Intelligent coal dust removal scheme generation method based on extension

Abstract

The invention discloses an intelligent coal dust removal scheme generation method based on extension. The method comprises the following steps: analyzing various preset dust removal methods by utilizing an event element theory to obtain parameters corresponding to the various dust removal methods; according to the parameters corresponding to the various dust removal methods, scores of the various dust removal methods are calculated; according to the scores of the various dust removal methods, selecting an optimal dust removal method; constructing a transformation tree according to the optimal dust removal method and the corresponding parameter selection; and traversing and adjusting the conversion tree to obtain an optimal dust removal scheme. The scheme generated by the method is good in landing effect, and the generated scheme can better fit an actual dust removal scene.

Description

Technical Field

[0001] The present invention relates to the technical field of data mining and intelligent decision-making applications, and more specifically, to a method for generating an intelligent coal dust removal solution based on extenics. Background Art

[0002] As an important source of energy, coal still occupies an important position in the global energy structure. Dust removal, as one of the key links in coal mine production, not only affects the health of workers, but is also directly related to the safe production and economic benefits of coal mines.

[0003] Numerous approaches exist for coal dust removal, but their efficiency and costs vary widely. Each solution has its own advantages and disadvantages, and technical decisions carry a high opportunity cost. Traditional approaches focus solely on the final solution, hindering the comparative decision-making and phased implementation of individual options, resulting in poor implementation results.

[0004] Prior art discloses a transformation tree method for intelligently generating quality improvement solutions. This method calculates the degree of transformation by obtaining parameters, optional parameter values, and their corresponding qualified production results during production. Then, based on the degree of transformation and optional parameter values, a transformation tree is constructed and traversed in reverse order to obtain transformation solutions. Finally, the transformation solutions are ranked by their merit to obtain the optimal production solution. This method primarily targets the selection of production solutions during the production process and does not address the selection of coal dust removal solutions. Summary of the Invention

[0005] The present invention addresses the drawbacks of existing coal dust removal solutions, which are difficult to implement step by step and have poor implementation effects, and provides a method for generating an intelligent coal dust removal solution based on extenics. The method has the characteristic of generating a solution with good implementation effects.

[0006] The primary purpose of the present invention is to solve the above technical problems, and the technical solutions of the present invention are as follows:

[0007] A method for generating an intelligent coal dust removal solution based on extenics, comprising:

[0008] S1: Use event-element theory to analyze various preset dust removal methods and obtain the corresponding parameters of various dust removal methods;

[0009] S2: Calculating scores for the various dust removal methods based on the parameters corresponding to the various dust removal methods;

[0010] S3: selecting the best dust removal method according to the scores of the various dust removal methods;

[0011] S4: constructing a transformation tree according to the optimal dust removal method and corresponding parameter selection;

[0012] S5: Traverse and adjust the transformation tree to obtain the optimal dust removal solution.

[0013] Furthermore, the preset dust removal method described in step S1 includes: spray dust removal, magnetized water dust removal, ventilation dust removal, and coal seam water injection dust removal.

[0014] Furthermore, the dust removal method scoring formula is as follows:

[0015] Dust removal method score = w1 × pollution level score + w2 × long-term cost score + w3 × versatility score

[0016] +w4×dust removal efficiency score

[0017] w1, w2, w3, and w4 all represent weight coefficients.

[0018] Furthermore, the calculation formula for the pollution degree score is as follows:

[0019] Pollution degree score = (chemical additive ratio × 20 + (water consumption × 5) + (secondary pollution × 20

[0020] The chemical additive ratio indicates the proportion of chemical substances in the dust remover, the water consumption indicates the water consumption per hour, and the secondary pollution indicates whether wastewater or waste residue is generated.

[0021] Furthermore, the calculation formula of the long-term cost score is as follows:

[0022]

[0023] Equipment life indicates the average number of years the equipment is used, maintenance cycle indicates the interval between two equipment maintenance, and energy consumption indicates the operating power of the equipment per hour.

