State prediction method and device for coal mine power supply system

Abstract

The invention provides a state prediction method and device for a coal mine power supply system. The method comprises the following steps: monitoring the current operation state of a target parameter in the coal mine power supply system in real time; wherein the operation state of the target parameter comprises any one or more of the opening and closing state of an air door, the opening and closing state of a non-electric lock door, the starting state of a fan and the power load state; acquiring a preset state transition matrix corresponding to the target parameter; wherein the preset state transition matrix comprises a transition probability value of the target parameter between any two operation states; and based on the current operation state of the target parameter and a preset state transition matrix, predicting a conditional probability that the target parameter is converted from the current operation state to the target operation state. According to the method, the future operation states of the air door, the interlocking door, the fan and the power load can be predicted, and a data basis is provided for accurate control of the coal mine power supply system on the air door, the interlocking door, the fan and the power load.

Description

Technical Field

[0001] The present invention relates to the technical field of state prediction, and in particular, to a method and device for predicting the state of a coal mine power supply system. Background Art

[0002] At present, the coal mine power supply system usually relies on manual control or partial simple interlock control methods. The ventilation condition and power load distribution are adjusted by manually monitoring and operating the states of air doors, interlock doors and fans. The opening and closing of the air doors and interlock doors in the connecting roadway are realized by manual or simple electrical logic control. There is a lack of automatic state prediction for air doors, interlock doors, fans and power loads. Usually, it is necessary to rely on manual experience for control, lacking state prediction and early adjustment functions, and it is difficult to respond to sudden failures in a timely manner, which is likely to lead to accidents. Although the coal mine power supply system has basic control capabilities, in a complex mine environment, simply relying on manual operation or simple logic control can no longer meet the real-time linkage requirements of multi-state equipment. Therefore, the existing coal mine power supply system lacks the ability to predict the operating states of air doors, interlock doors, fans and power loads, resulting in a relatively high failure risk degree of the coal mine power supply system. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method and device for predicting the state of a coal mine power supply system, which can predict the future operating states of air doors, interlock doors, fans and power loads, provides a data basis for the precise control of air doors, interlock doors, fans and power loads by the coal mine power supply system, and reduces the failure risk of the coal mine power supply system.

[0004] In order to achieve the above purpose, the technical solutions adopted in the embodiments of the present invention are as follows:

[0005] In the first aspect, an embodiment of the present invention provides a method for predicting the state of a coal mine power supply system, including:

[0006] Real-time monitoring of the current operating state of target parameters in the coal mine power supply system; wherein, the operating state of the target parameters includes any one or more of the opening and closing state of the air door, the opening and closing state of the non-powered interlock door, the starting state of the fan, and the power load state;

[0007] Obtaining a preset state transition matrix corresponding to the target parameters; wherein, the preset state transition matrix includes the transition probability values between any two operating states of the target parameters;

[0008] Based on the current operating state of the target parameters and the preset state transition matrix, predicting the conditional probability that the target parameters change from the current operating state to the target operating state.

[0009] Further, an embodiment of the present invention provides a first possible implementation manner of the first aspect. Among them, the step of predicting the conditional probability that the target parameter changes from the current operating state to the target operating state based on the current operating state of the target parameter and the preset state transition matrix includes:

[0010] Obtain the target probability value corresponding to the transfer of the target parameter from the current operating state to the target operating state from the preset state transition matrix;

[0011] Obtain the initial probability value corresponding to the target parameter in the current operating state, and predict the conditional probability that the target parameter changes from the current operating state to the target operating state based on the initial probability value and the target probability value.

[0012] Further, an embodiment of the present invention provides a second possible implementation manner of the first aspect. Among them, the step of predicting the conditional probability that the target parameter changes from the current operating state to the target operating state based on the initial probability value and the target probability value includes:

[0013] Calculate the product of the initial probability and the target probability value to obtain the conditional probability that the target parameter changes from the current operating state to the target operating state.

[0014] Further, an embodiment of the present invention provides a third possible implementation manner of the first aspect. Among them, the step of establishing the preset state transition matrix corresponding to the target parameter includes:

[0015] Obtain the historical operating state data of the target parameter, and determine the occurrence probability of the target parameter in each operating state based on the historical operating state data;

[0016] Determine the transition probability value between every two operating states of the target parameter based on the occurrence probability of the target parameter in each operating state;

[0017] Establish the preset state transition matrix corresponding to the target parameter based on the transition probability value between every two operating states of the target parameter.

[0018] Further, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect. Among them, the step of determining the transition probability value between every two operating states of the target parameter based on the occurrence probability of the target parameter in each operating state includes:

[0019] Obtain the occurrence probability of the starting operating state in every two operating states;

[0020] Obtain the occurrence probability of the terminal operating state in every two operating states;

[0021] Determine the transition probability value between each two operating states based on the occurrence probability of the starting operating state and the occurrence probability of the ending operating state.

