Line loss prediction and order sending method based on data

Abstract

The invention relates to the technical field of line loss prediction, and particularly discloses a data-based line loss prediction and order sending method, which comprises the following steps: acquiring T-2 antenna loss data, T being the current day, calculating the T-2 antenna loss rate according to the line loss data, calculating the line loss rate prediction values of the T-1 and Tth days by using a grey correlation improved least square method and a grey prediction GM (1, 1) model, and sending the line loss rate prediction values of the T-1 and Tth days. The severity of different line loss conditions is compared, and the line loss condition of the (T-2) th day and the predicted line loss conditions of the (T-1) th day and the Tth day are dispatched to managers according to the sequence from heavy to light, the RPA program is used for replacing manual work to automatically collect line loss data, the prediction algorithm is easy to implement, the occupied memory space is small, the prediction result is accurate and reliable, intelligent dispatching can be achieved, and the method is suitable for large-scale popularization and application. Therefore, the management personnel can visually understand the line loss condition and take corresponding measures to treat the line loss in time.

Description

Technical Field

[0001] The present invention relates to the technical field of line loss prediction, and in particular to a data-based line loss prediction and dispatching method. Background Art

[0002] Line loss is one of the comprehensive core economic and technical indicators of power grid enterprises. The analysis, prediction and management of line loss is an important way to reduce carbon emissions and tap the potential of energy conservation and consumption reduction in power grids. At present, the significance of line loss management in the power industry is multifaceted. For power distribution, by comparing the actual situation of line loss with international and domestic advanced indicators, the main cause of line loss can be found, and then relevant measures can be formulated to reduce line loss and improve the efficiency of electric energy use. For marketing, the significance lies in effectively reducing power line losses and improving power supply quality through scientific coordination and comprehensive management, so as to provide stable and reliable power guarantee for social and economic development and people's lives.

[0003] In power grid enterprises, line losses are generally divided into two different types of losses: substation line losses and distribution line line losses. The low-voltage substation is a key link in the power grid system that connects users and power transmission, and is located at the end of the power supply system. Due to the complex and diverse topology of the low-voltage distribution network, the wide power supply area, and the difficulty in management, the substation has become a part with relatively large power losses. Substation line loss mainly refers to the process of transmitting and distributing electric energy in the substation distribution network. Due to the impedance of the distribution lines and equipment, a certain amount of active power loss will be generated when current flows through. This loss is usually affected by many factors, including the topology of the distribution network, load conditions, weather conditions, etc. The substation line loss rate is an important indicator that can measure the efficiency and economy of the power system operation, and is also an important link in energy saving, loss reduction and efficiency improvement.

[0004] Distribution line loss refers to the phenomenon that in the entire distribution network, due to the resistance heat loss, induction electromagnetic wave loss and capacitive current loss caused by the current passing through the wire, the electric energy is lost during the transmission process. Similar to the substation line loss, the distribution line loss is also related to factors such as the length, material, cross-sectional area and the current passing through the line. Both types of line losses can be estimated and controlled through certain calculation methods and prediction models, thereby reducing power loss and improving the operating efficiency and economy of the power system.

[0005] At present, the line loss data of the power grid enterprises are mainly collected by the integrated power and line loss management system. The system can manually obtain three indicators of negative loss, high loss and line loss non-compliance rate in the line loss of the distribution line on the T-2 day, including 6 tables. However, the system has certain limitations in line loss analysis, prediction and early warning for line loss management, and it is unable to inform the relevant area or line management personnel to manage the line loss in time. Therefore, how to realize the automatic collection, analysis, prediction and intelligent dispatch of line loss data is of great significance to strengthening line loss management, reducing the burden on the grassroots and improving operational efficiency. Summary of the invention

[0006] The purpose of the present invention is to provide a data-based line loss prediction and dispatching method to solve the problems of limitations in line loss analysis, prediction and early warning and the inability to dispatch orders in a timely manner in the prior art.

