A state prediction method and device for a coal mine power supply system

Abstract

The application provides a state prediction method and device for a coal mine power supply system, which 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 interlocking door, the starting state of a fan and the power load state; obtaining a preset state transition matrix corresponding to the target parameter; wherein the preset state transition matrix comprises a transition probability value between any two operation states of the target parameter; and predicting the conditional probability of the target parameter changing from the current operation state to a target operation state based on the current operation state of the target parameter and the preset state transition matrix. The application can predict the future operation state of the air door, the interlocking door, the fan and the power load, and provides a data basis for the accurate control of the air door, the interlocking door, the fan and the power load by the coal mine power supply system.

Description

Technical Field

[0001] This invention relates to the field of state prediction technology, and in particular to a state prediction method and apparatus for a coal mine power supply system. Background Technology

[0002] Currently, coal mine power supply systems typically rely on manual control or simple interlocking control methods. Ventilation and power load distribution are adjusted by manually monitoring and operating the status of air doors, interlocking doors, and fans. The opening and closing of air doors and interlocking doors in connecting roadways are achieved manually or through simple electrical logic control. There is a lack of automatic status prediction for air doors, interlocking doors, fans, and power loads. Control usually relies on human experience, lacking status prediction and advance adjustment functions, making it difficult to respond promptly to sudden faults and easily leading to accidents. Although coal mine power supply systems possess basic control capabilities, in the complex environment of mines, relying solely on manual operation or simple logic control is insufficient to meet the real-time linkage requirements of multi-state equipment. Therefore, existing coal mine power supply systems lack the ability to predict the operating status of air doors, interlocking doors, fans, and power loads, resulting in a high risk of failure. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a state prediction method and device for a coal mine power supply system, which can predict the future operating state of dampers, interlocking doors, fans and power loads, providing a data basis for the precise control of dampers, interlocking doors, fans and power loads in the coal mine power supply system, and reducing the failure risk of the coal mine power supply system.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows:

[0005] In a first aspect, embodiments of the present invention provide a state prediction method for a coal mine power supply system, comprising:

[0006] The current operating status of target parameters in the coal mine power supply system is monitored in real time; wherein the operating status of the target parameters includes any one or more of the following: the opening and closing status of the damper, the opening and closing status of the non-electric interlocking door, the starting status of the fan, and the power load status.

[0007] Obtain the preset state transition matrix corresponding to the target parameter; wherein, the preset state transition matrix includes the transition probability value of the target parameter between any two operating states;

[0008] 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.

[0009] Furthermore, this embodiment of the invention provides a first possible implementation of the first aspect, wherein the step of 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 includes:

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

[0011] Obtain the initial probability value of the target parameter corresponding to 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.

[0012] Furthermore, this embodiment of the invention provides a second possible implementation of the first aspect, wherein the step of predicting 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 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 running state to the target running state.

[0014] Furthermore, this embodiment of the invention provides a third possible implementation of the first aspect, wherein the step of establishing the preset state transition matrix corresponding to the target parameter includes:

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

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

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

[0018] Furthermore, this embodiment of the invention provides a fourth possible implementation of the first aspect, wherein the step of determining the transition probability value of the target parameter between every two operating states based on the occurrence probability of the target parameter in each operating state includes:

[0019] Obtain the probability of the occurrence of the initial running state in every two running states;

[0020] Obtain the probability of the occurrence of the final running state in every two running states;

[0021] The transition probability value between each pair of operating states is determined based on the probability of the occurrence of the initial operating state and the probability of the occurrence of the final operating state.

[0022] Furthermore, this embodiment of the invention provides a fifth possible implementation of the first aspect, wherein the step of determining the transition probability value between every two running states based on the probability of occurrence of the initial running state and the probability of occurrence of the final running state includes:

[0023] Calculate the first product of the probability of the initial running state and the first weight, calculate the second product of the probability of the final running 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, this embodiment of the invention provides a sixth possible implementation of the first aspect, wherein the opening and closing states of the 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 fan's open / closed states include a start-up state and a shut-down state;

[0027] The power load status includes low load status, medium load status and high load status.

