CN117391357A - A dispatching self-checking system for power grid dispatching and operation management based on big data - Google Patents

Abstract

The invention discloses a dispatching self-checking system for power grid dispatching and operation management based on big data, which belongs to the technical field of power grid dispatching and operation management and includes a dispatching information module, an evaluation module and an early warning module; the dispatching information module is used to obtain the generated dispatches in real time. Scheduling detailed data of instructions; the evaluation module is used to evaluate the scheduling details data corresponding to the scheduling instructions, obtain the scheduling instructions collected by the scheduling information module and the corresponding scheduling details data; match the corresponding evaluation items based on the preset evaluation matching table, according to Each evaluation item collects corresponding evaluation item data; matches the corresponding reference instruction according to the identified scheduling instruction, and identifies the scheduling range and scheduling effect set corresponding to the reference instruction; evaluates the evaluation item data based on the obtained scheduling range and scheduling effect set, and obtains The corresponding evaluation results are generated according to the obtained evaluation results, and the corresponding scheduling abnormality early warning signals are generated; the early warning module is used to provide abnormal early warning to the corresponding managers.

Description

A dispatching self-checking system for power grid dispatching and operation management based on big data

Technical field

The invention belongs to the technical field of power grid dispatching and operation management, and is specifically a dispatching self-checking system for power grid dispatching and operation management based on big data.

Background technique

As the power grid structure becomes increasingly complex and dispatching services increase, the information and work that dispatchers need to pay attention to are becoming increasingly heavy. In recent years, computer technology has become increasingly mature, and the power grid dispatching management system has been realized. The idea of computer-aided business dispatching has been adopted, which has improved the digital level of power grid dispatching operations and improved the level of safe and stable operation of the power grid.

However, the existing dispatching power grid operation and management system requires manual execution by dispatching personnel, lacks system self-checking functions, is highly dependent on manual work, and is prone to dispatching errors.

For example, the Chinese patent application with publication number CN115719139A discloses a dispatching self-checking system for power grid dispatching and operation management. The dispatching self-examination system for power grid dispatching operation management includes: a shift management function module, an electromechanical group tab management function module and a dispatching business execution status inspection function module; the shift management function module includes a shift preparation sub-module, The shift execution sub-module and the post-shift analysis sub-module; the electromechanical group tab management function module includes an organic furnace defect sub-module, a unit start-stop sub-module, a unit analysis sub-module and a unit statistics sub-module. By combining and improving the shift management module and the unit management module, the system's real-time automatic self-checking function is realized.

However, the dispatching self-checking of the above scheme is not comprehensive enough. Therefore, in order to realize intelligent self-checking of power grid dispatching, the present invention provides a dispatching self-checking system for power grid dispatching operation management based on big data.

Contents of the invention

In order to solve the problems existing in the above solution, the present invention provides a dispatching self-checking system for power grid dispatching operation management based on big data to solve the existing power grid dispatching self-checking problem.

The object of the present invention can be achieved through the following technical solutions:

A dispatching self-checking system for power grid dispatching and operation management based on big data, including a dispatching information module, an evaluation module and an early warning module;

The scheduling information module is used to obtain the scheduling detailed data of each generated scheduling instruction in real time.

Further, the collection method of scheduling detailed data includes:

Connect with the corresponding power grid dispatching system and identify the dispatching instructions generated in the power grid dispatching system in real time;

Determine the influencing factors corresponding to different dispatching instructions in power grid dispatching, set corresponding detailed data items according to each influencing factor; and set evaluation items for each detailed data item; combine the obtained detailed data items and the corresponding evaluation Integrate the items and establish a corresponding evaluation matching table;

Match corresponding detailed data items from the evaluation matching table according to the identified scheduling instructions, perform corresponding data collection based on each detailed data item, and integrate the collected data to form scheduling detailed data.

The evaluation module is used to evaluate the scheduling details data corresponding to the scheduling instructions, obtain the scheduling instructions collected by the scheduling information module and the corresponding scheduling details data in real time; match the corresponding evaluation items based on the preset evaluation matching table, and according to each evaluation item Collect corresponding evaluation item data;

Match the corresponding reference instructions according to the identified scheduling instructions, and identify the scheduling range and scheduling effect set corresponding to the reference instructions;

The evaluation item data is evaluated based on the obtained scheduling scope and scheduling effect set, and corresponding evaluation results are obtained. The evaluation results include evaluation qualification and evaluation abnormality, and corresponding scheduling abnormality early warning signals are generated based on the obtained evaluation results.