[0024] Furthermore, the calculation formula of the versatility score is as follows:

[0025]

[0026] The minimum dust particle size indicates the minimum dust particle size that can be handled, the humidity range span indicates the operating humidity range of the equipment, and the installation method indicates whether the equipment is movable.

[0027] Furthermore, the dust removal efficiency score formula is as follows:

[0028]

[0029] Furthermore, the method for constructing the transformation tree includes:

[0030] S401: Classifying the parameters of the optimal dust removal method into modifiable parameters and non-modifiable parameters based on whether they can be modified after production;

[0031] S402: taking all the unmodifiable parameters as root nodes; taking the root nodes as root node groups;

[0032] S403: Calculating the conversion degrees of all the modifiable parameters respectively;

[0033] S404: Sort all modifiable parameters in ascending order according to the conversion degree of the modifiable parameters to obtain a modifiable parameter sequence;

[0034] S404: sequentially selecting a modifiable parameter from the parameter sequence as a first modifiable parameter;

[0035] S405: deriving corresponding child nodes from the nodes of the root node group according to the modifiable value of the first modifiable parameter;

[0036] S406: Using the derived child nodes as a new root node group;

[0037] S407: Replace the first modifiable parameter and execute step S405 until every modifiable parameter in the modifiable parameter sequence is traversed.

[0038] Furthermore, the calculation formula of the conversion degree is as follows:

[0039]

[0040] P(T(A)→B) represents the frequency of modifiable parameters failing to reach the target, P(A→B) represents the frequency of modifiable parameters failing to reach the target, and T(A)=C represents that option A with known modifiable parameters is converted into option C with known modifiable parameters after adjustment.

[0041] An intelligent coal dust removal solution generation system based on extenics, including:

[0042] Method analysis module: Use event-element theory to analyze various preset dust removal methods and obtain the corresponding parameters of various dust removal methods;

[0043] Scoring calculation module: Calculates scores of various dust removal methods based on the parameters corresponding to the various dust removal methods;

[0044] Method selection module: selects the best dust removal method according to the scores of the various dust removal methods;

[0045] Conversion tree construction module: constructing a conversion tree according to the optimal dust removal method and corresponding parameter selection;

[0046] Conversion tree traversal module: traverses and adjusts the conversion tree to obtain the optimal dust removal solution.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention uses event-element theory to analyze various pre-defined dust removal methods and obtain corresponding parameters for each method. Based on these parameters, the method scores are calculated. Based on these scores, the optimal method is selected. A transformation tree is constructed based on the optimal method and its corresponding parameters. The transformation tree is then traversed and adjusted to obtain the optimal dust removal solution. This method generates a solution that is highly effective and more suitable for actual dust removal scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flow chart of a method for generating an intelligent coal dust removal solution based on extenics provided in Example 1.

[0050] Figure 2 Schematic diagram of the transformation tree provided in Example 1. DETAILED DESCRIPTION

[0051] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0052] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0053] It is understandable to those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0054] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0055] Example 1

[0056] like Figure 1 As shown, a method for generating an intelligent coal dust removal solution based on extenics includes:

[0057] S1: Use event-element theory to analyze various preset dust removal methods and obtain the corresponding parameters of various dust removal methods;

[0058] S2: Calculating scores for the various dust removal methods based on the parameters corresponding to the various dust removal methods;

[0059] S3: selecting the best dust removal method according to the scores of the various dust removal methods;

[0060] S4: constructing a transformation tree according to the optimal dust removal method and corresponding parameter selection;

[0061] S5: Traverse and adjust the transformation tree to obtain the optimal dust removal solution.

[0062] Furthermore, the preset dust removal method described in step S1 includes: spray dust removal, magnetized water dust removal, ventilation dust removal, and coal seam water injection dust removal.

