A method for evaluating the production capacity of complete sets of units in thin coal seam working faces

Abstract

The present invention provides a method for evaluating the production capacity of a complete set of units in a thin coal seam working face, comprising the following steps: step 1) obtaining the theoretical production capacity Qb of the complete set of units, in units of 10,000 tons / year; obtaining the three-machine matching self-evaluation index Kp; obtaining the working face condition comprehensive evaluation index Kg; obtaining the coal mining adaptability evaluation index Km; step 2) establishing a working face estimated production formula: Qs=Qb*Kp*Kg*Km; wherein, Qb=Nt*K*Tr*Vmax*C*B*Hc*γ. The method for evaluating the production capacity of a complete set of units in a thin coal seam working face evaluates the production capacity of the complete set of units in a thin coal seam working face by respectively evaluating the three-machine matching self-evaluation index, the working face condition comprehensive evaluation index and the coal mining adaptability evaluation index, obtains an accurate prediction of the production capacity, and provides assistance for the construction of new working faces and the upgrading and transformation of existing working faces.

Description

Technical Field

[0001] The invention relates to a complete set of units for a thin coal seam working face, and in particular to a capacity evaluation method for a complete set of units for a thin coal seam working face. Background Art

[0002] As the country's planning for coal mine capacity construction becomes more and more sophisticated, major coal companies have increasingly precise requirements for capacity prediction of new and existing capacity transformation. The theoretical maximum capacity of working face equipment generally depends on the minimum value of the coal cutting capacity, frame shifting capacity and transportation system conveying capacity of the coal mining machine. According to the "GB / T 37806-2019 Overall Technical Conditions for Comprehensive Coal Mining Working Faces in Thin Coal Seams", when selecting equipment, the production capacity of the coal mining machine shall not be less than 110% of the designed production capacity of the working face, the conveying capacity of the scraper conveyor shall not be less than 110% of the production capacity of the coal mining machine, the frame shifting speed of the hydraulic support shall match the normal coal cutting speed of the coal mining machine, the output pressure and flow of the emulsion pump station shall be able to meet the initial support force and frame shifting speed requirements of the hydraulic support, the conveying capacity of the transfer machine shall not be less than 110% of the conveying capacity of the scraper conveyor, and the crushing capacity of the crusher shall not be less than 110% of the conveying capacity of the transfer machine. Therefore, the theoretical maximum capacity of equipment that is strictly matched in accordance with this standard depends only on the production capacity of the coal mining machine.

[0003] However, the above is only the theoretical maximum capacity. The self-matching situation of the complete set of units in the thin coal seam working face, the working face's own conditions and mining adaptability all have a great impact on the actual capacity. At present, there is no relevant evaluation method to guide the accurate prediction of actual capacity and equipment selection. Unreasonable capacity prediction often leads to unreasonable equipment selection. If the selection is too small, the actual capacity will not meet the standard, and if the selection is too large, there will be a waste of investment. Therefore, it is important to study the actual capacity evaluation method of the thin coal seam working face and accurately predict the actual capacity.

[0004] At present, there is no evaluation method system for predicting the actual production capacity of working faces. Mine construction, capacity planning and equipment selection are mostly based on rough estimates based on past experience and the production capacity of similar working faces. It is difficult to make more accurate predictions, which often leads to unreasonable equipment selection. If the selection is too small, the actual production capacity will not meet the standard, and if the selection is too large, there will be a waste of investment.

