Control method, device and equipment for ultra-large mining height working face based on closed-loop feedback

Abstract

The present application provides a method, device and equipment for controlling an ultra-large mining height working face based on closed-loop feedback, which relates to the field of data processing technology. The method includes: collecting first multi-source control parameters of the ultra-large mining height working face; calculating the equipment risk assessment value based on the first multi-source control parameters; generating control instructions for multiple devices of the ultra-large mining height working face based on the equipment risk assessment value, and transmitting the control instructions to the multiple devices respectively so that each of the devices executes the control instructions; after each of the devices executes the control instructions, collecting second multi-source control parameters of each of the devices after executing the control instructions; based on the second multi-source control parameters, determining whether the equipment operation status of the ultra-large mining height working face meets the preset performance requirements. The method provided in the embodiment of the present application can improve the energy efficiency and safety of the entire ultra-large mining height working face.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and equipment for controlling an ultra-large mining height working face based on closed-loop feedback. Background Art

[0002] In the relevant technologies, hydraulic supports and shearers are important equipment in ultra-large mining height working faces. At present, hydraulic support control usually adopts pressure compensation control, such as the Bang-Bang control strategy. When the column pressure is lower than the set value, full flow liquid is supplied, and it is cut off after reaching the threshold. This may cause pressure oscillations and may cause frequent failure of seals. Coal shearer height control is usually implemented using cubic spline interpolation combined with PID, but it will fail when the geological changes suddenly. Moreover, the sampling period of hydraulic support pressure data is not synchronized with the shearer positioning data, which will lead to a large error in the coordinated control time alignment, resulting in a high proportion of reactive power consumption in the hydraulic system. This will affect the energy efficiency and safety of ultra-large mining height working faces. Summary of the Invention

[0003] This application provides a method, device, and equipment for controlling an ultra-high mining height working face based on closed-loop feedback. The technical solution of this application is as follows:

[0004] In a first aspect, the present application provides a method for controlling an ultra-high mining height working face based on closed-loop feedback, comprising:

[0005] Collect the first multi-source control parameters of the ultra-large mining height working face;

[0006] Calculating a device risk assessment value based on the first multi-source control parameter;

[0007] generating control instructions for a plurality of devices of the ultra-large mining height working face based on the device risk assessment value, and transmitting the control instructions to the plurality of devices respectively so that each of the devices executes the control instructions;

[0008] After each of the devices executes the control instruction, collecting a second multi-source control parameter of each of the devices after executing the control instruction;

[0009] Based on the second multi-source control parameter, it is determined whether the equipment operation status of the ultra-large mining height working face meets the preset performance requirements.

[0010] In a second aspect, the present application provides a closed-loop feedback-based ultra-large mining height working face control device, comprising:

[0011] A first data acquisition module is used to acquire first multi-source control parameters of the ultra-large mining height working face;

[0012] a data calculation module, configured to calculate a device risk assessment value based on the first multi-source control parameter;

[0013] An instruction control module is used to generate control instructions for multiple devices of the ultra-large mining height working face based on the equipment risk assessment value, and transmit the control instructions to the multiple devices respectively so that each of the devices executes the control instructions;

[0014] A second data acquisition module is configured to acquire, after each of the devices executes the control instruction, a second multi-source control parameter of each of the devices;

[0015] The control module is used for the data acquisition module, and is used to determine whether the equipment operation status of the ultra-large mining height working face meets the preset performance requirements based on the second multi-source control parameters.

[0016] In a third aspect, the present application provides an electronic device, comprising:

[0017] processor;

[0018] a memory for storing instructions executable by the processor;

[0019] The processor is configured to execute the instructions to implement the method described in the first aspect.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the method described in the first aspect when the computer program is executed by a processor.

[0021] In a fifth aspect, the present application provides a computer program product, comprising a computer program / instruction, which implements the method described in the first aspect when executed by a processor.

[0022] The technical solution disclosed in this application brings at least the following beneficial effects:

[0023] In an embodiment of the present application, a system collects first multi-source control parameters for an ultra-large mining height working face; calculates equipment risk assessment values ​​based on the first multi-source control parameters; generates control instructions for multiple devices in the ultra-large mining height working face based on the equipment risk assessment values; transmits the control instructions to each of the multiple devices so that each device executes the control instructions; collects second multi-source control parameters for each device after executing the control instructions; and determines whether the equipment operation in the ultra-large mining height working face meets preset performance requirements based on the second multi-source control parameters. In this way, by collecting multi-source control parameters for the ultra-large mining height working face, calculating equipment risk assessment values, and generating control instructions based on these values, precise control and closed-loop verification of equipment such as hydraulic supports and shearers can be achieved. This effectively reduces pressure fluctuations in hydraulic supports, increases the service life of seals, and maintains high control stability for shearers during sudden geological changes. Furthermore, by synchronously collecting and processing multi-source data, time alignment errors in coordinated control can be reduced, reducing reactive power consumption in the hydraulic system, thereby improving the energy efficiency and safety of the entire ultra-large mining height working face.

[0024] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0026] Figure 1 A flow chart of a method for controlling an ultra-large mining height working face based on closed-loop feedback provided in an embodiment of the present application;

[0027] Figure 2 This is a flow chart of a method for controlling an ultra-large mining height working face based on closed-loop feedback provided in an embodiment of the present application;

[0028] Figure 3 This is a schematic structural diagram of a closed-loop feedback-based ultra-large mining height working face control device provided in an embodiment of the present application;

[0029] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to enable ordinary people in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0031] It should be noted that the terms "first," "second," and the like in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential sequence. It should be understood that the data used in this manner are interchangeable where appropriate so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. On the contrary, they are merely examples of devices and methods consistent with some aspects of this application.