[0028] Secondly, embodiments of the present invention also provide a state prediction device for a coal mine power supply system, comprising:

[0029] The monitoring module is used to monitor the current operating status of target parameters in the coal mine power supply system in real time; wherein, the operating status of the target parameters includes any one or more of the following: the opening and closing status of the damper, the opening and closing status of the power-off interlocking door, the starting status of the fan, and the power load status.

[0030] The acquisition module is used to acquire a preset state transition matrix corresponding to the target parameter; wherein, the preset state transition matrix includes the transition probability value of the target parameter between any two running states;

[0031] The prediction module is used to predict 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.

[0032] Thirdly, embodiments of the present invention provide a coal mine power supply system, including: a processor and a storage device;

[0033] The storage device stores a computer program that, when executed by the processor, performs the method as described in any of the first aspects.

[0034] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, performs the steps of the method described in any of the first aspects above.

[0035] This invention provides a method and apparatus for predicting the state of a coal mine power supply system. The method includes: real-time monitoring of the current operating state of a target parameter in the coal mine power supply system; wherein the operating state of the target parameter includes any one or more of the following: the opening and closing state of a damper, the opening and closing state of a non-electric interlocking door, the starting state of a fan, and the power load state; obtaining a preset state transition matrix corresponding to the target parameter; wherein the preset state transition matrix includes the transition probability value of the target parameter between any two operating states; and 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. This invention monitors any one or more target parameters in a coal mine power supply system, including the opening and closing status of dampers, the opening and closing status of interlocking doors without power, the start-up status of fans, and the status of power load. Based on the current operating status of these target parameters and a preset state transition matrix, it predicts the conditional probability of the target parameters transitioning from their current operating status to their target operating status. This allows for the prediction of the future operating status of dampers, interlocking doors, fans, and power loads, providing a data foundation for precise control of these components in the coal mine power supply system. This enables timely adjustments to the system in case of anomalies or emergencies, avoiding delays due to human error and reducing the risk of failure in the coal mine power supply system.

[0036] Other features and advantages of the embodiments of the present invention will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the techniques described above in the embodiments of the present invention.

[0037] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0038] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

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

[0040] Figure 2 This invention provides a state transition flowchart for a damper, an interlocking door, and a fan, according to an embodiment of the invention.

[0041] Figure 3 A schematic diagram of the state prediction device for a coal mine power supply system provided in an embodiment of the present invention is shown. Detailed Implementation

[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 in conjunction with the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0043] The coal mine power supply system is the fundamental guarantee for safe production in coal mines, and its core lies in achieving efficient, stable, and safe operation. The coal mine working environment is complex, and the normal operation of power supply and ventilation equipment is crucial. In particular, the condition of the air doors in connecting roadways directly affects ventilation quality, thereby impacting the mine's safety conditions.

[0044] Currently, coal mine power supply systems typically rely on manual control or some simple interlocking control methods. Ventilation and power load distribution are adjusted by manually monitoring and operating the status of air doors, interlocking doors, and fans. Typically, the opening and closing of air doors and interlocking doors in connecting roadways are achieved manually or through simple electrical logic control, lacking flexible adaptive adjustment capabilities. In the event of emergencies or equipment failures, the system can only rely on alarms and shutdown measures, lacking status prediction and advance adjustment functions, making it difficult to respond promptly to sudden faults and easily leading to accidents. Furthermore, the reliance on manual management not only increases workload and operating costs but also significantly reduces the system's response speed and emergency handling capabilities. Although coal mine power supply systems possess basic control capabilities, in the complex environment of mines, relying solely on manual operation or simple logic control is insufficient to meet the real-time linkage requirements of multi-state equipment. Therefore, existing coal mine power supply systems lack the ability to predict changes in the operating status of air doors, interlocking doors, fans, and power loads, resulting in a high degree of failure risk in coal mine power supply systems.

[0045] To address the aforementioned issues, this invention provides a method and apparatus for predicting the state of a coal mine power supply system. The following provides a detailed description of the embodiments of this invention.