[0005] The present invention constructs an evaluation method for the production capacity of a complete set of units in a thin coal seam working face through in-depth research on the three-machine matching, working face conditions and mining adaptability that have an impact on the actual production capacity of a thin coal seam fully mechanized mining working face. Summary of the invention

[0006] The purpose of the present invention is to address the deficiencies in the prior art and to provide a method for evaluating the capacity of a complete set of units in a thin coal seam working face, which incorporates a three-machine matching self-evaluation index, a comprehensive evaluation index of working face conditions and a coal mining adaptability evaluation index, so as to make capacity estimation more accurate, guide the construction of new working faces, and guide the upgrading of existing working faces.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a method for evaluating the production capacity of a complete set of units in a thin coal seam working face, comprising the following steps:

[0008] Step 1) Obtain the theoretical capacity Qb of the complete set of units, in units of 10,000 tons / year; obtain the three-machine matching self-evaluation index Kp; obtain the comprehensive evaluation index of the working face condition Kg; obtain the coal mining adaptability evaluation index Km;

[0009] Step 2) Establish the formula for estimated production of the working face:

[0010] Qs=Qb*Kp*Kg*Km——————————————————————————(1);

[0011] Where, Qb = Nt*K*Tr*Vmax*C*B*Hc*γ—————————————(2);

[0012] Nt——Number of working face production days per year, days / year;

[0013] K——average daily operating rate of coal mining machine, 0.7~0.95;

[0014] Tr——Daily production time of fully mechanized mining face, min / day;

[0015] Vmax——maximum coal cutting speed of the shearer, m / min;

[0016] C——working face recovery rate, 92% to 98%;

[0017] B——coal mining machine cutting depth, m;

[0018] Hc——coal cutting height of fully mechanized mining working face, m;

[0019] γ——bulk density of coal, t / m 3 .

[0020] Based on the above, the coal mining adaptability evaluation index Km is obtained by the following method:

[0021] Step a: List the readable indicators of thin coal seam working face mining and select the key indicators that affect the actual production capacity;

[0022] Step b: Obtain historical data of each mine with a thin coal seam working face, extract the key indicators selected in step a from the obtained historical data, and simultaneously obtain the actual production capacity data of the corresponding mine to build a database;

[0023] Step c: Through the weight analysis method and the relationship between each key indicator in the historical data and the actual capacity data, the key indicators are quantitatively scored and the impact coefficient of each key indicator is assigned;

[0024] Step d: Establish the relationship model between key indicators, theoretical capacity and actual capacity:

[0025] Km=K1*N1+K2*N2+……————————————————————(3);

[0026] K1, K2... are influence coefficients;

[0027] N1, N2... are key indicator scores;

[0028] Km is the total score;

[0029] The larger the Km value, the closer the actual capacity is to the theoretical capacity;

[0030] Step e: Take historical data to backtest formula (3) to verify its accuracy.

[0031] Based on the above, the three-machine matching self-evaluation index Kp is obtained by the following steps:

[0032] Step A: Select key indicators and assign scoring rules and weights to each key indicator in the total score;

[0033] Step B: Under the specific thin coal seam size limitation, the indicators in step A are allocated, the scores of each indicator are calculated according to the scoring rules, and finally the total score is obtained by adding the product of the scores of each indicator and the weight of the indicator;

[0034] Step C: When a single indicator scores zero, the indicator must be reallocated until no single indicator is zero;

[0035] Step D: When the total score is lower than 60 points, it is necessary to improve the comprehensive score by optimizing the correlation of various supporting indicators until the total score is higher than 60 points, and obtain a feasible supporting plan. According to the feasible supporting plan, the three machines are produced and installed.

[0036] Based on the above, the comprehensive evaluation index Kg of the working face condition is obtained by the following steps:

[0037] Step ① According to the parameter data of the thin coal seam working face, key indicators are selected, including coal seam inclination, coal seam hardness, fault drop influence, working face strike inclination, working face strike length, working face width, gas concentration, floor stability, immediate roof stability and basic roof level;

[0038] Step ②: Divide different evaluation criteria and scores for each key indicator;

[0039] Step ③ Determine the weight value of each key indicator;

[0040] Step ④ Calculate the evaluation score of the thin coal seam working face by weighted summing the scores of the key indicators;

[0041] Step 5: Input the above-mentioned calculation model for calculating the evaluation score of the thin coal seam working face into the system to form a learning model. In the learning model, the key indicators are set as input values, the weight values ​​are set as variables, and the calculation score of the thin coal seam working face is set as the target value;

