Resource communication method and system based on intelligent fusion terminal

Abstract

The invention relates to the field of digital information transmission, in particular to a resource communication method and system based on an intelligent fusion terminal. The method comprises the steps of collecting current data of the electric energy meter in real time, dividing windows according to a current abrupt change condition, calculating a corresponding window abnormity evaluation value according to the data of each window, comparing the window abnormity evaluation value with a preset window abnormity evaluation threshold to obtain an abnormal window, and calculating an electric power risk evaluation value of the electric energy meter according to the window abnormity evaluation value of the abnormal window. And matching communication resources according to the power risk evaluation values from large to small to transmit electric energy meter data. According to the method, the data cleanliness and the risk response capability can be improved.

Description

Technical Field

[0001] This application relates to the field of digital information transmission, and in particular to a resource communication method and system based on an intelligent fusion terminal. Background Technology

[0002] As the construction of smart grids progresses, bottlenecks in the data acquisition and communication architecture of distribution substations are gradually becoming apparent. Although the existing three-tier architecture has replaced the traditional unified collection mode of the main station, problems such as unstable data quality, delayed fault warnings, and low utilization of communication resources have become prominent after the large-scale access of distributed energy and new electrical equipment, increasing the risks to power safety.

[0003] Existing technologies are insufficient to effectively filter electromagnetic interference and noise caused by equipment malfunctions, and lack the ability to accurately identify and eliminate abnormal data; a risk warning and evaluation system has not been established, making it impossible to quickly locate safety hazards; data transmission does not combine risk levels with communication resources, resulting in low network bandwidth utilization, delays in uploading critical information, and impacting the efficiency of main station decision-making. Summary of the Invention

[0004] To address the problem that existing technologies lack the ability to accurately identify and remove abnormal data, thus failing to quickly locate security risks, this application provides a resource communication method and system based on an intelligent fusion terminal.

[0005] Firstly, this application provides a resource communication method based on an intelligent converged terminal, employing the following technical solution: A resource communication method based on an intelligent converged terminal includes the following steps: Real-time acquisition of current data from electricity meters; division of windows based on current abrupt changes; calculation of the corresponding window anomaly evaluation value based on the data of each window and comparison with the preset window anomaly evaluation threshold to obtain anomaly windows; calculation of the electricity meter's power risk evaluation value based on the window anomaly evaluation value of the anomaly windows; and matching communication resources to transmit electricity meter data from largest to smallest based on the power risk evaluation value. The process of dividing the window based on the current abrupt change includes: calculating the first current anomaly level at each moment, setting a current anomaly level threshold, and recording the moment as the anomaly start point if the first current anomaly level is greater than the preset current anomaly level threshold, otherwise not recording it; constructing a real-time second current anomaly level based on the anomaly start point, and recording the moment as the anomaly end point if the second current anomaly level is less than the preset current anomaly level, thereby obtaining the window composed of the anomaly start point and the anomaly end point, as well as the number of current data and the current data sequence within the window; The step of calculating the corresponding window anomaly evaluation value based on the data of each window includes: taking any window as the target window, calculating the similarity of the number of current data contained in the target window and the number of current data contained in other arbitrary windows, calculating the overall similarity of the current based on the current data sequence in the target window and the current data sequence contained in other arbitrary windows, and calculating the difference between 1 and the product of the similarity of the number of current data and the overall similarity of the current as the window anomaly evaluation value of the target window.

[0006] Furthermore, the method for calculating the degree of the first current anomaly includes: calculating the absolute value of the difference between the outlier distance at the previous moment and the average distance from the end of the previous window to the previous moment and 1 as the degree of the first current anomaly. If there is no previous window endpoint, then change to time 1.

[0007] Furthermore, the outlier distance calculation method includes: using a spline function to fit the real-time continuous current data to obtain a real-time current curve by the ratio of the real-time current data to the current data at the previous moment, and calculating the Euclidean distance from the current to the current curve at each moment as the outlier distance.