[0046] This embodiment provides a state prediction method for a coal mine power supply system. This method can be applied to coal mine power supply systems. See [link / reference]. Figure 1The flowchart shown is for a state prediction method for a coal mine power supply system. This method mainly includes the following steps:

[0047] Step S102: Monitor the current operating status of target parameters in the coal mine power supply system in real time;

[0048] The operating status of the above target parameters includes any one or more of the following: the opening and closing status of the damper, the opening and closing status of the non-electric interlocking door, the start-up status of the fan, and the power load status; the damper status fed back by the damper control mechanism is obtained in real time, the power load of the coal mine power supply network is obtained in real time, and the power load status is determined based on the power load.

[0049] In one specific implementation, the opening and closing states of the damper include a fully open state, a partially open state, and a fully closed state; the damper is a connecting roadway damper.

[0050] The opening and closing states of a door without an electric interlock include an open state and a closed state;

[0051] The fan's operating status includes both the start-up state and the shutdown state;

[0052] Electrical load status includes low load, medium load, and high load. In practical applications, the electrical load can be divided into multiple load intervals to obtain the current electrical load of the coal mine power supply system. This electrical load is the sum of the active power of each node in the coal mine power supply network. The current electrical load status is determined based on the load interval in which the current electrical load is located.

[0053] Step S104: Obtain the preset state transition matrix corresponding to the target parameters;

[0054] The preset state transition matrix includes the transition probability value of the target parameter between any two operating states; the transition probability of each target parameter when changing state between any two operating states is calculated, and the preset state transition matrix corresponding to the target parameter is established based on the transition probability.

[0055] The aforementioned preset state transition matrix includes the state transition matrix for the opening and closing state of the damper, the state transition matrix for the opening and closing state of the non-electric interlocking door, the state transition matrix for the start-up state of the fan, and the state transition matrix for the power load state.

[0056] For example, if the operating states of the connecting roadway airlock door include fully open state S0, partially open state S1, and fully closed state S2, then the preset state transition matrix corresponding to the connecting roadway airlock door is:

[0057]

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

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

[0060] The aforementioned 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 damper fully open, the aforementioned target operating state can be any one of the fully open state, partially open state, and fully closed state. Thus, the conditional probability of the damper changing from the fully open state to the fully open state, partially open state, and fully closed state can be calculated.

[0061] The conditional probability of a target parameter transitioning from its current operating state to its target operating state is related to both the current operating state of the target parameter and the transition probability to the target state. Based on the current operating state of the target parameter and a preset state transition matrix, the conditional probability of the target parameter transitioning from its current operating state to the target operating state can be predicted, thus accurately predicting the probability of the target parameter becoming the target operating state in the future. This method can clearly describe the uncertainties of dampers, non-electrically interlocked doors, and fans (i.e., electrical loads) during various state changes, thereby enabling the assessment and control of system stability and safety.

[0062] The aforementioned conditional probability can be applied to precise system control. It determines whether the conditional probability of a target parameter changing from its current operating state to its target operating state is greater than a preset threshold. If the conditional probability is greater than the preset threshold and the target operating state is the system's desired operating state, it indicates that the target parameter will change in the desired direction, and no intervention is needed. If the conditional probability is less than the preset threshold and the target operating state is the system's desired operating state, it indicates that the target parameter will not change in the desired direction in the future, and the system equipment should be controlled in advance to make the target parameter develop in the desired direction. For example, suppose the target parameter is a damper, and the damper is currently in a fully open state at time t. According to the above method, the conditional probability of the damper changing from a fully open state to a fully closed state at the next time t+1 is 16%, meaning there is a 16% probability that the damper will close. The probability of the damper closing at the next time is relatively small. We need to determine whether the conditional probability of the damper changing from a fully open state to a fully closed state is greater than a preset threshold (60% to 80%). If 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, we will execute the corresponding control strategy to make the damper change to a fully closed state at the next time to ensure the stable operation of the coal mine power supply system.

[0063] For example, when the ventilation door of the connecting roadway is not closed, it may affect the ventilation system and thus the coal mine power supply network. Poor ventilation may lead to problems such as heat dissipation of electrical equipment, affecting the stability of power supply. According to the ventilation door control strategy, the start and stop of the fan should be adjusted in a timely manner. For example, when the ventilation door is not closed and insufficient ventilation or abnormal air pressure is detected, the fan may be started to enhance ventilation and ensure the normal operation environment of electrical equipment. When ventilation returns to normal or other abnormal situations occur (such as the risk of fan failure), the fan may be shut down. This balances the relationship between ventilation and power supply system, maintains the stability and safety of the mine power supply system, and enables it to adapt to changes in working conditions under different ventilation door states, so as to achieve adaptive intelligent control.

