Coal mine working face sectional parameter self-adaptive control method, device and system

Abstract

The present disclosure provides a coal mine working face segmented parameter adaptive control method, device and system, and relates to the technical field of coal mining equipment control. In some embodiments of the present disclosure, the coal mine working face is divided into multiple independent control sections based on physical distance, an edge controller is arranged in each independent control section, accurate control and rapid response of the equipment in each independent control section can be realized, and the system response speed is improved; each edge controller independently controls the adjustment of the operating parameters of the equipment in the independent control section according to the adaptive adjustment amount of the operating parameters, and the universality of parameter configuration is improved; the central adjustment instruction of the independent control section issued by the central control computer is received, and the central adjustment instruction of the independent control section is executed; it is determined that the cooperative work of different control sections is more efficient, resource waste and conflicts are reduced, and the probability of system failure is reduced.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of coal mining equipment control, and particularly relates to a coal mining face segmented parameter adaptive control method, device, system, equipment and storage medium. BACKGROUND

[0002] At present, the coal mining face control system mainly includes the following kinds: electro-hydraulic control system: taking electro-hydraulic proportional valve as the core, realizing accurate control of hydraulic support, coal mining machine and other equipment. Frequency conversion control system: through the frequency converter, the running speed of the coal mining machine, conveyor and other equipment is adjusted, realizing efficient and energy-saving production. Centralized control system: using computer, communication, automation and other technologies, realizing centralized monitoring, scheduling and management of the coal mining production process. Intelligent control system: using artificial intelligence, big data and other technologies, realizing automation and intelligentization of the coal mining production process.

[0003] At present, in the application scene of the coal mine super-long working face, there are many devices in the super-long working face, the control signal transmission distance is far, which leads to slow system response speed; the geological conditions of the super-long working face are complex, the existing control system parameter setting is difficult to adapt to different working conditions, and the parameter configuration universality is low; there are many devices in the super-long working face, so the probability of failure is large, the existing control system is difficult to realize rapid diagnosis and processing, and the probability of system failure is large. SUMMARY

[0004] The present disclosure provides a coal mining face segmented parameter adaptive control method, device, system, equipment and storage medium to at least solve the problems of slow system response speed, low parameter configuration universality and large system failure probability.

[0005] The technical solution of the present disclosure is as follows:

[0006] The present disclosure provides a coal mining face segmented parameter adaptive control method, device, system, equipment and storage medium to at least solve the problems of slow system response speed, low parameter configuration universality and large system failure probability.

[0007] Collecting the running parameters of the equipment in the target independent control section corresponding to the target edge controller; wherein the coal mining face is evenly divided into multiple independent control sections based on physical distance, one edge controller is arranged in each independent control section, and the target edge controller is any one of the multiple edge controllers;

[0008] According to the running parameters, the equipment state is evaluated to obtain an equipment state evaluation result, and the equipment state evaluation result and the running parameters are reported to a central control computer;

[0009] According to the equipment state evaluation result, a running parameter adaptive adjustment amount is generated;

[0010] According to the operation parameter adaptive adjustment amount, an operation parameter adjustment is performed on the device, and a device state is evaluated, to obtain an adjusted operation parameter and an adjusted device state evaluation result, and the adjusted operation parameter and the adjusted device state evaluation result are reported to the central control computer;

[0011] An independent control section central adjustment instruction issued by the central control computer is received, and the independent control section central adjustment instruction is executed; wherein the independent control section central adjustment instruction is a cross-section collaborative optimization adjustment parameter generated by the central control computer based on the adjusted operation parameters and the adjusted device state evaluation results of a plurality of the edge controllers, and is an instruction generated and issued based on the cross-section collaborative optimization adjustment parameter.

[0012] Optionally, the operation parameters include support pressure, cutting motor current, conveyor vibration frequency, ambient temperature gradient, and speed value of the conveyor.

[0013] Optionally, the operation parameters include support pressure, cutting motor current, conveyor vibration frequency, and device rated vibration frequency; the device state evaluation result is a section health index; and the evaluation of the device state according to the operation parameters to obtain the device state evaluation result includes:

[0014] According to the support pressure, the cutting motor current, the conveyor vibration frequency, and the device rated vibration frequency, the section health index is calculated.

[0015] The section health index SHI j is calculated according to the following formula:

[0016]

[0017] wherein k is a dynamic adjustment coefficient, and takes a value of 0.5; e is Euler number; w1 is a support pressure weight parameter; P j is support pressure; w2 is a cutting motor current weight parameter; I j is cutting motor current; w3 is a conveyor vibration frequency weight parameter; f j is conveyor vibration frequency; f nom is device rated vibration frequency.

[0018] Optionally, the device state evaluation result is a section health index; and the generation of the operation parameter adaptive adjustment amount according to the device state evaluation result includes:

[0019] According to the section health index and a basic proportion coefficient, the operation parameter adaptive adjustment amount is calculated.

[0020] The operation parameter adaptive adjustment amount ΔK pThe calculation formula of the SHI is:

[0021]

[0022] wherein, ΔK p0 is a basic proportional coefficient, SHI j is a segmented health index.

