Intelligent shunting method and system for dispatching and transporting coal railway transportation traveling crane

Abstract

The invention provides an intelligent shunting method and system for coal railway transportation traveling crane allocation and transportation, and belongs to the field of coal railway transportation. The method comprises the following steps: acquiring shunting matching factor element data corresponding to each preset shunting reference item; inputting the distribution matching factor element data corresponding to each preset distribution reference item into the trained intelligent coal transportation distribution model for intelligent distribution, and obtaining and outputting optimal distribution operation plan flow direction information; wherein the coal transportation and allocation intelligent shunting model is constructed based on a pre-configured shunting rule matrix, the shunting rule matrix comprises multiple shunting rules, and the shunting rule matrix is used for carrying out step-by-step shunting matching on shunting matching factor element data corresponding to each preset shunting reference item according to various shunting rules in sequence. The efficiency, the accuracy and the stability of dispatching and shunting operation of the coal railway transportation traveling crane are improved, meanwhile, errors of manual operation and personal subjective operation are reduced, and the problem of manual intervention risks is effectively avoided.

Description

Technical Field

[0001] The present invention relates to the field of coal railway transportation, and in particular to an intelligent diversion method for coal railway transportation driving and dispatching, an intelligent diversion system for coal railway transportation driving and dispatching, a machine-readable storage medium and an electronic device. Background Art

[0002] Coal railway transportation and dispatching is a complex, tedious and error-prone task that relies on manual offline grouping management and distribution flow, and often has problems such as low work efficiency, high workload for dispatchers and poor stability.

[0003] The rules for coal railway transportation, dispatching and diversion are complex. If we rely too much on manual allocation management, the diversion rules cannot be solidified. In addition, each dispatcher has different subjectivity in diversion judgments, which leads to the problem of strong subjective operation. This brings the risk of subjective manual intervention to coal enterprises and dispatchers, and cannot effectively guarantee the efficiency and accuracy of coal railway transportation, dispatching and diversion operations. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide an intelligent diversion method and system for coal railway transportation driving and dispatching, so as to at least solve the above-mentioned problem of how to effectively improve the efficiency, accuracy and stability of coal railway transportation driving and dispatching and diversion operations.

[0005] In order to achieve the above-mentioned object, the first aspect of the present invention provides an intelligent diversion method for coal railway transportation driving and dispatching, comprising:

[0006] Based on the diversion data of multiple preset diversion reference items of the target coal railway, the diversion matching factor element data corresponding to each preset diversion reference item is obtained;

[0007] The diversion matching factor element data corresponding to each preset diversion reference item is input into the trained coal transportation and dispatching intelligent diversion model for intelligent diversion, and the optimal transportation operation plan flow direction information is obtained and output; wherein, the coal transportation and dispatching intelligent diversion model is constructed based on a preconfigured diversion rule matrix, and the diversion rule matrix contains a variety of diversion rules. The diversion rule matrix is ​​used to perform step-by-step diversion matching on the diversion matching factor element data corresponding to each preset diversion reference item according to various diversion rules in turn, so as to obtain the diversion matching factor element data corresponding to the optimal transportation operation plan flow direction information.

[0008] Optionally, the diversion data of the preset diversion reference item includes diversion demand data of the coal demand diversion plan, and the diversion demand data of the coal demand diversion plan includes at least the demand user identity data of the coal demand diversion plan, the user arrival data, the user demand coal type data, the coal type data, the demand train number, the train diversion interface information, the demand priority data and the vehicle type attribute data;

[0009] The above-mentioned diversion data based on multiple preset diversion reference items of the target coal railway obtains the diversion matching factor element data corresponding to each preset diversion reference item, including:

[0010] According to the diversion demand data of the coal demand diversion plan of the target coal railway, the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan is constructed; wherein,

[0011] The construction rules of the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan include:

[0012] From the diversion demand data of the coal demand diversion plan of the target coal railway, select the demand user identity data, user arrival data and train diversion interface information of the coal demand diversion plan as the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan; wherein DEM = {D 1 , D 2 , D 6}, D 1 D is the user identity data of the coal demand diversion plan, D2 is the user arrival data of the coal demand diversion plan, and D 6 It is the train diversion interface information of the coal demand diversion plan, and DEM is the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan.

[0013] Optionally, the diversion data of the preset diversion reference items include diversion supply data of the coal supply plan, and the diversion supply data of the coal supply plan at least includes train number data, train coal type data, train model data, train loading order number data, train loading station data, loading dedicated line data, train coal source mine point information, train waybill information and train first and last car number attribute data of the coal supply plan;

[0014] The above-mentioned diversion data based on multiple preset diversion reference items of the target coal railway obtains the diversion matching factor element data corresponding to each preset diversion reference item, including:

[0015] According to the diversion supply data of the coal supply plan of the target coal railway, the diversion matching factor element data corresponding to the diversion supply data of the coal supply plan is constructed; wherein,

[0016] The construction rules of the diversion matching factor element data corresponding to the diversion supply data of the coal supply plan include:

[0017] Based on the pre-configured diversion rule matrix, a diversion factor element set excluded by the diversion rule matrix is ​​constructed; wherein NDEM = {R1 1 , R1 2 , R1 3 , R1 4 , R1 5 , R1 6 , R1 7 , R1 8 , R1 9 , R1 10 , R1 11}, NDEM is the set of diversion factor elements excluded by the diversion rule matrix, R1 1 To exclude the transport type information for diversion, R1 2 To exclude coal type information, R1 3 To divert and exclude coal type information, R1 4 To exclude vehicle model information, R1 5 To exclude loading station information, R1 6 To divert and exclude coal source mining point information, R1 7 To exclude user information, R1 8 To divert and exclude coal supplier information, R1 9 To divert and exclude the dedicated loading line information, R1 10 To divert and exclude the shipping train information, R1 11 Exclude special loading notes for diversion;

[0018] The diversion supply data of the coal supply plan of the target coal railway that matches the diversion factor element set excluded by the diversion rule matrix are excluded to obtain the diversion matching factor element data corresponding to the diversion supply data of the coal supply plan.

[0019] Optionally, the diversion supply data of the above-mentioned coal supply plan also includes train operation plan data;

[0020] The rules for obtaining train operation plan data include:

[0021] According to the train waybill information of the coal supply plan, retrieve the train operation plan data corresponding to the coal supply plan;

[0022] The train operation plan data includes train operation line data, train map station planned departure time information and train map station actual departure time information.

