A mine emulsion delivery method, system, terminal device and storage medium

Abstract

The application relates to the technical field of mining engineering, in particular to a mining emulsion conveying method and system, a terminal device and a storage medium. The method comprises the following steps: if the process control detection result is normal, fluid property detection results corresponding to emulsion conveying parameters are obtained; if the fluid property detection result is abnormal, corresponding fluid abnormal items are obtained, and abnormal factor analysis is performed on the fluid abnormal items to generate corresponding abnormal analysis results; if the abnormal analysis result is an internal-external correlation, an abnormal factor distribution indication set of internal property abnormality and external factor abnormality corresponding to the fluid abnormal items is generated; a fluid abnormality elimination strategy corresponding to the abnormal factor distribution indication set is executed to generate corresponding fluid anomaly elimination process feedback information. The mining emulsion conveying method, system, terminal device and storage medium can effectively analyze and process the abnormality existing in the emulsion conveying process in a timely manner, thereby improving the coal mine production efficiency.

Description

Technical Field

[0001] This application relates to the field of mining engineering technology, and in particular to a method, system, terminal equipment and storage medium for conveying mining emulsion. Background Technology

[0002] Coal mine emulsification pump station systems are mainly used for the transportation and supply of emulsions during coal mine production. A typical emulsion pumping station system consists of the following parts: Emulsion storage: Emulsion pumping station systems usually have emulsion storage tanks to store large quantities of emulsion to meet the needs of coal mine production; Emulsion transportation: The system uses pumps and pipelines to transport the emulsion from the storage tanks to various equipment at the coal mine production site, such as blasting equipment, hydraulic supports, drilling equipment, crushing equipment, and conveying equipment; Emulsion pressure regulation: The system typically has pressure regulating devices to adjust the emulsion transportation pressure according to the actual needs of the coal mine production site, meeting the working requirements of different equipment; Emulsion flow control: The system can control the emulsion transportation flow rate by adjusting valves and flow meters according to the actual needs of the coal mine production site, meeting the working requirements of different equipment; Emulsion temperature control: The system can regulate the emulsion temperature using heaters or coolers to meet the special requirements of the coal mine production site, such as preventing the emulsion from solidifying at low temperatures; Safety protection: The system typically has safety protection devices, such as pressure protection, temperature protection, and leakage protection, to ensure the safe and reliable transportation of the emulsion.

[0003] In practical applications, traditional manual monitoring and operation methods are typically used to control coal mine emulsification pump station systems and equipment. However, manual operation is prone to errors, and the slow feedback of relevant information makes it difficult for coal mining enterprises to understand the actual transportation status of the emulsion in a timely manner. Consequently, it is impossible to analyze and deal with any abnormalities in the emulsion transportation process in a timely and effective manner, which reduces the efficiency of coal mine production operations. Summary of the Invention

[0004] In order to improve the efficiency of coal mine production operations, this application provides a method, system, terminal equipment and storage medium for conveying mining emulsion.

[0005] Firstly, this application provides, including the following steps:

[0006] Obtain the operational requirements parameters corresponding to the mining functional modules;

[0007] Match the emulsion delivery parameters corresponding to the operational requirements parameters;

[0008] Execute the emulsion delivery parameters to obtain the corresponding theoretical and actual operating parameters;

[0009] If the difference between the theoretical operation parameters and the actual operation parameters exceeds a preset operation parameter threshold, a corresponding difference detection instruction is generated.

[0010] Execute the difference detection command to obtain the process control detection results corresponding to the emulsion delivery parameters;

[0011] If the process control detection result is abnormal, the corresponding abnormal control item is obtained, and the abnormal elimination strategy corresponding to the abnormal control item is executed to generate the corresponding process elimination process feedback information.

[0012] If the process control detection result is normal, then obtain the fluid property detection result corresponding to the emulsion delivery parameters;

[0013] If the fluid property detection result is abnormal, the corresponding fluid anomaly item is obtained, and anomaly factor analysis is performed on the fluid anomaly item to generate the corresponding anomaly analysis result;

[0014] If the anomaly analysis results are correlated internally and externally, then an anomaly factor distribution indicator set corresponding to the fluid anomaly item and the anomaly of the internal property and external factors is generated;

[0015] The fluid anomaly elimination strategy corresponding to the abnormal factor distribution indicator set is executed, and the corresponding fluid anomaly elimination process feedback information is generated.

