Power distribution network equipment loss monitoring method, device, equipment and storage medium

Abstract

The application provides a power distribution network equipment loss monitoring method and device, equipment and a storage medium, belonging to the technical field of power distribution networks, which comprises the following steps: obtaining input end detection data and output end detection data corresponding to each detection time of a target device; calculating the input end power of the target device based on the input end detection data, calculating the output end power of the target device based on the output end detection data, and calculating the actual loss of the target device based on the input end power and the output end power; calculating the theoretical line loss between the input end and the output end of the target device based on the transmission current of the target device and a preset line resistance; and determining the additional loss of the target device at each detection time based on the actual loss and the theoretical line loss of the target device. The power distribution network equipment loss monitoring method, device, equipment and storage medium provided by the application can provide accurate data support for the operation and maintenance management of the power distribution network.

Description

Technical Field

[0001] This application belongs to the field of power distribution network technology, and more specifically, relates to a method, device, equipment and storage medium for monitoring the loss of power distribution network equipment. Background Technology

[0002] During the distribution of electrical energy from the power grid to the user side (such as residential / industrial electrical equipment), certain losses occur in the distribution network equipment, such as line losses and transformer losses. Distribution network equipment losses reflect the planning, design, operation, and management level of the power grid and are important technical indicators of the power system. Furthermore, by analyzing equipment losses, high-loss equipment can be identified, allowing for timely maintenance and ensuring the efficient operation of the distribution network.

[0003] Therefore, in order to further improve the operation of the distribution network, it is urgent to propose a more accurate method for monitoring the losses of distribution network equipment. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, equipment and storage medium for monitoring the loss of distribution network equipment, which can provide accurate data support for the operation and maintenance management of distribution networks.

[0005] A first aspect of this application provides a method for monitoring losses in power distribution network equipment, including: The system acquires the input and output detection data of the target device at each detection moment. The input detection data includes input voltage data and input current data, and the output detection data includes output voltage data and output current data. The input detection data at each detection moment is either the input detection data at that detection moment or the input detection data at multiple moments within the power grid cycle in which the detection moment occurs. The output detection data at each detection moment is either the output detection data at that detection moment or the output detection data at multiple moments within the power grid cycle in which the detection moment occurs. The input power of the target device is calculated based on the input detection data, the output power of the target device is calculated based on the output detection data, and the actual loss of the target device is calculated based on the input power and the output power. The theoretical line loss between the input and output terminals of the target device is calculated based on the transmission current of the target device and the preset line resistance; the transmission current of the target device is the input current data or the output current data of the target device. The additional loss of the target device at each detection time is determined based on the actual loss of the target device and the theoretical line loss.

[0006] A second aspect of this application provides a power distribution network equipment loss monitoring device, comprising: The data acquisition module is used to acquire the input terminal detection data and output terminal detection data of the target device at each detection moment; the input terminal detection data includes input terminal voltage data and input terminal current data, and the output terminal detection data includes output terminal voltage data and output terminal current data; the input terminal detection data corresponding to each detection moment is the input terminal detection data at that detection moment or the input terminal detection data at multiple moments within the power grid cycle in which the detection moment is located, and the output terminal detection data corresponding to each detection moment is the output terminal detection data at that detection moment or the output terminal detection data at multiple moments within the power grid cycle in which the detection moment is located; The actual loss calculation module is used to calculate the input power of the target device based on the input detection data, calculate the output power of the target device based on the output detection data, and calculate the actual loss of the target device based on the input power and the output power. The theoretical line loss calculation module is used to calculate the theoretical line loss between the input and output terminals of the target device based on the transmission current of the target device and a preset line resistance; the transmission current of the target device is the input current data or the output current data of the target device. An additional loss calculation module is used to determine the additional loss of the target device at each detection time based on the actual loss of the target device and the theoretical line loss.

[0007] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for monitoring losses in power distribution network equipment.

[0008] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for monitoring losses in power distribution network equipment.

[0009] The beneficial effects of the distribution network equipment loss monitoring method, device, equipment, and storage medium provided in this application embodiment are as follows: This embodiment first calculates the input power of the target device based on the input detection data at each detection time, and the output power based on the output detection data. Then, it calculates the actual loss of the target device based on the input and output power. Next, it calculates the normal line transmission loss between the input and output terminals of the target device based on the transmission current and a preset line resistance. This line transmission loss is taken as the theoretical line loss of the target device. Subtracting the theoretical line loss from the actual loss yields an additional loss that more accurately characterizes the operating status of the target device, thus providing accurate data support for the operation and maintenance management of the distribution network. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating a method for monitoring losses in power distribution network equipment according to an embodiment of this application; Figure 2 A schematic diagram of a power distribution network provided in an embodiment of this application; Figure 3 This is a structural block diagram of a power distribution network equipment loss monitoring device provided in an embodiment of this application; Figure 4 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0013] It is understood that in the embodiments of this application, data such as user information are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0014] It should be noted that the terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0015] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0016] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for monitoring losses in power distribution network equipment according to an embodiment of this application. The method can be executed by electronic equipment and may include: S101: Acquire the input and output detection data of the target device at each detection time; the input detection data includes input voltage data and input current data, and the output detection data includes output voltage data and output current data; the input detection data corresponding to each detection time is the input detection data at that detection time or the input detection data at multiple times within the power grid cycle in which the detection time is located, and the output detection data corresponding to each detection time is the output detection data at that detection time or the output detection data at multiple times within the power grid cycle in which the detection time is located.

[0017] In this embodiment, the target device may include a line or a transformer, and multiple detection times may be determined according to a preset time period (e.g., one hour). At each detection time, the input voltage data, input current data, output voltage data, and output current data of the target device are acquired.