[0023] Optionally, the diversion data of the above-mentioned preset diversion reference items include diversion strategy data, and the diversion strategy data includes diversion user information, diversion user arrival information, diversion user map interface information, dispatch type information, user arrival daily loading and unloading capacity information, user loading and unloading train interval information, train arrival operation time information, arrival station can load and unload vehicle types and heavy vehicle number starting information and arrival station can load and unload vehicle number rule information;

[0024] The above-mentioned diversion data based on multiple preset diversion reference items of the target coal railway obtains the diversion matching factor element data corresponding to each preset diversion reference item, including:

[0025] Based on DEM R = {R2 1 , R2 2 , R2 3}, construct the diversion matching factor element data DEM corresponding to the diversion strategy data R ;

[0026] The diversion matching factor element data corresponding to the diversion strategy data and the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan are merged to obtain the planned demand factor element data, and the planned demand factor element data represents all the diversion matching factor element data corresponding to the coal demand diversion plan; wherein,

[0027] The expression of the planned demand factor element data is as follows:

[0028] Among them, R2 1 To divert user information, R2 2 To divert user arrival information, R2 3 To divert user graphics interface information, R2 4 R2 is the transport type information. 5 Provides daily loading and unloading capacity information for users at the station, R2 6 Provide users with train interval information, R2 7 R2 is the train arrival time information. 8 R2 is the information about the types of vehicles that can be unloaded at the arrival station and the starting number of heavy vehicles. 9 DEM is the information of the rules for the unloading number of the arrival station. T DEM is the element data of planning demand factor. 1 D is the demand factor element for direct business diversion. 1 The user identity data for the coal demand diversion plan, D 2 The user arrival data for coal demand diversion plan, D 3 The coal type information required by users for coal demand diversion plan, D 4The coal type quantity information required by users for coal demand diversion plan, D 5 The user demand urgency level information for coal demand diversion plan, D 6 The user arrival map of the coal demand diversion plan is the interface information, D 7 Plan date information for user requirements, D 8 The user demand for coal demand diversion plan can be unloaded at the station. 9 Special user requirements for coal demand diversion plan, DEM 1L To ensure the supply of demand factor elements for direct business diversion, DEM 1S Divert non-guaranteed supply demand factor elements for direct business, DEM 2L To ensure the supply of demand factor elements for water business diversion, DEM 2S Divert non-guaranteed supply demand factor elements for launching business.

[0029] Optionally, the above diversion strategy data also includes diversion strategy date information, multiple diversion strategy type information and strategy execution ratio information corresponding to each diversion strategy type;

[0030] The formula for constructing the diversion matching factor element data C corresponding to the diversion strategy data includes:

[0031]

[0032] Among them, ST 1 Indicates that the diversion strategy is a strategy of cyclically executing the first diversion plan. The first diversion plan is to first divert the direct supply guarantee demand plan, then divert the direct non-supply guarantee demand plan, then divert the water supply guarantee demand plan, and finally divert the water non-supply guarantee demand plan; ST 2 Indicates that the diversion strategy is a strategy of cyclically executing the second diversion plan. The second diversion plan is to first divert the water supply guarantee demand plan, then divert the water supply non-guaranteed demand plan, then divert the direct supply guarantee demand plan, and finally divert the direct non-guaranteed demand plan; ST 3 Indicates that the diversion strategy is a strategy of cyclically executing the third diversion plan. The third diversion plan is to first divert the direct supply guarantee demand plan, then divert the water supply guarantee demand plan, then divert the direct non-supply guarantee demand plan, and finally divert the water non-supply guarantee demand plan; ST 4 It indicates that the diversion strategy is a strategy of cyclically executing the fourth diversion plan. The fourth diversion plan is to first divert the water supply guarantee demand plan, then divert the direct supply guarantee demand plan, then divert the water non-supply guarantee demand plan, and finally divert the direct non-supply guarantee demand plan; X% is the strategy execution ratio information corresponding to each diversion strategy type.

[0033] Optionally, the multiple diversion rules include at least one or more of a diversion exclusion rule, a diversion priority rule, an end-to-end rule, a railway skylight influence rule, and a vehicle type matching rule;

[0034] The diversion exclusion rule is used to screen the diversion matching factor element data corresponding to each preset diversion reference item according to a preset set of diversion factor elements for exclusion;

[0035] The diversion priority rule is used to screen the diversion matching factor element data corresponding to each preset diversion reference item according to the priority of each diversion strategy type;

[0036] The end-to-end rule is used to screen the diversion matching factor element data corresponding to each preset diversion reference item according to a plurality of preset end-to-end feature object information, wherein the preset end-to-end feature object information includes user identity information;

[0037] The railway skylight impact rule is used to screen the diversion matching factor element data corresponding to each preset diversion reference item according to the railway skylight key feature data, and the railway skylight key feature data includes the skylight date information, the skylight start time information, the skylight end time information, the skylight impact line information and the skylight impact line corresponding station information;

[0038] The vehicle type matching rule is used to screen the diversion matching factor element data corresponding to each preset diversion reference item according to the train vehicle type data of the coal supply plan.

[0039] The second aspect of the present invention provides an intelligent shunting system for coal railway transportation, including:

[0040] A diversion matching factor element acquisition module, for obtaining diversion matching factor element data corresponding to each preset diversion reference item based on the diversion data of multiple preset diversion reference items of the target coal railway;

[0041] The intelligent diversion module is used to input the diversion matching factor element data corresponding to each preset diversion reference item into the trained coal transportation and dispatching intelligent diversion model for intelligent diversion, and obtain and output the optimal transportation operation plan flow direction information; wherein, the coal transportation and dispatching intelligent diversion model is constructed based on a preconfigured diversion rule matrix, and the diversion rule matrix contains a variety of diversion rules. The diversion rule matrix is ​​used to perform step-by-step diversion matching on the diversion matching factor element data corresponding to each preset diversion reference item according to various diversion rules in turn, so as to obtain the diversion matching factor element data corresponding to the optimal transportation operation plan flow direction information.

[0042] In a third aspect of the present invention, a machine-readable storage medium is provided, on which instructions are stored, and when the instructions are executed by a processor, the processor is configured to execute the above-mentioned intelligent diversion method for coal railway transportation driving and dispatching.

[0043] In a fourth aspect of the present invention, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the intelligent diversion method for railway coal transportation operation is implemented.

[0044] Through the above technical scheme, a method and system for intelligent diversion of coal railway transportation and dispatching is provided, which obtains the diversion matching factor element data corresponding to each preset diversion reference item from the diversion data of multiple preset diversion reference items of the collected target coal railway. The diversion matching factor element data corresponding to each preset diversion reference item is input into the trained coal transportation and dispatching intelligent diversion model, and the coal transportation and dispatching intelligent diversion model performs step-by-step diversion matching on the diversion matching factor element data corresponding to each preset diversion reference item according to various diversion rules in the diversion rule matrix, so as to realize the intelligent diversion of transportation and dispatching of the target coal railway, and obtain and output the flow direction information of the optimal dispatching operation plan. The method and system solidify the diversion rule matrix structure, integrate the diversion matching factor element data, and then automatically complete the diversion operation by triggering the calculation of the coal transportation and dispatching intelligent diversion model, thereby improving the efficiency, accuracy and stability of the coal railway transportation and dispatching diversion operation, while reducing the errors of manual operation and personal subjective operation, and effectively avoiding the problem of manual intervention risk.