[0016] By adopting the above technical solution, the theoretical and actual operating parameters provided by emulsion delivery to the corresponding operating equipment in coal mine production operations are compared. If the parameter difference between the two exceeds the preset operating parameter threshold, it indicates that the current emulsion delivery status does not fully support the normal operation of the mining equipment. A corresponding difference detection command is then executed. Since the control parameters during emulsion delivery have a significant impact on the operation of coal mine equipment, process control detection is performed first. If any abnormalities are detected, corresponding elimination strategies are implemented, and real-time feedback information on the process elimination progress between emulsion delivery and the corresponding coal mine equipment is generated. If the process control detection result is normal, further adjustments are made to the emulsion delivery... The fluid properties are subjected to relevant safety tests. If the test results are abnormal and there is an internal and external correlation, in order to accurately analyze and characterize the relevant abnormal factors during the transportation of the emulsion, and thus improve the elimination effect of emulsion fluid anomalies, the internal property anomalies and external factor anomalies of the current fluid anomaly item are specifically analyzed, and the distribution indicator set of the abnormal factors corresponding to the fluid anomaly item is generated. Then, the corresponding fluid anomaly elimination strategy is executed and the fluid anomaly elimination process feedback information is recorded in real time. Because the actual abnormal situation that occurs during the transportation of the emulsion is analyzed and processed in depth, the anomalies can be eliminated in a timely and effective manner and feedback can be provided in real time, thereby improving the production efficiency of the coal mine.

[0017] Optionally, after executing the emulsion delivery parameters and obtaining the corresponding theoretical and actual operating parameters, the following steps are also included:

[0018] If the difference between the theoretical operation parameters and the actual operation parameters does not exceed the preset operation parameter threshold, then the source of the difference corresponding to the difference in operation parameters is obtained;

[0019] If there are multiple sources of difference, then each source of difference is divided and classified according to a preset difference class standard to generate a corresponding target difference class.

[0020] Based on the correlation coefficient between each target difference class and the difference in the operation parameters, a primary processing priority corresponding to the target difference class is set, and the correlation coefficient is proportional to the primary processing priority.

[0021] If there are multiple difference influencing factors in the target difference class, then according to the sensitivity of each difference influencing factor in the corresponding target difference class, a secondary processing priority is set for the difference influencing factor. The sensitivity is proportional to the secondary processing priority, and the primary priority is higher than the secondary priority.

[0022] Based on the primary processing priority and the secondary processing priority, the error reduction strategy corresponding to the difference in the job parameters is matched and executed.

[0023] By adopting the above technical solution, the minor sources of difference in the emulsion during transportation are classified into various target difference categories according to the preset differentiation criteria. This facilitates the analysis and processing of anomalies in the emulsion transportation process. Then, a primary processing priority is set according to the correlation coefficient between each target difference category and the difference in operating parameters. Next, a secondary processing priority is set according to the influence factors of each difference in each target difference category and their sensitivity in the corresponding target difference category. Then, the anomalies are eliminated according to the above primary and secondary processing priorities, thereby improving the efficiency of anomaly investigation and processing in the emulsion transportation process.

[0024] Optionally, if the process control detection result is abnormal, the corresponding abnormal control item is obtained, and the abnormal elimination strategy corresponding to the abnormal control item is executed to generate the corresponding process elimination process feedback information, including the following steps:

[0025] If the process control detection result is abnormal, then the damage indication item corresponding to the abnormal control item is obtained;

[0026] If there are multiple damage indicators, an anomaly distribution table corresponding to the anomaly control item is generated based on the damage parameters of each damage indicator.

[0027] Match the anomaly elimination strategy corresponding to the anomaly distribution table;

[0028] The anomaly elimination strategy is executed to generate the process anomaly elimination process feedback information corresponding to the anomaly control item.

