A data display optimization method and system based on power channel business

Abstract

The present invention relates to the technical field of power information data processing, and in particular to a data display optimization method and system based on power channel business, the method comprising: collecting or acquiring power data and influencing factor data of each node of the power channel business in time series, and determining the associated nodes of each node; monitoring the fluctuation of each influencing factor data in time series, and determining the related nodes under each influencing factor; acquiring the power data of the related nodes under each influencing factor, and determining the influence coefficient of a single influencing factor on the corresponding related node; determining the fluctuation threshold of each influencing factor based on the percentage of the unit change rate of each influencing factor on the related node; acquiring the influencing factor data of each node in real time, determining the related nodes based on the fluctuation threshold, and displaying the influence correlation. The present invention reduces the amount of data processing of the power channel business and improves the efficiency and accuracy of power business data processing, providing strong support for intelligent decision-making.

Description

Technical Field

[0001] The present invention relates to the technical field of power information data processing, and in particular to a data display optimization method and system based on power channel business. Background Art

[0002] With the development of smart grids and ubiquitous power Internet of Things, the power industry is gradually shifting towards intelligence and automation. In this process, a large amount of power business data is generated, processed and analyzed. These power business data include not only real-time data on power production, transmission and consumption, but also historical data, forecast data and data analyzed by various algorithms. These power business data are large in volume, diverse in types and change rapidly. Traditional data display methods can no longer meet complex and changing business needs.

[0003] By analyzing and processing the business data of the power channel in real time and monitoring the entire business process, bottlenecks and problems in the business process can be discovered in a timely manner, thereby optimizing the business process, improving efficiency and performance, and reflecting certain characteristics of the power data to a certain extent, thereby providing effective basis and support, and facilitating relevant personnel to make business decisions and optimize the power system.

[0004] Therefore, there is an urgent need for an efficient, accurate and intuitive data display optimization method for power channel business to solve the above technical problems. Summary of the invention

[0005] To this end, the present invention provides a data display optimization method based on electric power channel business, so as to overcome the technical problem that the data display of electric power channel business in the prior art is not efficient and intuitive.

[0006] To achieve the above object, the present invention provides a data display optimization method based on power channel business, comprising:

[0007] Step S1, collecting or acquiring power data and influencing factor data of each node of the power channel business in time sequence, and determining the associated nodes of each node respectively;

[0008] Step S2, respectively monitoring the time series data fluctuation of each influencing factor data collected by each node, determining the correlation of each associated node under each influencing factor, and determining the relevant nodes under each influencing factor;

[0009] Step S3, respectively obtaining power data of relevant nodes under various influencing factors, forming several power data fluctuation curves of single independent variables, and determining the influence coefficient of a single influencing factor on the corresponding relevant node;

[0010] Step S4, determining a fluctuation threshold of a single influencing factor based on the influence percentage of the unit change rate of the single influencing factor on the relevant nodes;

[0011] Step S5, based on the influencing factor data of each node of the power channel business acquired in real time, the corresponding related nodes are determined based on the corresponding fluctuation threshold, and the influence correlation of the determined related nodes is displayed to prompt the influence coefficient of a single influencing factor that exceeds the fluctuation threshold on each related node.

[0012] Further, in step S1, determining the associated node of each node includes:

[0013] Step S11, determining a target node based on each node of the power channel business;

[0014] Step S12, obtaining the connection relationship of the power channel business where the target node is located;

[0015] Step S13, based on the connection relationship, obtaining a number of nodes that are directly or indirectly connected to the target node, wherein the connection relationship ends at a power supply node or a power consumption node.

[0016] Furthermore, in step S2, it includes:

[0017] Step S21, based on a preset time period, collect the time series data of each influencing factor and the time series data of the power data at each node, and draw the fluctuation curve of the data of each influencing factor and the fluctuation curve of the power data of each node accordingly;

[0018] Step S22, determining the mutation time period and change interval of each influencing factor and each node power data respectively according to the fluctuation curve of each influencing factor data and the fluctuation curve of each node power data;

[0019] Step S23, calculating the mutation coincidence between each influencing factor and each node power data in the mutation time period, and calculating the interval coincidence between each influencing factor and each node power data in the change interval;

[0020] Step S24, determining the correlation of each associated node under each influencing factor based on the mutation overlap and the interval overlap;

[0021] Step S25: determining the related nodes of each node under the single influencing factor based on the correlation and the correlation threshold.

[0022] Furthermore, in step S22, it includes:

[0023] Step S221, determining a number of mutation factor points based on the fluctuation curves of the influencing factor data, and determining a number of mutation nodes based on the fluctuation curves of the power data of each node;

[0024] Step S222, determining the highest mutation factor point based on the plurality of mutation factor points, and determining the highest mutation node based on the plurality of mutation nodes;

[0025] Step S223, determining the mutation time period based on the highest mutation factor point and the highest mutation node;

[0026] Step S224: determining the variation interval based on the remaining mutation factor points excluding the highest mutation factor point and the remaining mutation nodes excluding the highest mutation node.

[0027] Furthermore, in step S23, it includes:

[0028] Standardizing the power data of each influencing factor and each node in the mutation time period and the change interval respectively;

[0029] Determine the mutation coincidence degree based on the standardized influencing factors and the power data of each node in the mutation time period;

[0030] The interval overlap is determined based on the influencing factors in the change interval segment after standardization and the power data of each node.