[0045] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0047] Figure 1 It is a flow chart of an intelligent diversion method for coal railway transportation driving and dispatching provided by an embodiment of the present invention;

[0048] Figure 2 It is a flow chart of another intelligent diversion method for coal railway transportation driving and dispatching provided by one embodiment of the present invention;

[0049] Figure 3 It is a block diagram of an intelligent shunting system for coal railway transportation driving and dispatching provided by one embodiment of the present invention;

[0050] Figure 4 It is a schematic diagram of the structure of an electronic device provided by a preferred embodiment of the present invention.

[0051] Description of Reference Numerals

[0052] 10 - electronic device, 100 - processor, 101 - memory, 102 - computer program. DETAILED DESCRIPTION

[0053] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0054] Figure 1 This is a flow chart of an intelligent diversion method for coal railway transportation traffic dispatching provided by one embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides an intelligent diversion method for coal railway transportation driving and dispatching, comprising:

[0055] S110: Based on the diversion data of multiple preset diversion reference items of the target coal railway, obtain the diversion matching factor element data corresponding to each preset diversion reference item;

[0056] S120: Input the diversion matching factor element data corresponding to each preset diversion reference item into the trained coal transportation and dispatching intelligent diversion model for intelligent diversion, and obtain and output the optimal transportation operation plan flow direction information; wherein, the coal transportation and dispatching intelligent diversion model is constructed based on a preconfigured diversion rule matrix, and the diversion rule matrix contains a variety of diversion rules. The diversion rule matrix is ​​used to perform step-by-step diversion matching on the diversion matching factor element data corresponding to each preset diversion reference item according to various diversion rules in turn, so as to obtain the diversion matching factor element data corresponding to the optimal transportation operation plan flow direction information.

[0057] Specifically, the method obtains the diversion matching factor element data corresponding to each preset diversion reference item from the diversion data of multiple preset diversion reference items of the collected target coal railway. The diversion matching factor element data corresponding to each preset diversion reference item is input into the trained coal transportation and dispatching intelligent diversion model. The coal transportation and dispatching intelligent diversion model performs step-by-step diversion matching on the diversion matching factor element data corresponding to each preset diversion reference item according to various diversion rules in the diversion rule matrix, so as to realize the intelligent diversion of the transportation and dispatching of the target coal railway, and obtain and output the flow direction information of the optimal dispatching operation plan. The method solidifies the diversion rule matrix structure, integrates the diversion matching factor element data, and then automatically completes the diversion operation by triggering the calculation of the coal transportation and dispatching intelligent diversion model, thereby improving the efficiency, accuracy and stability of the coal railway transportation and dispatching diversion operation, while reducing the errors of manual operation and personal subjective operation, and effectively avoiding the problem of manual intervention risk.

[0058] In the actual application of the method, the method collects the company's daily mine production or outsourcing supply plan, railway train real-time operation plan and customer's daily coal diversion demand plan, and obtains the corresponding diversion matching factor elements based on the supply plan, railway train real-time operation plan and customer's daily coal diversion demand plan, and then obtains the maintained diversion rule matrix to perform intelligent matching analysis and calculation, and cyclic training simulation is used to select the best matching railway driving and dispatching operation direction for output, so as to effectively solve the problems of cumbersome manual calculation, complex operation, low work efficiency, high work intensity and poor stability in the existing coal railway transportation driving and dispatching work. Intelligent diversion through the present invention can improve the work efficiency of dispatchers, reduce work intensity and improve stability, ensure the smooth coal railway transportation, maximize coal transportation capacity, and reduce the subjective operation risk of manual diversion.

[0059] Please refer to Figure 2 , Figure 2It is a flow chart of another intelligent diversion method for coal railway transportation and train dispatching provided by an embodiment of the present invention. In some implementations of the present embodiment, the diversion data of the above-mentioned preset diversion reference item includes the diversion demand data of the coal demand diversion plan, and the diversion demand data of the coal demand diversion plan includes at least the demand user identity data of the coal demand diversion plan, the user arrival data, the user demand coal type data, the coal type data, the required number of trains, the train diversion interface information, the demand priority data and the vehicle type attribute data (wherein, the demand user data of the coal demand diversion plan refers to the arrival user related information of the final flow direction of the coal-loaded train in the train diversion plan; the user arrival data refers to the railway station information of the train received by the user in the train diversion plan; the user demand coal type data refers to the coal type information required to be received by the user in the train diversion plan; the coal type data refers to the type of coal required to be received by the user in the train diversion plan, that is, which type of coal can be received; the required number of trains refers to the number of trains required to be allocated by the arrival user in the train diversion plan; the train diversion interface information Refers to the railway station information that the train passes through when diverting; the demand priority data refers to a sequence required in the train diversion plan; the vehicle type attribute data refers to the requirements for the vehicle type when the train diversion plan is made); the diversion data based on multiple preset diversion reference items of the target coal railway are used to obtain the diversion matching factor element data corresponding to each preset diversion reference item, including: according to the diversion demand data of the coal demand diversion plan of the target coal railway, construct the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan; wherein, the construction rule of the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan includes: selecting the demand user identity data, user arrival data and train diversion interface information of the coal demand diversion plan from the diversion demand data of the coal demand diversion plan of the target coal railway as the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan; wherein, DEM={D 1 , D 2 , D 6}, D 1 The user identity data for the coal demand diversion plan, D 2 The user arrival data for coal demand diversion plan, D 6 It is the train diversion interface information of the coal demand diversion plan, and DEM is the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan.

[0060] In some implementations of the present embodiment, the rule for selecting the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan from the diversion demand data of the coal demand diversion plan includes: each diversion demand data of the coal demand diversion plan has a corresponding matching value and weight value in the corresponding coal demand diversion plan, and the matching value and weight value of each diversion demand data of the coal demand diversion plan are input into a preset rule library (the preset rule library contains a rich data selection rules), so as to select the diversion demand data according to different data selection rules, and select the diversion demand data of the coal demand diversion plan corresponding to the maximum multiplication and addition result of the matching value and the weight value (that is, D 1 , D 2 and D 6 ) is the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan.

[0061] Specifically, the diversion demand data of the coal demand diversion plan is collected, and the demand user identity data, user arrival data and train diversion interface information of the coal demand diversion plan are obtained as the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan, so as to construct the demand planning module in the intelligent diversion model of coal transportation and dispatching.