[0064] The state prediction method for 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 dampers, the opening and closing states of the interlocking doors, the starting states of the fans, and the power load states of the coal mine power supply system. Based on the current operating state of the target parameters and a preset state transition matrix, it predicts the conditional probability of the target parameters changing from the current operating state to the target operating state. This method can predict the future operating states of the dampers, interlocking doors, fans, and power loads, providing a data foundation for the precise control of the dampers, interlocking doors, fans, and power loads in the coal mine power supply system. It enables the system to adjust in a timely manner when abnormalities or emergencies occur, avoids delays caused by human error, and reduces the failure risk of the coal mine power supply system.

[0065] In one embodiment, this embodiment provides an implementation method for predicting the conditional probability of the target parameter changing from the current running state to the target running state based on the current running state of the target parameter and a preset state transition matrix. The specific steps are as follows:

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

[0067] The preset state transition matrix corresponding to the target parameter stores the transition probability value between every two operating states. The target probability value corresponding to the transition of the target parameter from the current operating state to the target operating state is obtained from the preset state transition matrix corresponding to the target parameter. For example, if the target parameter is the operating state of the damper, the current operating state of the damper is fully open, and the target state is partially open, the target probability value P(S0→S2) corresponding to the transition of the damper in the connecting roadway from the fully open state S0 to the fully closed state S2 is obtained from the preset state transition matrix corresponding to the opening and closing states of the damper.

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

[0069] The initial probability value of the target parameter in the current operating state can be determined based on the historical operating state data of the target parameter. For example, the probability of the current operating state of the target parameter in the historical operating states can be calculated to obtain the initial probability value of the target parameter in the current operating state.

[0070] In one specific implementation, the product of the initial probability and the target probability value is calculated to obtain the conditional probability that the target parameter changes from the current running state to the target running state.

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

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

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

[0074] The probability of occurrence of the target parameter in each operating state is determined by the ratio of occurrence of each operating state of the target parameter in the historical operating state data.

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

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

[0077] When the target parameter is the open / closed state of the door without electric interlock, the transition probability value between each two operating states includes: the transition probability value of the door without electric interlock from the open state to the open state, the transition probability value of the door without electric interlock from the open state to the closed state, the transition probability value of the door without electric interlock from the closed state to the open state, and the transition probability value of the door without electric interlock from the closed state to the closed state.

[0078] When the target parameter is the on / off state of the fan, the transition probability value between any two operating states includes: the transition probability value of the fan from the start state to the start state, the transition probability value of the fan from the start state to the off state, the transition probability value of the fan from the off state to the start state, and the transition probability value of the fan from the off state to the off state.

[0079] In one specific implementation, the probability of the occurrence of the initial running state in every two running states is obtained; the probability of the occurrence of the final running state in every two running states is obtained; and the transition probability value between every two running states is determined based on the probability of the occurrence of the initial running state and the probability of the occurrence of the final running state.

[0080] The state before the transition between any two running states is taken as the starting running state, and the state after the transition is taken as the ending running state. The weighted sum of the probability of the occurrence of the starting running state and the probability of the occurrence of the ending running state is calculated to obtain the transition probability value between any two running states.

[0081] Calculate the first product of the probability of the initial running state and the first weight, calculate the second product of the probability of the final running state and the second weight, and calculate the sum of the first and second products to obtain the transition probability value; where the sum of the first and second weights is 1.

[0082] The formula for calculating the transition probability between each pair of operating states can be:

[0083] P = P0·w1 + P1·w2

[0084] Where P0 is the probability of the initial running state, P1 is the probability of the final running state, w1 is the first weight (greater than or equal to 0 and less than or equal to 1), and w2 is the second weight.

[0085] In another implementation, the formula for calculating the transition probability between any two operating states can be:

[0086] P = P0·(1-R) ​​+ P1·R

[0087] 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. This transition probability can be a constant. By adjusting the value of R, the state of the target parameter under different operating conditions can be described.

