraction unit, used to extract regular mutation values ​​and mutation time intervals from the plurality of second historical power data; the regular mutation value refers to the second historical power data greater than the delimiting value, and the mutation time interval refers to the time interval in which the regular mutation value is located;

[0046] A first judgment unit is used for adjusting the initial predicted power data according to the regular mutation value to obtain target predicted power data if the current sub-period is in the mutation time interval;

[0047] The second judgment unit is used to use the initial predicted power data as target predicted power data if the current sub-cycle is not in the mutation time interval; the target predicted power data is used for planning and scheduling power generation equipment.

[0048] Furthermore, the computing unit is used for:

[0049] If the number of the plurality of second historical sub-periods is less than a preset number, calculating the change rate of the plurality of second historical sub-periods;

[0050] If the change rate is less than the first threshold, taking an average value of the second historical power data corresponding to a plurality of the second historical sub-periods as the initial predicted power data;

[0051] If the change rate is not less than the first threshold, multiplying the second historical power data average value by the redundancy coefficient to obtain the initial predicted power data;

[0052] If the number of the plurality of second historical sub-periods is not less than a preset number, dividing the plurality of first historical sub-periods into a plurality of first historical sub-period sets, and dividing the plurality of second historical sub-periods into a plurality of second historical sub-period sets;

[0053] Matching a target first historical sub-period set and a target second historical sub-period set at the same period position;

[0054] Subtracting a first sum of the first historical power data in the target first historical sub-period set from a second sum of the second historical power data in the target second historical sub-period set to obtain a plurality of difference values;

[0055] If the number of the plurality of differences greater than the preset value is greater than a second threshold, the maximum value of the power data in the target first historical sub-period set and the target second historical sub-period set is used as the initial predicted power data;

[0056] If the number of the multiple differences that are greater than the preset value is not greater than the second threshold, the average value of the power data in the target second historical sub-period set is used as the initial predicted power data.

[0057] A third aspect of an embodiment of the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the power detection method described in the first aspect when executing the computer program.

[0058] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the power detection method described in the first aspect are implemented.

[0059] Compared with the prior art, the embodiment of the present invention has the following beneficial effects: the first historical power data and the second historical power data of multiple electric energy meters in the previous detection cycle and the current detection cycle are obtained, ensuring the comprehensiveness and timeliness of the data. Through the comprehensive analysis of the first historical power data and the second historical power data, the system can accurately calculate the initial predicted power data of the current sub-period, providing a reliable starting point for the prediction. The regular mutation value and its corresponding time interval are extracted from the second historical power data. This function enables the system to have the ability to identify abnormal power consumption behavior and adjust the prediction results in time. When the current sub-period is in the mutation time interval, the system dynamically adjusts the initial predicted power data according to the regular mutation value to ensure the accuracy of the prediction results. This flexible adjustment mechanism greatly improves the reliability and practicality of the prediction. The target predicted power data finally obtained will be used to plan and dispatch power generation equipment, so that the power system can be intelligently managed according to the user's power consumption, thereby optimizing the configuration of power resources and improving the power supply efficiency. In summary, this scheme significantly improves the accuracy of power user power consumption prediction through precise data analysis and dynamic adjustment mechanism, ensuring the stability of the power system and the reliability of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0061] Figure 1 A schematic flow chart of a power detection method provided by the present invention is shown;

[0062] Figure 2A schematic diagram of a power detection device provided by an embodiment of the present invention is shown;

[0063] Figure 3 A schematic diagram of a terminal device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0064] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0065] The embodiments of the present invention provide a power detection method and device to solve the technical problem of how to predict the power consumption of power users based on the power consumption of power users monitored by power meters and in combination with abnormal power consumption of power users.

