A coal mine production process control system

Abstract

The present invention provides a coal mine production process control system, which relates to the field of process management technology and includes: a data acquisition module for acquiring data of each production process in a mine in real time and outputting initial data; a preprocessing module for preprocessing the initial data to obtain data to be analyzed; a model training module for training a model to be trained selected from a model database in combination with preset data resources, outputting an alternative model, and outputting an alternative model that meets preset performance conditions as an analysis model; a data analysis module for analyzing the data to be analyzed based on the analysis model, outputting process analysis results, and updating parameters and optimizing performance of the analysis model in combination with preset data resources and cloud data. The present invention can monitor the data of each production process in a mine in real time, promptly discover potential risks of each production process, continuously update parameters and optimize performance of the model, and ensure the real-time and accuracy of the process analysis results.

Description

Technical Field

[0001] The present invention relates to the technical field of process management, and in particular to a coal mine production process management and control system. Background Art

[0002] The coal mine production process is a complex, semi-continuous industrial process, encompassing tunneling, coal mining, conveyor belt transport, buffer bunkers, hoisting (or inclined shaft transport), washing, and loading. Each link is closely interconnected, and an anomaly in any one link can disrupt the entire production chain, directly impacting the mine's overall production efficiency. Ensuring the smooth and efficient operation of the coal mine production line is not only essential for daily mine output but also crucial to the safe production of the entire mine.

[0003] Traditional production process control often relies on manual experience, overly reliant on the subjective experience of dispatchers and exhibiting significant lags. Only when an anomaly occurs in a process does feedback reach process management, and only after reporting to the decision-making authority can adjustments be made. This model is slow to respond and lacks a holistic optimization perspective. Faced with an increasingly complex production environment, the limitations of traditional process control models are becoming increasingly prominent, making it difficult to effectively respond to unexpected situations during production.

[0004] Therefore, the present invention provides a coal mine production process control system. Summary of the Invention

[0005] The present invention provides a coal mine production process control system for real-time monitoring of data of various production processes in the mine, and performs real-time and predictive analysis on the data through analysis models to promptly discover potential risks in various production processes. The system can also continuously update parameters and optimize performance of the model to ensure the real-time and accuracy of process analysis results, thereby ensuring production safety.

[0006] The present invention provides a coal mine production process control system, comprising:

[0007] Data acquisition module, used to obtain data of each production process in the mine in real time and output initial data;

[0008] A preprocessing module, configured to preprocess the initial data using a preset preprocessing method to obtain data to be analyzed;

[0009] The model training module is used to train the model to be trained selected from the model database in combination with preset data resources, output the candidate model, and output the candidate model that meets the preset performance conditions as the analysis model;

[0010] The data analysis module is used to analyze the data to be analyzed based on the analysis model, output the process analysis results, and update the parameters and optimize the performance of the analysis model based on the process analysis results and in combination with the preset data resources and cloud data.

[0011] Preferably, the data acquisition module includes:

[0012] The process classification submodule is used to divide the production processes in the mine into categories based on the monitoring priority of each production process and obtain a process classification table;

[0013] The data acquisition submodule is used to obtain data of each production process in the process classification table in real time using preset monitoring equipment set at preset positions, and output initial data.

[0014] Preferably, the data acquisition submodule includes:

[0015] A demand acquisition unit is used to acquire the monitoring requirements corresponding to each production process, and summarize and output the system monitoring requirements in combination with the monitoring priorities corresponding to each production process;

[0016] An equipment selection unit is configured to obtain preset monitoring equipment corresponding to each production process based on the system monitoring requirements and in combination with a preset requirement-equipment matching table, determine a preset location for installing each preset monitoring equipment, and output a process-equipment comparison table;

[0017] The data acquisition unit is used to obtain data of each production process in the mine in real time through the preset monitoring equipment at each preset position based on the process-equipment comparison table, and comprehensively output initial data.

[0018] Preferably, the pre-processing module includes:

[0019] a method selection submodule for extracting features from the initial data, constructing a first feature set based on the extracted features, and acquiring a preset preprocessing method that matches the first feature set in combination with a preset feature-method mapping table;

[0020] The preprocessing submodule is configured to preprocess the initial data based on the preset preprocessing method to obtain first data, and output the first data that meets the first threshold condition as data to be analyzed.

[0021] Preferably, the model training module includes:

[0022] The model selection submodule is used to select a model to be trained that matches the system monitoring requirements in the model database based on the preset requirements-model matching table;

[0023] The model training submodule is used to select preset data resources from a preset resource pool, train the to-be-trained model based on the preset data resources, and output an alternative model;

[0024] The model output submodule is used to obtain performance requirement information of the model, output preset performance conditions based on the performance requirement information, and output the candidate model that meets the preset performance judgment conditions as the analysis model.

[0025] Preferably, the model training submodule includes:

[0026] A data selection unit, configured to obtain a data selection instruction and select corresponding preset data resources from a target resource pool based on the data selection instruction;

[0027] A data partitioning unit is used to partition the preset data resources using a preset data partitioning method and output a training data set, a validation data set, and a test data set;

[0028] The model training unit is used to train and optimize the performance of the model to be trained based on the training data set, the verification data set and the test data set, and output an alternative model.

