A coal mine energy optimization control method and system

Abstract

The present invention discloses a coal mine energy optimization control method and system, which relates to the field of intelligent control technology. The method includes: based on the target coal mine energy area, real-time collection of multiple coal mine equipment operation data sets for classification evaluation, positioning and obtaining a set of equipment to be energy optimized, extracting associated parameters, obtaining a set of equipment associated characteristic parameters, and obtaining a coal mine equipment optimization degree set through simulation analysis and evaluation, matching it with the equipment optimization control execution strategy, and when a preset optimization degree threshold is met, optimizing the control parameters, obtaining the equipment optimization control parameter set for coal mine energy optimization control. The present invention solves the technical problem that the traditional coal mine energy management method in the prior art relies on experience and fixed management models, cannot adjust the operation strategy in time according to the equipment status, and affects the energy utilization efficiency, and achieves the technical effect of improving energy utilization through real-time equipment operation status monitoring and equipment control optimization.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a coal mine energy optimization control method and system. Background Art

[0002] In coal mine operations, the operation of mechanical equipment is the main source of energy consumption, including mining equipment, ventilation systems, drainage equipment and transportation systems. These equipment operate under high-load and high-intensity working environments, and have huge energy demands. With the expansion of coal mine scale and the improvement of mechanization, energy consumption issues have become more prominent, and energy costs have become an important factor affecting the operating efficiency of coal mines.

[0003] Traditional coal mine energy management methods usually rely on experience and fixed management models, lacking real-time monitoring of equipment operating status and data-driven optimization methods. As a result, when equipment ages or operating conditions change, it is impossible to adjust the operating strategy in time, which in turn affects energy efficiency. Summary of the invention

[0004] The present application provides a coal mine energy optimization control method and system, which is used to solve the technical problem that the traditional coal mine energy management method in the prior art relies on experience and fixed management models, cannot adjust the operation strategy in time according to the equipment status, and affects the energy utilization efficiency.

[0005] In a first aspect of the present application, a method for coal mine energy optimization control is provided, the method comprising: obtaining a target coal mine energy area, performing an Internet of Things deployment analysis on the target coal mine energy area, and obtaining an Internet of Things group perception network; acquiring multiple operating data sets of multiple coal mine equipment in real time through the Internet of Things group perception network, identifying energy utilization rates of the multiple operating data sets, and obtaining multiple equipment energy utilization rates; classifying and evaluating the multiple coal mine equipment based on the multiple equipment energy utilization rates, and locating and obtaining a set of equipment to be energy optimized; extracting associated parameters of the set of equipment to be energy optimized, respectively, to obtain a set of equipment associated characteristic parameters, and using the set of equipment associated characteristic parameters to perform simulation analysis and evaluation to obtain a set of coal mine equipment optimization degrees; constructing an equipment optimization control execution strategy, matching the coal mine equipment optimization degree set with the equipment optimization control execution strategy, and executing equipment parameter optimization instructions when a preset optimization degree threshold is met; optimizing control parameters of the set of equipment to be energy optimized based on the equipment parameter optimization instructions, obtaining a set of equipment optimization control parameters, and performing coal mine energy optimization control through the set of equipment optimization control parameters.

[0006] In a second aspect of the present application, a coal mine energy optimization control system is provided, the system comprising: an Internet of Things deployment analysis module, the Internet of Things deployment analysis module is used to obtain a target coal mine energy area, perform Internet of Things deployment analysis on the target coal mine energy area, and obtain an Internet of Things group perception network; an energy utilization rate identification module, the energy utilization rate identification module is used to collect and obtain multiple operating data sets of multiple coal mine equipment in real time through the Internet of Things group perception network, perform energy utilization rate identification on the multiple operating data sets, and obtain multiple equipment energy utilization rates; an equipment classification evaluation module, the equipment classification evaluation module is used to classify and evaluate the multiple coal mine equipment based on the energy utilization rates of the multiple equipment, and locate and obtain a set of equipment to be energy optimized; a simulation analysis evaluation module, the simulation analysis evaluation module The simulated analysis and evaluation module is used to extract the associated parameters of the equipment set to be energy optimized, obtain the equipment associated characteristic parameter set, use the equipment associated characteristic parameter set to perform simulation analysis and evaluation, and obtain the coal mine equipment optimization degree set; the execution strategy matching module, the execution strategy matching module is used to construct an equipment optimization control execution strategy, match the coal mine equipment optimization degree set with the equipment optimization control execution strategy, and execute the equipment parameter optimization instruction when the preset optimization degree threshold is met; the coal mine energy optimization control module, the coal mine energy optimization control module is used to optimize the control parameters of the equipment set to be energy optimized based on the equipment parameter optimization instruction, obtain the equipment optimization control parameter set, and perform coal mine energy optimization control through the equipment optimization control parameter set.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The present application provides a coal mine energy optimization control method and system, which relate to the field of intelligent control technology. Based on the target coal mine energy area, the operation data set of multiple coal mine equipment is collected in real time, the set of equipment to be energy optimized is located, and the related parameters are extracted for simulation analysis and evaluation to obtain the coal mine equipment optimization degree set, which is matched with the equipment optimization control execution strategy. When the preset optimization degree threshold is met, the control parameters are optimized, and the equipment optimization control parameter set is obtained to perform coal mine energy optimization control. This solves the technical problem that the traditional coal mine energy management method in the prior art relies on experience and fixed management models, and cannot adjust the operation strategy in time according to the equipment status, thus affecting the energy utilization efficiency. This achieves the technical effect of improving energy utilization through real-time equipment operation status monitoring and equipment control optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a coal mine energy optimization control method provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of a process for obtaining a set of equipment optimization control parameters in a coal mine energy optimization control method provided in an embodiment of the present application;

[0012] Figure 3 A schematic diagram of the structure of a coal mine energy optimization control system provided in an embodiment of the present application.