[0062] In some implementations of this embodiment, the diversion demand data D of the coal demand diversion plan is obtained: D 1 , D 2 , D 3 ......D n , where D 1 The user identity data for the coal demand diversion plan, D 2 The user arrival data for coal demand diversion plan, D 3 The coal type information required by users for coal demand diversion plan, D 4 The coal type quantity information required by users for coal demand diversion plan, D 5 The user demand urgency level information for coal demand diversion plan, D 6 The user arrival map of the coal demand diversion plan is the interface information, D 7 Plan date information for user requirements, D 8 The user demand for coal demand diversion plan can be unloaded at the station. 9 Provide special user demand information for coal demand diversion plan; the formula for constructing the planned demand factor element is DEM = D 1 +D 2 +D 6 , DEM 2 =D 7 +D 1 +D4 +D 6 .

[0063] In some implementations of the present embodiment, the diversion data of the preset diversion reference item includes the diversion supply data of the coal supply plan, and the diversion supply data of the coal supply plan includes at least the train number data, train coal type data, train model data, train loading order number data, train loading station data, loading dedicated line data, train coal source mine point information, train waybill information and train first and last car number attribute data of the coal supply plan (wherein, the train number data of the coal supply plan refers to the train number from which the loaded coal is to be distributed; the train coal type refers to the coal type information loaded by this train; the train model, train loading order number data, train loading station data, loading dedicated line data, train coal source mine point information, train waybill information and train first and last car number attribute data Point information, train waybill information and train first and last car number attribute data are all related attribute information of the loaded coal to be allocated to the running train); the above-mentioned diversion data based on multiple preset diversion reference items of the target coal railway obtains the diversion matching factor element data corresponding to each preset diversion reference item, including: according to the diversion supply data of the coal supply plan of the target coal railway, constructing the diversion matching factor element data corresponding to the diversion supply data of the coal supply plan; wherein, the construction rules of the diversion matching factor element data corresponding to the diversion supply data of the coal supply plan include: based on the pre-configured diversion rule matrix, constructing the diversion factor element set excluded by the diversion rule matrix; wherein, NDEM={R1 1 , R1 2 , R1 3 , R1 4 , R1 5 , R1 6 , R1 7 , R1 8 , R1 9 , R1 10 , R1 11}, NDEM is the set of diversion factor elements excluded by the diversion rule matrix, R1 1 To exclude the transport type information for diversion, R1 2 To exclude coal type information, R1 3 To divert and exclude coal type information, R1 4 To exclude vehicle model information, R1 5 To exclude loading station information, R1 6 To divert and exclude coal source mining point information, R1 7 To exclude user information, R1 8 To divert and exclude coal supplier information, R1 9 To divert and exclude the dedicated loading line information, R1 10 To divert and exclude the shipping train information, R1 11To exclude special loading notes for diversion (where R1 1 Diversion exclusion transport type information refers to the exclusion of transport types that are not diverted. For example, some vehicle types are not allocated to direct flow stations and can only be allocated to water flow stations. Therefore, these vehicle types are excluded when allocating direct plan requirements; R1 2 The excluded coal type in diversion refers to the coal type that does not match the coal type required by the station during diversion. For example, if the coal type required by the station is electric coal, the raw coal will be excluded; R1 3 Excluded coal types refer to those coal types that do not meet the coal type requirements at the station during diversion. For example, if the coal type required at the station is power coal, other types of coal except power coal will be excluded; R1 4 Diversion exclusion models refer to models that do not meet the arrival requirements during diversion. For example, if only C80 trains can be connected to the station, other C64\70 non-C80 trains will be excluded; R1 5 Diversion exclusion loading station means that the diversion arrival user does not accept the coal loaded from certain specific loading stations. In this case, the trains sent from this specific loading station need to be excluded from this arrival user during diversion; R1 6 Divert and exclude coal source mining points, same as R1 5 Exclusion restrictions on the coal loading mine area at the previous link of the loading station; R1 7 Diversion exclusion users refer to the restriction of arriving users during diversion; R1 8 Diversion exclusion of coal source suppliers refers to the restriction on the supplier of coal loaded in the divided train during diversion; R1 9 Diversion exclusion of dedicated loading lines refers to the restriction of loading lines for the divided trains during diversion; R1 10 Diversion exclusion dispatch train number information refers to the restrictions on the divided train numbers during diversion, such as excluding the train number at the beginning of the trial from the direct arrival station and only flowing to the port arrival station; R1 11 The diversion exclusion of special loading remarks information refers to the remarks made on the mining area when loading during diversion, such as "only entering the port for launching", which will be excluded from the direct arrival station during diversion); the diversion supply data that matches the diversion factor element set excluded by the diversion rule matrix in the diversion supply data of the coal supply plan of the target coal railway is excluded to obtain the diversion matching factor element data corresponding to the diversion supply data of the coal supply plan.

[0064] Specifically, the coal supply plan diversion supply data S is obtained: S1, S2, S3...Sn, where S1 is the train number information, S2 is the train coal type information, S3 is the train car number information, S4 is the train source coal source mine information, S5 is the train loading station information, S6 is the train diversion station departure time information, S7 is the customer information, S8 is the customer arrival information, S9 is the customer arrival map interface information, S10 is the customer arrival dedicated line information, S11 is the customer arrival map interface information, S12 is the customer arrival dedicated line information, S13 is the train dispatch type information, S14 is the train weight exchange number information, S15 is the train weight exchange time information, S16 is the train model information, and S17 is the train special remarks information; construct the planning supply module factor element SUP of the coal transportation and dispatch intelligent diversion model T : The supply plan factor element that finally enters the intelligent diversion model for coal transportation and dispatching (i.e., the diversion matching factor element data corresponding to the diversion supply data of the coal supply plan) is SUP T , the supply plan SUP excludes and diverts the non-compliant NDEM data and then performs calculation and matching.

[0065] Specifically, the data on the trains that can be supplied and allocated for the coal enterprise's daily production and purchase (i.e., the diversion supply data of the coal supply plan) are collected, and characteristic information such as train coal type data, train model data, train loading station data, loading dedicated line data, train coal source mining point information, and train first and last car number attribute data are obtained as diversion matching factor element data corresponding to the diversion supply data of the coal supply plan, so as to build a planning supply module for the intelligent diversion model of coal transportation and dispatching.

[0066] In some implementations of the present embodiment, the above-mentioned diversion supply data of the coal supply plan also includes train operation plan data; the rules for obtaining the train operation plan data include: according to the train waybill information of the coal supply plan, the train operation plan data corresponding to the coal supply plan is retrieved; wherein, the train operation plan data includes train operation line data, the planned departure time information of the stations passed by the train, and the actual departure time information of the stations passed by the train (wherein, the train operation line data refers to the route of each train, the planned departure time information of the stations passed by the train refers to the planned departure time of each station passed by the train, and the actual departure time information of the stations passed by the train refers to the actual departure time of each station passed by the train). Specifically, by obtaining the train operation plan data, the data features in the planning supply module of the intelligent diversion model of coal transportation and dispatching can be assisted and improved.

