Coal mine intelligent operation efficiency standardization evaluation model construction method suitable for differentiated geological conditions

Abstract

The invention relates to the technical field of coal mine intelligent mining, in particular to a coal mine intelligent operation efficiency standardization evaluation model construction method suitable for differentiated geological conditions, and the method comprises the steps: obtaining equipment operation data on a working surface, and calculating key indexes; the key indexes comprise a hydraulic support automation rate, a hydraulic support manual intervention rate, a coal mining machine automation rate, a coal mining machine manual intervention rate and a working face operator number; constructing an intelligent evaluation basic model based on the key indexes under the reference condition; working face geological conditions are obtained, the intelligent evaluation basic model is adjusted based on the working face geological conditions so as to optimize the intelligent evaluation model, and the corrected FAI is output. According to the method, a universal and standardized mathematical model is constructed, and the influence of external environment difference can be stripped, so that the operation efficiency of the system under different environment conditions can be accurately quantified and compared.

Description

Technical Field

[0001] This application relates to the field of intelligent coal mining technology, specifically to a method for constructing a standardized evaluation model for the intelligent operation efficiency of coal mines applicable to different geological conditions. Background Technology

[0002] With the continuous deepening of intelligent coal mine construction, the evaluation system for the operation level of fully mechanized mining faces is undergoing a systematic transformation from qualitative description and static acceptance to quantitative analysis and dynamic monitoring. Currently, multiple technical implementation solutions have been developed within the industry.

[0003] In the coal mining industry, the geological environment of the working face is complex and variable, and equipment operation is significantly affected by the working face environment. However, existing equipment operation performance evaluation models are mostly built around the equipment's own operating parameters. These performance evaluation models are mostly general weighted scoring or simple classification compensation, focusing only on basic indicators such as equipment speed, load rate, and failure frequency for single-dimensional evaluation. The evaluation dimensions are singular and one-sided, failing to form a multi-dimensional, full-process performance evaluation system, and making it difficult to fully reflect the actual operating status and true performance level of the equipment. Summary of the Invention

[0004] The technical problem this invention aims to solve is that existing equipment operation performance evaluation models are unable to fully reflect the actual operating status and true performance level of equipment.

[0005] Therefore, this invention provides a method for constructing a standardized evaluation model for intelligent operation efficiency of coal mines applicable to different geological conditions. This method constructs a general and standardized mathematical model that can isolate the influence of external environmental differences, thereby accurately quantifying and comparing the system's own operation efficiency under different environmental conditions.

[0006] The technical solution adopted by this invention to solve its technical problem is: A method for constructing a standardized evaluation model for the intelligent operation efficiency of coal mines applicable to differentiated geological conditions includes: S1. Obtain equipment operating data on the working surface and calculate key indicators, including the automation rate of the hydraulic support. Hydraulic support manual intervention rate Automation rate of coal mining machines Human intervention rate of coal mining machines Number of workers at the working face ; S2, Construct an intelligent evaluation basic model based on key indicators under benchmark conditions; S3: Obtain the geological conditions of the working face, adjust the intelligent evaluation basic model based on the geological conditions of the working face to optimize the intelligent evaluation model, and output the corrected FAI.

[0007] Furthermore, the intelligent evaluation model is a model of the advanced operational level that the intelligent system should achieve under benchmark geological conditions, setting indicators under benchmark conditions. , , and Indicators based on baseline conditions , , and Calculate the target automation operation index of the fully mechanized mining face under the benchmark conditions. , , ,in, For stent weighting coefficient; This is the weighting coefficient for coal mining machinery.

[0008] Furthermore, in step S3, a geological condition influence quantification matrix M is defined, and the specific expected performance level of the working face is calculated to optimize the intelligent evaluation model. Matrix M includes adjustment coefficients. ,in, This represents the expected reduction factor of the automation rate (ar) due to this condition. It is a negative value. This represents the coefficient representing the expected increase in the rate of human intervention (mr) due to this condition. It is a positive value.

[0009] Furthermore, under specific geological conditions ,in, ; ; ; ,in, Geological adjustment coefficient, , , This represents the effect of the condition on the automation rate. The expected reduction coefficient This represents the rate of artificial intervention under this condition. The expected increase coefficient, Furthermore, the FAI OAI is the working face automation operation index based on key indicators. The working face target automation operation index under the benchmark conditions. The automated operation index of the working face target under specific geological conditions, the , , , .