[0013] Explanation of the reference numerals: Internet of Things deployment analysis module 11, energy utilization identification module 12, equipment classification evaluation module 13, simulation analysis evaluation module 14, execution strategy matching module 15, coal mine energy optimization control module 16. DETAILED DESCRIPTION

[0014] The present application provides a coal mine energy optimization control method and system, which is used to solve the technical problem that the traditional coal mine energy management method in the prior art relies on experience and fixed management models, cannot adjust the operation strategy in time according to the equipment status, and affects the energy utilization efficiency.

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0017] Embodiment 1

[0018] like Figure 1 As shown, the present application provides a coal mine energy optimization control method, the method comprising:

[0019] P10: Obtain the target coal mine energy area, perform IoT deployment analysis on the target coal mine energy area, and obtain the IoT group perception network.

[0020] Furthermore, step P10 of the embodiment of the present application further includes:

[0021] P11: Classify the target coal mine energy area by functional attributes and divide the attribute areas to determine the coal mine energy attribute division area set; P12: Analyze the data collection volume of the coal mine equipment in the coal mine energy attribute division area set in turn to obtain the data collection volume information of the coal mine area; P13: Based on the data collection volume information of the coal mine area, perform balanced subdivision of the monitoring points of the coal mine energy attribute division area set to obtain a coal mine monitoring network node set, wherein each node in the coal mine monitoring network node set includes a data collection unit and a data communication unit; P14: Deploy the Internet of Things perception network for the target coal mine energy area based on the coal mine monitoring network node set to obtain the Internet of Things group perception network.

[0022] It should be understood that the specific coal mine area that needs energy optimization, namely the target coal mine energy area, is determined, and the deployment analysis of Internet of Things (IoT) devices is carried out in this area in order to establish an IoT group perception network to ensure that the data of each device can be effectively monitored and managed.

[0023] First, the target coal mine energy area is classified by functional attributes, that is, it is classified according to the function and purpose of different equipment in the coal mine area. For example, some areas may be mainly responsible for the operation of coal mining equipment, while other areas may focus on ventilation or drainage systems. The classification of these functional attributes helps to clarify the energy demand characteristics of each area. Subsequently, based on the classification results of functional attributes, the target coal mine area is divided into attribute areas to determine the set of coal mine energy attribute division areas. The purpose of this step is to divide the entire coal mine energy area into multiple independent attribute areas for subsequent detailed monitoring and management.

[0024] Furthermore, in each coal mine energy attribute division area, the data collection volume of coal mine equipment in the area is analyzed. Each coal mine equipment is usually equipped with corresponding data sensors, which are responsible for collecting the operation data of the equipment. Due to the different number, functions and application scenarios of different equipment, the data collection volume of its sensors may vary. By analyzing the data collection volume of each device, that is, the amount of data collected by the sensor per unit time, the overall data collection volume information of the coal mine area is obtained. This information is very important for the subsequent deployment of the monitoring network.

[0025] Next, based on the data collection volume information of the coal mine area, the set of coal mine energy attribute division areas is evenly subdivided into monitoring points. For example, if the amount of equipment data collected in a certain area is large, it may cause delays in data transmission or excessive network load. Therefore, it is necessary to subdivide the monitoring points in these high data volume areas to ensure load balance of data collection and avoid monitoring loopholes caused by untimely data transmission. After balanced subdivision, a set of coal mine monitoring network nodes is generated. Each node in this node set usually includes two key units: a data collection unit and a data communication unit. The data collection unit is responsible for collecting the operating data of the equipment in the area, while the data communication unit ensures that the data can be transmitted to the central monitoring system or other processing units in a timely manner.

[0026] Finally, based on the coal mine monitoring network node set generated above, the IoT perception network is deployed in the target coal mine energy area. This deployment process refers to setting up each node according to the plan to form an IoT group perception network covering the entire target area, achieving comprehensive and real-time monitoring of the target coal mine energy area, and laying a data foundation for subsequent energy optimization control.

[0027] P20: Acquire multiple operating data sets of multiple coal mine equipment in real time through the IoT group sensing network, identify energy utilization rates of the multiple operating data sets, and obtain energy utilization rates of multiple devices.

[0028] Furthermore, step P20 of the embodiment of the present application further includes:

[0029] P21: Perform outlier identification and data standardization processing on the multiple operating data sets respectively to obtain multiple standard equipment operating data sets; P22: Acquire data collection attribute information of the multiple coal mine equipment, and determine multiple equipment energy utilization evaluation indicators based on the data collection attribute information of the multiple coal mine equipment; P23: Perform associated data extraction on the multiple operating data sets based on the multiple equipment energy utilization evaluation indicators to obtain multiple equipment energy utilization evaluation associated data; P24: Perform energy utilization calculations based on the multiple equipment energy utilization evaluation associated data respectively to obtain the energy utilization rates of the multiple equipment.