[0018] For details, please refer to Figure 2 Each node of the power distribution network is equipped with a power distribution terminal, among which, Figure 2Distribution terminals 1, 2, and 3 are located on the main lines, while distribution terminals 2-1 and 2-2 are located on branch lines. Each distribution terminal can be a Data Transfer Unit (DTU) or a Feeder Terminal Unit (FTU). Each distribution terminal is equipped with a BeiDou module and a timekeeping chip (e.g., 8025T). The BeiDou module receives and decodes high-precision satellite time signals, providing a microsecond-level time reference and calibrating the internal clock of the distribution terminal. The 8025T is a highly stable timekeeping chip that maintains time output with its own high-precision crystal oscillator when satellite signals are interrupted, compensating for clock drift. The BeiDou module and the timekeeping chip work together to achieve nanosecond-level time synchronization across all distribution terminals.

[0019] Meanwhile, the analog-to-digital conversion module (ADC sampling module) inside each power distribution terminal is calibrated in real time according to the internal system time of the equipment. For example, each power distribution terminal adjusts the ADC trigger sampling counter once at the top of the hour (0 minutes, 0 seconds, and 0 milliseconds of every hour). When adjusting, the counter is aligned with the millisecond edge, so that the sampling pace of each power distribution terminal is basically consistent, and all power distribution terminals achieve simultaneous synchronous sampling.

[0020] For each target device, the input voltage and current data can be detected through the power distribution terminal at the input end of the target device; simultaneously, the output voltage and current data can be detected through the power distribution terminal at the output end of the target device. The input and output voltage data can each include three-phase voltage data and zero-sequence voltage data, and the input and output current data can each include three-phase current data and zero-sequence current data.

[0021] In this embodiment, the input and output detection data of the target device are obtained based on the existing power distribution terminals of the power distribution network, without the need for additional sensors, thereby reducing detection costs.

[0022] S102: Calculate the input power of the target device based on the input detection data, calculate the output power of the target device based on the output detection data, and calculate the actual loss of the target device based on the input power and the output power.

[0023] In this embodiment, the input power of the target device can be obtained based on the input voltage data and input current data of the target device, and the output power of the target device can be obtained based on the output voltage data and output current data of the target device.

[0024] Specifically, the input terminal detection data corresponding to each detection moment can include only the input terminal detection data at that detection moment. In this case, multiplying the input terminal voltage data and the input terminal current data at that detection moment yields the instantaneous power at the input terminal at that detection moment. Similarly, the output terminal detection data corresponding to each detection moment can include only the output terminal detection data at that detection moment. In this case, multiplying the output terminal voltage data and the output terminal current data at that detection moment yields the instantaneous power at the output terminal at that detection moment.

[0025] In this embodiment, the input terminal detection data corresponding to each detection time may also include input terminal detection data from multiple times within the power grid cycle in which the detection time is located. In this case, the average input power within the entire power grid cycle can be obtained based on the input terminal detection data from multiple times within the power grid cycle in which the detection time is located, and this average power can be used as the input power at the detection time. Similarly, the output terminal detection data corresponding to each detection time may also include output terminal detection data from multiple times within the power grid cycle in which the detection time is located. In this case, the average output power within the entire power grid cycle can be obtained based on the output terminal detection data from multiple times within the power grid cycle in which the detection time is located, and this average power can be used as the output power at the detection time.

[0026] Based on the input power and output power, the actual loss of the target device can be obtained by subtracting the output power from the input power.

[0027] Taking the target device as a line as an example, the actual line loss can be obtained by subtracting the output power from the input power of the line according to the current direction. Taking the target device as a transformer as an example, the high-voltage side of the transformer can be taken as the input end and the low-voltage side as the output end. The actual transformer loss can be obtained by subtracting the low-voltage side power from the high-voltage side power.

[0028] S103: Calculate the theoretical line loss between the input and output terminals of the target device based on the transmission current of the target device and the preset line resistance; the transmission current of the target device is the input current data or the output current data of the target device.

[0029] In this embodiment, the preset line resistance can be calculated based on the line material, length, and cross-sectional area. The specific calculation formula is as follows: ; in, This indicates the preset line resistance. It is the resistivity of the circuit (approximately 0.0175 Ω·mm² / m for copper). It is the line length, in meters (m). It is the cross-sectional area of ​​the line, and the unit is mm².

[0030] Taking the target device as an example, since the input and output currents of the same line are equal, the input current data or the output current data can be used as the transmission current, and the preset circuit resistance can be used as the transmission current. Substituting the transmission current I into the power calculation formula This yields the theoretical line loss of the line.

[0031] Taking a transformer as an example, the theoretical line loss on the high-voltage side can be calculated based on the high-voltage side current and the line resistance on the high-voltage side. At the same time, the theoretical line loss on the low-voltage side can be calculated based on the low-voltage side current and the line resistance on the low-voltage side. Then, the theoretical line loss on the high-voltage side and the theoretical line loss on the high-voltage side are added together to obtain the theoretical line loss of the transformer.

[0032] S104: Determine the additional loss of the target device at each detection time based on the actual loss and theoretical line loss of the target device.

[0033] In this embodiment, the additional losses of the target equipment can be obtained by subtracting the theoretical line losses from the actual losses of the target equipment. The additional losses can more accurately characterize the unexpected losses caused by factors such as faults in the target equipment (e.g., winding aging, poor contact), three-phase asymmetry, or zero-sequence anomalies, thereby providing accurate data support for the operation and maintenance management of the power grid.