[0032] It should be noted that in the embodiments of the present application, there may be certain software, components, models, etc. that already exist in the industry. They should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0033] In related technologies, hydraulic support control is typically achieved through pressure compensation, typically employing a bang-bang control strategy. When column pressure falls below a set value (e.g., 28 MPa), full fluid flow is applied; when the threshold is reached, fluid flow is cut off. Angle-to-back control typically uses a dual-axis tilt sensor to detect support tilt. When the lateral tilt angle exceeds 3°, pressure replenishment is triggered on one column. This approach is prone to pressure oscillation. For example, when mining at a height above 7.5 m, if the pressure regulation overshoot reaches 35%, seals frequently fail. Furthermore, when adjacent hydraulic supports are raised and lowered simultaneously, hydraulic shock waves are easily generated. When the pressure fluctuation propagation speed reaches 150 m / s, system resonance can occur. For electro-hydraulic control of hydraulic supports, pressure sensors and tilt sensors are typically used to achieve automatic tracking. However, as the mining height increases, the roof pressure distribution exhibits nonlinearity, making the PID (Proportional-Integral-Derivative) control method used in related technologies difficult to adapt to this situation. For shearer height control, cubic spline interpolation is typically used to construct roof and floor curves, combined with PID control of drum height. However, this approach is limited by its failure in the event of sudden geological changes. For example, when encountering a fault (e.g., a hardness change >15%), a surge in cutting resistance can cause PID integral term saturation, resulting in trajectory deviations of up to 200mm. Furthermore, in extremely high-height working faces, the support pressure data sampling period (e.g., 500ms) and the shearer positioning data period (e.g., 100ms) can become out of sync, leading to large coordinated control time alignment errors (e.g., up to 0.4s). Furthermore, at an 8m mining height, the hydraulic system's reactive power consumption can increase from the typical 18% to 34%.

[0034] Based on this, the embodiment of the present application provides a method for controlling an ultra-large mining height working face based on closed-loop feedback, which can collect real-time data of the ultra-large mining height working face (such as multi-source control parameters) to provide a basis for subsequent analysis and decision-making; it can collect key parameters such as hydraulic support pressure, coal mining machine position and roof separation, integrate data from different sources, and obtain the overall state of the working face; the collected multi-source control parameters can be fused and calculated to obtain more comprehensive working face state information, evaluate the risk of equipment operation, and provide a basis for control decisions; it can also perform equipment risk assessment values ​​based on the fused equipment state information to identify possible safety hazards, and use a distributed decision-making algorithm based on the equipment risk assessment values ​​to generate control quantities for multiple devices in the ultra-large mining height working face; and, through the contract network protocol, efficient task allocation can be achieved; at the same time, the equipment working state can be adjusted according to the control instructions, and each device can perform corresponding operations according to the received control instructions, and verify the control effect of the ultra-large mining height working face through closed-loop feedback to ensure the stability and safety of the system operation.

[0035] In this way, through multi-source data fusion and distributed decision-making, equipment can be controlled more accurately and production efficiency can be improved. Equipment risk assessment values ​​help identify potential risks in advance, take preventive measures, and reduce the accident rate; improved contract network protocols and bid response mechanisms can more effectively allocate tasks and improve resource utilization; closed-loop verification can ensure the effectiveness of control instructions, and through feedback adjustment, the stability and reliability of the entire system can be improved; moreover, the automatic execution of ultra-large mining height working face control based on closed-loop feedback can improve the degree of automation, reduce manual operations and decision-making, reduce labor intensity and human errors, thereby improving the applicability of the method provided in the embodiment of this application and improving the continuity, efficiency and stability of system production.

[0036] The following describes in detail the technical solutions provided by various embodiments of the present application in conjunction with the accompanying drawings.

[0037] Figure 1 This is a flow chart of a method for controlling a large mining height working face based on closed-loop feedback provided in an embodiment of the present application. This method can be applied to a control center computer. Figure 1 As shown, the ultra-large mining height working face control method based on closed-loop feedback may include the following steps:

[0038] S101, collecting the first multi-source control parameters of the ultra-large mining height working face.

[0039] In an embodiment of the present application, multi-source control parameters of an ultra-large mining height working face, namely, first multi-source control parameters, can be collected. For example, data such as the pressure change rate, roof separation, current mining height of the working face, and hydraulic support stiffness matrix of the ultra-large mining height working face can be collected. Alternatively, parameters such as the hydraulic support pressure vector, the three-dimensional position of the shearer, and the roof separation scalar can also be collected. An ultra-large mining height working face refers to a working face that uses a one-time full-height mining process to mine coal in an extremely thick coal seam. The mining height of the ultra-large mining height working face (i.e., the height at which the shearer cuts the coal seam) is relatively high (usually greater than 6 meters, and can even reach 8.8 meters, 10 meters, or more). This type of working face can achieve one-time mining of thicker coal seams through advanced coal mining technology and equipment, with high efficiency, high resource recovery rate, and intelligence. As an example, the first multi-source control parameters of the ultra-large mining height working face can be collected through sensors, monitoring systems, and the like.

