Decision-making method based on dustproof effect abnormity of transfer point of underground coal mine belt conveyor

Abstract

The invention relates to a decision-making method based on dustproof effect abnormity of a transfer point of an underground coal mine belt conveyor, and belongs to the technical field of coal mine operation. Firstly, based on scene characteristics of a mine belt transfer point, an expert knowledge base and an expert execution base of the mine belt transfer point are established; and through matching of the expert knowledge base, the reasons causing the abnormal dust falling effect of the transfer point are searched according to the priorities. And finally, based on the reason of the abnormal dust falling effect, through analysis of the expert knowledge base and the expert execution library, reasoning an operation group which needs to be executed at present, and completing automatic execution of the operation group. Therefore, by adopting the embodiment of the invention, the quick positioning of the abnormal dust falling effect of the transfer point and the automatic execution of the abnormal problem solution can be realized by establishing the expert database, the effective control of the dust concentration of the transfer point of the belt conveyor is completed, and the intelligent construction of dust prevention of a coal mine is facilitated.

Description

Technical Field

[0001] The invention belongs to the technical field of coal mine operation, and relates to a decision-making method based on abnormal dust prevention effect of a transfer point of a belt conveyor in an underground coal mine. Background Art

[0002] In the coal mining process, dust pollution is one of the serious occupational hazards, posing a serious threat to the health and safety of miners. The belt conveyor transfer point is one of the areas with more serious dust pollution in underground coal mines. Since a large amount of dust is generated during the transportation of coal, if the dust is not effectively controlled, it will cause the dust to spread in the tunnel, causing the following problems:

[0003] Miners who inhale dust for a long time are prone to occupational diseases such as pneumoconiosis, which seriously threatens their lives and health.

[0004] When the dust reaches a certain concentration, explosion accidents are likely to occur, causing casualties and property losses.

[0005] Dust will reduce the operating efficiency of equipment, shorten the service life of equipment, and affect the normal production of coal mines.

[0006] In order to effectively control dust pollution at the transfer point of the belt conveyor, the existing technology mainly adopts the following measures:

[0007] Install dust removal fans near the transfer point to exhaust the dust out of the tunnel by exhausting air.

[0008] Install a spray device near the transfer point to moisten the dust and settle it by spraying water mist.

[0009] The belt conveyor should be sealed to prevent dust leakage.

[0010] However, the existing technology has the following problems:

[0011] Existing technologies cannot achieve remote monitoring of dust concentration at transfer points, and it is difficult to detect abnormal dust prevention effects in a timely manner.

[0012] When the dust prevention effect is abnormal, it is difficult to quickly locate the cause of the abnormality, resulting in delayed processing.

[0013] Manual adjustment of equipment parameters and troubleshooting are required, which is inefficient and prone to errors.

[0014] Therefore, the existing technology is difficult to meet the needs of rapid processing of abnormal dust prevention effects at transfer points of belt conveyors in underground coal mines. There is an urgent need for an expert decision-making method that can remotely monitor, quickly locate causes and automatically execute. Summary of the invention

[0015] In view of this, the purpose of the present invention is to provide a decision-making method based on the abnormal dust prevention effect at the transfer point of the underground coal mine belt conveyor. By establishing an expert knowledge base and an expert execution base, the cause of the abnormal dust reduction at the transfer point can be quickly located, and a solution can be given and automatically executed.

[0016] In order to achieve the above object, the present invention provides the following technical solutions:

[0017] A decision-making method based on abnormal dust prevention effect at a transfer point of a belt conveyor in an underground coal mine comprises the following steps:

[0018] S1: Based on the scenario characteristics of the mine belt transfer point, establish the expert knowledge base and expert execution library of the coal mine belt transfer point.

[0019] The process includes the following steps:

[0020] S101: Obtain all devices associated with the dust concentration at the coal mine belt transfer point and the modifiable operating parameters of the devices, including touch spray and its operating parameters, dust collector and its operating parameters, dust concentration sensor and its equipment parameters, etc.;

[0021] S102: Setting the trigger condition for abnormal cause search at the belt transfer point. In this case, the trigger condition is defined as calculating the average value of dust concentration in a certain area at regular intervals; when the average value is greater than a certain set value, the search algorithm is executed;

[0022] S103: For different equipment types, an expert knowledge base of abnormal causes of belt transfer points is established; the expert knowledge base of abnormal causes needs to construct content according to the equipment type of the transfer point, equipment operating status parameters and operating parameters that affect the dust reduction effect; refer to the suggestions given by dust prevention experts and equipment manufacturers to prioritize the abnormal contents.