[0034] As can be seen from the above, this embodiment first calculates the input power of the target device based on the input detection data at each detection time, calculates the output power based on the output detection data, and calculates the actual loss of the target device based on the input and output power. Then, based on the transmission current of the target device and the preset line resistance, it calculates the normal line transmission loss between the input and output of the target device, uses this line transmission loss as the theoretical line loss of the target device, and subtracts the theoretical line loss from the actual loss of the target device. The resulting additional loss can more accurately characterize the operating status of the target device, thereby providing accurate data support for the operation and maintenance management of the distribution network.

[0035] In one embodiment of this application, the theoretical line loss between the input and output terminals of the target device is calculated based on the transmission current of the target device and a preset line resistance, including: Obtain the temperature of the circuit and determine the resistance adjustment coefficient based on the temperature of the circuit; The preset line resistance is adjusted based on the resistance adjustment coefficient to obtain the adjusted line resistance; The theoretical line loss is determined based on the adjusted line resistance and the transmission current of the target equipment.

[0036] In this embodiment, considering that line resistance is significantly affected by temperature, to further improve the accuracy of theoretical line loss detection, this embodiment determines a resistance adjustment coefficient based on the line temperature and adjusts the preset line resistance based on the resistance adjustment coefficient. Specifically, the resistance adjustment coefficient can be calculated using the following formula: ; in, Indicates the resistance adjustment factor. Indicates the temperature coefficient of resistance. This is a preset constant; when the circuit material is copper, When the circuit material is aluminum, ; Indicates the temperature of the circuit. Indicates the reference temperature.

[0037] Based on the obtained resistance adjustment coefficient, multiplying the resistance adjustment coefficient by the preset line resistance will yield the adjusted line resistance.

[0038] In one embodiment of this application, the input terminal detection data corresponding to each detection time includes input terminal detection data from multiple times within the power grid cycle in which the detection time occurs, and the output terminal detection data corresponding to each detection time includes output terminal detection data from multiple times within the power grid cycle in which the detection time occurs. In this embodiment, calculating the input terminal power of the target device based on the input terminal detection data and calculating the output terminal power of the target device based on the output terminal detection data includes: Discrete Fourier transforms are performed on the input voltage data, input current data, output voltage data, and output current data at multiple times within the power grid cycle of each detection time to obtain the effective values ​​of the input fundamental voltage, input fundamental current, input fundamental power factor angle, output fundamental voltage, output fundamental current, and output fundamental power factor angle of the target device within that power grid cycle. The input power of the target device is determined based on the effective value of the input fundamental voltage, the effective value of the input fundamental current, and the input fundamental power factor angle. The output power of the target device is determined based on the effective value of the fundamental voltage, the effective value of the fundamental current, and the fundamental power factor angle at the output terminal.

[0039] In this embodiment, the average input power over the entire power grid cycle can be calculated based on input detection data from multiple moments within the power grid cycle at each detection moment, and this average input power is used as the input power at that detection moment. Similarly, the average output power over the entire power grid cycle can be calculated based on output detection data from multiple moments within the power grid cycle at each detection moment, and this average output power is used as the output power at that detection moment. Using this method can eliminate the influence of instantaneous noise, thereby reducing the loss calculation deviation caused by noise interference.

[0040] Specifically, each distribution terminal starts recording and storing waveform data at the top of the hour (0:00:00:00:00), storing waveform data for one power grid cycle. The waveform data includes data from eight channels: three-phase current and three-phase voltage, zero-sequence voltage and zero-sequence current, with each waveform lasting 20ms and sampling at 256 points. Among them, zero-sequence voltage and zero-sequence current are mainly used to monitor three-phase voltage imbalance and grounding faults.

[0041] In this embodiment, the electronic device can be the monitoring master station in the power distribution network. Each power distribution terminal device transmits the stored waveform data to the monitoring master station through GPRS and 101 protocol. The monitoring master station summarizes the data and processes it based on Discrete Fourier Transform (DFT) to obtain the effective values ​​of three-phase voltage, three-phase voltage phase, effective values ​​of three-phase current, and three-phase current phase.

[0042] Taking the recorded waveform data of phase A voltage in the three-phase input as an example, the effective value and phase of phase A voltage can be obtained by performing a discrete Fourier transform on the recorded waveform data of phase A voltage. The specific calculation process is as follows: (1) Calculate the DFT coefficients: ; in, ; In the above formula, This represents the DFT coefficient of the kth harmonic in phase A voltage. This represents the waveform data of phase A voltage, where N represents the number of waveform data points. In this embodiment, N=256. For complex exponential rotation variables, It is the imaginary unit.

[0043] (2) Calculate the fundamental component: In this embodiment, only the fundamental frequency loss is calculated to avoid interference from harmonic losses and accurately capture the loss changes caused by target equipment faults such as "equipment aging" and "loose connections". Therefore, only the fundamental frequency component is calculated, and the calculation process is as follows: ; in, This represents the real part of the fundamental component. It represents the imaginary part of the fundamental component.

[0044] Based on this, the effective value of the fundamental component of the phase A voltage is: ; The fundamental phase angle of phase A voltage is: .

[0045] (3) Calculate the fundamental power factor angle and fundamental power: Using the same method as for phase A voltage, the fundamental effective value of phase B voltage can be obtained through discrete Fourier transform. The fundamental phase of phase B voltage The fundamental effective value of phase C voltage The fundamental phase of the C-phase voltage The fundamental effective value of phase A current The fundamental phase of phase A current The fundamental effective value of phase B current The fundamental phase of phase B current The fundamental effective value of phase C current Phase of the fundamental frequency of the C-phase current .

