High-efficiency energy-saving fully-mechanized coal mining electromechanical driving method

Abstract

The invention provides a high-efficiency energy-saving fully-mechanized coal mine electromechanical driving method, and relates to the field of coal mining. The efficient and energy-saving type fully-mechanized coal mine electromechanical driving method comprises the steps that energy optimization configuration is conducted, specifically, electric power, hydraulic pressure and mechanical energy are subjected to optimization configuration according to the specific requirements of coal mining and the energy supply condition, so that efficient utilization of energy is achieved; the intelligent control system adopts an advanced intelligent control system to monitor and adjust the electromechanical driving equipment in real time; energy is recycled, and in the operation process of the equipment, mechanical energy and potential energy are converted into electric energy through an energy recycling technology and stored or directly used for driving other equipment; and maintenance and fault prediction: carrying out preventive maintenance and fault prediction on the related programs by using big data analysis and machine learning technologies. The energy purchase cost is directly reduced by reducing the energy consumption, the equipment fault and downtime are reduced by preventive maintenance and fault prediction, and the maintenance cost and the production loss are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of coal mining, and in particular to a high-efficiency and energy-saving electromechanical driving method for a fully mechanized coal mining unit. Background Art

[0002] In the field of modern coal mining, fully mechanized mining technology has become a key means to improve production efficiency and resource utilization. However, with the continuous rise in energy prices and increasingly stringent environmental regulations, the traditional electromechanical drive method for fully mechanized coal mining faces huge challenges. Traditional drive systems often have problems such as high energy consumption, low efficiency, high failure rate and expensive maintenance costs. These problems not only increase the operating costs of coal mining enterprises, but also have an adverse impact on the environment.

[0003] With the advancement of science and technology, especially the rapid development of intelligent control technology, big data analysis, machine learning and energy recovery technology, new ideas and means have been provided to solve the above problems. The intelligent control system can monitor the operating status of the equipment in real time, and ensure that the equipment operates in the best working state by accurately adjusting the operating parameters, thereby improving energy efficiency. Machine learning and big data analysis technologies make it possible to conduct in-depth analysis of equipment operation data, which provides data support for predictive maintenance and fault prediction, helps to reduce unplanned downtime and extend the service life of equipment. The application of energy recovery technology can recycle and reuse the energy generated during the operation of the equipment, further improving the efficiency of energy utilization. Therefore, technicians in this field provide an efficient and energy-saving electromechanical drive method for comprehensive coal mining to solve the problems raised in the above background technology. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides an efficient and energy-saving electromechanical drive method for a fully mechanized coal mine, which solves the problems of high energy consumption, low efficiency, high failure rate and expensive maintenance cost in traditional drive systems.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a highly efficient and energy-saving fully mechanized coal mining electromechanical driving method, comprising:

[0008] Energy optimization configuration: according to the specific needs of coal mining and energy supply, the power, hydraulic and mechanical energy are optimized to achieve efficient use of energy;

[0009] Intelligent control system: using advanced intelligent control system to monitor and adjust electromechanical drive equipment in real time to ensure that the equipment operates in the best working condition;

[0010] Energy recovery and utilization: during the operation of the equipment, mechanical energy and potential energy are converted into electrical energy through energy recovery technology and stored or directly used to drive other equipment;

[0011] Maintenance and fault prediction: Use big data analysis and machine learning technology to perform preventive maintenance and fault prediction on the equipment involved, reduce unplanned downtime, and improve equipment operation efficiency.

[0012] Preferably, the energy optimization configuration comprises the following steps:

[0013] S1. Data collection and analysis: Collect energy consumption data of various equipment in the coal mining process, including electricity, hydraulic energy and mechanical energy, analyze the energy demand patterns of different mining stages and equipment, and the calculation formula is:

[0014]

[0015] Among them, E i is the energy consumption of the ith device, n is the total number of devices;

[0016] S2. Energy demand forecast: Based on historical data and mining plans, energy demand in the future is forecasted. The calculation formula is:

[0017] E dernand =f(history,plan)

[0018] Among them, f is the prediction function, history is the historical data, and plan is the mining plan;

[0019] S3. Energy supply analysis: Analyze the available energy supply, including grid power supply and self-generation capacity. The calculation formula is:

[0020]

[0021] Among them, S j is the supply of the jth energy supply source, and m is the total number of supply sources;

[0022] S4. Establishment of energy optimization model. Establishment of energy optimization model, taking into account various factors such as energy cost, equipment efficiency and mining progress. The specific calculation formula is:

[0023]

[0024] Among them, C i is the energy cost of the ith device, E i is the amount of energy allocated;

[0025] Constraint: Energy allocation does not exceed the equipment demand E i ≤E demand,i ;

[0026] Total energy distribution does not exceed energy supply

[0027] Equipment operation efficiency constraint η i ≥η min,i , where η i is the operating efficiency of the ith device, η min,i It is the minimum efficiency requirement of the equipment;

[0028] S5. Intelligent algorithm solution: Use intelligent algorithm to solve the energy optimization model and obtain the optimal energy allocation plan. The calculation formula is:

[0029]

[0030] in, is the energy allocation of the ith device in the kth iteration, is the adjustment amount;

[0031] S6. Dynamic adjustment and implementation: According to real-time monitoring data, the energy allocation plan is dynamically adjusted to ensure efficient use of energy. The dynamic adjustment formula is:

[0032]

[0033] in, is the adjusted energy allocation, is the energy distribution before adjustment, ΔE i is the adjustment amount.

[0034] Preferably, the intelligent control system comprises the following steps:

[0035] S1. Sensor network deployment: Deploy sensor networks on comprehensive mining equipment to monitor the operating parameters of the equipment in real time, including voltage, current, temperature and pressure. The calculation formula for the sensor data acquisition rate is:

[0036]

[0037] Among them, N samples is the number of samples collected, T is the collection time;

[0038] S2. Data collection and processing: The sensor data is transmitted to the central control system in real time through the data collection system. The data is processed and analyzed using big data analysis technology to identify the equipment operation status. The calculation formula for data processing delay is:

[0039] L=T received -T sent

[0040] Among them, T received is the data receiving time, T sent is the data sending time;

[0041] Data analysis accuracy:

[0042]

[0043] Among them, N correct is the amount of data analyzed correctly, N total is the total amount of data;

[0044] S3. Intelligent adjustment: Based on the analysis results, the intelligent control system automatically adjusts the operating parameters of the equipment, for example, the speed of the motor and the pressure of the hydraulic system to optimize the performance of the equipment. The formula for calculating the adjustment parameters is:

[0045] P new =P current +K×(S setpoint -S current )

[0046] Among them, P new is the new tuning parameter, P current is the current parameter, K is the adjustment coefficient, S setpoint is the set point, S current is the current state value;

[0047] S4. Human-machine interaction interface: It provides an intuitive human-machine interaction interface, so that operators can view the equipment operation status in real time and make necessary interventions;

[0048] S5. Predictive maintenance: using sensors to collect equipment operation data and analyzing it through the cloud platform to achieve preventive maintenance and fault prediction of equipment;

[0049] S6. Remote monitoring and diagnosis: through the remote monitoring system, the equipment operation status can be monitored in real time, and remote fault diagnosis and maintenance guidance can be carried out.

[0050] Preferably, the energy recovery and utilization comprises the following steps:

[0051] S1. Energy recovery device installation: Energy recovery devices are installed on coal mining machines and conveyor equipment, including generators and hydraulic energy recovery systems;

[0052] S2. Energy conversion and storage, converting mechanical energy and potential energy into electrical energy and storing it through energy storage devices;

[0053] S3. Energy recycling: using the stored electrical energy to drive other devices or feeding it back into the power grid to achieve energy recycling;

[0054] S4. Energy recovery system optimization: according to the actual operation situation, the energy recovery system is optimized and adjusted to improve the energy recovery efficiency;

[0055] S5. Energy recovery benefit evaluation: evaluate the economic benefits of the energy recovery system, including energy cost savings and reduced carbon emissions.

[0056] Preferably, the maintenance and fault prediction comprises the following steps:

[0057] S1. Data collection and analysis: Use sensors to collect equipment operating data, including parameters of vibration, temperature, pressure and current, and identify the equipment's operating status and potential failure modes through data analysis;

[0058] S2. Establish a prediction model. Use machine learning technology to establish an equipment failure prediction model to predict possible equipment failures based on historical data and real-time data.

[0059] S3. Preventive maintenance: formulate a preventive maintenance plan based on the prediction results, regularly inspect and maintain the equipment to reduce the probability of failure;

[0060] S4. Remote monitoring and diagnosis: through the remote monitoring system, the equipment operation status can be monitored in real time, and remote fault diagnosis and maintenance guidance can be carried out;

[0061] S5. Maintenance effect evaluation: evaluate the maintenance effect, including indicators of equipment operation efficiency and failure rate.