[0010] Furthermore, it also includes step S4, in which key performance indicators are optimized based on the efficiency of reducing manpower, in order to optimize the corrected [result]. .

[0011] Furthermore, based on the weighting coefficient of the efficiency of reducing manpower Optimization methods include: Data preparation: Extract valid data samples from all working faces within a certain period from the platform. Each sample includes: the calculated comprehensive automation level index of the support structure. Comprehensive Automation Level Indicators of Coal Mining Machines And the corresponding performance indicators, including the number of workers on the work surface. ; Correlation analysis: Calculate separately and correlation coefficient ,as well as and correlation coefficient ; Weight optimization calculation: The correlation coefficient is normalized and used as the basis for optimizing the weights. The normalized result is obtained. , ; Smooth iteration: Iterative updates are performed using exponential smoothing. ,in, This is a smoothing factor (e.g., 0.2) used to control the update magnitude.

[0012] Furthermore, in step S4, the optimization baseline conditions are based on advanced levels. Thus, the corrected version is updated. .

[0013] Furthermore, in step S4, the initial geological adjustment coefficient is adjusted based on the equipment operating data. Perform calibration to update the corrected version. .

[0014] Furthermore, the initial geological adjustment coefficient based on equipment operation data The calibration method is as follows: For a specific geological condition, all working face data with the same geological conditions were selected from the database as the experimental group, while a batch of working face data that did not have this geological condition but were similar in other major geological conditions were selected as the control group. The mean values ​​of each key indicator in the experimental group and the mean values ​​of each key indicator in the control group were calculated respectively. Based on the ratio of changes in the key indicator ar between the experimental and control groups, the initial... The value is calibrated; Based on the ratio of changes in the key indicator MR between the experimental and control groups, the initial... The value is calibrated.

[0015] The beneficial effects of this invention are that it establishes an intelligent evaluation model for automated equipment operation, embedding adjustment parameters for geological conditions, adjustment parameters for reducing manpower and energy efficiency, and advanced adjustment parameters, thereby enabling dynamic updates to the model output FAI, making the output evaluation more comprehensive and objective.

[0016] The construction of this model provides a universal mathematical modeling method that separates environmental differences from performance evaluation, and can be extended to other fields affected by natural conditions such as agriculture and wind power.

[0017] The model output (FAI) is a dimensionless, standardized value that facilitates longitudinal and lateral comparisons across units and time periods. The model encourages achieving or exceeding expected levels under harsh environments through management and technical efforts, fairly measuring the "degree of subjective effort." Attached Figure Description

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Figure 1 This is a schematic diagram of the evaluation model construction method in this invention. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0021] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, features defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0022] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0023] A method for constructing a standardized evaluation model for the intelligent operation efficiency of coal mines applicable to differentiated geological conditions includes the following steps: Step S1: Construct a feature vector library of geological conditions at the working face. S1.1 Obtain working environment data The equipment operating environment data includes the geological conditions of the working face. The classification of geological conditions is the basis for implementing differentiated evaluation. Based on the survey data, this plan proposes six key constraints that significantly restrict the intelligent operation of fully mechanized mining, as follows: Table 1 Key Constraints

[0024] The geological properties of each working face are described by all its key constraints, forming a standardized geological condition file, which provides a basis for subsequent accurate matching of evaluation standards.

[0025] S1.2 Constructing Geological Condition Feature Vectors For each fully mechanized mining face to be evaluated, a digital geological profile is established. This profile consists of a set of predefined geological condition characteristic indicators, forming a multi-dimensional geological condition feature vector, which is stored in the working face profile unit. Geological condition characteristic indicators include, but are not limited to: Coal seam occurrence characteristic indicators include: coal seam thickness type (extremely thin, thin, medium thick, thick, extra thick), coal seam dip angle classification (gently dipping, dipping, steeply dipping), and coal seam stability (stable, relatively stable, unstable, extremely unstable). Geological structural characteristics indicators include the degree of fault development, fold complexity, and density of collapse columns, which are classified into simple, medium, complex, and extremely complex levels according to the "Detailed Rules for Coal Mine Geological Work". Top and bottom plate condition indicators: including the stability level of the top plate (stable, moderately stable, unstable), and the lithology and bearing capacity of the bottom plate (hard, medium-hard, soft). Mining process characteristics include: mining method (one-time full-height mining, top coal caving mining) and working face layout (strike longwall, dip longwall). Geological indicators of hazards include gas level (low gas, high gas, coal and gas outburst), hydrogeological type (simple, medium, complex, extremely complex), and rock burst hazard (none, weak, medium, strong).