[0046] Furthermore, the fundamental phase of phase A voltage is... Phase of the fundamental frequency of phase A current Subtracting them, we get the power factor angle of phase A. The fundamental phase of phase B voltage Phase of the fundamental frequency of phase B current Subtracting them, we get the power factor angle of phase B. The fundamental phase of the C-phase voltage Phase of the fundamental frequency of the C-phase current Subtracting them, we get the C-phase power factor angle. .

[0047] Based on this, the input power can be obtained. for: .

[0048] Using the same calculation method as the input power, the output power of the target device can be obtained based on the output detection data at multiple times within the power grid cycle at each detection time.

[0049] As can be seen from the above, this embodiment calculates the average input power and average output power within one power grid cycle, which are used as the input power and output power respectively, thus eliminating the influence of instantaneous noise. At the same time, this embodiment only calculates the fundamental loss, which can accurately capture the loss changes caused by target equipment faults such as "equipment aging" and "poor connection", avoiding the interference of harmonic loss.

[0050] In one embodiment of this application, the method for monitoring losses in power distribution network equipment further includes: The fluctuation level of the additional loss of the target device is determined based on the additional loss of the target device at multiple detection times within a first set time period; If the fluctuation level value is greater than the fluctuation threshold, the first prompt message will be output; the first prompt message is used to indicate that there is a loose connection fault in the target device. If the fluctuation level is less than or equal to the fluctuation threshold, a linear equation is fitted based on the additional losses of the target device at multiple detection times within a second set time period; if the slope of the linear equation is greater than the slope threshold, a second prompt message is output; the second prompt message is used to indicate that the target device has an aging fault; wherein, the second set time period includes the first set time period.

[0051] In this embodiment, it is considered that different faults of the target equipment have different extra loss evolution patterns. For example, when the connectors or line connection points of the target equipment are loose, the contact resistance will change abruptly due to factors such as vibration and heat, causing sudden fluctuations in extra loss. That is, the extra loss will rise and fall sharply in a short period of time, and the fluctuation range will exceed the normal range. As another example, during the aging process of the target equipment, such as conductor oxidation and insulation deterioration, the contact resistance or equivalent resistance increases monotonically with time, and the extra loss increases steadily and rapidly, and its trend conforms to the linear growth law.

[0052] Therefore, based on the additional loss at each detection moment, the presence of a fault in the target device can be determined by monitoring the trend of the additional loss. For example, by calculating the fluctuation value of the additional loss within a first set period (e.g., one month), the degree of abrupt change in the additional loss can be quantified. When the fluctuation value exceeds a fluctuation threshold, it indicates that the target device has a loose connection fault, and the corresponding first prompt message is output. The fluctuation value of the additional loss can be obtained by calculating the standard deviation of the additional loss at multiple detection moments within the first set period. The fluctuation threshold is a preset constant, which can be calibrated by those skilled in the art according to actual conditions; for example, the fluctuation threshold can be 25% of the average additional loss within the first set period.

[0053] If the target device does not have a loose connection fault, the presence of an aging fault can be determined by monitoring the trend of additional losses at multiple detection points over a longer period (a second set time period, such as 6 months). Specifically, the average of the additional losses at multiple detection points each month can be taken as the additional losses for that month, and then a linear equation y=px+q can be fitted based on the additional loss data over 6 months. Here, y represents the additional losses, and x represents the month.

[0054] Based on this, when the slope p of the straight line exceeds the slope threshold, it indicates that the rate of loss growth exceeds the normal aging range, and is judged as an aging-to-acceleration fault. The slope threshold is a preset constant; for example, it could be 5% of the initial additional loss q.

[0055] As can be seen from the above, this embodiment first judges the loose connection fault based on the change of additional loss in the first set period. When it is determined that there is no loose connection fault, the long-term additional loss data is used to judge the aging fault that is too fast. This can avoid misjudgment of aging fault caused by short-term data fluctuations.

[0056] In one embodiment of this application, the method for monitoring losses in power distribution network equipment further includes: Acquire multi-dimensional feature data of the target device; the multi-dimensional feature data includes the ambient temperature, ambient humidity, service life and failure rate of the target device at multiple detection times within a third set time period before the current time. The additional losses of the target device at multiple detection times within the third set time period, as well as multi-dimensional feature data, are input into a pre-trained Lightweight Gradient Boosting Model (LightGBM model) to obtain the first additional loss prediction result of the target device in future time periods; The additional losses of the target device at multiple detection times within the third set time period are input into a pre-trained exponential smoothing model to obtain the second additional loss prediction result of the target device in future time periods. The first additional loss prediction result and the second additional loss prediction result are weighted and summed to obtain the third additional loss prediction result. The operation and maintenance management strategy for the target equipment is determined based on the third additional loss prediction results.

[0057] In this embodiment, additional losses of the target device can be obtained at multiple detection times within a third predetermined time period (e.g., 12 months) prior to the current time, forming a loss time series. Simultaneously, ambient temperature, ambient humidity, service life, and failure rate are collected at multiple detection times within the third predetermined time period as multi-dimensional feature data. The service life refers to the number of years of use up to the detection time, and the failure rate refers to the failure rate up to the detection time, such as the number of failures within a year.

[0058] Based on this, the additional losses of the target device at multiple detection times within a third defined time period, along with multi-dimensional feature data, are input into a pre-trained LightGBM model to obtain the first additional loss prediction result for the target device in future time periods. The LightGBM model can capture the impact of multi-dimensional factors of the environment and the target device itself on the additional losses, making the prediction results of additional losses more consistent with reality.

