Information processing method for distributed power supply settlement process

Abstract

The invention provides an information processing method for a distributed power supply settlement process, and particularly relates to the field of information processing, comprising the following steps: timely and synchronously acquiring distributed power supply values of a distributed power supply total end at multiple orientation points; performing outlier fluctuation test analysis on the distributed power supply values of all the azimuth points in sequence to obtain discrete amplitudes of information points corresponding to all the azimuth points in sequence; any orientation point is taken as a reference orientation, and discrete amplitudes of all power supply information of information points of all time points corresponding to all orientations under the influence of all discrete variables are obtained; any time point is taken as a reference time point, and the amplitude of the total end of the distributed power supply at the reference time point is calculated; according to the wave amplitude and the discrete amplitude, obtaining the product of the discrete amplitude of the information point of the corresponding time point of the reference azimuth point and the weight coefficient; and analyzing and calculating to obtain corresponding weight constants of the information points collected at each time point.

Description

Technical Field

[0001] The present invention proposes an information processing method for a distributed power supply settlement process, and specifically relates to the field of information processing. Background Art

[0002] With the rapid development of the Internet, people are increasingly relying on data reception and transmission in their daily lives, and data noise has become an important issue that affects the quality of data communication and user experience. Data noise refers to various interferences generated during network transmission, such as signal overlap, signal attenuation, signal distortion, etc. In order to improve the reliability of network transmission and data quality, filtering and noise reduction technology has become a hot topic of research. The main causes of data noise are as follows: 1. Electromagnetic interference: frequency interference of electrical equipment, electromagnetic wave interference, etc. will cause data noise; 2. Poor quality of transmission media: For example, the use of old cables, optical fibers and other transmission media is prone to signal attenuation and distortion; 3. Network congestion: When the network traffic is too large and the network equipment cannot bear it, it will cause data packet loss or delay, thereby generating data noise; 4. Software problems: Imperfect or defective implementation of the network protocol stack will also cause data noise.

[0003] Data noise has a great impact on network communications, which is mainly manifested in the following aspects: 1. Data loss: Data noise will cause the loss of data packets, which will affect the integrity and transmission efficiency of the data; 2. Data delay: Data noise will cause the delay of data packets, which will affect the transmission speed and response time of the network; 3. Data distortion: Data noise will cause the distortion of data packets, so that the received data is inconsistent with the sent data, thus affecting the reliability and accuracy of the data; 4. Impact on user experience: Data noise will lead to a decline in user experience, such as video freezes, audio interruptions and other problems.

[0004] At present, due to the data noise and data clutter generated by the above-mentioned reasons, the transmitted distributed power value has a large error. To put it more specifically, the stability and reliability of the collected distributed power value do not meet the requirements. Generally, a denoising algorithm is used to operate on the obtained distributed power value.

[0005] The traditional weighted moving average method is a denoising algorithm used for numerical signals. In the weighted moving average method, the weight constant is selected based on the empirical method and trial algorithm. After the selection, it is a constant. Therefore, when denoising and removing clutter from the distributed power value, the size of the weight constant cannot be flexibly determined based on the obvious degree of specific clutter fluctuations. Therefore, the algorithm cannot improve the accuracy of the distributed power value, ensure its accuracy and stability, and thus cannot optimize the value well.

[0006] Therefore, the present invention provides an information processing method for a distributed power supply settlement process. Summary of the invention

[0007] In order to solve the deficiencies in the prior art, the present invention provides an analysis operation to obtain the corresponding weight constants of the information points collected at each time point; after obtaining the corresponding weight constants, a denoising and clutter removal algorithm operation is performed on the information point at the current time point based on the corresponding weight constants of the information points at each time point; and then the denoised stable information point data is saved for the subsequent analysis of the reliability stability of the distributed power supply value.

[0008] The present invention adopts the following technical solution.

[0009] The present invention provides an information processing method for a distributed power supply settlement process, comprising the following steps:

[0010] S1: timely and synchronously obtain the distributed power values ​​of the distributed power main terminal located at multiple prescription locations;

[0011] S2: Perform outlier fluctuation test analysis on the distributed power values ​​of all azimuth points in sequence to obtain the discrete amplitudes of each information point corresponding to all azimuth points in sequence;

[0012] S3: Take any one point as the reference point, and obtain the discrete amplitude of each power information under the influence of each discrete variable at the information point of all points at all times; take any time point as the reference time point, and calculate the amplitude of the distributed power source terminal at the reference time point;

[0013] S4: According to the amplitude and discrete amplitude, obtain the product of the discrete amplitude of the information point at the corresponding time point of the reference azimuth point and the weight coefficient;

[0014] S5: Analyze and calculate to obtain the corresponding weight constants of the information points collected at each time point.