[0023] S104: Based on the equipment experimental data of the belt transfer point, an expert knowledge base of the equipment operating conditions of the belt transfer point is established; by conducting a dust reduction test experiment on a single equipment at the belt transfer point, the experimental results are organized into an expert knowledge base of the equipment operating conditions of the belt transfer point.

[0024] S105: According to the equipment operation parameters, an expert execution library of the belt transfer point is established; according to the various equipment parameters obtained in S101, the executable behaviors are divided into four categories:

[0025] (1) Manual prompts: These prompts include device failures, offline status, and other behaviors that require manual inspection and repair;

[0026] (2) Automatic start-up category: This includes the automatic start-up behavior of the device;

[0027] (3) Automatic adjustment class: This class includes the behavior of automatic adjustment of device parameters.

[0028] (4) Automatic callback type: When the dust reduction effect factors are resolved, the parameters are automatically called back to the lower limit of the adjustment value or the operating status callback.

[0029] S2: Through matching of the expert knowledge base, the causes of abnormal dust reduction at the transfer point are found according to the priority.

[0030] It includes the following steps:

[0031] S201: Establish a query link table based on the expert knowledge base of abnormal causes of belt transfer points;

[0032] The principles for establishing the query list are as follows:

[0033] A linked list represents the query link of the abnormal cause of a device;

[0034] The order of nodes in the linked list is determined by the priority in the expert knowledge base of the abnormality cause;

[0035] Each node in the linked list contains the logic for determining the cause of the exception based on a certain parameter;

[0036] S202: Find the reason for the abnormal dust reduction effect at the transfer point by matching the data on the linked list; when the trigger condition is met, the algorithm will create multiple threads, and each thread will independently search for the cause of the abnormality in a linked list.

[0037] S3: Based on the reasons for the abnormal dust reduction effect, through the analysis of the expert knowledge base and the expert execution library, it is inferred that the operation group currently needs to be executed, and the automatic execution of the operation group is completed.

[0038] After obtaining the abnormal cause of dust fall, first analyze which execution operations are required for the abnormal cause according to the expert execution library; for manual prompt and automatic start classes, the corresponding execution operation sequence will be directly generated; for automatic adjustment classes, first parse the content in the equipment operation condition expert knowledge base to analyze whether the records in the library can meet the operation parameters of the current scenario; based on the selected operation parameter results, infer which execution method needs to be used for parameter adjustment; match the operation parameters and execution methods in the expert execution library to infer the corresponding operation sequence;

[0039] Combine the operation sequences and push or issue instructions synchronously through multi-threading to realize the automated execution process of the operation group;

[0040] When the cause of the exception is resolved, the automatic callback operation in the expert execution library will be executed; since the cause of the exception may require multiple operations to resolve, in order to achieve the accuracy of the callback process, a stack is used to store the abnormal dust reduction causes during the execution of the method. The storage process must ensure that each dust reduction abnormality factor in the stack is unique; when the cause of the exception is resolved, the order of automatic callback operations is determined by the stack popping order.

[0041] A device based on the decision-making method, comprising:

[0042] Acquisition module: used to obtain all equipment related to the dust concentration of the coal mine belt transfer point and the modifiable operating parameters of the equipment;

[0043] Trigger module: used to set the trigger conditions for abnormal cause finding at the belt transfer point;

[0044] Knowledge base module: used to establish an expert knowledge base of abnormal causes of belt transfer points, an expert knowledge base of equipment operating conditions, and an expert execution base;

[0045] Query module: used to find the reasons for abnormal dust reduction effect at the transfer point according to the priority through matching of the expert knowledge base;

[0046] Execution module: It is used to infer the operation group that needs to be executed currently based on the cause of abnormal dust reduction effect through analysis of expert knowledge base and expert execution library, and complete the automatic execution of the operation group;

[0047] The acquisition module transmits the collected equipment operation parameters and dust concentration data to the trigger module;

[0048] The trigger module determines whether to start the abnormal cause search process according to the preset trigger conditions;

[0049] When the trigger module determines that the search process needs to be started, the knowledge base module is called; the knowledge base module provides an expert knowledge base of abnormal causes, an expert knowledge base of equipment operating conditions, and an expert execution base for use in subsequent steps;

[0050] The query module matches the expert knowledge base of abnormal causes and the expert knowledge base of equipment operating conditions to find the cause of abnormal dust prevention effect; the query module is responsible for analyzing the equipment operating parameters and dust concentration data, and comparing them with the rules in the knowledge base to determine the cause of the abnormality;

[0051] The query module passes the found exception cause to the execution module; the execution module infers the operations that need to be performed based on the exception cause and the expert execution library, and executes them automatically.