[0059] Meanwhile, the additional losses of the target device at multiple detection times within the third set time period are input into a pre-trained exponential smoothing model to obtain the second additional loss prediction result of the target device in future time periods.

[0060] In this embodiment, the exponential smoothing model can accurately capture the temporal trend of additional losses. By weighted and fused the second additional loss prediction result obtained based on the exponential smoothing model and the first additional loss prediction result obtained based on the LightGBM model, the advantages of both models can be combined to obtain an accurate third prediction result. Using this third prediction result for the operation and maintenance management of the target equipment is beneficial for formulating reasonable operation and maintenance strategies and optimizing resource allocation.

[0061] In one embodiment of this application, the weighted summation of the first additional loss prediction result and the second additional loss prediction result includes: Obtain the reference ranges for ambient temperature, ambient humidity, service life, and failure rate; Count the number of feature dimensions that exceed the corresponding reference range among ambient temperature, ambient humidity, service life, and failure rate; If the number of feature dimensions exceeding the corresponding reference range is greater than a preset number, the weight corresponding to the first additional loss prediction result is set to the first value, and the weight corresponding to the second additional loss prediction result is set to the second value; wherein, the first value is greater than the second value, and the sum of the first value and the second value is 1; If the number of feature dimensions that exceed the corresponding reference range is less than or equal to the preset number, the weight corresponding to the first additional loss prediction result is set to the second value, and the weight corresponding to the second additional loss prediction result is set to the first value. Based on the respective weights of the first and second additional loss prediction results, a weighted sum is calculated between the first and second additional loss prediction results.

[0062] In this embodiment, features such as ambient temperature, ambient humidity, service life, and failure rate all have corresponding reference ranges. For example, the reference range for ambient temperature can be -20℃ to 40℃, the reference range for ambient humidity can be 30% to 80%, the reference range for service life can be 15 years, and the failure rate can be 0.3 times / year. For each dimension of features, data from multiple detection times for that dimension are compared with the reference range for that dimension. If the proportion of data exceeding the reference range is greater than 50%, then that dimension is marked as a feature dimension that exceeds the corresponding reference range.

[0063] If the number of feature dimensions exceeding the corresponding reference range is greater than the preset number (e.g., 3), it indicates that there are many abnormal factors and the mutual influence between features is stronger. In this case, the weight corresponding to the first additional loss prediction result is set to a larger first value (e.g., 0.7), and the weight corresponding to the second additional loss prediction result is set to a smaller second value (e.g., 0.3), which can accurately quantify the superposition effect of abnormal factors on additional loss.

[0064] Correspondingly, if the number of feature dimensions exceeding the corresponding reference range is less than or equal to the preset number, it indicates that there are fewer abnormal factors and the mutual influence between features is weak. In this case, setting the weight corresponding to the first additional loss prediction result to a smaller second value (e.g., 0.3) and setting the weight corresponding to the second additional loss prediction result to a larger first value (e.g., 0.7) can prevent the LightGBM model from causing prediction fluctuations due to excessive attention to minor factors.

[0065] As can be seen from the above, this embodiment compares ambient temperature, ambient humidity, service life, and failure rate with their respective reference ranges to assess the number of abnormal factors. Based on the number of abnormal factors, it determines the weights of the first and second additional loss prediction results. This approach can fully leverage the advantages of both the LightGBM model and the exponential smoothing model, thereby further improving the prediction results of additional losses.

[0066] In one embodiment of this application, the future time period includes a first future sub-time period and a second future sub-time period, and the power distribution network equipment loss monitoring method further includes: Obtain meteorological forecast data for the first future sub-period and historical meteorological data for the corresponding second future sub-period; The first environmental type for the future period is determined based on meteorological forecast data for the first future sub-period and historical meteorological data for the corresponding second future sub-period. The second environmental type for future periods is determined based on the ambient temperature and humidity at multiple detection times of the target device within a third set time period. If the first environment type and the second environment type are different environment types, the first additional loss prediction result is corrected based on the preset environment correction coefficient to obtain the corrected first additional loss prediction result. The third additional loss prediction result is obtained by weighted summing of the first and second additional loss prediction results, including: The corrected first additional loss prediction result and the second additional loss prediction result are weighted and summed to obtain the third additional loss prediction result.

[0067] In this embodiment, considering that the first additional loss prediction result is obtained by the LightGBM model based on multi-dimensional feature data within a third set time period, where the multi-dimensional feature data includes ambient temperature and ambient humidity, if the meteorological data in the future period changes significantly compared to the meteorological data in the third set time period, it will lead to a large difference between the ambient temperature and ambient humidity in the future period and the ambient temperature and ambient humidity in the third set time period, thereby causing a large deviation in the first additional loss prediction result obtained based on the ambient temperature and ambient humidity in the third set time period.

[0068] To address the aforementioned issues, this embodiment divides the future time period into a first future sub-period and a second future sub-period. The first future sub-period may include the next 15 days, and the second future sub-period may include the next 16 days to 3 months. Meteorological forecast data for the first future sub-period can be obtained from a meteorological platform. Simultaneously, historical meteorological data from the same period last year for the second future sub-period is obtained as the corresponding historical meteorological data for the second future sub-period. Based on this, meteorological data for the future time period is determined using the meteorological forecast data from the first future sub-period and the corresponding historical meteorological data for the second future sub-period.