[0030] Optionally, the operation data of coal mine equipment is collected in real time through the IoT group perception network, and the energy utilization rate is identified and analyzed. First, in order to ensure the accuracy and reliability of subsequent analysis, it is necessary to identify outliers and standardize the data for the multiple collected operation data sets. Outlier identification aims to eliminate extreme or unreasonable data points caused by equipment failure, data transmission errors, etc., and the normal range of data can be defined by statistical methods (such as box plot method, Z-score method, etc.). Subsequently, through data standardization processing (such as minimum-maximum normalization, Z-score standardization, etc.), data with different dimensions and different value ranges are converted into a unified format for subsequent comparison and analysis. The output of multiple standard equipment operation data sets removes noise and is comparable.

[0031] Next, in order to accurately evaluate the energy utilization rate of coal mine equipment, it is necessary to determine the corresponding energy utilization evaluation indicators based on the data collection attribute information of the equipment (including data collection type, format, value range, etc.). These indicators are a direct reflection of the energy efficiency of the equipment and are usually closely related to the ratio of the equipment's energy output energy to input energy. Specifically, it is to determine the necessary and effective data attributes for calculating the energy utilization rate of each device, such as power consumption, output, operating time, etc. Through this process, a set of energy efficiency evaluation systems are tailored for each device.

[0032] Based on the energy utilization evaluation indicators determined in the previous step, data directly related to these evaluation indicators are extracted from the preprocessed standard equipment operation data set, that is, related data extraction. This process can use database query, data mining and other technical means to efficiently filter out key information from massive data, build a related data set that reflects the energy utilization of the equipment, and provide direct data support for subsequent energy utilization calculations.

[0033] Finally, based on the extracted data on energy utilization evaluation of multiple devices, the energy utilization of each device is calculated according to a predetermined calculation method (such as directly calculating the ratio of energy output to input, or using a more complex energy efficiency evaluation model). The output of this step is the energy utilization value of multiple devices, which intuitively reflects the energy efficiency of the equipment and provides a scientific basis for energy conservation, emission reduction and energy efficiency improvement of coal mining enterprises.

[0034] Furthermore, step P21 of the embodiment of the present application further includes:

[0035] P21-1: Determine multiple equipment abnormal data evaluation indicators based on the data acquisition attribute information respectively, and the multiple equipment abnormal data evaluation indicators include consistency evaluation, threshold verification and integrity evaluation; P21-2: Based on the multiple equipment abnormal data evaluation indicators, perform abnormal value identification on the multiple operation data sets respectively to obtain multiple equipment abnormal operation data; P21-3: Determine multiple equipment abnormal data standardization processing procedures based on the multiple equipment abnormal data evaluation indicators; P21-4: Based on the multiple equipment abnormal data standardization processing procedures, perform standardization processing on the multiple equipment abnormal operation data respectively to obtain the multiple standard equipment operation data sets.

[0036] Specifically, the specific process of performing outlier identification and data standardization processing on the multiple operating data sets may be, first, determining the abnormal data evaluation indicators of multiple devices based on the data acquisition attribute information of each device. These evaluation indicators include: consistency evaluation, that is, checking whether there is significant inconsistency between the data before and after. For example, some sensor data changes dramatically in a short period of time, which may be caused by equipment failure or sensor problems. Threshold verification, that is, evaluating whether the data value exceeds the normal range of the data type. For example, if the voltage or current data exceeds the predetermined safety range, exception processing may be required. Integrity evaluation, that is, checking whether the data is missing or incomplete. For example, interruptions or errors in the data acquisition process may cause some data to be missing.

[0037] Furthermore, based on the abnormal data evaluation index determined above, outlier identification is performed on multiple operating data sets. By comparing the abnormal data evaluation index, abnormal data points that do not meet the consistency, exceed the threshold range or have incomplete data are identified. In this way, abnormal operating data of multiple devices can be effectively screened out.

[0038] For the identified abnormal data, determine the corresponding standardized processing procedures according to the abnormal data evaluation indicators of different devices. For different abnormal situations of different devices, formulate specific standardized processing steps. For example, for data exceeding the threshold, it can be processed by data correction or elimination; for incomplete data, the missing data can be supplemented by data interpolation.

[0039] Finally, based on the determined abnormal data standardization processing procedure, the abnormal operation data of each device is standardized. Through standardization, the abnormal data is converted into a standard format and range that meets the system requirements. These processed data are finally integrated into multiple standard equipment operation data sets, providing reliable basic data for subsequent energy utilization identification and calculation.

[0040] P30: Classify and evaluate the multiple coal mine equipment based on the energy utilization rates of the multiple equipment, and locate and obtain a set of equipment to be energy optimized.

[0041] It should be understood that the energy utilization rates of multiple coal mining equipment are classified and evaluated to identify and locate the set of equipment that needs energy optimization. Specifically, first, based on the energy utilization rates of multiple equipment calculated in the previous steps, these coal mining equipment are classified and evaluated. According to the energy utilization efficiency of each equipment, the equipment is divided into different categories. For example, equipment can be divided into categories with energy utilization rates that meet the standards, energy utilization rates that are close to meeting the standards, and energy utilization rates that do not meet the standards.