[0023] Optionally, the method further comprises:

[0024] The central control computer constructs a dynamic coupling model based on the adjusted operating parameters and the adjusted equipment state evaluation results of the plurality of edge controllers, and generates a global collaborative instruction based on a collaborative optimization objective function; wherein the global collaborative instruction includes an independent control segment central adjustment instruction corresponding to each independent control segment;

[0025] The coupling strength C j，j+1 between adjacent independent control segments in the dynamic coupling model is calculated according to the formula:

[0026]

[0027] wherein, δ is a coupling attenuation coefficient, SHI j and SHI j+1 are the segmented health indexes of the jth and j+1th independent control segments, and e is Euler's number;

[0028] The formula of the collaborative optimization objective function is:

[0029]

[0030] wherein, v j and v j+1 are the speed values of the jth and j+1th conveyors, v ref is a global reference speed, v tol is a speed tracking tolerance, which is 0.1, λ is an energy consumption weight coefficient, γ is a collaborative weight coefficient between independent control segments, and n is the number of independent control segments; P j is a support pressure.

[0031] Optionally, the method further comprises:

[0032] The independent control segment central adjustment instruction and the local instruction are fused to generate an execution instruction;

[0033] The calculation formula of the execution instruction is:

[0034]

[0035] wherein, α jis a central adjustment instruction weight, (1- a j ) is a local instruction weight, is an independent control section central adjustment instruction, is a local instruction;

[0036] The central adjustment instruction weight a j The calculation formula is:

[0037]

[0038] Wherein, k is the weight steepness coefficient, the sensitivity of the control mode switching, e is Euler number, SHI j is the current independent control section segment health index.

[0039] The embodiment of the present disclosure also provides a coal mine working face segmented parameter adaptive control device, which is applied to a target edge controller and comprises:

[0040] A collection module is configured to collect operation parameters of equipment in a target independent control section corresponding to the target edge controller; wherein, the coal mine working face is divided into a plurality of independent control sections based on physical distance, each of the independent control sections is provided with an edge controller, and the target edge controller is any one of the plurality of edge controllers.

[0041] A first evaluation module is configured to evaluate the equipment state according to the operation parameters, obtain an equipment state evaluation result, and report the equipment state evaluation result and the operation parameters to a central control computer;

[0042] A generation module is configured to generate an operation parameter adaptive adjustment amount according to the equipment state evaluation result;

[0043] A second evaluation module is configured to adjust the operation parameters of the equipment and evaluate the equipment state according to the operation parameter adaptive adjustment amount, obtain an adjusted operation parameter and an adjusted equipment state evaluation result, and report the adjusted operation parameter and the adjusted equipment state evaluation result to the central control computer;

[0044] A receiving module is configured to receive an independent control section central adjustment instruction issued by the central control computer and execute the independent control section central adjustment instruction; wherein, the independent control section central adjustment instruction is a cross-section collaborative optimization adjustment parameter generated by the central control computer based on the adjusted operation parameters and the adjusted equipment state evaluation results of the plurality of edge controllers, and is an instruction generated and issued based on the cross-section collaborative optimization adjustment parameter.

[0045] The embodiment of the present disclosure further provides a coal mine working face segmented parameter self-adaptive control system, comprising: a plurality of edge controllers, a central control computer in communication connection with the plurality of edge controllers, and a plurality of devices in an independent control section in communication connection with each of the edge controllers;

[0046] a target edge controller, collecting an operation parameter of a device in a target independent control section corresponding to the target edge controller; wherein the coal mine working face is averagely divided into a plurality of independent control sections based on physical distance, each of the independent control sections is provided with an edge controller, the target edge controller is any one of the plurality of edge controllers; evaluating a device state according to the operation parameter to obtain a device state evaluation result, and reporting the device state evaluation result and the operation parameter to a central control computer; generating an operation parameter self-adaptive adjustment amount according to the device state evaluation result; adjusting the operation parameter of the device and evaluating the device state according to the operation parameter self-adaptive adjustment amount to obtain an adjusted operation parameter and an adjusted device state evaluation result, and reporting the adjusted operation parameter and the adjusted device state evaluation result to the central control computer; receiving an independent control section central adjustment instruction issued by the central control computer, and executing the independent control section central adjustment instruction;

[0047] the central control computer, generating a cross-section collaborative optimization adjustment parameter based on the adjusted operation parameter and the adjusted device state evaluation result of the plurality of edge controllers, generating an independent control section central adjustment instruction based on the cross-section collaborative optimization adjustment parameter, and issuing the independent control section central adjustment instruction to the target edge controller.

[0048] The embodiment of the present disclosure further provides an electronic device, comprising:

[0049] a processor;

[0050] a memory for storing processor-executable instructions;

[0051] The processor is configured to execute the instructions to implement each step in the above method.

[0052] The embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement each step in the above method.

