Fire coal dispatching plan real-time updating method based on risk identification

Abstract

The invention discloses a fire coal allocation plan real-time updating method based on risk identification. The method comprises the steps of obtaining influence factors and preset data and obtaining a plurality of preset data combinations; generating a predicted fire coal dispatching plan of the preset data combination, and comparing the predicted fire coal dispatching plan with the fire coal dispatching plan to obtain a plurality of fire coal dispatching parameter deviation values; analyzing the fire coal allocation parameter deviation value to obtain a comprehensive difference evaluation value, setting a risk coefficient according to the comprehensive difference evaluation value, and constructing a risk identification model; determining a fire coal dispatching plan associated with the abnormal influence factors, determining a corresponding real-time data combination, and determining a real-time risk coefficient corresponding to the fire coal dispatching plan based on the real-time data combination and a risk identification model; a plurality of updating models are constructed, the corresponding updating models are selected according to the real-time risk coefficients, real-time updating instructions corresponding to the fire coal dispatching plans are generated, updating strategies of the fire coal dispatching plans are reasonably formulated, the completion rate of the dispatching plans is increased, and economic losses are reduced.

Description

Technical Field

[0001] The present application relates to the technical field of coal transportation, and in particular to a method for real-time updating of coal transportation plans based on risk identification. Background Art

[0002] At present, thermal power generation is still the main mode of power generation, so the safe acquisition, transportation and management of fuel for thermal power generation has become extremely important. The rationality of the coal transportation plan can not only make fuel scheduling safer, but also reduce the cost of fuel scheduling.

[0003] However, the existing coal transportation plan updating method has a delay, cannot timely discover and identify the risks of changes in influencing factors on the coal transportation plan, and cannot reasonably formulate an updating strategy for the coal transportation plan, resulting in a reduced transportation completion rate or serious economic losses. Summary of the Invention

[0004] In order to solve the above technical problems, the present application provides a real-time updating method for coal transportation plan based on risk identification. By determining the influencing factors of the coal transportation plan and the preset data of the influencing factors, multiple preset data combinations are constructed and corresponding predicted coal transportation plans are obtained. A comprehensive difference evaluation value is generated based on the comparison results of multiple predicted coal transportation plans and coal transportation plans. A risk coefficient is set according to the comprehensive difference evaluation value, and a risk identification model is constructed. The real-time risk coefficient is determined according to the risk identification model and the real-time data combination. The corresponding update model is selected according to the real-time line coefficient and a real-time update instruction is generated. A reasonable update strategy for the coal transportation plan is formulated to improve the completion rate of the transportation plan and reduce economic losses.

[0005] In some embodiments of the present application, a method for real-time updating of coal transportation plans based on risk identification is provided, including: Acquire multiple influencing factors when formulating a coal transportation plan and multiple preset data for each influencing factor, and randomly combine the preset data of different influencing factors to obtain multiple preset data combinations; generating a predicted coal transportation plan based on multiple preset data combinations based on a coal transportation plan prediction model, comparing the predicted coal transportation parameters in the predicted coal transportation plan with the target coal transportation parameters of the coal transportation plan to obtain multiple coal transportation parameter deviations; Analyze the deviation of coal transportation parameters to obtain a comprehensive difference evaluation value, set a risk coefficient based on the comprehensive difference evaluation value, and build a risk identification model for the corresponding coal transportation plan based on the preset data combination and risk coefficient; Obtain real-time data of influencing factors and determine whether there are any anomalies. If so, determine the coal transportation plan associated with the abnormal influencing factors and determine the corresponding real-time data combination. Based on the real-time data combination and the risk identification model, determine the real-time risk coefficient of the corresponding coal transportation plan; Construct multiple update models, select a corresponding update model according to the real-time risk coefficient, and generate a real-time update instruction corresponding to the coal transportation plan, wherein the update model includes a first update model, a second update model, and a third update model.

[0006] In some embodiments of the present application, obtaining multiple influencing factors when formulating a coal transportation plan and multiple preset data for each influencing factor includes: Acquiring multiple influencing factors and standard data of the influencing factors when formulating a coal transportation plan, and generating a predicted fluctuation curve of the standard data of each influencing factor, wherein the coal transportation plan includes multiple coal transportation parameters; Mapping each influencing factor with the affected coal transportation parameter to obtain an influencing factor-parameter mapping table, wherein the influencing factor is mapped to at least one coal transportation parameter; Preset multiple division nodes, divide the predicted fluctuation curve into multiple predicted fluctuation intervals according to the division nodes, and obtain historical impact characteristics of the predicted fluctuation intervals of the influencing factors on the corresponding coal transportation parameters in the influencing factor-parameter mapping table, wherein the historical impact characteristics include historical update instructions for the corresponding coal transportation parameters and the historical difference degree of the coal transportation parameters before and after the historical update instructions; Generate an impact evaluation value of a predicted fluctuation range of the influencing factor based on the number of coal transportation parameters whose historical difference between the coal transportation parameters before and after the historical update instruction is greater than a preset difference threshold and the difference between the historical difference and the preset difference threshold; The calculation formula of the impact evaluation value is: ; Where Y is the application evaluation value, n0 is the number of coal transportation parameters whose historical difference between the coal transportation parameters before and after the historical update instruction is greater than the preset difference threshold, and N is the total number of coal transportation parameters mapped by the current influencing factor in the influencing factor-parameter mapping table. is the historical difference of the s-th coal transportation parameter, and y1 is the impact assessment conversion coefficient; Generate the impact evaluation value of each predicted fluctuation interval of the influencing factor in sequence; If the impact evaluation value is greater than the preset impact evaluation value threshold, the impact evaluation value difference is calculated, the number of samples collected for the corresponding predicted fluctuation interval is set according to the impact evaluation value difference, and the preset data for the corresponding predicted fluctuation interval is collected according to the number of samples collected; If the impact evaluation value is less than the preset impact evaluation value threshold, the mean of the data in the corresponding predicted fluctuation interval is calculated and set as the preset data of the corresponding predicted fluctuation interval; According to the multiple predicted fluctuation intervals, multiple preset data corresponding to the influencing factors are obtained, and a preset data sequence A, A (a1, a2, ... aj) is constructed, where ai is the i-th preset data and j is the total number of preset data of the influencing factors; The preset data in the preset data sequence of multiple influencing factors of the same coal transportation plan are randomly combined to obtain multiple preset data combinations of the coal transportation plan.