[0026] Each indicator is quantified into standardized feature values, forming a geological condition feature vector G = (g1, g2, ..., g...). n ), where g i This represents the value of the i-th geological condition characteristic index.

[0027] Step S2: Real-time automatic acquisition of raw equipment operation data and identification of core events. Establish a data communication interface with the intelligent fully mechanized coal mining system (including the electro-hydraulic control system of hydraulic supports, the coal mining machine control system, the working face transportation system, and the personnel positioning system) to achieve fully automatic, real-time acquisition of key runtime sequence data, specifically including: Equipment status timing signals: Coal mining machine operating status (automatic / manual mode, traction speed, drum position), hydraulic support control mode (automatic follow-up / manual control), support action (lifting column, lowering column, moving support, pushing conveyor) trigger signals, support pressure data; Personnel location data: Identification and timestamps of personnel entering the work area and end area during the production shift; Production system status signals: emulsion pump station start / stop status, working face conveyor operation status.

[0028] Based on a predefined rules engine, the following core production events are identified in real time from the above time-series data stream: Automatic cutting event of coal mining machine: When the coal mining machine is in "memory cutting" or "planned cutting" automatic mode and the traction speed is greater than the set threshold, the automatic cutting time will start to accumulate; when it switches to manual mode or the speed is lower than the threshold, the event ends.

[0029] Coal mining machine manual intervention event: If a coal mining machine remote control operation signal or HMI interface manual adjustment command is detected during the automatic cutting process, it is marked as a manual intervention.

[0030] Automatic hydraulic support following event: When the coal mining machine passes the support position, and the support automatically completes the "lowering the support-moving the support-raising the support" cycle within the set time window without human intervention, it is counted as a successful automatic following event.

[0031] Manual intervention event for hydraulic support: If manual operation (including operation of adjacent supports or remote intervention) is detected during automatic follow-up or after automatic follow-up fails, it is counted as one manual intervention.

[0032] Step S3: Calculate equipment-level operation monitoring indicators Online evaluation indicators for intelligent fully mechanized mining faces are core tools for measuring the effectiveness of intelligent operation. This design, based on the principles of systematicity, measurability, and guidance, focuses on core process data regarding "whether the system is in use" and "whether the operation is smooth," and constructs an evaluation index for the automation rate of hydraulic supports (…). ), hydraulic support manual intervention rate ( ), Coal mining machine automation rate ( ), Coal mining machine manual intervention rate ( ), number of workers at the working face (including end face) Five key indicators were used to calculate the automation operation monitoring indicators for key equipment in the fully mechanized mining face. These indicators included the overall automation rate of the fully mechanized mining face (...). ), Comprehensive manual intervention rate of fully mechanized mining face ( ), Overall Automation Rate (OAR) of Longwall Mining Face, Automation Operation Index of Longwall Mining Face ( By automatically and in real-time acquiring data from existing intelligent systems in coal mines, subjective judgment is avoided.

[0033] S3.1 Key Indicator Calculation S3.1.1 Automation rate of hydraulic supports ( ) Automation rate of hydraulic supports ( The ratio refers to the proportion of hydraulic supports passed by the coal mining machine during coal mining operations when the hydraulic support automation control function is enabled, out of the total number of hydraulic supports passed by the coal mining machine. This ratio directly measures the degree of normalized use of the intelligent following function of the support system. The higher the ratio, the better the level of automation application.

[0034] The calculation formula is as follows:

[0035] In the formula, the number of automated follow-up support shifts is the number of hydraulic supports that the coal mining machine passes through when the hydraulic supports are under automated control during coal mining operations. Total number of passes for the coal mining machine – the total number of hydraulic supports that the coal mining machine passes over during coal mining operations.

[0036] S3.1.2 Manual intervention rate of hydraulic supports ( ) Manual intervention rate of hydraulic supports ( This refers to the proportion of manual intervention required for hydraulic supports during coal mining operations, when the hydraulic supports are under automated control, relative to the total number of hydraulic supports passed by the coal mining machine. This ratio reflects the proportion of automated operation where manual intervention is still necessary for correction or compensation. A smaller value indicates a stronger system adaptability and more stable operation.