[0053] The technical solution provided by the embodiment of the present disclosure at least brings the following beneficial effects:

[0054] In some embodiments of the present disclosure, the operating parameters of the equipment in the target independent control section corresponding to the target edge controller are collected; wherein the coal mining face is divided into multiple independent control sections based on physical distance, and each independent control section is provided with an edge controller, which can realize accurate control and rapid response of the equipment in each independent control section, and improve the system response speed; the target edge controller is any one of the multiple edge controllers; the equipment state is evaluated according to the operating parameters to obtain an equipment state evaluation result, and the equipment state evaluation result and the operating parameters are reported to the central control computer; the operating parameter adaptive adjustment amount is generated according to the equipment state evaluation result; the operating parameters of the equipment are adjusted according to the operating parameter adaptive adjustment amount, and the equipment state is evaluated to obtain an adjusted operating parameter and an adjusted equipment state evaluation result, and the adjusted operating parameter and the adjusted equipment state evaluation result are reported to the central control computer; each edge controller adjusts the operating parameters of the equipment in the independent control section according to the operating parameter adaptive adjustment amount, improves the universality of parameter configuration; receives the independent control section central adjustment instruction issued by the central control computer, and executes the independent control section central adjustment instruction; wherein the independent control section central adjustment instruction is a cross-section collaborative optimization adjustment parameter generated by the central control computer based on the adjusted operating parameters and the adjusted equipment state evaluation results of the multiple edge controllers, which determines that the collaborative work of different control sections is more efficient, reduces resource waste and conflict, and reduces the probability of system failure.

[0055] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0056] The accompanying drawings incorporated in and forming a part of the specification illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure without imposing undue limitations on the disclosure.

[0057] Figure 1 A flowchart of a coal mining face segmented parameter adaptive control method according to an exemplary embodiment of the present disclosure is shown in the figure;

[0058] Figure 2 A structure diagram of a coal mining face segmented parameter adaptive control device according to an exemplary embodiment of the present disclosure is shown in the figure;

[0059] Figure 3 A structure diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown in the figure. DETAILED DESCRIPTION

[0060] In order for those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings.

[0061] It should be noted that the terms "first", "second" and the like in the specification and claims of the present disclosure and the above-described drawings are used to distinguish similar objects, and do not necessarily have to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure 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 the present disclosure. Rather, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0062] It should be noted that the user information involved in the present disclosure includes but is not limited to: user equipment information and user personal information; the collection, storage, use, processing, transmission, provision and disclosure of user information in the present disclosure comply with the provisions of relevant laws and regulations, and do not violate public order and good customs.

[0063] In order to solve the above technical problems, in some embodiments of the present disclosure, the running parameters of the equipment in the target independent control section corresponding to the target edge controller are collected; wherein the coal mining face is divided into multiple independent control sections based on physical distance, and one edge controller is arranged in each independent control section, which can realize accurate control and fast response of the equipment in each independent control section, and improve the system response speed; the target edge controller is any one of the multiple edge controllers; the equipment state is evaluated according to the running parameters to obtain an equipment state evaluation result, and the equipment state evaluation result and the running parameters are reported to a central control computer; an adaptive adjustment amount of the running parameters is generated according to the equipment state evaluation result; the running parameters of the equipment are adjusted according to the adaptive adjustment amount of the running parameters, and the equipment state is evaluated to obtain an adjusted running parameter and an adjusted equipment state evaluation result, and the adjusted running parameter and the adjusted equipment state evaluation result are reported to the central control computer; each edge controller adjusts the running parameters of the equipment in the independent control section according to the adaptive adjustment amount of the running parameters, improving the universality of parameter configuration; the independent control section central adjustment instruction issued by the central control computer is received and executed; wherein the independent control section central adjustment instruction is a cross-section collaborative optimization adjustment parameter generated by the central control computer based on the adjusted running parameters and the adjusted equipment state evaluation results of the multiple edge controllers, which determines that different control sections work more efficiently, reduces resource waste and conflict, and reduces the probability of system failure.

[0064] The technical solutions provided by the embodiments of the present disclosure will be described in detail below in conjunction with the drawings.

[0065] Figure 1 A flowchart of a coal mine working face segmented parameter adaptive control method is provided for an exemplary embodiment of the present disclosure. As shown in Figure 1 the method comprises:

[0066] S101: Collect the running parameters of the equipment in the target independent control section corresponding to the target edge controller; wherein the coal mine working face is divided into multiple independent control sections based on physical distance, each independent control section is provided with an edge controller, and the target edge controller is any one of the multiple edge controllers;

[0067] S102: According to the running parameters, the equipment state is evaluated to obtain the equipment state evaluation result, and the equipment state evaluation result and the running parameters are reported to the central control computer;

[0068] S103: According to the equipment state evaluation result, the running parameter adaptive adjustment amount is generated;

[0069] S104: According to the running parameter adaptive adjustment amount, the running parameter adjustment of the equipment and the evaluation of the equipment state are carried out, the adjusted running parameter and the adjusted equipment state evaluation result are obtained, and the adjusted running parameter and the adjusted equipment state evaluation result are reported to the central control computer;

[0070] S105: Receive the independent control section central adjustment instruction issued by the central control computer, and execute the independent control section central adjustment instruction; wherein the independent control section central adjustment instruction is a cross-section collaborative optimization adjustment parameter generated by the central control computer based on the adjusted running parameters and the adjusted equipment state evaluation results of the multiple edge controllers, and is an instruction generated and issued based on the cross-section collaborative optimization adjustment parameter.

[0071] In this embodiment, the execution subject of the above method is the edge controller. The edge controller is a computing device located at the edge of the network, which is used for real-time data processing, analysis and decision-making near the data source, and is a key component in the edge computing architecture.