[0029] By adopting the above technical solution and generating a corresponding anomaly distribution table, the distribution of anomalies can be identified in a timely manner. At the same time, by matching the anomaly elimination strategy corresponding to the anomaly distribution table, anomalies can be handled in a targeted manner, thereby improving the efficiency of anomaly handling.

[0030] Optionally, if there are multiple damage indicators, generating an anomaly distribution table corresponding to the anomaly control item based on the damage parameters of each damage indicator includes the following steps:

[0031] If there are multiple damage indicators, the damage parameters are analyzed according to the preset damage judgment criteria to determine the abnormal target stage corresponding to the damage indicator;

[0032] By combining each of the damage indicators and the corresponding abnormal target stage, an abnormal distribution table corresponding to the abnormal control item is generated.

[0033] By adopting the above technical solution and combining various damage indicators with the corresponding abnormal target stages, the generated anomaly distribution table can provide more comprehensive anomaly information, thereby providing more reference information for subsequent anomaly handling and improving anomaly handling efficiency.

[0034] Optionally, if the fluid property detection result is abnormal, the corresponding fluid anomaly item is obtained, and anomaly factor analysis is performed on the fluid anomaly item to generate the corresponding anomaly analysis result, including the following steps:

[0035] If the fluid property detection result is abnormal, then anomaly factor analysis is performed on the fluid anomaly item, and corresponding fluid property analysis data is generated;

[0036] If the fluid property analysis data meets the preset response property criteria, then the fluid anomaly item is generated as the anomaly analysis result of the internal and external correlation.

[0037] By adopting the above technical solution, anomaly factor analysis of fluid anomalies can be performed, which can more accurately identify the causes of anomalies. At the same time, based on fluid property analysis data, detailed information showing the anomaly can be quickly obtained, thereby improving the accuracy and efficiency of anomaly handling.

[0038] Optionally, if the anomaly analysis results are correlated internally and externally, generating the anomaly factor distribution indicator set corresponding to the fluid anomaly item's intrinsic property anomaly and external factor anomaly includes the following steps:

[0039] If the anomaly analysis result is an internal-external correlation, then obtain the anomaly interference factor corresponding to the internal property anomaly and the external factor anomaly of the fluid anomaly item;

[0040] If there is co-directional interference among the abnormal interference factors, then the initiating factor and the inducing factor among the abnormal interference factors are determined, and the corresponding abnormal factor distribution indicator set is generated by combining the correlation coefficient between the initiating factor and the inducing factor.

[0041] If there is reverse interference among the abnormal interference factors, then the increasing and decreasing factors among the abnormal interference factors are determined, and the corresponding abnormal factor distribution indicator set is generated by combining the offsetting factors between the increasing and decreasing factors.

[0042] By adopting the above technical solution, the abnormal interference factors of the internal property abnormalities and external factor abnormalities corresponding to the fluid abnormal items can be obtained. Regardless of whether there is unidirectional or reverse interference between the abnormal interference factors, the corresponding abnormal factor distribution indicator set can be generated by determining the corresponding initiating factor, inducing factor, increasing factor and decreasing factor. This allows for more accurate location of the source of the abnormality and improves the accuracy of abnormality handling.

[0043] Optionally, after executing the fluid anomaly elimination strategy corresponding to the anomaly factor distribution indicator set and generating the corresponding fluid anomaly elimination process feedback information, the following steps are also included:

[0044] Based on the feedback information of the fluid elimination process, dynamic elimination indication data corresponding to the abnormal interference factors in the abnormal factor distribution indication set is generated;

[0045] Based on the dynamic anomaly elimination indicator data, an anomaly elimination equilibrium curve corresponding to the emulsion is generated.

[0046] By adopting the above technical solution, based on the anomaly elimination equilibrium curve corresponding to the emulsion, the anomaly elimination process of the emulsion can be analyzed and controlled more accurately, thereby improving the accuracy and efficiency of anomaly handling.