[0007] In some embodiments of the present application, a plurality of coal transportation parameter deviations are obtained, including: The coal transportation plan prediction model is obtained by performing neural network training based on the power plant data, resource unit data, and target transportation data when the current coal transportation plan is formulated as static input data, the historical data combination of multiple influencing parameters as dynamic input data, and the formulated historical coal transportation plan as output data. generating a predicted coal transportation plan for each preset data combination based on a coal transportation plan prediction model, wherein the predicted coal transportation plan includes a plurality of predicted coal transportation parameters; The predicted coal transportation parameters of each predicted coal transportation plan are compared with the target coal transportation parameters of the coal transportation plan to obtain the coal transportation parameter deviation.

[0008] In some embodiments of the present application, the deviation of coal transportation parameters is analyzed to obtain a comprehensive difference evaluation value, including: Pre-set multiple evaluation indicators; Determine the coal transportation parameter associated with each evaluation index according to the degree of correlation between the coal transportation parameter and the evaluation index, compare the coal transportation parameter deviation value of the corresponding coal transportation parameter with the corresponding preset deviation threshold value in the evaluation index, and obtain the deviation difference value; Generate a reference evaluation value of the corresponding evaluation index according to the deviation difference of the coal transportation parameters associated with the same evaluation index; A comprehensive difference evaluation value is generated based on the reference evaluation values of all evaluation indicators and the weight coefficients of the corresponding evaluation indicators.

[0009] In some embodiments of the present application, setting a risk factor according to the comprehensive difference evaluation value includes: Presetting a first preset evaluation value interval, a second preset evaluation value interval, a third preset evaluation value interval and a fourth preset evaluation value interval; When the comprehensive difference evaluation value is within the first preset evaluation value range, the risk coefficient is set to the first preset risk coefficient; When the comprehensive difference evaluation value is within the second preset evaluation value range, the risk coefficient is set to the second preset risk coefficient; When the comprehensive difference evaluation value is within the third preset evaluation value range, the risk coefficient is set to the third preset risk coefficient; When the comprehensive difference evaluation value is in the fourth preset evaluation value interval, the risk coefficient is set to the fourth preset risk coefficient.

[0010] In some embodiments of the present application, obtaining real-time data of influencing factors and determining whether they are abnormal, and if so, determining a coal transportation plan associated with the abnormal influencing factors, includes: Obtain the real-time data of the influencing factors and subtract them from the corresponding standard data to obtain the real-time data difference; If the real-time data difference is greater than the preset difference threshold, it is determined that an abnormality has occurred and the factors affecting the abnormality are screened out; Obtain the application coefficient of the abnormal influencing factors when formulating the coal transportation plan, determine the coal transportation parameters affected by the abnormal influencing factors according to the influencing factor-parameter mapping table, and obtain the weight coefficient of the corresponding coal transportation parameters; Generate correlation coefficients between abnormal influencing factors and coal transportation plans based on application coefficients and weight coefficients of affected coal transportation parameters; The calculation formula of the correlation coefficient is: ; Wherein, G is the correlation coefficient, w is the application coefficient, g1 is the first correlation conversion coefficient, q1 is the first preset weight coefficient, g is the second correlation conversion coefficient, q2 is the second preset weight coefficient, m is the number of coal transportation parameters affected by the abnormal influencing factor, and q0z is the weight coefficient of the zth affected coal transportation parameter; Pre-set correlation coefficient threshold; If the correlation coefficient is greater than the correlation coefficient threshold, the corresponding coal transportation plan is set as the coal transportation plan associated with the abnormal influencing factor.

[0011] In some embodiments of the present application, determining a real-time risk coefficient of a corresponding coal transportation plan based on a real-time data combination and a risk identification model includes: Constructing a risk identification model for each coal transportation plan based on multiple preset data combinations and corresponding risk coefficients; Generate a real-time data combination corresponding to the coal transportation plan based on the influencing factors when the associated coal transportation plan is formulated and the real-time data of the influencing factors; The real-time data combination is input into the risk identification model of the corresponding coal transportation plan to obtain the real-time risk coefficient of the corresponding coal transportation plan.