[0007] In order to solve the above technical problems, the present invention provides a data-based line loss prediction and dispatching method, which is characterized by comprising the following steps:

[0008] S1. Collect the line loss data on the T-2th day, where T is the current day. The line loss data includes power supply, power sales, independent variables affecting line loss, and line loss influencing indicators. Calculate the line loss rate on the T-2th day based on the power supply and power sales, and obtain the line loss situation on the T-2th day;

[0009] S2. According to the independent variables affecting line loss and the line loss influencing indicators, the predicted line loss rate values ​​of the T-1 and T days are calculated using the least square method improved by grey correlation and the grey prediction GM (1,1) model to obtain the predicted line loss conditions of the T-1 and T days;

[0010] S3. Compare the severity of the line loss situation, and assign the line loss situation on day T-2 and the predicted line loss situation on days T-1 and T to the management personnel in order from severe to mild.

[0011] Preferably, the Cyclone RPA designer is used to design an RPA program for the integrated power and line loss management system. The RPA program is used to automatically collect and organize line loss data, and the line loss data is stored in the background server in the form of an Excel table.

[0012] Preferably, the line loss rate calculation formula is: line loss rate = (line loss power / power supply) 100% = (power supply-power sales) / power supply 100% = (1-power sales / power supply) × 100%.

[0013] Preferably, the independent variables affecting line loss include voltage, current, power factor, equipment load rate and grid operation mode, and the line loss influencing indicators include enterprise economic benefits, power quality, equipment operation efficiency and power supply reliability.

[0014] Preferably, the collected line loss data is parsed, and parsing the line loss data includes data preprocessing and data analysis, the data preprocessing is to remove null values ​​and abnormal values, and the data analysis is to calculate the mean value and standard deviation.

[0015] Preferably, the parsed line loss data is converted into a chart using the displayData function of the chart.js library, and displayed on the web side of the integrated power and line loss management system.

[0016] Preferably, the least square method improved by grey correlation is used to predict line loss:

[0017] Assume that n independent variable sequences affecting line loss form a matrix:

[0018]

[0019] Where m is the number of line loss impact indicators, we get:

[0020] X i ′=(x i ′(1),x i ′(2),...,x i ′(m)) T (i=1,2,...,n);

[0021] The dimensionless sequence forms a matrix:

[0022]

[0023] Calculate the absolute difference between the corresponding elements of each evaluated object indicator sequence and the reference sequence one by one: x0(k)-x i (k)(k=1,...,m,i=1,...,n);

[0024] Calculate the correlation coefficient:

[0025]

[0026] Where ρ is the resolution coefficient, which takes the value of (0,1) and calculates the correlation order:

[0027]

[0028] The least squares linear regression model is used for training to obtain the line loss rate prediction value k1;

[0029] Line loss prediction using the grey prediction GM (1,1) model:

[0030] Assume that n independent variable sequences affecting line loss form a sequence:

[0031] x (0)=[x (0) (1),x (0) (2),...,x (0) (n)];

[0032] Do an accumulation calculation, recorded as 1-AGO:

[0033]

[0034] Get the generated sequence:

[0035] x (1) =[x (1) (1),x (1) (2),...,x (1) (n)];

[0036] Get the mean series:

[0037] z(w)=0.5x(w)+0.5x(w-1);

[0038] Establish a grey system solution, the GM(1,1) first-order differential equation is:

[0039]

[0040] List the Y and B matrices:

[0041]

[0042] Parameter vector to be identified Φ=[au] T , and Y = BΦ, the parameter vector obtained by the least squares method is:

[0043]

[0044] Get its discrete solution:

[0045]

[0046] Get the line loss rate prediction value k2:

[0047]

[0048] Finally, the weighted line loss rate prediction value K is obtained:

[0049] K = g1k1 + g2k2;

[0050] Among them, g1 and g2 are weights, g1+g2=1.

[0051] Preferably, g1=g2=0.5.

[0052] Preferably, the line loss situation is obtained by calculating the line loss rate: a line loss rate of >6% is high loss, a line loss rate of <0% is negative loss, and both high loss and negative loss are substandard.