Further, the method of matching corresponding reference instructions according to the identified scheduling instructions includes:

Determine the corresponding candidate instructions, and set the corresponding dispatch range and dispatch effect set according to the historical power grid dispatch data corresponding to the candidate instructions; integrate the obtained candidate instructions with the corresponding dispatch scope and dispatch effect set to form a corresponding reference library ;

Identify the scheduling instructions, input the obtained scheduling instructions into the reference library for matching, obtain the corresponding candidate instructions, mark the matching candidate instructions as reference instructions, and obtain the scheduling range and scheduling effect set corresponding to the reference instructions.

Further, when the corresponding reference instruction is not matched in the reference library, a corresponding scheduling exception warning signal is generated.

Further, methods for evaluating the evaluation item data based on the obtained scheduling scope and scheduling effect set include:

Calculate the similarity value between the evaluation item data and the scheduling effect set, and mark the obtained similarity value as XS;

Mark the evaluation items as i, i=1, 2,...,n; n is the number of evaluation items; according to the isolated forest algorithm Calculate the single evaluation value f(xi) of each evaluation item, where xi is the evaluation item data of the corresponding evaluation item, and U is the corresponding scheduling range;

According to the comprehensive evaluation formula Calculate the corresponding comprehensive evaluation value PGF, where λ is the judgment coefficient, when/> When it is not equal to 0, it is directly determined that the evaluation is abnormal. When/> When equal to 0, the influence of λ is not considered;

When the comprehensive evaluation value is greater than the threshold X1, the evaluation is judged to be qualified;

When the comprehensive evaluation value is not greater than the threshold X1, the evaluation is determined to be abnormal.

Further, the evaluation module also includes a cumulative evaluation unit, which is used to perform cumulative analysis on qualified scheduling instructions and obtain corresponding cumulative analysis results. The cumulative analysis results include normal cumulative analysis and abnormal cumulative analysis. According to the obtained The cumulative analysis results generate corresponding scheduling abnormality warning signals.

Further, the working methods of the cumulative evaluation unit include:

Identify qualified scheduling instructions in real time, mark them as target instructions, identify the scheduling detailed data of the target instructions, and set the corresponding scheduling unfavorable curve according to the obtained scheduling detailed data; identify the scheduling unfavorable function G(t) corresponding to the unfavorable scheduling curve;

Preset the scheduling limit table; match the corresponding scheduling limit value from the scheduling limit value table according to the detailed scheduling data, and mark the obtained scheduling limit value as PB;

According to the cumulative evaluation formula Calculate the corresponding cumulative evaluation value, where to is the corresponding estimated scheduling duration; PVD is the cumulative evaluation value;

Mark the cumulative evaluation results of scheduling instructions with a cumulative evaluation value greater than 0 as cumulative analysis exceptions;

Mark the cumulative evaluation results of scheduling instructions whose cumulative evaluation value is not greater than 0 as cumulative analysis normal.

Further, the setting method of the unfavorable scheduling curve includes:

Establish a disadvantage analysis model, conduct analysis through the established disadvantage analysis model, and obtain the corresponding initial disadvantage value and change coefficient curve;

Mark the obtained initial unfavorable value as BNo; match the corresponding change coefficient from the change coefficient curve according to the corresponding time, and mark the obtained change coefficient as μ; then the unfavorable value at the corresponding time is BN=BNo×(1+μ), In the formula, BN is an unfavorable value;

Taking the scheduling duration as the horizontal axis and the unfavorable value as the vertical axis, combine each unfavorable value and the corresponding time to form a scheduling unfavorable curve, and obtain the scheduling unfavorable function of the scheduling unfavorable curve, and label the scheduling unfavorable function as G(t).

The early warning module is used to provide abnormal early warning to the corresponding management personnel, identify the scheduling abnormal early warning signal generated by the evaluation module in real time, and provide abnormal early warning to the management personnel according to the preset abnormal early warning method according to the identified scheduling abnormal early warning signal.