[0031] Furthermore, in step S3, it includes:

[0032] Step S31, for a single influencing factor, determining a first influencing coefficient of the single influencing factor on the single relevant node according to a power data fluctuation curve of a single relevant node in a preset time period and an influencing factor fluctuation curve;

[0033] Step S32, for a single influencing factor, determining a second influencing coefficient of the single influencing factor on the multiple related nodes according to the power data fluctuation curve of the multiple related nodes in the preset time period and the influencing factor fluctuation curve;

[0034] Step S33: determining the influence coefficient based on the first influence coefficient and the second influence coefficient.

[0035] Furthermore, in step S4, determining the unit change rate of the single influencing factor includes:

[0036] Determine the change amount of the influencing factor between any two mutation factor points based on the plurality of mutation factor points;

[0037] The unit change rate of the single influencing factor is determined based on the change amount of the influencing factor.

[0038] Further, in step S4, determining the impact percentage includes:

[0039] The influence percentage of the unit change rate of the single influencing factor on the related node is determined based on the unit change rate of the single influencing factor and the influence coefficient.

[0040] Further, in step S4, determining the fluctuation threshold includes: comparing the influence percentage of the unit change rate of a single influencing factor on the relevant node with a preset percentage, and determining the fluctuation threshold based on the comparison result.

[0041] Furthermore, in step S5, it includes:

[0042] Step S51, determining a target node based on a user instruction trigger or based on an influencing factor exceeding the fluctuation threshold of a corresponding node;

[0043] Step S52, determining the related nodes corresponding to the target node based on the influencing factor data under the target node and the fluctuation threshold;

[0044] Step S53, displaying the target node, the related nodes corresponding to the target node, and displaying the corresponding influence coefficient based on the related nodes;

[0045] The display of the nodes corresponding to each influencing factor is different, including different display colors, different flashing frequencies, different display positions, and different display push objects.

[0046] A second aspect of the present invention provides a data display optimization system based on electric power channel business, which is used to execute the above method, wherein the system comprises a data acquisition module, a data fluctuation monitoring module, a data analysis module and a data display module;

[0047] The data collection module is used to collect power data and influencing factor data of each node of the power channel business and determine the associated nodes of each node;

[0048] The data fluctuation monitoring module is used to monitor the time series data fluctuation of each influencing factor data collected by each node, determine the correlation of each associated node under each influencing factor, and determine the relevant nodes under each influencing factor;

[0049] The data analysis module forms several single-variable power data fluctuation curves based on the power data acquired by the data acquisition module and the data fluctuation monitoring module, and determines the influence coefficient of a single influencing factor on the corresponding related nodes and the fluctuation threshold of a single influencing factor;

[0050] The data display module displays the target node, the related nodes corresponding to the target node and their corresponding influence coefficients based on the influencing factor data and corresponding fluctuation thresholds of each node of the power channel business acquired in real time.

[0051] Compared with the prior art, the beneficial effect of the present invention is that, through accurate time series data collection and analysis, the associated nodes of each node in the power channel business are effectively identified, and then the correlation of each associated node under each influencing factor is determined, so as to narrow the node association range, reduce the amount of calculation for subsequent data processing and analysis, and determine the influence coefficient and fluctuation threshold of each influencing factor on the related nodes. The nodes affected by each influencing factor data are determined through the fluctuation threshold and the real-time collected influencing factor data, and the influence coefficient of the influencing factor on the related nodes is intuitively displayed, which greatly improves the efficiency and accuracy of power business data processing and provides strong support for the stable operation and intelligent decision-making of the power system.

[0052] Furthermore, the present invention collects the time series data of each influencing factor and the time series data of the power data under each node in a preset time period, effectively captures the fluctuation characteristics of each influencing factor and the power node data, thereby determining the respective mutation time period and change interval segment, and accurately evaluates and determines the correlation of each associated node under each influencing factor by calculating the mutation overlap and interval overlap, and identifies the related nodes of each node accordingly. This further improves the accuracy and efficiency of data processing, ensures the accurate judgment of data relevance, greatly improves the efficiency and accuracy of power business data processing, and provides strong support for the stable operation and intelligent decision-making of the power system.

[0053] Furthermore, the present invention determines the highest mutation factor point and the highest mutation node by respectively identifying the mutation factor points in the data fluctuation curve of each influencing factor and the mutation nodes in the power data fluctuation curve of each node, thereby effectively defining the mutation time period. At the same time, the remaining mutation factor points and the remaining mutation nodes are further used to delineate the change interval segments. This fine-grained time period division significantly improves the accuracy of data fluctuation analysis, while further ensuring the accurate judgment of data correlation, greatly improving the efficiency and accuracy of power business data processing, and providing strong support for the stable operation and intelligent decision-making of the power system.

[0054] Furthermore, the present invention ensures the consistency and comparability of data of different magnitudes when calculating mutation overlap and interval overlap by standardizing the influencing factors and power data of each node in the mutation time period and change interval, thereby accurately reflecting the degree of correlation between each influencing factor and power node data in a specific time period, thereby further improving the efficiency and accuracy of power business data processing, and providing strong support for the stable operation and intelligent decision-making of the power system.

[0055] Furthermore, the present invention carefully distinguishes the impact of a single influencing factor on the power data at a single node and the impact of a single influencing factor on the power data at multiple nodes. The impact of a single influencing factor on the power data at a single node is the first impact coefficient, and the impact of a single influencing factor on the power data at multiple nodes is the second impact coefficient. The final impact coefficient is then determined, and the influence of a single influencing factor in the power channel business system is comprehensively evaluated, making the impact analysis more three-dimensional and multi-dimensional, thereby further ensuring the accurate judgment of data correlation, greatly improving the efficiency and accuracy of power business data processing, and providing strong support for the stable operation and intelligent decision-making of the power system.