[0053] Preferably, the line loss situations are divided into four types: A. non-compliance on the T-2 day, B. non-compliance on the T-1 day or the T day, C. line loss rate exceeding 90% of the warning line on the T-2 day, D. line loss rate exceeding 90% of the warning line on the T-1 day or the T day, exceeding the 90% warning line means line loss rate > 5.4% or line loss rate < 0.9%;

[0054] For the above four situations, the dispatch weight values ​​are set to 0.50, 0.25, 0.20 and 0.05 respectively. If situation A exists, it is set as Class A, and if situation A does not exist, it is set as Class B. The dispatch priority of Class A is higher than that of Class B. The dispatch order is as follows: first notify the management personnel of Class A with higher dispatch priority, and then notify the management personnel of Class B with lower dispatch priority. When the dispatch priorities are the same, calculate the dispatch weight values, and give priority to notifying the line loss situations with higher total dispatch weight values. When the total dispatch weight values ​​of the same category are the same, the party with higher single dispatch weight value will be notified first.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention uses RPA program to automatically collect and organize line loss data, which is more accurate and reliable than manual means. At the same time, the RPA program design method adopts multiple error prevention and exception handling mechanisms, and uses multi-function processes and multi-interface configurations to improve the robustness of the program. In addition, RPA technology itself has the characteristics of non-intrusive, non-service interface and non-data transmission, which can achieve agile delivery and agile development, and shorten the delivery cycle. Secondly, the least squares method improved by grey correlation and the grey prediction GM (1,1) model are simple to calculate, easy to implement, occupy little memory space, and have high accuracy, which makes up for the shortcomings of the integrated power and line loss management system in line loss prediction. The analysis results are displayed in a digital form on the web, so that managers can intuitively understand the line loss situation. In addition, a dispatching method is proposed, which can promptly notify the relevant substation or distribution line managers to manage the line loss, make the line loss notification more orderly, strengthen the line loss management, and improve the operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flowchart of the RPA program data collection program in an embodiment of the present invention;

[0058] Figure 2 A flowchart of a line loss data prediction program according to an embodiment of the present invention;

[0059] Figure 3 This is a flowchart of a dispatching procedure according to an embodiment of the present invention;

[0060] Figure 4 It is an overall flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0062] like Figure 1-4 As shown, a data-based line loss prediction and dispatching method according to an embodiment of the present invention includes the following steps:

[0063] S1. Use Cyclone RPA Designer to design an RPA program for the integrated power and line loss management system, which automatically collects the line loss data of the substation and distribution line on the T-2 day, where T is the current day. Data collection can be automatically performed at 9:00 every day. The RPA program is more accurate and reliable than manual methods. It adopts multiple error prevention and exception handling mechanisms, and uses multi-function processes and multi-interface configurations to improve the robustness of the program. In addition, RPA technology itself has the characteristics of non-intrusiveness, non-service interface, and non-data transmission, which can achieve agile delivery and agile development, and shorten the delivery cycle.

[0064] Line loss data includes power supply, power sales, independent variables affecting line loss and line loss influencing indicators. Independent variables affecting line loss include voltage, current, power factor, equipment load rate and grid operation mode. Line loss influencing indicators include enterprise economic benefits, power quality, equipment operation efficiency and power supply reliability. Line loss data is deduplicated and abnormal data is screened out. The line loss rate on the T-2 day is calculated based on power supply and power sales. The line loss rate calculation formula is:

[0065] Line loss rate = (line loss power / power supply) 100% = (power supply - power sales) / power supply 100% = (1 - power sales / power supply) × 100%;

[0066] According to the line loss rate, the line loss situation on the T-2 day is obtained. The line loss situation is divided into high loss and negative loss. The line loss rate is greater than 6% for high loss, and the line loss rate is less than 0% for negative loss. Both high loss and negative loss are not up to standard. The line loss data is divided into six Excel tables, named "Distribution line line loss not up to standard details yyyymmdd", "Distribution line line loss negative loss number details yyyymmdd", "Distribution line line loss high loss number details yyyymmdd", "Substation area line loss not up to standard details yyyymmdd", "Substation area line loss negative loss number details yyyymmdd d", "Details of high loss of line loss in substation area yyyymmdd", yyyy is year, mm is month, dd is date, merge the six Excel tables into a folder named "Result file yyyy-mm-dd", and determine whether there is a result file of the T-2 day on the server. If not, store the "Result file yyyy-mm-dd" on the server. If it exists, delete and silently overwrite the original file, and store the current file on the server to ensure that there is only one copy of the file on the server, thus ensuring the accuracy and consistency of the data.