[0066] First, the present invention provides a power detection method. Figure 1 , Figure 1 FIG. 2 shows a schematic flow chart of a power detection method provided by the present invention. Figure 1 As shown, the power detection method may include the following steps:

[0067] Step 101: Acquire a plurality of first historical power data detected by a plurality of electric energy meters in a plurality of first historical sub-cycles in a previous detection cycle and a plurality of second historical power data detected by a plurality of second historical sub-cycles in a current detection cycle; wherein the previous detection cycle includes a plurality of first historical sub-cycles, and the current detection cycle includes a plurality of second historical sub-cycles;

[0068] The cycle duration of the previous detection cycle and the current detection cycle includes but is not limited to annual, quarterly and monthly. In this embodiment, the previous detection cycle and the current detection cycle are both based on annual cycle duration. The sub-cycles in the previous detection cycle and the current detection cycle are based on daily cycle duration.

[0069] The current detection cycle refers to the detection cycle in which the current sub-cycle is located, and the second historical sub-cycle refers to the sub-cycle in the current detection cycle that is located before the current sub-cycle. The previous detection cycle is located before the current detection cycle.

[0070] The electric energy meter has a communication unit, which sends the detected historical power data to the power detection equipment at the management level in the same area through data communication; the power detection equipment can process the power data.

[0071] Step 102: Calculate initial predicted power data of the current sub-period according to the plurality of first historical power data and the plurality of second historical power data;

[0072] It is worth noting that since there are many factors affecting electricity consumption forecasting, and personal factors are the dominant factors, electricity consumption forecasting cannot achieve high-precision forecasting. Therefore, the electricity forecasting method in this application is to make rough calculations and forecasts based on user usage habits to ensure sufficient power supply and avoid overpowering.

[0073] The multiple first historical power data and the multiple second historical power data are compared to obtain the initial predicted power data of the current sub-period. The specific logic is as follows:

[0074] Specifically, step 102 specifically includes steps 1021 to 1028:

[0075] Step 1021: If the number of the second historical sub-periods is less than a preset number, calculating the change rate of the second historical sub-periods;

[0076] It should be noted that, for the prediction of the initial predicted power data, it is necessary to rely on the multiple second historical power data detected in each of the second historical sub-periods. Different prediction methods need to be adopted for the second historical sub-periods with a smaller number and the second historical sub-periods with a larger number (that is, the two situations are predicted separately to improve the prediction accuracy). Therefore, when the number of the multiple second historical sub-periods is less than the preset number, the rate of change of the multiple second historical sub-periods is calculated. The rate of change of the multiple second historical sub-periods can be calculated by variance and the like. The greater the rate of change of the multiple second historical power data, the greater the fluctuation of the second historical power data. The smaller the rate of change of the multiple second historical power data, the smaller the fluctuation of the second historical power data.

[0077] Step 1022: if the change rate is less than the first threshold, taking the average value of the second historical power data corresponding to a plurality of the second historical sub-periods as the initial predicted power data;

[0078] The change rate is less than the first threshold, indicating that the fluctuation of the second historical power data is small, so the original values ​​of multiple second historical sub-periods are more referenceable. Therefore, the average value of the second historical power data corresponding to multiple second historical sub-periods can be used as the initial predicted power data.

[0079] Step 1023: If the change rate is not less than the first threshold, multiplying the second historical power data average value by the redundancy coefficient to obtain the initial predicted power data;

[0080] If the rate of change is not less than the first threshold, it means that the second historical power data fluctuates greatly, so the original values ​​of multiple second historical sub-periods are not very useful for reference. In order to ensure the stable operation of the power grid, for power data with large fluctuations, the average value of the second historical power data can be multiplied by the redundancy coefficient to ensure that sufficient power can be provided to avoid power grid instability.

[0081] Step 1024: if the number of the plurality of second historical sub-periods is not less than a preset number, dividing the plurality of first historical sub-periods into a plurality of first historical sub-period sets, and dividing the plurality of second historical sub-periods into a plurality of second historical sub-period sets;

[0082] When the number of the plurality of second historical sub-periods is not less than the preset number, another prediction method needs to be adopted. First, the plurality of first historical sub-periods need to be divided into a plurality of first historical sub-period sets and the plurality of second historical sub-periods need to be divided into a plurality of second historical sub-period sets according to the fixed set length.