[0012] In some embodiments of the present application, multiple update models are constructed, including: Presetting a first preset risk coefficient interval, a second preset risk coefficient interval, and a third preset risk coefficient interval; Obtaining a preset data combination whose risk coefficient is within a first preset risk coefficient interval and a coal transportation parameter deviation corresponding to the preset data combination, analyzing the coal transportation parameter deviation based on a preset objective function and a first constraint condition to obtain a plurality of first update strategies, and constructing a first update model, wherein the first update model is used to obtain a real-time update instruction for the coal transportation plan at the current moment; Obtaining historical data combinations whose risk factors fall within a second preset risk factor interval and coal transportation parameter deviations corresponding to the historical data interval combinations, analyzing the coal transportation parameter deviations based on a preset objective function and a second constraint condition to obtain a plurality of second update strategies, and constructing a second update model, the second update model being used to obtain real-time update instructions for the coal transportation plan at the current moment and for the first preset future time period; Obtaining historical data combinations whose risk factors fall within a third preset risk factor interval and coal transportation parameter deviations corresponding to the historical data interval combinations, analyzing the coal transportation parameter deviations based on a preset objective function and a third constraint condition to obtain multiple third update strategies, and constructing a third update model, the third update model being used to obtain update instructions for the real-time coal transportation plan for the current moment and a second preset future time period; Among them, the preset objective functions include the objective function of minimizing the demurrage rate and the objective function of maximizing the completion rate of the transportation plan; Among them, the first preset risk coefficient interval is smaller than the second preset risk coefficient interval, the second preset risk coefficient interval is smaller than the third preset risk coefficient interval, and the first preset future time period is smaller than the second preset future time period.

[0013] In some embodiments of the present application, selecting a corresponding update model according to the real-time risk coefficient and generating a real-time update instruction corresponding to the coal transportation plan include: Determine a standard coal transportation plan for the real-time data combination, compare the standard coal transportation parameters of the standard coal transportation plan with the real-time coal transportation parameters of the coal transportation plan, and obtain a deviation of the real-time coal transportation parameters; When the real-time risk coefficient is within a first preset risk coefficient range, a first update model is selected, an update strategy for the real-time coal transportation parameter deviation at the current moment is determined based on the first update model, and a real-time update instruction corresponding to the coal transportation plan is generated according to the update strategy; When the real-time risk coefficient is within the second preset risk coefficient range, a second update model is selected, an update strategy for the real-time coal transportation parameter deviation at the current moment and the first preset future time period is determined based on the second update model, and a real-time update instruction corresponding to the coal transportation plan is generated according to the update strategy; When the real-time risk coefficient is in the third preset risk coefficient range, the third update model is selected, and the update strategy of the real-time coal transportation parameter deviation at the current moment and the second preset future time period is determined according to the third update model, and the real-time update instructions of the corresponding coal transportation plan are generated according to the update strategy.

[0014] Compared with the prior art, the real-time update method of the coal transportation plan based on risk identification in the embodiment of the present application has the following advantages: By determining the influencing factors of the coal transportation plan and the preset data of the influencing factors, multiple preset data combinations are constructed to obtain the corresponding predicted coal transportation plan, and a comprehensive difference evaluation value is generated based on the comparison results of multiple predicted coal transportation plans and the coal transportation plan. The risk coefficient is set according to the comprehensive difference evaluation value, and a risk identification model is constructed. The real-time risk coefficient is determined based on the risk identification model and the real-time data combination. The corresponding update model is selected according to the real-time line coefficient and a real-time update instruction is generated. A reasonable update strategy for the coal transportation plan is formulated to improve the completion rate of the transportation plan and reduce economic losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a method for real-time updating of coal transportation plan based on risk identification in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0017] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0018] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. Throughout this application, unless otherwise specified, "plurality" means two or more.

[0019] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0020] like Figure 1 As shown, the real-time updating method of the coal transportation plan based on risk identification in the embodiment of the present application includes: Step S101: obtaining multiple influencing factors and multiple preset data of each influencing factor when formulating a coal transportation plan, and randomly combining the preset data of different influencing factors to obtain multiple preset data combinations; Step S102: generating a predicted coal transportation plan based on multiple preset data combinations based on the coal transportation plan prediction model, comparing the predicted coal transportation parameters in the predicted coal transportation plan with the target coal transportation parameters of the coal transportation plan, and obtaining multiple coal transportation parameter deviations; Step S103: Analyze the deviation of the coal transportation parameters to obtain a comprehensive difference evaluation value, set a risk coefficient based on the comprehensive difference evaluation value, and construct a risk identification model corresponding to the coal transportation plan based on the preset data combination and the risk coefficient; Step S104: Acquire real-time data of influencing factors and determine whether there is an anomaly. If so, determine the coal transportation plan associated with the abnormal influencing factors, determine the corresponding real-time data combination, and determine the real-time risk coefficient of the corresponding coal transportation plan based on the real-time data combination and the risk identification model; Step S105: construct multiple update models, select a corresponding update model according to the real-time risk coefficient and generate a real-time update instruction corresponding to the coal transportation plan, wherein the update model includes a first update model, a second update model and a third update model.