[0062] (III) Beneficial effects

[0063] The present invention provides an efficient and energy-saving electromechanical driving method for a fully mechanized coal mining operation. It has the following beneficial effects:

[0064] 1. In the present invention, the energy utilization efficiency can be significantly improved and energy waste can be reduced through energy optimization configuration, intelligent control system and energy recovery and utilization. The energy optimization configuration ensures the rational distribution of energy and avoids excessive energy consumption. The intelligent control system monitors and adjusts the equipment operation status in real time to ensure that the equipment operates in the best working state. The energy recovery and utilization technology recovers and reuses the energy generated during the operation of the equipment, further improving the energy utilization efficiency.

[0065] 2. In the present invention, energy procurement costs are directly reduced by reducing energy consumption, and preventive maintenance and fault prediction reduce equipment failures and downtime, thereby reducing maintenance costs and production losses.

[0066] 3. In the present invention, through intelligent maintenance and fault prediction, potential problems of equipment can be discovered and solved in time, thereby extending the service life of the equipment, reducing the wear and aging of the equipment, and allowing the equipment to be discovered and handled in the early stages of the problem, avoiding the deterioration of the problem. DETAILED DESCRIPTION

[0067] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0068] Embodiment 1:

[0069] The embodiment of the present invention provides a high-efficiency and energy-saving fully mechanized coal mining electromechanical driving method, comprising:

[0070] Energy optimization configuration: according to the specific needs of coal mining and energy supply, the power, hydraulic and mechanical energy are optimized to achieve efficient use of energy;

[0071] Intelligent control system: using advanced intelligent control system to monitor and adjust electromechanical drive equipment in real time to ensure that the equipment operates in the best working condition;

[0072] Energy recovery and utilization: during the operation of the equipment, mechanical energy and potential energy are converted into electrical energy through energy recovery technology and stored or directly used to drive other equipment;

[0073] Maintenance and fault prediction: Use big data analysis and machine learning technology to perform preventive maintenance and fault prediction on the equipment involved, reduce unplanned downtime, and improve equipment operation efficiency.

[0074] Energy optimization configuration includes the following steps:

[0075] S1. Data collection and analysis: Collect energy consumption data of various equipment in the coal mining process, including electricity, hydraulic energy and mechanical energy, analyze the energy demand patterns of different mining stages and equipment, and the calculation formula is:

[0076]

[0077] Among them, E i is the energy consumption of the ith device, n is the total number of devices;

[0078] S2. Energy demand forecast: Based on historical data and mining plans, energy demand in the future is forecasted. The calculation formula is:

[0079] Edernand =f(history,plan)

[0080] Among them, f is the prediction function, history is the historical data, and plan is the mining plan;

[0081] S3. Energy supply analysis: Analyze the available energy supply, including grid power supply and self-generation capacity. The calculation formula is:

[0082]

[0083] Among them, S j is the supply of the jth energy supply source, and m is the total number of supply sources;

[0084] S4. Establishment of energy optimization model. Establishment of energy optimization model, taking into account various factors such as energy cost, equipment efficiency and mining progress. The specific calculation formula is:

[0085]

[0086] Among them, C i is the energy cost of the ith device, E i is the amount of energy allocated;

[0087] Constraint: Energy allocation does not exceed the equipment demand E i ≤E demand,i ;

[0088] Total energy distribution does not exceed energy supply

[0089] Equipment operation efficiency constraint η i ≥η min,i , where η i is the operating efficiency of the ith device, η min,i It is the minimum efficiency requirement of the equipment;

[0090] S5. Intelligent algorithm solution: Use intelligent algorithm to solve the energy optimization model and obtain the optimal energy allocation plan. The calculation formula is:

[0091]

[0092] in, is the energy allocation of the ith device in the kth iteration, is the adjustment amount;

[0093] S6. Dynamic adjustment and implementation: According to real-time monitoring data, the energy allocation plan is dynamically adjusted to ensure efficient use of energy. The dynamic adjustment formula is:

[0094]

[0095] in, is the adjusted energy allocation, is the energy distribution before adjustment, ΔE i is the adjustment amount.