[0083] Step 1025: Matching a target first historical sub-period set and a target second historical sub-period set at the same period position;

[0084] The first historical sub-period sets in the previous detection cycle are matched one by one with the second historical sub-period sets in the current detection cycle, and the first historical sub-period sets and the second historical sub-period sets that are successfully matched are used as the target first historical sub-period sets and the target second historical sub-period sets.

[0085] Step 1026: subtracting a first sum of the first historical power data in the target first historical sub-period set from a second sum of the second historical power data in the target second historical sub-period set to obtain a plurality of difference values;

[0086] The sum of the one-to-one corresponding target first historical sub-period set and the target second historical sub-period set is subtracted to obtain a plurality of difference values. The plurality of difference values ​​are used to represent the difference in electricity consumption at the same cycle position between the previous year and the current year.

[0087] Step 1027: If the number of the plurality of differences greater than the preset value is greater than the second threshold, the maximum value of the power data in the target first historical sub-period set and the target second historical sub-period set is used as the initial predicted power data;

[0088] When multiple differences are greater than the preset values, it indicates that the user has regularly increased the load, reduced the load, or changed the electricity usage habits between the previous year and the current year, making the multiple differences greater than the preset values. Therefore, when the number of multiple differences greater than the preset value is greater than the second threshold, the maximum value of the power data in the target first historical sub-period set and the target second historical sub-period set is used as the initial predicted power data to ensure that sufficient power can be provided to avoid grid instability and the like.

[0089] Step 1028: If the number of the multiple differences that are greater than the preset value is not greater than the second threshold, the average value of the power data in the target second historical sub-period set is used as the initial predicted power data.

[0090] When the number of multiple differences greater than the preset value is not greater than the second threshold, it indicates that the user only has normal fluctuations in electricity usage between the previous year and the current year. Therefore, the average value of the electricity data in the target second historical sub-period set can be directly used as the initial predicted electricity data.

[0091] In this embodiment, by analyzing multiple first and second historical power data, the accuracy of the initial predicted power data of the current sub-period can be effectively improved. When the number of second historical sub-periods is insufficient, the change rate is used as the basis for judgment, and the prediction method is flexibly adjusted; when the number is sufficient, the data fluctuations are accurately captured through set matching and difference analysis to ensure the reliability of the prediction results. This method not only improves the flexibility and accuracy of the prediction, but also helps to optimize power scheduling and resource allocation, thereby improving the efficiency of the overall system.

[0092] Step 103: extracting regular mutation values ​​and mutation time intervals from the plurality of second historical power data; the regular mutation value refers to the second historical power data greater than the delimiting value, and the mutation time interval refers to the time interval in which the regular mutation value is located;

[0093] In the field of power detection, mutation values ​​refer to values ​​that have an abnormal numerical relationship with surrounding power data, including individual mutation values ​​and regular mutation values. Individual mutation values ​​refer to irregular mutation values ​​caused by individual reasons of users, which are mainly formed by the personal usage habits or purposes of each user. Regular mutation values ​​are often formed by universal factors such as seasonality or festivals. Since regular mutation values ​​are universal for all users, the initial predicted power data can be adjusted by regular mutation values ​​to improve the prediction accuracy.