[0072] This disclosure improves the overall control accuracy and efficiency of the working face by dividing the working face into several independent control segments and setting an edge controller for each segment. This enables precise control and rapid response of equipment within each segment. The edge controllers can collect and analyze equipment operating parameters in real time, promptly identify and address potential problems, reduce equipment failures and downtime, and thus enhance the stability and reliability of the entire system. Adaptive parameter adjustment is achieved: based on equipment status assessment results, the edge controllers can generate adaptive adjustment values ​​for operating parameters, allowing the equipment to automatically adjust to its optimal operating state according to actual working conditions, improving production efficiency. The central control computer can generate cross-segment collaborative optimization adjustment parameters based on data from all edge controllers, ensuring more efficient collaborative operation between different control segments and reducing resource waste and conflicts. Through real-time monitoring and adaptive adjustment, equipment wear and damage can be reduced, extending equipment lifespan and thus lowering maintenance costs. This method effectively monitors equipment status, promptly identifies safety hazards, and prevents accidents by adjusting parameters, improving the safety of coal mine production. The segmented control method facilitates system expansion and maintenance; newly added control segments can be easily integrated into the existing system, improving system flexibility and scalability. A highly automated control system can reduce reliance on manual operation, lower the demand for human resources, and reduce the possibility of human error.

[0073] In some embodiments of this disclosure, the operating parameters include at least one of the following: support pressure, cutting motor current, conveyor vibration frequency, ambient temperature gradient, conveyor speed, and equipment rated vibration frequency. Equipment condition assessment results can be segmented health indices.

[0074] In some embodiments of this disclosure, the equipment status is evaluated based on operating parameters to obtain equipment status evaluation results. One possible approach is to calculate a segmental health index based on support pressure, cutting motor current, conveyor vibration frequency, and equipment rated vibration frequency.

[0075] Segmented Health Index (SHI) j The calculation formula is:

[0076]

[0077] Where k is the dynamic adjustment coefficient, with a value of 0.5; e is the Euler number; w1 is the support pressure weighting parameter; P j It is the support pressure; w2 is the cutting motor current weighting parameter; I j It is the cutting motor current; w3 is the conveyor vibration frequency weighting parameter; f j It is the vibration frequency of the conveyor; f nom It is the rated vibration frequency of the equipment.

[0078] The above-mentioned embodiments of the present disclosure can monitor key parameters such as support pressure and conveyor vibration frequency in real time by monitoring and evaluating the operating parameters of the equipment in real time, so as to discover potential safety risks such as support instability or conveyor overload in time, and thus take measures to prevent accidents. Potential failures or performance degradation can be predicted, so as to realize predictive maintenance and reduce unexpected downtime. The calculation of the segmented health index helps to identify the performance differences of different segments of the working face, and the operating state of the equipment is optimized by adjusting parameters, so as to improve the overall production efficiency. By adjusting parameters such as cutting motor current and conveyor speed, the energy consumption of the equipment can be optimized, and unnecessary energy waste can be reduced. By avoiding long-time operation of the equipment in a poor state, wear and tear can be reduced, and the service life of the equipment can be prolonged. The introduction of the concept of health index makes the control system more intelligent, and the control strategy can be automatically adjusted according to the actual operating state of the equipment. By collecting and analyzing a large amount of equipment operating data, data support can be provided for the management layer, and the data-based decision-making ability can be enhanced.

[0079] In some embodiments of the present disclosure, an operating parameter adaptive adjustment amount is generated according to the equipment state evaluation result. One implementable way is to calculate the operating parameter adaptive adjustment amount according to the segmented health index and the basic proportional coefficient. The proportional coefficient K p .

[0080] The calculation formula of the operating parameter adaptive adjustment amount ΔK p is as follows:

[0081]

[0082] wherein, ΔK p0 is the basic proportional coefficient, SHI j is the segmented health index.

[0083] The above-mentioned embodiments of the present disclosure can reduce the influence of external interference and model uncertainty on the control system through fuzzy PID control, improve the robustness of the system, and ensure stable control effect in a variable working environment; the use of fuzzy rule base simplifies the design process of complex control strategy, and makes the control logic more intuitive and easy to understand. By adjusting the PID parameters in real time, the operating state of the equipment can be optimized, energy consumption and equipment wear and tear can be reduced, and thus the operating cost can be reduced. The parameter adaptive adjustment amount is calculated based on the health index, which can more accurately match the actual needs of the equipment and improve the control precision. Automated parameter adjustment reduces the need for manual parameter adjustment and reduces the possibility of human error, thereby improving the automation level. By adjusting the PID parameters in real time, the equipment downtime caused by parameter mismatch can be reduced, and the continuity and efficiency of production can be improved.

[0084] In some embodiments of the present disclosure, the central control computer constructs a dynamic coupling model based on the adjusted operating parameters and the adjusted equipment state evaluation results of the plurality of edge controllers; and generates global collaborative instructions based on a collaborative optimization objective function; wherein the global collaborative instructions include independent control segment central adjustment instructions corresponding to each independent control segment. By constructing the dynamic coupling model, the central control computer can consider the interaction and influence between the control segments, thereby achieving global optimization control of the entire working face. The dynamic coupling model can identify and compensate for mutual interference between the segments, reduce system oscillation and instability factors, and enhance the stability of the overall system. The global collaborative instructions ensure the coordinated work between the independent control segments, improving the collaboration and consistency of the entire working face. The setting of the collaborative optimization objective function helps to optimize the production process and improve production efficiency and yield. Global optimization control can reduce excessive wear and tear and unnecessary maintenance of equipment, thereby reducing maintenance costs.