[0022] Furthermore, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, wherein the step of determining the transition probability value between each two operating states based on the occurrence probability of the starting operating state and the occurrence probability of the ending operating state includes:

[0023] Calculate the first product of the occurrence probability of the starting operating state and the first weight, calculate the second product of the occurrence probability of the ending operating state and the second weight, and calculate the sum of the first product and the second product to obtain the transition probability value; wherein the sum of the first weight and the second weight is 1.

[0024] Furthermore, an embodiment of the present invention provides a sixth possible implementation manner of the first aspect, wherein the opening and closing states of the air damper include a fully open state, a partially open state, and a fully closed state;

[0025] The opening and closing states of the non-electric interlocking door include an open state and a closed state;

[0026] The opening and closing states of the fan include a starting state and a closed state;

[0027] The power load states include a low load state, a medium load state, and a high load state.

[0028] In a second aspect, an embodiment of the present invention further provides a state prediction device for a coal mine power supply system, including:

[0029] A monitoring module, configured to monitor in real time the current operating state of target parameters in the coal mine power supply system; wherein the operating state of the target parameters includes any one or more of the opening and closing states of the air damper, the opening and closing states of the non-electric interlocking door, the starting state of the fan, and the power load state;

[0030] An acquisition module, configured to acquire a preset state transition matrix corresponding to the target parameters; wherein the preset state transition matrix includes the transition probability values between any two operating states of the target parameters;

[0031] A prediction module, configured to predict the conditional probability that the target parameter changes from the current operating state to a target operating state based on the current operating state of the target parameter and the preset state transition matrix.

[0032] In a third aspect, an embodiment of the present invention provides a coal mine power supply system, including: a processor and a storage device;

[0033] A computer program is stored on the storage device, and when the computer program is run by the processor, it executes the method described in any item of the first aspect.

[0034] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and when the computer program is run by a processor, it executes the steps of the method described in any item of the first aspect above.

[0035] An embodiment of the present invention provides a method and device for predicting the state of a coal mine power supply system. The method includes: real-time monitoring of the current operating state of target parameters in the coal mine power supply system; where the operating state of the target parameters includes any one or more of the opening and closing states of air doors, the opening and closing states of non-electric interlock doors, the starting state of fans, and the power load state; obtaining a preset state transition matrix corresponding to the target parameters; where the preset state transition matrix includes the transition probability values between any two operating states of the target parameters; based on the current operating state of the target parameters and the preset state transition matrix, predicting the conditional probability of the target parameters changing from the current operating state to the target operating state. By monitoring any one or more target parameters such as the opening and closing states of air doors, the opening and closing states of non-electric interlock doors, the starting state of fans, and the power load state of the coal mine power supply system in the present invention, and predicting the conditional probability of the target parameters changing from the current operating state to the target operating state according to the current operating state of the target parameters and the preset state transition matrix, the future operating states of air doors, interlock doors, fans, and power loads can be predicted, providing a data basis for the precise control of air doors, interlock doors, fans, and power loads in the coal mine power supply system, enabling the system to adjust in time when abnormal or emergency situations occur, avoiding delays caused by human misoperations, and reducing the failure risk of the coal mine power supply system.

[0036] Other features and advantages of the embodiments of the present invention will be described in the subsequent description, or some features and advantages can be inferred from the description or determined without doubt, or can be known by implementing the above technologies of the embodiments of the present invention.

[0037] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. Description of the Drawings

[0038] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required to be used in the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 The flowchart of a method for predicting the state of a coal mine power supply system provided by an embodiment of the present invention is shown;

[0040] Figure 2 The flowchart of the state transition of an air door, an interlocking door and a fan provided by an embodiment of the present invention is shown;

[0041] Figure 3 The schematic structural diagram of a device for predicting the state of a coal mine power supply system provided by an embodiment of the present invention is shown. Detailed implementation manners

[0042] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be described below with reference to the accompanying drawings. Apparently, the described embodiments are some but not all of the embodiments of the present invention.

[0043] The coal mine power supply system is the basic guarantee for the safe production of coal mines, and its core lies in realizing the efficient, stable and safe operation of the power supply system. The coal mine operation environment is complex, and the normal operation of power supply equipment and ventilation equipment is crucial. In particular, the state of the air door in the connection roadway will directly affect the ventilation quality, thus affecting the safety conditions of the mine.