[0029] Preferably, the data analysis module includes:

[0030] A data analysis submodule is used to analyze the data to be analyzed to obtain sub-data corresponding to each production process;

[0031] The level information acquisition submodule is used to obtain the monitoring level information of each sub-data and summarize and output the process monitoring level table;

[0032] A comparison table generation submodule is used to combine the process monitoring level table and the sub-data corresponding to each production process under each monitoring level to output a monitoring level-data comparison table;

[0033] An anomaly identification submodule is used to extract features from the sub-data of the production process at each monitoring level in the monitoring level-data comparison table, construct a second feature set, and perform feature matching on the data features in the second feature set in combination with a preset abnormal feature database to obtain an anomaly identification sub-result corresponding to the sub-data of the production process at each monitoring level, and summarize and output the anomaly analysis results;

[0034] A historical data acquisition submodule, configured to select from a historical database first historical process data that matches the numerical features of the data in the second feature set and second historical process data that matches the temporal features of the data in the second feature set;

[0035] a format conversion submodule, configured to obtain data format information of the sub-data, the first historical process data, and the second historical process data corresponding to each monitoring level, and perform format conversion on the sub-data, the first historical process data, and the second historical process data corresponding to each monitoring level in combination with the format requirement information of the analysis model, to obtain first standard data, second standard data, and third standard data, respectively;

[0036] A real-time analysis submodule, configured to perform real-time analysis on the first standard data corresponding to the production process at each monitoring level based on the analysis model, and output real-time analysis results;

[0037] A prediction analysis submodule is used to perform prediction analysis on the second standard data and the third standard data based on the analysis model, obtain prediction data of each production process at each monitoring level within a preset time period in the future, and output the prediction analysis results;

[0038] A comprehensive analysis submodule is used to analyze the abnormal analysis results, real-time analysis results and predictive analysis results using a preset comprehensive analysis method, and output a comprehensive analysis result;

[0039] A historical prediction data acquisition submodule is used to acquire historical prediction data matching the analysis model in a historical database;

[0040] The result verification submodule is used to perform time calibration and data verification on the historical forecast data and the real-time analysis results under each same production process, and output the forecast data verification results;

[0041] The model optimization submodule is used to optimize the comprehensive analysis results and the prediction data verification results in combination with preset optimization indicators, and adjust the parameters and optimize the performance of the analysis model based on the optimization analysis results.

[0042] Preferably, the model optimization submodule includes:

[0043] an analysis unit, configured to analyze the comprehensive analysis result to obtain a first sub-result corresponding to the abnormal analysis result, a second sub-result corresponding to the real-time analysis result, and a third sub-result corresponding to the predictive analysis result;

[0044] a feature extraction unit, configured to extract features from the first sub-result, the second sub-result, the third sub-result, and the prediction data verification result, and construct an abnormal feature set, a real-time feature set, a prediction feature set, and a verification feature set based on the extracted features;

[0045] An indicator selection unit is used to select preset optimization indicators from an indicator database and construct a first indicator set matching the abnormal feature set, a second indicator set matching the real-time feature set, a third indicator set matching the prediction feature set, and a fourth indicator set matching the verification feature set;

[0046] a relationship graph acquisition unit, configured to acquire a mapping relationship between preset optimization indicators in the first indicator set, the second indicator set, the third indicator set, and the fourth indicator set, and output an indicator relationship graph;

[0047] an optimization analysis unit, configured to perform optimization analysis on the comprehensive analysis result and the prediction data verification result based on the first indicator set, the second indicator set, the third indicator set, the fourth indicator set, and the indicator relationship diagram, to obtain an optimization analysis result;

[0048] ;

[0049] in, Indicates the optimization analysis results; represents the exponential function; Represents the comprehensive correction coefficient corresponding to the data calculation error under each preset optimization indicator in the first indicator set, the second indicator set, the third indicator set, and the fourth indicator set; Indicates the total number of preset optimization indicators in the i-th indicator set; i=1 corresponds to the first indicator set; i=2 corresponds to the second indicator set; i=3 corresponds to the third indicator set; i=4 corresponds to the fourth indicator set; represents the optimization standard parameter corresponding to the jth preset optimization indicator in the i-th indicator set; Indicates the actual parameter value corresponding to the jth preset optimization indicator in the i-th indicator set in the comprehensive analysis results and the prediction data verification results; represents the optimization weight coefficient corresponding to the jth preset optimization indicator in the i-th indicator set; represents the multiple correlation coefficient between the jth preset optimization indicator in the i-th indicator set and the remaining t-1 preset optimization indicators in each indicator set, and t represents the total number of preset optimization indicators in the first indicator set, the second indicator set, the third indicator set, and the fourth indicator set;

[0050] The model optimization unit is used to combine the preset result-strategy-instruction comparison table to obtain the model parameter performance optimization strategy and the optimization instructions under the corresponding strategy that match the optimization analysis results, and adjust the parameters and optimize the performance of the analysis model based on the model parameter performance optimization strategy and optimization instructions.

[0051] The present invention provides a coal mine production process control system that monitors and optimizes coal mine production processes through the collaboration of data acquisition, preprocessing, model training, and data analysis modules. From initial data to data to be analyzed, and then to the training and optimization of analysis models, the system continuously improves itself to better adapt to changes in the coal mine production environment. The present invention can monitor each production process in real time and provide accurate process analysis results, helping managers make more accurate decisions. It can also achieve real-time optimization of the model, improve the production efficiency of each production process, and ensure the efficient operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 It is a schematic diagram of the framework of a coal mine production process control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0055] like Figure 1 As shown, an embodiment of the present invention provides a coal mine production process control system, including:

[0056] Data acquisition module, used to obtain data of each production process in the mine in real time and output initial data;

[0057] A preprocessing module is used to preprocess the initial data using a preset preprocessing method to obtain data to be analyzed;

[0058] The model training module is used to train the model to be trained selected from the model database in combination with preset data resources, output the candidate model, and output the candidate model that meets the preset performance conditions as the analysis model;

[0059] The data analysis module is used to analyze the data to be analyzed based on the analysis model, output the process analysis results, and update the parameters and optimize the performance of the analysis model based on the process analysis results and in combination with preset data resources and cloud data.