[0037] The calculation formula is as follows:

[0038] In the formula, "automatic follow-up support shift" refers to the number of hydraulic supports manually moved during coal mining operations when the hydraulic supports are under automated control, expressed as a unit support. Total number of passes by the coal mining machine – During coal mining operations, under the automated control function of the hydraulic supports, the total number of hydraulic supports passed by the coal mining machine, expressed as a unit of support.

[0039] S3.1.3 Coal mining machine automation rate ( ) Coal mining machine automation rate ( The ratio () refers to the proportion of hydraulic supports passed by the coal mining machine under automated control during coal mining operations, out of the total number of hydraulic supports passed by the machine. This ratio directly measures the degree of routine use of intelligent coal cutting functions such as memory cutting or planned cutting. A higher ratio indicates a better level of automation.

[0040] The calculation formula is as follows:

[0041] In the formula, "automatic coal cutting passes through" refers to the number of hydraulic supports that the coal mining machine passes through under automated control during coal mining operations, expressed as a unit of support. Total number of coal mining machine operations – the total number of hydraulic supports that the coal mining machine passes over during coal mining operations, expressed as a unit of support.

[0042] S3.1.4 Coal mining machine manual intervention rate ( ) Manual intervention rate of coal mining machines ( This refers to the ratio of the number of hydraulic supports passed by the coal mining machine during manual remote control operation to the total number of hydraulic supports passed by the coal mining machine under automated control. This ratio reflects the degree of manual intervention required during intelligent coal cutting due to geological changes, model inaccuracies, and other reasons. The smaller the value, the stronger the system's self-adaptability and the more stable its operation.

[0043] The calculation formula is as follows:

[0044] In the formula, the number of manual remote control operation frames refers to the number of hydraulic supports that the coal mining machine passes through during coal mining operations, expressed as a unit frame. Total number of automated coal cutting passes – the total number of hydraulic supports passed by the coal mining machine in the automated control state during coal mining operations, expressed as a unit of support.

[0045] S3.1.5 Number of workers on the working face ( ) Number of workers at the working face ( This refers to the average positional data of personnel within the work area (including the end points) during a production shift. This indicator is used to verify whether intelligent systems have truly reduced the number of personnel working in hazardous areas and is a standard for measuring effectiveness.

[0046] The calculation method is as follows:

[0047] In the formula, —The number of personnel at the work site (including the end) during a specific time period (without a change in the number of personnel) within the statistical period, expressed in person; —The duration corresponding to the number of people, in minutes.

[0048] Calculation of Automated Operation Monitoring Indicators for Key Equipment in S3.2 Longwall Mining Face S3.2.1 Comprehensive automation rate of fully mechanized mining face ( ) The overall automation rate of fully mechanized mining faces ( The value is a weighted average of the automation rate of hydraulic supports and the automation rate of coal mining machines. This indicator is used to comprehensively measure the degree of automation coverage in the two core processes of support and cutting. The higher the value, the higher the overall degree of automation of the working face and the better the foundation for the routine application of intelligent systems.

[0049] The calculation formula is as follows:

[0050] In the formula, —Stent weighting coefficient; — Coal mining machinery weighting coefficient.

[0051] Note: The following movement of hydraulic supports is the foundation and prerequisite for ensuring the safety and continuous advancement of the working face. The stability and coverage of its automated operation have a more direct and significant impact on overall efficiency. To reflect the fundamental nature of support following, the support weight coefficient is 0.7, the coal mining machine weight coefficient is 0.3, and so on.

[0052] S3.2.2 Comprehensive manual intervention rate of fully mechanized mining face ( ) Comprehensive manual intervention rate in fully mechanized mining faces ( This is the weighted average of the manual intervention rate for hydraulic supports and coal mining machines. This indicator reflects the frequency with which manual intervention is still required for correction or adjustment during automated operation. A lower value indicates stronger system adaptability and stability, and smoother automated operation.

[0053] The calculation formula is as follows:

[0054] S3.2.3 Overall automation rate of fully mechanized mining face ( ) Overall automation rate of fully mechanized mining faces ( ) is in the overall automation rate ( Based on the above, this is the net value after deducting efficiency losses caused by human intervention. This indicator more accurately reflects the level of "effective automation" after deducting ineffective or inefficient automation. The higher the value, the better the overall effectiveness of the work surface in balancing automation coverage and operational quality.

[0055] The calculation formula is as follows:

[0056] S3.2.4 Automation Operation Index of Longwall Mining Face ( ) Automated operation index of working face ( The index is a quantitative measure of the overall automation level of a single working face. It directly reflects the absolute operating status of the intelligent fully mechanized mining system and is independent of geological conditions.