[0042] The basis for classification evaluation can be a preset energy utilization standard or threshold. The actual energy utilization of each device is compared with the energy utilization standard or threshold to determine the energy efficiency performance of the device. This process not only helps to identify devices with low energy utilization, but also finds devices with energy utilization close to the threshold, which is convenient for further optimization. After the classification evaluation, the devices with substandard energy utilization are located and identified to form a set of devices to be energy optimized. This set includes all devices that fail to meet the preset energy utilization standards, usually due to low equipment operating efficiency, excessive energy consumption or potential failures. It can lay the foundation for subsequent energy optimization control and ensure that optimization measures can be implemented accurately.

[0043] P40: Extract associated parameters of the equipment set to be energy optimized respectively to obtain an equipment associated characteristic parameter set, use the equipment associated characteristic parameter set to perform simulation analysis and evaluation to obtain a coal mine equipment optimization degree set.

[0044] Furthermore, step P40 of the embodiment of the present application also includes:

[0045] P41: Construct a set of equipment operation simulation scenarios, perform parameter analysis and orthogonal arrangement on the equipment-related characteristic parameter set based on the equipment operation simulation scenario set, and obtain an equipment operation scenario simulation control parameter table; P42: Perform equipment operation simulation tests in sequence according to the equipment operation scenario simulation control parameter table to obtain an equipment operation scenario simulation data set; P43: Perform energy utilization impact assessment on the equipment operation scenario simulation data set according to the equipment operation simulation scenario set to obtain a multi-simulation scenario equipment optimization degree set; P44: Perform weighted calculation and integration on the multi-simulation scenario equipment optimization degree set according to the equipment set to be energy optimized to obtain the coal mine equipment optimization degree set.

[0046] In a possible embodiment of the present application, the associated parameters of the set of equipment to be optimized are extracted respectively, that is, the potential optimization space of the set of equipment to be optimized is explored, and the associated parameters are extracted for each equipment to be optimized. These parameters include but are not limited to the equipment's operating time, load changes, ambient temperature, maintenance history, etc., which together constitute a set of equipment-associated characteristic parameters. The set of equipment-associated characteristic parameters is used to perform simulation analysis and evaluation to predict the impact of different parameter combinations on equipment performance and energy utilization, and finally obtain a preliminary optimization degree set of coal mine equipment.

[0047] Specifically, first, based on the extraction of device-related parameters, a set of device operation simulation scenarios is constructed. Each simulation scenario represents a different combination of control parameters, such as the device's operating time, output power, heating or cooling strategy, etc. By setting multiple simulation scenarios, the impact of different operating conditions on the energy utilization of the device can be comprehensively evaluated. The device-related characteristic parameters in these simulation scenarios are analyzed and orthogonally arranged in detail to generate a device operation scenario simulation control parameter table. This parameter table lists the specific control parameter combinations under each simulation scenario, providing clear operating steps for subsequent simulation tests.

[0048] Furthermore, according to the equipment operation scenario simulation control parameter table, the equipment is run in turn for simulation tests. The simulation test simulates different equipment operation conditions, observes and records the energy utilization of the equipment under these conditions. Each simulation test generates a equipment operation scenario simulation data set. These data sets record key data such as energy consumption and output efficiency of the equipment in different scenarios, providing a measured basis for subsequent optimization evaluation.

[0049] Next, based on the generated equipment operation scenario simulation data set, the impact assessment of the equipment energy utilization rate in each simulation scenario is performed. The key to the assessment is to analyze the impact of different control parameter combinations on the improvement of energy utilization rate. Exemplarily, after the control parameters in each simulation scenario are adjusted, the change in energy utilization rate is calculated. For example, under certain parameter combinations, the energy utilization rate is significantly improved, and these improved percentage values ​​represent the equipment optimization degree in the scenario. Finally, the optimization degree in each simulation scenario is integrated to form a multi-simulation scenario equipment optimization degree set.

[0050] Finally, the optimization degree set of multiple simulation scenarios is weighted and integrated according to the set of equipment to be optimized. The weighted calculation takes into account the importance of each simulation scenario. By weighting the optimization degree under each scenario, it can more accurately reflect which scenarios contribute the most to equipment optimization. After the weighted calculation of the scenario, the optimization degree of each device is further integrated to finally generate the optimization degree set of coal mine equipment. This set can provide an important reference for the optimization control decision of the equipment.

[0051] P50: Construct an equipment optimization control execution strategy, match the equipment optimization control execution strategy based on the coal mine equipment optimization degree set, and execute equipment parameter optimization instructions when a preset optimization degree threshold is met.

[0052] Specifically, we build an equipment optimization control execution strategy, and based on the optimization degree set of coal mine equipment, we optimize or replace the equipment in a targeted manner to improve the overall energy utilization rate. First, we build an equipment optimization control execution strategy, which is a comprehensive strategy framework that includes specific optimization measures for different equipment and different optimization levels. The formulation of this strategy relies on an in-depth analysis of the optimization degree set of coal mine equipment, as well as a comprehensive consideration of equipment performance, maintenance history, cost-effectiveness, and other aspects.

[0053] Next, the coal mine equipment optimization set is accurately matched with the equipment optimization control execution strategy. Pattern recognition and decision support technology are used to automatically analyze the optimization level of each device through algorithms and determine the most suitable optimization path. For example, for equipment with low optimization, it may be identified that these devices have degraded performance due to long-term use or after fault repair, and simple parameter adjustment optimization may have limited effect on improving their energy utilization. For such equipment, equipment replacement instructions are generated, and enterprises are advised to consider upgrading to fundamentally improve energy efficiency.