Compared with the prior art, the beneficial effects of the present invention are:

Through the cooperation between the dispatch information module, the evaluation module and the early warning module, the dispatch self-examination of the power grid dispatch operation management is realized, and the evaluation item data is evaluated according to the dispatch scope and dispatch effect set, and based on the matching in the reference library As a result, the accumulated evaluation unit performs cumulative analysis on qualified dispatching instructions, etc., to realize self-examination from multiple angles, making the dispatching self-inspection more comprehensive and ensuring the normal operation of power grid dispatching; realizing intelligent self-inspection of power grid dispatching.

Description of the drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following will briefly introduce the drawings needed to describe the embodiments or the prior art. Obviously, the drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without exerting creative efforts.

Figure 1 is a functional block diagram of the present invention.

Detailed ways

The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

As shown in Figure 1, a dispatching self-checking system for power grid dispatching and operation management based on big data includes a dispatching information module, an evaluation module and an early warning module;

The dispatch information module is used to obtain relevant dispatch detailed data of each generated dispatch instruction in real time; connect to the corresponding power grid dispatch system, obtain the dispatch instructions generated in the power grid dispatch system in real time, and collect the dispatch instructions corresponding to the dispatch instructions according to the generated dispatch instructions. Scheduling detailed data refers to data related to generating the scheduling instruction.

Specifically, the influencing factors of different dispatching instructions in power grid dispatching can be determined based on the relevant specifications of power grid dispatching, dispatching common sense, etc., and corresponding detailed data items can be set according to each influencing factor; and the evaluation items of each detailed data item can be set according to subsequent evaluation requirements. , used to indicate from which angles the scheduling data of this detailed data item should be evaluated; all are set manually, because it is common knowledge in this field, and it is more convenient and faster to set it manually; each detailed data obtained Integrate the items and corresponding evaluation items to establish a corresponding evaluation matching table;

Corresponding data collection is carried out based on the corresponding detailed data items in the evaluation matching table, and the scheduling detailed data is formed after integration.

The evaluation module is used to evaluate the scheduling details data corresponding to the scheduling instructions, obtain the scheduling instructions collected by the scheduling information module and the corresponding scheduling details data in real time; match the corresponding evaluation items based on the preset evaluation matching table, and according to each evaluation item Collect corresponding evaluation item data;

Match the corresponding reference instructions according to the identified scheduling instructions, and identify the scheduling range and scheduling effect set corresponding to the reference instructions;

The evaluation item data is evaluated based on the obtained scheduling scope and scheduling effect set, and corresponding evaluation results are obtained. The evaluation results include evaluation qualification and evaluation abnormality, and corresponding scheduling abnormality early warning signals are generated based on the obtained evaluation results.

Among them, the method of matching corresponding reference instructions according to the identified scheduling instructions includes:

Identify various dispatching instructions based on historical power grid dispatching data, match various dispatching instructions that may be generated by the power grid based on the detailed data of the power grid, and mark the dispatching instructions that may be generated by the power grid as candidates for selection. Because of different power grid conditions, The dispatch instructions that may be generated are different, and some dispatch instructions cannot be generated at all or should not be generated in the power grid;

The corresponding dispatch range is summarized based on the historical power grid dispatch data corresponding to the instruction to be selected, that is, the dispatch range to which the instruction to be selected is adapted under the normal allowed state is set. It can be integrated and summarized based on the corresponding dispatch conditions, and then performed manually. Corresponding adjustments are made to form the scheduling range of the candidate instruction; and the scheduling effect set corresponding to the scheduling range is set accordingly, that is, the corresponding scheduling effect is set according to the historical scheduling data of the corresponding scheduling conditions, and summarized to form the scheduling effect corresponding to the scheduling range. Set, the scheduling effect is the scheduling effect formed by the scheduling instruction under the scheduling conditions.

Integrate the obtained candidate instructions with the corresponding dispatch range and dispatch effect set to form a reference library for the power grid;

Identify the scheduling instructions, input the obtained scheduling instructions into the reference library for matching, obtain the corresponding candidate instructions, mark the matching candidate instructions as reference instructions, and obtain the scheduling range and scheduling effect set corresponding to the reference instructions.

In one embodiment, if no reference instruction is matched, a corresponding scheduling exception warning signal is generated.