[0067] In some implementations of the present embodiment, the diversion data of the above-mentioned preset diversion reference items include diversion strategy data, and the diversion strategy data includes diversion user information, diversion user arrival information, diversion user map interface information, transportation type information, user arrival daily loading and unloading capacity information, user loading and unloading train interval information, train arrival operation time information, arrival station can load and unload vehicle types and heavy vehicle starting information and arrival station can load and unload vehicle number rule information (wherein, the diversion user information refers to the final receiving user information of the coal loaded on the shunting train flowing to the arrival station; the diversion user arrival information is the railway station where this user receives the goods; the diversion user map interface information is the railway station information that needs to be passed to reach this user; the transportation type is the driving and distributing person The classification of coal transportation business by personnel; the daily loading and unloading capacity of users at the station refers to the number of coal trains that can be unloaded at the receiving station every day; the interval time information of users loading and unloading trains refers to the interval time that should be reserved when users load and unload multiple trains; the running time information of trains at the station refers to the time required for the train to travel from the diversion station to the final arrival station; the vehicle type that can be unloaded at the arrival station refers to the vehicle type that can be received at the final arrival station of the train; the starting information of heavy vehicle number refers to the starting information of the train number loaded with coal; the vehicle number rule that can be unloaded at the arrival station refers to the requirements of the train terminal station for the vehicle number that can be received); the above-mentioned diversion data based on multiple preset diversion reference items of the target coal railway obtains the diversion matching factor element data corresponding to each preset diversion reference item, including: according to DEM R = {R2 1 , R2 2 , R2 3}, construct the diversion matching factor element data DEM corresponding to the diversion strategy data R ; The diversion matching factor element data corresponding to the diversion strategy data and the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan are merged to obtain the planned demand factor element data, and the planned demand factor element data represents all the diversion matching factor element data corresponding to the coal demand diversion plan; wherein, the expression of the planned demand factor element data is as follows:

[0068] Among them, R2 1 To divert user information, R2 2 To divert user arrival information, R2 3 To divert user graphics interface information, R2 4 R2 is the transport type information. 5 Daily loading and unloading capacity information for users at the station, R2 6 Provide users with train interval information, R2 7 R2 is the train arrival time information. 8 R2 is the information about the types of vehicles that can be unloaded at the arrival station and the starting number of heavy vehicles. 9DEM is the information of the rules for the unloading number of the arrival station. T DEM is the element data of planning demand factor. 1 D is the demand factor element for direct business diversion. 1 The user identity data for the coal demand diversion plan, D 2 The user arrival data for coal demand diversion plan, D 3 The coal type information required by users for coal demand diversion plan, D 4 The coal type quantity information required by users for coal demand diversion plan, D 5 The user demand urgency level information for coal demand diversion plan, D 6 The user arrival map of the coal demand diversion plan is the interface information, D 7 Plan date information for user requirements, D 8 The user demand for coal demand diversion plan can be unloaded at the station. 9 Special user requirements for coal demand diversion plan, DEM 1L To ensure the supply of demand factor elements for direct business diversion, DEM 1S Divert non-guaranteed supply demand factor elements for direct business, DEM 2L To ensure the supply of demand factor elements for water business diversion, DEM 2S Divert non-guaranteed supply demand factor elements for launching business.

[0069] It should be noted that direct business / launching business: For coal railway transportation business, the dispatching personnel made two business distinctions according to unloading at stations along the railway and loading into the port when diverting trains. Unloading along the railway is divided into direct business, and unloading at the port is called launching business. Diversion guaranteed supply demand / diversion non-guaranteed supply demand: The planned arrival demand of the train is the basis for specifying the flow direction when the train is diverted. According to the planned demand, it is allocated to the appropriate station for unloading. At this time, the system identifies and distinguishes the stations that must be guaranteed to arrive first for special needs, and prioritizes the flow direction for diversion guaranteed supply demand, and allocates it second for diversion non-guaranteed supply demand.

[0070] In some implementations of this embodiment, the above-mentioned diversion strategy data also includes diversion strategy date information, multiple diversion strategy type information and strategy execution ratio information corresponding to each diversion strategy type (wherein, the diversion strategy date information refers to the diversion strategy being executed according to the diversion strategy date to determine the corresponding diversion plan date. Multiple diversion strategy types refer to ST 1 、ST 2 、ST 3 、ST 4 Several different diversion strategies; such as ST 1 Indicates that it is allocated to DEM during diversion sorting1L Trains under the direct supply guarantee plan should take priority over DEM 1S Direct trains not under the guaranteed supply plan, DEM 1S Direct trains that are not under the guaranteed supply plan should take precedence over DEM 2L Trains under the water supply plan, DEM 2L Trains under the water supply guarantee plan should take priority over DEM 2S The strategy execution ratio information corresponding to each diversion strategy type is respectively represented as follows: In the above diversion plan, such as DEM 1L The train plan demand for the direct supply guarantee plan is 10 trains. When maintaining the diversion strategy, we maintain the X% percentage to 50%. In fact, when the diversion strategy is executed, it will be allocated according to 10 trains * 50% = 5 trains); the construction formula of the diversion matching factor element data C corresponding to the diversion strategy data includes:

[0071] Among them, ST 1 Indicates that the diversion strategy is a strategy of cyclically executing the first diversion plan. The first diversion plan is to first divert the direct supply guarantee demand plan, then divert the direct non-supply guarantee demand plan, then divert the water supply guarantee demand plan, and finally divert the water non-supply guarantee demand plan; ST 2 Indicates that the diversion strategy is a strategy of cyclically executing the second diversion plan. The second diversion plan is to first divert the water supply guarantee demand plan, then divert the water supply non-guaranteed demand plan, then divert the direct supply guarantee demand plan, and finally divert the direct non-guaranteed demand plan; ST 3 Indicates that the diversion strategy is a strategy of cyclically executing the third diversion plan. The third diversion plan is to first divert the direct supply guarantee demand plan, then divert the water supply guarantee demand plan, then divert the direct non-supply guarantee demand plan, and finally divert the water non-supply guarantee demand plan; ST 4 It indicates that the diversion strategy is a strategy of cyclically executing the fourth diversion plan. The fourth diversion plan is to first divert the water supply guarantee demand plan, then divert the direct supply guarantee demand plan, then divert the water non-supply guarantee demand plan, and finally divert the direct non-supply guarantee demand plan; X% is the strategy execution ratio information corresponding to each diversion strategy type.