[0063] Furthermore, the dust removal method scoring formula is as follows:

[0064] Dust removal method score = w1 × pollution level score + w2 × long-term cost score + w3 × versatility score

[0065] +w4×dust removal efficiency score

[0066] w1, w2, w3, and w4 all represent weight coefficients.

[0067] Furthermore, the calculation formula for the pollution degree score is as follows:

[0068] Pollution degree score = (chemical additive ratio × 20 + (water consumption × 5) + (secondary pollution × 20

[0069] The chemical additive ratio indicates the proportion of chemical substances in the dust remover (0% means no addition), water consumption (m 3 / h) represents the water consumption per hour, and secondary pollution indicates whether wastewater or waste residue is generated (0 = no, 1 = yes).

[0070] If the score ranges from 0 to 15, it is low pollution, 16 to 35 is medium pollution, and 36 to 100 is high pollution.

[0071] Furthermore, the calculation formula of the long-term cost score is as follows:

[0072]

[0073] Equipment life (years) indicates the average number of years the equipment is used, maintenance cycle (months) indicates the interval between two equipment maintenances, and energy consumption (kW) indicates the operating power of the equipment per hour.

[0074] If the score ranges from 0 to 30, it is low cost, 31 to 60 is medium cost, and 61 to 100 is high cost.

[0075] Furthermore, the calculation formula of the versatility score is as follows:

[0076]

[0077] The minimum dust particle size (μm) indicates the minimum particle size of dust that can be handled, the humidity range span indicates the operating humidity range of the equipment, and the installation method indicates whether the equipment is movable (0 for fixed type and 1 for mobile type).

[0078] If the score range is 0-49, it is low general use, 50-79 is medium general use, and 80-100 is high general use.

[0079] Furthermore, the dust removal efficiency score formula is as follows:

[0080]

[0081] It should be noted that the pollution degree score, long-term cost score, versatility score, and dust removal efficiency score need to be standardized, as follows:

[0082] Assume that the original score of an indicator is x, and its possible value range is [x min ,x max ]

[0083]

[0084] S max and S min are the maximum and minimum values of the target interval.

[0085] Furthermore, the method for constructing the transformation tree includes:

[0086] S401: Classifying the parameters of the optimal dust removal method into modifiable parameters and non-modifiable parameters based on whether they can be modified after production;

[0087] S402: taking all the unmodifiable parameters as root nodes; taking the root nodes as root node groups;

[0088] S403: Calculating the conversion degrees of all the modifiable parameters respectively;

[0089] S404: Sort all modifiable parameters in ascending order according to the conversion degree of the modifiable parameters to obtain a modifiable parameter sequence;

[0090] S404: sequentially selecting a modifiable parameter from the parameter sequence as a first modifiable parameter;

[0091] S405: deriving corresponding child nodes from the nodes of the root node group according to the modifiable value of the first modifiable parameter;

[0092] S406: Using the derived child nodes as a new root node group;

[0093] S407: Replace the first modifiable parameter and execute step S405 until every modifiable parameter in the modifiable parameter sequence is traversed.

[0094] In a specific embodiment, if the dust removal method direction v, drilling length l, hole diameter d, and hole spacing x parameters are difficult to modify after production due to restrictions on production lines and tool standards, they are designated as unmodifiable parameters and placed at the root node of the transformation tree. Its parameters are divided into corresponding categories as fixed combinations.

[0095] In a specific embodiment, if there are multiple non-adjustable parameters, the information gain ratios of the non-adjustable parameters may be calculated and the root node may be constructed by arranging the information gain ratios in descending order.

[0096] The formula for information gain is as follows:

[0097]

[0098] Furthermore, the calculation formula of the conversion degree is as follows:

[0099]

[0100] P(T(A)→B) represents the frequency of modifiable parameters failing to reach the target, P(A→B) represents the frequency of modifiable parameters failing to reach the target, and T(A)=C represents that option A with known modifiable parameters is converted into option C with known modifiable parameters after adjustment.