[0085] Optionally, the coupling strength C j，j+1 between adjacent independent control segments in the dynamic coupling model is calculated by the following formula:

[0086]

[0087] wherein, δ is the coupling attenuation coefficient, SHI j and SHI j+1 are the segment health indexes of the jth and j+1th independent control segments, and e is Euler's number.

[0088] The formula of the collaborative optimization objective function is:

[0089]

[0090] wherein, v j and v j+1 are the speed values of the jth and j+1th conveyors, v ref is the global reference speed, v tol is the speed tracking tolerance, which is 0.1, λ is the energy consumption weight coefficient, γ is the collaborative weight coefficient between independent control segments, and n is the number of independent control segments; P j is the support pressure.

[0091] The above embodiments of the present disclosure can accurately represent the interaction between adjacent control sections by defining a coupling strength formula, thereby better coordinating the operation between sections. The coupling attenuation coefficient allows the system to adaptively adjust the coupling strength according to the actual operation, making the model more flexible and able to adapt to different working conditions. The collaborative optimization objective function takes into account the speed values of each section conveyor, which helps to maintain the balance of the entire working face production speed and avoid low efficiency caused by speed mismatch. The introduction of the global reference speed enables each section conveyor to track a unified standard, improving the consistency of production and product quality. The setting of the energy consumption weight coefficient makes the optimization objective function consider the optimization of energy consumption while pursuing production efficiency, which helps to reduce overall energy consumption.

[0092] In some embodiments of the present disclosure, the edge controller fuses the independent control section central adjustment instruction and the local instruction to generate an execution instruction.

[0093] Execution instruction The calculation formula is:

[0094]

[0095] wherein, α j is the central adjustment instruction weight, (1-α j ) is the local instruction weight, is the independent control section central adjustment instruction, is the local instruction.

[0096] The calculation formula of the central adjustment instruction weight α j is:

[0097]

[0098] wherein, k is the weight steepness coefficient, the sensitivity of the control mode switching, e is Euler number, SHI j is the current independent control section section health index.

[0099] The above embodiments of the present disclosure effectively combine the central adjustment instruction and the local instruction through the fusion formula, enhance the synergy of central control and edge control, and improve the overall performance of the control system. The introduction of the weight coefficient enables the control system to flexibly adjust the control strategy according to the current working section condition, and better adapt to the dynamic changes of the working face. The section health index SHI jThe greater the global instruction proportion is, the greater the weight steepness coefficient is. The weight steepness coefficient allows the system to automatically adjust the weight of the central adjustment instruction according to the segmented health index, so that the system can maintain stable operation when facing different health states. The segmented parameter adaptive control system for the coal mine ultra-long working face is used to realize the segmented parameter adaptive control method for the coal mine ultra-long working face. The adaptive control system comprises a plurality of edge controllers, a communication module and a central control computer. The plurality of edge controllers are in communication connection with the central control computer through the communication module. The plurality of edge controllers are in communication connection with a plurality of devices in the corresponding independent control section.

[0100] In combination with the above description of each embodiment, the present disclosure further provides a segmented parameter adaptive control system for a coal mine working face, comprising: a plurality of edge controllers, a central control computer in communication connection with the plurality of edge controllers, and a plurality of devices in an independent control section in communication connection with each edge controller. Wherein the plurality of edge controllers are in communication connection with the central control computer through a communication module. The communication module can be based on wired communication, and can also realize wireless communication based on a locally constructed wireless network, such as Lora wireless technology, etc.

[0101] Wherein, the target edge controller collects the operating parameters of the devices in the target independent control section corresponding to the target edge controller; wherein the coal mine working face is evenly divided into a plurality of independent control sections based on physical distance, and each independent control section is provided with an edge controller, and the target edge controller is any one of the plurality of edge controllers; the device state is evaluated according to the operating parameters to obtain a device state evaluation result, and the device state evaluation result and the operating parameters are reported to the central control computer; an operating parameter adaptive adjustment amount is generated according to the device state evaluation result; the operating parameters of the devices are adjusted according to the operating parameter adaptive adjustment amount, and the device state is evaluated to obtain adjusted operating parameters and adjusted device state evaluation results, and the adjusted operating parameters and the adjusted device state evaluation results are reported to the central control computer; the independent control section central adjustment instruction issued by the central control computer is received and executed;

[0102] The central control computer generates a cross-section collaborative optimization adjustment parameter based on the adjusted operating parameters and the adjusted device state evaluation results of the plurality of edge controllers, generates an independent control section central adjustment instruction based on the cross-section collaborative optimization adjustment parameter, and issues the independent control section central adjustment instruction to the target edge controller.