[0015] In a further preferred embodiment of the present invention, step S1 further comprises:

[0016] Timely and synchronously obtain the distributed power value of the distributed power source main terminal located at multiple prescription points, the distributed power value includes the information point collected corresponding to each time point at any prescription point, and the information point obtains the corresponding power supply information under the influence of no less than two discrete variables; when the number of discrete variables is 3, the discrete variables include signal decibel value, carrier-to-noise ratio and noise coefficient.

[0017] In a further preferred embodiment of the present invention, step S2 further comprises:

[0018] The discrete amplitude of each information point is obtained by the analysis and calculation of the isolation forest algorithm. All the information points collected by sensors at all azimuth points are analyzed by the isolation forest algorithm to perform outlier fluctuation test analysis, and then the corresponding outlier scores of all the information points are obtained, and finally the discrete amplitude of the information point at a certain time point corresponding to a certain azimuth point is obtained.

[0019] In a further preferred embodiment of the present invention, step S3 further comprises:

[0020] Take any azimuth point as the reference azimuth, analyze and calculate the discrete amplitudes of each power information of the information point at each time point corresponding to the reference azimuth under the influence of different discrete variables, and then obtain the discrete amplitudes of each power information of the information points at all azimuths corresponding to all time points under the influence of various discrete variables in this way; take any time point as the reference time point, and calculate the amplitude of the distributed power source main terminal at the reference time point based on the discrete amplitudes of the power information of all azimuth information points at the reference time point under the influence of various discrete variables.

[0021] According to a further preferred embodiment of the present invention, the discrete amplitude calculation formula is:

[0022] First, a regional array is set as Cg, and its array length is H. Based on all the information points in any direction, in each power information group formed under the influence of each discrete variable, a power information is selected as the midpoint of the regional array Cg. According to the current power information and the power information group formed based on a time point under the influence of a discrete variable where the current power information is located, all the power information in the regional array Cg with a length of H is selected, and the discrete amplitude between the current power information and the remaining power information in the regional array Cg where the current power information is located is calculated. The calculation formula is:

[0023]

[0024] Explain the formula in detail. s,v,t F is the discrete amplitude between the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the remaining power information in the area array Cg where the power information is located; s,v,t For f s,v,t The maximum and minimum algorithm processing is the final result used in subsequent calculations; M is abbreviated as the formula L s,v,t is the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point; is the average of all power information in the area array Cg where the power information affected by the tth parameter variable is located among the information points collected by the sth azimuth point at the vth time point; H is the length of the area array Cg where the power information affected by the tth parameter variable is located among the information points collected by the sth azimuth point at the vth time point; is the βth power information in the remaining power information in the area array Cg where the power information affected by the tth parameter variable is located among the information points collected by the sth azimuth point at the vth time point; min(f s,v,t ) is the minimum value of the discrete amplitude between the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the remaining power information in the area array Cg where the power information is located; max(f s,v,t ) is the maximum value of the discrete amplitude between the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the rest of the power information in the regional array Cg where the power information is located.

[0025] In a further preferred embodiment of the present invention, the amplitude calculation formula is:

[0026]

[0027] Explain the formula in detail. v is the amplitude of the distributed power value collected at the vth time point; A is the total count of the azimuth points where the sensors are placed; B is the number of discrete variables of the collected information points; F' is the mean of the discrete amplitudes of the power information affected by the tth parameter variable in the information points collected by all azimuth points at the vth time point; s,v,t is the product of the discrete amplitude of the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the weight coefficient; s,v,t is the discrete amplitude of the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point, d s is the distance between the sth azimuth point and the main terminal of the distributed power generation.

[0028] 7. The information processing method of the distributed power generation settlement process according to claim 5, characterized in that the step S4 further comprises:

[0029] According to the discrete amplitude of each information point and the amplitude at the time point corresponding to the current information point, the discrete amplitude of all information points is multiplied by the weight coefficient. The calculation formula is:

[0030] E′ s,v =E s,v ×(1-Wv ).

[0031] Explain the formula in detail. s,v is the discrete amplitude of the information point at the vth time point corresponding to the sth prescription site; E' s,v is the product of the discrete amplitude of the information point at the vth time point corresponding to the sth prescription site and the weight coefficient; W v is the amplitude of the distributed power source terminal collected at the vth time point.