[0042] Step ⑥ extract the selected key indicators from the historical data and enter them into the database, and at the same time enter the theoretical capacity and actual capacity data of the corresponding thin coal seam working face into the database;

[0043] Step 7: Establish a backtest adjustment mechanism, substitute the key indicators in the database into the learning model for calculation, obtain the target quantity, and verify the accuracy of the target quantity value based on the difference between theoretical capacity and actual capacity;

[0044] Step ⑧ While backtesting and adjusting, continuously optimize the weight value so that the target quantity matches the difference between theoretical production capacity and actual production capacity, and finally obtain the optimized formula as the evaluation formula for evaluating thin coal seam working faces.

[0045] Based on the above, in step ②, each key indicator is divided into three levels of scores according to its own evaluation criteria, namely ≥90 points, 60-90 points and ≤60 points.

[0046] Based on the above, the method further includes step f: taking the influence coefficient as a variable, establishing a learning model through backtesting verification, and adjusting the value of the influence coefficient to make the backtest data closer to the real data.

[0047] Based on the above, in step d, if the score of a unidirectional indicator is zero, the indicator needs to be reallocated until no single indicator is zero.

[0048] Compared with the prior art, the present invention has outstanding substantial features and remarkable progress. Specifically, the present invention quantitatively evaluates the three-machine matching, working face condition and mining adaptability that have an important impact on production capacity, calculates the scores of the three as a percentage, and uses them as an index. By multiplying them with the theoretical production capacity, a relatively accurate actual production capacity is estimated.

[0049] Theoretically, the smaller the impact on theoretical production capacity, the higher the score on the percentage system, the closer it is to 1 as an index, and the closer the value of theoretical production capacity is to the actual production capacity.

[0050] This evaluation method uses calculations to make accurate production capacity forecasts, providing a basis and support for decision-making by regulatory agencies and coal mine managers. It can not only be used to guide the construction of new working faces and equipment selection, but also to upgrade and transform existing working faces. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of the method for evaluating the production capacity of a complete set of units in a thin coal seam working face of the present invention.

[0052] Figure 2 It is a flow chart for obtaining the coal mining adaptability evaluation index Km of the thin coal seam working face in the present invention.

[0053] Figure 3 This is a flow chart for obtaining the theoretical production capacity Kp of a complete set of units for a thin coal seam working face in the present invention.

[0054] Figure 4 It is a flow chart for obtaining the three-machine matching self-evaluation index Kg of the thin coal seam working face in the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is further described in detail below through specific implementation methods.

[0056] like Figure 1-Figure 4 As shown, a method for evaluating the production capacity of a complete set of units in a thin coal seam working face comprises the following steps:

[0057] Step 1) Obtain the theoretical capacity Qb of the complete set of units, in units of 10,000 tons / year; obtain the three-machine matching self-evaluation index Kp; obtain the comprehensive evaluation index of the working face condition Kg; obtain the coal mining adaptability evaluation index Km;

[0058] Step 2) Establish the formula for estimated production of the working face:

[0059] Qs=Qb*Kp*Kg*Km——————————————————————————(1);

[0060] Where, Qb = Nt*K*Tr*Vmax*C*B*Hc*γ—————————————(2);

[0061] Nt——Number of working face production days per year, days / year;

[0062] K——average daily operating rate of coal mining machine, 0.7~0.95;

[0063] Tr——Daily production time of fully mechanized mining face, min / day;

[0064] Vmax——maximum coal cutting speed of the shearer, m / min;

[0065] C——working face recovery rate, 92% to 98%;

[0066] B——coal mining machine cutting depth, m;

[0067] Hc——coal cutting height of fully mechanized mining working face, m;

[0068] γ——bulk density of coal, t / m 3 .

[0069] Wherein, the coal mining adaptability evaluation index Km is obtained by the following method:

[0070] Step a: List the readable indicators of thin coal seam working face mining and select the key indicators that affect the actual production capacity.