[0067] S2. Call the JavaScript library and use the readExcelFile function on the web side of the integrated power and line loss management system to read the Excel table in the server. The parameter received by the readExcelFile function is the path of the Excel file. The read data is preprocessed, including removing null values ​​and outliers, so as to facilitate subsequent line loss data mining and analysis. The preprocessed line loss data is analyzed, and statistical indicators such as the mean and standard deviation are calculated. The displayData function of the chart.js library is used to convert the calculated line loss data into charts, such as line charts and bar charts, and the charts are displayed on the web side of the integrated power and line loss management system.

[0068] According to the independent variables affecting line loss and the line loss influencing indicators, the line loss rate is predicted. First, the least square method improved by grey correlation is used to calculate the predicted value of the line loss rate on the T-1 and T days:

[0069] Assume that n independent variable sequences affecting line loss form a matrix:

[0070]

[0071] Where m is the number of line loss impact indicators, we get:

[0072] X i ′=(x i ′(1),x i ′(2),...,x i ′(m)) T (i=1,2,...,n);

[0073] The dimensionless sequence forms a matrix:

[0074]

[0075] Calculate the absolute difference between the corresponding elements of each evaluated object indicator sequence and the reference sequence one by one:

[0076] x0(k)-x i (k)(k=1,...,m,i=1,...,n);

[0077] Calculate the correlation coefficient:

[0078]

[0079] Where ρ is the resolution coefficient, which takes the value of (0,1) and calculates the correlation order:

[0080]

[0081] Depending on the actual situation, it is also possible to calculate the correlation order of weighted sequences:

[0082]

[0083] Where W k is the weight of each indicator,

[0084] The least squares linear regression model is used to train these key factors and obtain the line loss rate prediction value k1;

[0085] Then, the grey prediction GM(1,1) model is used to calculate the line loss rate prediction value on day T-1 and day T:

[0086] Assume that n independent variable sequences affecting line loss form a sequence:

[0087] x (0) =[x (0) (1),x (0) (2),...,x (0) (n)];

[0088] Do an accumulation calculation, recorded as 1-AGO:

[0089]

[0090] Get the generated sequence:

[0091] x (1) =[x (1) (1),x (1) (2),...,x (1) (n)];

[0092] Get the mean series:

[0093] z(w)=0.5x(w)+0.5x(w-1);

[0094] Establish a grey system solution, the GM(1,1) first-order differential equation is:

[0095]

[0096] List the Y and B matrices:

[0097]

[0098] Parameter vector to be identified Φ=[au] T , and Y = BΦ, the parameter vector obtained by the least squares method is:

[0099] Get its discrete solution:

[0100]

[0101] Get the line loss rate prediction value k2:

[0102]

[0103] Finally, the weighted line loss rate prediction value K is obtained:

[0104] K = g1k1 + g2k2;

[0105] Among them, g1 and g2 are weights, g1+g2=1. In this embodiment, g1=g2=0.5. Different weight values ​​can also be assigned according to the preference for the prediction model. According to the predicted value of the line loss rate, the predicted line loss situation of the T-1th and Tth days is obtained. The grey correlation improved least squares method and grey prediction GM(1,1) model used here are simple to calculate, easy to implement, small in memory space, and high in accuracy, which makes up for the shortcomings of the integrated power and line loss management system in line loss prediction.

[0106] Each time the algorithm is modeled, an error test is required. The relative error value and the level deviation value are checked through the model residual test to evaluate the fitting effect of the model. The relative error value should be less than 20%, and the level deviation value should be less than 0.2.

[0107] After the prediction is completed, the latest prediction results are displayed in the form of charts on the web side of the integrated power and line loss management system, so that managers can intuitively understand the line loss situation. Step S2 is repeated at regular intervals, and a timer can be set to automatically execute at 12:00 every day to continuously monitor and analyze line loss data.

[0108] S3. Each manager is in charge of multiple lines in the area, and each line may correspond to different line loss situations. The severity of the line loss situations is compared, and the line loss situation on day T-2 and the predicted line loss situation on days T-1 and T are assigned to the manager in order from severe to mild.

[0109] First, the existing line loss situations are divided into four types: A. There is non-compliance on the T-2 day, B. It is predicted that there will be non-compliance on the T-1 day or the T day, C. On the T-2 day, there is a line loss rate that exceeds the warning line of 90%, and D. It is predicted that there will be a line loss rate that exceeds the warning line of 90% on the T-1 or T day. The line loss rate exceeding the 90% warning line is greater than 5.4% or less than 0.9%.