[0094] In the solution provided in the embodiment of the present application, by collecting historical power data detected by multiple electric energy meters in historical detection sub-periods, the historical power data of each detection sub-period is compared, individual mutation values ​​and regular mutation values ​​are distinguished, and the time interval of the regular mutation value is extracted. The extraction logic of the regular mutation value and the mutation time interval is as follows:

[0095] Specifically, step 103 specifically includes steps 1031 to 1037:

[0096] Step 1031: Acquire a plurality of second historical power data collected by a plurality of electric energy meters respectively;

[0097] Step 1032: sorting the plurality of second historical power data according to the numerical relationship between them, and dividing the plurality of second historical power data into three equal parts from small to large, to obtain a first array to be screened, a second array to be screened, and a third array to be screened;

[0098] A plurality of second historical power data collected by the same power meter is obtained, and the plurality of second historical power data are arranged from large to small, and are divided into three equal parts to obtain a first array to be screened, a second array to be screened, and a third array to be screened.

[0099] Step 1033: Calculate the mean of multiple data in the second array to be screened;

[0100] Step 1034: multiplying the mean value by the redundancy coefficient to obtain a cutoff value;

[0101] Step 1035: taking the second historical power data in the third array to be screened that is greater than the threshold value as the initial mutation value;

[0102] For the multiple second historical power data collected by each electric energy meter, steps 1032 to 1053 are respectively executed to obtain the initial mutation value corresponding to each electric energy meter.

[0103] Specifically, step 1035 specifically includes: extracting the second historical power data greater than the delimiting value in the third array to be screened; and taking the median of multiple second historical power data greater than the delimiting value as the initial mutation value.

[0104] Step 1036: determining a regular mutation value according to initial mutation values ​​corresponding to each of the plurality of electric energy meters;

[0105] Based on the distribution characteristics of the initial mutation values ​​corresponding to multiple electric energy meters, the regular mutation values ​​are extracted. The specific process is as follows:

[0106] Step 1036 specifically includes steps A1 to A4:

[0107] Step A1: Match the second time point corresponding to each initial mutation value;

[0108] Step A2: Counting the number of initial mutation values ​​at each of the second time points in a plurality of electric energy meters;

[0109] Since regular mutation values ​​are universal for power data collected by most electric energy meters, when regular mutation values ​​exist, most of the initial mutation values ​​corresponding to multiple electric energy meters are at the same second time point. Therefore, this example screens regular mutation values ​​based on this rule.

[0110] Step A3: If the number of the initial mutation values ​​is greater than a third threshold, confirming that the initial mutation value is the regular mutation value;

[0111] Step A4: If the number of the initial mutation values ​​is not greater than the third threshold, the initial mutation values ​​are confirmed to be individual mutation values.

[0112] In this embodiment, the technical solution effectively counts the number of initial mutation values ​​in multiple electric energy meters by matching the corresponding relationship between the initial mutation value and the second time point, thereby accurately distinguishing regular mutation values ​​from individual mutation values. When the number of initial mutation values ​​exceeds the set third threshold, the system can automatically confirm that it is a regular mutation value, thereby improving the reliability and accuracy of data analysis. On the contrary, if the threshold is not reached, it is identified as an individual mutation value.

[0113] Step 1037: Determine the mutation time interval according to the first time point at which the regular mutation value is located.

[0114] In this embodiment, the power data is divided into three equal parts and the mean of the second array to be screened is calculated, so that the screening process is targeted. This step can effectively remove outliers and ensure that the data relied on by subsequent analysis is more stable, thereby improving the recognition accuracy of mutation values. By multiplying the mean value with the redundancy coefficient to obtain the demarcation value, the sensitivity of mutation recognition can be flexibly adjusted. This method enhances the adaptability of the scheme, enabling it to maintain good performance under different data environments. The initial mutation value screened out based on the demarcation value can efficiently identify abnormal changes in power data. This process makes the extraction of regular mutation values ​​more intuitive and convenient for subsequent analysis and decision-making. By determining the first time point of the regular mutation value, the mutation time interval can be clearly identified, providing an important time reference. This technical effect can provide an important basis for the monitoring and maintenance of the power system, and help improve the reliability and safety of the system. In summary, the technical solution can effectively identify regular mutations in power data through scientific data processing and analysis, and has high practical value and application potential.