[0071] Step b: Obtain historical data of each mine with a thin coal seam working face, extract the key indicators selected in step a from the obtained historical data, and simultaneously obtain the actual production capacity data of the corresponding mine to build a database;

[0072] Step c: Through the weight analysis method and the relationship between each key indicator in the historical data and the actual capacity data, the key indicators are quantitatively scored and the impact coefficient of each key indicator is assigned;

[0073] Step d: Establish the relationship model between key indicators, theoretical capacity and actual capacity:

[0074] Km=K1*N1+K2*N2+……————————————————————(3);

[0075] K1, K2... are influence coefficients;

[0076] N1, N2... are key indicator scores;

[0077] Km is the total score;

[0078] The larger the Km value, the closer the actual capacity is to the theoretical capacity;

[0079] Step e: Take historical data to backtest formula (3) to verify its accuracy.

[0080] Step f: Use the influence coefficient as a variable, establish a learning model through backtesting verification, and adjust the value of the influence coefficient to make the backtest data closer to the real data.

[0081] Among them, the determination of key indicators first selects directly related indicators through objective factors, and the remaining indicators are determined by the expert team through scoring.

[0082] The key indicators include:

[0083] Support strength adaptability: If the allowable bottom plate load is less than 6Mpa, a bottom lifting mechanism must be provided, and 100 points are given if it is provided, and 0 points are given if it is not provided. If the allowable bottom plate load is 6-16, 100 points are given if it is provided, and 50 points are given if it is not provided. If the allowable bottom plate load is more than 16, 100 points are given if it is provided, and 80 points are given if it is not provided. The weight is 0.05.

[0084] Mining height adaptability: The minimum mining height is not greater than the average thickness of the coal seam minus the coefficient of variation, which is 100 points; the minimum mining height is between the average thickness of the coal seam minus the coefficient of variation and the average thickness of the coal seam, which is 80 points; the minimum mining height is between the average thickness of the coal seam and the average thickness of the coal seam + the coefficient of variation, which is 40 points; the minimum mining height is greater than the average thickness of the coal seam + the coefficient of variation, which is 0 points; the weight is 0.12;

[0085] Support strength adaptability: New working face: 100 points if the selected support strength is greater than 1.2 times the theoretical calculated support strength, 60 points if the selected support strength is between the theoretical calculated support strength and 1.2 times the theoretical calculated support strength, and 0 points if it is less than the theoretical calculated support strength; Existing working face: 100 points if the opening rate of the periodic pressure safety valve is less than 10%, 60 points if it is between 10% and 40%, and 0 points if it is more than 40%, with a weight of 0.08;

[0086] Adaptability of working face inclination: when it is greater than 15°, the coal mining machine and support must have anti-fall devices, with 100 points, 15-30 without 50 points, and above 30 without 0 points; when it is greater than 20°, the conveyor must have anti-slip devices, with 100 points, 20-30 without 50 points, and above 30 without 00-15°, with bottom adjustment 100 points, without 80 points; when it is greater than 15°, with bottom adjustment 100 points, 15-25 without 50 points, and above 25 without 0 points; the weight is 0.11;

[0087] Adaptability of the inclination angle: when it is greater than 15°, the conveyor must have a sliding device, with a score of 100, 0-15° without a score of 90, 15°-30° without a score of 60, 30°-45° without a score of 30, and above 45° without a score of 0; the weight is 0.06;

[0088] Roof adaptability: When the direct roof is of Class 1, timely support is awarded 100 points, and untimely support is awarded 60 points; when the direct roof is of Class 2, timely support is awarded 100 points, and untimely support is awarded 80 points; when the direct roof is of Class 3 or 4, timely support is awarded 100 points, and untimely support is awarded 90 points; the weight is 0.06;

[0089] Adaptability to tunnel layout: 80% for breaking the bottom along the top or breaking the top along the bottom, 60% for breaking the top and breaking the bottom; weight is 0.09;