[0096] The intelligent control system includes the following steps:

[0097] S1. Sensor network deployment: Deploy sensor networks on comprehensive mining equipment to monitor the operating parameters of the equipment in real time, including voltage, current, temperature and pressure. The calculation formula for the sensor data acquisition rate is:

[0098]

[0099] Among them, N samples is the number of samples collected, T is the collection time;

[0100] S2. Data collection and processing: The sensor data is transmitted to the central control system in real time through the data collection system. The data is processed and analyzed using big data analysis technology to identify the equipment operation status. The calculation formula for data processing delay is:

[0101] L=T received -T sent

[0102] Among them, T received is the data receiving time, T sent is the data sending time;

[0103] Data analysis accuracy:

[0104]

[0105] Among them, N correct is the amount of data analyzed correctly, N total is the total amount of data;

[0106] S3. Intelligent adjustment: Based on the analysis results, the intelligent control system automatically adjusts the operating parameters of the equipment, for example, the speed of the motor and the pressure of the hydraulic system to optimize the performance of the equipment. The formula for calculating the adjustment parameters is:

[0107] P new =P current +K×(S setpoint -S current )

[0108] Among them, P new is the new tuning parameter, P current is the current parameter, K is the adjustment coefficient, S setpoint is the set point, Scurrent is the current state value;

[0109] S4. Human-machine interaction interface: It provides an intuitive human-machine interaction interface, so that operators can view the equipment operation status in real time and make necessary interventions;

[0110] S5. Predictive maintenance: using sensors to collect equipment operation data and analyzing it through the cloud platform to achieve preventive maintenance and fault prediction of equipment;

[0111] S6. Remote monitoring and diagnosis: through the remote monitoring system, the equipment operation status can be monitored in real time, and remote fault diagnosis and maintenance guidance can be carried out.

[0112] Energy recovery includes the following steps:

[0113] S1. Energy recovery device installation: Energy recovery devices are installed on coal mining machines and conveyor equipment, including generators and hydraulic energy recovery systems;

[0114] S2. Energy conversion and storage, converting mechanical energy and potential energy into electrical energy and storing it through energy storage devices;

[0115] S3. Energy recycling: using the stored electrical energy to drive other devices or feeding it back into the power grid to achieve energy recycling;

[0116] S4. Energy recovery system optimization: according to the actual operation situation, the energy recovery system is optimized and adjusted to improve the energy recovery efficiency;

[0117] S5. Energy recovery benefit evaluation: evaluate the economic benefits of the energy recovery system, including energy cost savings and reduced carbon emissions.

[0118] Maintenance and failure prediction includes the following steps:

[0119] S1. Data collection and analysis: Use sensors to collect equipment operating data, including parameters of vibration, temperature, pressure and current, and identify the equipment's operating status and potential failure modes through data analysis;

[0120] S2. Establish a prediction model. Use machine learning technology to establish an equipment failure prediction model to predict possible equipment failures based on historical data and real-time data.

[0121] S3. Preventive maintenance: formulate a preventive maintenance plan based on the prediction results, regularly inspect and maintain the equipment to reduce the probability of failure;

[0122] S4. Remote monitoring and diagnosis: through the remote monitoring system, the equipment operation status can be monitored in real time, and remote fault diagnosis and maintenance guidance can be carried out;

[0123] S5. Maintenance effect evaluation: evaluate the maintenance effect, including indicators of equipment operation efficiency and failure rate.

[0124] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An efficient and energy-saving electromechanical driving method for a fully mechanized coal mine, characterized in that: include: Energy optimization configuration: according to the specific needs of coal mining and energy supply, the power, hydraulic and mechanical energy are optimized to achieve efficient use of energy; Intelligent control system: using advanced intelligent control system to monitor and adjust electromechanical drive equipment in real time to ensure that the equipment operates in the best working condition; Energy recovery and utilization: during the operation of the equipment, mechanical energy and potential energy are converted into electrical energy through energy recovery technology and stored or directly used to drive other equipment; Maintenance and fault prediction: Use big data analysis and machine learning technology to perform preventive maintenance and fault prediction on the equipment involved, reduce unplanned downtime, and improve equipment operation efficiency.