[0088] Step 3): Establish a preset state transition matrix corresponding to the target parameter based on the transition probability value between every two operating states of the target parameter.

[0089] The transition probability values ​​of the target parameter between every two running states are added to a preset state transition matrix. The starting running states of the transition probability values ​​in each row of the preset state transition matrix 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 of the connecting roadway, P(S0→S0)+P(S0→S1)+P(S0→S2)=1.

[0091] The state prediction method for the coal mine power supply system provided in this embodiment can monitor the state changes of dampers, interlocking doors, fans, and power loads in the coal mine power supply network in real time. By calculating the transition probability of each state, it can accurately calculate the state change probability of dampers, interlocking doors, fans, and power loads. Compared with the traditional method that relies on manual and simple logic control, this embodiment applies the state transition matrix model to the coal mine power supply network. It uses the state transition matrix to predict and adjust the operating state of equipment, so that the system can automatically respond and adjust when abnormal or sudden situations occur, avoiding human error or delay. At the same time, through the interconnection and mutual control of damper closure feedback device and non-electric interlocking doors, it ensures the coordinated work between various devices, so that the power supply and ventilation system of the mine in the complex environment has obtained efficient, safe and intelligent protection.

[0092] Based on the foregoing embodiments, this embodiment provides a specific example of applying the aforementioned state prediction method for coal mine power supply systems to perform adaptive intelligent control of coal mine power supply networks:

[0093] See also Figure 2 The flowchart shown illustrates the state transitions of the damper, interlock door, and fan. When the fan is not powered, the interlock door is not open, and the damper is fully open: the initial state of the system is that the fan is not powered, the interlock door is not open, and the damper is fully open. When the fan starts, the system will make corresponding adjustments based on the state of the damper.

[0094] If the damper is not closed, the system receives a damper closure signal and the linkage control system adjusts accordingly based on the damper's state. If the damper is opened without electrical interlock, the system changes the damper's state according to state transition rules. For example, suppose the damper transitions from the open state (K=1) to the closed state (K... * =0).

[0095] When the damper is fully closed, the feedback device sends a signal back to the linkage control system, indicating that the damper is closed, and the non-electrically interlocked door status changes from open (W=1) to closed (W=1). * =0)(When the damper is not closed, the received closing signal may seem to simply close the damper, but in reality, this signal is closely linked to the state transition rules in the system. Under the adaptive intelligent control system, the state transition rules are set based on the system's comprehensive monitoring and analysis of the entire coal mine's power supply network and ventilation, etc. For example, it may take into account factors such as the current ventilation demand and the degree to which the stability of the power supply network is affected by ventilation. After the signal is triggered, the system will determine whether it is necessary to further adjust the operating status of other related equipment such as fans 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 damper action, but a coordinated operation with the overall adaptive intelligent control.)

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

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

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

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

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

[0101] At this point, if the powerless interlock door opens (W=1), the damper closes and the interlock door is also in the open state. If the damper opens fully again, the system will receive the damper closing signal again, and the state change formula can be expressed as:

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

[0103] Even when the damper is fully open, the system still uses a state change matrix to regulate and ensure that the states of the damper and the interlocking door are coordinated.

[0104] When the linkage control system receives a signal that the door is not open due to lack of power interlock, the damper closes again. At this time, the system will execute the operation of closing the damper, and according to K... * Update the state W of the interlocking door according to the changes:

[0105] W * =f(W,K)=f(1,0)→W * =0

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

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

[0108] In this state, the damper is fully closed, and the interlock door is open. The system will adjust its state based on different signals from the damper and interlock door. For example, when a signal indicating that the damper is open is received, the damper will open, and the state variable K will change from 0 to 1. As the system operates further, the linkage control system may receive a signal indicating that the interlock door is open when there is no power, causing changes in the state of the damper and interlock door as follows:

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

[0110] At this point, the damper changes from closed to open, and the damper status and the interlock door status will be updated synchronously. If a signal indicating that the damper is closed is received at this time, the status will change again.