[0101] Substituting the known modifiable parameters into option C, we get:

[0102] T(A)=C

[0103]

[0104] represents the confidence of option C with modifiable parameters, and P(A) represents the frequency of option A with known modifiable parameters.

[0105] In a specific embodiment, the traversal in step S5 is a reverse order traversal, including:

[0106] S501: Using the leaf node of the tree branch corresponding to the dust removal solution as the second node;

[0107] S502: The parent node corresponding to the second node is used as the third node; and the nodes excluding the second node from the child nodes of the third node are used as a child node group;

[0108] S503: Determine whether there is a node in the child node group; if so, execute step S504; if not, execute step S506;

[0109] S504: Select a node in the child node group as the first node, and replace the parameter optional value represented by the first node with the parameter optional value corresponding to the second node in the dust removal solution to form a new dust removal solution;

[0110] S505: Remove the first node from the child node group and execute step S503;

[0111] S506: Determine whether the third node is a root node; if so, execute step S508; if not, execute step S507;

[0112] S507: Set the third node as the new second node, and the parent node of the third node as the new third node; remove the new second node from the child nodes of the new third node as a new child node group; and execute step S503;

[0113] S508: The obtained new dust removal solution is used as a conversion solution.

[0114] In a specific embodiment, such as in the wet dust removal process, compared with the spray dust removal method, the use of bubble dust removal will greatly improve the dust removal efficiency K, and its core influencing parameter tool u has a higher conversion degree than other parameters in the conversion degree calculation.

[0115]

[0116] In the dry dust removal process, the dust removal efficiency K of the air curtain dust isolation method is smaller than that of the ventilation dust removal method. Therefore, for its tool parameter w, the smaller dust removal efficiency increment will cause the calculated conversion degree to be smaller than the conversion degree E1.

[0117]

[0118] Therefore, tool parameter u is placed as a child node in the transformation tree construction due to its higher transformation degree, while tool parameter w is placed as a parent node.

[0119] In a specific embodiment, Figure 2 As shown in the figure, assuming only the dust removal methods are modified and compared, the corresponding influencing parameters are ranked from low to high by conversion degree, tool u, tool w, tool m, and component c. If the given combination [u1, w1, m1, c1] is converted and only component c is adjusted, and if adjusting to c2 or c4 improves dust removal efficiency, then the improved solutions [u1, w1, m1, c2] and [u1, w1, m1, c4] are generated. If the cost of adjusting to c4 exceeds the required range, only the combination [u1, w1, m1, c2] is retained as the output alternative. The final selected solution is the improved solution for the given combination [u1, w1, m1, c1] that meets the cost and efficiency requirements and can improve dust removal efficiency.

[0120] An intelligent coal dust removal solution generation system based on extenics, including:

[0121] Method analysis module: Use event-element theory to analyze various preset dust removal methods and obtain the corresponding parameters of various dust removal methods;

[0122] Scoring calculation module: Calculates scores of various dust removal methods based on the parameters corresponding to the various dust removal methods;

[0123] Method selection module: selects the best dust removal method according to the scores of the various dust removal methods;

[0124] Conversion tree construction module: constructing a conversion tree according to the optimal dust removal method and corresponding parameter selection;

[0125] Conversion tree traversal module: traverses and adjusts the conversion tree to obtain the optimal dust removal solution.

[0126] The same or similar reference numerals correspond to the same or similar components;

[0127] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0128] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for generating an intelligent coal dust removal solution based on extenics, characterized in that: include: S1: Use event-element theory to analyze various preset dust removal methods and obtain the corresponding parameters of various dust removal methods; S2: Calculating scores for the various dust removal methods based on the parameters corresponding to the various dust removal methods; S3: selecting the best dust removal method according to the scores of the various dust removal methods; S4: constructing a transformation tree according to the optimal dust removal method and corresponding parameter selection; S5: Traverse and adjust the transformation tree to obtain the optimal dust removal solution.