[0115] Step 104: If the current sub-cycle is in the mutation time interval, adjusting the initial predicted power data according to the regular mutation value to obtain target predicted power data;

[0116] Since the current sub-period is in the mutation time interval, the power data of the current sub-period may be close to the regular mutation value. Therefore, the initial predicted power data can be adjusted by the regular mutation value to improve the accuracy of the target predicted power data. The specific logic is as follows:

[0117] Specifically, step 104 includes steps 1041 to 1043:

[0118] Step 1041: if the initial predicted power data is greater than the regularity mutation value, taking the initial predicted power data as the target predicted power data;

[0119] Step 1041: If the initial predicted power data is not greater than the regular mutation value, then calculating the difference between the regular mutation value and the initial predicted power data;

[0120] Step 1041: Match the preset numerical interval in which the difference value is located, multiply the initial predicted power data by the adjustment coefficient corresponding to the preset numerical interval to obtain the target predicted power data; wherein different preset numerical intervals correspond to different adjustment coefficients.

[0121] Different preset value intervals set different adjustment coefficients to meet the adjustment needs of different differences.

[0122] In this embodiment, when the initial predicted power data is greater than the regular mutation value, the data is directly used as the target predicted power data, ensuring high accuracy under normal fluctuations. For the initial predicted power data that is not greater than the regular mutation value, by calculating the difference between it and the mutation value, the preset numerical interval is used to match the adjustment coefficient, which can effectively capture the subtle changes in data fluctuations. This correction mechanism improves the ability to respond to emergencies. Different preset numerical intervals correspond to different adjustment coefficients, so that this solution can flexibly adapt to a variety of scenarios and needs, and improve the applicability of the system. Through the combination of the above mechanisms, the error of the target predicted power data in the case of mutation is significantly reduced, which improves the stability and security of the power system and ensures the power supply needs of users.

[0123] Step 105: If the current sub-cycle is not in the mutation time interval, the initial predicted power data is used as the target predicted power data; the target predicted power data is used for planning and scheduling power generation equipment.

[0124] In this embodiment, the first historical power data and the second historical power data of multiple electric energy meters in the previous detection cycle and the current detection cycle are obtained to ensure the comprehensiveness and timeliness of the data. Through the comprehensive analysis of the first historical power data and the second historical power data, the system can accurately calculate the initial predicted power data of the current sub-period, providing a reliable starting point for the prediction. The regular mutation value and its corresponding time interval are extracted from the second historical power data. This function enables the system to have the ability to identify abnormal power consumption behavior and adjust the prediction results in time. When the current sub-period is in the mutation time interval, the system dynamically adjusts the initial predicted power data according to the regular mutation value to ensure the accuracy of the prediction results. This flexible adjustment mechanism greatly improves the reliability and practicality of the prediction. The target predicted power data finally obtained will be used to plan and dispatch power generation equipment, so that the power system can be intelligently managed according to the user's power consumption, thereby optimizing the configuration of power resources and improving the power supply efficiency. In summary, this scheme significantly improves the accuracy of power user power consumption prediction through precise data analysis and dynamic adjustment mechanism, and ensures the stability of the power system and the reliability of power supply.

[0125] like Figure 2 The present invention provides a power detection device, see Figure 2 , Figure 2 A schematic diagram of a power detection device provided by the present invention is shown. Figure 2 The power detection device shown comprises:

[0126] An acquisition unit 21 is used to acquire a plurality of first historical power data detected by a plurality of electric energy meters in a plurality of first historical sub-cycles in a previous detection cycle and a plurality of second historical power data detected by a plurality of second historical sub-cycles in a current detection cycle; wherein the previous detection cycle includes a plurality of first historical sub-cycles, and the current detection cycle includes a plurality of second historical sub-cycles;

[0127] A calculation unit 22, configured to calculate initial predicted power data of a current sub-period according to a plurality of the first historical power data and a plurality of the second historical power data;