[0044] At present, the coal mine power supply system usually relies on manual control or some simple interlocking control methods, and adjusts the ventilation condition and power load distribution by manually monitoring and operating the states of the air door, interlocking door and fan. Usually, the opening and closing of the air door and interlocking door in the connection roadway are realized by manual or simple electrical logic control, and the system lacks flexible adaptive adjustment ability. When emergencies or equipment failures occur, the system can only rely on alarm and shutdown measures, lacking the state prediction and early adjustment functions, and it is difficult to respond to sudden failures in a timely manner, which is likely to lead to accidents. At the same time, the management method relying on manual labor not only increases the workload and operation cost, but also greatly reduces the reaction speed and emergency handling ability of the system. Although the coal mine power supply system has basic control capabilities, in a complex mine environment, simply relying on manual operation or simple logic control cannot meet the real-time linkage requirements of multi-state equipment. Therefore, the existing coal mine power supply system lacks the ability to predict the changes in the operating states of the air door, interlocking door, fan and power load, resulting in a relatively high degree of fault risk for the coal mine power supply system.

[0045] To improve the above problems, a method and a device for predicting the state of a coal mine power supply system provided by an embodiment of the present invention are introduced in detail below.

[0046] This embodiment provides a method for predicting the state of a coal mine power supply system, and this method can be applied to a coal mine power supply system. Refer to Figure 1Flowchart of the method for predicting the state of the coal mine power supply system shown, the method mainly includes the following steps:

[0047] Step S102, monitor the current operating state of the target parameters in the coal mine power supply system in real time;

[0048] The operating state of the above target parameters includes any one or more of the opening and closing state of the air door, the opening and closing state of the non-electric interlock door, the starting state of the fan, and the power load state; obtain the air door state feedback by the air door control mechanism in real time, obtain the power load of the coal mine power supply network in real time, and determine the power load state according to the power load;

[0049] In a specific embodiment, the opening and closing state of the air door includes the fully open state, the partially open state, and the fully closed state; the air door is the connection roadway air door;

[0050] The opening and closing state of the non-electric interlock door includes the open state and the closed state;

[0051] The starting state of the fan includes the starting state and the closed state;

[0052] The power load state includes the low load state, the medium load state, and the high load state. In practical applications, the power load can be divided into multiple load intervals, obtain the current power load of the coal mine power supply system, the power load is the sum of the active powers of each node in the coal mine power supply network, and determine the current power load state according to the load interval where the current power load is located.

[0053] Step S104, obtain the preset state transition matrix corresponding to the target parameters;

[0054] Among them, the preset state transition matrix includes the transition probability values between any two operating states of the target parameters; calculate the transition probabilities when each target parameter changes its state between every two operating states respectively, and establish the preset state transition matrix corresponding to the target parameters according to the transition probabilities.

[0055] The above preset state transition matrix includes the state transition matrix of the opening and closing state of the air door, the state transition matrix of the opening and closing state of the non-electric interlock door, the state transition matrix of the starting state of the fan, and the state transition matrix of the power load state.

[0056] For example, assume that the operating state of the connection roadway air door includes the fully open state S0, the partially open state S1, and the fully closed state S2, then the preset state transition matrix corresponding to the connection roadway air door is:

[0057]

[0058] Among them, P(S0→S0) is the target probability value corresponding to the transfer of the air door in the connection roadway from the fully open state S0 to the fully open state S0, P(S0→S1) is the target probability value corresponding to the transfer of the air door in the connection roadway from the fully open state S0 to the partially open state S1, and so on. The preset state transition matrix includes the transfer probability values between any two operating states.

[0059] Step S106, based on the current operating state of the target parameter and the preset state transition matrix, predict the conditional probability of the target parameter changing from the current operating state to the target operating state.

[0060] The above-mentioned target operating state can be any operating state of the target parameter. For example, if the current operating state of the target parameter is the fully open state of the air door, the above-mentioned target operating state can be any one of the fully open state, the partially open state, and the fully closed state, so that the conditional probabilities of the air door changing from the fully open state to the fully open state, the partially open state, and the fully closed state respectively can be calculated.

[0061] The conditional probability of the target parameter changing from the current operating state to the target operating state is related to both the current operating state of the target parameter and the transfer probability of transferring to the target state. By predicting the conditional probability of the target parameter changing from the current operating state to the target operating state based on the current operating state of the target parameter and the preset state transition matrix, the probability of the target parameter changing to the target operating state in the future can be accurately predicted. It can clearly describe the uncertainty in the process of various state changes of the air door, the door without electric interlock, the fan, and the power load, so as to realize the evaluation and control of the system stability and security.