Methods for evaluating the evaluation item data based on the obtained scheduling scope and scheduling effect set include:

The existing similarity algorithm is used to calculate the similarity value between the evaluation item data and the scheduling effect set, that is, the scheduling effect to be achieved according to the evaluation item data is compared with the scheduling effect set. If it is within the scheduling effect set, the similarity value is 1. Otherwise, compare it with the most solved scheduling effect, and reduce the similarity value according to the corresponding difference; you can use the existing similarity calculation method for calculation, or you can establish a corresponding similarity evaluation model based on the CNN network or DNN network. , the corresponding training set is manually set for training. The training set includes various preset scheduling effect combinations and corresponding set similarity values. The corresponding similarity values are obtained by analyzing the similarity evaluation model after successful training. ; Because neural networks are existing technologies in this field, the specific establishment and training process will not be described in detail in the present invention.

Mark the evaluation items as i, i=1, 2,...,n; n is the number of evaluation items; according to the isolated forest algorithm Calculate the single evaluation value f(xi) of each evaluation item, where xi is the evaluation item data of the corresponding evaluation item, and U is the corresponding scheduling range.

Mark the obtained similarity value as XS, according to the comprehensive evaluation formula Calculate the corresponding comprehensive evaluation value PGF, where λ is the judgment coefficient, when/> When it is not equal to 0, it is directly determined that the evaluation is abnormal. When/> When equal to 0, the influence of λ is not considered;

When the comprehensive evaluation value is greater than the threshold X1, the evaluation is judged to be qualified;

When the comprehensive evaluation value is not greater than the threshold X1, the evaluation is determined to be abnormal.

In one embodiment, further scheduling analysis is performed on the qualified scheduling instructions, and a cumulative evaluation unit is provided in the evaluation module. The cumulative evaluation unit is used to perform cumulative analysis on the qualified scheduling instructions to obtain the corresponding cumulative analysis results. , the cumulative analysis results include normal cumulative analysis and abnormal cumulative analysis, and the corresponding scheduling abnormality early warning signal is generated based on the obtained cumulative analysis results.

Among them, the working methods of the cumulative evaluation unit include:

Identify qualified scheduling instructions in real time, mark them as target instructions, identify the scheduling detailed data of the target instructions, and set the corresponding scheduling unfavorable curve according to the obtained scheduling detailed data; identify the scheduling unfavorable function G(t) corresponding to the unfavorable scheduling curve;

The scheduling limits corresponding to different scheduling levels are preset and adjusted by the user, such as the scheduling limits corresponding to different scheduling volume intervals. The scheduling limit means that the cumulative value corresponding to the corresponding scheduling cannot exceed the limit, and is adjusted by the user. Each scheduling limit must comply with the corresponding specifications; organize the set scheduling limits to form a scheduling limit table used by the user; match the corresponding scheduling limits from the scheduling limit table according to the detailed scheduling data, and obtain the The scheduling limit is marked PB;

According to the cumulative evaluation formula Calculate the corresponding cumulative evaluation value PVD, where to is the corresponding estimated scheduling duration;

Mark the cumulative evaluation results of scheduling instructions with a cumulative evaluation value greater than 0 as cumulative analysis exceptions;

Mark the cumulative evaluation results of scheduling instructions whose cumulative evaluation value is not greater than 0 as cumulative analysis normal.

Among them, the dispatching unfavorable curve is based on the current dispatching conditions to simulate the continuous unfavorable situation change curve when the power grid is dispatched. The horizontal axis is time and the vertical axis is unfavorable value. The unfavorable value is based on dispatching distance loss, safety factors, dispatching efficiency, Relevant equipment and other relevant data are evaluated, and change as the dispatch time continues, and the corresponding adverse value is benchmarked against the comprehensive possible loss, that is, the possible economic potential corresponding to various factors is estimated based on the existing facility conditions and detailed dispatch data. The losses are accumulated to obtain the comprehensive expected loss, which is converted into the corresponding unfavorable value. The corresponding conversion coefficient is preset and multiplied; then combined with the impact of changes over time, the unfavorable value is gradually increased, which requires The corresponding change coefficient is preset according to the facility conditions, and the change coefficient gradually increases with time. The unfavorable value at that time can be obtained by multiplying the unfavorable value by the change coefficient at the corresponding time.