[0128] An extraction unit 23 is used to extract regular mutation values ​​and mutation time intervals from the plurality of second historical power data; the regular mutation value refers to the second historical power data greater than the delimiting value, and the mutation time interval refers to the time interval in which the regular mutation value is located;

[0129] A first judgment unit 24 is configured to adjust the initial predicted power data according to the regular mutation value to obtain target predicted power data if the current sub-cycle is in the mutation time interval;

[0130] The second judgment unit 25 is used to use the initial predicted power data as target predicted power data if the current sub-cycle is not in the mutation time interval; the target predicted power data is used for planning and scheduling power generation equipment.

[0131] The present invention provides a power detection device, which obtains the first historical power data and the second historical power data of multiple electric energy meters in the previous detection cycle and the current detection cycle, ensuring the comprehensiveness and timeliness of the data. Through the comprehensive analysis of the first historical power data and the second historical power data, the system can accurately calculate the initial predicted power data of the current sub-period, providing a reliable starting point for the prediction. The regular mutation value and its corresponding time interval are extracted from the second historical power data. This function enables the system to have the ability to identify abnormal power consumption behavior and adjust the prediction results in time. When the current sub-period is in the mutation time interval, the system dynamically adjusts the initial predicted power data according to the regular mutation value to ensure the accuracy of the prediction results. This flexible adjustment mechanism greatly improves the reliability and practicality of the prediction. The target predicted power data finally obtained will be used to plan and dispatch power generation equipment, so that the power system can be intelligently managed according to the user's power consumption, thereby optimizing the configuration of power resources and improving the power supply efficiency. In summary, this scheme significantly improves the accuracy of power user power consumption prediction through precise data analysis and dynamic adjustment mechanism, ensuring the stability of the power system and the reliability of power supply.

[0132] Figure 3 FIG. 1 is a schematic diagram of a terminal device provided by an embodiment of the present invention. Figure 3 As shown, a terminal device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a power detection program. When the processor 30 executes the computer program 32, the steps in each of the above-mentioned power detection method embodiments are implemented, such as Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of each unit in the above-mentioned device embodiments are realized, for example, Figure 2 Function of the unit shown.

[0133] Exemplarily, the computer program 32 may be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 32 in the terminal device 3. For example, the computer program 32 may be divided into the following specific functions of each unit:

[0134] An acquisition unit, used to acquire a plurality of first historical power data detected by a plurality of electric energy meters in a plurality of first historical sub-cycles in a previous detection cycle and a plurality of second historical power data detected by a plurality of second historical sub-cycles in a current detection cycle; wherein the previous detection cycle includes a plurality of first historical sub-cycles, and the current detection cycle includes a plurality of second historical sub-cycles;

[0135] a calculation unit, configured to calculate initial predicted power data of a current sub-period according to a plurality of the first historical power data and a plurality of the second historical power data;

[0136] An extraction unit, used to extract regular mutation values ​​and mutation time intervals from the plurality of second historical power data; the regular mutation value refers to the second historical power data greater than the delimiting value, and the mutation time interval refers to the time interval in which the regular mutation value is located;

[0137] A first judgment unit is used for adjusting the initial predicted power data according to the regular mutation value to obtain target predicted power data if the current sub-period is in the mutation time interval;

[0138] The second judgment unit is used to use the initial predicted power data as target predicted power data if the current sub-cycle is not in the mutation time interval; the target predicted power data is used for planning and scheduling power generation equipment.

[0139] The terminal device includes but is not limited to a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of a terminal device 3 and does not constitute a limitation on the terminal device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0140] The processor 30 may be a central processing unit (CPU), or 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.