[0060] In this embodiment, initial data refers to the raw data of each production process in the mine obtained from the data acquisition module, including but not limited to the equipment operating parameters of each production process in the mine and the temperature, humidity, gas concentration and other parameters of the surrounding environment;

[0061] In this embodiment, the preset preprocessing method: a preset data preprocessing method is used to process the initial data, including but not limited to data cleaning, denoising, normalization and other operations;

[0062] In this embodiment, the data to be analyzed: data prepared for analysis obtained after being processed by the preprocessing module;

[0063] In this embodiment, preset data resources: pre-set data resources used for model training and data analysis, including but not limited to real-time process data and historical process data corresponding to each production process;

[0064] In this embodiment, the model database: a database storing various models, from which the model training module selects the model to be trained;

[0065] In this embodiment, the model to be trained is a model to be trained selected from the model database;

[0066] In this embodiment, the candidate model: a model that meets the preset performance conditions after being trained by the model training module;

[0067] In this embodiment, the preset performance conditions: pre-set model performance indicators are used to screen out candidate models that meet the conditions;

[0068] In this embodiment, the analysis model: a candidate model after screening, used by the data analysis module to analyze the data to be analyzed;

[0069] In this embodiment, the process analysis result is the result obtained by the data analysis module after analyzing the data to be analyzed based on the analysis model;

[0070] In this embodiment, cloud data refers to data resources stored in the cloud, which are used to update parameters and optimize performance of the analysis model in combination with preset data resources and process analysis results;

[0071] In this embodiment, parameter update and performance optimization: based on cloud data and process analysis results, the parameters of the analysis model are updated and the performance is optimized to improve the accuracy and efficiency of the model.

[0072] The implementation principle and beneficial effects of this embodiment: The present invention realizes the monitoring and optimization of the coal mine production process through the collaboration of data collection, preprocessing, model training and data analysis modules. From the initial data to the data to be analyzed and then to the training and optimization of the analysis model, the system continuously improves itself to better adapt to the changes in the coal mine production environment. The present invention can monitor each production process in real time and provide accurate process analysis results to help managers make more accurate decisions. It can also realize real-time optimization of the model, improve the production efficiency of each production process, and ensure the efficient operation of the system.

[0073] The embodiment of the present invention provides a coal mine production process control system, a data acquisition module, including:

[0074] The process classification submodule is used to divide the production processes in the mine into categories based on the monitoring priority of each production process and obtain a process classification table;

[0075] The data acquisition submodule is used to obtain the data of each production process in the process classification table in real time using the preset monitoring equipment set at the preset position, and output the initial data.

[0076] In this embodiment, monitoring priority: a priority order set according to the importance and urgency of the production process;

[0077] In this embodiment, the process classification table is a table divided according to the monitoring priority and the characteristics of the production process, used to record the monitoring priority of each production process;

[0078] In this embodiment, preset location: pre-set location information is used to determine the placement of monitoring equipment to ensure comprehensiveness and effectiveness of data collection;

[0079] In this embodiment, preset monitoring equipment: pre-set monitoring equipment is used to obtain data from each production process in real time for subsequent analysis and control. For example, in a coal mine production scenario, the monitoring priority is set to the coal mining process as the highest, the mine ventilation process as the second, and the mine drainage process as the lowest. According to this priority order, the system establishes a process classification table and installs corresponding monitoring equipment at preset locations. These devices obtain data from each process in real time. The system collects and manages data in a timely manner according to the monitoring priority to ensure the smooth progress of the mine production process.

[0080] The implementation principles and beneficial effects of this embodiment are as follows: The present invention achieves real-time acquisition and classified management of data from various production processes within a mine through the collaboration of the process classification submodule and the data acquisition submodule. Monitoring priorities are used to determine the operating order of monitoring equipment, while the process classification table helps the system effectively classify and manage data. The present invention can prioritize monitoring and acquisition of data from key production processes, ensuring the stability and safety of the production process. At the same time, it helps the system classify and manage data from various production processes, facilitating subsequent analysis and decision-making, promptly responding to changes in the production environment, and improving the efficiency and accuracy of production control.

[0081] The embodiment of the present invention provides a coal mine production process control system, the data acquisition submodule including:

[0082] The demand acquisition unit is used to obtain the monitoring requirements corresponding to each production process, and combine the monitoring priorities corresponding to each production process to summarize and output the system monitoring requirements;

[0083] The equipment selection unit is used to obtain the preset monitoring equipment corresponding to each production process based on the system monitoring requirements and the preset requirements-equipment matching table, determine the preset location for the installation of each preset monitoring equipment, and output the process-equipment comparison table;

[0084] The data acquisition unit is used to obtain data of various production processes in the mine in real time through preset monitoring equipment at various preset locations based on the process-equipment comparison table, and comprehensively output initial data.

[0085] In this embodiment, monitoring requirements: the requirements of each production process for monitoring data, including the parameters to be monitored, frequency, accuracy, etc.;

[0086] In this embodiment, the system monitoring demand: summarizes the demand for monitoring data of each production process, which is the comprehensive demand of the entire system for monitoring data;

[0087] In this embodiment, the preset requirement-equipment matching table: a table containing the mapping relationship between monitoring requirements and monitoring equipment, which is pre-set and helps the equipment selection unit determine the preset monitoring equipment corresponding to each production process and the installation location of these equipment;

[0088] In this embodiment, the process-equipment comparison table records the correspondence between each production process and the preset monitoring equipment, and is used to guide the data acquisition unit to obtain data of each production process in real time. For example, in a coal mine production scenario, the monitoring requirements include the need to monitor temperature and humidity in the mine ventilation process, and the need to monitor geological parameters in the coal mining process. Based on these monitoring requirements, combined with the monitoring priority and the preset requirements-equipment matching table, the system determines the monitoring equipment and installation location corresponding to each production process, and establishes a process-equipment comparison table. The data acquisition unit obtains data of each production process in real time through this table to ensure that the system can effectively monitor and control the mine production process.