[0054] For devices whose optimization degree meets the preset optimization degree threshold, the control parameter optimization strategy is adopted. These parameters may include equipment operation time, load distribution, working mode adjustment, etc. Through fine adjustment, the energy utilization rate of these devices can be maximized. In this process, advanced control algorithms such as model predictive control (MPC) and optimization algorithms (such as genetic algorithms and particle swarm optimization) can be used to assist in formulating the optimal control parameter combination.

[0055] When executing equipment parameter optimization instructions, ensure the accuracy and executability of the instructions. By integrating with the production management system of coal mining enterprises, the optimization instructions are sent to the corresponding equipment control system in real time to achieve remote parameter adjustment and optimization. At the same time, a feedback mechanism is established to continuously monitor the operating status of the optimized equipment and changes in energy utilization to ensure the effectiveness of the optimization measures, and make necessary adjustments and optimizations according to actual conditions.

[0056] P60: Optimize the control parameters of the equipment set to be energy optimized based on the equipment parameter optimization instruction, obtain an equipment optimization control parameter set, and perform coal mine energy optimization control through the equipment optimization control parameter set.

[0057] Further, such as Figure 2As shown, step P60 of the embodiment of the present application also includes:

[0058] P61: Determine the target equipment optimization degree set and the target equipment energy utilization rate set based on the equipment set to be energy optimized; P62: Preset the equipment energy utilization rate benchmark, calculate and obtain the target equipment energy utilization rate set and the equipment energy utilization rate deviation set of the equipment energy utilization rate benchmark; P63: Perform control parameter optimization analysis on the equipment energy utilization rate deviation set based on the target equipment optimization degree set, and obtain the equipment control parameter selection threshold set; P64: Construct an optimization target fitness function, perform global optimization within the equipment control parameter selection threshold set based on the optimization target fitness function, and obtain the equipment optimization control parameter set.

[0059] It should be understood that, based on the equipment parameter optimization instructions, the control parameters of the energy optimization equipment set are optimized to ultimately achieve coal mine energy optimization control.

[0060] First, according to the set of devices to be energy optimized, the optimization degree set and energy utilization rate set of the target devices are determined. The optimization degree set represents the optimization potential of each device evaluated in the previous step, while the energy utilization rate set reflects the energy utilization efficiency of these devices in their current state. By comparing and analyzing these sets, we can further identify which devices need to be optimized, so as to formulate more accurate control strategies.

[0061] Next, the equipment energy utilization rate benchmark is preset. This benchmark is determined based on the production needs of the coal mine, equipment characteristics and industry standards, and serves as the energy utilization rate standard for the equipment under the ideal operating state. By calculating the difference between the target equipment energy utilization rate set and the equipment energy utilization rate benchmark, the equipment energy utilization rate deviation set is obtained. This deviation set indicates the gap between the current operating state of the equipment and the ideal state, providing an important reference for subsequent optimization analysis.

[0062] Furthermore, based on the target device optimization degree set, the device energy utilization rate deviation set is analyzed for control parameter optimization. The core of this step is to identify the key control parameters that affect energy utilization by analyzing the relationship between device optimization degree and energy utilization rate deviation, and determine the optimal selection range of these parameters, that is, the device control parameter selection threshold set. These threshold sets define the value range that different control parameters should maintain during the optimization process, ensuring that the device can achieve the best energy utilization effect during operation.

[0063] After completing the generation of the control parameter selection threshold set, construct the optimization target fitness function. This function can be defined according to different optimization objectives, such as maximizing energy conversion efficiency, minimizing operating costs, reducing pollutant emissions, etc. These objectives are usually fitted and constructed by analyzing the historical data and actual operating conditions of the equipment. Based on the constructed optimization target fitness function, a global optimization is performed within the device control parameter selection threshold set. Global optimization refers to finding the optimal solution among all possible parameter combinations to ensure that the equipment can achieve the best energy utilization under current conditions. Finally, the equipment optimization control parameter set is obtained. These parameters will be applied to the actual equipment control to guide the equipment operation, which can not only reduce operating costs, but also reduce energy waste and improve the overall performance of the equipment, thereby realizing the intelligent management and control of coal mine energy.

[0064] Furthermore, step P64 of the embodiment of the present application also includes:

[0065] P64-1: Randomly select multiple device control parameter sets within the device control parameter selection threshold set, use the optimization target fitness function to evaluate and calculate the multiple device control parameter sets, and obtain multiple device control effect fitnesses; P64-2: Perform gradient ascent calculation and optimization direction analysis based on the multiple device control effect fitnesses to determine the device control parameter optimization direction set; P64-3: Iteratively search and evaluate the device control parameter selection threshold set according to the device control parameter optimization direction set until the preset convergence requirement is reached, and obtain the device optimization control parameter set.

[0066] Specifically, the specific process of performing global optimization within the device control parameter selection threshold set based on the optimization target fitness function may be, first, randomly selecting multiple device control parameter sets within the device control parameter selection threshold set. Each control parameter set represents a set of possible device operation configurations, which will be evaluated in subsequent steps. Subsequently, these randomly selected device control parameter sets are evaluated and calculated using the optimization target fitness function. The evaluation result of the fitness function reflects the effect of each set of control parameters in achieving specific optimization goals (such as maximizing energy efficiency, minimizing costs, etc.), and ultimately generates multiple device control effect fitness values.