[0103] The following uses specific embodiments to illustrate the implementation principle of the coal mine working face segmented parameter adaptive control method and system of the present disclosure:

[0104] A coal mine 400 meters long working face is divided into 8 independent control sections, each 50 meters. Among them, 1-4 sections are geologically stable sections, with initial support pressure of 28 MPa, cutting motor current of 90 A, conveyor vibration frequency of 48 Hz, and temperature gradient of 5 ℃ / m; 5-8 sections are geologically broken sections, with support pressure of 35 MPa, current of 130 A, vibration frequency of 55 Hz, and temperature gradient of 12 ℃ / m.

[0105] State evaluation: calculated by the segmented health index (SHI) formula: the SHI value of the first section is 0.32 (good condition), and the SHI value of the fifth section reaches 0.81 (abnormal condition). When calculating, the dynamic adjustment coefficient k=0.5, the weight parameters w1=0.4 (pressure), w2=0.3 (current), and w3=0.3 (vibration equipment rated vibration frequency fnom=50Hz dynamic) are used.

[0106] Parameter adjustment: based on the SHI value for adaptive adjustment: the first section enters energy-saving mode, the proportional coefficient Kp is adjusted from the base value 2.5 to 2.1, and the cutting speed is reduced by 10%; the fifth section enters protection mode, Kp increases to 3.3, and the support pressure increases by 15%.

[0107] Synergistic optimization: the central control computer calculates that the combined coefficient of the fourth section (stable section) and the fifth section (broken section) is 0.35, which is significantly lower than 0.92 between stable sections. After optimization, the instruction requires the fourth section speed to be adjusted from 2.4 m / s to 2.45 m / s, and the fifth section to be increased from 2.1 m / s to 2.25 m / s, with a target synchronous speed of 2.5 m / s and an allowable deviation of 0.1 m / s.

[0108] Instruction execution: the fourth section uses 70% central instruction weight (a=0.7), the final execution speed is 2.45 m / s, the frequency converter PWM duty cycle is 65%, and the pressure is stable at 29 MPa; the fifth section uses 95% central instruction weight (a=0.95), the execution speed is 2.25 m / s, the hydraulic valve opening is increased to 85%, and the vibration frequency is reduced to 52 Hz.

[0109] Implementation effect: the whole working face speed synchronization error is reduced from ±0.15 m / s to +0.03 m / s, the ton coal energy consumption is reduced from 3.2 kW·h / t to 2.4 kW·h / t, and the fault response time is shortened from 10 minutes to 1.2 minutes. The pressure of the high-risk fifth section is reduced from 35 MPa to 32 MPa, which avoids the roof fall accident: the current of the stable first section is reduced from 90 A to 82 A, which prolongs the service life of the equipment, and the whole conveying system runs smoothly without coal stacking phenomenon.

[0110] The scheme is implemented in a 430m working face of a coal mine, and the failure rate is reduced by 70%, the production capacity is increased by 18%, and safe and efficient production is realized. Through dynamic weight distribution and precise collaborative control, the control problem caused by uneven geological conditions of the ultra-long working face is effectively solved.

[0111] Figure 2 A structural diagram of a coal mine working face segmented parameter adaptive control device 20 is provided for the exemplary embodiments of the present disclosure. As shown in the figure, the coal mine working face segmented parameter adaptive control device 20 includes a collection module 21, a first evaluation module 22, a generation module 23, a second evaluation module 24, and a receiving module 25. Figure 2

[0112] The collection module 21 is configured to collect the operating parameters of the equipment in the target independent control section corresponding to the target edge controller; the coal mine working face is divided into multiple independent control sections based on physical distance, and each independent control section is provided with an edge controller, and the target edge controller is any one of the multiple edge controllers;

[0113] The first evaluation module 22 is configured to evaluate the equipment state according to the operating parameters to obtain an equipment state evaluation result, and report the equipment state evaluation result and the operating parameters to a central control computer;

[0114] The generation module 23 is configured to generate an operating parameter adaptive adjustment amount according to the equipment state evaluation result;

[0115] The second evaluation module 24 is configured to adjust the operating parameters of the equipment and evaluate the equipment state according to the operating parameter adaptive adjustment amount, to obtain an adjusted operating parameter and an adjusted equipment state evaluation result, and report the adjusted operating parameter and the adjusted equipment state evaluation result to the central control computer;

[0116] The receiving module 25 is configured to receive an independent control section central adjustment instruction issued by the central control computer, and execute the independent control section central adjustment instruction; wherein the independent control section central adjustment instruction is a cross-section collaborative optimization adjustment parameter generated by the central control computer based on the adjusted operating parameters and the adjusted equipment state evaluation results of the multiple edge controllers, and is an instruction generated and issued based on the cross-section collaborative optimization adjustment parameter.

[0117] As to the device in the above-mentioned embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments relating to the method, and will not be described in detail here.

[0118] Figure 3 A structural diagram of an electronic device is provided for the exemplary embodiments of the present disclosure. As shown in the figure, Figure 3 ​As shown, the electronic device includes a memory 31 and a processor 32. In addition, the electronic device also includes a power supply component 33 and a communication component 34.

[0119] The memory 31 is configured to store computer programs and can be configured to store other various data to support operations on the electronic device. Examples of these data include instructions for any application or method operating on the electronic device.

[0120] The memory 31 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0121] The communication component 34 is configured to perform data transmission with other devices.