[0032] In a further preferred embodiment of the present invention, step S5 further comprises:

[0033] According to the product value of the obtained discrete amplitude and the weight coefficient, the limit value of the corresponding information point is analyzed and calculated; at the same time, according to the limit value of each information point, the denoising and clutter removal algorithm operation is performed on each information point in the distributed power value to obtain the distributed power value after denoising and clutter removal; the calculation formula of the weight constant is:

[0034]

[0035] Explain the formula in detail. s,v is the weight constant of the information point at the vth time point corresponding to the sth prescription site; E' s,v It is the product of the discrete amplitude of the information point at the vth time point corresponding to the sth prescription site and the weight coefficient.

[0036] A further preferred embodiment of the present invention is that the weight constant obtains a specific value of the weight constant based on an exponential moving average method, and the size of the weight constant represents data fluctuation and discreteness, and also includes a response level to the difference between the estimated value and the actual value.

[0037] The beneficial effects of the present invention are that, compared with the prior art, it can timely and synchronously obtain the distributed power values ​​of the distributed power main terminal located at multiple prescription points; perform outlier fluctuation test analysis on the distributed power values ​​of all azimuth points in turn, and obtain the discrete amplitudes of each information point corresponding to all azimuth points in turn; take any azimuth point as the reference azimuth, and obtain the discrete amplitudes of each power information of the information points corresponding to all azimuth points at all time points under the influence of each discrete variable; take any time point as the reference time point, and calculate the amplitude of the distributed power main terminal at the reference time point; based on the amplitude and discrete amplitude, obtain the product of the discrete amplitude of the information point corresponding to the reference azimuth point and the weight coefficient; analyze and calculate to obtain the corresponding weight constants of the information points collected at each time point. The above technical scheme filters and averages the data, making the data more reliable, stable and secure, specifically including the following functions:

[0038] 1. Remove noise: In actual data collection, due to various reasons, there is often some noise in the data, which will interfere with the analysis and processing of the data and even lead to wrong conclusions. By denoising the data, these noises can be removed or weakened to make the data more accurate.

[0039] 2. Reduce fluctuations: Some data may have large fluctuations, which may be caused by the influence of the external environment or the characteristics of the data itself. By denoising the data, these fluctuations can be reduced and the data can be made more stable.

[0040] 3. Eliminate outliers: Sometimes there may be some outliers in the data. These outliers may be caused by problems with data collection or the characteristics of the data itself. By denoising the data, these outliers can be eliminated or weakened, making the data more reliable.

[0041] 4. Optimize data analysis: For some data analysis models, denoising can make the data more consistent with the model assumptions, thereby improving the accuracy and reliability of data analysis.

[0042] In summary, data denoising can effectively improve the quality and reliability of data, improve the accuracy and reliability of data analysis, and is an important part of data acquisition and processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of an information processing method of a distributed power supply settlement process disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0045] See also Figure 1 In an embodiment of the present invention, a method for processing information of a distributed power supply settlement process is provided, and the specific steps are as follows:

[0046] In S1, the distributed power value of the distributed power source main terminal located at multiple prescription locations is obtained in a timely and synchronous manner, and the distributed power value includes the information points collected corresponding to each time point at any location, and the information points obtain the corresponding power supply information under the influence of no less than two discrete variables.

[0047] In the embodiments of the present invention, the distributed power source is a power system that disperses power generation equipment near users or loads. It is different from the traditional centralized power generation system in that the power generation equipment is transferred from the centralized power station to the vicinity of users or loads. The distributed power source terminal system is usually composed of multiple small power generation devices, such as solar photovoltaic panels, wind turbine generators, micro gas turbines, etc. These power generation devices can be distributed in cities, buildings, communities or industrial parks, etc., to generate electricity according to local needs. The distributed power supply system also includes energy storage devices and smart grid technology to improve energy utilization efficiency, reduce energy loss and optimize power supply.

[0048] At the same time, the scheme for obtaining the required distributed power value in this example is: placing sensors at corresponding points in multiple directions outward from the distributed power main terminal as the center point, and synchronously obtaining distributed power value samples through the sensors for subsequent calculation and analysis. The number of points is linearly related to the number of placed sensors, that is, the number of placed sensors is M, M>1, and the number of points is also M.

[0049] In the implementation of the present invention, in order to ensure that the obtained distributed power value samples have higher accuracy, more sensors should be used to obtain richer data samples. The placement requirements of the sensors should first be that the engineers should place sensors at multiple locations outward from the distributed power main terminal as the center point, and then sample the required distributed power values ​​in real time, and at the same time, record the interval lengths between all sensors and the distributed power main terminal in sequence, and then record the corresponding information collected by the sensors at any location at each time point as information points. The information points should obtain corresponding power information under the influence of no less than two discrete variables.

[0050] In the implementation of the present invention, the data sampling frequency of the sensor is set in advance to be once per second.