[0067] Furthermore, after obtaining the fitness values ​​of multiple device control effects, a gradient ascent calculation is performed based on these fitness values. Gradient ascent calculation is an optimization method that determines which parameter adjustment direction can most quickly improve the fitness value of the device under the current conditions by calculating the gradient of the fitness value as the parameters change. Through this analysis, a set of optimization directions for device control parameters is determined. This set includes the direction with the fastest fitness gradient increase, that is, the parameter optimization direction that is most likely to achieve global optimization through parameter adjustment.

[0068] Furthermore, according to the determined device control parameter optimization direction set, the device control parameter selection threshold set is iteratively optimized. Iterative optimization means that after each calculation, the system gradually adjusts the control parameters according to the optimization direction, and re-evaluates the fitness after each adjustment. This process will continue until the fitness value of the system reaches the preset convergence requirement, that is, further adjustment of the control parameters can no longer significantly improve the fitness value, or the change in the fitness value is lower than a certain set threshold.

[0069] Finally, a set of optimized control parameters for the equipment is obtained. After multiple iterations and optimizations, the control parameters in this set represent the optimal configuration for the operation of the equipment under the current conditions, which can maximize the energy efficiency of the equipment or meet other specific optimization goals. Through these steps, the control parameters of the equipment can be accurately adjusted to achieve global optimization. This method can not only effectively improve the energy utilization rate of the equipment, but also reduce unnecessary energy waste, and ultimately realize the intelligent management of coal mine equipment operation and efficient use of energy.

[0070] In summary, the embodiments of the present application have at least the following technical effects:

[0071] This application collects the operating data sets of multiple coal mine equipment in real time based on the target coal mine energy area, locates the set of equipment to be optimized for energy, extracts related parameters for simulation analysis and evaluation, obtains the coal mine equipment optimization degree set, matches it with the equipment optimization control execution strategy, and optimizes the control parameters when the preset optimization degree threshold is met, obtains the equipment optimization control parameter set for coal mine energy optimization control. The technical effect of optimizing equipment control through real-time equipment operation status monitoring and analysis, improving energy utilization and the overall benefits and sustainability of coal mine operations is achieved.

[0072] Embodiment 2

[0073] Based on the same inventive concept as the coal mine energy optimization control method in the aforementioned embodiment, Figure 3 As shown, the present application provides a coal mine energy optimization control system, and the system and method embodiments in the present application embodiments are based on the same inventive concept. The system includes:

[0074] The Internet of Things deployment analysis module 11 is used to obtain a target coal mine energy area, perform Internet of Things deployment analysis on the target coal mine energy area, and obtain an Internet of Things group perception network.

[0075] The energy utilization rate identification module 12 is used to collect and obtain multiple operating data sets of multiple coal mine equipment in real time through the Internet of Things group perception network, identify the energy utilization rates of the multiple operating data sets, and obtain energy utilization rates of multiple devices.

[0076] The equipment classification and evaluation module 13 is used to classify and evaluate the multiple coal mine equipment based on the energy utilization rates of the multiple equipment, and locate and obtain a set of equipment to be energy optimized.

[0077] The simulation analysis and evaluation module 14 is used to extract the associated parameters of the equipment set to be energy optimized, obtain the equipment associated characteristic parameter set, use the equipment associated characteristic parameter set to perform simulation analysis and evaluation, and obtain the coal mine equipment optimization degree set.

[0078] The execution strategy matching module 15 is used to construct an equipment optimization control execution strategy, match the equipment optimization control execution strategy based on the coal mine equipment optimization degree set, and execute the equipment parameter optimization instruction when the preset optimization degree threshold is met.

[0079] The coal mine energy optimization control module 16 is used to optimize the control parameters of the set of equipment to be energy optimized based on the equipment parameter optimization instruction, obtain the equipment optimization control parameter set, and perform coal mine energy optimization control through the equipment optimization control parameter set.

[0080] Furthermore, the Internet of Things deployment analysis module 11 is also used to perform the following steps:

[0081] Classifying the target coal mine energy area by functional attributes and dividing the attribute areas, and determining a set of coal mine energy attribute division areas;

[0082] Performing data collection volume analysis on the coal mine equipment in the coal mine energy attribute division area set in turn to obtain coal mine area data collection volume information;

[0083] Based on the data collection amount information of the coal mine area, the set of coal mine energy attribute division areas is subdivided into monitoring points in a balanced manner to obtain a set of coal mine monitoring network nodes, each node in the set of coal mine monitoring network nodes includes a data collection unit and a data communication unit;

[0084] Based on the coal mine monitoring network node set, an Internet of Things perception network is deployed in the target coal mine energy area to obtain the Internet of Things group perception network.

[0085] Furthermore, the energy utilization rate identification module 12 is further configured to perform the following steps:

[0086] Perform outlier identification and data standardization processing on the multiple operating data sets respectively to obtain multiple standard equipment operating data sets; obtain data collection attribute information of the multiple coal mine equipment, and determine multiple equipment energy utilization evaluation indicators based on the data collection attribute information of the multiple coal mine equipment; extract associated data from the multiple operating data sets based on the multiple equipment energy utilization evaluation indicators to obtain multiple equipment energy utilization evaluation associated data; perform energy utilization calculations based on the multiple equipment energy utilization evaluation associated data to obtain the multiple equipment energy utilization rates.