[0021] In this embodiment, the influencing factors include resource conditions, berthing plans, weather conditions, and procedures, and the preset data refers to data that has a significant impact on the coal transportation parameters in the coal transportation plan and the corresponding coal transportation parameters need to be updated.

[0022] In this embodiment, the target coal transportation parameters refer to the coal transportation parameters in the coal transportation plan formulated based on standard data of influencing factors.

[0023] In some embodiments of the present application, obtaining multiple influencing factors when formulating a coal transportation plan and multiple preset data for each influencing factor includes: Acquiring multiple influencing factors and standard data of the influencing factors when formulating a coal transportation plan, and generating a predicted fluctuation curve of the standard data of each influencing factor, wherein the coal transportation plan includes multiple coal transportation parameters; Mapping each influencing factor with the affected coal transportation parameter to obtain an influencing factor-parameter mapping table, wherein the influencing factor is mapped to at least one coal transportation parameter; Preset multiple division nodes, divide the predicted fluctuation curve into multiple predicted fluctuation intervals according to the division nodes, and obtain historical impact characteristics of the predicted fluctuation intervals of the influencing factors on the corresponding coal transportation parameters in the influencing factor-parameter mapping table, wherein the historical impact characteristics include historical update instructions for the corresponding coal transportation parameters and the historical difference degree of the coal transportation parameters before and after the historical update instructions; Generate an impact evaluation value of a predicted fluctuation range of the influencing factor based on the number of coal transportation parameters whose historical difference between the coal transportation parameters before and after the historical update instruction is greater than a preset difference threshold and the difference between the historical difference and the preset difference threshold; The calculation formula of the impact evaluation value is: ; Where Y is the application evaluation value, n0 is the number of coal transportation parameters whose historical difference between the coal transportation parameters before and after the historical update instruction is greater than the preset difference threshold, and N is the total number of coal transportation parameters mapped by the current influencing factor in the influencing factor-parameter mapping table. is the historical difference of the s-th coal transportation parameter, and y1 is the impact assessment conversion coefficient; Generate the impact evaluation value of each predicted fluctuation interval of the influencing factor in sequence; If the impact evaluation value is greater than the preset impact evaluation value threshold, the impact evaluation value difference is calculated, the number of samples collected for the corresponding predicted fluctuation interval is set according to the impact evaluation value difference, and the preset data for the corresponding predicted fluctuation interval is collected according to the number of samples collected; If the impact evaluation value is less than the preset impact evaluation value threshold, the mean of the data in the corresponding predicted fluctuation interval is calculated and set as the preset data of the corresponding predicted fluctuation interval; According to the multiple predicted fluctuation intervals, multiple preset data corresponding to the influencing factors are obtained, and a preset data sequence A, A (a1, a2, ... aj) is constructed, where ai is the i-th preset data and j is the total number of preset data of the influencing factors; The preset data in the preset data sequence of multiple influencing factors of the same coal transportation plan are randomly combined to obtain multiple preset data combinations of the coal transportation plan.

[0024] In this embodiment, when formulating a real-time coal transportation plan, it is necessary to set it based on the standard data of multiple influencing factors. During the actual transportation process, the standard data of the influencing factors will produce fluctuations of varying degrees. A predicted fluctuation curve of the standard data of the influencing factors is generated based on the fluctuation range and fluctuation trend of the standard data.

[0025] In this embodiment, the impact evaluation value refers to the evaluation of the historical update instructions of the corresponding data in the predicted fluctuation interval on the corresponding coal transportation parameters and the update degree of the coal transportation parameters. When the impact evaluation value is larger, it means that the data in the corresponding predicted fluctuation interval has a greater impact on the coal transportation parameters. The number of data collected in the predicted fluctuation interval is set according to the difference in the impact evaluation value. When the difference in the impact evaluation value is larger, the number of data collected should be more, thereby laying the foundation for the subsequent construction of multiple preset data combinations and risk identification models, and improving the accuracy of judgment on the risk coefficient of the coal transportation plan caused by changes in influencing factors.

[0026] In some embodiments of the present application, a plurality of coal transportation parameter deviations are obtained, including: The coal transportation plan prediction model is obtained by performing neural network training based on the power plant data, resource unit data, and target transportation data when the current coal transportation plan is formulated as static input data, the historical data combination of multiple influencing parameters as dynamic input data, and the formulated historical coal transportation plan as output data. generating a predicted coal transportation plan for each preset data combination based on a coal transportation plan prediction model, wherein the predicted coal transportation plan includes a plurality of predicted coal transportation parameters; The predicted coal transportation parameters of each predicted coal transportation plan are compared with the target coal transportation parameters of the coal transportation plan to obtain the coal transportation parameter deviation.

[0027] In this embodiment, power plant data includes inventory data, daily consumption forecast data, incoming coal data, etc., resource unit data includes coal supply data, suppliers, coal types, etc., and target transportation data includes arrival and unloading port date, power plant flow data, etc.

[0028] In this embodiment, static input data is data that does not change, dynamic input data refers to data that is constructed based on different historical data combinations of influencing factors and is data that will change, and historical coal transportation plans are formulated based on static input data and different historical data combinations of influencing factors, thereby constructing a coal transportation plan prediction model, and predicting the preset data combination based on the coal transportation plan prediction model to obtain multiple predicted coal transportation plans, laying the foundation for the subsequent determination of the risk coefficient of the coal transportation plan and the construction of a risk identification model, improving the accuracy of the risk identification model, and timely generating update strategies, improving the update effect of the coal transportation plan, improving the completion rate of the transportation plan and reducing economic losses.