[0089] The implementation principles and beneficial effects of this embodiment: Through the collaboration of the demand acquisition unit, the equipment selection unit, and the data acquisition unit, the present invention determines the monitoring equipment and location based on monitoring requirements and acquires data from each production process in real time. The present invention can provide customized monitoring solutions based on the monitoring requirements of each production process, ensuring the accuracy and comprehensiveness of the monitoring data. By presetting the demand-equipment matching table and the process-equipment comparison table, the selection and installation location of monitoring equipment can be continuously optimized, improving the efficiency of data collection.

[0090] An embodiment of the present invention provides a coal mine production process control system, a preprocessing module, including:

[0091] A method selection submodule is used to extract features from the initial data, construct a first feature set based on the extracted features, and obtain a preset preprocessing method that matches the first feature set in combination with a preset feature-method mapping table;

[0092] The preprocessing submodule is configured to preprocess the initial data based on a preset preprocessing method to obtain first data, and output the first data that meets a first threshold condition as data to be analyzed.

[0093] In this embodiment, the first feature set is constructed by extracting features from the initial data using the method selection submodule. This feature set is used to describe important features of the data. For example, for vibration data in coal mine production, the system extracts features such as frequency and amplitude from the raw vibration data, then selects an appropriate preprocessing method based on a preset feature-method mapping table to process the data, and ultimately outputs data that meets the threshold conditions for subsequent analysis.

[0094] In this embodiment, the preset feature-method mapping table is used to record the mapping relationship between the preset features and the preset preprocessing methods, so as to help the system select an appropriate preprocessing method;

[0095] In this embodiment, the first data: data obtained after preprocessing;

[0096] In this embodiment, the first threshold condition is a condition used to filter data. Data that meets the condition will be output as data to be analyzed. It is usually used to filter out data with higher quality or that meets specific standards.

[0097] The implementation principles and beneficial effects of this embodiment are as follows: The present invention utilizes the collaboration of a method selection submodule and a preprocessing submodule to extract features and preprocess initial data to obtain data that meets analysis requirements. A preset feature-method mapping table helps the system select appropriate preprocessing methods, ensuring the accuracy and effectiveness of data processing. By setting a first threshold condition, the present invention enables the system to automatically filter data and further analyze data that meets the threshold condition, thereby improving data quality and usability.

[0098] The embodiment of the present invention provides a coal mine production process control system, including a model training module, comprising:

[0099] The model selection submodule is used to select the to-be-trained model that matches the system monitoring requirements from the model database based on the preset requirements-model matching table;

[0100] The model training submodule is used to select preset data resources from a preset resource pool, train the to-be-trained model based on the preset data resources, and output an alternative model;

[0101] The model output submodule is used to obtain the performance requirement information of the model, output the preset performance conditions based on the performance requirement information, and output the alternative models that meet the preset performance judgment conditions as analysis models.

[0102] In this embodiment, the preset requirement-model matching table records the matching relationship between the system monitoring requirements and the model, helping the system to select the appropriate model to be trained according to the monitoring requirements;

[0103] In this embodiment, the preset resource pool stores preset data resources, from which the model training submodule selects data for model training;

[0104] In this embodiment, performance requirement information: performance requirements that the model needs to meet during training and application, including but not limited to accuracy, recall rate, error range, etc. Specifically, for example, a neural network model is selected for coal mine production data prediction. The model training submodule selects historical production data from a preset resource pool for model training, and based on the performance requirement information, requires the model accuracy to reach more than 90%. Finally, the model output submodule outputs an analysis model that meets the preset performance conditions, which can be used for predictive analysis of production data.

[0105] The implementation principles and beneficial effects of this embodiment are as follows: Through the collaboration of the model selection submodule, the model training submodule, and the model output submodule, the present invention selects a to-be-trained model that meets the system monitoring requirements, performs model training based on data resources in a preset resource pool, and outputs an analysis model that meets preset performance conditions based on performance requirement information. By setting performance requirement information and outputting alternative models that meet performance requirements, the present invention improves the model's predictive accuracy and stability, helping decision makers better understand data and make accurate decisions.

[0106] The embodiment of the present invention provides a coal mine production process control system, including a model training submodule, comprising:

[0107] A data selection unit, configured to obtain a data selection instruction and select corresponding preset data resources from a target resource pool based on the data selection instruction;

[0108] A data partitioning unit is used to partition the preset data resources using a preset data partitioning method and output a training data set, a validation data set, and a test data set;

[0109] The model training unit is used to train and optimize the performance of the model to be trained based on the training data set, validation data set, and test data set, and output an alternative model.

[0110] In this embodiment, the data selection instruction instructs the data selection unit to select specific data resources from the target resource pool, and may be a selection instruction for a specific time period, specific parameters, etc.;

[0111] In this embodiment, the target resource pool: stores various data resources required by the system, such as historical production data, sensor data, etc.;

[0112] In this embodiment, the preset data partitioning method specifies how to divide the entire data set into a training data set, a validation data set, and a test data set, for example, random partitioning, proportional partitioning, time partitioning, etc.;

[0113] In this embodiment, the training data set is a data set used for model training, and the model learns parameters through this data;

[0114] In this embodiment, the validation dataset is used to evaluate the performance of the model and adjust hyperparameters during the training process to avoid model overfitting;

[0115] In this embodiment, the test data set is used to ultimately evaluate the generalization ability and performance of the model. The model is tested on this part of the data to verify its performance on unseen data. For example, in the coal mine production management and control system, the data selection unit selects the production data of the past year from the target resource pool as the preset data resource according to the data selection instruction. The data partitioning unit uses a time partitioning method to divide the data into a training data set (80%), a verification data set (10%) and a test data set (10%). The model training unit uses these data sets to train and optimize the performance of the to-be-trained model, and outputs an alternative model for subsequent analysis.