[0087] Furthermore, the energy utilization rate identification module 12 is further configured to perform the following steps:

[0088] According to the data collection attribute information, a plurality of equipment abnormal data evaluation indicators are determined respectively, and the plurality of equipment abnormal data evaluation indicators include consistency evaluation, threshold value verification and integrity evaluation; based on the plurality of equipment abnormal data evaluation indicators, abnormal value identification is performed on the plurality of operation data sets respectively to obtain a plurality of equipment abnormal operation data; according to the plurality of equipment abnormal data evaluation indicators, a plurality of equipment abnormal data standardization processing procedures are determined; based on the plurality of equipment abnormal data standardization processing procedures, the plurality of equipment abnormal operation data are standardized to obtain the plurality of standard equipment operation data sets.

[0089] Furthermore, the simulation analysis and evaluation module 14 is also used to perform the following steps:

[0090] Construct a set of equipment operation simulation scenarios, perform parameter analysis and orthogonal arrangement on the equipment-related characteristic parameter set based on the equipment operation simulation scenario set, and obtain an equipment operation scenario simulation control parameter table; perform equipment operation simulation tests in sequence according to the equipment operation scenario simulation control parameter table to obtain an equipment operation scenario simulation data set; perform energy utilization impact assessment on the equipment operation scenario simulation data set according to the equipment operation simulation scenario set to obtain a multi-simulation scenario equipment optimization degree set; perform weighted calculation and integration on the multi-simulation scenario equipment optimization degree set according to the equipment set to be energy optimized to obtain the coal mine equipment optimization degree set.

[0091] Furthermore, the coal mine energy optimization control module 16 is also used to perform the following steps:

[0092] Based on the set of equipment to be energy optimized, determine the target equipment optimization degree set and the target equipment energy utilization rate set; preset the equipment energy utilization rate benchmark, calculate and obtain the target equipment energy utilization rate set and the equipment energy utilization rate deviation set of the equipment energy utilization rate benchmark; based on the target equipment optimization degree set, perform control parameter optimization analysis on the equipment energy utilization rate deviation set to obtain the equipment control parameter selection threshold set; construct an optimization target fitness function, and perform global optimization within the equipment control parameter selection threshold set based on the optimization target fitness function to obtain the equipment optimization control parameter set.

[0093] Furthermore, the coal mine energy optimization control module 16 is also used to perform the following steps:

[0094] A plurality of device control parameter sets are randomly selected within the device control parameter selection threshold set, and the plurality of device control parameter sets are evaluated and calculated using the optimization target fitness function to obtain a plurality of device control effect fitnesses; a gradient ascent calculation and optimization direction analysis are performed based on the plurality of device control effect fitnesses to determine a device control parameter optimization direction set; and an iterative optimization evaluation is performed on the device control parameter selection threshold set according to the device control parameter optimization direction set until a preset convergence requirement is reached to obtain the device optimization control parameter set.

[0095] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0096] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0097] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A coal mine energy optimization control method, characterized in that: The method comprises: Obtain a target coal mine energy area, perform IoT deployment analysis on the target coal mine energy area, and obtain an IoT group perception network; Acquire multiple operating data sets of multiple coal mine equipment in real time through the IoT group sensing network, identify energy utilization rates of the multiple operating data sets, and obtain energy utilization rates of multiple equipment; Classify and evaluate the plurality of coal mine equipment based on the energy utilization rates of the plurality of equipment, and locate and obtain a set of equipment to be energy optimized; Extracting associated parameters of the equipment set to be energy optimized respectively to obtain an equipment associated characteristic parameter set, and using the equipment associated characteristic parameter set to perform simulation analysis and evaluation to obtain a coal mine equipment optimization degree set; Constructing an equipment optimization control execution strategy, matching the equipment optimization control execution strategy with the coal mine equipment optimization degree set, and executing equipment parameter optimization instructions when a preset optimization degree threshold is met; Based on the equipment parameter optimization instruction, control parameters of the equipment set to be energy optimized are optimized to obtain an equipment optimization control parameter set, and coal mine energy optimization control is performed through the equipment optimization control parameter set; Wherein, the coal mine equipment optimization degree set is obtained, including: Constructing a set of equipment operation simulation scenarios, performing parameter analysis and orthogonal arrangement on the equipment-associated characteristic parameter set based on the set of equipment operation simulation scenarios, and obtaining a table of equipment operation scenario simulation control parameters; Perform equipment operation simulation tests in sequence according to the equipment operation scenario simulation control parameter table to obtain an equipment operation scenario simulation data set; Performing an energy utilization impact assessment on the equipment operation scenario simulation data set according to the equipment operation simulation scenario set to obtain a multi-simulation scenario equipment optimization degree set; The multi-simulation scenario equipment optimization degree sets are weighted and integrated according to the equipment set to be energy optimized to obtain the coal mine equipment optimization degree set.

2. A coal mine energy optimization control method as claimed in claim 1, characterized in that: The obtaining of the IoT group perception network includes: Classifying the target coal mine energy area by functional attributes and dividing the attribute areas, and determining a set of coal mine energy attribute division areas; Performing data collection volume analysis on the coal mine equipment in the coal mine energy attribute division area set in turn to obtain coal mine area data collection volume information; Based on the data collection amount information of the coal mine area, the set of coal mine energy attribute division areas is subdivided into monitoring points in a balanced manner to obtain a set of coal mine monitoring network nodes, each node in the set of coal mine monitoring network nodes includes a data collection unit and a data communication unit; Based on the coal mine monitoring network node set, an Internet of Things perception network is deployed in the target coal mine energy area to obtain the Internet of Things group perception network.