2. The high-efficiency and energy-saving electromechanical driving method for fully mechanized coal mining according to claim 1 is characterized in that: The energy optimization configuration includes the following steps: S1. Data collection and analysis: Collect energy consumption data of various equipment in the coal mining process, including electricity, hydraulic energy and mechanical energy, analyze the energy demand patterns of different mining stages and equipment, and the calculation formula is: Among them, E i is the energy consumption of the ith device, n is the total number of devices; S2. Energy demand forecast: Based on historical data and mining plans, energy demand in the future is forecasted. The calculation formula is: E dernand =f(history,plan) Among them, f is the prediction function, history is the historical data, and plan is the mining plan; S3. Energy supply analysis: Analyze the available energy supply, including grid power supply and self-generation capacity. The calculation formula is: Among them, S j is the supply of the jth energy supply source, and m is the total number of supply sources; S4. Establishment of energy optimization model. Establishment of energy optimization model, taking into account various factors such as energy cost, equipment efficiency and mining progress. The specific calculation formula is: Among them, C i is the energy cost of the ith device, E i is the amount of energy allocated; Constraint: Energy allocation does not exceed the equipment demand E i ≤E demand,i ; Total energy distribution does not exceed energy supply Equipment operation efficiency constraint η i ≥η min,i , where η i is the operating efficiency of the ith device, η min,i It is the minimum efficiency requirement of the equipment; S5. Intelligent algorithm solution: Use intelligent algorithm to solve the energy optimization model and obtain the optimal energy allocation plan. The calculation formula is: in, is the energy allocation of the ith device in the kth iteration, is the adjustment amount; S6. Dynamic adjustment and implementation: According to real-time monitoring data, the energy allocation plan is dynamically adjusted to ensure efficient use of energy. The dynamic adjustment formula is: in, is the adjusted energy allocation, is the energy distribution before adjustment, ΔE i is the adjustment amount.

3. The high-efficiency and energy-saving electromechanical driving method for fully mechanized coal mining according to claim 1 is characterized in that: The intelligent control system comprises the following steps: S1. Sensor network deployment: Deploy sensor networks on comprehensive mining equipment to monitor the operating parameters of the equipment in real time, including voltage, current, temperature and pressure. The calculation formula for the sensor data acquisition rate is: Among them, N samples is the number of samples collected, T is the collection time; S2. Data collection and processing: The sensor data is transmitted to the central control system in real time through the data collection system. The data is processed and analyzed using big data analysis technology to identify the equipment operation status. The calculation formula for data processing delay is: L=T received -T sent Among them, T received is the data receiving time, T sent is the data sending time; Data analysis accuracy: Among them, N correct is the amount of data analyzed correctly, N total is the total amount of data; S3. Intelligent adjustment: Based on the analysis results, the intelligent control system automatically adjusts the operating parameters of the equipment, for example, the speed of the motor and the pressure of the hydraulic system to optimize the performance of the equipment. The formula for calculating the adjustment parameters is: P new =P current +K×(S setpoint -S current ) Among them, P new is the new tuning parameter, P current is the current parameter, K is the adjustment coefficient, S setpoint is the set point, S current is the current state value; S4. Human-machine interaction interface: It provides an intuitive human-machine interaction interface, so that operators can view the equipment operation status in real time and make necessary interventions; S5. Predictive maintenance: using sensors to collect equipment operation data and analyzing it through the cloud platform to achieve preventive maintenance and fault prediction of equipment; S6. Remote monitoring and diagnosis: through the remote monitoring system, the equipment operation status can be monitored in real time, and remote fault diagnosis and maintenance guidance can be carried out.

4. The high-efficiency and energy-saving electromechanical driving method for fully mechanized coal mining according to claim 1 is characterized in that: The energy recovery and utilization comprises the following steps: S1. Energy recovery device installation: Energy recovery devices are installed on coal mining machines and conveyor equipment, including generators and hydraulic energy recovery systems; S2. Energy conversion and storage, converting mechanical energy and potential energy into electrical energy and storing it through energy storage devices; S3. Energy recycling: using the stored electrical energy to drive other devices or feeding it back into the power grid to achieve energy recycling; S4. Energy recovery system optimization: according to the actual operation situation, the energy recovery system is optimized and adjusted to improve the energy recovery efficiency; S5. Energy recovery benefit evaluation: evaluate the economic benefits of the energy recovery system, including energy cost savings and reduced carbon emissions.

5. The high-efficiency and energy-saving electromechanical driving method for fully mechanized coal mining according to claim 1 is characterized in that: The maintenance and failure prediction comprises the following steps: S1. Data collection and analysis: Use sensors to collect equipment operating data, including parameters of vibration, temperature, pressure and current, and identify the equipment's operating status and potential failure modes through data analysis; S2. Establish a prediction model. Use machine learning technology to establish an equipment failure prediction model to predict possible equipment failures based on historical data and real-time data. S3. Preventive maintenance: formulate a preventive maintenance plan based on the prediction results, regularly inspect and maintain the equipment to reduce the probability of failure; S4. Remote monitoring and diagnosis: through the remote monitoring system, the equipment operation status can be monitored in real time, and remote fault diagnosis and maintenance guidance can be carried out; S5. Maintenance effect evaluation: evaluate the maintenance effect, including indicators of equipment operation efficiency and failure rate.

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