[0051] In the implementation of the present invention, it is further supplemented that based on different information sources, that is, the distributed power values ​​collected by sensors located at their respective azimuth points, the final sampling of the distributed power value samples can be completed by a large number of sensors, and the accuracy of the distributed power value samples collected by the sensors due to the insufficient number of sensors is avoided as much as possible, and the value transmission has certain fluctuations due to the characteristics of the sensors themselves, thereby affecting the accuracy, reliability and integrity of the transmission signal. Therefore, in summary, more sensors are placed to obtain distributed power value samples with richer levels and a wider range.

[0052] In the embodiment of the present invention, the information point at any time point can be three corresponding power supply information obtained under the influence of three discrete variables, and the discrete variables include signal decibel value, carrier-to-noise ratio and noise factor. In this embodiment, the number of discrete variables is 3, but based on other embodiments, the discrete variables can be 4 or 5, and the number of discrete variables can be taken according to the specific embodiment. For example, when the number of discrete variables is 4, the discrete variables of the information point include signal decibel value, carrier-to-noise ratio, noise factor and noise equivalent bandwidth.

[0053] In S2, the distributed power values ​​of all azimuth points are subjected to outlier fluctuation test analysis in sequence to obtain the discrete amplitudes of each information point corresponding to all azimuth points in sequence.

[0054] In the implementation of the present invention, it is further supplemented that all information points collected by sensors at all azimuth points are analyzed by the isolation forest algorithm of the prior art solution, and then the corresponding outlier scores of all information points are obtained, and the outlier scores of all information points are converted to a range between [-1, 1] by a maximum and minimum algorithm processing method, and finally the discrete amplitude E of the information point at the vth time point corresponding to the sth prescription point is obtained. s,v .

[0055] In S3, any azimuth point is taken as the reference azimuth, and the discrete amplitudes of various power information of the information points at each time point corresponding to the reference azimuth under the influence of different discrete variables are analyzed and calculated, and then the discrete amplitudes of various power information of the information points at all azimuths corresponding to all time points under the influence of various discrete variables are obtained by analogy; any time point is taken as the reference time point, and the amplitude of the distributed power source main terminal at the reference time point is calculated based on the discrete amplitudes of the power information of all azimuth information points at the reference time point under the influence of various discrete variables.

[0056] In the embodiment of the present invention, according to the information points obtained at a certain position, power information based on the influence of the same discrete variable is selected in the information points, and the obtained power information can construct a power information group about the time sequence; according to the power information associated with each time point in the power information group and the difference of the power information influenced by the same discrete variable within a certain limit, the power information at all time points and the discrete amplitude of the power information under the influence of the same discrete variable are obtained by analysis and calculation, and the analysis process is as follows:

[0057] First, a regional array is set as Cg, and its array length is H. Based on all the information points in any direction, in each power information group formed under the influence of each discrete variable, a power information is selected as the midpoint of the regional array Cg. According to the current power information and the power information group formed based on a time point under the influence of a discrete variable where the current power information is located, all the power information in the regional array Cg with a length of H is selected, and the discrete amplitude between the current power information and the remaining power information in the regional array Cg where the current power information is located is calculated. The calculation formula is:

[0058]

[0059] Explain the formula in detail. s,v,t F is the discrete amplitude between the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the remaining power information in the area array Cg where the power information is located; s,v,t For f s,v,t The maximum and minimum algorithm processing is the final result used in subsequent calculations; M is abbreviated as the formula L s,v,t is the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point; is the average of all power information in the area array Cg where the power information affected by the tth parameter variable is located among the information points collected by the sth azimuth point at the vth time point; H is the length of the area array Cg where the power information affected by the tth parameter variable is located among the information points collected by the sth azimuth point at the vth time point; is the βth power information in the remaining power information in the area array Cg where the power information affected by the tth parameter variable is located among the information points collected by the sth azimuth point at the vth time point; min(f s,v,t ) is the minimum value of the discrete amplitude between the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the remaining power information in the area array Cg where the power information is located; max(f s,v,t ) is the maximum value of the discrete amplitude between the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the rest of the power information in the regional array Cg where the power information is located.

[0060] The present invention is further explained in the implementation, It is the absolute value of the difference between the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the average of all the power information in the regional array Cg where the power information is located. The higher the result value, the more obvious the difference between the current power information and the rest of the power information in the regional array Cg where the current power information is located; the lower the result value, the more obvious the similarity between the current power information and the rest of the power information in the regional array Cg where the current power information is located; The result value represents the concentration trend of all power information in the area array Cg where the current power information is located. The larger the calculation result, the more dispersed the value arrangement of all power information in the area array Cg where the current power information is located; the smaller the calculation result, the more obvious the concentration trend of all power information in the area array Cg where the current power information is located. The higher the result of is, the higher the result value of M is, that is, The smaller the result of the operation, the The larger the entropy value between M and M, the higher the difference between the current power information and the power information of the regional array Cg where the current power information is located, and the more obvious the concentration trend of the values ​​of all power information in the current regional array Cg, and the larger the discrete amplitude between the current power information and the power information of the regional array Cg where the current power information is located; on the contrary, the smaller the discrete amplitude between the current power information and the power information of the regional array Cg where the current power information is located.