[0029] In some embodiments of the present application, the deviation of coal transportation parameters is analyzed to obtain a comprehensive difference evaluation value, including: Pre-set multiple evaluation indicators; Determine the coal transportation parameter associated with each evaluation index according to the degree of correlation between the coal transportation parameter and the evaluation index, compare the coal transportation parameter deviation value of the corresponding coal transportation parameter with the corresponding preset deviation threshold value in the evaluation index, and obtain the deviation difference value; Generate a reference evaluation value of the corresponding evaluation index according to the deviation difference of the coal transportation parameters associated with the same evaluation index; A comprehensive difference evaluation value is generated based on the reference evaluation values of all evaluation indicators and the weight coefficients of the corresponding evaluation indicators.

[0030] In this embodiment, the evaluation indicators include economic evaluation indicators, plan completion rate evaluation indicators, loading and unloading efficiency evaluation indicators, etc. For example, the economic evaluation indicators refer to the demurrage difference and profit difference corresponding to the deviation of the coal transportation parameters. The plan completion rate evaluation indicators refer to the transportation completion rate difference and transportation plan difference before and after adjustment corresponding to the deviation of the coal transportation parameters. The loading and unloading efficiency evaluation indicators refer to the month-on-month loading time and the difference in procedure processing time corresponding to the deviation of the coal transportation parameters.

[0031] In this embodiment, a comprehensive difference evaluation value is generated based on multiple evaluation indicators to obtain the degree of difference between the predicted coal transportation plan and the pre-set coal transportation plan, thereby determining the risk coefficient of the coal transportation plan under different changes in influencing factors, and constructing a risk identification model to lay the foundation for the subsequent selection of update models and real-time update instructions based on real-time risk coefficients, thereby improving the timeliness and accuracy of update instructions, improving the completion rate of transportation plans and reducing economic losses.

[0032] In some embodiments of the present application, setting a risk factor according to the comprehensive difference evaluation value includes: Presetting a first preset evaluation value interval, a second preset evaluation value interval, a third preset evaluation value interval and a fourth preset evaluation value interval; When the comprehensive difference evaluation value is within the first preset evaluation value range, the risk coefficient is set to the first preset risk coefficient; When the comprehensive difference evaluation value is within the second preset evaluation value range, the risk coefficient is set to the second preset risk coefficient; When the comprehensive difference evaluation value is within the third preset evaluation value range, the risk coefficient is set to the third preset risk coefficient; When the comprehensive difference evaluation value is in the fourth preset evaluation value interval, the risk coefficient is set to the fourth preset risk coefficient.

[0033] In this embodiment, the first preset evaluation value interval < the second preset evaluation value interval < the third preset evaluation value interval < the fourth preset evaluation value interval, and the first preset risk coefficient < the second preset risk coefficient < the third preset risk coefficient < the fourth preset risk coefficient.

[0034] In this embodiment, the larger the preset evaluation value range of the comprehensive difference evaluation value is, the greater the preset data combination corresponding to the predicted coal transportation plan will be, which means that the preset data combination corresponding to the predicted coal transportation plan will have a greater impact on the economy, plan completion rate, and loading and unloading efficiency of the coal transportation plan. That is, the greater the risk coefficient should be, the more reasonably the coal transportation plan should be updated, thereby improving the transportation plan completion rate and reducing economic losses.

[0035] In some embodiments of the present application, obtaining real-time data of influencing factors and determining whether they are abnormal, and if so, determining a coal transportation plan associated with the abnormal influencing factors, includes: Obtain the real-time data of the influencing factors and subtract them from the corresponding standard data to obtain the real-time data difference; If the real-time data difference is greater than the preset difference threshold, it is determined that an abnormality has occurred and the factors affecting the abnormality are screened out; Obtain the application coefficient of the abnormal influencing factors when formulating the coal transportation plan, determine the coal transportation parameters affected by the abnormal influencing factors according to the influencing factor-parameter mapping table, and obtain the weight coefficient of the corresponding coal transportation parameters; Generate correlation coefficients between abnormal influencing factors and coal transportation plans based on application coefficients and weight coefficients of affected coal transportation parameters; The calculation formula of the correlation coefficient is: ; Wherein, G is the correlation coefficient, w is the application coefficient, g1 is the first correlation conversion coefficient, q1 is the first preset weight coefficient, g is the second correlation conversion coefficient, q2 is the second preset weight coefficient, m is the number of coal transportation parameters affected by the abnormal influencing factor, and q0z is the weight coefficient of the zth affected coal transportation parameter; Pre-set correlation coefficient threshold; If the correlation coefficient is greater than the correlation coefficient threshold, the corresponding coal transportation plan is set as the coal transportation plan associated with the abnormal influencing factor.

[0036] In this embodiment, the first correlation conversion coefficient and the second correlation conversion coefficient refer to converting the application coefficient and the weight coefficient of the coal transportation parameter into coefficients of the same dimension, that is, obtaining the correlation coefficient.