[0040] It can be understood that the ultra-large mining height working face control method based on closed-loop feedback provided in the embodiment of the present application can be executed periodically. In this way, when collecting the first multi-source control parameters of the ultra-large mining height working face, the first multi-source control parameters of the ultra-large mining height working face within the set period can be collected.

[0041] S102: Calculate a device risk assessment value based on the first multi-source control parameter.

[0042] In an embodiment of the present application, after collecting the first multi-source control parameters of the ultra-large mining height working face, a risk assessment value for the equipment, also known as the equipment risk assessment value, can be calculated based on the first multi-source control parameters. By evaluating the equipment risk assessment value of the equipment, a data basis can be provided for subsequent processing. As a quantitative risk indicator, it can make the decision-making process faster and more accurate. Furthermore, by evaluating the equipment risk assessment value, a data basis can also be provided for risk prevention of the equipment.

[0043] In a possible implementation, the first multi-source control parameter includes a pressure change rate, a roof separation amount, a current mining height of the working face, and a hydraulic support stiffness matrix;

[0044] The formula for calculating the equipment risk assessment value Risklevel based on the first multi-source control parameter is as follows:

[0045]

[0046] in, f 1 (x) is the pressure change rate risk term, P is the rate of change of pressure; f 2 (x) is the roof separation risk item, △L is the roof separation, His the current mining height of the working face; f 3 (x) is the risk item of hydraulic support stiffness variation, K is the real-time stiffness matrix, K 0 is the initial stiffness matrix, is a measure of the overall variance of the matrix, a is the normalization coefficient, w 1. w 2. w 3 is the weight coefficient.

[0047] In this way, by evaluating the pressure change rate, roof delamination and hydraulic support stiffness variation, potential safety risks can be discovered in a timely manner, preventive measures can be taken, and accidents can be avoided; by monitoring the stiffness changes of the hydraulic support, maintenance and overhaul activities can be planned more effectively to ensure the normal operation of the equipment; the relationship between roof delamination and mining height is also taken into account, which helps to maintain the stability of the working face and prevent accidents such as roof collapse; at the same time, the equipment risk assessment value takes into account multiple factors, which can enable the system to automatically adjust the control strategy according to different working conditions and enhance the system's adaptability.

[0048] S103, generating control instructions for multiple devices of the ultra-large mining height working face based on the device risk assessment value, and transmitting the control instructions to the multiple devices respectively, so that each device executes the control instructions.

[0049] In an embodiment of the present application, after calculating a device risk assessment value, a control instruction for each device can be generated based on the device risk assessment value. For example, the control amount for each device can be determined based on the device risk assessment value. Then, a control instruction for each device can be generated based on the control amount for each device, and the control instruction can be transmitted to each device. In this way, after receiving the corresponding control instruction, each device can perform the corresponding operation based on the control instruction.

[0050] S104 , after each device executes the control instruction, collecting the second multi-source control parameters of each device after executing the control instruction.

[0051] In the embodiments of the present application, after each device executes a control instruction, the device's parameters also change. At this point, multi-source control parameters for each device can be collected, i.e., second multi-source control parameters. For example, the second multi-source control parameters may include the device's control instruction and PID controller parameters. It is understood that the second multi-source control parameters may also include, for example, a hydraulic support pressure vector, a roof separation scalar, a decision function, and the like.

[0052] S105: Based on the second multi-source control parameter, determine whether the equipment operation status of the ultra-large mining height working face meets the preset performance requirements.

[0053] In an embodiment of the present application, after collecting the second multi-source control parameters of each device after executing the control instruction, the operating status of the ultra-large mining height working face can be verified based on the second multi-source control parameters, thereby achieving closed-loop feedback of the ultra-large mining height working face control method. For example, by defining preset performance requirements and calculating actual performance indicators, and comparing the two, it can be determined whether the operating status of the equipment in the ultra-large mining height working face meets the preset performance requirements, thereby achieving closed-loop feedback of the operating status of the equipment in the ultra-large mining height working face.

[0054] In an embodiment of the present application, a system collects first multi-source control parameters for an ultra-large mining height working face; calculates equipment risk assessment values ​​based on the first multi-source control parameters; generates control instructions for multiple devices in the ultra-large mining height working face based on the equipment risk assessment values; transmits the control instructions to each of the multiple devices so that each device executes the control instructions; collects second multi-source control parameters for each device after executing the control instructions; and determines whether the equipment operation in the ultra-large mining height working face meets preset performance requirements based on the second multi-source control parameters. In this way, by collecting multi-source control parameters for the ultra-large mining height working face, calculating equipment risk assessment values, and generating control instructions based on these values, precise control and closed-loop verification of equipment such as hydraulic supports and shearers can be achieved. This effectively reduces pressure fluctuations in hydraulic supports, increases the service life of seals, and maintains high control stability for shearers during sudden geological changes. Furthermore, by synchronously collecting and processing multi-source data, time alignment errors in coordinated control can be reduced, reducing reactive power consumption in the hydraulic system, thereby improving the energy efficiency and safety of the entire ultra-large mining height working face.