[0062] The above-mentioned conditional probability can be applied to the precise control of the system. Judge whether the conditional probability of the target parameter changing from the current operating state to the target operating state is greater than the preset threshold. If the conditional probability is greater than the preset threshold and the target operating state is the expected operating state of the system, it indicates that the target parameter will change in the expected direction of the system and no intervention control is required; if the conditional probability is less than the preset threshold and the target operating state is the expected operating state of the system, it indicates that the target parameter will not change in the expected direction of the system in the future, and the system equipment is controlled in advance to make the target parameter develop in the expected direction of the system. For example, let the target parameter be the air door, and the current operating state of the air door at the current time t is the fully open state. According to the above method, the conditional probability of the air door changing from the fully open state to the fully closed state at the next time t + 1 is calculated to be 16%, that is, there is a 16% probability that the air door will close. The probability of the air door closing at the next time is relatively small. Judge whether the conditional probability of the air door changing from the fully open state to the fully closed state is 16% greater than the preset threshold (60% - 80%). When the conditional probability is less than the preset threshold and the fully closed state is the expected operating state of the system at the next time, execute the corresponding control strategy to make the air door become fully closed at the next time to ensure the stable operation of the coal mine power supply system.

[0063] For example, when the air door in the crossheading is not closed, it may affect the ventilation system and then have an impact on the coal mine power supply network. For instance, poor ventilation may lead to problems such as heat dissipation of electrical equipment, affecting power supply stability. Adjusting the start and stop of the fan in a timely manner according to the air door control strategy may involve starting the fan when the air door is not closed and insufficient ventilation volume or abnormal air pressure is detected to enhance ventilation and ensure a normal operating environment for electrical equipment; while closing the fan when ventilation returns to normal or other abnormal situations occur (such as the risk of fan failure), thereby balancing the relationship between ventilation and the power supply system, maintaining the stability and safety of the mine power supply system, enabling it to adapt to the working condition changes under different air door states, and achieving adaptive intelligent control.

[0064] The above method for predicting the state of the coal mine power supply system provided in this embodiment monitors any one or more target parameters among the opening and closing states of the air doors, the opening and closing states of the non-electric interlock doors, the start states of the fans, and the power load states of the coal mine power supply system, and predicts the conditional probability of the target parameter changing from the current operating state to the target operating state according to the current operating state of the target parameter and the preset state transition matrix. It can predict the future operating states of the air doors, interlock doors, fans, and power loads, providing a data basis for the precise control of the air doors, interlock doors, fans, and power loads in the coal mine power supply system, enabling the system to adjust in a timely manner when abnormalities or emergencies occur, avoiding delays caused by human misoperations, and reducing the failure risk of the coal mine power supply system.

[0065] In one embodiment, the present embodiment provides an implementation manner for predicting the conditional probability of a target parameter changing from the current operating state to the target operating state based on the current operating state of the target parameter and the preset state transition matrix. The specific steps can be executed as follows:

[0066] Step (1): Obtain the target probability value corresponding to the target parameter changing from the current operating state to the target operating state from the preset state transition matrix;

[0067] The preset state transition matrix corresponding to the target parameter stores the transition probability values between every two operating states. Obtain the target probability value corresponding to the target parameter changing from the current operating state to the target operating state from the preset state transition matrix corresponding to the target parameter. For example, if the target parameter is the operating state of the air door, the current operating state of the air door is the fully open state, and the target state is the partially open state, obtain the target probability value P(S0→S2) corresponding to the crossheading air door changing from the fully open state S0 to the fully closed state S2 from the preset state transition matrix corresponding to the opening and closing states of the air door.

[0068] Step (2): Obtain the initial probability value corresponding to the target parameter in the current operating state, and predict the conditional probability of the target parameter changing from the current operating state to the target operating state based on the initial probability value and the target probability value.

[0069] The initial probability value corresponding to the above target parameter in the current operating state can be determined according to the historical operating state data of the target parameter. For example, the occurrence probability of the current operating state of the target parameter in the historical operating state can be calculated to obtain the initial probability value corresponding to the target parameter in the current operating state.

[0070] In a specific implementation manner, calculate the product of the initial probability and the target probability value to obtain the conditional probability of the target parameter changing from the current operating state to the target operating state.

[0071] For example, let the target parameter be the operating state of the air door in the connection roadway. The current operating state of the air door in the connection roadway at the current moment is the fully open state S0. The target probability value corresponding to the air door in the connection roadway transferring from the fully open state S0 to the fully closed state S2 is P(S0→S2). The initial probability value corresponding to the air door in the fully open state is P(S0). Then the conditional probability P(S2|S0) of the air door in the connection roadway changing from the fully open state S0 to the fully closed state S2 at the next moment is P(S0→S2)×P(S0).

[0072] In one embodiment, this embodiment provides the steps for establishing the preset state transition matrix corresponding to the target parameter:

[0073] Step 1): Obtain the historical operating state data of the target parameter, and determine the occurrence probability of the target parameter in each operating state based on the historical operating state data;

[0074] Determine the occurrence probability of the target parameter in each operating state according to the occurrence ratio of each operating state of the target parameter in the historical operating state data.