[0052] A decision-making device based on the decision-making method, comprising:

[0053] Sensor: used to detect dust concentration at the transfer point of the belt conveyor;

[0054] Controller: used to receive sensor data and execute the functions of the device;

[0055] Actuator: used to execute control instructions issued by the controller;

[0056] The sensor transmits the collected dust concentration data to the controller via the data line; the sensor is responsible for monitoring the dust concentration around the transfer point of the belt conveyor and transmitting the data to the controller for analysis and processing;

[0057] The controller sends the control instructions to the actuator through the control line; the controller generates the control instructions according to the sensor data and the preset control logic, and sends them to the actuator through the control line to control the operation of the dust removal equipment or the spray system;

[0058] The sensor is directly connected to the actuator or indirectly connected to the actuator through a controller; the dust concentration data monitored by the sensor is directly used to control the actuator.

[0059] A storage medium stores the decision-making method.

[0060] The beneficial effects of the present invention are:

[0061] (1) Through the dust reduction test experiment on a single device, the experimental results are organized into an expert knowledge base of the equipment operating conditions at the belt transfer point, providing a basis for subsequent parameter adjustment. Through the expert knowledge base of equipment operating conditions, the operating parameters in the current scenario can be analyzed more accurately, and the appropriate parameter adjustment method can be selected, thereby more effectively controlling the dust concentration.

[0062] (2) When the trigger condition is met, the algorithm will create multiple threads, each of which independently searches for the cause of the exception in a linked list to improve the search efficiency. Multi-threaded parallel search for the cause of the exception can significantly improve the search efficiency and shorten the processing time.

[0063] (3) For automatic adjustment operations, first parse the content in the equipment operating condition expert knowledge base to see if the records in the analysis base can meet the operating parameters of the current scenario. Then, based on the selected operating parameter results, infer which execution method needs to be used for parameter adjustment. Avoid parameter callback errors: Using a stack to store the cause of abnormal dust reduction can ensure the accuracy of the callback process and avoid parameter callback errors.

[0064] (4) In order to achieve the accuracy of the callback process, a stack is used to store the abnormal dust reduction causes during the execution process. The storage process must ensure that each abnormal dust reduction factor in the stack is unique; when the abnormal cause is resolved, the order of automatic callback operations is determined by the stack popping order. More flexible solution: Based on the equipment operating condition expert knowledge base, multiple parameter adjustment methods can be selected, such as directly adjusting to a specified value or adjusting in a specified step size, so as to more flexibly respond to different abnormal situations.

[0065] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0067] Figure 1 It is a flowchart of the decision-making method provided in the embodiment of the present application;

[0068] Figure 2 It is a schematic diagram of a belt transfer point abnormality cause search linked list provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0070] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0071] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which 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 operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0072] See also Figure 1 The embodiment selects an underground coal mine belt transfer point equipped with a touch spray, a dust collector, and a dust concentration sensor as the implementation object of this implementation case.

[0073] S1: Based on the scenario characteristics of the mine belt transfer point, establish the expert knowledge base and expert execution library of the coal mine belt transfer point.

[0074] The process includes the following steps:

[0075] S101: Obtain all devices associated with the dust concentration at the coal mine belt transfer point and the modifiable operating parameters of the devices, including touch spray and its operating parameters, dust collector and its operating parameters, dust concentration sensor and its equipment parameters, etc.;

[0076] S102: Setting the trigger condition for abnormal cause search at the belt transfer point. In this case, the trigger condition is defined as calculating the average value of dust concentration in a certain area at regular intervals; when the average value is greater than a certain set value, the search algorithm is executed;

[0077] S103: Establish an expert knowledge base of abnormal causes of belt transfer points for different equipment types; the expert knowledge base of abnormal causes needs to construct content according to the equipment type of the transfer point, equipment operating status parameters and operating parameters that affect the dust reduction effect; refer to the suggestions given by dust prevention experts and equipment manufacturers to prioritize the abnormal contents; the expert knowledge base of abnormal causes of belt transfer points in this embodiment is shown in Table 1.