[0069] Furthermore, based on the average ambient temperature and average ambient humidity of each day in the future period, the weather type of each day is divided into normal temperature and humidity, high temperature and high humidity, or low temperature and low humidity, and the weather type that occurs most frequently in the future period is taken as the first environmental type of the future period; similarly, based on the ambient temperature and average ambient humidity at multiple detection times in the third set period, the average ambient temperature and average ambient humidity of each day in the third set period are determined, and the weather type of each day is divided into normal temperature and humidity, high temperature and high humidity, or low temperature and low humidity, and the weather type that occurs most frequently in the third set period is taken as the second environmental type of the future period.

[0070] Simultaneously, the additional loss growth rate under different environmental types can be statistically calculated in advance based on historical data of similar equipment, and the environmental correction coefficient can be determined. For example, taking the "normal temperature and humidity" environmental type as the benchmark, its corresponding environmental correction coefficient is 1.0, the environmental correction coefficient corresponding to the "high temperature and high humidity" environmental type is 1.2, and the environmental correction coefficient corresponding to the "low temperature and low humidity" environmental type is 0.9.

[0071] Based on this, if the first environment type and the second environment type are different, the first additional loss prediction result can be corrected based on a preset environmental correction coefficient to obtain a corrected first additional loss prediction result. The specific process includes: The amount of additional loss growth is determined based on the first additional loss prediction result and the additional loss at the current moment. The additional loss growth is corrected based on a preset environmental correction coefficient to obtain the corrected additional loss growth. The revised first additional loss prediction result is determined based on the revised additional loss growth and the additional loss at the current moment.

[0072] For example, based on meteorological forecast data for the first future sub-period and historical meteorological data for the corresponding second future sub-period, the first environmental type for the future period is high temperature and high humidity (corresponding environmental correction factor of 1.2). Based on the environmental temperature and humidity at multiple detection times within the third set period, the second environmental type for the future period is normal temperature and normal humidity (corresponding environmental correction factor of 1). The first additional loss prediction result is... The additional loss at the current moment is Then the additional loss increases It can be represented as: ; The first and second environmental types are different; therefore, the corrected prediction of the first additional loss is as follows: ; in, This indicates the corrected prediction of the first additional loss. This represents the environmental correction factor corresponding to the first environmental type. This represents the environmental correction factor corresponding to the second environmental type.

[0073] Finally, by weighting and summing the corrected first and second additional loss predictions, a more accurate third additional loss prediction can be obtained.

[0074] As can be seen from the above, when the ambient temperature and humidity in the future period differ greatly from those in the third set period, this embodiment can correct the first additional loss prediction result output by the LightGBM model based on the meteorological forecast data of the first future sub-period and the historical meteorological data corresponding to the second future sub-period, thereby further improving the accuracy of the first additional loss prediction result.

[0075] Based on the same inventive concept, this application also provides a distribution network equipment loss monitoring device for implementing the above-described distribution network equipment loss monitoring method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the distribution network equipment loss monitoring device provided below can be found in the limitations of the distribution network equipment loss monitoring method described above, and will not be repeated here.

[0076] This application provides a power distribution network equipment loss monitoring device, such as... Figure 3 As shown, the power distribution network equipment loss monitoring device 20 includes: a data acquisition module 21, an actual loss calculation module 22, a theoretical line loss calculation module 23, and an additional loss calculation module 24.

[0077] The data acquisition module 21 is used to acquire the input terminal detection data and output terminal detection data of the target device at each detection time. The input terminal detection data includes input terminal voltage data and input terminal current data, and the output terminal detection data includes output terminal voltage data and output terminal current data. The input terminal detection data corresponding to each detection time is the input terminal detection data at that detection time or the input terminal detection data at multiple times within the power grid cycle in which the detection time is located. The output terminal detection data corresponding to each detection time is the output terminal detection data at that detection time or the output terminal detection data at multiple times within the power grid cycle in which the detection time is located. The actual loss calculation module 22 is used to calculate the input power of the target device based on the input detection data, calculate the output power of the target device based on the output detection data, and calculate the actual loss of the target device based on the input power and the output power. The theoretical line loss calculation module 23 is used to calculate the theoretical line loss between the input and output terminals of the target device based on the transmission current of the target device and the preset line resistance; the transmission current of the target device is the input current data or the output current data of the target device. Additional loss calculation module 24 is used to determine the additional loss of the target device at each detection time based on the actual loss and theoretical line loss of the target device.

[0078] In one embodiment of this application, the theoretical line loss calculation module 23 is specifically used for: Obtain the temperature of the circuit and determine the resistance adjustment coefficient based on the temperature of the circuit; The preset line resistance is adjusted based on the resistance adjustment coefficient to obtain the adjusted line resistance; The theoretical line loss is determined based on the adjusted line resistance and the transmission current of the target equipment.

[0079] In one embodiment of this application, the input detection data corresponding to each detection time includes input detection data from multiple times within the power grid cycle in which the detection time occurs, and the output detection data corresponding to each detection time includes output detection data from multiple times within the power grid cycle in which the detection time occurs; the actual loss calculation module 22 is specifically used for: Discrete Fourier transforms are performed on the input voltage data, input current data, output voltage data, and output current data at multiple times within the power grid cycle of each detection time to obtain the effective values ​​of the input fundamental voltage, input fundamental current, input fundamental power factor angle, output fundamental voltage, output fundamental current, and output fundamental power factor angle of the target device within that power grid cycle. The input power of the target device is determined based on the effective value of the input fundamental voltage, the effective value of the input fundamental current, and the input fundamental power factor angle. The output power of the target device is determined based on the effective value of the fundamental voltage, the effective value of the fundamental current, and the fundamental power factor angle at the output terminal.