Specifically, the corresponding disadvantage analysis model can be established based on the CNN network or DNN network, and the corresponding training set can be established manually. The training set includes facility condition information, scheduling detailed data, and corresponding settings of the initial disadvantage value and change coefficient curve, and the change coefficient curve. The horizontal axis is time, and the vertical axis is the variation coefficient, and the increase gradually increases with the increase of time.

Mark the obtained initial unfavorable value as BNo; then the unfavorable value at subsequent time BN=BNo×(1+μ), where μ is the change coefficient of the corresponding time, input the corresponding time into the change coefficient curve for matching, and obtain the corresponding Variation coefficient; a scheduling disadvantage curve is formed based on the obtained disadvantage values and time, and the scheduling disadvantage function of the scheduling disadvantage curve can be obtained, and the scheduling disadvantage function is marked as G(t).

The early warning module is used to provide abnormal early warning to the corresponding management personnel, identify the scheduling abnormal early warning signal generated by the evaluation module in real time, and provide abnormal early warning to the management personnel according to the preset abnormal early warning method according to the identified scheduling abnormal early warning signal.

Identify various scheduling abnormality early warning signals, and preset corresponding abnormality early warning methods for each scheduling abnormality early warning signal; the specific abnormality early warning methods are set according to its specific management methods and management requirements.

Through the cooperation between the dispatch information module, the evaluation module and the early warning module, the dispatch self-examination of the power grid dispatch operation management is realized, and the evaluation item data is evaluated according to the dispatch scope and dispatch effect set, and based on the matching in the reference library As a result, the accumulated evaluation unit performs cumulative analysis on qualified dispatching instructions, etc., to realize self-examination from multiple angles, making the dispatching self-inspection more comprehensive and ensuring the normal operation of power grid dispatching; realizing intelligent self-inspection of power grid dispatching.

The above formulas are all numerical calculations after removing the dimensions. The formula is a formula closest to the real situation obtained by collecting a large amount of data for software simulation. The preset parameters and preset thresholds in the formula are set by those skilled in the field according to the actual situation. Or obtain a large amount of data through simulation.

The above embodiments are only used to illustrate the technical methods of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical methods of the present invention can be modified or equivalently substituted. without departing from the spirit and scope of the technical method of the present invention.

Claims (8)

1. The dispatching self-checking system for dispatching operation management of the power grid based on big data is characterized by comprising a dispatching information module, an evaluation module and an early warning module;

the scheduling information module is used for acquiring scheduling detailed data of each generated scheduling instruction in real time;

the evaluation module is used for evaluating the scheduling detail data corresponding to the scheduling instruction and acquiring the scheduling instruction acquired by the scheduling information module and the corresponding scheduling detail data in real time; matching corresponding evaluation items based on a preset evaluation matching table, and collecting corresponding evaluation item data according to each evaluation item;

according to the identified scheduling instruction matching the corresponding reference instruction, identifying a scheduling range and a scheduling effect set corresponding to the reference instruction;

evaluating the evaluation item data according to the obtained scheduling range and the scheduling effect set to obtain corresponding evaluation results, wherein the evaluation results comprise qualified evaluation and abnormal evaluation, and corresponding scheduling abnormal early warning signals are generated according to the obtained evaluation results;

the early warning module is used for carrying out abnormal early warning on corresponding management personnel, identifying the scheduling abnormal early warning signals generated by the evaluation module in real time, and carrying out abnormal early warning on the management personnel according to the identified scheduling abnormal early warning signals and a preset abnormal early warning mode.

2. The dispatching self-checking system for dispatching operation management of a power grid based on big data as set forth in claim 1, wherein the method for collecting dispatching detail data comprises the following steps:

docking a corresponding power grid dispatching system, and identifying dispatching instructions generated in the power grid dispatching system in real time;

determining influence factors corresponding to different dispatching instructions in power grid dispatching, and setting corresponding detail data items according to the influence factors; setting an evaluation item of each detail data item; integrating the obtained detail data items with the corresponding evaluation items, and establishing a corresponding evaluation matching table;

and matching corresponding detail data items from the evaluation matching table according to the identified scheduling instruction, carrying out corresponding data acquisition based on each detail data item, and integrating the acquired data to form scheduling detail data.