[0078] Table 1

[0079] Serial number Abnormal cause Severity 1 Dust removal fan vacuum starter failure I 2 Touch spray malfunction I 3 Dust concentration sensor failure I 4 Touch spray is not turned on II 5 Dust collector not started II 6 Touch spray offline III 7 Vacuum starter offline III 8 Dust concentration sensor offline III 9 Touch spray water pressure problem III 10 Touch spray delay time is too short III 11 Dust collector power is too small III

[0080] S104: Based on the equipment experimental data of the belt transfer point, an expert knowledge base of equipment operating conditions at the belt transfer point is established; by conducting a dust reduction test experiment on a single device at the belt transfer point, the experimental results are organized into an expert knowledge base of equipment operating conditions at the belt transfer point. Table 2 is an expert knowledge base of dust collectors at belt transfer points; Table 3 is an expert knowledge base of spraying at belt transfer points.

[0081] Table 2

[0082]

[0083] Table 3

[0084]

[0085] S105: Establish an expert execution library of the belt transfer point according to the equipment operation parameters; according to the various equipment parameters obtained in S101, the executable behaviors are divided into four categories in this embodiment, and the design results of the expert execution library are as follows:

[0086] (1) Manual prompt category: This includes prompts for equipment failure, offline, and other behaviors that require manual inspection and maintenance; the design of the expert execution library includes the pre-existing abnormal cause, execution method, and prompt content; the execution method includes push and page prompts. Push refers to sending the abnormal cause and execution suggestion to the designated coal mine manager in the form of a message, which is a directional sending of execution suggestions; page prompts refer to broadcasting the abnormal cause and prompt content directly on the dust prevention software platform. Only the monitoring room that has turned on the dust prevention software platform can receive the page prompts. Table 4 is an example of the expert execution library (manual prompt category).

[0087] Table 4

[0088]

[0089]

[0090] (2) Automatic start-up category: This category includes the automatic start-up behavior of the device; the device startup instructions are not shown in the table due to confidentiality. Table 5 is an example of the expert execution library (automatic start-up category).

[0091] Table 5

[0092]

[0093] (3) Automatic adjustment category: This category includes the behavior of automatic adjustment of equipment parameters. The automatic adjustment behavior can be divided into two categories: one is the behavior of adjusting a single operating parameter of the equipment to a specified value, and the other is the behavior of adjusting a single operating parameter of the equipment starting from a specified step size. Table 6 is an example of the expert execution library (automatic adjustment category).

[0094] Table 6

[0095]

[0096] (4) Automatic callback class: When the dust reduction effect factor is resolved, the parameter is called back to the lower limit of the adjustment value or the operating status is automatically called back; Table 7 is an example of the expert execution library (automatic callback class).

[0097] Table 7

[0098] Pre-existing abnormality reason Execution content Touch spray is not turned on Turn off touch spray Dust collector not started Close the vacuum starter Touch spray water pressure problem Adjust the water pressure to 2MPa Touch spray delay time is too short Adjust the spray delay time to 2s Dust collector power is too small Adjust the dust removal motor frequency to 50Hz in specified steps

[0099] S2: Through matching of the expert knowledge base, the causes of abnormal dust reduction at the transfer point are found according to the priority.

[0100] It includes the following steps:

[0101] S201: Based on the expert knowledge base of abnormal causes of belt transfer points, a query linked list is established, such as Figure 2 As shown; each node in the linked list contains the logic for determining whether it is an abnormal cause based on a certain parameter;

[0102] For example, when the touch spray water pressure subnode is found, the actual water pressure value of the current touch spray will be obtained in real time. If the water pressure value is less than the water pressure threshold, the touch spray water pressure subnode is determined to be an abnormal reason, and feedback is given that the touch spray water pressure is too low; if the water pressure is greater than or equal to the water pressure threshold, the touch spray water pressure subnode is not an abnormal reason. In this embodiment, the water pressure threshold is the lower limit of the adjustment, that is, 4MPA.

[0103] S202: by matching the data on the linked list, find the reason for the abnormal dust reduction effect at the transfer point; when the trigger condition is met, the algorithm will create multiple threads, each thread independently searches for the abnormal reason in a linked list. In this way, at most one abnormal reason is obtained for each device.

[0104] S3: Based on the reasons for the abnormal dust reduction effect, through the analysis of the expert knowledge base and the expert execution library, it is inferred that the operation group currently needs to be executed, and the automatic execution of the operation group is completed.