[0055] In some possible implementations, generating control instructions for multiple devices in an ultra-large mining height working face based on the equipment risk assessment value includes:

[0056] Adopting distributed decision-making, the control quantities of multiple equipment in ultra-large mining height working faces are generated based on equipment risk assessment values;

[0057] Use preset contract network protocols to pre-distribute control instructions for multiple devices;

[0058] generating control instructions for the plurality of devices based on the bid response feedback results when bid response feedback results for the plurality of devices are received;

[0059] Transmit control instructions of multiple devices to multiple devices respectively.

[0060] In an embodiment of the present application, when generating control instructions for multiple devices of an ultra-large mining height working face based on the equipment risk assessment value, the control quantities of multiple devices of the ultra-large mining height working face can be generated based on the calculated equipment risk assessment value first. For example, the control quantities of each device in the ultra-large mining height working face can be generated by a distributed decision-making method. Then, the control instructions of each device can be generated based on the control quantity of each device through a preset contract network protocol, and the control instructions of each device can be pre-allocated. After each device receives the pre-allocated control instruction, a response result, that is, a bid response feedback result, can be fed back, so that the central computer can generate the control instructions of each device based on the bid response feedback result of each device. Afterwards, the control instructions of each device can be sent to the corresponding device.

[0061] In a further possible implementation, distributed decision-making is adopted to generate control quantities of multiple equipment in an ultra-large mining height working face based on equipment risk assessment values, including:

[0062] A multi-agent distributed decision-making method is adopted to generate control quantities for multiple devices based on multi-objective optimization functions and equipment risk assessment values.

[0063] In an embodiment of the present application, a multi-objective optimization function and equipment risk assessment values ​​can be calculated through a multi-agent distributed decision-making method to generate control quantities for each device.

[0064] The multi-objective optimization function may be pre-set, as follows:

[0065]

[0066] in, u is the control quantity vector, u i It is i The control quantity of each device, u n is the nominal control quantity, r i It is i Risk assessment value of each device, u safe is the device security control threshold, R is the energy consumption weight matrix, J 1 (u) is the target function of the control quantity tracking the nominal value, J 2 (u) is the risk aversion objective function, J 3 (u) is the energy consumption minimization objective function, u T is the row vector of the control quantity vector (used to make the control quantity vector u and energy consumption weight matrixR multiply).

[0067] The multi-agent distributed decision-making method is used to calculate the control quantities of multiple devices based on the multi-objective optimization function and the equipment risk assessment value as follows:

[0068]

[0069] Among them, G d is the control quantity of the device, R a is the risk assessment value corresponding to the device (that is, the risk assessment result corresponding to the device), β is the decision algorithm parameter, and D is the decision function.

[0070] In a further possible implementation, the pre-set contract network protocol includes a bidding phase, a tendering phase, and a successful bid decision phase. The bidding phase involves sending control quantity task tuples to multiple devices individually; the tendering phase involves multiple devices providing feedback on bid vectors; and the successful bid decision phase involves generating control instructions for multiple devices based on the bid vectors. In this way, through the bidding and tendering process, tasks can be allocated based on the actual conditions and capabilities of each device, thereby achieving optimal resource allocation. The improved contract network protocol allows devices to quickly respond to task requirements, reducing decision-making time and improving the overall system response speed. Devices can provide feedback on their own status and preferences via bid vectors, allowing the control center computer to flexibly adjust control strategies based on real-time information. The control instruction generation during the successful bid decision phase ensures that the generated control instructions meet system performance requirements to the greatest extent possible, improving control effectiveness. Thus, through a structured bidding and tendering process, unnecessary communication overhead can be reduced and communication efficiency improved.

[0071] In a further possible implementation, determining whether the equipment operation status of the ultra-large mining height working face meets preset performance requirements based on the second multi-source control parameter includes:

[0072] Based on the second multi-source control parameter, calculate the equipment operation performance index of the ultra-large mining height working face:

[0073] Based on the equipment operation performance indicators, determine whether the equipment operation status of the ultra-large mining height working face meets the preset performance requirements.

[0074] In an embodiment of the present application, when determining whether the equipment operation status of the ultra-large mining height working face meets the preset performance requirements based on the second multi-source control parameter, that is, when performing closed-loop verification, the equipment operation performance index of the ultra-large mining height working face can be first calculated based on the second multi-source control parameter. The calculation formula can be as follows:

[0075] ;

[0076] Among them, V cis the closed-loop verification result, P s , L m , D1 is the second multi-source control parameter after executing the control instruction; C i is the control instruction, and K is the PID controller parameter.

[0077] Then, based on the equipment operation performance index, it can be determined whether the equipment operation status of the ultra-large mining height working face meets the preset performance requirements, thereby realizing closed-loop verification of the ultra-large mining height working face control method. Exemplarily, verification can be performed by threshold comparison, for example, comparing the equipment operation performance index with the preset performance requirement index. If the equipment operation performance index is greater than the preset performance requirement, it can be considered that the control effect meets expectations, the system is stable, the control instructions are effective, and the system performance is optimized; conversely, if the equipment operation performance index is less than or equal to the preset performance requirement, it can be considered that the control effect does not meet expectations.