[0057] The calculation method is as follows:

[0058] Step S4: Construct a dynamic correction model for geological conditions To fairly and scientifically reflect the objective constraints of different geological conditions on the operational performance of the intelligent system in the evaluation, this scheme introduces a geological condition correction mechanism. The overall approach is: First, define a model of the advanced operational level that the intelligent system should achieve under ideal (benchmark) geological conditions; Secondly, based on the actual geological conditions of a specific working face, the adverse effects are quantified to calculate the "acceptable" expected operating level under those geological conditions. Finally, the OAI represents the actual operating level of the working face. The OAI is compared with the expected level under the geological conditions to obtain a standardized and comparable final evaluation index (evaluated using FAI).

[0059] In this way, working faces with different mining difficulties can have their "effort level" and "intelligent application effectiveness" measured on the same scale, thereby creating positive incentives.

[0060] S4.1 Establish a benchmark operating performance model S4.1.1 Baseline Operation Model The benchmark operating model P0 defines the theoretical operating level that a mature working face, which fully utilizes advanced intelligent technologies, should achieve under ideal or simply simple geological conditions. It serves as an ideal reference for evaluation and is primarily used to calculate benchmark values ​​for geological condition corrections.

[0061] The target values ​​under the baseline conditions are recommended as follows (which can be adjusted according to industry best practices):

[0062]

[0063]

[0064]

[0065] According to the weights set in step S3.2.1 ( , ), calculate the comprehensive indicators under the benchmark model: (1) Target automation rate of fully mechanized mining face under benchmark conditions:

[0066] (2) Target human intervention rate of fully mechanized mining face under benchmark conditions:

[0067] (3) Overall automation rate of fully mechanized mining face under benchmark conditions:

[0068] (4) Target automation operation index of fully mechanized mining face under benchmark conditions:

[0069] S4.2: Construct a geological condition influence quantification matrix M to calculate the specific expected performance level of the working face. To quantify the impact of geological conditions on automated operation, a pair of adjustment coefficients is assigned to each type of key indicator. ), matrix M includes adjustment coefficients .in, This represents the effect of the condition on the automation rate ( The expected reduction coefficient (negative impact) This represents the effect of the condition on the rate of artificial intervention ( The expected increase coefficient (positive impact) is based on industry experience and survey data, and can be optimized in the future based on data accumulation.

[0070] Table 2. Adjustment Coefficients for Key Constraints

[0071] Step S5: Calculate the corrected standardized operating index For a working face with a geological condition set G, the acceptable operational model under the influence of geological conditions is calculated as follows: S5.1 Calculate the baseline target value under geological constraints Based on all the geological constraints of the working face, the α and β coefficients are summarized, and the baseline target values ​​are adjusted.

[0072]

[0073]

[0074]

[0075]

[0076] Note: The calculation results must be constrained within the interval [0,1], where i represents the i-th key constraint.

[0077] S5.2 Calculate the expected comprehensive operating index under the influence of geological conditions ( )

[0078] This represents a relatively good level of operation that is "acceptable" or "theoretically achievable" under the specific geological conditions of the working face, after scientific prediction.

[0079] S5.3 Calculation of the corrected automated operation index of the fully mechanized mining face ( ) use This represents the final revised automation operation index of the fully mechanized mining face, calculated as follows:

[0080] The essence is to measure the actual operating index of the working face ( ), and its expected index under geological conditions ( After normalizing the ratio, it is then mapped back to the benchmark index. On the scale of ). This indicates that the actual operation is better than expected under these geological conditions; This indicates that the expected outcome has been achieved; This indicates that expectations have not been met. All working surfaces All values ​​are based on a fair consideration of geological differences and are aligned with a unified baseline. Compare them.

[0081] Step S6: Evaluate the dynamic optimization of model parameters The correction coefficient K in this evaluation system includes the weighting coefficient ( ), geological adjustment coefficient ( ) and benchmark target value ( The initial settings of these parameters primarily rely on the experience of industry experts and preliminary research, constituting theoretical "prior values." However, whether these parameters accurately reflect the contribution of different automation stages to the ultimate goals of "reducing manpower, increasing safety, and improving efficiency," and whether they precisely quantify the objective constraints of various geological conditions, still requires verification and calibration using actual operational data. Deviations in parameter settings will directly affect the fairness of the evaluation results and the effectiveness of their guiding role. To address this issue, we plan to utilize the real operational data accumulated on the platform to establish a data-driven dynamic parameter optimization mechanism. This mechanism aims to continuously monitor and analyze the data to allow model parameters to evolve from "experience-based static settings" to "effectiveness-based dynamic optimization," ensuring that the evaluation system always scientifically and accurately reflects and guides the improvement of intelligent operation levels.