[0072] In some implementations of the present embodiment, the above-mentioned multiple diversion rules include at least one or more of diversion exclusion rules, diversion priority rules, end-to-end rules, railway skylight influence rules and vehicle type matching rules; the diversion exclusion rule is used to filter the diversion matching factor element data corresponding to each preset diversion reference item according to a preset set for excluding diversion factor elements; the diversion priority rule is used to filter the diversion matching factor element data corresponding to each preset diversion reference item according to the priority of each diversion strategy type; the end-to-end rule is used to filter the diversion matching factor element data corresponding to each preset diversion reference item according to multiple preset end-to-end feature object information, and the preset end-to-end feature object information includes user identity information; the railway skylight influence rule is used to filter the diversion matching factor element data corresponding to each preset diversion reference item according to the railway skylight key feature data, and the railway skylight key feature data includes skylight date information, skylight start time information, skylight end time information, skylight influence line information and skylight influence line corresponding station information; the vehicle type matching rule is used to filter the diversion matching factor element data corresponding to each preset diversion reference item according to the train vehicle type data of the coal supply plan.

[0073] It should be noted that the impact of railway skylights refers to "the window period caused by not drawing train lines in the train operation diagram or adjusting or reducing train operations to reserve time for construction and maintenance operations"; diversion exclusion rules: when the train is running, the dispatching personnel divide the train dispatching work into two flow categories: direct along the railway and launching into the port. The direct and launching businesses have different requirements for the source of loading, the vehicle model, unloading method and storage capacity at the unloading station, so corresponding exclusion rules are made for the direct and launching flow trains; diversion priority rules: for trains from the diversion station to The distance to the final destination, the types of vehicles that can be unloaded at the destination, and the loading and unloading capacity are all different. When diverting, the transportation personnel will prioritize and balance the diversion plan for the day; end-to-end rules: Since some mining areas only accept loading for some customers at the destination, it is necessary to directly divert these running trains; vehicle type matching rules: Due to the complex types of diversion trains, there are C80 large cars, C64 large cars, C70 cars, etc., and the equipment that can be unloaded at different unloading stations is also different, so it is necessary to match the types of vehicles that can be unloaded at each unloading station and then include them in the diversion calculation.

[0074] Further, obtain the railway skylight impact rule R3 in the diversion rule matrix: R3 1 、R3 2 、R3 3 ......R3 n , where R3 1 For sunroof date information, R3 2 R3 is the skylight start time information. 3For the skylight end time information, R3 4 For the skylight affecting the line information, R3 5 Provide the station information corresponding to the line affected by the skylight, etc.; construct the skylight calculation diversion factor element SK t :SK t =[R3 1 +(R3 3 +R3 2 )].

[0075] Furthermore, the diversion start transportation time is calculated based on the fixed station distance running time of the interval from X-1 station to X station and the interval from X+1 station to X station, and the time taken from the diversion start transportation station to the planned demand destination station is summarized; among which, the arrival time of the supply train is calculated as: Where X t m The final arrival time for customers, Si 1 t is the running time of the map station 1, Si 2 t is the running time of the second map station, Si n t It is the running time of the Nth map station.

[0076] Further, calculate whether the supply train will arrive on the same day or the next day: Calculate the arrival time range on the same day

[0077] Skylight Day

[0078]

[0079] Non-Skylight Day

[0080]

[0081] Calculate the next day arrival time range

[0082] Skylight Day

[0083]

[0084] Non-Skylight Day

[0085] Among them, DA n t To calculate the end time of the day when the skylight diversion is completed, DA n To calculate the end time of the diversion on non-skylight days, DA f t To calculate the start time of the next day of diversion during the skylight day, DA f To calculate the start time of the next day of diversion on non-skylight days;

[0086] Furthermore, the end-to-end rule R4 in the traffic diversion rule matrix: R4 1 、R4 2 、R4 3 ......R4 n , where R4 1 For the first end-to-end feature object information such as customer, R4 2 The second end-to-end feature object information, R4 3 The third end-to-end feature object information, R4 4 The fourth end-to-end feature object information, R4 5 For the fifth end-to-end feature object information, etc.; construct the end-to-end priority calculation diversion factor element: E2E is the factor for end-to-end priority calculation of traffic diversion rules. 1 Calculate the diversion rule factor for the first end-to-end priority, E2E 2 Calculate the diversion rule factor for the second end-to-end priority, E2E n Calculate the diversion rule factor for the nth end-to-end priority; E2E 1 Middle R4 1 For end-to-end customer elements, R4 2 For end-to-end coal information elements, R4 3 It is an end-to-end special remark information element. When calculating the intelligent diversion model of coal transportation and dispatching, the E2E end-to-end factor element is preferentially taken to obtain the characteristic value and participate in SUP T Match supply and demand with DEM1 / 2.

[0087] In some implementations of this embodiment, the coal transportation and dispatching intelligent diversion model obtains the final diversion demand planning factor element DEM of coal railway transportation driving dispatching by obtaining the basic data module, diversion strategy module, and diversion rule matrix module D, S, T, C, and R. T , Final diversion supply planning factor element SUP T , Diversion strategy factor element C-ST 1 / ST 2 / ST 3 / ST 4 , Skylight day diversion rule factor element DA n t / DA f t , non-skylight day diversion rule factor element DA n / DA f and E2E information of end-to-end diversion rule factor elements; the intelligent diversion model for coal transportation and dispatching is constructed as follows: Among them, Q is the diversion result output by the intelligent diversion model for coal transportation and dispatching, and LOOP is the loop training operation of the intelligent diversion model for coal transportation and dispatching.T With DEM T When matching: E2E data is matched first, and then the matching is performed according to the diversion strategy C rule. When matching, the train running time DA is referenced throughout the process. n t / DA f t / DA n / DA f and demand planning time DEM T -D7 matching point before and after demand planning, DEM T The demand plan is matched according to the diversion rules with priority given to the highest supply level, and the diversion result Q is output after matching a series of diversion rules such as far first then near, difficult first then easy, small coal types first then large coal types.

[0088] In summary, this method first obtains train traffic diversion data from basic data collection; then combines traffic diversion strategies and rules for optimal allocation; improves the stability and accuracy of coal railway transportation traffic diversion, and solves the problems of low efficiency and high workload of manual diversion in existing coal railway transportation traffic diversion, and reduces subjective human intervention.

[0089] Figure 3 This is a block diagram of an intelligent shunting system for coal railway transportation and vehicle dispatching provided by one embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an intelligent shunting system for coal railway transportation driving and dispatching, comprising:

[0090] A diversion matching factor element acquisition module, for obtaining diversion matching factor element data corresponding to each preset diversion reference item based on the diversion data of multiple preset diversion reference items of the target coal railway;

[0091] The intelligent diversion module is used to input the diversion matching factor element data corresponding to each preset diversion reference item into the trained coal transportation and dispatching intelligent diversion model for intelligent diversion, and obtain and output the optimal transportation operation plan flow direction information; wherein, the coal transportation and dispatching intelligent diversion model is constructed based on a preconfigured diversion rule matrix, and the diversion rule matrix contains a variety of diversion rules. The diversion rule matrix is ​​used to perform step-by-step diversion matching on the diversion matching factor element data corresponding to each preset diversion reference item according to various diversion rules in turn, so as to obtain the diversion matching factor element data corresponding to the optimal transportation operation plan flow direction information.