[0047] Secondly, this application provides a mining emulsion conveying system, comprising:

[0048] The operation parameter acquisition module is used to acquire the operation requirement parameters corresponding to the mining function modules;

[0049] The parameter matching module is used to match the emulsion delivery parameters corresponding to the operational requirements parameters;

[0050] The parameter execution module executes the emulsion delivery parameters and obtains the corresponding theoretical and actual operating parameters.

[0051] If the difference between the theoretical operation parameters and the actual operation parameters exceeds a preset operation parameter threshold, the difference detection module generates a corresponding difference detection instruction.

[0052] The process detection module is used to execute the difference detection command and obtain the process control detection results corresponding to the emulsion delivery parameters;

[0053] If the process control detection result is abnormal, the process elimination module is used to obtain the corresponding abnormal control item, execute the abnormal elimination strategy corresponding to the abnormal control item, and generate the corresponding process elimination process feedback information.

[0054] The fluid detection module is used to obtain the fluid property detection results corresponding to the emulsion delivery parameters if the process control detection result is normal.

[0055] An anomaly analysis module is used to obtain the corresponding fluid anomaly item if the fluid property detection result is abnormal, and to perform anomaly factor analysis on the fluid anomaly item to generate the corresponding anomaly analysis result.

[0056] Anomaly factor indication module: If the anomaly analysis result is related to internal and external factors, the anomaly factor indication module is used to generate anomaly factor distribution indication set corresponding to the internal property anomaly and external factor anomaly of the fluid anomaly item.

[0057] The fluid anomaly elimination module is used to execute the fluid anomaly elimination strategy corresponding to the anomaly factor distribution indication set and generate corresponding fluid anomaly elimination process feedback information.

[0058] By adopting the above technical solution, the difference detection module compares the theoretical and actual operating parameters provided by the emulsion delivery to the corresponding operating equipment in coal mine production operations. If the parameter difference exceeds the preset operating parameter threshold, it indicates that the current emulsion delivery status does not fully support the normal operation of the mining equipment. Subsequently, the process detection module executes the corresponding difference detection command. Since the control parameters during the emulsion delivery process have a significant impact on the operation of coal mine equipment, process control detection is performed first. If any abnormalities are detected, the process anomaly elimination module executes the corresponding elimination strategy and generates real-time feedback information on the process anomaly elimination progress between the emulsion delivery and the corresponding coal mine equipment. If the process control detection result is normal, further fluid detection is performed. The module performs relevant safety checks on the fluid properties of the emulsion. If the check results are abnormal and there are internal and external correlations, in order to accurately analyze and characterize the relevant abnormal factors during the transportation of the emulsion, and thus improve the elimination effect of fluid abnormalities, the abnormal factor indication module performs a specific analysis on the internal property abnormalities and external factor abnormalities of the current fluid abnormality item, and generates an abnormal factor distribution indication set corresponding to the fluid abnormality item. Then, the fluid anomaly elimination module executes the corresponding fluid anomaly elimination strategy and records the fluid anomaly elimination process feedback information in real time. Because the actual abnormal situation that occurs during the transportation of the emulsion is analyzed and processed in depth, the anomalies can be eliminated in a timely and effective manner and feedback can be provided in real time, thereby improving the production efficiency of the coal mine.

[0059] Thirdly, this application provides a terminal device, which adopts the following technical solution:

[0060] A terminal device includes a memory and a processor. The memory stores computer instructions that can run on the processor. When the processor loads and executes the computer instructions, it employs the aforementioned method for transporting mining emulsions.

[0061] By adopting the above technical solution, computer instructions are generated from the above-mentioned method for conveying mining emulsions and stored in a memory for loading and execution by a processor. Thus, a terminal device can be manufactured based on the memory and processor for convenient use.

[0062] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0063] A computer-readable storage medium storing computer instructions, wherein when the computer instructions are loaded and executed by a processor, the above-described method for conveying mining emulsions is employed.

[0064] By adopting the above technical solution, a method for conveying mining emulsion is used to generate computer instructions, which are then stored in a computer-readable storage medium for loading and execution by a processor. The computer-readable storage medium facilitates the reading and storage of computer instructions.