[0075] Step 2): Determine the transition probability value between every two operating states of the target parameter based on the occurrence probability of the target parameter in each operating state;

[0076] Calculate the transition probability values between the pairwise operating states of the target parameter. For example, if the target parameter is the power load state, the transition probability values between every two operating states include: the transition probability value of the power load state transferring from the low load state to the low load state, the transition probability value of the power load state transferring from the low load state to the medium load state, the transition probability value of the power load state transferring from the low load state to the high load state, the transition probability value of the power load state transferring from the medium load state to the low load state, the transition probability value of the power load state transferring from the medium load state to the medium load state, the transition probability value of the power load state transferring from the medium load state to the high load state, the transition probability value of the power load state transferring from the high load state to the low load state, the transition probability value of the power load state transferring from the high load state to the medium load state, and the transition probability value of the power load state transferring from the high load state to the high load state.

[0077] When the target parameter is the opening and closing state of the non-powered interlocking door, the transition probability values between every two operating states include: the transition probability value of the non-powered interlocking door transferring from the open state to the open state, the transition probability value of the non-powered interlocking door transferring from the open state to the closed state, the transition probability value of the non-powered interlocking door transferring from the closed state to the open state, and the transition probability value of the non-powered interlocking door transferring from the closed state to the closed state.

[0078] When the target parameter is the opening and closing state of the fan, the transition probability values between every two operating states include: the transition probability value of the fan transferring from the starting state to the starting state, the transition probability value of the fan transferring from the starting state to the closed state, the transition probability value of the fan transferring from the closed state to the starting state, and the transition probability value of the fan transferring from the closed state to the closed state.

[0079] In a specific embodiment, obtain the occurrence probability of the starting operating state in every two operating states; obtain the occurrence probability of the terminal operating state in every two operating states; determine the transition probability value between every two operating states based on the occurrence probability of the starting operating state and the occurrence probability of the terminal operating state.

[0080] Take the operating state before the transfer in every two operating states as the starting operating state, and the operating state after the transfer as the terminal operating state, and calculate the weighted sum of the occurrence probability of the starting operating state and the occurrence probability of the terminal operating state to obtain the transition probability value between every two operating states.

[0081] Calculate the first product of the occurrence probability of the starting operating state and the first weight, calculate the second product of the occurrence probability of the terminal operating state and the second weight, and calculate the sum of the first product and the second product to obtain the transition probability value; wherein, the sum of the first weight and the second weight is 1.

[0082] The calculation formula for the transition probability value between every two operating states described above may be as follows:

[0083] P = P0·w1 + P1·w2

[0084] Wherein, P0 is the occurrence probability of the starting operating state, P1 is the occurrence probability of the terminal operating state, w1 is the first weight, w1 is greater than or equal to 0 and less than or equal to 1, and w2 is the second weight.

[0085] In another embodiment, the calculation formula for the transition probability value between every two operating states may be as follows:

[0086] P = P0·(1 - R) + P1·R

[0087] Wherein, R is the transition probability that the target parameter is in the terminal operating state, R is greater than or equal to 0 and less than or equal to 1, and this transition probability may be a constant. By adjusting the value of R, the state of the target parameter under different working conditions can be described.

[0088] Step 3): Based on the transition probability values between every two operating states of the target parameter, establish a preset state transition matrix corresponding to the target parameter.

[0089] Add the transition probability values between every two operating states of the target parameter to the preset state transition matrix. In this preset state transition matrix, the starting operating states of the transition probability values in each row are the same, that is, the sum of the transition probability values in each row is 1.

[0090] For example, in the preset state transition matrix corresponding to the air door in the crossheading roadway, P(S0→S0) + P(S0→S1) + P(S0→S2) = 1.

[0091] The state prediction method of the coal mine power supply system provided in this embodiment can monitor the state changes of air doors, interlocking doors, fans and power loads in the coal mine power supply network in real time, and accurately calculate the state change probabilities of air doors, interlocking doors, fans and power loads by calculating the transition probabilities of each state. Compared with the traditional method that relies on manual and simple logic control, in this embodiment, the state transition matrix model is applied to the coal mine power supply network, and the state transition matrix is used to predict and adjust the operating states of equipment, so that the system can automatically respond and adjust when abnormal or sudden situations occur, avoiding human misoperation or delay. At the same time, through the interconnection and mutual control of the air door closing feedback device and the non-powered interlocking door, it is ensured that each device works in coordination, so that the power supply and ventilation systems in the mine under complex environments are provided with efficient, safe and intelligent guarantees.