[0141] The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal device 3. The memory 31 is used to store the computer program and other programs and data required by the roaming control device. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0142] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0143] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0144] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0145] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0146] An embodiment of the present invention provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0147] If the integrated unit 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 this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, 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 at least include: any entity or device that can carry the computer program code to the camera device / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0148] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0149] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0150] In the embodiments provided by the present invention, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0151] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed over multiple network units.

[0152] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0153] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0154] As used in the present specification and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to monitoring, depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is monitored" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is monitored" or "in response to monitoring [described condition or event]", depending on the context.

[0155] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0156] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0157] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A power detection method, characterized in that: The power detection method comprises: Acquire a plurality of first historical power data detected by a plurality of electric energy meters in a plurality of first historical sub-cycles in a previous detection cycle and a plurality of second historical power data detected by a plurality of second historical sub-cycles in a current detection cycle; wherein the previous detection cycle includes a plurality of first historical sub-cycles, and the current detection cycle includes a plurality of second historical sub-cycles; Calculating initial predicted power data of a current sub-period according to a plurality of the first historical power data and a plurality of the second historical power data; Extracting regular mutation values ​​and mutation time intervals from the plurality of second historical power data; the regular mutation value refers to the second historical power data greater than the delimiting value, and the mutation time interval refers to the time interval in which the regular mutation value is located; If the current sub-cycle is in the mutation time interval, adjusting the initial predicted power data according to the regular mutation value to obtain target predicted power data; If the current sub-cycle is not in the mutation time interval, the initial predicted power data is used as the target predicted power data; the target predicted power data is used for planning and scheduling power generation equipment.

2. The power detection method according to claim 1, characterized in that: The step of calculating the initial predicted power data of the current sub-cycle according to the plurality of the first historical power data and the plurality of the second historical power data comprises: If the number of the plurality of second historical sub-periods is less than a preset number, calculating the change rate of the plurality of second historical sub-periods; If the change rate is less than the first threshold, taking an average value of the second historical power data corresponding to a plurality of the second historical sub-periods as the initial predicted power data; If the change rate is not less than the first threshold, multiplying the second historical power data average value by the redundancy coefficient to obtain the initial predicted power data; If the number of the plurality of second historical sub-periods is not less than a preset number, dividing the plurality of first historical sub-periods into a plurality of first historical sub-period sets, and dividing the plurality of second historical sub-periods into a plurality of second historical sub-period sets; Matching a target first historical sub-period set and a target second historical sub-period set at the same period position; Subtracting a first sum of the first historical power data in the target first historical sub-period set from a second sum of the second historical power data in the target second historical sub-period set to obtain a plurality of difference values; If the number of the plurality of differences greater than the preset value is greater than a second threshold, the maximum value of the power data in the target first historical sub-period set and the target second historical sub-period set is used as the initial predicted power data; If the number of the multiple differences that are greater than the preset value is not greater than the second threshold, the average value of the power data in the target second historical sub-period set is used as the initial predicted power data.

3. The power detection method according to claim 1, characterized in that: The step of extracting regular mutation values ​​and mutation time intervals from the plurality of second historical power data comprises: Acquire a plurality of second historical power data respectively collected by a plurality of electric energy meters; Sort the plurality of second historical power data according to the numerical relationship between them, and divide the plurality of second historical power data into three equal parts from small to large, to obtain a first array to be screened, a second array to be screened, and a third array to be screened; Calculate the mean of multiple data in the second array to be screened; Multiplying the mean value by the redundancy coefficient to obtain a cutoff value; Taking the second historical power data in the third array to be screened that is greater than the threshold value as the initial mutation value; Determine a regular mutation value according to initial mutation values ​​corresponding to each of the plurality of electric energy meters; The mutation time interval is determined according to the first time point at which the regular mutation value is located.