[0090] Control mode: Manual control of the current rack scores 60 points, manual control of the adjacent rack scores 70 points, electro-hydraulic control scores 85 points, and automated control scores 95 points; the weight is 0.08;

[0091] Cleanliness control: If a reverse osmosis water treatment device, an automatic emulsion dispensing station, a high-pressure backwash filter station, a return liquid filter station or related equipment are configured, the score for only the osmosis water treatment device is 60, the score for only the automatic emulsion dispensing station is 50, the score for the reverse osmosis water treatment device + the automatic emulsion dispensing station is 90, the score for all four is 100, and the score for none of the four is 30; the weight is 0.04;

[0092] Power supply, liquid and water mode: long-distance supply gets 100 points, self-propelled equipment train gets 80 points, winch traction gets 60 points; weight is 0.05;

[0093] Belt and lifting system: If the conveying capacity of the belt and lifting system is greater than 1.2 times of the crushing capacity of the crusher, 100 points will be awarded; if it is between 1.2 times and 0.8 times, 100x-20 points will be awarded; if it is less than 0.8 times, 75x points will be awarded; the weight is 0.08;

[0094] Advanced support method: 90 points for advanced hydraulic support, 80 points for unit support, and 30 points for single support; the weight is 0.05;

[0095] Mining method: 100 points for full height mining at one time, 80 points for top coal caving mining; weight is 0.03;

[0096] Personnel technical level: The best equipment level used: 100 points for using automated equipment, 90 points for using electro-hydraulic control equipment, 80 points for using fully mechanized mining equipment, 50 points for only using general mining equipment, and 20 points for never using coal mining equipment; the weight is 0.05;

[0097] Mine management level: Coal mine safety production standardization management system level: Level 1 gets 100 points, Level 2 gets 80 points, Level 3 gets 60 points, and below Level 3 gets 0 points; the weight is 0.05.

[0098] In step d, if the score of a single indicator is zero, the indicator needs to be reallocated until no single indicator is zero. When the total score is less than 60 points, the comprehensive score is improved by optimizing the association of key indicators until the total score is higher than 60 points, and a reliable solution is obtained, and the mining adaptability of the thin coal seam working face is evaluated based on the solution.

[0099] Based on the traditional thin coal seam fully mechanized mining face mining work, key indicators are screened out, and then detailed data of key indicators are obtained from the working face systems and materials of various local mines. These data are summarized, and the influence coefficients are designed through weight analysis. A database is established, and then a relational model is used to calculate the correlation between key indicators, theoretical production capacity and actual production capacity. Backtesting verification is carried out to verify the accuracy, and qualified calculation results are selected as an evaluation system for evaluating the adaptability of thin coal seam working face mining, and accurate production capacity prediction is made.

[0100] The three-machine matching self-evaluation index Kp is obtained by the following steps:

[0101] Step A: Select key indicators, and assign scoring rules and weights in the total score to each key indicator, specifically: for the clearance indicator, the scoring rule is 0 points for less than 120 mm, (7x indicator value - 450) / 13 for between 120 mm and 250 mm, and 100 points for more than 250 mm, with a weight of 0.11; for the shearer bottom volume indicator, the scoring rule is 0 points for less than 100 mm, 0.8x indicator value - 20 for between 100 and 150 mm, and 100 points for more than 150 mm, with a weight of 0.14; for the shearer coal height indicator, the scoring rule is 0 points for less than 250 mm, 0.4x indicator value - 40 for between 250 and 350 mm, and 0.5x indicator value - 40 for more than 350 mm. The score is 100 points, with a weight of 0.18; the scoring rule for the hydraulic support chasing speed index is 0 points for less than 0.8 times, 100x index value -20 for 0.8-1.2 times, and 100 points for more than 1.2 times, with a weight of 0.08; the scoring rule for the coal mining machine top picking amount index is 0 points for less than 100mm, 0.8x index value -20 for 100-150mm, and 100 points for more than 150mm, with a weight of 0.14; the scoring rule for the tunnel passing space index is 0 points for less than 700mm, and 100 points for more than 700mm, with a weight of 0.05; the scoring rule for the clearance index between the coal mining machine sliding shoe and the scraper slot is 0 points for less than 40mm and more than 50mm, and 0 points for 40- 50mm is 100 points, with a weight of 0.06; the shovel spacing index, the scoring rule is less than 260mm, more than 290mm is 0 points, 260-290mm is 100 points, with a weight of 0.04; the beam end distance index, the scoring rule is less than 200mm, more than 350mm is 0 points, 200-350mm is 100 points, with a weight of 0.05; the pedestrian space index of the hydraulic support, the scoring rule is less than 700mm in length or less than 500mm in width is 0 points, more than 700mm in length and more than 500mm in width is 100 points, with a weight of 0.02; when the hydraulic support is at the maximum height, when the upper roller is mining the top coal, the conveyor swings up 5°, and the distance index from the rear edge of the roller to the front end of the top beam is scored as follows: 0 points for less than 100mm, 0.8x index value -20 for 100-150mm, 100 points for more than 150mm, and the weight is 0.05; when the coal mining machine, hydraulic support and scraper are moved, the hydraulic support is fully extended when the bottom lifting jack is fully extended, and the conveyor swings up 5°, marking the shortest distance index from the front end of the base to the lower end of the cable trough. The scoring rule is 0 points for less than 30mm, 2x index value for 30-50mm, and 100 points for more than 50mm, and the weight is 0.03; the scoring rule for the overlap index is 0 points for less than 100mm, 0.8x index value -20 for 100-150mm, and 100 points for more than 150mm, and the weight is 0.05;

[0102] Step B: Under the specific thin coal seam size limitation, the indicators in step A are allocated, the scores of each indicator are calculated according to the scoring rules, and finally the total score is obtained by adding the product of the scores of each indicator and the weight of the indicator;

[0103] Step C: When a single indicator scores zero, the indicator must be reallocated until no single indicator is zero;

[0104] Step D: When the total score is lower than 60 points, it is necessary to improve the comprehensive score by optimizing the correlation of various supporting indicators until the total score is higher than 60 points, and obtain a feasible supporting plan. According to the feasible supporting plan, the three machines are produced and installed.

[0105] Through a large number of practical cases and expert discussions, 13 key indicators were selected, and then quantitative scores were given according to the type and degree of impact of each key indicator on the output per unit time, and the weights of each indicator were allocated. Through a percentage-based scoring method, the optimal matching plan was selected according to the score. Specifically, the higher the score, the better the plan, which can enable the three machines after matching to maximize the equipment performance and achieve the highest production efficiency in thin coal seams with a certain spatial size. The indicator scoring method is highly operational, and for the first time, an optimization method for the three-machine matching plan for thin coal seams was given.

[0106] The comprehensive evaluation index Kg of the working face condition is obtained by the following steps:

[0107] Step ① According to the parameter data of the thin coal seam working face, key indicators are selected, including coal seam inclination, coal seam hardness, fault drop influence, working face strike inclination, working face strike length, working face width, gas concentration, floor stability, immediate roof stability and basic roof level;

[0108] Step ②: Divide different evaluation criteria and scores for each key indicator;

[0109] Step ③ Determine the weight value of each key indicator;

[0110] Step ④ Calculate the evaluation score of the thin coal seam working face by weighted summing the scores of the key indicators;

[0111] Step 5: Input the above-mentioned calculation model for calculating the evaluation score of the thin coal seam working face into the system to form a learning model. In the learning model, the key indicators are set as input values, the weight values ​​are set as variables, and the calculation score of the thin coal seam working face is set as the target value;