[0008] Furthermore, the second current anomaly degree calculation method is as follows: select the anomaly starting point, and calculate the average of the product of the time correction coefficient of the anomaly starting point and the first current anomaly degree of the corresponding anomaly starting point and the time correction coefficient of the anomaly starting point and the time correction coefficient of the corresponding time.

[0009] Further, the step of obtaining the abnormal window includes: comparing the window abnormality evaluation value corresponding to the target window with a preset window abnormality evaluation threshold; if the window abnormality evaluation value of the target window is greater than the preset window abnormality evaluation threshold, the target window is defined as a normal window and discarded; if the window abnormality evaluation value of the target window is less than or equal to the preset window abnormality evaluation threshold, the target window is defined as an abnormal window and retained.

[0010] Furthermore, the method for calculating the power risk assessment value of the electricity meter includes: calculating the slope of the window anomaly assessment value of all abnormal windows after one calculation using the least squares method, and defining the product of the slope and the cumulative value of the window anomaly assessment value of all abnormal windows as the power risk assessment value of the electricity meter.

[0011] Furthermore, the greater the slope of the window anomaly evaluation values ​​of all abnormal windows, the greater the increase in the window anomaly evaluation values ​​of the abnormal windows; the greater the cumulative value of the window anomaly evaluation values ​​of all abnormal windows, the more abnormal windows there are or the larger the window anomaly evaluation values ​​of the abnormal windows.

[0012] Secondly, this application provides a resource communication system based on an intelligent converged terminal, which adopts the following technical solution: A resource communication system based on an intelligent converged terminal includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the resource communication method based on an intelligent converged terminal described above is implemented.

[0013] This application has the following technical effects: This application achieves accurate identification of abnormal windows by dividing data windows through calculation of current mutation characteristics and comprehensively evaluating anomalies by combining data sequence similarity and window length similarity. This enables the accurate identification and separation of "abnormal windows" containing real potential hazards. This process effectively filters out transient noise caused by electromagnetic interference, ensuring the high quality and representativeness of subsequent analysis data and laying a reliable data foundation for accurate risk assessment.

[0014] This application constructs a dynamic and quantitative power risk assessment value based on the identified abnormal windows and by calculating the cumulative trend (cumulative value) and rate of change (slope) of the abnormal evaluation values ​​of the windows. This model can not only reflect the overall severity of the risk, but also capture the trend of accelerated deterioration of the risk, realizing a deeper understanding of the operating status of electricity meters from "abnormal detection" to "risk classification", and providing a scientific basis for priority dispatch.

[0015] This application prioritizes electricity meters from high to low based on their power risk assessment values ​​and automatically matches them with corresponding communication resources. This means that high-risk abnormal data will have priority access to high-speed, reliable transmission channels. This significantly optimizes network bandwidth utilization, ensures low-latency uploading of critical alarm information, thereby helping the main station platform achieve rapid response and accurate decision-making, and improving the overall system's security defense capabilities and operational efficiency. Attached Figure Description

[0016] The above and other objects, features, and advantages of the present invention will become readily apparent from the following detailed description of exemplary embodiments, accompanied by the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein: Figure 1 This is a flowchart of a resource communication method based on an intelligent fusion terminal provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This application discloses a resource communication method based on an intelligent fusion terminal, referring to... Figure 1 ,include: S1: Real-time acquisition of electricity meter current data.

[0019] Specifically, within a distribution area, under the control of a master station, the smart terminal communicates with the smart meters within the distribution area via a standard communication protocol to acquire real-time operational data, including current. The analog-to-digital converter (ADC) built into the smart meter is responsible for converting the analog signals collected by the meter into digital signals, which are then transmitted to the smart fusion terminal via the communication module. This process yields the real-time current data and its corresponding timestamps collected by the smart fusion terminal under the control of the master station.

[0020] S2: Divide the window according to the current change situation, calculate the corresponding window anomaly evaluation value according to the data of each window and compare it with the preset window anomaly evaluation threshold to obtain the abnormal window.

[0021] Specifically, in power systems, equipment switching, load changes, and radio waves can all generate electromagnetic interference, introducing abnormal data and affecting the results of subsequent analysis. Therefore, it is necessary to eliminate abnormal voltage and current values ​​to provide more accurate data for subsequent calculations. Current responds to system changes much faster and with greater amplitude than voltage, making it the primary signal source for anomaly detection. Therefore, it is necessary to detect instantaneous current.