[0061] In this embodiment, a regional array is first set, and its array length is H. The value of H must be 2n+1, where n is a natural number. This is to ensure that in the embodiment, a power information taken in the analysis and operation of discrete amplitudes is exactly the midpoint of the regional array, recorded as the midpoint value; if the total number of power information located before the power information to be analyzed is less than n, that is, the calculated power information cannot satisfy the requirement of being located exactly in the center of the regional array, then the power information located before the current power information is still included in the regional array, and at the same time, the regional array where the midpoint value is located is generally moved backward to ensure that the number of information in the group where the center value is located meets H. For example, a regional array is first set with an array length of 21. The discrete amplitude of the 4th power information in the power information group to be analyzed needs to be calculated. There are only 3 bits of power information before the power information, and the power information to be calculated cannot fall at the midpoint of the regional value. Then the power information to be calculated at the 4th bit is taken as the center value. The data of the regional array includes the first 3 bits of power information and the last 16 bits of power information of the current power information to be calculated, ensuring that the regional array takes 21 bits.

[0062] The implementation of the present invention further includes that in the analysis and calculation of the power data corresponding to the discrete amplitude, it is necessary to ensure that the power information falls in the center of the regional array as much as possible, that is, it can be ensured that the power information is indeed the center value of the regional array, and then in the subsequent analysis and calculation process of the discrete amplitude, the calculation error can be reduced to ensure the accuracy of the obtained discrete amplitude.

[0063] In addition to this embodiment, in other embodiments, it is also possible to directly select uninterrupted power information based on a preset number of boxes in the constructed power information group, that is, the operation information group, and then analyze and calculate all the power information in the operation information group to obtain all the corresponding discrete amplitudes. The operation information group is a part of the data selected in the power information group, so when the required power information is selected through the pre-set area array, the first power information and the last power information in the operation information group are both located in the value in the power information group, then the head and the last two power information in the operation information group can be analyzed and calculated according to the pre-set area array to obtain the discrete amplitude.

[0064] The present invention further explains that the corresponding power information obtained under the influence of any discrete variable is the calculation sample that can be selected in the above embodiment. For example, the discrete variables mentioned above include signal decibel value, carrier-to-noise ratio and noise coefficient, etc. Then, according to the corresponding power information obtained under the influence of each discrete variable at the collected information point, the discrete amplitude of the power information under the influence of each discrete variable can be obtained by performing corresponding analysis and calculation.

[0065] It is further explained in the implementation of the present invention that the power information based on a certain discrete variable in the distributed power value collected at a certain prescription point may produce certain fluctuations within a certain time range, and the fluctuations are classified as data changes within a reasonable range. However, when affected by the external environment, it is very likely that the collected power information will produce abnormal fluctuations, that is, the data may be erroneous data, false data, abnormal data and missing data. Then, the numerical dynamic difference between the current power information and the previous and subsequent power information will also increase sharply. Then, based on the corresponding power information under the influence of any discrete variable at the information points collected at all time points at all orientation points and the numerical dynamic difference between the adjacent power data, the discrete amplitudes of the information points collected at all time points can be analyzed and calculated.

[0066] The implementation of the present invention further explains that when obtaining the distributed power value, the engineer places sensors at different azimuth distances with the distributed power main terminal as the center point, so the closer to the distributed power main terminal, the higher the signal strength of the sensor will be, ensuring the high reliability and high transmission rate of the collected power information. Based on the above, when analyzing the distributed power value at a certain point in time, that is, the amplitude of the distributed power main terminal, the azimuth of each type of sensor at the distributed power main terminal, the angle between them, and the length of the interval should also be included in the analysis range.

[0067] In this embodiment, based on the discrete amplitudes of the power information under the influence of various discrete variables collected by all sensors at a specific time point, the amplitude of the power information collected at a specific time point is analyzed and calculated, and the analysis operation is:

[0068] According to S1, sensors are placed at multiple prescription locations with the distributed power supply terminal as the center point, and the interval length between each signal sensor and the distributed power supply terminal is recorded. At the same time, based on the interval distance and the discrete amplitude of the power supply information collected by all sensors at a specific time point under the influence of various discrete variables, the amplitude of the distributed power supply value collected at a specific time point is analyzed and calculated. The calculation formula is:

[0069]

[0070] Explain the formula in detail. v is the amplitude of the distributed power value collected at the vth time point; A is the total count of the azimuth points where the sensors are placed; B is the number of discrete variables of the collected information points; F' is the mean of the discrete amplitudes of the power information affected by the tth parameter variable in the information points collected by all azimuth points at the vth time point; s,v,t is the product of the discrete amplitude of the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the weight coefficient; s,v,t is the discrete amplitude of the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point, d s is the distance between the sth azimuth point and the main terminal of the distributed power generation.