[0112] Step ⑥ Extract the selected key indicators from the historical data and enter them into the database, and enter the theoretical capacity and actual capacity data of the corresponding thin coal seam working face into the database. In this step, the historical data comes from the indicator values ​​of various coal mine working faces across the country, as well as the actual capacity and theoretical capacity of the corresponding working face;

[0113] Step 7: Establish a backtest adjustment mechanism, substitute the key indicators in the database into the learning model for calculation, obtain the target quantity, and verify the accuracy of the target quantity value based on the difference between theoretical capacity and actual capacity;

[0114] Step ⑧ While backtesting and adjusting, continuously optimize the weight value so that the target quantity matches the difference between theoretical capacity and actual capacity, and finally obtain the optimized formula as the evaluation formula for evaluating thin coal seam working faces. In other words, in theory, the higher the target quantity score, the smaller the difference between theoretical capacity and actual capacity. By comparing a large amount of data, it is verified whether the calculated target quantity score matches the difference. If there is a mismatch, the weight value is continuously adjusted and optimized through the learning model to make the target quantity score match the difference between theoretical capacity and actual capacity, and obtain optimized weight data.

[0115] In step ②, the evaluation table of the impact of the thin coal seam working face's own conditions on production capacity is as follows:

[0116]

[0117]

[0118] As shown in the table above, each key indicator is divided into three levels according to their respective evaluation criteria, namely ≥90 points, 60-90 points and ≤60 points.

[0119] The calculation formulas for each key indicator are as follows:

[0120] Coal seam inclination:

[0121] Coal seam hardness: Fault impact degree: Working face inclination: Working face length: Working surface width: Gas concentration: Bottom plate stability: Direct top stability: Basic top level: Through in-depth research on the key indicators that affect the actual production capacity of the thin coal seam fully mechanized mining face itself, the type and degree of influence of each indicator on the output are quantified and scored. For example, the length of the working face, inclination, coal quality, faults, roof and floor stability, etc. all have a great impact. The weight of each indicator is allocated based on expert discussion and a large number of actual cases. The product of the quantitative score of each indicator and their respective weights is the score of the indicator. The sum of all indicator scores is the comprehensive score of the degree of influence. The higher the score, the better the condition of the thin coal seam working face itself, and the smaller the impact on the theoretical production capacity.

[0122] The obtained three-machine matching self-evaluation index Kp, working face condition comprehensive evaluation index Kg, coal mining adaptability evaluation index Km, and complete set unit theoretical capacity Qb, in units of 10,000 tons / year; the actual output forecast value of the working face is obtained through the formula in step 2), which provides a basis and support for the decision-making of regulatory agencies and coal mine managers. It can not only be used to guide the construction of new working faces and equipment selection, but also to upgrade and transform existing working faces.

[0123] In practical applications, after the development of the calculation program, you only need to enter the relevant parameters to quickly obtain the calculation results. It has the advantages of simple use, accurate results and strong practicality.

[0124] The system is more comprehensive and specific in predicting output value and has good guiding significance.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or some technical features can be replaced by equivalents without departing from the spirit of the technical solution of the present invention, which should be included in the scope of the technical solution for protection of the present invention.