2. The method for generating an intelligent coal dust removal solution based on extenics according to claim 1, characterized in that: The preset dust removal method described in step S1 includes: spray dust removal, magnetized water dust removal, ventilation dust removal, and coal seam water injection dust removal.

3. The method for generating an intelligent coal dust removal solution based on extenics according to claim 1, characterized in that: The formula for scoring the dust removal method is as follows: Dust removal method score = w1 × pollution level score + w2 × long-term cost score + w3 × versatility score + w4 × dust removal efficiency score w1, w2, w3, and w4 all represent weight coefficients.

4. The method for generating an intelligent coal dust removal solution based on extenics according to claim 3 is characterized in that: The calculation formula for the pollution degree score is as follows: Pollution degree score = (chemical additive ratio × 20) + (water consumption × 5) + (secondary pollution × 20) The chemical additive ratio indicates the proportion of chemical substances in the dust remover, the water consumption indicates the water consumption per hour, and the secondary pollution indicates whether wastewater or waste residue is generated.

5. The method for generating an intelligent coal dust removal solution based on extenics according to claim 3 is characterized in that: The long-term cost score is calculated as follows: Equipment life indicates the average number of years the equipment is used, maintenance cycle indicates the interval between two equipment maintenance, and energy consumption indicates the operating power of the equipment per hour.

6. The method for generating an intelligent coal dust removal solution based on extenics according to claim 3 is characterized in that: The calculation formula for the versatility score is as follows: The minimum dust particle size indicates the minimum dust particle size that can be handled, the humidity range span indicates the operating humidity range of the equipment, and the installation method indicates whether the equipment is movable.

7. The method for generating an intelligent coal dust removal solution based on extenics according to claim 3 is characterized in that: The formula for the dust removal efficiency score is as follows:

8. The method for generating an intelligent coal dust removal solution based on extenics according to claim 1, characterized in that: The method for constructing the transformation tree comprises: S401: Classifying the parameters of the optimal dust removal method into modifiable parameters and non-modifiable parameters based on whether they can be modified after production; S402: taking all the unmodifiable parameters as root nodes; taking the root nodes as root node groups; S403: Calculating the conversion degrees of all the modifiable parameters respectively; S404: Sort all modifiable parameters in ascending order according to the conversion degree of the modifiable parameters to obtain a modifiable parameter sequence; S404: sequentially selecting a modifiable parameter from the parameter sequence as a first modifiable parameter; S405: deriving corresponding child nodes from the nodes of the root node group according to the modifiable value of the first modifiable parameter; S406: Using the derived child nodes as a new root node group; S407: Replace the first modifiable parameter and execute step S405 until every modifiable parameter in the modifiable parameter sequence is traversed.

9. The method for generating an intelligent coal dust removal solution based on extenics according to claim 8, characterized in that: The calculation formula of the conversion degree is as follows: P(T(A)→B) represents the frequency of modifiable parameters failing to reach the target, P(A→B) represents the frequency of modifiable parameters failing to reach the target, and T(A)=C represents that option A with known modifiable parameters is converted into option C with known modifiable parameters after adjustment.

10. An intelligent coal dust removal scheme generation system based on extenics, applied to the dust removal scheme generation method according to any one of claims 1 to 9, characterized in that: include: Method analysis module: Use event-element theory to analyze various preset dust removal methods and obtain the corresponding parameters of various dust removal methods; Scoring calculation module: Calculates scores of various dust removal methods based on the parameters corresponding to the various dust removal methods; Method selection module: selects the best dust removal method according to the scores of the various dust removal methods; Conversion tree construction module: constructing a conversion tree according to the optimal dust removal method and corresponding parameter selection; Conversion tree traversal module: traverses and adjusts the conversion tree to obtain the optimal dust removal solution.

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