[0116] The implementation principles and beneficial effects of this embodiment are as follows: The present invention can select preset data resources in the target resource pool according to instructions, and divide the data into training, validation, and test data sets according to a preset data partitioning method, and perform model training and performance optimization based on these data sets. Through the collaboration of the data selection unit and the data partitioning unit, the system can efficiently utilize the data resources in the target resource pool and avoid data waste. At the same time, the training, validation, and test data sets are used for model training and performance evaluation, which helps to optimize the performance and generalization ability of the model.

[0117] The embodiment of the present invention provides a coal mine production process control system, a data analysis module, including:

[0118] The data analysis submodule is used to analyze the data to be analyzed and obtain the sub-data corresponding to each production process;

[0119] The level information acquisition submodule is used to obtain the monitoring level information of each sub-data and summarize and output the process monitoring level table;

[0120] The comparison table generation submodule is used to combine the process monitoring level table and the sub-data corresponding to each production process under each monitoring level to output the monitoring level-data comparison table;

[0121] The anomaly identification submodule is used to extract features from the sub-data of the production process at each monitoring level in the monitoring level-data comparison table, construct a second feature set, and perform feature matching on the data features in the second feature set in combination with a preset anomaly feature database to obtain anomaly identification sub-results corresponding to the sub-data of the production process at each monitoring level, and summarize and output the anomaly analysis results;

[0122] A historical data acquisition submodule is used to select from the historical database first historical process data that matches the data numerical features in the second feature set and second historical process data that matches the data temporal features in the second feature set;

[0123] a format conversion submodule, configured to obtain data format information of the sub-data, the first historical process data, and the second historical process data corresponding to each monitoring level, and perform format conversion on the sub-data, the first historical process data, and the second historical process data corresponding to each monitoring level in combination with the format requirement information of the analysis model, to obtain first standard data, second standard data, and third standard data, respectively;

[0124] A real-time analysis submodule, configured to perform real-time analysis on the first standard data corresponding to the production process at each monitoring level based on the analysis model, and output real-time analysis results;

[0125] The prediction analysis submodule is used to perform prediction analysis on the second standard data and the third standard data based on the analysis model, obtain the prediction data of each production process under each monitoring level within the future preset time period, and output the prediction analysis results;

[0126] The comprehensive analysis submodule is used to analyze the abnormal analysis results, real-time analysis results and predictive analysis results using a preset comprehensive analysis method and output the comprehensive analysis results;

[0127] The historical forecast data acquisition submodule is used to obtain historical forecast data that matches the analysis model in the historical database;

[0128] The result verification submodule is used to perform time calibration and data verification on the historical forecast data and real-time analysis results under each same production process, and output the forecast data verification results;

[0129] The model optimization submodule is used to optimize the comprehensive analysis results and the prediction data verification results in combination with preset optimization indicators, and to adjust the parameters and optimize the performance of the analysis model based on the optimization analysis results.

[0130] In this embodiment, sub-data refers to sub-data corresponding to each production process obtained after parsing the data to be analyzed, that is, specific data for each production process extracted from the original data. For example, if a production process is "coal mining", the corresponding sub-data is the mining depth data of the process;

[0131] In this embodiment, the monitoring level information: that is, the monitoring level of different production processes is used to determine the corresponding monitoring level and data processing method

[0132] In this embodiment, the process monitoring level table summarizes the monitoring level information corresponding to each production process, clarifies the monitoring priority of each production process, and helps the system perform monitoring and analysis;

[0133] In this embodiment, the monitoring level-data comparison table includes the monitoring level information and sub-data comparison table corresponding to each production process, which facilitates subsequent data analysis and abnormality identification.

[0134] In this embodiment, the second feature set is a feature set constructed by extracting features from the sub-data and is used for anomaly identification and analysis. For example, assuming that a production process is "coal mining", the second feature set includes features such as mining speed and mining depth change rate;

[0135] In this embodiment, the abnormal feature database is preset: it contains predefined abnormal feature information, which is used to match the data features in the second feature set to identify abnormal situations;

[0136] In this embodiment, the abnormality identification sub-result is the result obtained after feature extraction and abnormality identification of the sub-data of the production process at the monitoring level, for example, abnormal fluctuation of the mining depth exceeds a threshold, etc.;

[0137] In this embodiment, the abnormality analysis result: integrates the analysis results of the abnormality identification sub-results to guide subsequent decision-making and processing;

[0138] In this embodiment, the first historical process data: historical process data that matches the numerical features of the data in the second feature set;

[0139] In this embodiment, the second historical process data: historical process data that matches the temporal features in the second feature set;

[0140] In this embodiment, data format information refers to format information of various data, such as data type, data structure, etc.;

[0141] In this embodiment, the format requirement information: that is, the analysis model's requirement information on the data format of the data to be processed, to ensure the data's format consistency and compatibility;

[0142] In this embodiment, the first standard data is standard data that matches the format requirement information obtained after format conversion of the sub-data, and is used for subsequent real-time analysis;

[0143] In this embodiment, the second standard data: standard data matching the format requirement information obtained by format conversion of the first historical process data, used for subsequent prediction analysis;

[0144] In this embodiment, the third standard data: standard data that matches the format requirement information obtained after format conversion of the second historical process data, corresponds to the second standard data and is used for subsequent prediction analysis;

[0145] In this embodiment, the real-time analysis result: the result obtained after real-time analysis of the first standard data, outputting the current status evaluation result of a certain production process, such as normal, abnormal, etc.;

[0146] In this embodiment, the forecast data is data obtained by performing a forecast analysis on the second standard data and the third standard data through the analysis model, and is used to predict the production status or data of each production process in a certain time period in the future, for example, predicting the output change in the next week;