[0122] The processor 32 can execute computer instructions stored in the memory 31 to: collect running parameters of devices in a target independent control section corresponding to a target edge controller; wherein the coal mining face is divided into a plurality of independent control sections based on physical distance, and each independent control section is provided with an edge controller, and the target edge controller is any one of the plurality of edge controllers; evaluate the device state according to the running parameters to obtain a device state evaluation result, and report the device state evaluation result and the running parameters to a central control computer; generate a running parameter adaptive adjustment amount according to the device state evaluation result; adjust the running parameters of the devices and evaluate the device state according to the running parameter adaptive adjustment amount to obtain adjusted running parameters and an adjusted device state evaluation result, and report the adjusted running parameters and the adjusted device state evaluation result to the central control computer; receive an independent control section central adjustment instruction issued by the central control computer, and execute the independent control section central adjustment instruction; wherein the independent control section central adjustment instruction is a cross-section collaborative optimization adjustment parameter generated by the central control computer based on the adjusted running parameters and the adjusted device state evaluation result of the plurality of edge controllers, and is an instruction generated and issued based on the cross-section collaborative optimization adjustment parameter.

[0123] Correspondingly, the embodiments of the present disclosure also provide a computer readable storage medium storing a computer program. When the computer readable storage medium stores the computer program, and the computer program is executed by one or more processors, the one or more processors are caused to perform the steps in the method embodiments. Figure 1 Correspondingly, the embodiments of the present disclosure also provide a computer readable storage medium storing a computer program. When the computer readable storage medium stores the computer program, and the computer program is executed by one or more processors, the one or more processors are caused to perform the steps in the method embodiments.

[0124] Accordingly, the embodiments of the present disclosure also provide a computer program product, which includes computer programs / instructions, and the computer programs / instructions are executed by a processor Figure 1 The steps in the method embodiments.

[0125] The communication component in the above Figure 3 The communication component in the above

[0126] The power supply component in the above Figure 3 The power supply component in the above

[0127] The electronic device also includes a display screen and an audio component.

[0128] The display screen includes a screen, which can include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense a touch, a slide, and a gesture on the touch panel. The touch sensor can not only sense a boundary of a touching or a sliding action, but also detect a duration and a pressure related to a touch or a slide operation.

[0129] The audio component can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC) configured to receive external audio signals when the device in which the audio component is located is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in a memory or transmitted via a communication component. In some embodiments, the audio component also includes a speaker for outputting audio signals.

[0130] Those skilled in the art will appreciate that embodiments of the disclosure can be supplied as a method, a system, or a computer program product. Thus, the disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the disclosure can take the form of a computer program product on one or more computer readable storage media (including disks memory, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0131] The disclosure is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the disclosure. It should be understood that each flow and / or block in the flowchart and / or block diagrams, and a combination of flows and / or blocks in the flowchart and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing device, generate a means for implementing the functions specified in the flowchart and / or block diagrams of the flowchart and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus for implementing functions specified in the flowchart and / or block diagrams of one or more flows and / or blocks.

[0132] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufacture product including instruction apparatus, which implements the functions specified in the flowchart and / or block diagrams of the flowchart and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus for implementing functions specified in the flowchart and / or block diagrams of one or more flows and / or blocks.

[0133] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer implemented process, so that the instructions executed on the computer or other programmable device provide a means for implementing the functions specified in the flowchart and / or block diagrams of the flowchart and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus for implementing functions specified in the flowchart and / or block diagrams of one or more flows and / or blocks.

[0134] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0135] The memory can include non-persistent memory in the computer readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory. The memory is an example of computer readable media.

[0136] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0137] It should be noted that, in this text, relational terms such as "first" and "second" and the like are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element.

[0138] The above is only a specific embodiment of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest range consistent with the principles and novel features disclosed herein.

Claims

1. A segmented parameter adaptive control method for coal mine working faces, characterized in that, Applied to target edge controllers, including: The operating parameters of the equipment within the target independent control segment corresponding to the target edge controller are collected; wherein, the coal mine working face is divided into multiple independent control segments based on physical distance, and each independent control segment is equipped with an edge controller, and the target edge controller is any one of the multiple edge controllers; The equipment status is evaluated based on the operating parameters to obtain the equipment status evaluation result, and the equipment status evaluation result and the operating parameters are reported to the central control computer. Based on the equipment status assessment results, adaptive adjustment amounts for operating parameters are generated. Based on the adaptive adjustment amount of the operating parameters, the operating parameters of the device are adjusted and the device status is evaluated to obtain the adjusted operating parameters and the adjusted device status evaluation results, and the adjusted operating parameters and the adjusted device status evaluation results are reported to the central control computer. The system receives and executes the independent control segment central adjustment instruction issued by the central control computer. The independent control segment central adjustment instruction is a cross-segment collaborative optimization adjustment parameter generated by the central control computer based on the adjusted operating parameters of multiple edge controllers and the adjusted equipment status evaluation results. The instruction is generated and issued based on the cross-segment collaborative optimization adjustment parameter. The equipment status assessment result is a segmented health index; the step of generating adaptive adjustment amounts for operating parameters based on the equipment status assessment result includes: The adaptive adjustment amount of the operating parameters is calculated based on the segmented health index and the basic proportional coefficient. The adaptive adjustment amount of the operating parameters The calculation formula is: ； in, It is the basic proportional coefficient. It is a segmented health index; The method further includes: The central adjustment instructions and local instructions of the independent control segment are merged to generate execution instructions; The execution instructions The calculation formula is: ； in, It is the central government adjusting the weight of instructions. It is the local instruction weight. It is an independent control section central adjustment command. It is a local command; The weight of the central adjustment order The calculation formula is: ； in, It is the weighted steepness coefficient, which controls the sensitivity of mode switching. It is the Euler number. It is the segmented health index of the current independent control segment.