[0080] In one embodiment of this application, the additional loss calculation module 24 is specifically used for: The fluctuation level of the additional loss of the target device is determined based on the additional loss of the target device at multiple detection times within a first set time period; If the fluctuation level value is greater than the fluctuation threshold, the first prompt message will be output; the first prompt message is used to indicate that there is a loose connection fault in the target device. If the fluctuation level is less than or equal to the fluctuation threshold, a linear equation is fitted based on the additional losses of the target device at multiple detection times within a second set time period; if the slope of the linear equation is greater than the slope threshold, a second prompt message is output; the second prompt message is used to indicate that the target device has an aging fault; wherein, the second set time period includes the first set time period.

[0081] In one embodiment of this application, the additional loss calculation module 24 is specifically used for: Acquire multi-dimensional feature data of the target device; the multi-dimensional feature data includes the ambient temperature, ambient humidity, service life and failure rate of the target device at multiple detection times within a third set time period before the current time. The additional losses of the target device at multiple detection times within the third set time period, as well as multi-dimensional feature data, are input into the pre-trained LightGBM model to obtain the first additional loss prediction result of the target device in future time periods. The additional losses of the target device at multiple detection times within the third set time period are input into a pre-trained exponential smoothing model to obtain the second additional loss prediction result of the target device in future time periods. The first additional loss prediction result and the second additional loss prediction result are weighted and summed to obtain the third additional loss prediction result. The operation and maintenance management strategy for the target equipment is determined based on the third additional loss prediction results.

[0082] In one embodiment of this application, the additional loss calculation module 24 is further configured to: Obtain the reference ranges for ambient temperature, ambient humidity, service life, and failure rate; Count the number of feature dimensions that exceed the corresponding reference range among ambient temperature, ambient humidity, service life, and failure rate; If the number of feature dimensions exceeding the corresponding reference range is greater than a preset number, the weight corresponding to the first additional loss prediction result is set to the first value, and the weight corresponding to the second additional loss prediction result is set to the second value; wherein, the first value is greater than the second value, and the sum of the first value and the second value is 1; If the number of feature dimensions that exceed the corresponding reference range is less than or equal to the preset number, the weight corresponding to the first additional loss prediction result is set to the second value, and the weight corresponding to the second additional loss prediction result is set to the first value. Based on the respective weights of the first and second additional loss prediction results, a weighted sum is calculated between the first and second additional loss prediction results.

[0083] In one embodiment of this application, the future time period includes a first future sub-time period and a second future sub-time period, and the additional loss calculation module 24 is further used for: Obtain meteorological forecast data for the first future sub-period and historical meteorological data for the corresponding second future sub-period; The first environmental type for the future period is determined based on meteorological forecast data for the first future sub-period and historical meteorological data for the corresponding second future sub-period. The second environmental type for future periods is determined based on the ambient temperature and humidity at multiple detection times of the target device within a third set time period. If the first environment type and the second environment type are different environment types, the first additional loss prediction result is corrected based on the preset environment correction coefficient to obtain the corrected first additional loss prediction result. The third additional loss prediction result is obtained by weighted summing of the first and second additional loss prediction results, including: The corrected first additional loss prediction result and the second additional loss prediction result are weighted and summed to obtain the third additional loss prediction result.

[0084] See Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 4 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of each module / unit in the above-described device embodiments, for example... Figure 3 The functions of the data acquisition module 21, actual loss calculation module 22, theoretical line loss calculation module 23, and additional loss calculation module 24 are shown.

[0085] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0086] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0087] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store preset constants such as fluctuation thresholds, rate of change thresholds, and environmental correction factors.

[0088] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation method described in the power distribution equipment loss monitoring method provided in the embodiments of this application, or they can execute the implementation method of the electronic equipment described in the embodiments of this application, which will not be repeated here.

[0089] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0090] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0091] Those skilled in the art will recognize that the modules / units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0092] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules, units, or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or modules / units, or it may be an electrical, mechanical, or other form of connection.

[0094] The modules / units described as separate components may or may not be physically separate. Similarly, the components shown as modules / units may or may not be physical modules / units; they may be located in one place or distributed across multiple network modules / units. Some or all of the modules / units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0095] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0096] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for monitoring losses in power distribution network equipment, characterized in that, include: Acquire the input and output detection data of the target device at each detection time; The input terminal detection data includes input terminal voltage data and input terminal current data, and the output terminal detection data includes output terminal voltage data and output terminal current data; the input terminal detection data corresponding to each detection moment is the input terminal detection data at that detection moment or the input terminal detection data at multiple moments within the power grid cycle in which the detection moment is located, and the output terminal detection data corresponding to each detection moment is the output terminal detection data at that detection moment or the output terminal detection data at multiple moments within the power grid cycle in which the detection moment is located; The input power of the target device is calculated based on the input detection data, the output power of the target device is calculated based on the output detection data, and the actual loss of the target device is calculated based on the input power and the output power. Calculate the theoretical line loss between the input and output terminals of the target device based on the transmission current of the target device and the preset line resistance; The transmission current of the target device is the input current data or the output current data of the target device. The additional loss of the target device at each detection time is determined based on the actual loss of the target device and the theoretical line loss.

2. The method for monitoring losses in power distribution network equipment as described in claim 1, characterized in that, The calculation of the theoretical line loss between the input and output terminals of the target device based on the transmission current of the target device and the preset line resistance includes: The temperature of the line is obtained, and the resistance adjustment coefficient is determined based on the temperature of the line. The preset line resistance is adjusted based on the resistance adjustment coefficient to obtain the adjusted line resistance; The theoretical line loss is determined based on the adjusted line resistance and the transmission current of the target device.