[0110] Then, the dispatch weight values ​​of 0.50, 0.25, 0.20 and 0.05 are set for the above four situations respectively. Situation A is set as Class A if it exists, and set as Class B if it does not exist. The dispatch priority of Class A is higher than that of Class B. The dispatch order is as follows: notify the management personnel of Class A first, and then notify the management personnel of Class B. When the dispatch priorities are the same, calculate the dispatch weight values, and give priority to notifying the line loss situation with a higher total dispatch weight value. When the total dispatch weight values ​​of the same category are the same, the party with a higher single dispatch weight value will be notified first.

[0111] For example, the line loss in the area under the jurisdiction of manager No. 1 is 1 A and 4 Ds, the line loss in the area under the jurisdiction of manager No. 2 is 1 A and 1 C, the line loss in the area under the jurisdiction of manager No. 3 is 2 Bs and 1 C, and the line loss in the area under the jurisdiction of manager No. 4 is 10 Ds. At this time, because of the existence of A, the line loss of managers No. 1 and 2 is Class A, and the line loss of managers No. 3 and 4 is Class B. The dispatching priority of managers No. 1 and 2 is higher than that of managers No. 3 and 4. Since managers No. 1 and 2 have the same dispatching priority, the dispatching weight is calculated. Value, No. 1 = 1×0.5+4×0.05=0.7, No. 2 = 1×0.5+1×0.2=0.7. At this time, the dispatch weights of managers No. 1 and No. 2 are the same, but the situation of manager No. 2 takes precedence over the situation of manager No. 1, so manager No. 2 is given priority. Similarly, the dispatch weights of managers No. 3 and No. 4 are calculated to be 0.7 and 0.5 respectively, so manager No. 3 is given priority. In summary, according to the severity of the line loss, the dispatch order of the four managers is: No. 2, No. 1, No. 3, No. 4. At the same time, for each manager, the priority of line loss management is: A, B, C, D. This dispatch method can promptly notify the relevant substation or distribution line managers to manage the line loss, making the notification of line loss more orderly, strengthening line loss management, and improving operational efficiency.

[0112] The present invention uses RPA program to automatically collect and organize line loss data, which is more accurate and reliable than manual means. At the same time, the RPA program design method adopts multiple error prevention and exception handling mechanisms, and uses multi-function processes and multi-interface configurations to improve the robustness of the program. In addition, RPA technology itself has the characteristics of non-intrusive, non-service interface and non-data transmission, which can achieve agile delivery and agile development, and shorten the delivery cycle. Secondly, the least squares method improved by grey correlation and the grey prediction GM (1,1) model are simple to calculate, easy to implement, occupy little memory space, and have high accuracy, which makes up for the shortcomings of the integrated power and line loss management system in line loss prediction. The analysis results are displayed in a digital form on the web, so that managers can intuitively understand the line loss situation. In addition, a dispatching method is proposed, which can promptly notify the relevant substation or distribution line managers to manage the line loss, make the line loss notification more orderly, strengthen the line loss management, and improve the operation efficiency.

[0113] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A data-based line loss prediction and dispatching method, characterized in that: The following steps are involved: S1. Collect the line loss data on the T-2th day, where T is the current day. The line loss data includes power supply, power sales, independent variables affecting line loss, and line loss influencing indicators. Calculate the line loss rate on the T-2th day based on the power supply and power sales, and obtain the line loss situation on the T-2th day; S2. According to the independent variables affecting line loss and the line loss influencing indicators, the predicted line loss rate values ​​of the T-1 and T days are calculated using the least square method improved by grey correlation and the grey prediction GM (1,1) model to obtain the predicted line loss conditions of the T-1 and T days; S3. Compare the severity of the line loss situation, and assign the line loss situation on day T-2 and the predicted line loss situation on days T-1 and T to the management personnel in order from severe to mild.

2. The data-based line loss prediction and dispatching method according to claim 1, characterized in that: In step S1, the Cyclone RPA designer is used to design an RPA program for the integrated power and line loss management system. The RPA program is used to automatically collect and organize line loss data, and the line loss data is stored in the background server in the form of an Excel table.