[0082] S6.1 Weighting coefficients based on the efficiency of reducing manpower ( Optimization methods Initial weighting coefficients for the support and coal mining machine ( , This is based on the theoretical judgment of the fundamental and safety importance of support operations. However, whether this weighting ratio best reflects the core practical goal of "reducing manpower" requires data verification. Under different coal mines and processes, the effects of support automation and coal mining machinery automation on reducing the number of workers at the working face ( The actual contribution may differ.

[0083] Statistical analysis of historical data revealed that the "comprehensive automation level of hydraulic supports" and "comprehensive automation level of coal mining machines" were correlated with the "number of workers on the working face (…)" The association strength and relevance of ") are used to deduce and optimize the weighting coefficients in reverse. and The goal is to make the weight allocation more accurately reflect the contribution of automation in each process to the effectiveness of "reducing manpower," and to strengthen the guiding role of evaluation in reducing manpower and increasing efficiency.

[0084] The specific method is as follows: (1) Data preparation: Extract valid data samples from all working faces within a certain period (such as a quarter) from the platform. Each sample contains: the calculated comprehensive automation level index of the support structure. Comprehensive Automation Level Indicators of Coal Mining Machines And the corresponding effectiveness indicator: number of workers on the working face ( ).

[0085] (2) Correlation analysis: Calculate the correlations separately. and correlation coefficient ,as well as and correlation coefficient The correlation coefficient is calculated using the existing Spearman rank correlation coefficient method. The absolute value of the correlation coefficient reflects the degree to which the level of automation in this process affects the reduction of manpower.

[0086] (3) Weight optimization calculation: The correlation coefficient is normalized and used as the basis for weight optimization. A simplified optimization formula is as follows:

[0087]

[0088] This formula means that the level of automation in a particular step is more closely related to the effectiveness of reducing manpower, and that step will be given a higher weight in the overall evaluation.

[0089] (4) Smoothing iteration: To avoid drastic changes in weights due to fluctuations in single-period data, exponential smoothing is used for iterative updates:

[0090] in, This is a smoothing factor (e.g., 0.2) used to control the update magnitude.

[0091] S6.2 Geological adjustment coefficient based on equipment operation data ( Calibration method Initial geological adjustment coefficient ( The table is based on qualitative experience and case studies, and may not be able to accurately quantify the impact of each geological condition on the automation rate. ) and intervention rate ( The actual magnitude of the impact needs to be determined. For example, whether the estimate that a "large tilt angle" condition increases the intervention rate by 12% is universally applicable requires verification with large datasets.

[0092] Overall approach and expected results: The working faces will be grouped according to a single geological condition (e.g., "with steep dip" group vs. "without steep dip" control group), and key operational indicators between the groups will be compared. , , , The statistical differences were used to empirically measure the impact of the geological conditions, thereby calibrating the data. and Coefficients. The goal is to make the geological correction model more closely reflect objective reality and improve the horizontal fairness of the evaluation.

[0093] The specific method is as follows: (1) Division of control group and data screening: For a specific geological condition to be calibrated (such as "soft rock base"), all working face data with the same geological conditions are selected from the database as the experimental group, and a batch of working face data that do not have such geological conditions but are similar in other major geological conditions are selected as the control group.

[0094] (2) Analysis of differences in indicators: The differences between the experimental group and the control group were calculated respectively. , , , The average of these four indicators.

[0095] (3) Coefficient calculation and calibration: Impact coefficient on automation rate : Calculate the experimental group relative to the control group Average percentage decrease. For example, the control group. The average decrease was 85%, and the experimental group's was 70%. Therefore, the observed decrease rate was (85% - 70%) / 85% ≈ 17.6%. Compare this observed value with the initial... The values ​​(e.g., -0.12) are compared and calibrated according to certain rules (e.g., taking the weighted average of the observed values ​​and the initial values).