[0065] In summary, this application includes at least one of the following beneficial technical effects: It compares the theoretical and actual operating parameters provided by emulsion delivery to the corresponding operating equipment during coal mine production operations. If the parameter difference exceeds a preset operating parameter threshold, it indicates that the current emulsion delivery status does not fully support the normal operation of the mining equipment. A corresponding difference detection command is then executed. Since the control parameters during emulsion delivery have a significant impact on the operation of coal mine equipment, process control detection is performed first. If any abnormalities are detected, a corresponding elimination strategy is implemented, and real-time feedback information on the process elimination process between emulsion delivery and the corresponding coal mine equipment is generated. If the process control detection result is normal, further... The process involves conducting relevant safety tests on the fluid properties of the emulsion. If the test results are abnormal and there is an internal or external correlation, in order to accurately analyze and characterize the relevant abnormal factors during the transportation of the emulsion, and thus improve the elimination effect of fluid abnormalities, a specific analysis is performed on the internal property abnormalities and external factor abnormalities of the current fluid abnormality item. An abnormal factor distribution indicator set corresponding to the fluid abnormality item is generated, and then the corresponding fluid abnormality elimination strategy is executed and the fluid abnormality elimination process feedback information is recorded in real time. Because the actual abnormal situation that occurs during the transportation of the emulsion is analyzed and processed in depth, the abnormality can be eliminated in a timely and effective manner and feedback can be provided in real time, thereby improving the production efficiency of the coal mine. Attached Figure Description

[0066] Figure 1 This is a flowchart illustrating steps S101 to S110 of a mining emulsion transportation method according to this application.

[0067] Figure 2 This is a flowchart illustrating steps S201 to S205 of a mining emulsion transportation method according to this application.

[0068] Figure 3 This is a flowchart illustrating steps S301 to S304 of a mining emulsion transportation method according to this application.

[0069] Figure 4 This is a flowchart illustrating steps S401 to S402 of a mining emulsion transportation method according to this application.

[0070] Figure 5 This is a flowchart illustrating steps S501 to S502 of a mining emulsion transportation method according to this application.

[0071] Figure 6 This is a flowchart illustrating steps S601 to S603 of a mining emulsion transportation method according to this application.

[0072] Figure 7 This is a flowchart illustrating steps S701 to S702 of a mining emulsion transportation method according to this application.

[0073] Figure 8 This is a schematic diagram of a mining emulsion conveying system according to this application.

[0074] Explanation of reference numerals in the attached figures:

[0075] 1. Operation parameter acquisition module; 2. Parameter matching module; 3. Parameter execution module; 4. Difference detection module; 5. Process detection module; 6. Process anomaly elimination module; 7. Fluid detection module; 8. Anomaly factor analysis module; 9. Anomaly factor indication module; 10. Fluid anomaly elimination module. Detailed Implementation

[0076] The following is in conjunction with the appendix Figure 1-8 This application will be described in further detail.

[0077] This application discloses a method for conveying mining emulsions, such as... Figure 1 As shown, it includes the following steps:

[0078] S101. Obtain the operational requirement parameters corresponding to the mining functional modules;

[0079] S102. Match the emulsion delivery parameters to the operational requirements parameters;

[0080] S103. Execute the emulsion delivery parameters and obtain the corresponding theoretical and actual operating parameters;

[0081] S104. If the difference between the theoretical operation parameters and the actual operation parameters exceeds the preset operation parameter threshold, a corresponding difference detection instruction will be generated.

[0082] S105. Execute the difference detection command to obtain the process control detection results corresponding to the emulsion delivery parameters;

[0083] S106. If the process control detection result is abnormal, the corresponding abnormal control item is obtained, and the abnormal elimination strategy corresponding to the abnormal control item is executed to generate the corresponding process elimination process feedback information.

[0084] S107. If the process control detection result is normal, then obtain the fluid property detection result corresponding to the emulsion delivery parameters;

[0085] S108. If the fluid property detection result is abnormal, then obtain the corresponding fluid anomaly item, perform anomaly factor analysis on the fluid anomaly item, and generate the corresponding anomaly analysis result;

[0086] S109. If the anomaly analysis results are related to internal and external factors, then generate an anomaly factor distribution indicator set corresponding to the fluid anomaly item and the anomaly of the internal property and external factors.