3. The dispatching self-checking system for dispatching operation management of a power grid based on big data according to claim 1, wherein the method for matching corresponding reference instructions according to the identified dispatching instructions comprises the following steps:

determining a corresponding to-be-selected instruction, and setting a corresponding scheduling range and a scheduling effect set according to historical power grid scheduling data corresponding to the to-be-selected instruction; integrating the obtained to-be-selected instruction with the corresponding scheduling range and the scheduling effect set to form a corresponding reference library;

identifying a scheduling instruction, inputting the obtained scheduling instruction into a reference library for matching, obtaining a corresponding to-be-selected instruction, marking the matched to-be-selected instruction as a reference instruction, and obtaining a scheduling range and a scheduling effect set corresponding to the reference instruction.

4. The dispatching self-checking system for dispatching operation management of the power grid based on big data according to claim 3, wherein when no corresponding reference instruction is matched in the reference library, a corresponding dispatching abnormality early warning signal is generated.

5. The dispatching self-checking system for dispatching operation management of a power grid based on big data according to claim 1, wherein the method for evaluating the evaluation item data according to the obtained dispatching range and dispatching effect set comprises the following steps:

calculating a similarity value between the evaluation item data and the scheduling effect set, and marking the obtained similarity value as XS;

the evaluation item is labeled i, i=1, 2, … …, n; n is the number of evaluation items; according to an isolated forest algorithmCalculating a single evaluation value f (xi) of each evaluation item, wherein xi is the evaluation item of the corresponding evaluation itemData, U is a corresponding scheduling range;

according to the comprehensive evaluation formulaCalculating a corresponding comprehensive evaluation value PGF, wherein lambda is a judgment coefficient, when +.>When the value is not equal to 0, judging that the evaluation is abnormal; when->When equal to 0, the influence of lambda is not considered;

when the comprehensive evaluation value is larger than a threshold value X1, judging that the evaluation is qualified;

when the integrated evaluation value is not greater than the threshold value X1, it is determined that the evaluation is abnormal.

6. The dispatching self-checking system for power grid dispatching operation management based on big data according to claim 1, wherein the evaluation module further comprises an accumulation evaluation unit, the accumulation evaluation unit is used for performing accumulated analysis on dispatching instructions qualified in evaluation to obtain corresponding accumulated analysis results, the accumulated analysis results comprise accumulated analysis normal and accumulated analysis abnormal, and corresponding dispatching abnormality early warning signals are generated according to the obtained accumulated analysis results.

7. The dispatching self-checking system for dispatching operation management of power grid based on big data according to claim 6, wherein the working method of the accumulation evaluation unit comprises the following steps:

identifying and evaluating qualified scheduling instructions in real time, marking the scheduling instructions as target instructions, identifying scheduling detailed data of the target instructions, and setting corresponding scheduling adverse curves according to the obtained scheduling detailed data; identifying a scheduling disfunction G (t) corresponding to the scheduling disfunction curve;

presetting a scheduling limit table; matching corresponding scheduling limit values from a scheduling limit value table according to the scheduling detailed data, and marking the obtained scheduling limit values as PB;

according to the cumulative evaluation formulaCalculating corresponding accumulated evaluation values, wherein to is the corresponding estimated scheduling duration; PVD is a cumulative evaluation value;

marking the accumulated evaluation result of the scheduling instruction with the accumulated evaluation value larger than 0 as accumulated analysis abnormality;

and marking the accumulated evaluation result of the scheduling instruction with the accumulated evaluation value not greater than 0 as the accumulated analysis normal.

8. The dispatching self-checking system for dispatching operation management of a power grid based on big data according to claim 7, wherein the method for setting the dispatching adverse curve comprises the following steps:

establishing an unfavorable analysis model, and analyzing through the established unfavorable analysis model to obtain a corresponding initial unfavorable value and a change coefficient curve;

the initial adverse value obtained is marked BNo; matching corresponding change coefficients from the change coefficient curves according to the corresponding time, and marking the obtained change coefficients as mu; the adverse value of the corresponding time is bn= BNo × (1+μ), where BN is the adverse value;

taking the scheduling duration time as a horizontal axis and the adverse value as a vertical axis, forming a scheduling adverse curve by combining each adverse value and the corresponding time, obtaining a scheduling adverse function of the scheduling adverse curve, and marking the scheduling adverse function as G (t).