[0022] Specifically, the current data collected by smart meters is recorded synchronously in the form of correlated data pairs anchored by timestamps. Abnormal values ​​of current and voltage caused by electromagnetic interference will deviate from the normal range in the short term rather than change gradually. Under normal circumstances, voltage and current show a segmented periodic change with the increase of time. Therefore, the real-time current data is divided into windows.

[0023] It is important to note that within the window, voltage and current exhibit a stable trend. However, user behavior (such as starting and stopping high-power appliances) and line faults (such as short circuits and leakage) can immediately cause drastic changes in current. For example, during a short circuit, the peak current can reach 10 to 100 times the normal level, while the voltage is buffered by the grid's voltage regulation mechanism, resulting in minimal instantaneous changes. By dividing the window according to sudden current changes, transient anomalies can be quickly captured, providing a clear target for subsequent verification.

[0024] Specifically, dividing the window according to the current change includes: calculating the first current anomaly level at each moment, setting a current anomaly level threshold, and recording the moment as the anomaly start point if the first current anomaly level is greater than the preset current anomaly level threshold, otherwise not recording it; constructing a real-time second current anomaly level based on the anomaly start point, and recording the moment as the anomaly end point if the second current anomaly level is less than the preset current anomaly level, thereby obtaining the window composed of the anomaly start point and the anomaly end point, as well as the number of current data and the current data sequence within the window.

[0025] Specifically, the method for calculating the degree of the first current anomaly includes: calculating the absolute value of the difference between the outlier distance at the previous moment and the average distance from the end point of the previous window to the previous moment and 1; if there is no end point of the previous window, then the starting point of the first moment is used instead.

[0026] Specifically, the outlier distance calculation method includes: using a spline function to fit the real-time continuous current data to obtain the real-time current curve by the ratio of the real-time current data to the current data at the previous moment, and calculating the Euclidean distance from the current to the current curve at each moment as the outlier distance.

[0027] Specifically, the expression for calculating the degree of the first current anomaly is as follows: in, For the first The degree of the first current anomaly at time 1. The endpoint of the previous window to the [number]th [window]. The set of outlier distances at any given moment. For the first Outlier distance at time (this step calculates the first) Time relative to The mutation before the moment represents Everything was in a stable state before that moment.

[0028] For the first The outlier distance at time t = the distance from the end of the previous window to the first window. The difference in average outlier distance at time 1; the closer this value is to 1, the better the difference in average outlier distance at time 1. The outlier distance at time t = the distance from the end of the previous window to the first window. The smaller the difference in the average outlier distance at any given time, the lower the probability of an outlier.

[0029] It should be noted that, The endpoint of the previous window to the [number]th [window]. The set of outlier distances at time points, where if there is no previous window endpoint, it is changed to time point 1.

[0030] When the degree of the first current anomaly exceeds the preset anomaly threshold (0.5 in this embodiment), it indicates that the first current anomaly... If the current value at a given time is abnormal, record that time as the start of the abnormality. Otherwise, do not record it.

[0031] Specifically, the method for calculating the degree of the second current anomaly includes: selecting the starting point of the anomaly, and calculating the average of the product of the time correction coefficient at the starting point of the anomaly and the degree of the first current anomaly at the starting point of the anomaly and the corresponding time as the starting point of the anomaly and the degree of the first current anomaly at the corresponding time as the degree of the second current anomaly corresponding to the starting point of the anomaly.

[0032] Specifically, based on the abnormal starting points obtained above, a system is constructed using... The degree of the second current anomaly at the time of the anomaly's inception, and the expression for calculating the degree of the second current anomaly: in, Indicates Within the window where the time is the starting point of the anomaly The degree of the second current anomaly at time [time] Indicates the time from the end of the previous moment to the next moment. The set of outlier distances at any given moment. Indicates any other time. Indicates the first The distance from the group at any given moment, This represents the time correction coefficient. The larger the time difference from the start of the anomaly, the smaller this value becomes. This is because the further away from the start of the window, the more likely the fitting process is to deviate from the fitting curve when an anomaly occurs. However, when the current returns to the state before the start of the window, the outlier distance is also relatively large. Therefore, it is necessary to appropriately reduce the degree of the first current anomaly to ensure that the result of the degree of the second current anomaly converges.