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

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

[0113] System state changes depend not only on the current states of the dampers and interlocking doors, but also on the operating state of the fan. Through the coordination of multiple states, the system can dynamically adjust the opening and closing states of the dampers and interlocking doors. The state transition formulas described above illustrate how the system responds to different signals and makes corresponding adjustments. Assume the initial state is that the damper is fully open (K=1), the non-energized interlocking door is not open (W=0), and the fan is off. At this time, after receiving a fan-opening signal, the damper remains open. When the damper closes, the linkage control system adjusts the states of the damper and the non-energized interlocking door according to the feedback signal. If the system receives a non-energized interlocking door opening signal again, the damper will automatically close, and the interlocking door will synchronously adjust to open. This process can be expressed using a state transition matrix, ensuring that the control of each device conforms to the set rules and safety requirements. The detailed steps of the state transitions demonstrate the complex interaction between the dampers, interlocking doors, and fans in the coal mine power supply system. By using the state transition matrix and the state functions f(W, K) of the dampers and interlocking doors, the system's behavior under different conditions can be accurately described. Through intelligent control systems, coal mine power supply networks can make flexible control decisions in the face of different working conditions, ensuring the stable and safe operation of the system.

[0114] In the adaptive intelligent control method of coal mine power supply networks, the state transition probability calculation can effectively describe the transition law and stability of damper states. Based on the state change model, the transition probability between each state describes the likelihood of the damper switching from one state to another, providing a quantitative analysis of the system's operating trend. The formula for calculating the transition probability between any 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 running 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 method for calculating the conditional probability of the damper:

[0118] The operating state of the connecting roadway airlock can be represented by the airlock's state transition matrix. Assume the current state of the airlock is S. k-1 Through state transition, the next time step S can be calculated. k Conditional probability:

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

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

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

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

[0123] This indicates that, with the damper initially fully open, there is a 16% probability of a change to a closed state. This probabilistic model clearly describes the uncertainties of the damper during various state changes, thereby enabling the assessment and control of system stability and safety.

[0124] The aforementioned probability calculations and the introduction of the state transition matrix enable the system to dynamically assess the state change trends of dampers and interlocking doors, effectively predicting the stability and risks of system operation. This model provides a foundation for intelligent control of coal mine power supply networks, enabling the system to maintain precise automated control capabilities even in complex environments.

[0125] This embodiment reduces manual intervention and improves automation. By intelligently managing the mine power supply network, it reduces reliance on manual monitoring and operation. Employing a state transition matrix and intelligent linkage control strategy, it can perform real-time analysis and automatic adjustment of various states of the power supply system, thereby reducing the need for manual intervention and improving the automation level and operational efficiency of the power supply system. It also improves the problems of response delay, operational complexity, and insufficient risk response in traditional power supply networks. Through the implementation of state monitoring and adaptive linkage strategies, it achieves efficient, safe, and intelligent control of the mine power supply network, providing a strong guarantee for the safe operation of the coal mine power supply system.

[0126] Corresponding to the state prediction method for coal mine power supply systems provided in the above embodiments, this invention provides a state prediction device for coal mine power supply systems. (See attached image) Figure 3 The diagram shows a structural schematic of a state prediction device for a coal mine power supply system. The device includes the following modules:

[0127] The monitoring module 31 is used to monitor the current operating status of target parameters in the coal mine power supply system in real time; wherein, the operating status of the target parameters includes any one or more of the following: the opening and closing status of the damper, the opening and closing status of the non-electric interlocking door, the starting status of the fan, and the power load status.

[0128] The acquisition module 32 is used to acquire the preset state transition matrix corresponding to the target parameter; wherein, the preset state transition matrix includes the transition probability value of the target parameter between any two running states;

[0129] The prediction module 33 is used to predict the conditional probability of the target parameter changing from the current running state to the target running state based on the current running state of the target parameter and the preset state transition matrix.

[0130] The state prediction device for the coal mine power supply system provided by this invention monitors any one or more target parameters among the opening and closing states of the dampers, the opening and closing states of the interlocking doors, the starting states of the fans, and the power load states of the coal mine power supply system. Based on the current operating state of the target parameters and a preset state transition matrix, it predicts the conditional probability of the target parameters changing from their current operating state to their target operating state. This allows for the prediction of the future operating states of the dampers, interlocking doors, fans, and power loads, providing a data foundation for the precise control of these components in the coal mine power supply system. This enables the system to adjust promptly in the event of abnormalities or emergencies, avoiding delays due to human error and reducing the risk of failure in the coal mine power supply system.