[0092] Specifically, the system obtains the diversion matching factor element data corresponding to each preset diversion reference item from the diversion data of multiple preset diversion reference items of the collected target coal railway. The diversion matching factor element data corresponding to each preset diversion reference item is input into the trained coal transportation and dispatching intelligent diversion model. The coal transportation and dispatching intelligent diversion model performs step-by-step diversion matching on the diversion matching factor element data corresponding to each preset diversion reference item according to various diversion rules in the diversion rule matrix, so as to realize the intelligent diversion of the transportation and dispatching of the target coal railway, and obtain and output the flow direction information of the optimal dispatching operation plan. The system solidifies the diversion rule matrix structure, integrates the diversion matching factor element data, and then automatically completes the diversion operation by triggering the calculation of the coal transportation and dispatching intelligent diversion model, thereby improving the efficiency, accuracy and stability of the coal railway transportation and dispatching diversion operation, while reducing the errors of manual operation and personal subjective operation, and effectively avoiding the problem of manual intervention risks.

[0093] An embodiment of the present invention further provides a machine-readable storage medium, on which instructions are stored. When the instructions are executed by the processor 100, the processor 100 is configured to execute the above-mentioned intelligent diversion method for coal railway transportation driving and dispatching.

[0094] Machine-readable storage media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0095] The embodiment of the present invention also provides an electronic device 10, which includes a memory 101, a processor 100, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, the above-mentioned intelligent diversion method for coal railway transportation operation is implemented.

[0096] like Figure 4FIG. 1 is a schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 10 of this embodiment includes: a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, the steps in the above method embodiment are implemented. Alternatively, when the processor 100 executes the computer program 102, the functions of each module / unit in the above device embodiment are implemented.

[0097] Exemplarily, the computer program 102 may be divided into one or more modules / units, one or more modules / units are stored in the memory 101, and are executed by the processor 100 to complete the present invention. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 102 in the electronic device 10. For example, the computer program 102 may be divided into a split matching factor element acquisition module and an intelligent split module.

[0098] The electronic device 10 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 10 may include, but is not limited to, a processor 100 and a memory 101. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 10 and does not constitute a limitation of the electronic device 10. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0099] The processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0100] The memory 101 may be an internal storage unit of the electronic device 10, such as a hard disk or memory of the electronic device 10. The memory 101 may also be an external storage device of the electronic device 10, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 10. Further, the memory 101 may also include both an internal storage unit of the electronic device 10 and an external storage device. The memory 101 is used to store computer programs and other programs and data required by the electronic device 10. The memory 101 may also be used to temporarily store data that has been output or is to be output.

[0101] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0102] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program 102 products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program 102 product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program 102 products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by the computer program 102 instructions. These computer program 102 instructions can be provided to a processor 100 of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor 100 of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] These computer program 102 instructions may also be stored in a computer readable memory 101 that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory 101 produce an article of manufacture including an instruction device that implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0105] These computer program 102 instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0106] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0107] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. An intelligent diversion method for coal railway transportation driving and dispatching, characterized in that: include: Based on the diversion data of multiple preset diversion reference items of the target coal railway, the diversion matching factor element data corresponding to each preset diversion reference item is obtained; The diversion matching factor element data corresponding to each preset diversion reference item is input into the trained coal transportation and dispatching intelligent diversion model for intelligent diversion, and the optimal transportation operation plan flow direction information is obtained and output; wherein, the coal transportation and dispatching intelligent diversion model is constructed based on a preconfigured diversion rule matrix, and the diversion rule matrix contains a variety of diversion rules. The diversion rule matrix is ​​used to perform step-by-step diversion matching on the diversion matching factor element data corresponding to each preset diversion reference item according to various diversion rules in turn, so as to obtain the diversion matching factor element data corresponding to the optimal transportation operation plan flow direction information.

2. The intelligent diversion method for coal railway transportation and dispatching according to claim 1 is characterized in that: The diversion data of the preset diversion reference item includes diversion demand data of the coal demand diversion plan, and the diversion demand data of the coal demand diversion plan at least includes the identity data of the user who needs the coal demand diversion plan, the user's arrival data, the user's required coal type data, the coal type data, the required number of trains, the train diversion interface information, the demand priority data and the vehicle type attribute data; The diversion data based on multiple preset diversion reference items of the target coal railway, and obtaining the diversion matching factor element data corresponding to each preset diversion reference item, include: According to the diversion demand data of the coal demand diversion plan of the target coal railway, the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan is constructed; wherein, The construction rules of the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan include: The demand user identity data, user arrival data and train diversion interface information of the coal demand diversion plan are selected from the diversion demand data of the coal demand diversion plan of the target coal railway as the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan; wherein, DEM={D1, D2, D6}, D1 is the demand user identity data of the coal demand diversion plan, D2 is the user arrival data of the coal demand diversion plan, D6 is the train diversion interface information of the coal demand diversion plan, and DEM is the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan.

3. The intelligent diversion method for coal railway transportation driving and dispatching according to claim 1 is characterized in that: The diversion data of the preset diversion reference item includes the diversion supply data of the coal supply plan, and the diversion supply data of the coal supply plan at least includes the train number data, train coal type data, train model data, train loading order number data, train loading station data, loading dedicated line data, train coal source mine point information, train waybill information and train first and last car number attribute data of the coal supply plan; The diversion data based on multiple preset diversion reference items of the target coal railway, and obtaining the diversion matching factor element data corresponding to each preset diversion reference item, include: According to the diversion supply data of the coal supply plan of the target coal railway, the diversion matching factor element data corresponding to the diversion supply data of the coal supply plan is constructed; wherein, The construction rules of the diversion matching factor element data corresponding to the diversion supply data of the coal supply plan include: Based on the preconfigured diversion rule matrix, a diversion factor element set excluded by the diversion rule matrix is ​​constructed; wherein NDEM = {R11, R12, R13, R14, R15, R16, R17, R18, R19, R1 10 , R1 11 }, NDEM is the set of diversion factor elements excluded by the diversion rule matrix, R11 is the diversion exclusion transportation type information, R12 is the diversion exclusion coal type information, R13 is the diversion exclusion coal type information, R14 is the diversion exclusion vehicle model information, R15 is the diversion exclusion loading site information, R16 is the diversion exclusion coal source mine point information, R17 is the diversion exclusion user information, R18 is the diversion exclusion coal source supplier information, R19 is the diversion exclusion loading line information, R1 10 To divert and exclude the shipping train information, R1 11 Exclude special loading notes for diversion; The diversion supply data of the coal supply plan of the target coal railway that matches the diversion factor element set excluded by the diversion rule matrix are excluded to obtain the diversion matching factor element data corresponding to the diversion supply data of the coal supply plan.