[0033] When the abnormality level of the second current is less than the preset abnormality level threshold (0.5), it indicates that the second current is abnormal. The current at time 100 tends to stabilize and returns to the current state before the abnormal start point, and this time is recorded as the abnormal end point. A window consisting of the abnormal start point and the abnormal end point is then obtained.

[0034] Specifically, regarding the windows and the total number of current data points within the windows obtained through real-time data acquisition, when the acquired data becomes abnormal or a high-power appliance is suddenly turned on, the voltage and current values ​​will change drastically. The current recovers immediately after the instantaneous change. Windows with fewer current data points are extracted. Since the voltage and current values ​​will also change drastically when a high-power appliance is suddenly turned on, the current data within the windows will be further analyzed.

[0035] Specifically, abnormal values ​​caused by electromagnetic interference or voltage and current generated by suddenly turning on high-power electrical appliances are further calculated based on the current data in the window obtained above, and normal windows are eliminated to obtain abnormal windows.

[0036] Specifically, the calculation of the corresponding window anomaly evaluation value based on the data of each window includes: taking any window as the target window, calculating the similarity of the number of current data contained in the target window and the number of current data contained in other arbitrary windows, calculating the overall similarity of the current based on the current data sequence in the target window and the current data sequence contained in other arbitrary windows, and calculating the difference between 1 and the product of the similarity of the number of current data and the overall similarity of the current as the window anomaly evaluation value of the target window.

[0037] Specifically, taking any window as the target window, the window anomaly evaluation value of the target window is calculated. The expression for calculating the window anomaly evaluation value is as follows: in, This represents the window anomaly evaluation value of the target window. Indicates the first The number of current data points contained within each window. This indicates the number of current data points contained within the target window. This represents the current data sequence within the target window. Indicates the first Current data sequence within a window For time-normalized distance; This indicates the number of windows that have been obtained.

[0038] Indicates the first The degree of similarity between the currents in the two windows and the target window. The smaller the value, the more similar the currents in the two windows are. Indicates the first The similarity of the number of current data points contained in the window and the target window, i.e., the duration of the anomaly is consistent, the smaller the value, the closer they are.

[0039] Specifically, a threshold for the degree of current anomaly in the target window is set. When the anomaly evaluation value of the target window is greater than the threshold, it indicates that the current change within the window is similar to the current changes in multiple windows in the past, and the target window is a normal window and is discarded. Conversely, when the anomaly evaluation value of the target window is less than or equal to the threshold, the target window is an abnormal window and is retained.

[0040] S3: Calculate the power risk assessment value of the electricity meter based on the window anomaly assessment value of the anomaly window, and match the communication resources to transmit the electricity meter data according to the power risk assessment value from large to small.

[0041] Specifically, since the windows obtained after the above windows are removed are all windows with abnormal situations, the power risk assessment value of the abnormal windows is calculated and used to reallocate communication resources. The higher the power risk assessment value, the more priority is given to matching communication resources.

[0042] Specifically, the electricity risk assessment value of the electricity meter is represented by multiplying the slope obtained by the least squares method of the window anomaly assessment values ​​of all anomaly windows after one transmission with the cumulative value of the window anomaly assessment values ​​of all anomaly windows. The larger the slope, the greater the increase in the window anomaly assessment value of the anomaly window. A larger cumulative value of the window anomaly assessment values ​​of all anomaly windows indicates more anomaly windows or larger window anomaly assessment values ​​of the anomaly windows. The electricity meter data is transmitted in descending order of the real-time sorting results, according to the magnitude of all electricity risk assessment values.

[0043] This application also discloses a resource communication system based on an intelligent converged terminal, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a resource communication method based on an intelligent converged terminal according to this application is implemented.