[0092] On the basis of the foregoing embodiment, this embodiment provides a specific example of applying the state prediction method of the foregoing coal mine power supply system to perform adaptive intelligent control on the coal mine power supply network:

[0093] Refer to the state transition flow chart of the air damper, interlock door and fan shown in Figure 2 When the fan is not powered on, the interlock door is not opened, and the air damper is fully open: The initial state of the system is that the fan is not powered on, the interlock door is not opened, and the air damper is fully open. When the fan starts, the system will make corresponding adjustments according to the state of the air damper.

[0094] If the air damper is not closed, after the system receives the air damper closing signal, the linkage control system makes adjustments according to the state of the air damper. If the power-off interlock door is opened, the system changes the state of the air damper according to the state conversion rule. For example, assume that the air damper changes from the open state (K = 1) to the closed state (K * = 0).

[0095] When the air damper is fully closed, the feedback device sends the signal back to the linkage control system, indicating that the air damper has been closed, and the state quantity of the power-off interlock door changes from open (W = 1) to closed (W * = 0) (When the air damper is not closed, although the received closing signal seemingly only simply closes the air damper, in fact, this signal is closely related to the state conversion rule in the system. Under the adaptive intelligent control system, the state conversion rule is set based on the comprehensive monitoring and analysis of the entire coal mine power supply network and ventilation, etc. For example, it may consider factors such as the current ventilation demand and the degree of influence of ventilation on the stability of the power supply network. After this signal is triggered, the system will judge whether it is necessary to further adjust the operating states of other associated devices such as the fan according to these rules to ensure the stability and safety of the entire coal mine production system under different working conditions. It is not just a simple action of the air damper, but works in coordination with the overall adaptive intelligent control.).

[0096] This can be expressed by the following state change formula:

[0097] K * = f(W, K) = f(1, 1) → K * = 0

[0098] The above formula indicates that when the air damper is not closed and the interlock door is open, the air damper will close and the state will change.

[0099] When the fan is powered on, the interlock door is opened, and the air damper is not closed:

[0100] In this state, the system is already in the state where the fan is powered on and the interlock door is open. At this time, if the air damper starts to close (K * = 0), the feedback signal will be sent back to the linkage control system, the air damper will close, and the system state K * will change from 1 to 0.

[0101] At this time, if the non-powered interlock door is open (W = 1), the air damper is closed and the interlock door is also in the open state. If the air damper is fully opened again, the system will receive the air damper closed signal again, and the state change formula can be expressed as:

[0102] K * = f(W, K) = f(1, 1) → K * = 0

[0103] Even if the air damper is fully opened, the system still regulates through the state change matrix to ensure the coordinated states of the air damper and the interlock door.

[0104] When the linkage control system receives the signal that the non-powered interlock door is not open, the air damper closes again. At this time, the system will perform the operation of closing the air damper and update the state W of the interlock door according to the change of K * :

[0105] W * = f(W, K) = f(1, 0) → W * = 0

[0106] That is, after the air damper closes, the interlock door will change from open (W = 1) to closed (W * = 0).

[0107] When the fan is powered on, the interlock door is open, and the air damper is fully closed:

[0108] In this state, the air damper is fully closed and the interlock door is open. The system will adjust the state according to different signals of the air damper and the interlock door. For example, when receiving the signal that the air damper opens, the air damper will open, and the state variable K changes from 0 to 1. As the system further operates, the linkage control system may receive the signal that the non-powered interlock door is open, resulting in changes in the states of the air damper and the interlock door as follows:

[0109] K * = f(W, K) = f(0, 0) → K * = 1

[0110] At this time, the air damper changes from closed to open, and the states of the air damper and the interlock door will be updated synchronously. If the signal that the air damper closes is received at this time, the state will change again:

[0111] K * = f(W, K) = f(1, 1) → K * = 0

[0112] As the state changes, the linkage control system will continue to monitor the state of the non-powered interlock door. When the signal indicates that the non-powered interlock door is not open, the system will automatically perform the operation of closing the air damper.

[0113] The change of the system state not only depends on the current states of the air damper and the interlocking door, but also takes into account the working state of the fan. Through the coordination of multiple states, the system can dynamically adjust the opening and closing states of the air damper and the interlocking door. The above state transition formula reflects how the system responds to different signals and makes corresponding action adjustments. Suppose the initial state is that the air damper is fully open (K = 1), the non-powered interlocking door is not opened (W = 0), and the fan is in the closed state. At this time, after the system receives the fan start signal, the air damper remains open. When the air damper closes, the linkage control system adjusts the states of the air damper and the non-powered interlocking door according to the feedback signal. If the system then receives the non-powered interlocking door opening signal, the air damper will automatically close, and the interlocking door will also be adjusted to the open state synchronously. This process can be expressed by a state transition matrix to ensure that the control of each device complies with the set rules and safety requirements. The detailed steps of the state transition show the complex interaction among the air damper, the interlocking door, and the fan in the coal mine power supply system. By using the state transition matrix and the state function f(W, K) of the air damper and the interlocking door, the behavior of the system under different conditions can be accurately described. Through the intelligent control system, the coal mine power supply network can flexibly make control decisions in the face of different working conditions to ensure the stable and safe operation of the system.