[0056] Furthermore, the present invention analyzes the unit change rate of a single influencing factor in detail and combines it with the influence coefficient to accurately calculate the percentage of influence of the single influencing factor on the relevant nodes. Subsequently, a preset percentage threshold is determined, and based on this, the fluctuation threshold of the single influencing factor is reasonably determined, which improves the sensitivity to the volatility of the influencing factor, further warns of potential risks, greatly improves the efficiency and accuracy of power business data processing, and provides strong support for the stable operation and intelligent decision-making of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of the data display optimization method based on the power channel business of the present invention;

[0058] Figure 2 A flow chart for determining relevant nodes under various influencing factors for the present invention;

[0059] Figure 3 A flow chart for determining a mutation time period and a change interval for the present invention;

[0060] Figure 4 The present invention is a flow chart for determining the influence coefficient of a single influencing factor on corresponding related nodes. DETAILED DESCRIPTION

[0061] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0062] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0063] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0064] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0065] Example 1: Please refer to Figure 1 As shown, it is a flow chart of a data display optimization method based on electric power channel business of the present invention. Specifically, a specific embodiment of the present invention provides a data display optimization method based on electric power channel business, including:

[0066] Step S1, collecting or acquiring power data and influencing factor data of each node of the power channel business in time sequence, and determining the associated nodes of each node respectively;

[0067] Step S2, respectively monitoring the time series data fluctuation of each influencing factor data collected by each node, determining the correlation of each associated node under each influencing factor, and determining the relevant nodes under each influencing factor;

[0068] Step S3, respectively obtaining power data of relevant nodes under various influencing factors, forming several power data fluctuation curves of single independent variables, and determining the influence coefficient of a single influencing factor on the corresponding relevant node;

[0069] Step S4, determining a fluctuation threshold of a single influencing factor based on the influence percentage of the unit change rate of the single influencing factor on the relevant nodes;

[0070] Step S5, based on the influencing factor data of each node of the power channel business acquired in real time, the corresponding related nodes are determined based on the corresponding fluctuation threshold, and the influence correlation of the determined related nodes is displayed to prompt the influence coefficient of a single influencing factor that exceeds the fluctuation threshold on each related node.

[0071] It can be understood that in step S1, the nodes of the power channel business are respectively: power supply node, transportation node, sales node, operation and maintenance node and user node, and each node has corresponding influencing factors, for example: the influencing factors corresponding to the power supply node include: environmental factors, time factors, fault factors, etc., where environmental factors include temperature, humidity and wind speed, etc., time factors include spring, summer, autumn and winter, and fault factors include power generation equipment failure, etc.; the influencing factors corresponding to the sales node include: time factors and supply and demand factors, etc., where supply and demand factors include sudden increase in demand and insufficient power supply, etc. And the types of power data collected or obtained at each node are not exactly the same, for example, the power data collected or obtained at the power supply node is the power supply, the power data collected or obtained at the transportation node is the power loss, and the power data collected or obtained at the sales node is the power sales, and so on, the operation and maintenance node corresponds to the number of operation and maintenance users, and the power consumption node corresponds to the power consumption.

[0072] It can be understood that in step S2, after determining the associated nodes of each node, the time series data fluctuation of each influencing factor data under each node is monitored. There is a corresponding influencing factor data at each time point. After determining the correlation of each associated node under each influencing factor, the scope of the associated nodes can be further narrowed and named as related nodes.

[0073] It can be understood that in steps S3 and S4, a single influencing factor has more or less influence on the power data under the relevant nodes, so the influence percentage of the single influencing factor on the relevant nodes is determined based on the unit change rate of the single influencing factor and the influence coefficient of the single influencing factor on the relevant nodes, and then the fluctuation threshold of the single influencing factor is determined based on the influence percentage of the single influencing factor on the relevant nodes.

[0074] It can be understood that in step S5, based on the determined fluctuation threshold of each influencing factor and the real-time collected data of each influencing factor of each node, the relevant nodes that are highly correlated with the influencing factors and easily affected by the influencing factors can be determined, and while displaying the relevant nodes, the influence coefficient of a single influencing factor that exceeds the fluctuation threshold on each relevant node is prompted, so that business personnel can make corresponding decisions on the corresponding nodes.

[0075] Through accurate time series data collection and analysis, the associated nodes of each node in the power channel business can be effectively identified, and then the correlation of each associated node under each influencing factor can be determined, so as to narrow the node association range and reduce the amount of calculation for subsequent data processing and analysis, and at the same time determine the influence coefficient and fluctuation threshold of each influencing factor on the related nodes. The nodes affected by the data of each influencing factor are determined through the fluctuation threshold and the real-time collected data of each influencing factor, and the influence coefficient of the influencing factor on the related nodes is intuitively displayed, which greatly improves the efficiency and accuracy of power business data processing and provides strong support for the stable operation and intelligent decision-making of the power system.

[0076] In a specific embodiment, in step S1, determining the associated node of each node includes:

[0077] Step S11, determining a target node based on each node of the power channel business;

[0078] Step S12, obtaining the connection relationship of the power channel business where the target node is located;

[0079] Step S13, based on the connection relationship, obtaining a number of nodes that are directly or indirectly connected to the target node, wherein the connection relationship ends at a power supply node or a power consumption node.