[0147] In this embodiment, the forecast analysis results: after analyzing the forecast data, the forecast status of a certain production process in the future is output, for example, the forecast of coal mine output, equipment wear, etc.;

[0148] In this embodiment, the preset comprehensive analysis method is a pre-set method for comprehensively analyzing abnormal analysis results, real-time analysis results, and predictive analysis results, which can be a weighted average method that comprehensively considers abnormal conditions, real-time data, and predictive data, or a comprehensive judgment method based on rules and models;

[0149] In this embodiment, the comprehensive analysis result is a final result obtained through the comprehensive analysis method, combined with the results of abnormality analysis, real-time analysis, and predictive analysis, to provide a comprehensive data analysis conclusion. For example, the comprehensive analysis result shows that a production process at a certain monitoring level has an abnormality, the real-time analysis result shows that the data of the process is abnormal, and the predictive analysis result also shows that there may be problems with the process in the future.

[0150] In this embodiment, historical prediction data refers to historical prediction data stored in a historical database that matches the analysis model. In other words, the historical prediction data stored in the historical database by the analysis model is the prediction data obtained by the analysis model during a previous prediction analysis, and is used for comparison and verification with the real-time analysis results corresponding to the same time point, so as to continuously reduce the prediction error of the analysis model and optimize the prediction performance of the analysis model.

[0151] In this embodiment, time calibration and data verification: the accuracy of the predicted data is verified by comparing the timestamps and data content of the historical predicted data and the real-time analysis results to ensure the accuracy and reliability of the predicted data;

[0152] In this embodiment, the prediction data verification result: the verification conclusion drawn based on the time calibration and data verification results indicates the quality and accuracy of the prediction data. For example, the verification result shows that the prediction data is highly consistent with the actual data, and the prediction accuracy rate reaches 95%;

[0153] In this embodiment, the preset optimization index is an index or standard used to optimize the analysis of the comprehensive analysis results and the prediction data verification results, for example, accuracy, recall rate, error range and other indicators;

[0154] In this embodiment, the optimization analysis results are: optimization conclusions obtained after analyzing the comprehensive analysis results and the prediction data verification results according to the optimization indicators, including parameter adjustment or performance optimization suggestions, etc. For example, the conclusion obtained according to the optimization indicators is that certain parameters of the model need to be adjusted to improve the prediction accuracy.

[0155] The implementation principles and beneficial effects of this embodiment: Through the collaboration of various submodules, the present invention performs data parsing, level information acquisition, anomaly identification, historical data acquisition, format conversion, real-time analysis, predictive analysis, comprehensive analysis, historical forecast data acquisition, result verification, and model optimization, thereby achieving comprehensive monitoring and analysis of the production process. The present invention can comprehensively monitor and analyze abnormal conditions, real-time data, and future forecast data in the production process, perform optimization analysis based on optimization indicators, and adjust model parameters and optimize performance based on the optimization analysis results, thereby continuously improving the performance of the analysis model.

[0156] The embodiment of the present invention provides a coal mine production process control system, including a model optimization submodule, including:

[0157] an analysis unit, configured to analyze the comprehensive analysis result to obtain a first sub-result corresponding to the abnormal analysis result, a second sub-result corresponding to the real-time analysis result, and a third sub-result corresponding to the predictive analysis result;

[0158] a feature extraction unit, configured to extract features from the first sub-result, the second sub-result, the third sub-result, and the prediction data verification result, and construct an abnormal feature set, a real-time feature set, a prediction feature set, and a verification feature set based on the extracted features;

[0159] An indicator selection unit is used to select preset optimization indicators from the indicator database and construct a first indicator set matching the abnormal feature set, a second indicator set matching the real-time feature set, a third indicator set matching the prediction feature set, and a fourth indicator set matching the verification feature set;

[0160] A relationship graph acquisition unit is used to obtain the mapping relationship between the preset optimization indicators in the first indicator set, the second indicator set, the third indicator set, and the fourth indicator set, and output an indicator relationship graph;

[0161] an optimization analysis unit, configured to perform optimization analysis on the comprehensive analysis results and the prediction data verification results based on the first indicator set, the second indicator set, the third indicator set, the fourth indicator set, and the indicator relationship diagram, to obtain an optimization analysis result;

[0162] ;

[0163] in, Indicates the optimization analysis results; represents the exponential function; Represents the comprehensive correction coefficient corresponding to the data calculation error under each preset optimization indicator in the first indicator set, the second indicator set, the third indicator set, and the fourth indicator set; Indicates the total number of preset optimization indicators in the i-th indicator set; i=1 corresponds to the first indicator set; i=2 corresponds to the second indicator set; i=3 corresponds to the third indicator set; i=4 corresponds to the fourth indicator set; represents the optimization standard parameter corresponding to the jth preset optimization indicator in the i-th indicator set; Indicates the actual parameter value corresponding to the jth preset optimization indicator in the i-th indicator set in the comprehensive analysis results and the prediction data verification results; represents the optimization weight coefficient corresponding to the jth preset optimization indicator in the i-th indicator set; represents the multiple correlation coefficient between the jth preset optimization indicator in the i-th indicator set and the remaining t-1 preset optimization indicators in each indicator set, and t represents the total number of preset optimization indicators in the first indicator set, the second indicator set, the third indicator set, and the fourth indicator set;

[0164] The model optimization unit is used to combine the preset result-strategy-instruction comparison table to obtain the model parameter performance optimization strategy and the optimization instructions under the corresponding strategy that match the optimization analysis results, and adjust the parameters and optimize the performance of the analysis model based on the model parameter performance optimization strategy and optimization instructions.