[0114] In the adaptive intelligent control method of the coal mine power supply network, the change law and stability of the air damper state can be effectively described by calculating the state transition probability. According to the state change model, the transition probability between states describes the possibility of the air damper switching from one state to another, providing a quantitative analysis of the system operation trend. The calculation formula for the transition probability value between every two operating states can be:

[0115] P = P0·(1 - R) + P1·R

[0116] Where R is the transition probability of the target parameter being in the final operating state, R is greater than or equal to 0 and less than or equal to 1, and this transition probability can be a constant.

[0117] This embodiment provides a calculation method for the conditional probability of the air damper:

[0118] The operating state of the air damper in the connection roadway can be represented by the state transition matrix of the air damper. Suppose the state of the air damper at the current moment is S k-1 , through state transition, the conditional probability of S k at the next moment can be calculated:

[0119] P(S k ∣S k-1 ) = P(S k-1 →S k )×P(S k-1 )

[0120] Where P(S k-1 →Sk ) represents the transition probability from S k-1 to S k , and P(S k-1 ) is the initial probability of the current state. This conditional probability is used to predict the evolution trend of the damper's future state.

[0121] Assume that the initial state S0 is the fully open state of the damper, and the corresponding probability is P(S0) = 0.8, while the transition probability of the closed damper state S2 is P(S0→S2) = 0.2. Then, the conditional probability of the damper transitioning from the S0 state to the S2 state is:

[0122] P(S2|S0) = P(S0→S2) × P(S0) = 0.2 × 0.8 = 0.16

[0123] This indicates that when the damper is initially fully open, there is a 16% probability of a state change to the closed damper. Through this probability model, the uncertainty of the damper during various state changes can be clearly described, thereby enabling the assessment and control of the system's stability and safety.

[0124] The above probability calculation and the introduction of the state transition matrix enable the system to dynamically evaluate the state change trends of the damper and the interlock door, effectively predicting the stability and risks of the system operation. This model provides a basis for the intelligent control of the coal mine power supply network, enabling the system to maintain precise automatic control capabilities even in complex environments.

[0125] This embodiment reduces manual intervention and improves the automation level. By the intelligent management of the mine power supply network, the dependence on manual monitoring and manual operation is reduced. By using the state transition matrix and the intelligent linkage control strategy, real-time analysis and automatic adjustment of each state of the power supply system can be performed, thereby reducing the need for manual intervention and improving the automation level and operation efficiency of the power supply system; it improves the problems of response delay, complex operation, and insufficient risk response of the traditional power supply network. Through the implementation of state monitoring and adaptive linkage strategies, efficient, safe, and intelligent control of the mine power supply network is achieved, providing a strong guarantee for the safe operation of the coal mine power supply system.

[0126] Corresponding to the state prediction method of the coal mine power supply system provided in the above embodiment, an embodiment of the present invention provides a state prediction device for a coal mine power supply system. Refer to Figure 3 the structural schematic diagram of a state prediction device for a coal mine power supply system shown. The device includes the following modules:

[0127] A monitoring module 31, configured to monitor the current operating state of target parameters in the coal mine power supply system in real time; wherein, the operating state of the target parameters includes any one or more of the opening and closing state of the damper, the opening and closing state of the power-off interlock door, the starting state of the fan, and the power load state.

[0128] An acquisition module 32, configured to acquire a preset state transition matrix corresponding to a target parameter; wherein, the preset state transition matrix includes transition probability values of the target parameter between any two operating states.

[0129] A prediction module 33, configured to predict a conditional probability that the target parameter changes from the current operating state to the target operating state based on the current operating state of the target parameter and the preset state transition matrix.

[0130] The state prediction device of the coal mine power supply system provided by the present invention monitors any one or more target parameters of the opening and closing states of air doors, the opening and closing states of non-electric interlock doors, the starting states of fans, and the power load states of the coal mine power supply system, and predicts the conditional probability that the target parameter changes from the current operating state to the target operating state according to the current operating state of the target parameter and the preset state transition matrix, so as to predict the future operating states of air doors, interlock doors, fans, and power loads, providing a data basis for the precise control of air doors, interlock doors, fans, and power loads by the coal mine power supply system, enabling the system to adjust in time when abnormalities or emergencies occur, avoiding delays caused by human misoperations, and reducing the failure risk of the coal mine power supply system.

[0131] For the device provided in this embodiment, the implementation principle and the technical effects produced are the same as those in the previous embodiment. For a brief description, for the parts not mentioned in the device embodiment, reference may be made to the corresponding content in the previous method embodiment.