[0080] It can be understood that, in implementation, the target node is any one of the nodes of the power channel business. Based on the business process of the power channel business, each node has a node directly or indirectly connected to it. When the target node is a power supply node, the node directly connected to the power supply node is a transportation node, and the remaining nodes are indirectly connected. The remaining nodes include sales nodes, operation and maintenance nodes, and user nodes, and the connection relationship ends at the power supply node or the power consumption node; when the target node is a sales node, the node directly connected to the sales node is a user node and a transportation node, and the indirectly connected nodes are power supply nodes and operation and maintenance nodes, and the directly or indirectly connected nodes of the remaining nodes are analogous to this.

[0081] In a specific embodiment, see Figure 2 As shown, it is a flow chart of determining the relevant nodes under each influencing factor of the present invention. Specifically, in step S2, it includes:

[0082] Step S21, based on a preset time period, collect the time series data of each influencing factor and the time series data of the power data at each node, and draw the fluctuation curve of the data of each influencing factor and the fluctuation curve of the power data of each node accordingly;

[0083] Step S22, determining the mutation time period and change interval of each influencing factor and each node power data respectively according to the fluctuation curve of each influencing factor data and the fluctuation curve of each node power data;

[0084] Step S23, calculating the mutation coincidence between each influencing factor and each node power data in the mutation time period, and calculating the interval coincidence between each influencing factor and each node power data in the change interval;

[0085] Step S24, determining the correlation of each associated node under each influencing factor based on the mutation overlap and the interval overlap;

[0086] Step S25: determining the related nodes of each node under the single influencing factor based on the correlation and the correlation threshold.

[0087] It can be understood that in the preset time period, the power data collected at each node at each time point has corresponding data of each influencing factor. Based on each time point, each power data, and each influencing factor, the fluctuation curve of each influencing factor data and the fluctuation curve of each node power data can be drawn respectively. The independent variable of each influencing factor data fluctuation curve is each time point, and the dependent variable is each influencing data. The independent variable of each node power data fluctuation curve is each time point, and the dependent variable is each node power data. Based on the curve fluctuation, the mutation time period and change interval of each influencing factor and each node power data can be determined respectively. The mutation time period is the time period with the largest mutation of the influencing factor and the power data, and the change interval is the time period with obvious mutation of the influencing factor and the power data except the mutation time period. There is only one mutation time period corresponding to each influencing factor and the power data, and there are one or more change intervals, which are determined according to the actual situation.

[0088] It is understandable that under the influence of various influencing factors, the power data of each node will also change. Therefore, the mutation overlap and interval overlap between each influencing factor and each node power data are calculated in the mutation time period and the change interval to indicate the degree of influence of each influencing factor on the power data of each node, and then the correlation of each associated node of a certain node under each influencing factor is determined, and the related nodes are determined accordingly. In other words, the correlation of each associated node under each influencing factor is different. Therefore, when the correlation of each associated node under a single influencing factor is greater than the correlation threshold, it means that each associated node has a strong correlation under a single influencing factor and can be affected by a single influencing factor. At this time, it can be determined that the associated nodes of each node are the related nodes of each node.

[0089] In implementation, the preset time period is in the range of 15 to 30 days, preferably, the preset time period is 20 days, the interval between the time points is 1 to 3 days, preferably, the interval between the time points is 2 days, the correlation threshold is in the range of 0.5 to 1, preferably, the correlation threshold is 0.65. The preset time period, the interval between the time points, the range of the correlation threshold and the preferred value can be determined according to actual conditions, and will not be described in detail here.

[0090] The present invention collects the time series data of each influencing factor and the time series data of the power data under each node in a preset time period, effectively captures the fluctuation characteristics of each influencing factor and the power node data, thereby determining the respective mutation time period and change interval segment, and accurately evaluates and determines the correlation of each associated node under each influencing factor by calculating the mutation overlap and interval overlap, and identifies the related nodes of each node accordingly. This further improves the accuracy and efficiency of data processing, ensures the accurate judgment of data relevance, greatly improves the efficiency and accuracy of power business data processing, and provides strong support for the stable operation and intelligent decision-making of the power system.

[0091] See also Figure 3 As shown, it is a flow chart of determining the mutation time period and the change interval segment of the present invention. Specifically, in step S22, it includes:

[0092] Step S221, determining a number of mutation factor points based on the fluctuation curves of the influencing factor data, and determining a number of mutation nodes based on the fluctuation curves of the power data of each node;

[0093] Step S222, determining the highest mutation factor point based on the plurality of mutation factor points, and determining the highest mutation node based on the plurality of mutation nodes;

[0094] Step S223, determining the mutation time period based on the highest mutation factor point and the highest mutation node;

[0095] Step S224: determining the variation interval based on the remaining mutation factor points excluding the highest mutation factor point and the remaining mutation nodes excluding the highest mutation node.

[0096] It can be understood that the several mutation factor points are the points where the data in the data fluctuation curve of each influencing factor suddenly changes significantly, that is, peaks and troughs, and the several mutation nodes are the points where the data in the power data fluctuation curve of each node suddenly changes significantly, the highest mutation factor point is the highest point in the data fluctuation curve of each influencing factor, and the highest mutation node is the highest point in the power data fluctuation curve of each node.

[0097] Specifically, in step S223, the mutation time period corresponding to each influencing factor is determined based on the highest mutation factor point and the preset first time period, and the mutation time period corresponding to the power data of each node is determined based on the highest mutation node and the preset first time period.