[0037] In this embodiment, the application coefficient refers to the importance of abnormal influencing factors in formulating a coal transportation plan, the weight coefficient of the coal transportation parameter refers to the importance to the transportation purpose of the coal transportation plan, and the coal transportation plan associated with the abnormal influencing factors refers to the coal transportation plan affected by the abnormal influencing factors.

[0038] In some embodiments of the present application, determining a real-time risk coefficient of a corresponding coal transportation plan based on a real-time data combination and a risk identification model includes: Constructing a risk identification model for each coal transportation plan based on multiple preset data combinations and corresponding risk coefficients; Generate a real-time data combination corresponding to the coal transportation plan based on the influencing factors when the associated coal transportation plan is formulated and the real-time data of the influencing factors; The real-time data combination is input into the risk identification model of the corresponding coal transportation plan to obtain the real-time risk coefficient of the corresponding coal transportation plan.

[0039] In some embodiments of the present application, multiple update models are constructed, including: Presetting a first preset risk coefficient interval, a second preset risk coefficient interval, and a third preset risk coefficient interval; Obtaining a preset data combination whose risk coefficient is within a first preset risk coefficient interval and a coal transportation parameter deviation corresponding to the preset data combination, analyzing the coal transportation parameter deviation based on a preset objective function and a first constraint condition to obtain a plurality of first update strategies, and constructing a first update model, wherein the first update model is used to obtain a real-time update instruction for the coal transportation plan at the current moment; Obtaining historical data combinations whose risk factors fall within a second preset risk factor interval and coal transportation parameter deviations corresponding to the historical data interval combinations, analyzing the coal transportation parameter deviations based on a preset objective function and a second constraint condition to obtain a plurality of second update strategies, and constructing a second update model, the second update model being used to obtain real-time update instructions for the coal transportation plan at the current moment and for the first preset future time period; Obtaining historical data combinations whose risk factors fall within a third preset risk factor interval and coal transportation parameter deviations corresponding to the historical data interval combinations, analyzing the coal transportation parameter deviations based on a preset objective function and a third constraint condition to obtain multiple third update strategies, and constructing a third update model, the third update model being used to obtain update instructions for the real-time coal transportation plan for the current moment and a second preset future time period; Among them, the preset objective functions include the objective function of minimizing the demurrage rate and the objective function of maximizing the completion rate of the transportation plan; Among them, the first preset risk coefficient interval is smaller than the second preset risk coefficient interval, the second preset risk coefficient interval is smaller than the third preset risk coefficient interval, and the first preset future time period is smaller than the second preset future time period.

[0040] In this embodiment, the first constraint condition, the second constraint condition and the third constraint condition are all time constraints, namely the current moment, the current moment and the first preset future period, the current moment and the second preset future period, respectively. The corresponding preset objective function is satisfied within the corresponding time constraint condition, and the corresponding update strategy is obtained and the corresponding update model is constructed.

[0041] In this embodiment, the first update model, the second update model and the third update model refer to the update models for the current moment, the short term and the long term, respectively. The corresponding update models are constructed according to the corresponding risk coefficients, the objective functions set in advance and the reasonable constraint time, which lays the foundation for the subsequent selection of reasonable update models and the determination of real-time update instructions, thereby improving the update effect of the coal transportation plan, thereby improving the completion rate of the transportation plan and reducing economic losses.

[0042] In some embodiments of the present application, selecting a corresponding update model according to the real-time risk coefficient and generating a real-time update instruction corresponding to the coal transportation plan include: Determine a standard coal transportation plan for the real-time data combination, compare the standard coal transportation parameters of the standard coal transportation plan with the real-time coal transportation parameters of the coal transportation plan, and obtain a deviation of the real-time coal transportation parameters; When the real-time risk coefficient is within a first preset risk coefficient range, a first update model is selected, an update strategy for the real-time coal transportation parameter deviation at the current moment is determined based on the first update model, and a real-time update instruction corresponding to the coal transportation plan is generated according to the update strategy; When the real-time risk coefficient is within the second preset risk coefficient range, a second update model is selected, an update strategy for the real-time coal transportation parameter deviation at the current moment and the first preset future time period is determined based on the second update model, and a real-time update instruction corresponding to the coal transportation plan is generated according to the update strategy; When the real-time risk coefficient is in the third preset risk coefficient range, the third update model is selected, and the update strategy of the real-time coal transportation parameter deviation at the current moment and the second preset future time period is determined according to the third update model, and the real-time update instructions of the corresponding coal transportation plan are generated according to the update strategy.

[0043] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and replacements can be made without departing from the technical principles of the present application. These improvements and replacements should also be regarded as the scope of protection of the present application.

Claims

1. A real-time updating method for coal transportation plan based on risk identification, characterized in that: include: Acquire multiple influencing factors when formulating a coal transportation plan and multiple preset data for each influencing factor, and randomly combine the preset data of different influencing factors to obtain multiple preset data combinations; generating a predicted coal transportation plan based on multiple preset data combinations based on a coal transportation plan prediction model, comparing the predicted coal transportation parameters in the predicted coal transportation plan with the target coal transportation parameters of the coal transportation plan to obtain multiple coal transportation parameter deviations; Analyze the deviation of coal transportation parameters to obtain a comprehensive difference evaluation value, set a risk coefficient based on the comprehensive difference evaluation value, and build a risk identification model for the corresponding coal transportation plan based on the preset data combination and risk coefficient; Obtain real-time data of influencing factors and determine whether there are any anomalies. If so, determine the coal transportation plan associated with the abnormal influencing factors and determine the corresponding real-time data combination. Based on the real-time data combination and the risk identification model, determine the real-time risk coefficient of the corresponding coal transportation plan; Construct multiple update models, select a corresponding update model according to the real-time risk coefficient, and generate a real-time update instruction corresponding to the coal transportation plan, wherein the update model includes a first update model, a second update model, and a third update model.