[0105] First, the system uses the expert execution library to analyze the identified abnormal dust fall causes to determine the necessary execution operations. For operations that require manual prompts or automatic start, the system will directly build the corresponding execution operation sequence. For operations that require automatic adjustment, the system will refer to the data in the equipment operation condition expert knowledge base to analyze whether the records in the library can meet the operation requirements of the current scenario.

[0106] Specifically, taking the touch spray water pressure problem as an example, if the actual water pressure detected is 1.5MPa, the system will judge according to the preset parameter adjustment table (as shown in Table 6) that the actual water pressure is lower than the specified adjustment lower limit. Therefore, the system will execute the instruction to adjust the water pressure to 2MPa to ensure the ideal dust reduction effect.

[0107] In another case, if the actual water pressure is 2MPa, the system will further evaluate whether it needs to be adjusted. According to Table 6, the actual water pressure is no less than the lower limit of adjustment. At this time, the system will analyze the belt transfer point spray knowledge expert library to determine whether there are better operating parameters. If there are (for example, 6MPa is determined to be a more suitable parameter), the system will perform the operation of adjusting the water pressure to 6MPa. If there are no more suitable parameters in the library, the system will gradually adjust the touch spray water pressure according to the preset step size until the specified upper limit is reached.

[0108] In this way, the system can ensure that the spray system operates under optimal parameters, effectively improving dust reduction efficiency while reducing resource waste.

[0109] When performing adjustment operations, the system will monitor the adjustment effects in real time to ensure that each adjustment step is moving towards resolving the cause of the abnormality.

[0110] For automatic adjustment operations, the system uses an intelligent reasoning algorithm that can dynamically adjust parameters based on historical data and real-time feedback to adapt to the ever-changing working environment.

[0111] The system's operation sequence generation process fully considers the operation priority and safety factors, ensuring that while solving dust reduction anomalies, it will not affect the safety of production equipment and personnel.

[0112] Combine the operation sequences, push or issue instructions synchronously through multi-threading, and realize the automated execution process of the operation group; when the cause of the exception is resolved, the corresponding parameter callback operation in the expert execution library will be automatically executed; because the cause of the exception may require multiple operations to resolve, in order to achieve the accuracy of the callback process, the abnormal dust reduction cause is stored in a stack during the execution process, and the storage process must ensure that each dust reduction abnormal factor in the stack is unique; when the cause of the exception is resolved, the order of automatic callback operations is determined by the stack popping order.

[0113] For example, in a specific dust reduction abnormality handling scenario, the method of the present invention successfully solved the dust reduction abnormality problem after three rounds of expert decision-making processes. In the first round of operation, the system identified the abnormal reason why the touch spray was not turned on, and quickly executed the operation of turning on the touch spray. In the second round of operation, the system detected that the delay time of the touch spray was too short to achieve the ideal dust reduction effect, so the delay time was adjusted to 4 seconds. In the third round, it was further found that the delay time still needed to be optimized, so the delay time was increased again to 6 seconds to enhance the dust reduction effect.

[0114] During the entire processing process, two key abnormal reasons were saved in the record stack: the record at the bottom of the stack indicated that the touch spray was not turned on initially, while the record at the top of the stack pointed to the problem of the touch spray delay time. When executing the automatic callback parameters, the system follows the principle of last in first out, first handles the abnormal cause at the top of the stack, calls back the spray delay time to 2 seconds, and then executes the operation of turning off the touch spray, so as to ensure the stability of the system and the durability of the dust reduction effect.

[0115] Through such refined management and control, the present invention not only achieves rapid response and efficient handling of dust fall anomalies, but also ensures that after the anomaly is resolved, the system can be restored to the preset optimal operating state in an orderly manner, thereby improving the safety and reliability of the entire coal mine production environment.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A decision-making method based on the abnormal dust prevention effect of the transfer point of the underground coal mine belt conveyor, characterized in that: The following steps are involved: S1: Based on the scenario characteristics of the mine belt transfer point, an expert knowledge base and expert execution base for the coal mine belt transfer point are established, including: S101: Obtain all equipment associated with the dust concentration at the coal mine belt transfer point and the modifiable operating parameters of the equipment; S102: Setting trigger conditions for abnormal cause finding at the belt transfer point; S103: Establish an expert knowledge base of abnormal causes of belt transfer points for different equipment types; S104: establishing an expert knowledge base of equipment operating conditions at the belt transfer point based on equipment experimental data at the belt transfer point; S105: according to the equipment operation parameters, an expert execution library of the belt transfer point is established, and executable behaviors are divided into manual prompting, automatic start, automatic adjustment and automatic callback; S2: Through matching the expert knowledge base, find the reasons that lead to abnormal dust reduction effect at the transfer point according to the priority; S3: Based on the reasons for the abnormal dust reduction effect, through the analysis of the expert knowledge base and the expert execution library, it is inferred that the operation group currently needs to be executed, and the automatic execution of the operation group is completed.