[0096] Impact coefficient of intervention rate : Calculate the experimental group relative to the control group Average increase rate. For example, the control group. The average increase was 10%, and the experimental group's was 22%. Therefore, the observed increase rate was (22% - 10%) / 10% = 120%. This observed rate was then compared with the initial... The value (e.g., +0.15) is used for calibration.

[0097] (4) Multiple regression-assisted calibration: For complex situations involving multiple intertwined geological conditions, a multiple linear regression model can be used. or As the dependent variable, regression is performed with multiple geological conditions (converted into 0 / 1 dummy variables or degree variables) as independent variables. The magnitude and direction of the regression coefficients can be directly used as calibration. and This is an important basis.

[0098] S6.3 Benchmark target value based on advanced level ( Rolling update method Benchmark target value (e.g.) =0.95) represents the advanced level under ideal conditions. With the popularization of intelligent technologies, the improvement of operator proficiency, and the enhancement of equipment reliability, the industry's actual "advanced level" will dynamically rise. A fixed benchmark target value may lead to an excessively low evaluation ceiling, failing to continuously incentivize innovation and breakthroughs.

[0099] Establish a benchmark management mechanism to regularly (e.g., annually) identify the "benchmark working face" with the best actual performance under various geological conditions. Use its high-level operational data as a reference to progressively and continuously update the benchmark target values ​​applicable throughout the region. The goal is to ensure that the "full score" of the evaluation system keeps pace with industry technological progress and continuously leads the level of intelligent operation to a higher level.

[0100] Detailed method description: (1) Selection of benchmark working faces: In each geological condition classification combination (major categories may be appropriately merged), based on its modified index FAI and original operating data, the top 5%-10% of working faces are selected as the current period's "benchmark working faces". Ensure the advanced nature, representativeness and data authenticity of the benchmarks.

[0101] (2) Benchmark data extraction and analysis: Analyze the actual performance of these benchmark working faces under their own geological conditions. , , , These values ​​represent the "realistically advanced level" achievable under such conditions at the current stage, since they have done their best within their objective limitations.

[0102] (3) The target value update calculation can be performed in multiple ways. This embodiment provides two different methods: the target value update calculation is performed using method A or method B. Method A (Quantitation Method): Calculate a certain index (e.g., ...) for all benchmark working faces. The upper quartile (75th percentile) of the target area. This value means that 25% of the benchmark working surfaces have reached this level or above, making them strong candidates for a new round of benchmark target values.

[0103] Method B (Trend Extrapolation): Plot the curve of the historical baseline value of the indicator over time, combine it with the trend of technological progress, and use linear or curve fitting to perform appropriate forward-looking extrapolation to determine the new target value.

[0104] (4) Prudent updating and release: The updating of benchmark target values ​​should be relatively cautious to avoid frequent and large changes. A "small steps, quick progress" strategy can be adopted, with each update not exceeding 5%. The updated target values ​​need to be reviewed by experts and then uniformly released and applied at the beginning of the new evaluation cycle to ensure the stability and authority of the evaluation standards.

[0105] Step S7: Execution Level Grading and Visualization Furthermore, based on the revised fully mechanized mining face automation operation index ( The operational level of intelligent fully mechanized mining faces is divided into four levels: Grade A (Excellent): ≥90 indicates that the intelligent system at the working face operates efficiently, with in-depth automation and minimal human intervention, reaching a leading level within the limits of geological conditions.

[0106] Grade B (Good): 80≤ <90. This indicates that the intelligent system is operating stably, the automation functions are being used effectively, and it has a good foundation for routine operation.

[0107] Grade C (Qualified): 60≤ <80. This indicates that the intelligent system has basically achieved operation, but the level of automation or stability needs to be improved, and there is still a lot of manual intervention.

[0108] Grade D (Needs improvement): <60. This indicates that the intelligent system is not performing well and the automation functions are not being effectively utilized, requiring significant improvement.

[0109] The evaluation rating will serve as an important basis for assessing the level of intelligent operation in coal mines, providing policy support, and recognizing outstanding achievements.

[0110] Operational data is analyzed and displayed through visualization units. Based on the evaluation results, the coal mine analyzes weaknesses, formulates and implements improvement measures, forming a closed-loop management system of "evaluation-feedback-improvement".

[0111] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined by the scope of the claims.