[0078] In some possible implementations, the first multi-source control parameter may also include the pressure change rate, roof delamination, current working face mining height, and hydraulic support stiffness matrix. The equipment risk assessment value, Risklevel, is calculated based on the pressure change rate, roof delamination, current working face mining height, and hydraulic support stiffness matrix. Thus, by evaluating the pressure change rate, roof delamination, and hydraulic support stiffness variation, potential safety risks can be promptly identified, preventative measures can be taken, and accidents can be avoided. Quantified risk indicators can also be provided to the control center, making the decision-making process faster and more accurate. Monitoring changes in hydraulic support stiffness can also enable more effective planning of maintenance and overhaul activities to ensure the normal operation of the equipment. Furthermore, the relationship between roof delamination and mining height is considered, which helps maintain the stability of the working face and prevent accidents such as roof collapse. By integrating multiple factors to calculate the equipment risk assessment value, the system can automatically adjust the control strategy according to different working conditions, enhancing its adaptive capabilities.

[0079] Accordingly, at this time, the method for calculating the equipment risk assessment value can be as follows:

[0080]

[0081] in, f 1 (x) is the pressure change rate risk term, P is the rate of change of pressure; f 2 (x) is the roof separation risk item, △L is the roof separation, H is the current mining height of the working face; f 3 (x) is the risk item of hydraulic support stiffness variation, K is the real-time stiffness matrix,K 0 is the initial stiffness matrix, is a measure of the overall variance of the matrix, a is the normalization coefficient, w 1. w 2. w 3 is the weight coefficient.

[0082] In some possible implementations, the first multi-source control parameter may further include a hydraulic support pressure vector, a three-dimensional position of a coal mining machine, and a roof separation scalar;

[0083] The above-mentioned ultra-large mining height working face control method based on closed-loop feedback may further include:

[0084] Equipment status fusion is calculated based on the hydraulic support pressure vector, coal mining machine three-dimensional position, and roof separation scalar.

[0085] In an embodiment of the present application, device state fusion can also be calculated. For example, the device state fusion can be calculated based on the hydraulic support pressure vector, the three-dimensional position of the coal mining machine, and the roof separation scalar in the first multi-source control parameter. The calculation method can be as follows:

[0086]

[0087] in, P k is the hydraulic support pressure vector, S k is the three-dimensional position of the shearer, L k is the roof separation scalar, △u p It is the flow regulation amount of the hydraulic support pump station. V s is the shearer speed command, α is the pressure transmission coefficient (e.g. it can be taken as 0.15), β is the displacement dynamic coefficient (e.g. it can be taken as 0.8), I 3 represents the third-order identity matrix, W k is the process noise, △t is the sampling time interval.

[0088] In this way, by fusing multi-source data such as hydraulic support pressure, coal mining machine position and roof separation, a more accurate fusion estimate of the equipment state can be obtained, thereby improving the response speed and accuracy of the control system; the dynamic coefficients in the state equation can also be used by the system to predict the future equipment state, which helps to make decisions in advance and reduce control errors caused by reaction lag; by considering the pump station flow regulation of the hydraulic support and the coal mining machine speed instruction, resources can be allocated more effectively, and energy utilization and production efficiency can be improved; accurate state estimation helps prevent equipment overload and improper operation, reduce equipment wear, extend equipment service life, and thus reduce maintenance costs; at the same time, by real-time monitoring and prediction of roof separation, timely measures can be taken to prevent roof accidents and improve the safety of the working face; and the state equation takes process noise into account, so that the algorithm can adapt to the uncertainty under different working conditions and improve the robustness of the system.

[0089] In order to make the ultra-large mining height working face control method based on closed-loop feedback provided by the embodiment of the present disclosure clearer, the following is explained with reference to specific examples. Figure 2 The ultra-large mining height working face control method based on closed-loop feedback provided in the embodiment of the present application may include the following steps:

[0090] Step 1: The control center computer collects multi-source control parameters of the ultra-large mining height working face, including hydraulic support pressure parameters, coal mining machine position parameters, and roof separation parameters;

[0091] Step 2: The control center computer performs equipment status fusion calculations for a set period;

[0092] Step 3: The control center computer performs equipment risk assessment at a set period;

[0093] Step 4: The control center computer uses distributed decision-making to generate control quantities for multiple devices in the ultra-high mining height working face based on equipment risk assessment;

[0094] Step 5: The control center computer uses the improved contract network protocol to pre-assign task instructions. Multiple devices submit bids in response to the instruction assignment from the control center computer. The control center computer generates control instructions for the multiple devices based on the bid response feedback results and transmits them to the multiple devices.

[0095] Step 6: Multiple devices execute the control instructions;

[0096] Step 7: The control center computer performs closed-loop verification of the control effect based on the multi-source control parameters after the control instruction.

[0097] In this way, real-time data (such as multi-source control parameters) of the ultra-large mining height working face can be collected to provide a basis for subsequent analysis and decision-making; key parameters such as hydraulic support pressure, coal mining machine position and roof separation can be collected, and data from different sources can be integrated to obtain the overall status of the working face; the collected multi-source control parameters can be fused and calculated to obtain more comprehensive working face status information, evaluate the risks of equipment operation, and provide a basis for control decisions; equipment risk assessment values ​​can also be performed based on the fused equipment status information to identify possible safety hazards. Based on the equipment risk assessment values, a distributed decision-making algorithm is used to generate control quantities for multiple devices in the ultra-large mining height working face; and, through the contract network protocol, efficient task allocation can be achieved; at the same time, the equipment working status can be adjusted according to the control instructions, and each device can perform corresponding operations according to the received control instructions, and the control effect of the ultra-large mining height working face can be verified through closed-loop feedback to ensure the stability and safety of the system operation.