[0044] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0045] In this application, the aforementioned memory can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory (DRAM), dynamic random access memory (DRAM), static random access memory (SRAM), or any other medium that can be used to store required information and can be accessed by an application program, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device.

[0046] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A resource communication method based on an intelligent converged terminal, characterized in that, Including the following steps: Real-time acquisition of current data from electricity meters; division of windows based on current abrupt changes; calculation of the corresponding window anomaly evaluation value based on the data of each window and comparison with the preset window anomaly evaluation threshold to obtain anomaly windows; calculation of the electricity meter's power risk evaluation value based on the window anomaly evaluation value of the anomaly windows; and matching communication resources to transmit electricity meter data from largest to smallest based on the power risk evaluation value. The process of dividing the window based on the current abrupt change includes: calculating the first current anomaly level at each moment, setting a current anomaly level threshold, and recording the moment as the anomaly start point if the first current anomaly level is greater than the preset current anomaly level threshold, otherwise not recording it; constructing a real-time second current anomaly level based on the anomaly start point, and recording the moment as the anomaly end point if the second current anomaly level is less than the preset current anomaly level, thereby obtaining the window composed of the anomaly start point and the anomaly end point, as well as the number of current data and the current data sequence within the window; The step of calculating the corresponding window anomaly evaluation value based on the data of each window includes: taking any window as the target window, calculating the similarity of the number of current data contained in the target window and the number of current data contained in other arbitrary windows, calculating the overall similarity of the current based on the current data sequence in the target window and the current data sequence contained in other arbitrary windows, and calculating the difference between 1 and the product of the similarity of the number of current data and the overall similarity of the current as the window anomaly evaluation value of the target window.

2. The resource communication method based on an intelligent fusion terminal according to claim 1, characterized in that, The method for calculating the degree of the first current anomaly includes: calculating the absolute value of the difference between the outlier distance at the previous moment and the average distance from the end of the previous window to the previous moment and 1 as the degree of the first current anomaly. If there is no previous window endpoint, then change to time 1.

3. The resource communication method based on an intelligent fusion terminal according to claim 2, characterized in that, The outlier distance calculation method includes: using a spline function to fit the real-time continuous current data to obtain a real-time current curve by the ratio of the real-time current data to the current data at the previous moment, and calculating the Euclidean distance from the current to the current curve at each moment as the outlier distance.

4. The resource communication method based on an intelligent fusion terminal according to claim 1, characterized in that, The second current anomaly degree calculation method is as follows: Select the anomaly starting point, and calculate the average of the product of the time correction coefficient of the anomaly starting point and the first current anomaly degree of the corresponding anomaly starting point and the time correction coefficient of the anomaly starting point and the time correction coefficient of the corresponding time.

5. The resource communication method based on an intelligent fusion terminal according to claim 1, characterized in that, The process of obtaining abnormal windows includes: comparing the window abnormality evaluation value corresponding to the target window with a preset window abnormality evaluation threshold; if the window abnormality evaluation value of the target window is greater than the preset window abnormality evaluation threshold, the target window is defined as a normal window and discarded; if the window abnormality evaluation value of the target window is less than or equal to the preset window abnormality evaluation threshold, the target window is defined as an abnormal window and retained.

6. The resource communication method based on an intelligent fusion terminal according to claim 1, characterized in that, The method for calculating the power risk assessment value of the electricity meter includes: calculating the slope of the window anomaly assessment value of all abnormal windows after one calculation using the least squares method, and defining the product of the slope and the cumulative value of the window anomaly assessment value of all abnormal windows as the power risk assessment value of the electricity meter.

7. A resource communication method based on an intelligent fusion terminal according to claim 6, characterized in that, The greater the slope of the window anomaly evaluation values ​​of all abnormal windows, the greater the increase in the window anomaly evaluation values ​​of the abnormal windows; the greater the cumulative value of the window anomaly evaluation values ​​of all abnormal windows, the more abnormal windows there are or the larger the window anomaly evaluation values ​​of the abnormal windows.

8. A resource communication system based on an intelligent converged terminal, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement a resource communication method based on an intelligent fusion terminal according to any one of claims 1-7.

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