[0098] It can be understood that the length of the mutation time period of each influencing factor and the mutation time period of each node power data can be the same, so that the data of each influencing factor and the power data of each node collected in the mutation time period can be subsequently standardized and the mutation overlap and interval overlap can be calculated.

[0099] In a specific embodiment, when the mutation time period of each influencing factor is the same as the mutation time period of each node power data, assuming that the timestamp corresponding to the highest mutation factor point of a single influencing factor is , the first time period is preset to , then the mutation time period of a single influencing factor can be defined as ; Similarly, assuming that the timestamp corresponding to the highest mutation node of the power data of a single node is , then the mutation time period corresponding to the power data of a single node can be defined as .

[0100] In implementation, the value range of the preset first time period is 1 day to 4 days. Preferably, the preset first time period is 2 days. The value range and preferred value of the preset first time period can be determined according to actual conditions and will not be repeated here.

[0101] In implementation, when the lengths of the change intervals of each influencing factor and the change intervals of the power data of each node are the same, the change intervals can be determined based on the timestamps corresponding to the peaks in the remaining mutation factor points and the remaining mutation nodes and the preset second time period. The method for determining the mutation time period is the same as the method for determining the mutation time period when the lengths of the mutation time periods of each influencing factor and the mutation time periods of the power data of each node are the same, and the timestamp does not include the highest factor mutation point and the highest mutation node. The value range of the preset second time period is 0.5 days to 2 days. Preferably, the preset second time period is 1 day. The value range and preferred value of the preset second time period can be determined according to actual conditions. It is generally defined as the same as the length of the preset first time period, which will not be repeated here.

[0102] In another specific embodiment, when the lengths of the change intervals of each influencing factor and the change intervals of the power data of each node are different, a change interval can be determined based on the timestamps corresponding to the troughs of each influencing factor, without considering the intervals formed by the timestamps corresponding to the troughs on both sides of the highest factor mutation point. A change interval can be determined based on the timestamps corresponding to the troughs of the power data of each node, without considering the intervals formed by the timestamps corresponding to the troughs on both sides of the highest mutation node.

[0103] The present invention determines the highest mutation factor point and the highest mutation node by respectively identifying the mutation factor points in the data fluctuation curve of each influencing factor and the mutation nodes in the power data fluctuation curve of each node, thereby effectively defining the mutation time period. At the same time, the remaining mutation factor points and the remaining mutation nodes are further used to delineate the change interval segments. This fine-grained time period division significantly improves the accuracy of data fluctuation analysis, while further ensuring the accurate judgment of data correlation, greatly improving the efficiency and accuracy of power business data processing, and providing strong support for the stable operation and intelligent decision-making of the power system.

[0104] Specifically, in step S23, it includes:

[0105] Standardizing the power data of each influencing factor and each node in the mutation time period and the change interval respectively;

[0106] Determine the mutation coincidence degree based on the standardized influencing factors and the power data of each node in the mutation time period;

[0107] The interval overlap is determined based on the influencing factors in the change interval segment after standardization and the power data of each node.

[0108] In implementation, Min-Max can be used to standardize the power data of each influencing factor and each node in the mutation time period and the change interval. The method of standardizing the power data of each influencing factor and each node in the mutation time period and the change interval can be determined according to actual conditions and will not be repeated here.

[0109] When Min-Max is used for processing, the standardized data are all in the range of [0,1]. Data of individual influencing factors The calculation formula is as follows:

[0110] (1)

[0111] in, is the data of the i-th single influencing factor in the standardized mutation time period, is the first mutation period before the standardization treatment Data of a single influencing factor, is the smallest single influencing factor in the mutation period before the standardized treatment. is the largest single influencing factor in the mutation period before standardization. .

[0112] The standardized mutation time period for a single node Power data The calculation formula is as follows:

[0113] (2)

[0114] in, The first node under the standardized mutation time period Power data, The single node in the mutation time period before the standardization process Power data, To standardize the minimum power data of a single node in the pre-mutation period, To standardize the maximum power data of a single node in the mutation period before processing, .

[0115] After standardization, the single change interval Data of individual influencing factors The calculation formula is as follows:

[0116] (3)

[0117] in, The first Data of a single influencing factor, is the first Data of a single influencing factor, is the smallest single influencing factor in a single change interval before standardization. is the largest single influencing factor in a single change interval before standardization. .

[0118] After the standardization, the single node under the single change interval segment Power data The calculation formula is as follows:

[0119] (4)

[0120] in, For a single node in a standardized single change interval segment, Power data, The first node under a single change interval before standardization Power data, It is the minimum power data of a single node in a single change interval before standardization. It is the maximum power data of a single node in a single change interval before standardization. .

[0121] In implementation, the mutation coincidence and interval coincidence can be calculated based on the area coincidence of each influencing factor data fluctuation curve and each node power data fluctuation curve in the mutation time period and change interval.

[0122] In a specific embodiment, the calculation formula for the mutation coincidence degree of power data of a single influencing factor and a single node in a mutation time period is as follows:

[0123] (5)

[0124] in, is the mutation coincidence degree of power data of a single influencing factor and a single node in the mutation time period, It is the area overlap between the fluctuation curve of a single influencing factor data in the mutation time period and the fluctuation curve of power data at a single node. It is the area enclosed by the fluctuation curve of the data of a single influencing factor and the X-axis during the mutation period. It is the area enclosed by the power data fluctuation curve of a single node in the mutation time period and the X-axis.