[0071] The present invention is further explained in the implementation, The result is the product of the discrete amplitude of the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the weight coefficient. If the interval between the sensor at the current azimuth point and the distributed power supply main terminal is shorter, it means that the information flow captured by the sensor at the current azimuth point is more stable, and it also means that the reliability and authenticity of the discrete amplitude of the current information point are higher. On the contrary, it means that the reliability and authenticity of the discrete amplitude of the current information point are lower.

[0072] The present invention is further explained in the implementation, The result is the entropy value of the product of the discrete amplitude of each power supply information affected by all parameter variables and the weight coefficient obtained by the sensors of all azimuth points at the vth time point and the product of the total count of the azimuth points and the number of discrete variables. The smaller the result of the entropy value, the higher the similarity of the discrete amplitude of each power supply information affected by all parameter variables obtained at the current time point. At the same time, it also shows in a deeper level that the smaller the amplitude range of the information point obtained at the current time point, the more obvious the data concentration trend. On the contrary, the larger the result of the entropy value, the larger the amplitude range of the information point obtained at the current time point, and the more obvious the dispersion of the data arrangement.

[0073] In summary, the embodiments provided by the present invention can analyze and calculate the amplitude of the distributed power value at a specific point in time based on the interval length between the sensors at each azimuth point and the distributed power source main terminal and the discrete amplitude of each power source information obtained by the sensors at all azimuth points at a specific point in time under the influence of all discrete variables.

[0074] In S4, the product of the discrete amplitude of the information point at the corresponding time point of the reference azimuth point and the weight coefficient is obtained according to the amplitude and discrete amplitude calculated in steps S2 and S3.

[0075] It is further explained in the implementation of the present invention that for the information points collected at a randomly selected time point, the smaller the amplitude range at the current time point, the more stable the information flow captured by the sensor. It also indicates that the reliability and authenticity of the discrete amplitude of the information point at the current time point are higher. Therefore, based on the discrete amplitudes of all information points and the amplitude at the time point corresponding to the current information point, an algorithm operation of multiplying the discrete amplitudes of all information points by a weight coefficient can be performed.

[0076] In this embodiment, the discrete amplitudes of all information points are multiplied by the weight coefficient according to the discrete amplitudes of each information point and the amplitude at the time point corresponding to the current information point. The calculation formula is:

[0077] E′ s,v =E s,v ×(1-W v ).

[0078] Explain the formula in detail. s,v is the discrete amplitude of the information point at the vth time point corresponding to the sth prescription site; E' s,v is the product of the discrete amplitude of the information point at the vth time point corresponding to the sth prescription site and the weight coefficient; W v is the amplitude of the distributed power source terminal collected at the vth time point.

[0079] It is further explained in the implementation of the present invention that for the information points collected at a randomly selected time point, the smaller the amplitude range at the current time point, the more stable the information flow captured by the sensor. It also indicates that the reliability and authenticity of the discrete amplitude of the information point at the current time point are higher, that is, the product value of the discrete amplitude of the information point collected at the current time point and the weight coefficient is higher; conversely, the product value is lower.

[0080] In S5, the weight constant of the corresponding information point is analyzed and calculated according to the product value of the discrete amplitude and the weight coefficient obtained in step S4; at the same time, a denoising and clutter removal algorithm operation is performed on each information point in the distributed power value according to the weight constant of each information point to obtain the distributed power value after denoising and clutter removal.

[0081] The implementation of the present invention further includes that, according to the exponential sliding average method, the size of the weight constant represents the data fluctuation and discreteness, and also includes the reaction level of the difference between the estimated value and the actual value. The larger the weight constant, the stronger the data denoising effect, and the more sensitive the reaction to the change of the actual data. The smaller the weight constant, the weaker the data denoising effect, and the slower the reaction to the change of the actual data. The higher the discrete amplitude, the greater the numerical difference between the information point and the adjacent information point, and the more obvious the noise fluctuation of the current information point. Then, when performing the denoising operation on the information point with a higher current discrete amplitude, a relatively large weight constant is used to make the current information point have a more significant denoising effect after the operation, and the noise of the current data point can be removed more efficiently to ensure the stability of the data as a whole. Therefore, the more obvious the noise fluctuation is, the larger the weight constant is, the milder the noise fluctuation is, and the smaller the weight constant is. The larger the product value of the discrete amplitude of the current data point and the weight coefficient is, the larger the value of the corresponding weight constant of the current information point is, and on the contrary, the smaller the value of the corresponding weight constant of the current information point is.