3. A coal mine energy optimization control method as claimed in claim 1, characterized in that: The energy utilization rates of multiple devices are obtained, including: Performing outlier identification and data standardization processing on the multiple operating data sets respectively to obtain multiple standard equipment operating data sets; Acquire data collection attribute information of the plurality of coal mine equipment, and determine a plurality of equipment energy utilization rate evaluation indicators according to the data collection attribute information of the plurality of coal mine equipment; Extracting associated data from the multiple operating data sets based on the multiple equipment energy utilization rate evaluation indicators to obtain multiple equipment energy utilization rate evaluation associated data; Energy utilization rates are calculated based on the plurality of equipment energy utilization rate evaluation related data to obtain the plurality of equipment energy utilization rates.

4. A coal mine energy optimization control method as claimed in claim 3, characterized in that: The obtaining of multiple standard equipment operation data sets includes: Determining a plurality of device abnormal data evaluation indicators according to the data collection attribute information respectively, wherein the plurality of device abnormal data evaluation indicators include consistency evaluation, threshold value inspection and integrity evaluation; Based on the multiple equipment abnormal data evaluation indicators, outlier identification is performed on the multiple operation data sets respectively to obtain multiple equipment abnormal operation data; Determining a plurality of standardized processing procedures for abnormal data of equipment according to the plurality of evaluation indicators for abnormal data of equipment; Based on the multiple device abnormal data standardization processing programs, the multiple device abnormal operation data are standardized to obtain the multiple standard device operation data sets.

5. A coal mine energy optimization control method as claimed in claim 1, characterized in that: The obtaining of a set of equipment optimization control parameters includes: Determining a target device optimization degree set and a target device energy utilization rate set according to the set of devices to be energy optimized; Preset a device energy utilization rate benchmark, calculate and obtain the target device energy utilization rate set and the device energy utilization rate deviation set of the device energy utilization rate benchmark; Based on the target device optimization degree set, a control parameter optimization analysis is performed on the device energy utilization rate deviation set to obtain a device control parameter selection threshold set; An optimization target fitness function is constructed, and based on the optimization target fitness function, a global optimization is performed within the device control parameter selection threshold set to obtain the device optimization control parameter set.

6. A coal mine energy optimization control method as claimed in claim 5, characterized in that: The obtaining of the device optimization control parameter set includes: Randomly select multiple device control parameter sets within the device control parameter selection threshold set, and evaluate and calculate the multiple device control parameter sets using the optimization target fitness function to obtain multiple device control effect fitnesses; Performing gradient ascent calculation and optimization direction analysis based on the fitness of the control effects of the multiple devices to determine a set of optimization directions for the device control parameters; The device control parameter selection threshold set is iteratively evaluated according to the device control parameter optimization direction set until a preset convergence requirement is reached to obtain the device optimization control parameter set.

7. A coal mine energy optimization control system, characterized in that: The system comprises: An Internet of Things deployment analysis module, the Internet of Things deployment analysis module is used to obtain a target coal mine energy area, perform Internet of Things deployment analysis on the target coal mine energy area, and obtain an Internet of Things group perception network; An energy utilization rate identification module, the energy utilization rate identification module is used to collect and obtain multiple operating data sets of multiple coal mine equipment in real time through the Internet of Things group perception network, perform energy utilization rate identification on the multiple operating data sets, and obtain energy utilization rates of multiple devices; An equipment classification and evaluation module, the equipment classification and evaluation module is used to classify and evaluate the plurality of coal mine equipment based on the energy utilization rates of the plurality of equipment, and locate and obtain a set of equipment to be energy optimized; A simulation analysis and evaluation module, which is used to extract associated parameters of the equipment set to be energy optimized, obtain an equipment associated characteristic parameter set, and use the equipment associated characteristic parameter set to perform simulation analysis and evaluation to obtain a coal mine equipment optimization degree set; An execution strategy matching module, wherein the execution strategy matching module is used to construct an equipment optimization control execution strategy, match the equipment optimization control execution strategy based on the coal mine equipment optimization degree set, and execute the equipment parameter optimization instruction when a preset optimization degree threshold is met; A coal mine energy optimization control module, the coal mine energy optimization control module is used to optimize the control parameters of the set of equipment to be energy optimized based on the equipment parameter optimization instruction, obtain the equipment optimization control parameter set, and perform coal mine energy optimization control through the equipment optimization control parameter set; A device operation scenario simulation control parameter table acquisition module, the device operation scenario simulation control parameter table acquisition module is used to construct a device operation simulation scenario set, and perform parameter analysis and orthogonal arrangement on the device-related feature parameter set based on the device operation simulation scenario set to obtain a device operation scenario simulation control parameter table; An equipment operation simulation test module, wherein the equipment operation simulation test module is used to perform equipment operation simulation tests in sequence according to the equipment operation scenario simulation control parameter table to obtain an equipment operation scenario simulation data set; An energy utilization rate impact assessment module, the energy utilization rate impact assessment module is used to perform energy utilization rate impact assessment on the device operation scenario simulation data set according to the device operation simulation scenario set to obtain a multi-simulation scenario device optimization degree set; A weighted calculation integration module is used to perform weighted calculation integration on the equipment optimization degree sets of the multiple simulation scenarios according to the equipment set to be energy optimized, so as to obtain the coal mine equipment optimization degree set.

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