[0132] Corresponding to the method and device provided in the previous embodiment, an embodiment of the present invention further provides a coal mine power supply system, which includes: a processor and a memory. A computer program that can run on the processor is stored in the memory. When the processor executes the computer program, the steps of the method provided in the above embodiment are implemented.

[0133] An embodiment of the present invention provides a computer-readable medium, wherein the computer-readable medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to implement the method described in the above embodiment.

[0134] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the above-described system can refer to the corresponding process in the previous embodiment, and will not be described in detail here.

[0135] In addition, in the description of the embodiments of the present invention, unless otherwise clearly defined and limited, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0136] If the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0137] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0138] Finally, it should be noted that the above-mentioned embodiments are only specific embodiments of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting them. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for predicting the state of a coal mine power supply system, characterized in that, Including: Real-time monitoring of the current operating status of target parameters in the coal mine power supply system; wherein, the operating status of the target parameters includes any one or more of the opening and closing status of air doors, the opening and closing status of non-electric interlock doors, the starting status of fans, and the power load status. Obtaining a preset state transition matrix corresponding to the target parameters; wherein, the preset state transition matrix includes the transition probability values between any two operating states of the target parameters. Based on the current operating status of the target parameters and the preset state transition matrix, predicting the conditional probability that the target parameters change from the current operating status to the target operating status.

2. The state prediction method according to claim 1, wherein The step of predicting the conditional probability that the target parameters change from the current operating status to the target operating status based on the current operating status of the target parameters and the preset state transition matrix includes: Obtaining the target probability value corresponding to the transfer of the target parameters from the current operating status to the target operating status from the preset state transition matrix. Obtaining the initial probability value corresponding to the target parameters in the current operating status, and predicting the conditional probability that the target parameters change from the current operating status to the target operating status based on the initial probability value and the target probability value.

3. The state prediction method according to claim 2, wherein The step of predicting the conditional probability that the target parameters change from the current operating status to the target operating status based on the initial probability value and the target probability value includes: Calculating the product of the initial probability and the target probability value to obtain the conditional probability that the target parameters change from the current operating status to the target operating status.

4. The state prediction method according to claim 1, wherein The establishment step of the preset state transition matrix corresponding to the target parameters includes: Obtaining the historical operating status data of the target parameters, and determining the occurrence probability of the target parameters in each operating status based on the historical operating status data. Determining the transition probability value between every two operating states of the target parameters based on the occurrence probability of the target parameters in each operating status. Establishing a preset state transition matrix corresponding to the target parameters based on the transition probability values between every two operating states of the target parameters.

5. The state prediction method according to claim 4, wherein The step of determining the transition probability value between every two operating states of the target parameters based on the occurrence probability of the target parameters in each operating status includes: Obtaining the occurrence probability of the starting operating state in every two operating states. Obtaining the occurrence probability of the terminal operating state in every two operating states. Determining the transition probability value between every two operating states based on the occurrence probability of the starting operating state and the occurrence probability of the terminal operating state.

6. The state prediction method according to claim 5, wherein The step of determining the transition probability value between every two operating states based on the occurrence probability of the starting operating state and the occurrence probability of the terminal operating state includes: Calculating the first product of the occurrence probability of the starting operating state and the first weight, calculating the second product of the occurrence probability of the terminal operating state and the second weight, and calculating the sum of the first product and the second product to obtain the transition probability value; wherein, the sum of the first weight and the second weight is 1.

7. The state prediction method according to any one of claims 1-6, characterized in that The opening and closing states of the air damper include the fully open state, the partially open state, and the fully closed state; The opening and closing states of the non-electric interlock door include the open state and the closed state; The opening and closing states of the fan include the start state and the closed state; The power load states include the low load state, the medium load state, and the high load state.

8. A state prediction device for a coal mine power supply system, characterized in that, It includes: A monitoring module for real-time monitoring of the current operating state of target parameters in the coal mine power supply system; wherein, the operating state of the target parameters includes any one or more of the opening and closing states of the air damper, the opening and closing states of the non-electric interlock door, the start state of the fan, and the power load state; An acquisition module for acquiring the preset state transition matrix corresponding to the target parameters; wherein, the preset state transition matrix includes the transition probability values between any two operating states of the target parameters; A prediction module for predicting the conditional probability of the target parameters changing from the current operating state to the target operating state based on the current operating state of the target parameters and the preset state transition matrix.

9. A coal mine power supply system, characterized in that, It includes: A processor and a storage device; A computer program is stored on the storage device, and the computer program, when run by the processor, executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored, characterized in that, When the computer program is run by the processor, it executes the steps of the method according to any one of claims 1 to 7 above.

Images