2. A decision-making method based on abnormal dust prevention effect at the transfer point of a belt conveyor in an underground coal mine according to claim 1, characterized in that: The expert execution library contains the following contents: Manual prompts: include prompts of equipment failure or offline behavior that requires manual inspection and repair; Automatic start class: contains the automatic start behavior of the device; Automatic adjustment class: contains the behavior of automatic adjustment of device parameters; Automatic callback type: When the dust reduction effect factors are resolved, the parameters are called back to the lower limit of the adjustment value or the operating status is automatically called back.

3. The decision-making method based on the abnormal dust prevention effect of the transfer point of the underground coal mine belt conveyor according to claim 1 is characterized by: In S3, after obtaining the abnormal cause of dust reduction, first analyze which execution operations are required for the abnormal cause according to the expert execution library; for the manual prompt type and the automatic start type, the corresponding execution operation sequence will be directly generated; for the automatic adjustment type, first parse according to the content in the expert knowledge base of the equipment operating conditions, and analyze whether the records in the library can meet the operating parameters of the current scenario; based on the results of selecting the operating parameters, infer which execution method needs to be used for parameter adjustment; match the operating parameters and execution methods in the expert execution library, and thus infer the corresponding operation sequence.

4. The decision-making method based on the abnormal dust prevention effect of the transfer point of the underground coal mine belt conveyor according to claim 1 is characterized in that: In S3, when the cause of the exception is resolved, the corresponding parameter callback operation in the expert execution library will be automatically executed.

5. A device based on the decision-making method according to any one of claims 1 to 4, characterized in that: include: Acquisition module: used to obtain all equipment related to the dust concentration of the coal mine belt transfer point and the modifiable operating parameters of the equipment; Trigger module: used to set the trigger conditions for abnormal cause finding at the belt transfer point; Knowledge base module: used to establish an expert knowledge base of abnormal causes of belt transfer points, an expert knowledge base of equipment operating conditions, and an expert execution base; Query module: used to find the reasons for abnormal dust reduction effect at the transfer point according to the priority through matching of the expert knowledge base; Execution module: It is used to infer the operation group that needs to be executed currently based on the cause of abnormal dust reduction effect through analysis of expert knowledge base and expert execution library, and complete the automatic execution of the operation group; The acquisition module transmits the collected equipment operation parameters and dust concentration data to the trigger module; The trigger module determines whether to start the abnormal cause search process according to the preset trigger conditions; When the trigger module determines that the search process needs to be started, the knowledge base module is called; The knowledge base module provides an expert knowledge base of abnormal causes, an expert knowledge base of equipment operating conditions, and an expert execution base for use in subsequent steps; The query module matches the abnormal cause expert knowledge base and the equipment operation condition expert knowledge base to find the cause of the abnormal dust prevention effect; The query module is responsible for analyzing the equipment operating parameters and dust concentration data, and comparing them with the rules in the knowledge base to determine the cause of the abnormality; The query module passes the found exception cause to the execution module; the execution module infers the operations that need to be performed based on the exception cause and the expert execution library, and executes them automatically.

6. A decision-making device based on the decision-making method according to any one of claims 1 to 4, characterized in that: include: Sensor: used to detect dust concentration at the transfer point of the belt conveyor; Controller: used to receive sensor data and execute the functions of the device; Actuator: used to execute control instructions issued by the controller; The sensor transmits the collected dust concentration data to the controller via the data line; the sensor is responsible for monitoring the dust concentration around the transfer point of the belt conveyor and transmitting the data to the controller for analysis and processing; The controller sends the control instructions to the actuator through the control line; the controller generates the control instructions according to the sensor data and the preset control logic, and sends them to the actuator through the control line to control the operation of the dust removal equipment or the spray system; The sensor is directly connected to the actuator or indirectly connected to the actuator through a controller; the dust concentration data monitored by the sensor is directly used to control the actuator.

7. A storage medium, characterized in that: The decision-making method according to any one of claims 1 to 4 is stored.

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