3. The method for monitoring losses in power distribution network equipment as described in claim 1, characterized in that, The input detection data corresponding to each detection time is the input detection data of multiple times within the power grid cycle in which the detection time is located, and the output detection data corresponding to each detection time includes the output detection data of multiple times within the power grid cycle in which the detection time is located. The calculation of the input power of the target device based on the input detection data and the calculation of the output power of the target device based on the output detection data include: Discrete Fourier transforms are performed on the input voltage data, input current data, output voltage data, and output current data at multiple times within the power grid cycle of each detection time to obtain the effective value of the fundamental voltage, effective value of the fundamental current, input power factor angle, effective value of the fundamental voltage, effective value of the fundamental current, and effective value of the fundamental power factor angle of the target device within that power grid cycle. The input power of the target device is determined based on the effective value of the input fundamental voltage, the effective value of the input fundamental current, and the input fundamental power factor angle. The output power of the target device is determined based on the effective value of the fundamental voltage at the output terminal, the effective value of the fundamental current at the output terminal, and the fundamental power factor angle at the output terminal.

4. The method for monitoring losses in power distribution network equipment as described in claim 1, characterized in that, Also includes: The fluctuation level of the additional loss of the target device is determined based on the additional loss of the target device at multiple detection times within a first set time period; If the fluctuation level value is greater than the fluctuation threshold, the first prompt message will be output; The first prompt message is used to indicate that the target device has a loose connection fault; If the fluctuation level value is less than or equal to the fluctuation threshold, a linear equation is fitted based on the additional losses of the target device at multiple detection times within a second set time period; if the slope of the linear equation is greater than the slope threshold, a second prompt message is output; the second prompt message is used to indicate that the target device has an aging fault; wherein, the second set time period includes the first set time period.

5. The method for monitoring losses in power distribution network equipment as described in claim 1, characterized in that, Also includes: Acquire multi-dimensional feature data of the target device; the multi-dimensional feature data includes the ambient temperature, ambient humidity, service life and failure rate of the target device at multiple detection times within a third set time period before the current time; The additional losses of the target device at multiple detection times within the third set time period, along with the multi-dimensional feature data, are input into a pre-trained LightGBM model to obtain the first additional loss prediction result of the target device in future time periods. The additional losses of the target device at multiple detection times within a third set time period are input into a pre-trained exponential smoothing model to obtain a second additional loss prediction result for the target device in future time periods. The first additional loss prediction result and the second additional loss prediction result are weighted and summed to obtain the third additional loss prediction result. The operation and maintenance management strategy for the target equipment is determined based on the third additional loss prediction result.

6. The method for monitoring losses in power distribution network equipment as described in claim 5, characterized in that, The weighted summation of the first additional loss prediction result and the second additional loss prediction result includes: Obtain the reference ranges for the ambient temperature, ambient humidity, service life, and failure rate, respectively. Count the number of feature dimensions that exceed the corresponding reference range among the ambient temperature, ambient humidity, service life, and failure rate; If the number of feature dimensions exceeding the corresponding reference range is greater than a preset number, the weight corresponding to the first additional loss prediction result is set to a first value, and the weight corresponding to the second additional loss prediction result is set to a second value; wherein, the first value is greater than the second value, and the sum of the first value and the second value is 1; If the number of feature dimensions that exceed the corresponding reference range is less than or equal to a preset number, the weight corresponding to the first additional loss prediction result is set to the second value, and the weight corresponding to the second additional loss prediction result is set to the first value. Based on the respective weights of the first additional loss prediction result and the second additional loss prediction result, a weighted sum is performed on the first additional loss prediction result and the second additional loss prediction result.

7. The method for monitoring losses in power distribution network equipment as described in claim 5, characterized in that, The future time period includes the first future sub-time period and the second future sub-time period, and the power distribution network equipment loss monitoring method further includes: Obtain meteorological forecast data for the first future sub-period and historical meteorological data for the second future sub-period; The first environmental type for the future period is determined based on the meteorological forecast data for the first future sub-period and the historical meteorological data corresponding to the second future sub-period. The second environmental type for the future time period is determined based on the ambient temperature and humidity of the target device at multiple detection times within the third set time period. If the first environment type and the second environment type are different environment types, the first additional loss prediction result is corrected based on the preset environment correction coefficient to obtain the corrected first additional loss prediction result. The step of weighted summing of the first additional loss prediction result and the second additional loss prediction result to obtain the third additional loss prediction result includes: The corrected first additional loss prediction result and the second additional loss prediction result are weighted and summed to obtain the third additional loss prediction result.

8. A power distribution network equipment loss monitoring device, characterized in that, include: The data acquisition module is used to acquire the input and output detection data of the target device at each detection time. The input terminal detection data includes input terminal voltage data and input terminal current data, and the output terminal detection data includes output terminal voltage data and output terminal current data; the input terminal detection data corresponding to each detection moment is the input terminal detection data at that detection moment or the input terminal detection data at multiple moments within the power grid cycle in which the detection moment is located, and the output terminal detection data corresponding to each detection moment is the output terminal detection data at that detection moment or the output terminal detection data at multiple moments within the power grid cycle in which the detection moment is located; The actual loss calculation module is used to calculate the input power of the target device based on the input detection data, calculate the output power of the target device based on the output detection data, and calculate the actual loss of the target device based on the input power and the output power. The theoretical line loss calculation module is used to calculate the theoretical line loss between the input and output terminals of the target device based on the transmission current of the target device and the preset line resistance. The transmission current of the target device is the input current data or the output current data of the target device. An additional loss calculation module is used to determine the additional loss of the target device at each detection time based on the actual loss of the target device and the theoretical line loss.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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