[0098] In this way, through multi-source data fusion and distributed decision-making, equipment can be controlled more accurately and production efficiency can be improved. Equipment risk assessment values ​​help identify potential risks in advance, take preventive measures, and reduce the accident rate; improved contract network protocols and bid response mechanisms can more effectively allocate tasks and improve resource utilization; closed-loop verification can ensure the effectiveness of control instructions, and through feedback adjustment, the stability and reliability of the entire system can be improved; moreover, the automatic execution of ultra-large mining height working face control based on closed-loop feedback can improve the degree of automation, reduce manual operations and decision-making, reduce labor intensity and human errors, thereby improving the applicability of the method provided in the embodiment of this application and improving the continuity, efficiency and stability of system production.

[0099] In step 2, the state equation of the device state fusion calculation is:

[0100]

[0101] in, P k is the hydraulic support pressure vector, S k is the three-dimensional position of the shearer, L k is the roof separation scalar, △u p It is the flow regulation amount of the hydraulic support pump station. V s is the shearer speed command, α is the pressure transmission coefficient (e.g. it can be taken as 0.15), β is the displacement dynamic coefficient (e.g. it can be taken as 0.8), I 3 represents the third-order identity matrix, W kis the process noise, △t is the sampling time interval.

[0102] In step 3, the equipment risk assessment value is calculated as:

[0103]

[0104] in, f 1 (x) is the pressure change rate risk term, P is the rate of change of pressure; f 2 (x) is the roof separation risk item, △L is the roof separation, H is the current mining height of the working face; f 3 (x) is the risk item of hydraulic support stiffness variation, K is the real-time stiffness matrix, K 0 is the initial stiffness matrix, is a measure of the overall variance of the matrix, a is the normalization coefficient, w 1. w 2. w 3 is the weight coefficient.

[0105] In step 4, when generating the control variables of multiple devices in the ultra-large mining height working face, the multi-objective optimization function is:

[0106]

[0107] in, u is the control quantity vector, u i It is i The control quantity of each device, u n is the nominal control quantity, r i It is i Risk assessment value of each device, u safe is the device security control threshold, R is the energy consumption weight matrix, J 1 (u) is the target function of the control quantity tracking the nominal value, J 2 (u) is the risk aversion objective function, J 3 (u) is the energy consumption minimization objective function, u T It is the row vector of the control quantity vector (used to multiply the control quantity vector u with the energy consumption weight matrix R).

[0108] The following formula is used to generate the control quantity of multiple devices:

[0109]

[0110] Among them, G d is the control quantity of the device, R a is the risk assessment value corresponding to the device (that is, the risk assessment result corresponding to the device), β is the decision algorithm parameter, and D is the decision function.

[0111] In step 5, the improved contract network protocol includes a bidding phase, a tendering phase, and a successful bid decision phase. The bidding phase involves the control center computer sending control quantity task tuples to multiple devices. The bidding phase involves multiple devices feeding back tender vectors to the control center computer. The successful bid decision phase involves the control center computer generating control instructions for multiple devices based on the tender vectors. The control instruction generation formula for the successful bid decision phase is:

[0112]

[0113] Among them, C i is the generated device control instruction, T P is the bid vector, δ is the bid response algorithm, and Bid is the winning bid decision function.

[0114] In step 7, the control center computer can use a multi-index fusion closed-loop verification algorithm to perform closed-loop verification on the control effect. The formula of the multi-index fusion closed-loop verification algorithm is as follows:

[0115] ;

[0116] Among them, V c is the closed-loop verification result, P s , L m , D1 is the second multi-source control parameter after executing the control instruction; C i is the control instruction, and K is the PID controller parameter.

[0117] By integrating multiple indicators, the algorithm can more accurately assess the actual effects of control commands, thereby adjusting and optimizing control parameters and improving control accuracy. Closed-loop verification ensures the stability of the control system during actual operation. Through real-time monitoring and adjustment, the possibility of system oscillation and overshoot is reduced. Multi-indicator integration considers multiple control parameters, helping to comprehensively evaluate control effectiveness, thereby optimizing control performance and improving production efficiency. The algorithm provides real-time feedback on control effects, enabling the control center computer to quickly adjust to the dynamic changes in the working surface environment.

[0118] The following is a description of the closed-loop feedback-based ultra-large mining height working face control method of the present application with reference to specific examples, as follows:

[0119] Step 1, data collection:

[0120] Hydraulic support pressure parameters: [24.5MPa, 23.8MPa, 25.2MPa];

[0121] Coal mining machine position parameters: [10.2m, 3.5m, 1.8m];

[0122] Roof separation parameter: 0.6m;

[0123] Step 2: fusion calculation of equipment status;

[0124] State equation parameters:

[0125] Hydraulic support pressure vector: [24.5, 23.8, 25.2];

[0126] Coal mining machine 3D position: [10.2, 3.5, 1.8];

[0127] Roof separation scalar: 0.6;

[0128] Hydraulic support pump station flow adjustment: 0.3m³ / min;

[0129] Coal mining machine speed command: 2.5m / min;

[0130] Pressure transmission coefficient: 0.15;

[0131] Displacement dynamic coefficient: 0.8;

[0132] Process noise: [0.01, 0.02, 0.03];

[0133] Step 3, equipment risk assessment;

[0134] Risk assessment parameters:

[0135] Pressure change rate risk item: 0.2;

[0136] Roof separation risk item: 0.15;