[0165] In this embodiment, the first sub-result is a sub-result corresponding to the abnormality analysis result, which is parsed from the comprehensive analysis result and contains detailed information about the abnormality in the production process. For example, if the comprehensive analysis result shows that an abnormality occurs in a certain production link, the first sub-result may include information such as the specific abnormality type, duration of the abnormality, and scope of impact of the link;

[0166] In this embodiment, the second sub-result is a sub-result corresponding to the real-time analysis result, which is parsed from the comprehensive analysis result and provides detailed information about the current production status. For example, if the real-time analysis result shows that the operating status of a certain device is unstable, the second sub-result may include information such as device operating parameters, sensor data, and device operating time.

[0167] In this embodiment, the third sub-result is a sub-result corresponding to the forecast analysis result, parsed from the comprehensive analysis result. It provides forecast information about future production conditions. For example, if the forecast analysis result shows that a certain production indicator will decline within the next week, the third sub-result may include the predicted extent of the decline, possible causes, and recommended countermeasures.

[0168] In this embodiment, the abnormal feature set is a feature set extracted from the first sub-result, which is used to describe the characteristics of the abnormal situation, including but not limited to the abnormality type, abnormality occurrence time, abnormality impact range, abnormality duration, etc.

[0169] In this embodiment, the real-time feature set is a feature set extracted from the second sub-result, used to describe the characteristics of the real-time production status, including but not limited to real-time information such as equipment operating parameters, sensor data, and environmental conditions;

[0170] In this embodiment, the prediction feature set is a feature set extracted from the third sub-result, used to describe the characteristics of future production conditions, including but not limited to prediction indicators, prediction time period, prediction trend, possible influencing factors, and other characteristics;

[0171] In this embodiment, the verification feature set is a feature set used to verify the accuracy of the predicted data and to compare the difference between the predicted data and the actual data, including but not limited to features such as the verification time period, verification indicators, actual data, and predicted data;

[0172] In this embodiment, the indicator database: a database storing preset optimization indicators, used to provide optional indicators in the subsequent indicator selection process, including but not limited to various indicators that can be used for optimization analysis, such as production equipment operating parameters, environmental monitoring data, and production indicators;

[0173] In this embodiment, the first indicator set is a set of preset optimization indicators corresponding to the abnormal feature set;

[0174] In this embodiment, the second indicator set is a set of preset optimization indicators corresponding to the real-time feature set;

[0175] In this embodiment, the third indicator set is a set of preset optimization indicators corresponding to the prediction feature set;

[0176] In this embodiment, the fourth indicator set: a set consisting of preset optimization indicators corresponding to the verification feature set;

[0177] In this embodiment, the indicator relationship diagram: that is, a mapping relationship diagram between indicators, is used to help understand the correlation between the indicators;

[0178] In this embodiment, the preset result-strategy-instruction comparison table: a comparison table containing the mapping relationship between the optimization analysis results and the model parameter performance optimization strategies and optimization instructions, which is used to match the input optimization analysis results to the corresponding model parameter performance optimization strategies and optimization instructions;

[0179] In this embodiment, the model parameter performance optimization strategy: a strategy for adjusting model parameters and optimizing performance determined based on the optimization analysis results;

[0180] In this embodiment, the optimization instruction is to adjust and update the specific parameters of the model according to the specific operation instructions provided by the model parameter performance optimization strategy.

[0181] The implementation principle and beneficial effects of this embodiment: The present invention can achieve parameter adjustment and performance optimization of the analysis model through the collaboration of various units such as the parsing unit, feature extraction unit, indicator selection unit, and relationship graph acquisition unit. The present invention can adjust the parameters and optimize the performance of the analysis model in real time, thereby continuously improving the data analysis performance of the analysis model, ensuring the accuracy of the model analysis results, and thus improving the efficiency and accuracy of the production process control system.