[0125] The calculation formula for the interval overlap of a single influencing factor and a single node under the kth change interval is as follows:

[0126] (6)

[0127] in, is the interval overlap between a single influencing factor and a single node under the kth change interval, is the area overlap between the single influencing factor data fluctuation curve under the kth change interval and the power data fluctuation curve under a single node, is the area enclosed by the data fluctuation curve of a single influencing factor and the X-axis in the kth change interval, The power data fluctuation curve of a single node in the kth change interval is The area enclosed by the axis.

[0128] The present invention ensures the consistency and comparability of data of different magnitudes when calculating mutation overlap and interval overlap by standardizing the influencing factors and power data of each node in the mutation time period and change interval, and further accurately reflects the degree of correlation between the influencing factors and the power node data in a specific time period, thereby further improving the efficiency and accuracy of power business data processing, and providing strong support for the stable operation and intelligent decision-making of the power system.

[0129] See also Figure 4 As shown, it is a flow chart of determining the influence coefficient of a single influencing factor on a corresponding related node in the present invention. Specifically, in step S3, it includes:

[0130] Step S31, for a single influencing factor, determining a first influencing coefficient of the single influencing factor on the single relevant node according to a power data fluctuation curve of the single relevant node in a preset time period and an influencing factor fluctuation curve;

[0131] Step S32, for a single influencing factor, determining a second influencing coefficient of the single influencing factor on the multiple related nodes according to the power data fluctuation curve of the multiple related nodes in the preset time period and the influencing factor fluctuation curve;

[0132] Step S33: determining the influence coefficient based on the first influence coefficient and the second influence coefficient.

[0133] It can be understood that, within a preset time period, the number of power data of the single relevant node and the number of single influencing factor data are the same.

[0134] In a specific embodiment, The influencing factors and The first influence coefficient of the relevant nodes The calculation formula is as follows:

[0135] (7)

[0136] in, For the The influencing factor data, For the The first Power data, For the The mean of all data of the influencing factors, For the The mean value of power data under relevant nodes.

[0137] No. The second influence coefficient of each influencing factor and all related nodes The calculation formula is as follows:

[0138] (8)

[0139] in, For the The relevant node weights, For the The influencing factors and The first influence coefficient of the relevant nodes, is the number of related nodes. Assuming the target node is the power supply node and the single influencing factor is winter, the related nodes are the transportation node, the power consumption node, the sales node, and the operation and maintenance node. Winter will first affect the power consumption of the power consumption node and the related losses of the transportation node. Therefore, the weight of the transportation node is 0.3, the weight of the power consumption node is 0.4, the weight of the sales node is 0.2, and the weight of the operation and maintenance node is 0.1. Specific weights It can be determined based on actual conditions and is not specifically limited or elaborated here.

[0140] No. The influencing factors and The influence coefficient of the relevant nodes The calculation formula is as follows:

[0141] (9)

[0142] in, For the The influencing factors and The weight of the first influence coefficient of the relevant nodes, For the The weight of the influencing factor and the second influence coefficient of all related nodes, and The sum of is 1, and .

[0143] In implementation, the The value is 0.6 to 0.9, preferably, is 0.8, The value is 0.1 to 0.4, preferably, is 0.2, and The value range and preferred value can be determined according to the actual situation, and as long as and The sum of them can be 1, and no further details will be given here.

[0144] The present invention accurately distinguishes the impact of a single influencing factor on the power data at a single node and the impact of a single influencing factor on the power data at multiple nodes, wherein the impact of a single influencing factor on the power data at a single node is a first impact coefficient, and the impact of a single influencing factor on the power data at multiple nodes is a second impact coefficient, and then determines the final impact coefficient, comprehensively evaluates the impact degree of a single influencing factor in the power channel business system, makes the impact analysis more three-dimensional and multi-dimensional, thereby further ensuring the accurate judgment of data relevance, greatly improving the efficiency and accuracy of power business data processing, and providing strong support for the stable operation and intelligent decision-making of the power system.

[0145] Specifically, in step S4, determining the unit change rate of the single influencing factor includes:

[0146] Determine the change amount of the influencing factor between any two mutation factor points based on the plurality of mutation factor points;

[0147] The unit change rate of the single influencing factor is determined based on the change amount of the influencing factor.

[0148] It can be understood that the change in the influencing factor between any two mutation factor points reflects the increase or decrease in the influencing factor between the two time points corresponding to the any two mutation factor points.

[0149] For the The mutation factor point and the The change amount of the influencing factors between the mutation factor points is also the first The amount of change of influencing factors, The calculation formula is as follows:

[0150] (10)

[0151] in, For the The influencing factor data of each mutation factor point, For the The influencing factor data of each mutation factor point.

[0152] No. The mutation factor point and the Unit change rate between mutation factor points The calculation formula is as follows:

[0153] (11)

[0154] (12)

[0155] Unit change rate of a single influencing factor The calculation formula is as follows:

[0156] (13)

[0157] in, is the sum of the influencing factor variables between any two mutation factor points of a single influencing factor, The number of mutation factor points for a single influencing factor.

[0158] Specifically, in step S4, determining the impact percentage includes:

[0159] The influence percentage of the unit change rate of the single influencing factor on the related node is determined based on the unit change rate of the single influencing factor and the influence coefficient.

[0160] It can be understood that the impact percentage can help understand the extent to which the power data under the relevant node is affected by a single influencing factor.

[0161] The calculation formula for the influence percentage of the unit change rate of a single influencing factor on the relevant node is as follows:

[0162] (14)

[0163] Among them, k is the sensitivity of a single influencing factor change to the performance of related nodes, is the initial value of a single influencing factor.