[0082] The present invention further explains that according to the product value of the discrete amplitude of each data point and the weight coefficient, the weight constant of the current information point is analyzed and calculated, and the calculation formula is: Explain the formula in detail. s,v is the weight constant of the information point at the vth time point corresponding to the sth prescription site; E' s,v It is the product of the discrete amplitude of the information point at the vth time point corresponding to the sth prescription site and the weight coefficient.

[0083] Based on all the above steps, the corresponding weight constants of the information points collected at each time point can be finally analyzed and calculated; after obtaining the corresponding weight constants, the denoising and clutter removal algorithm operation is performed on the information points at the current time point according to the corresponding weight constants of the information points at each time point; then the denoised stable information point data is saved for the subsequent analysis of the reliability and stability of the distributed power supply values.

[0084] The beneficial effects of the present invention are that, compared with the prior art, it can timely and synchronously obtain the distributed power values ​​of the distributed power main terminal located at multiple prescription points; perform outlier fluctuation test analysis on the distributed power values ​​of all azimuth points in turn, and obtain the discrete amplitudes of each information point corresponding to all azimuth points in turn; take any azimuth point as the reference azimuth, and obtain the discrete amplitudes of each power information of the information points corresponding to all azimuth points at all time points under the influence of each discrete variable; take any time point as the reference time point, and calculate the amplitude of the distributed power main terminal at the reference time point; based on the amplitude and discrete amplitude, obtain the product of the discrete amplitude of the information point corresponding to the reference azimuth point and the weight coefficient; analyze and calculate to obtain the corresponding weight constants of the information points collected at each time point. The above technical scheme filters and averages the data, making the data more reliable, stable and secure, specifically including the following functions:

[0085] 1. Remove noise: In actual data collection, due to various reasons, there is often some noise in the data, which will interfere with the analysis and processing of the data and even lead to wrong conclusions. By denoising the data, these noises can be removed or weakened to make the data more accurate.

[0086] 2. Reduce fluctuations: Some data may have large fluctuations, which may be caused by the influence of the external environment or the characteristics of the data itself. By denoising the data, these fluctuations can be reduced and the data can be made more stable.

[0087] 3. Eliminate outliers: Sometimes there may be some outliers in the data. These outliers may be caused by problems with data collection or the characteristics of the data itself. By denoising the data, these outliers can be eliminated or weakened, making the data more reliable.

[0088] 4. Optimize data analysis: For some data analysis models, denoising can make the data more consistent with the model assumptions, thereby improving the accuracy and reliability of data analysis.

[0089] In summary, data denoising can effectively improve the quality and reliability of data, improve the accuracy and reliability of data analysis, and is an important part of data acquisition and processing.

[0090] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0091] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0092] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0093] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for processing information of a distributed power generation settlement process, characterized in that: The following steps are involved: S1: timely and synchronously obtain the distributed power values ​​of the distributed power main terminal located at multiple prescription locations; S2: Perform outlier fluctuation test analysis on the distributed power values ​​of all azimuth points in sequence to obtain the discrete amplitudes of each information point corresponding to all azimuth points in sequence; S3: Take any one point as the reference point, and obtain the discrete amplitude of each power information under the influence of each discrete variable at the information point of all points at all times; take any time point as the reference time point, and calculate the amplitude of the distributed power source terminal at the reference time point; S4: According to the amplitude and discrete amplitude, obtain the product of the discrete amplitude of the information point at the corresponding time point of the reference azimuth point and the weight coefficient; S5: Analyze and calculate to obtain the corresponding weight constants of the information points collected at each time point.

2. The information processing method of a distributed power generation settlement process according to claim 1 is characterized in that: The step S1 further comprises: The distributed power value of the distributed power source terminal located at multiple prescription sites is obtained in a timely and synchronous manner, and the distributed power value includes the information points collected corresponding to each time point at any location, and the information points obtain the corresponding power source information under the influence of no less than two discrete variables.

3. The information processing method of a distributed power generation settlement process according to claim 2 is characterized in that: When the number of discrete variables is 3, the discrete variables include signal decibel value, carrier-to-noise ratio and noise factor.