[0131] The device provided in this embodiment has the same implementation principle and technical effect as the aforementioned embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0132] Corresponding to the methods and apparatus provided in the foregoing embodiments, this embodiment of the invention also provides a coal mine power supply system, which includes: a processor and a memory, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps of the methods provided in the foregoing embodiments.

[0133] This invention provides a computer-readable medium storing computer-executable instructions. When these computer-executable instructions are invoked and executed by a processor, they cause the processor to implement the methods described in the above embodiments.

[0134] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system described above can be referred to the corresponding process in the foregoing embodiments, and will not be repeated here.

[0135] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0136] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the 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 to cause a computer device (which may be a personal computer, server, or 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 capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0137] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0138] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting the state of a coal mine power supply system, characterized in that, include: The current operating status of target parameters in the coal mine power supply system is monitored in real time; wherein the operating status of the target parameters includes any one or more of the following: the opening and closing status of the damper, the opening and closing status of the non-electric interlocking door, the starting status of the fan, and the power load status. Obtain the preset state transition matrix corresponding to the target parameter; wherein, the preset state transition matrix includes the transition probability value of the target parameter between any two operating states; 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; The steps for establishing the preset state transition matrix corresponding to the target parameters include: Obtain historical operating status data of the target parameter, and determine the probability of occurrence of the target parameter in each operating state based on the historical operating status data; The transition probability value of the target parameter between every two operating states is determined based on the occurrence probability of the target parameter in each operating state; A preset state transition matrix corresponding to the target parameter is established based on the transition probability value between every two operating states of the target parameter.

2. The state prediction method according to claim 1, characterized in that, The step of 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 includes: Obtain the target probability value corresponding to the transition of the target parameter from the current running state to the target running state from the preset state transition matrix; Obtain the initial probability value of the target parameter corresponding to 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.

3. The state prediction method according to claim 2, characterized in that, The step of predicting 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 includes: 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 running state to the target running state.

4. The state prediction method according to claim 1, characterized in that, The step of determining the transition probability value of the target parameter between every two operating states based on the occurrence probability of the target parameter in each operating state includes: Obtain the probability of the occurrence of the initial running state in every two running states; Obtain the probability of the occurrence of the final running state in every two running states; The transition probability value between each pair of operating states is determined based on the probability of the occurrence of the initial operating state and the probability of the occurrence of the final operating state.

5. The state prediction method according to claim 4, characterized in that, The step of determining the transition probability value between every two running states based on the probability of the occurrence of the initial running state and the probability of the occurrence of the final running state includes: Calculate the first product of the probability of the initial running state and the first weight, calculate the second product of the probability of the final running 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.

6. The state prediction method according to any one of claims 1-5, characterized in that, The opening and closing states of the damper include fully open, partially open, and fully closed. The opening and closing states of the non-electric interlocking door include an open state and a closed state; The fan's open / closed states include a start-up state and a shut-down state; The power load status includes low load status, medium load status and high load status.

7. A state prediction device for a coal mine power supply system, characterized in that, include: The monitoring module is used to monitor the current operating status of target parameters in the coal mine power supply system in real time; wherein, the operating status of the target parameters includes any one or more of the following: the opening and closing status of the damper, the opening and closing status of the power-off interlocking door, the starting status of the fan, and the power load status. The acquisition module is used to acquire a preset state transition matrix corresponding to the target parameter; wherein, the preset state transition matrix includes the transition probability value of the target parameter between any two running states; The prediction module is used to predict the conditional probability of the target parameter changing from the current running state to the target running state based on the current running state of the target parameter and the preset state transition matrix. A module is established to acquire historical operating state data of the target parameter, determine the occurrence probability of the target parameter in each operating state based on the historical operating state data, determine the transition probability value of the target parameter between every two operating states based on the occurrence probability of the target parameter in each operating state, and establish a preset state transition matrix corresponding to the target parameter based on the transition probability value of the target parameter between every two operating states.

8. A coal mine power supply system, characterized in that, include: Processors and storage devices; The storage device stores a computer program that, when executed by the processor, performs the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by the processor to perform the steps of the method described in any one of claims 1 to 6.

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