[0087] S110. Execute the fluid anomaly elimination strategy corresponding to the abnormal factor distribution indicator set and generate corresponding fluid anomaly elimination process feedback information.

[0088] In step S101, the mining functional module refers to various operating equipment at the coal mine production site, such as blasting equipment, hydraulic supports, drilling equipment, crushing equipment, and conveying equipment. The operational requirement parameters corresponding to the mining functional module refer to the technical parameters or operating conditions required for each functional module to perform its specific task in mining operations.

[0089] For example, the drilling speed of drilling equipment in daily coal mine production operations is the operational requirement parameter of drilling equipment. This parameter determines the working efficiency of drilling equipment. If a coal mine needs to complete a large number of drilling operations in a short period of time, then the drilling speed of the drilling equipment needs to meet the corresponding standard.

[0090] In step S102, the emulsion delivery parameters refer to a series of physical and chemical performance parameters that the corresponding emulsion needs to meet during the delivery process in the operation of the mining functional module. These parameters mainly include the pressure, flow rate, temperature, and medium of the emulsion. The emulsion delivery system stores an execution mapping table of the operational requirements parameters for each mining functional module and the corresponding emulsion delivery parameters. Known specific operational requirements parameters can be matched to suitable emulsion delivery parameters through this execution mapping table.

[0091] For example, the emulsion delivery parameters for drilling equipment include emulsion flow rate, pressure, and temperature. Emulsion flow rate refers to the volume of emulsion passing through the drilling equipment per unit time; the flow rate directly affects the cooling and cutting effects of the drilling equipment. Emulsion pressure refers to the pressure of the emulsion within the drilling equipment; the pressure directly affects the emulsion delivery efficiency and the working efficiency of the drilling equipment. The temperature of the emulsion affects its viscosity and fluidity, thus influencing its delivery effect within the drilling equipment.

[0092] In step S103, the theoretical operating parameters refer to the expected operating parameters of the mining functional module obtained through theoretical calculation based on the emulsion's delivery parameters. For example, based on the emulsion's pressure, flow rate, temperature, and medium, the expected drilling depth, drilling speed, and drilling diameter of the drilling equipment can be calculated.

[0093] Secondly, actual operating parameters refer to the actual operating parameters of the mining functional modules obtained through measurement during actual operation. For example, these are obtained by measuring the actual drilling depth, drilling speed, and drilling diameter of the drilling equipment.

[0094] It should be noted that the execution of emulsion delivery parameters is completed by the emulsion delivery system. For example, the pump is the core equipment in the circulation system; it is responsible for drawing the emulsion from the storage tank and delivering it to the drill bit section of the drilling equipment at a certain pressure and flow rate.

[0095] In step S104, the preset operating parameter threshold refers to a standard or limit set for the mining functional module to measure whether the difference between the actual operating parameters and the theoretical operating parameters is within an acceptable range. This threshold is set based on factors such as experience, equipment performance, and safety standards.

[0096] For example, in drilling equipment applications, the theoretical operating parameters for emulsion delivery to power the drilling equipment include the emulsion's flow rate, pressure, temperature, and viscosity. These parameters are calculated based on factors such as equipment design, formation properties, borehole depth, and diameter, representing the ideal state the emulsion delivery system should achieve. Actual operating parameters, on the other hand, are the actual flow rate, pressure, temperature, and viscosity of the emulsion measured by sensors and other equipment during the actual drilling process.

[0097] Furthermore, if the difference between the theoretical operating parameters and the actual operating parameters exceeds the preset operating parameter threshold, it indicates that an abnormality has occurred in the current process of the emulsion being delivered to the relevant mining functional modules, and a difference detection command for detecting the actual delivery status of the emulsion is then generated.