4. The information processing method of a distributed power generation settlement process according to claim 1 is characterized in that: The step S2 further comprises: The discrete amplitude of each information point is obtained by the analysis and calculation of the isolation forest algorithm. All the information points collected by sensors at all azimuth points are analyzed by the isolation forest algorithm to perform outlier fluctuation test analysis, and then the corresponding outlier scores of all the information points are obtained, and finally the discrete amplitude of the information point at a certain time point corresponding to a certain azimuth point is obtained.

5. The information processing method of a distributed power generation settlement process according to claim 1 is characterized in that: The step S3 further comprises: Take any azimuth point as the reference azimuth, analyze and calculate the discrete amplitudes of each power information of the information point at each time point corresponding to the reference azimuth under the influence of different discrete variables, and then obtain the discrete amplitudes of each power information of the information points at all azimuths corresponding to all time points under the influence of various discrete variables in this way; take any time point as the reference time point, and calculate the amplitude of the distributed power source main terminal at the reference time point based on the discrete amplitudes of the power information of all azimuth information points at the reference time point under the influence of various discrete variables.

6. The information processing method of the distributed power generation settlement process according to claim 5 is characterized in that: The discrete amplitude calculation formula is: First, a regional array is set as Cg, and its array length is H. Based on all the information points in any direction, in each power information group formed under the influence of each discrete variable, a power information is selected as the midpoint of the regional array Cg. According to the current power information and the power information group formed based on a time point under the influence of a discrete variable where the current power information is located, all the power information in the regional array Cg with a length of H is selected, and the discrete amplitude between the current power information and the remaining power information in the regional array Cg where the current power information is located is calculated. The calculation formula is: Explain the formula in detail. s,v,t F is the discrete amplitude between the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the remaining power information in the area array Cg where the power information is located; s,v,t For f s,v,t The maximum and minimum algorithm processing is the final result used in subsequent calculations; M is abbreviated as the formula L s,v,t is the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point; is the average of all power information in the area array Cg where the power information affected by the tth parameter variable is located among the information points collected by the sth azimuth point at the vth time point; H is the length of the area array Cg where the power information affected by the tth parameter variable is located among the information points collected by the sth azimuth point at the vth time point; is the βth power information in the remaining power information in the area array Cg where the power information affected by the tth parameter variable is located among the information points collected by the sth azimuth point at the vth time point; min(f s,v,t ) is the minimum value of the discrete amplitude between the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the remaining power information in the area array Cg where the power information is located; max(f s,v,t ) is the maximum value of the discrete amplitude between the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the rest of the power information in the regional array Cg where the power information is located.

7. The information processing method of the distributed power generation settlement process according to claim 6 is characterized in that: The amplitude calculation formula is: Explain the formula in detail. v is the amplitude of the distributed power value collected at the vth time point; A is the total count of the azimuth points where the sensors are placed; B is the number of discrete variables of the collected information points; F' is the mean of the discrete amplitudes of the power information affected by the tth parameter variable in the information points collected by all azimuth points at the vth time point; s,v,t is the product of the discrete amplitude of the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point and the weight coefficient; s,v,t is the discrete amplitude of the power information affected by the tth parameter variable in the information point collected by the sth azimuth point at the vth time point, d s is the distance between the sth azimuth point and the main terminal of the distributed power generation.

8. The information processing method of a distributed power generation settlement process according to claim 1 is characterized in that: The step S4 further comprises: According to the discrete amplitude of each information point and the amplitude at the time point corresponding to the current information point, the discrete amplitude of all information points is multiplied by the weight coefficient. The calculation formula is: HAVE BEEN' s,v =E s,v ×(1-W v ); Explain the formula in detail. s,v is the discrete amplitude of the information point at the vth time point corresponding to the sth prescription site; E' s,v is the product of the discrete amplitude of the information point at the vth time point corresponding to the sth prescription site and the weight coefficient; W v is the amplitude of the distributed power source terminal collected at the vth time point.

9. The information processing method of a distributed power generation settlement process according to claim 1 is characterized in that: The step S5 further comprises: According to the product value of the obtained discrete amplitude and the weight coefficient, the limit value of the corresponding information point is analyzed and calculated; at the same time, according to the limit value of each information point, the denoising and clutter removal algorithm operation is performed on each information point in the distributed power value to obtain the distributed power value after denoising and clutter removal; the calculation formula of the weight constant is: Explain the formula in detail. s,v is the weight constant of the information point at the vth time point corresponding to the sth prescription site; E' s,v It is the product of the discrete amplitude of the information point at the vth time point corresponding to the sth prescription site and the weight coefficient.

10. The information processing method of the distributed power generation settlement process according to claim 9 is characterized in that: The weight constant is obtained by using the exponential moving average method to obtain a specific value of the weight constant. The size of the weight constant represents data fluctuation and discreteness, and also includes the response level to the difference between the estimated value and the actual value.

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