[0164] The present invention analyzes the unit change rate of a single influencing factor in detail and combines it with the influence coefficient to accurately calculate the percentage of influence of the single influencing factor on the relevant nodes. Subsequently, a preset percentage threshold is determined, and based on this, the fluctuation threshold of the single influencing factor is reasonably determined, which improves the sensitivity to the volatility of the influencing factor, further warns of potential risks, greatly improves the efficiency and accuracy of power business data processing, and provides strong support for the stable operation and intelligent decision-making of the power system.

[0165] Specifically, in step S4, determining the fluctuation threshold includes: comparing the influence percentage of the unit change rate of a single influencing factor on the relevant node with a preset percentage, and determining the fluctuation threshold based on the comparison result.

[0166] It can be understood that if the influence percentage of the unit change rate of the single influencing factor on the relevant node is greater than or equal to the preset percentage, the fluctuation threshold is correspondingly set to the average value of the change amounts of several influencing factors of several unit change rates corresponding to the single influencing factor.

[0167] If the influence percentage of the unit change rate of the single influencing factor on the relevant node is less than the preset percentage, it means that the single influencing factor has a small influence on the relevant node.

[0168] In a specific embodiment, the fluctuation threshold The calculation formula is as follows:

[0169] (15)

[0170] in, The unit change rate of the single influencing factor has an influence percentage on the related node that is greater than or equal to the unit change rate of the preset percentage. The amount of change of influencing factors, The number of changes in the influencing factors corresponding to the unit change rate whose influence percentage of the unit change rate of the single influencing factor on the related node is greater than or equal to the preset percentage.

[0171] In implementation, the value range of the preset percentage is 40% to 60%. Preferably, the preset percentage is 45%. The value range and preferred value of the preset percentage can be determined according to actual conditions and will not be repeated here.

[0172] Specifically, in step S5, it includes:

[0173] Step S51, determining a target node based on a user instruction trigger or based on an influencing factor exceeding the fluctuation threshold of a corresponding node;

[0174] Step S52, determining the related nodes corresponding to the target node based on the influencing factor data under the target node and the fluctuation threshold;

[0175] Step S53, displaying the target node, the related nodes corresponding to the target node, and displaying the corresponding influence coefficient based on the related nodes;

[0176] The display of the nodes corresponding to each influencing factor is different, including different display colors, different flashing frequencies, different display positions, and different display push objects.

[0177] It can be understood that the system can determine the target node through user instructions. After determining the target node, the relevant nodes corresponding to the target node can be further determined based on the comparison results of the influencing factor data collected in real time and the corresponding fluctuation threshold, and the influence coefficient of each influencing factor on the relevant nodes can be displayed.

[0178] In a specific embodiment, assuming that the target node triggered by the user instruction is a power supply node, the power supply node is highlighted in yellow, long-lit without flashing, and the display position is located above the relevant node in the display screen. Then the system will automatically determine the relevant node corresponding to the target node based on the influencing factor data under the target node and the fluctuation threshold. Assuming that the loss of the transportation node exceeds the fluctuation threshold, the loss is one of the influencing factors, then the transportation node is displayed as a relevant node of the power supply node, and the display method is red highlighting, displayed below the corresponding target node, and the flashing frequency is 1s / time to 2s / time, and the influence coefficient is marked next to the transportation node, for example, "0.3" means that the loss reaches 30%.

[0179] Embodiment 2: The present invention also provides a data display optimization system based on electric power channel business, which is used to execute the method described in Embodiment 1, and the system includes a data acquisition module, a data fluctuation monitoring module, a data analysis module and a data display module;

[0180] The data collection module is used to collect power data and influencing factor data of each node of the power channel business and determine the associated nodes of each node;

[0181] The data fluctuation monitoring module is used to monitor the time series data fluctuation of each influencing factor data collected by each node, determine the correlation of each associated node under each influencing factor, and determine the relevant nodes under each influencing factor;

[0182] The data analysis module forms several single-variable power data fluctuation curves based on the power data acquired by the data acquisition module and the data fluctuation monitoring module, and determines the influence coefficient of a single influencing factor on the corresponding related nodes and the fluctuation threshold of a single influencing factor;

[0183] The data display module displays the target node, the related nodes corresponding to the target node and their corresponding influence coefficients based on the influencing factor data and corresponding fluctuation thresholds of each node of the power channel business acquired in real time.

[0184] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A data display optimization method based on power channel business, characterized in that: The steps include: Step S1, collecting or acquiring power data and influencing factor data of each node of the power channel business in time sequence, and determining the associated nodes of each node according to the connection relationship between the nodes; Step S2, monitoring the time series data fluctuation of each influencing factor data collected by each node to draw an influencing factor data fluctuation curve, analyzing the correlation of the associated nodes of each node under each influencing factor based on the influencing factor data fluctuation curve, and based on the correlation and with reference to a preset correlation threshold, screening out the related nodes of each node under each influencing factor from the associated nodes of each node; Step S3, respectively obtaining power data of relevant nodes under each influencing factor, forming a plurality of power data fluctuation curves of single independent variables, and determining a first influence coefficient of a single influencing factor on a single relevant node and a second influence coefficient of a single influencing factor on multiple relevant nodes based on the power data fluctuation curve of each node and the influencing factor data fluctuation curve, and determining the influence coefficient of a single influencing factor on the corresponding relevant node by combining the first influence coefficient and the second influence coefficient through weight allocation and weighted summation; Step S4, determining the amount of change of the influencing factor according to the mutation factor point in the influencing factor data fluctuation curve of the single influencing factor, which is used to determine the unit change rate of the single influencing factor, determining the influence percentage of the unit change rate of the single influencing factor on the relevant node based on the unit change rate and the influence coefficient of the single influencing factor, comparing the influence percentage of the unit change rate of the single influencing factor on the relevant node with a preset percentage, and determining the fluctuation threshold of the single influencing factor based on the comparison result; Step S5, based on the influencing factor data of each node of the power channel business acquired in real time, the corresponding related nodes are determined based on the corresponding fluctuation threshold, and the influence correlation of the determined related nodes is displayed to prompt the influence coefficient of a single influencing factor that exceeds the fluctuation threshold on each related node.