4. The intelligent diversion method for coal railway transportation and dispatching according to claim 3 is characterized in that: The diversion supply data of the coal supply plan also includes train operation plan data; The acquisition rules of the train operation plan data include: According to the train waybill information of the coal supply plan, retrieve the train operation plan data corresponding to the coal supply plan; The train operation plan data includes train operation line data, train map station planned departure time information and train map station actual departure time information.

5. The intelligent diversion method for coal railway transportation and dispatching according to claim 2 is characterized in that: The diversion data of the preset diversion reference item includes diversion strategy data, and the diversion strategy data includes diversion user information, diversion user arrival information, diversion user map interface information, dispatch type information, user arrival daily loading and unloading capacity information, user loading and unloading train interval information, train arrival operation time information, arrival station can load and unload vehicle type and heavy vehicle number starting information and arrival station can load and unload vehicle number rule information; The diversion data based on multiple preset diversion reference items of the target coal railway, and obtaining the diversion matching factor element data corresponding to each preset diversion reference item, include: Based on DEM R = {R21, R22, R23}, construct the diversion matching factor element data DEM corresponding to the diversion strategy data R ; The diversion matching factor element data corresponding to the diversion strategy data and the diversion matching factor element data corresponding to the diversion demand data of the coal demand diversion plan are merged to obtain the planned demand factor element data, wherein the planned demand factor element data represents all the diversion matching factor element data corresponding to the coal demand diversion plan; wherein, The expression of the planned demand factor element data is as follows: R21 is the diversion user information, R22 is the diversion user arrival information, R23 is the diversion user map interface information, R24 is the dispatch type information, R25 is the user's daily loading and unloading capacity information at the station, R26 is the user's loading and unloading train interval information, R27 is the train arrival time information, R28 is the arrival station can be unloaded vehicle type and heavy vehicle start information, R29 is the arrival station can be unloaded vehicle number rule information, DEM T is the planned demand factor element data, DEM1 is the direct business diversion demand factor element, D1 is the demand user identity data of the coal demand diversion plan, D2 is the user arrival data of the coal demand diversion plan, D3 is the user demand coal type information of the coal demand diversion plan, D4 is the user demand coal type quantity information of the coal demand diversion plan, D5 is the user demand urgency level information of the coal demand diversion plan, D6 is the user arrival map interface information of the coal demand diversion plan, D7 is the user demand plan date information, D8 is the user demand arrival at the coal demand diversion plan. The information of the type of vehicle that can be unloaded, D9 is the user demand special requirements remark information of the coal demand diversion plan, DEM 1L To ensure the supply of demand factor elements for direct business diversion, DEM 1S Divert non-guaranteed supply demand factor elements for direct business, DEM 2L To ensure the supply of demand factor elements for water business diversion, DEM 2S Divert non-guaranteed supply demand factor elements for launching business.

6. The intelligent diversion method for coal railway transportation and dispatching according to claim 5 is characterized in that: The diversion strategy data also includes diversion strategy date information, multiple diversion strategy type information and strategy execution ratio information corresponding to each diversion strategy type; The construction formula of the diversion matching factor element data C corresponding to the diversion strategy data includes: Among them, ST1 indicates that the diversion strategy is a strategy of cyclically executing the first diversion plan, wherein the first diversion plan is to first divert the direct supply guarantee demand plan, then divert the direct non-supply guarantee demand plan, then divert the water supply guarantee demand plan, and finally divert the water non-supply guarantee demand plan; ST2 indicates that the diversion strategy is a strategy of cyclically executing the second diversion plan, wherein the second diversion plan is to first divert the water supply guarantee demand plan, then divert the water non-supply guarantee demand plan, then divert the direct supply guarantee demand plan, and finally divert the direct non-supply guarantee demand plan; ST3 indicates the diversion strategy It is a strategy for cyclically executing the third diversion plan, and the third diversion plan is to first divert the direct supply guarantee demand plan, then divert the water supply guarantee demand plan, then divert the direct non-supply guarantee demand plan, and finally divert the water non-supply guarantee demand plan; ST4 indicates that the diversion strategy is a strategy for cyclically executing the fourth diversion plan, and the fourth diversion plan is to first divert the water supply guarantee demand plan, then divert the direct supply guarantee demand plan, then divert the water non-supply guarantee demand plan, and finally divert the direct non-supply guarantee demand plan; X% is the strategy execution ratio information corresponding to each diversion strategy type.

7. The intelligent diversion method for coal railway transportation and dispatching according to claim 6 is characterized in that: The multiple diversion rules at least include one or more of a diversion exclusion rule, a diversion priority rule, an end-to-end rule, a railway skylight influence rule, and a vehicle type matching rule; The diversion exclusion rule is used to screen the diversion matching factor element data corresponding to each preset diversion reference item according to a preset set of diversion factor elements for exclusion; The diversion priority rule is used to screen the diversion matching factor element data corresponding to each preset diversion reference item according to the priority of each diversion strategy type; The end-to-end rule is used to screen the diversion matching factor element data corresponding to each preset diversion reference item according to a plurality of preset end-to-end feature object information, wherein the preset end-to-end feature object information includes user identity information; The railway skylight impact rule is used to screen the diversion matching factor element data corresponding to each preset diversion reference item according to the railway skylight key feature data, and the railway skylight key feature data includes skylight date information, skylight start time information, skylight end time information, skylight impact line information and skylight impact line corresponding station information; The vehicle type matching rule is used to screen the diversion matching factor element data corresponding to each preset diversion reference item according to the train vehicle type data of the coal supply plan.

8. An intelligent diversion system for coal railway transportation, characterized in that: include: A diversion matching factor element acquisition module, for obtaining diversion matching factor element data corresponding to each preset diversion reference item based on the diversion data of multiple preset diversion reference items of the target coal railway; An intelligent diversion module is used to input the diversion matching factor element data corresponding to each preset diversion reference item into the trained coal transportation and dispatching intelligent diversion model for intelligent diversion, and obtain and output the optimal transportation operation plan flow direction information; wherein, the coal transportation and dispatching intelligent diversion model is constructed based on a preconfigured diversion rule matrix, and the diversion rule matrix contains a variety of diversion rules. The diversion rule matrix is ​​used to perform step-by-step diversion matching on the diversion matching factor element data corresponding to each preset diversion reference item according to various diversion rules in turn, so as to obtain the diversion matching factor element data corresponding to the optimal transportation operation plan flow direction information.

9. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by the processor, the processor is configured to execute the intelligent diversion method for coal railway transportation operation and dispatching as described in any one of claims 1 to 7.

10. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the intelligent diversion method for coal railway transportation operation and dispatching as described in any one of claims 1 to 7 is implemented.

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