Claims

1. A method for constructing a standardized evaluation model for the intelligent operation efficiency of coal mines applicable to differentiated geological conditions, characterized in that, include: S1, acquire real-time equipment operation data on the working surface and calculate key indicators, including the automation rate of the hydraulic support. Hydraulic support manual intervention rate Automation rate of coal mining machines Human intervention rate of coal mining machines ; S2, Construct an intelligent evaluation basic model based on key indicators under benchmark conditions; S3: Obtain the geological conditions of the working face, adjust the intelligent evaluation basic model based on the geological conditions of the working face to optimize the intelligent evaluation model, and output the corrected FAI.

2. The method for constructing a standardized evaluation model for intelligent operation efficiency of coal mines applicable to differentiated geological conditions as described in claim 1, characterized in that, The aforementioned intelligent evaluation model is a model of the advanced operational level that the intelligent system should achieve under benchmark geological conditions, setting indicators under benchmark conditions. , , and Indicators based on baseline conditions , , and Calculate the target automation operation index of the fully mechanized mining face under the benchmark conditions. , , ,in, For stent weighting coefficient; This is the weighting coefficient for coal mining machinery.

3. The data-driven intelligent evaluation model parameter dynamic optimization method according to claim 2, characterized in that, In step S3, a geological condition influence quantification matrix M is defined, and the specific expected performance level of the working face is calculated to optimize the intelligent evaluation model. Matrix M includes adjustment coefficients. ,in, This represents the expected reduction factor of the automation rate (ar) due to this condition. It is a negative value. This represents the coefficient representing the expected increase in the rate of human intervention (mr) due to this condition. It is a positive value.

4. The data-driven intelligent evaluation model parameter dynamic optimization method according to claim 2, characterized in that, Under specific geological conditions ,in, ; ; ; ,in, Geological adjustment coefficient, , , This represents the effect of the condition on the automation rate. The expected reduction coefficient This represents the rate of artificial intervention under this condition. The expected increase coefficient.

5. The method for constructing a standardized evaluation model for intelligent operation efficiency of coal mines applicable to differentiated geological conditions, as described in claim 4, is characterized in that... The FAI OAI is the working face automation operation index based on key indicators. The working face target automation operation index under the benchmark conditions. The automated operation index of the working face target under specific geological conditions, the , , , .

6. The method for constructing a standardized evaluation model for intelligent operation efficiency of coal mines applicable to differentiated geological conditions as described in claim 1, characterized in that, The process also includes step S4, in which key performance indicators are optimized based on the efficiency of reducing manpower, in order to optimize the corrected... .

7. The method for constructing a standardized evaluation model for intelligent operation efficiency of coal mines applicable to differentiated geological conditions, as described in claim 6, is characterized in that... Weighting coefficients based on the effectiveness of reducing manpower Optimization methods include: Data preparation: Extract valid data samples from all working faces within a certain period from the platform. Each sample includes: the calculated comprehensive automation level index of the support structure. Comprehensive Automation Level Indicators of Coal Mining Machines And the corresponding performance indicators, including the number of workers on the work surface. ; Correlation analysis: Calculate separately and correlation coefficient ,as well as and correlation coefficient ; Weight optimization calculation: The correlation coefficient is normalized and used as the basis for optimizing the weights. The normalized result is obtained. , ; Smooth iteration: Iterative updates are performed using exponential smoothing. ,in, This is a smoothing factor (e.g., 0.2) used to control the update magnitude.

8. The method for constructing a standardized evaluation model for intelligent operation efficiency of coal mines applicable to differentiated geological conditions as described in claim 1, characterized in that, In step S4, based on the advanced level optimization benchmark conditions Thus, the corrected version is updated. .

9. The method for constructing a standardized evaluation model for intelligent operation efficiency of coal mines applicable to differentiated geological conditions as described in claim 1, characterized in that, In step S4, the initial geological adjustment coefficient is adjusted based on the equipment operating data. Perform calibration to update the corrected version. .

10. The method for constructing a standardized evaluation model for intelligent operation efficiency of coal mines applicable to differentiated geological conditions, as described in claim 9, is characterized in that... Initial geological adjustment coefficient based on equipment operation data The calibration method is as follows: For a specific geological condition, all working face data with the same geological conditions were selected from the database as the experimental group, while a batch of working face data that did not have this geological condition but were similar in other major geological conditions were selected as the control group. The mean values ​​of each key indicator in the experimental group and the mean values ​​of each key indicator in the control group were calculated respectively. Based on the ratio of changes in the key indicator ar between the experimental and control groups, the initial... The value is calibrated; Based on the ratio of changes in the key indicator MR between the experimental and control groups, the initial... The value is calibrated.

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