Claims

1. A method for evaluating the production capacity of a complete set of units in a thin coal seam working face, characterized in that: The following steps are involved: Step 1) Obtain the theoretical capacity Qb of the complete set of units, in units of 10,000 tons / year; obtain the three-machine matching self-evaluation index Kp; obtain the comprehensive evaluation index of the working face condition Kg; obtain the coal mining adaptability evaluation index Km; Step 2) Establish the formula for estimated production of the working face: Qs=Qb*Kp*Kg*Km——————————————————————————(1); Where, Qb = Nt*K*Tr*Vmax*C*B*Hc*γ—————————————(2); Nt——Number of annual production days of the working face, days / year; K——average daily operating rate of coal mining machine, 0.7~0.95; Tr——Daily production time of fully mechanized mining face, min / day; Vmax——maximum coal cutting speed of the shearer, m / min; C——working face recovery rate, 92% to 98%; B——coal mining machine cutting depth, m; Hc——coal cutting height of fully mechanized mining working face, m; γ——bulk density of coal, t / m 3 The coal mining adaptability evaluation index Km is obtained by the following method: Step a: List the readable indicators of thin coal seam working face mining and select the key indicators that affect the actual production capacity; Step b: Obtain historical data of each mine with a thin coal seam working face, extract the key indicators selected in step a from the obtained historical data, and simultaneously obtain the actual production capacity data of the corresponding mine to build a database; Step c: Through the weight analysis method and the relationship between each key indicator in the historical data and the actual capacity data, the key indicators are quantitatively scored and the impact coefficient of each key indicator is assigned; Step d: Establish the relationship model between key indicators, theoretical capacity and actual capacity: Km=K1*N1+K2*N2+……————————————————————(3); K1, K2... are influence coefficients; N1, N2... are key indicator scores; Km is the total score; The larger the Km value, the closer the actual capacity is to the theoretical capacity; Step e: Take historical data to backtest formula (3) to verify its accuracy; the three-machine matching self-evaluation index Kp is obtained by the following steps: Step A: Select key indicators and assign scoring rules and weights to each key indicator in the total score; Step B: Under the specific thin coal seam size limitation, the indicators in step A are allocated, the scores of each indicator are calculated according to the scoring rules, and finally the total score is obtained by adding the product of the scores of each indicator and the weight of the indicator; Step C: When a single indicator scores zero, the indicator must be reallocated until no single indicator is zero; Step D: When the total score is lower than 60 points, it is necessary to improve the comprehensive score by optimizing the correlation of various supporting indicators until the total score is higher than 60 points, and obtain a feasible supporting scheme. According to the feasible supporting scheme, the three machines are produced and installed; the comprehensive evaluation index Kg of the working face condition is obtained by the following steps: Step ① According to the parameter data of the thin coal seam working face, key indicators are selected, including coal seam inclination, coal seam hardness, fault drop influence, working face strike inclination, working face strike length, working face width, gas concentration, floor stability, immediate roof stability and basic roof level; Step ②: Divide different evaluation criteria and scores for each key indicator; Step ③ Determine the weight value of each key indicator; Step ④ Calculate the evaluation score of the thin coal seam working face by weighted summing the scores of the key indicators; Step 5: Input the above-mentioned calculation model for calculating the evaluation score of the thin coal seam working face into the system to form a learning model. In the learning model, the key indicators are set as input values, the weight values ​​are set as variables, and the calculation score of the thin coal seam working face is set as the target value; Step ⑥ extracts the selected key indicators from the historical data and enters them into the database, and at the same time enters the theoretical capacity and actual capacity data of the corresponding thin coal seam working face into the database; Step 7: Establish a backtest adjustment mechanism, substitute the key indicators in the database into the learning model for calculation, obtain the target quantity, and verify the accuracy of the target quantity value based on the difference between theoretical capacity and actual capacity; Step ⑧ While backtesting and adjusting, continuously optimize the weight value so that the target quantity matches the difference between theoretical production capacity and actual production capacity, and finally obtain the optimized formula as the evaluation formula for evaluating thin coal seam working faces.

2. The method for evaluating the production capacity of a complete set of units in a thin coal seam working face according to claim 1 is characterized in that: In step ②, each key indicator is divided into three levels according to its own evaluation criteria, namely ≥90 points, 60-90 points and ≤60 points.

3. The method for evaluating the production capacity of a complete set of units in a thin coal seam working face according to claim 2 is characterized in that: The method also includes step f: taking the influence coefficient as a variable, establishing a learning model through backtesting verification, and adjusting the value of the influence coefficient to make the backtest data closer to the real data.

4. The method for evaluating the production capacity of a complete set of units in a thin coal seam working face according to claim 2, characterized in that: In step d, if the score of a single indicator is zero, the indicator needs to be reallocated until no single indicator is zero.

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