[0098] For example, the theoretical operating parameters of an emulsion are a flow rate of 100 liters / minute, a pressure of 2000 Pa, a temperature of 25°C, and a viscosity of 1 Pa·s. The actual operating parameters are an emulsion flow rate of 95 liters / minute, a pressure of 1900 Pa, a temperature that may rise to 30°C, and a viscosity that increases to 1.2 Pa·s. If the preset flow rate threshold is 5 liters / minute, the pressure threshold is 100 Pa, the temperature threshold is 2°C, and the viscosity threshold is 0.1 Pa·s, then in this example, the differences in flow rate and pressure are within the threshold range, but the differences in temperature and viscosity exceed the threshold, and a temperature and viscosity difference detection command is then executed.

[0099] In step S105, the process control detection result is one of the detection methods for executing the above-mentioned difference detection instructions. Specifically, process control detection refers to the real-time monitoring and recording of parameters such as flow rate, pressure, temperature, and viscosity of the emulsion during the emulsion transportation process using various sensors and detection equipment to ensure the normal operation of the emulsion transportation process.

[0100] In step S106, if the process control detection result is abnormal, it means that during the emulsion conveying process, some parameters (such as flow rate, pressure, temperature, viscosity, etc.) are significantly different from the preset theoretical parameters and exceed the normal range, which may affect the conveying effect of the emulsion or the normal operation of the equipment.

[0101] Among these, obtaining the corresponding anomaly control item refers to determining the control measures that need to be taken based on the type and severity of the anomaly. For example, if the temperature of the emulsion is detected to be too high, then the corresponding anomaly control item is to lower the temperature of the emulsion.

[0102] Secondly, implementing the anomaly elimination strategy corresponding to the anomaly control item refers to formulating and executing corresponding operational strategies based on the determined control measures to eliminate the anomaly. Continuing the example above, if it is necessary to lower the temperature of the emulsion, then possible strategies are to reduce the heating of the emulsion or increase the cooling.

[0103] Furthermore, generating corresponding process feedback information means that after executing the anomaly elimination strategy, the system will re-check the emulsion parameters to confirm whether the anomaly has been eliminated. If the anomaly has been eliminated, the system will generate corresponding feedback information to inform the operator or other systems that the emulsion delivery process has returned to normal. If the anomaly still exists, the system will generate corresponding feedback information to indicate that the anomaly elimination strategy needs to be continued.

[0104] In step S107, if the process control detection result is normal, it means that all parameters (such as flow rate, pressure, temperature, viscosity, etc.) in the emulsion delivery process are within the preset normal range, and the emulsion delivery process is running well.

[0105] Secondly, in this scenario, the system acquires the fluid property detection results corresponding to the emulsion delivery parameters. Specifically, the system detects the physical and chemical properties of the emulsion, including but not limited to its density, viscosity, surface tension, and pH value. These property detection results provide a deeper understanding of the emulsion's properties and state, as well as the changes that may occur during delivery. For example, the density and viscosity of the emulsion affect its flow performance in the pipeline, surface tension affects the contact between the emulsion and the pipeline wall, and pH value reflects the emulsion's acidity or alkalinity—all important factors influencing the emulsion delivery effect.

[0106] In step S108, if the fluid property detection result is abnormal, it means that some physical or chemical properties of the emulsion (such as density, viscosity, surface tension, pH value, etc.) are significantly different from the preset theoretical values ​​and exceed the normal range, which may affect the delivery effect of the emulsion or the normal operation of the equipment.

[0107] Furthermore, obtaining the corresponding fluid anomaly item refers to determining the nature of the anomaly that needs attention and handling based on its type and severity. For example, if the viscosity of an emulsion is detected to be too high, then the corresponding fluid anomaly item is the viscosity of the emulsion.

[0108] Anomaly factor analysis for fluid anomalies refers to analyzing the possible causes of a given anomaly based on its identified characteristics. Continuing the example above, if the viscosity of the emulsion is too high, possible causes include problems with the emulsion formulation, issues with the emulsification process, and the influence of temperature.

[0109] Secondly, generating corresponding anomaly analysis results means creating a detailed analysis report based on the results of the anomaly factor analysis. This report will include a detailed description of the anomaly, possible causes, and suggested solutions.