2. The method for real-time updating of coal transportation plan based on risk identification according to claim 1, characterized in that: Obtain multiple influencing factors when formulating coal transportation plans and multiple preset data for each influencing factor, including: Acquiring multiple influencing factors and standard data of the influencing factors when formulating a coal transportation plan, and generating a predicted fluctuation curve of the standard data of each influencing factor, wherein the coal transportation plan includes multiple coal transportation parameters; Mapping each influencing factor with the affected coal transportation parameter to obtain an influencing factor-parameter mapping table, wherein the influencing factor is mapped to at least one coal transportation parameter; Preset multiple division nodes, divide the predicted fluctuation curve into multiple predicted fluctuation intervals according to the division nodes, and obtain historical impact characteristics of the predicted fluctuation intervals of the influencing factors on the corresponding coal transportation parameters in the influencing factor-parameter mapping table, wherein the historical impact characteristics include historical update instructions for the corresponding coal transportation parameters and the historical difference degree of the coal transportation parameters before and after the historical update instructions; Generate an impact evaluation value of a predicted fluctuation range of the influencing factor based on the number of coal transportation parameters whose historical difference between the coal transportation parameters before and after the historical update instruction is greater than a preset difference threshold and the difference between the historical difference and the preset difference threshold; The calculation formula of the impact evaluation value is: ； Where Y is the application evaluation value, n0 is the number of coal transportation parameters whose historical difference between the coal transportation parameters before and after the historical update instruction is greater than the preset difference threshold, and N is the total number of coal transportation parameters mapped by the current influencing factor in the influencing factor-parameter mapping table. is the historical difference of the s-th coal transportation parameter, and y1 is the impact assessment conversion coefficient; Generate the impact evaluation value of each predicted fluctuation interval of the influencing factor in sequence; If the impact evaluation value is greater than the preset impact evaluation value threshold, the impact evaluation value difference is calculated, the number of samples collected for the corresponding predicted fluctuation interval is set according to the impact evaluation value difference, and the preset data for the corresponding predicted fluctuation interval is collected according to the number of samples collected; If the impact evaluation value is less than the preset impact evaluation value threshold, the mean of the data in the corresponding predicted fluctuation interval is calculated and set as the preset data of the corresponding predicted fluctuation interval; According to the multiple predicted fluctuation intervals, multiple preset data corresponding to the influencing factors are obtained, and a preset data sequence A, A (a1, a2, ... aj) is constructed, where ai is the i-th preset data and j is the total number of preset data of the influencing factors; The preset data in the preset data sequence of multiple influencing factors of the same coal transportation plan are randomly combined to obtain multiple preset data combinations of the coal transportation plan.

3. The method for real-time updating of coal transportation plan based on risk identification according to claim 2, characterized in that: Obtain multiple coal transportation parameter deviations, including: The coal transportation plan prediction model is obtained by performing neural network training based on the power plant data, resource unit data, and target transportation data when the current coal transportation plan is formulated as static input data, the historical data combination of multiple influencing parameters as dynamic input data, and the formulated historical coal transportation plan as output data. generating a predicted coal transportation plan for each preset data combination based on a coal transportation plan prediction model, wherein the predicted coal transportation plan includes a plurality of predicted coal transportation parameters; The predicted coal transportation parameters of each predicted coal transportation plan are compared with the target coal transportation parameters of the coal transportation plan to obtain the coal transportation parameter deviation.

4. The method for real-time updating of coal transportation plan based on risk identification according to claim 3, characterized in that: Analyze the deviation of coal transportation parameters to obtain comprehensive difference evaluation values, including: Pre-set multiple evaluation indicators; Determine the coal transportation parameter associated with each evaluation index according to the degree of correlation between the coal transportation parameter and the evaluation index, compare the coal transportation parameter deviation value of the corresponding coal transportation parameter with the corresponding preset deviation threshold value in the evaluation index, and obtain the deviation difference value; Generate a reference evaluation value of the corresponding evaluation index according to the deviation difference of the coal transportation parameters associated with the same evaluation index; A comprehensive difference evaluation value is generated based on the reference evaluation values of all evaluation indicators and the weight coefficients of the corresponding evaluation indicators.

5. The method for real-time updating of coal transportation plan based on risk identification according to claim 4, characterized in that: The risk factor is set based on the comprehensive difference evaluation value, including: Presetting a first preset evaluation value interval, a second preset evaluation value interval, a third preset evaluation value interval and a fourth preset evaluation value interval; When the comprehensive difference evaluation value is within the first preset evaluation value range, the risk coefficient is set to the first preset risk coefficient; When the comprehensive difference evaluation value is within the second preset evaluation value range, the risk coefficient is set to the second preset risk coefficient; When the comprehensive difference evaluation value is within the third preset evaluation value range, the risk coefficient is set to the third preset risk coefficient; When the comprehensive difference evaluation value is in the fourth preset evaluation value interval, the risk coefficient is set to the fourth preset risk coefficient.