3. The data-based line loss prediction and dispatching method according to claim 1, characterized in that: In step S1, the line loss rate is calculated as follows: line loss rate = (line loss power / power supply) 100% = (power supply - power sales) / power supply 100% = (1 - power sales / power supply) × 100%.

4. The data-based line loss prediction and dispatching method according to claim 1, characterized in that: In step S1, the independent variables affecting line loss include voltage, current, power factor, equipment load rate and grid operation mode, and the line loss influencing indicators include enterprise economic benefits, power quality, equipment operation efficiency and power supply reliability.

5. The data-based line loss prediction and dispatching method according to claim 4, characterized in that: In the step S2, the collected line loss data is parsed, and the parsing of the line loss data includes data preprocessing and data analysis. The data preprocessing is to remove null values ​​and abnormal values, and the data analysis is to calculate the mean value and standard deviation.

6. The data-based line loss prediction and dispatching method according to claim 5, characterized in that: In the step S2, the parsed line loss data is converted into a chart using the displayData function of the chart.js library, and displayed on the web side of the integrated power and line loss management system.

7. The data-based line loss prediction and dispatching method according to claim 5, characterized in that: In step S2, the line loss prediction is performed using the least square method improved by grey correlation: Assume that n independent variable sequences affecting line loss form a matrix: Where m is the number of line loss impact indicators, we get: X i ′=(x i ′(1),x i ′(2),...,x i ′(m)) T (i=1,2,...,n); The dimensionless sequence forms a matrix: Calculate the absolute difference between the corresponding elements of each evaluated object indicator sequence and the reference sequence one by one: x0(k)-x i (k)(k=1,...,m,i=1,...,n); Calculate the correlation coefficient: Where ρ is the resolution coefficient, which takes the value of (0,1) and calculates the correlation order: The least squares linear regression model is used for training to obtain the line loss rate prediction value k1; Line loss prediction using the grey prediction GM (1,1) model: Assume that n independent variable sequences affecting line loss form a sequence: x (0) =[x (0) (1),x (0) (2),...,x (0) (n)]; Do an accumulation calculation, recorded as 1-AGO: Get the generated sequence: x (1) =[x (1) (1),x (1) (2),...,x (1) (n)]; Get the mean series: z(w)=0.5x(w)+0.5x(w-1); Establish a grey system solution, the GM(1,1) first-order differential equation is: List the Y and B matrices: Parameter vector to be identified Φ=[au] T , and Y = BΦ, the parameter vector obtained by the least squares method is: Get its discrete solution: Get the line loss rate prediction value k2: Finally, the weighted line loss rate prediction value K is obtained: K = g1k1 + g2k2; Among them, g1 and g2 are weights, g1+g2=1.

8. The data-based line loss prediction and dispatching method according to claim 7, characterized in that: In the step S2, g1=g2=0.

5.

9. The data-based line loss prediction and dispatching method according to claim 1, characterized in that: In the step S1, the line loss situation is obtained by calculating the line loss rate: a line loss rate of more than 6% is high loss, a line loss rate of less than 0% is negative loss, and both high loss and negative loss are not up to standard.

10. The data-based line loss prediction and dispatching method according to claim 9, characterized in that: In step S3, the line loss situations are divided into four types: A. non-compliance on day T-2; B. non-compliance on day T-1 or day T; C. line loss rate exceeding 90% of the warning line on day T-2; D. line loss rate exceeding 90% of the warning line on day T-1 or day T; exceeding the 90% warning line means line loss rate > 5.4% or line loss rate < 0.9%; For the above four situations, the dispatch weight values ​​are set to 0.50, 0.25, 0.20 and 0.05 respectively. If situation A exists, it is set as Class A, and if situation A does not exist, it is set as Class B. The dispatch priority of Class A is higher than that of Class B. The dispatch order is as follows: first notify the management personnel of Class A with higher dispatch priority, and then notify the management personnel of Class B with lower dispatch priority. When the dispatch priorities are the same, calculate the dispatch weight values, and give priority to notifying the line loss situations with higher total dispatch weight values. When the total dispatch weight values ​​of the same category are the same, the party with higher single dispatch weight value will be notified first.

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