[0137] Hydraulic support stiffness variation risk item: 0.1;

[0138] Weight coefficient: [0.5, 0.3, 0.2];

[0139] Step 4, distributed decision making;

[0140] Control quantity generation results:

[0141] Device 1 control capacity: 5kW; Device 2 control capacity: 3kW; Device 3 control capacity: 4kW;

[0142] Step 5, contract network agreement;

[0143] Bidding phase: the control center sends the task tuple; bidding phase: the device feeds back the bidding vector;

[0144] Bid-winning decision stage: Generate control instructions;

[0145] Step 6, execute the control instruction; device 1 executes the control amount: 5kW; device 2 executes the control amount: 3kW; device 3 executes the control amount: 4kW;

[0146] Step 7, closed-loop verification;

[0147] Closed-loop verification results: The control effect is in line with expectations and the system is stable.

[0148] Closed-loop verification results: control instructions are effective and system performance is optimized.

[0149] The specific implementation and technical effects of each step of this embodiment are similar to those of the above method embodiment and will not be repeated here.

[0150] Based on the same inventive concept, the embodiment of the present application also provides a super-large mining height working face control device based on closed-loop feedback. Figure 3 As shown, the ultra-large mining height working face control device 300 based on closed-loop feedback includes:

[0151] The first data acquisition module 310 is used to acquire first multi-source control parameters of the ultra-large mining height working face;

[0152] A data calculation module 320 is configured to calculate a device risk assessment value based on the first multi-source control parameter;

[0153] An instruction control module 330 is configured to generate control instructions for multiple devices of the ultra-large mining height working face based on the device risk assessment value, and transmit the control instructions to the multiple devices respectively so that each device executes the control instructions;

[0154] A second data acquisition module 340 is configured to acquire, after each device executes the control instruction, a second multi-source control parameter of each device;

[0155] The control module 350 is configured to determine whether the equipment operation status of the ultra-large mining height working face meets preset performance requirements based on the second multi-source control parameters.

[0156] In a possible implementation, the instruction control module 330 is configured to:

[0157] Adopting distributed decision-making, the control quantities of multiple equipment in ultra-large mining height working faces are generated based on equipment risk assessment values;

[0158] Pre-allocating control instructions for the plurality of devices using a preset contract network protocol;

[0159] Upon receiving the bid response feedback results of the plurality of devices, generating control instructions for the plurality of devices based on the bid response feedback results;

[0160] The control instructions of the multiple devices are transmitted to the multiple devices respectively.

[0161] In a possible implementation, the instruction control module 330 is configured to:

[0162] A multi-agent distributed decision-making method is adopted to generate control quantities of multiple devices based on a multi-objective optimization function and the device risk assessment value.

[0163] In one possible implementation, the preset contract network protocol includes a bidding stage, a tendering stage, and a bid-winning decision stage, wherein the bidding stage refers to sending control quantity task tuples to the multiple devices respectively; the bidding stage refers to the multiple devices feeding back bid vectors; and the bid-winning decision stage refers to generating control instructions for the multiple devices based on the bid vectors.

[0164] In a possible implementation, the control module 350 is configured to:

[0165] Based on the second multi-source control parameter, the equipment operation performance index of the ultra-large mining height working face is calculated:

[0166] Based on the equipment operation performance indicators, determine whether the equipment operation status of the ultra-large mining height working face meets the preset performance requirements.

[0167] In a possible implementation manner, the first multi-source control parameter includes pressure change rate, roof separation amount, current mining height of the working face, and hydraulic support stiffness matrix;

[0168] The data calculation module 320 is used to:

[0169]

[0170] in, f 1 (x) is the pressure change rate risk term, P is the rate of change of pressure; f 2 (x) is the roof separation risk item, △L is the roof separation, H is the current mining height of the working face; f 3 (x) is the risk item of hydraulic support stiffness variation, K is the real-time stiffness matrix,K 0 is the initial stiffness matrix, is a measure of the overall variance of the matrix, a is the normalization coefficient, w 1. w 2. w 3 is the weight coefficient.

[0171] In a possible implementation manner, the first multi-source control parameter includes a hydraulic support pressure vector, a three-dimensional position of a coal mining machine, and a roof separation scalar;

[0172] The ultra-large mining height working face control device 300 based on closed-loop feedback further includes a state calculation module for:

[0173] The equipment state fusion is calculated based on the hydraulic support pressure vector, the three-dimensional position of the coal mining machine, and the roof separation scalar.

[0174] The specific implementation method and technical effects of the device provided in the embodiment of the present application are similar to those of the above-mentioned method embodiment and will not be repeated here.

[0175] According to embodiments of the present application, the present application also discloses an electronic device, a computer-readable storage medium, and a computer program product.

[0176] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present application is shown. The electronic device 400 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0177] like Figure 4 As shown, electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. Various programs and data required for the operation of electronic device 400 may also be stored in RAM 403. Computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0178] Multiple components in the electronic device 400 are connected to the I / O interface 405, including an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0179] The computing unit 401 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 executes the various methods and processes described above, such as the closed-loop feedback-based ultra-high mining face control method. For example, in some embodiments, the closed-loop feedback-based ultra-high mining face control method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the closed-loop feedback-based ultra-high mining face control method described above can be performed. Alternatively, in other embodiments, the computing unit 401 may be configured in any other appropriate manner (for example, by means of firmware) to execute the ultra-large mining height working face control method based on closed-loop feedback.