6. The method for real-time updating of coal transportation plan based on risk identification according to claim 5, characterized in that: Obtain real-time data on influencing factors and determine whether they are abnormal. If so, determine the coal transportation plan associated with the abnormal influencing factors, including: Obtain the real-time data of the influencing factors and subtract them from the corresponding standard data to obtain the real-time data difference; If the real-time data difference is greater than the preset difference threshold, it is determined that an abnormality has occurred and the factors affecting the abnormality are screened out; Obtain the application coefficient of the abnormal influencing factors when formulating the coal transportation plan, determine the coal transportation parameters affected by the abnormal influencing factors according to the influencing factor-parameter mapping table, and obtain the weight coefficient of the corresponding coal transportation parameters; Generate correlation coefficients between abnormal influencing factors and coal transportation plans based on application coefficients and weight coefficients of affected coal transportation parameters; The calculation formula of the correlation coefficient is: ； Wherein, G is the correlation coefficient, w is the application coefficient, g1 is the first correlation conversion coefficient, q1 is the first preset weight coefficient, g is the second correlation conversion coefficient, q2 is the second preset weight coefficient, m is the number of coal transportation parameters affected by the abnormal influencing factor, and q0z is the weight coefficient of the zth affected coal transportation parameter; Pre-set correlation coefficient threshold; If the correlation coefficient is greater than the correlation coefficient threshold, the corresponding coal transportation plan is set as the coal transportation plan associated with the abnormal influencing factor.

7. The method for real-time updating of coal transportation plan based on risk identification according to claim 6, characterized in that: Determine the real-time risk factor of the corresponding coal transportation plan based on real-time data combination and risk identification model, including: Constructing a risk identification model for each coal transportation plan based on multiple preset data combinations and corresponding risk coefficients; Generate a real-time data combination corresponding to the coal transportation plan based on the influencing factors when the associated coal transportation plan is formulated and the real-time data of the influencing factors; The real-time data combination is input into the risk identification model of the corresponding coal transportation plan to obtain the real-time risk coefficient of the corresponding coal transportation plan.

8. The method for real-time updating of coal transportation plan based on risk identification according to claim 7, characterized in that: Build multiple update models, including: Presetting a first preset risk coefficient interval, a second preset risk coefficient interval, and a third preset risk coefficient interval; Obtaining a preset data combination whose risk coefficient is within a first preset risk coefficient interval and a coal transportation parameter deviation corresponding to the preset data combination, analyzing the coal transportation parameter deviation based on a preset objective function and a first constraint condition to obtain a plurality of first update strategies, and constructing a first update model, wherein the first update model is used to obtain a real-time update instruction for the coal transportation plan at the current moment; Obtaining historical data combinations whose risk factors fall within a second preset risk factor interval and coal transportation parameter deviations corresponding to the historical data interval combinations, analyzing the coal transportation parameter deviations based on a preset objective function and a second constraint condition to obtain a plurality of second update strategies, and constructing a second update model, the second update model being used to obtain real-time update instructions for the coal transportation plan at the current moment and for the first preset future time period; Obtaining historical data combinations whose risk factors fall within a third preset risk factor interval and coal transportation parameter deviations corresponding to the historical data interval combinations, analyzing the coal transportation parameter deviations based on a preset objective function and a third constraint condition to obtain multiple third update strategies, and constructing a third update model, the third update model being used to obtain update instructions for the real-time coal transportation plan for the current moment and a second preset future time period; Among them, the preset objective functions include the objective function of minimizing the demurrage rate and the objective function of maximizing the completion rate of the transportation plan; Among them, the first preset risk coefficient interval is smaller than the second preset risk coefficient interval, the second preset risk coefficient interval is smaller than the third preset risk coefficient interval, and the first preset future time period is smaller than the second preset future time period.

9. The method for real-time updating of coal transportation plan based on risk identification according to claim 8, characterized in that: Select the corresponding update model based on the real-time risk factor and generate real-time update instructions for the corresponding coal transportation plan, including: Determine a standard coal transportation plan for the real-time data combination, compare the standard coal transportation parameters of the standard coal transportation plan with the real-time coal transportation parameters of the coal transportation plan, and obtain a deviation of the real-time coal transportation parameters; When the real-time risk coefficient is within a first preset risk coefficient range, a first update model is selected, an update strategy for the real-time coal transportation parameter deviation at the current moment is determined based on the first update model, and a real-time update instruction corresponding to the coal transportation plan is generated according to the update strategy; When the real-time risk coefficient is within the second preset risk coefficient range, a second update model is selected, an update strategy for the real-time coal transportation parameter deviation at the current moment and the first preset future time period is determined based on the second update model, and a real-time update instruction corresponding to the coal transportation plan is generated according to the update strategy; When the real-time risk coefficient is in the third preset risk coefficient range, the third update model is selected, and the update strategy of the real-time coal transportation parameter deviation at the current moment and the second preset future time period is determined according to the third update model, and the real-time update instructions of the corresponding coal transportation plan are generated according to the update strategy.

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