2. The data display optimization method based on the power channel business according to claim 1 is characterized in that: In step S1, determining the associated node of each node includes: Step S11, determining a target node based on each node of the power channel business; Step S12, obtaining the connection relationship of the power channel business where the target node is located; Step S13, based on the connection relationship, obtaining a number of nodes that are directly or indirectly connected to the target node, wherein the connection relationship ends at a power supply node or a power consumption node.

3. The data display optimization method based on the power channel business according to claim 1 is characterized in that: In step S2, it includes: Step S21, based on a preset time period, collect the time series data of each influencing factor and the time series data of the power data at each node, and draw the fluctuation curve of the data of each influencing factor and the fluctuation curve of the power data of each node accordingly; Step S22, determining mutation factor points according to the fluctuation curves of the influencing factor data, determining mutation nodes according to the fluctuation curves of the power data of each node, and determining mutation time periods and change intervals of each influencing factor and each node power data based on the mutation factor points and mutation nodes, respectively. The mutation time period is the time period with the largest mutation of the influencing factor and power data determined based on the highest mutation factor point and the highest mutation node, and the change interval is the time period with mutation of the influencing factor and power data except the mutation time period; Step S23, calculating the mutation coincidence between each influencing factor and each node power data in the mutation time period, and calculating the interval coincidence between each influencing factor and each node power data in the change interval; Step S24, determining the correlation of each associated node under each influencing factor based on the mutation overlap and the interval overlap; Step S25: determining the related nodes of each node under the single influencing factor based on the correlation and the correlation threshold.

4. The data display optimization method based on the power channel business according to claim 3 is characterized in that: In step S22, it includes: Step S221, determining a number of mutation factor points based on the fluctuation curves of the influencing factor data, and determining a number of mutation nodes based on the fluctuation curves of the power data of each node; Step S222, determining the highest mutation factor point based on the plurality of mutation factor points, and determining the highest mutation node based on the plurality of mutation nodes; Step S223, determining the mutation time period based on the highest mutation factor point and the highest mutation node; Step S224: determining the variation interval based on the remaining mutation factor points excluding the highest mutation factor point and the remaining mutation nodes excluding the highest mutation node.

5. The data display optimization method based on the power channel business according to claim 3 is characterized in that: In step S23, it includes: Standardizing the power data of each influencing factor and each node in the mutation time period and the change interval respectively; Determine the mutation coincidence degree based on the standardized influencing factors and the power data of each node in the mutation time period; The interval overlap is determined based on the influencing factors in the change interval segment after standardization and the power data of each node.

6. The data display optimization method based on the power channel business according to claim 4 is characterized in that: In step S4, determining the unit change rate of the single influencing factor includes: Determine the change amount of the influencing factor between any two mutation factor points based on the plurality of mutation factor points; The unit change rate of the single influencing factor is determined based on the change amount of the influencing factor.

7. The data display optimization method based on the power channel business according to claim 6 is characterized in that: In step S4, determining the impact percentage includes: The influence percentage of the unit change rate of the single influencing factor on the related node is determined based on the unit change rate of the single influencing factor and the influence coefficient.

8. The data display optimization method based on the power channel business according to claim 7 is characterized in that: In step S4, determining the fluctuation threshold includes: comparing the influence percentage of the unit change rate of a single influencing factor on the relevant node with a preset percentage, and determining the fluctuation threshold based on the comparison result.

9. The data display optimization method based on the power channel business according to claim 1 is characterized in that: In step S5, it includes: Step S51, determining a target node based on a user instruction trigger or based on an influencing factor exceeding the fluctuation threshold of a corresponding node; Step S52, determining the related nodes corresponding to the target node based on the influencing factor data under the target node and the fluctuation threshold; Step S53, displaying the target node, the related nodes corresponding to the target node, and displaying the corresponding influence coefficient based on the related nodes; The display of the nodes corresponding to each influencing factor is different, including different display colors, different flashing frequencies, different display positions, and different display push objects.

10. A data display optimization system based on power channel business, used to execute the method according to any one of claims 1 to 9, characterized in that: The system includes a data acquisition module, a data fluctuation monitoring module, a data analysis module and a data display module; The data collection module is used to collect power data and influencing factor data of each node of the power channel business and determine the associated nodes of each node; The data fluctuation monitoring module is used to monitor the time series data fluctuation of each influencing factor data collected by each node, determine the correlation of each associated node under each influencing factor, and determine the relevant nodes under each influencing factor; The data analysis module forms several single-variable power data fluctuation curves based on the power data acquired by the data acquisition module and the data fluctuation monitoring module, and determines the influence coefficient of a single influencing factor on the corresponding related nodes and the fluctuation threshold of a single influencing factor; The data display module displays the target node, the related nodes corresponding to the target node and their corresponding influence coefficients based on the influencing factor data and corresponding fluctuation thresholds of each node of the power channel business acquired in real time.

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