2. The method according to claim 1, characterized in that, The operating parameters include: support pressure, cutting motor current, conveyor vibration frequency, ambient temperature gradient, and conveyor speed.

3. The method according to claim 1, characterized in that, The operating parameters include: support pressure, cutting motor current, conveyor vibration frequency, and equipment rated vibration frequency; the equipment condition assessment result is a segmented health index; the equipment condition assessment based on the operating parameters to obtain the equipment condition assessment result includes: The segment health index is calculated based on the support pressure, the cutting motor current, the conveyor vibration frequency, and the equipment rated vibration frequency. The segmented health index The calculation formula is: ； in, is the dynamic adjustment coefficient, with a value of 0.5; e is the Euler number; It is the support pressure weight parameter; It is the support pressure; It is the weighting parameter for cutting off the motor current; It cuts off the motor current; It is the conveyor vibration frequency weighting parameter; It is the vibration frequency of the conveyor; It is the rated vibration frequency of the equipment.

4. The method according to claim 1, characterized in that, The method further includes: The central control computer constructs a dynamic coupling model based on the adjusted operating parameters and adjusted device status evaluation results of multiple edge controllers; and generates global collaborative instructions based on a collaborative optimization objective function; wherein, the global collaborative instructions include the central adjustment instructions for each independent control segment corresponding to each independent control segment; The coupling strength between adjacent independent control segments in the dynamic coupling model The calculation formula is: ； in, It is the coupling attenuation coefficient. and These are the segmented health indices for the j-th and j+1-th independent control segments, respectively, and e is the Euler number; The formula for the collaborative optimization objective function is: ； in, These are the speed values ​​of the j-th and j+1-th conveyor segments, respectively. It is the global reference speed. This is the speed tracking tolerance, with a value of 0.

1. It is the energy consumption weighting coefficient. is the inter-control segment coordination weighting coefficient, and n is the number of independent control segments; It is support pressure.

5. A segmented parameter adaptive control device for a coal mine working face, characterized in that, The method described in any one of claims 1-4, applied to a target edge controller, comprises: The acquisition module is used to acquire the operating parameters of the equipment in the target independent control segment corresponding to the target edge controller; wherein, the coal mine working face is divided into multiple independent control segments based on physical distance, and each independent control segment is equipped with an edge controller, and the target edge controller is any one of the multiple edge controllers; The first evaluation module is used to evaluate the equipment status based on the operating parameters, obtain the equipment status evaluation result, and report the equipment status evaluation result and the operating parameters to the central control computer. The generation module is used to generate adaptive adjustment amounts for operating parameters based on the equipment status assessment results. The second evaluation module is used to adjust the operating parameters of the device and evaluate the device status based on the adaptive adjustment amount of the operating parameters, obtain the adjusted operating parameters and the adjusted device status evaluation results, and report the adjusted operating parameters and the adjusted device status evaluation results to the central control computer. The receiving module is used to receive the independent control segment central adjustment instruction issued by the central control computer and execute the independent control segment central adjustment instruction; wherein, the independent control segment central adjustment instruction is a cross-segment collaborative optimization adjustment parameter generated by the central control computer based on the adjusted operating parameters of multiple edge controllers and the adjusted equipment status evaluation results, and an instruction generated and issued based on the cross-segment collaborative optimization adjustment parameter.

6. A segmented parameter adaptive control system for a coal mine working face, characterized in that, The method according to any one of claims 1-4 includes: a plurality of edge controllers, a central control computer communicatively connected to the plurality of edge controllers, and a plurality of devices in an independent control segment communicatively connected to each of the edge controllers; The target edge controller collects operating parameters of equipment within a target independent control segment corresponding to the target edge controller. The coal mine working face is divided into multiple independent control segments based on physical distance, and each independent control segment is equipped with an edge controller. The target edge controller is any one of these edge controllers. The device status is evaluated based on the operating parameters to obtain a device status evaluation result, and the device status evaluation result and the operating parameters are reported to the central control computer. An adaptive adjustment amount for the operating parameters is generated based on the device status evaluation result. The operating parameters are adjusted and the device status is evaluated based on the adaptive adjustment amount to obtain adjusted operating parameters and an adjusted device status evaluation result, which are then reported to the central control computer. The central control computer receives and executes the independent control segment central adjustment command. The central control computer generates cross-segment collaborative optimization adjustment parameters based on the adjusted operating parameters of multiple edge controllers and the adjusted device status evaluation results, and generates independent control segment central adjustment instructions based on the cross-segment collaborative optimization adjustment parameters, and sends the independent control segment central adjustment instructions to the target edge controller.

7. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute instructions to implement the steps of the method as described in any one of claims 1-4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-4.

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