[0182] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A coal mine production process control system, characterized in that: include: Data acquisition module, used to obtain data of each production process in the mine in real time and output initial data; A preprocessing module, configured to preprocess the initial data using a preset preprocessing method to obtain data to be analyzed; The model training module is used to train the model to be trained selected from the model database in combination with preset data resources, output the candidate model, and output the candidate model that meets the preset performance conditions as the analysis model; A data analysis module is used to analyze the data to be analyzed based on the analysis model, output process analysis results, and update parameters and optimize the performance of the analysis model based on the process analysis results and in combination with the preset data resources and cloud data; The data analysis module includes: A data parsing submodule is used to parse the data to be analyzed to obtain sub-data corresponding to each production process; a level information acquisition submodule is used to obtain monitoring level information of each sub-data and summarize and output a process monitoring level table; a comparison table generation submodule is used to combine the process monitoring level table and the sub-data corresponding to each production process under each monitoring level to output a monitoring level-data comparison table; an abnormality identification submodule is used to extract features of the sub-data of the production process under each monitoring level in the monitoring level-data comparison table, and construct a second feature set, and perform feature matching on the data features in the second feature set in combination with a preset abnormal feature database to obtain abnormality identification sub-results corresponding to the sub-data of the production process under each monitoring level, and summarize and output abnormality analysis results; a historical data acquisition submodule is used to select from the historical database the first historical process data that matches the data numerical features in the second feature set and the second historical process data that matches the data temporal features in the second feature set; a format conversion submodule is used to obtain data format information of the sub-data, first historical process data and second historical process data corresponding to each monitoring level, and combine the format requirement information of the analysis model to match the data format of each monitoring level to the sub-data of the production process under each monitoring level. The corresponding sub-data, the first historical process data and the second historical process data are format-converted to obtain the first standard data, the second standard data and the third standard data respectively; a real-time analysis sub-module is used to perform real-time analysis on the first standard data corresponding to the production process of each monitoring level based on the analysis model, and output the real-time analysis results; a prediction analysis sub-module is used to perform prediction analysis on the second standard data and the third standard data based on the analysis model, obtain the prediction data of each production process under each monitoring level within a preset time period in the future, and output the prediction analysis results; a comprehensive analysis sub-module is used to analyze the abnormal analysis results, real-time analysis results and prediction analysis results using a preset comprehensive analysis method, and output the comprehensive analysis results; a historical prediction data acquisition sub-module is used to obtain historical prediction data matching the analysis model in the historical database; a result verification sub-module is used to perform time calibration and data verification on the historical prediction data and the real-time analysis results under each same production process, and output the prediction data verification results; a model optimization sub-module is used to optimize the comprehensive analysis results and the prediction data verification results in combination with preset optimization indicators, and adjust the parameters and optimize the performance of the analysis model based on the optimization analysis results; The model optimization submodule includes: a parsing unit for parsing the comprehensive analysis result to obtain a first sub-result corresponding to the abnormal analysis result, a second sub-result corresponding to the real-time analysis result, and a third sub-result corresponding to the predictive analysis result; a feature extraction unit for extracting features from the first sub-result, the second sub-result, the third sub-result, and the prediction data verification result, and constructing an abnormal feature set, a real-time feature set, a predictive feature set, and a verification feature set based on the extracted features; an indicator selection unit for selecting preset optimization indicators in an indicator database, and constructing a first indicator set matching the abnormal feature set, a second indicator set matching the real-time feature set, a third indicator set matching the predictive feature set, and a verification feature set matching the prediction feature set. The fourth indicator set matched by the verification feature set; a relationship diagram acquisition unit, used to obtain the mapping relationship between each preset optimization indicator in the first indicator set, the second indicator set, the third indicator set and the fourth indicator set, and output the indicator relationship diagram; an optimization analysis unit, used to optimize and analyze the comprehensive analysis results and the prediction data verification results based on the first indicator set, the second indicator set, the third indicator set, the fourth indicator set and the indicator relationship diagram to obtain the optimization analysis results; a model optimization unit, used to obtain the model parameter performance optimization strategy matching the optimization analysis results and the optimization instructions under the corresponding strategy in combination with the preset result-strategy-instruction comparison table, and adjust the parameters and optimize the performance of the analysis model based on the model parameter performance optimization strategy and the optimization instructions.

2. A coal mine production process control system according to claim 1, characterized in that: The data acquisition module includes: The process classification submodule is used to divide the production processes in the mine into categories based on the monitoring priority of each production process and obtain a process classification table; The data acquisition submodule is used to obtain data of each production process in the process classification table in real time using preset monitoring equipment set at preset positions, and output initial data.

3. A coal mine production process control system according to claim 2, characterized in that: The data acquisition submodule includes: A demand acquisition unit is used to acquire the monitoring requirements corresponding to each production process, and summarize and output the system monitoring requirements in combination with the monitoring priorities corresponding to each production process; An equipment selection unit is configured to obtain preset monitoring equipment corresponding to each production process based on the system monitoring requirements and in combination with a preset requirement-equipment matching table, determine a preset location for installing each preset monitoring equipment, and output a process-equipment comparison table; The data acquisition unit is used to obtain data of each production process in the mine in real time through the preset monitoring equipment at each preset position based on the process-equipment comparison table, and comprehensively output initial data.

4. A coal mine production process control system according to claim 1, characterized in that: The preprocessing module includes: a method selection submodule for extracting features from the initial data, constructing a first feature set based on the extracted features, and acquiring a preset preprocessing method that matches the first feature set in combination with a preset feature-method mapping table; The preprocessing submodule is configured to preprocess the initial data based on the preset preprocessing method to obtain first data, and output the first data that meets the first threshold condition as data to be analyzed.

5. A coal mine production process control system according to claim 3, characterized in that: The model training module includes: The model selection submodule is used to select a model to be trained that matches the system monitoring requirements in the model database based on the preset requirements-model matching table; The model training submodule is used to select preset data resources from a preset resource pool, train the to-be-trained model based on the preset data resources, and output an alternative model; The model output submodule is used to obtain performance requirement information of the model, output preset performance conditions based on the performance requirement information, and output the candidate model that meets the preset performance judgment conditions as the analysis model.

6. A coal mine production process control system according to claim 5, characterized in that: The model training submodule includes: A data selection unit, configured to obtain a data selection instruction and select corresponding preset data resources from a target resource pool based on the data selection instruction; A data partitioning unit is used to partition the preset data resources using a preset data partitioning method and output a training data set, a validation data set, and a test data set; The model training unit is used to train and optimize the performance of the model to be trained based on the training data set, the verification data set and the test data set, and output an alternative model.

7. A coal mine production process control system according to claim 1, characterized in that: The optimizing analysis of the comprehensive analysis results and the prediction data verification results to obtain the optimized analysis results includes: According to the formula ; in, Indicates the optimization analysis results; represents the exponential function; Represents the comprehensive correction coefficient corresponding to the data calculation error under each preset optimization indicator in the first indicator set, the second indicator set, the third indicator set, and the fourth indicator set; Indicates the total number of preset optimization indicators in the i-th indicator set; i=1 corresponds to the first indicator set; i=2 corresponds to the second indicator set; i=3 corresponds to the third indicator set; i=4 corresponds to the fourth indicator set; represents the optimization standard parameter corresponding to the jth preset optimization indicator in the i-th indicator set; Indicates the actual parameter value corresponding to the jth preset optimization indicator in the i-th indicator set in the comprehensive analysis results and the prediction data verification results; represents the optimization weight coefficient corresponding to the jth preset optimization indicator in the i-th indicator set; It represents the multiple correlation coefficient between the jth preset optimization indicator in the i-th indicator set and the remaining t-1 preset optimization indicators in each indicator set, and t represents the total number of preset optimization indicators in the first indicator set, the second indicator set, the third indicator set, and the fourth indicator set.

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