4. The power detection method according to claim 3, characterized in that: The step of determining the regular mutation value according to the initial mutation values ​​corresponding to the plurality of electric energy meters comprises: Match the second time point corresponding to each initial mutation value; Counting the number of initial mutation values ​​at each of the second time points in a plurality of electric energy meters; If the number of the initial mutation values ​​is greater than a third threshold, confirming that the initial mutation value is the regular mutation value; If the number of the initial mutation values ​​is not greater than the third threshold, the initial mutation value is confirmed to be an individual mutation value.

5. The power detection method according to claim 3, characterized in that: The step of using the second historical power data in the third array to be screened that is greater than the delimiting value as the initial mutation value comprises: Extracting second historical power data greater than the threshold value in the third array to be screened; The median of a plurality of second historical power data that are greater than the boundary value is used as the initial mutation value.

6. The power detection method according to claim 1, characterized in that: If the current detection sub-cycle is in the mutation time interval, the step of adjusting the initial predicted power data according to the regular mutation value to obtain the target predicted power data includes: If the initial predicted power data is greater than the regularity mutation value, the initial predicted power data is used as the target predicted power data; If the initial predicted power data is not greater than the regular mutation value, then calculating the difference between the regular mutation value and the initial predicted power data; Match the preset numerical interval in which the difference value is located, multiply the initial predicted power data by the adjustment coefficient corresponding to the preset numerical interval, and obtain the target predicted power data; wherein different preset numerical intervals correspond to different adjustment coefficients.

7. A power detection device, characterized in that: The power detection device comprises: An acquisition unit, used to acquire a plurality of first historical power data detected by a plurality of electric energy meters in a plurality of first historical sub-cycles in a previous detection cycle and a plurality of second historical power data detected by a plurality of second historical sub-cycles in a current detection cycle; wherein the previous detection cycle includes a plurality of first historical sub-cycles, and the current detection cycle includes a plurality of second historical sub-cycles; a calculation unit, configured to calculate initial predicted power data of a current sub-period according to a plurality of the first historical power data and a plurality of the second historical power data; An extraction unit, used to extract regular mutation values ​​and mutation time intervals from the plurality of second historical power data; the regular mutation value refers to the second historical power data greater than the delimiting value, and the mutation time interval refers to the time interval in which the regular mutation value is located; A first judgment unit is used for adjusting the initial predicted power data according to the regular mutation value to obtain target predicted power data if the current sub-period is in the mutation time interval; The second judgment unit is used to use the initial predicted power data as target predicted power data if the current sub-cycle is not in the mutation time interval; the target predicted power data is used for planning and scheduling power generation equipment.

8. The power detection method according to claim 7, characterized in that: The computing unit is used for: If the number of the plurality of second historical sub-periods is less than a preset number, calculating the change rate of the plurality of second historical sub-periods; If the change rate is less than the first threshold, taking an average value of the second historical power data corresponding to a plurality of the second historical sub-periods as the initial predicted power data; If the change rate is not less than the first threshold, multiplying the second historical power data average value by the redundancy coefficient to obtain the initial predicted power data; If the number of the plurality of second historical sub-periods is not less than a preset number, dividing the plurality of first historical sub-periods into a plurality of first historical sub-period sets, and dividing the plurality of second historical sub-periods into a plurality of second historical sub-period sets; Matching a target first historical sub-period set and a target second historical sub-period set at the same period position; Subtracting a first sum of the first historical power data in the target first historical sub-period set from a second sum of the second historical power data in the target second historical sub-period set to obtain a plurality of difference values; If the number of the plurality of differences greater than the preset value is greater than a second threshold, the maximum value of the power data in the target first historical sub-period set and the target second historical sub-period set is used as the initial predicted power data; If the number of the multiple differences that are greater than the preset value is not greater than the second threshold, the average value of the power data in the target second historical sub-period set is used as the initial predicted power data.

9. A terminal device, characterized in that: The terminal device comprises: a memory, a processor, and a power detection program stored in the memory and executable on the processor, wherein the power detection program is configured to implement the steps in the power detection method according to any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the power detection method according to any one of claims 1 to 6 are implemented.

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