[0180] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0181] The program code of the computer program product for implementing the method of the present application can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0182] In the context of this application, a computer-readable storage medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may be a machine-readable signal medium or a machine-readable storage medium. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0183] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0184] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0185] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, establishing a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical servers and VPS services ("Virtual Private Servers" or "VPS"). The server may also be a server in a distributed system or a server integrated with blockchain.

[0186] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this application can be achieved. This is not a limitation herein.

[0187] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A method for controlling a super-large mining height working face based on closed-loop feedback, characterized in that: include: Collect the first multi-source control parameters of the ultra-large mining height working face; Calculating a device risk assessment value based on the first multi-source control parameter; generating control instructions for a plurality of devices of the ultra-large mining height working face based on the device risk assessment value, and transmitting the control instructions to the plurality of devices respectively so that each of the devices executes the control instructions; After each of the devices executes the control instruction, collecting a second multi-source control parameter of each of the devices after executing the control instruction; determining, based on the second multi-source control parameter, whether the equipment operation condition of the ultra-large mining height working face meets preset performance requirements; The first multi-source control parameters include pressure change rate, roof separation amount, current mining height of the working face, and hydraulic support stiffness matrix; The method of calculating the equipment risk assessment value Risklevel based on the first multi-source control parameter is as follows: in, f 1 (x) is the pressure change rate risk term, p is the rate of change of pressure; f 2 (x) is the roof separation risk item, △L is the roof separation, H is the current mining height of the working face; f 3 (x) is the risk item of hydraulic support stiffness variation, K is the real-time stiffness matrix, K 0 is the initial stiffness matrix, is a measure of the overall difference of the matrix, α is the normalization coefficient, w 1. w 2. w 3 is the weight coefficient.

2. The closed-loop feedback-based ultra-large mining height working face control method according to claim 1, characterized in that: The generating of control instructions for multiple devices of the ultra-large mining height working face based on the equipment risk assessment value includes: Adopting distributed decision-making, the control quantities of multiple equipment in ultra-large mining height working faces are generated based on equipment risk assessment values; Pre-allocating control instructions for the plurality of devices using a preset contract network protocol; Upon receiving the bid response feedback results of the plurality of devices, generating control instructions for the plurality of devices based on the bid response feedback results; The control instructions of the multiple devices are transmitted to the multiple devices respectively.

3. The closed-loop feedback-based ultra-large mining height working face control method according to claim 2, characterized in that: The distributed decision-making method generates control quantities for multiple devices in an ultra-large mining height working face based on the equipment risk assessment value, including: A multi-agent distributed decision-making method is adopted to generate control quantities of multiple devices based on a multi-objective optimization function and the device risk assessment value.

4. The method for controlling a super-large mining height working face based on closed-loop feedback according to claim 2, characterized in that: The preset contract network protocol includes a bidding stage, a tendering stage and a bid-winning decision stage, wherein the bidding stage refers to sending control quantity task tuples to the multiple devices respectively; the bidding stage refers to the multiple devices feeding back bid vectors; and the bid-winning decision stage refers to generating control instructions for the multiple devices based on the bid vectors.

5. The method for controlling a super-large mining height working face based on closed-loop feedback according to claim 1, characterized in that: The determining, based on the second multi-source control parameter, whether the equipment operation condition of the ultra-large mining height working face meets the preset performance requirements includes: Based on the second multi-source control parameter, the equipment operation performance index of the ultra-large mining height working face is calculated: Based on the equipment operation performance indicators, determine whether the equipment operation status of the ultra-large mining height working face meets the preset performance requirements.

6. The method for controlling a super-large mining height working face based on closed-loop feedback according to claim 1, characterized in that: The first multi-source control parameters include a hydraulic support pressure vector, a three-dimensional position of a coal mining machine, and a roof separation scalar; The method further comprises: The equipment state fusion is calculated based on the hydraulic support pressure vector, the three-dimensional position of the coal mining machine, and the roof separation scalar.

7. A super-large mining height working face control device based on closed-loop feedback, characterized in that: include: A first data acquisition module is used to acquire first multi-source control parameters of the ultra-large mining height working face; a data calculation module, configured to calculate a device risk assessment value based on the first multi-source control parameter; An instruction control module is used to generate control instructions for multiple devices of the ultra-large mining height working face based on the equipment risk assessment value, and transmit the control instructions to the multiple devices respectively so that each of the devices executes the control instructions; A second data acquisition module is configured to acquire, after each of the devices executes the control instruction, a second multi-source control parameter of each of the devices; A control module, used for a data acquisition module, for determining whether the equipment operation status of the ultra-large mining height working face meets the preset performance requirements based on the second multi-source control parameter; The first multi-source control parameters include pressure change rate, roof separation amount, current mining height of the working face, and hydraulic support stiffness matrix; The method of calculating the equipment risk assessment value Risklevel based on the first multi-source control parameter is as follows: in, f 1 (x) is the pressure change rate risk term, p is the rate of change of pressure; f 2 (x) is the roof separation risk item, △L is the roof separation, H is the current mining height of the working face; f 3 (x) is the risk item of hydraulic support stiffness variation, K is the real-time stiffness matrix, K 0 is the initial stiffness matrix, is a measure of the overall difference of the matrix, α is the normalization coefficient, w 1. w 2. w 3 is the weight coefficient.

8. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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