[0110] In step S109, if the anomaly analysis result is an internal-external correlation, it indicates that the cause of the fluid property anomaly includes both the inherent properties of the emulsion itself and factors in the external environment or operation process. Subsequently, an anomaly factor distribution indicator set corresponding to the fluid anomaly item and the anomaly of the inherent properties and external factors is generated. This means that based on the anomaly analysis result, the factors leading to the anomaly are classified and organized to form an indicator set containing both inherent property anomaly factors and external factor anomaly factors.

[0111] Internal abnormalities include problems with the emulsion formulation, inappropriate component ratios, and incomplete emulsification. External abnormalities include unsuitable operating conditions such as temperature, pressure, and flow rate, or equipment malfunctions and human error that cause abnormalities in the emulsion during transport.

[0112] Secondly, the distribution indicator set of abnormal factors can clearly show the distribution of various abnormal factors, which helps operators or systems better analyze the causes of abnormalities and take targeted measures to deal with them. For example, if the proportion of intrinsic abnormal factors is found to be large, it may be necessary to adjust the emulsion formula or optimize the emulsification process; if the proportion of external abnormal factors is found to be large, it may be necessary to adjust the operating conditions or repair the equipment.

[0113] In step S110, executing the fluid anomaly elimination strategy corresponding to the anomaly factor distribution indicator set means formulating and implementing corresponding strategies to eliminate fluid anomalies based on the anomaly factor distribution indicator set. These strategies may include adjusting the emulsion formulation, optimizing the emulsification process, adjusting operating conditions, and repairing equipment.

[0114] Furthermore, generating corresponding fluid anomaly elimination process feedback information refers to collecting and recording relevant data and information during the execution of the anomaly elimination strategy, forming a feedback message. This feedback message can include the process, results, and effects of the strategy execution.

[0115] It should be noted that the feedback information provided can help operators or other systems understand the implementation status of the strategy, evaluate its effectiveness, and make subsequent adjustments and optimizations. For example, if a strategy is found to be ineffective, it may need to be adjusted; if a strategy is found to be very effective, it may need to be applied to other similar situations.

[0116] The mining emulsion conveying method provided in this embodiment compares the theoretical and actual operating parameters provided to the corresponding operating equipment during emulsion conveying in coal mine production operations. If the parameter difference between the two exceeds a preset operating parameter threshold, it indicates that the current emulsion conveying status does not fully support the normal operation of the mining equipment. A corresponding difference detection command is then executed. Since the control parameters during emulsion conveying have a significant impact on the operation of coal mine equipment, process control detection is performed first. If any abnormalities are detected, a corresponding elimination strategy is executed, and real-time feedback information on the process elimination progress between emulsion conveying and the corresponding coal mine equipment is generated. If the process control detection result is normal, further adjustments are made to the emulsion conveying process. The fluid properties of the emulsion are subjected to relevant safety tests. If the test results are abnormal and there is an internal and external correlation, in order to accurately analyze and characterize the relevant abnormal factors during the transportation of the emulsion, and thus improve the elimination effect of emulsion fluid anomalies, the internal property anomalies and external factor anomalies of the current fluid anomaly item are specifically analyzed, and the distribution indicator set of the abnormal factors corresponding to the fluid anomaly item is generated. Then, the corresponding fluid anomaly elimination strategy is executed and the fluid anomaly elimination process feedback information is recorded in real time. Because the actual abnormal situation that occurs during the transportation of the emulsion is analyzed and processed in depth, the anomalies can be eliminated in a timely and effective manner and feedback can be provided in real time, thereby improving the production efficiency of the coal mine.

[0117] In one embodiment of this example, such as Figure 2 As shown, after step S103, which involves executing the emulsion delivery parameters and obtaining the corresponding theoretical and actual operating parameters, the following steps are also included:

[0118] S201. If the difference between the theoretical operation parameters and the actual operation parameters does not exceed the preset operation parameter threshold, then obtain the source of the difference corresponding to the difference in operation parameters;

[0119] S202. If there are multiple sources of difference, then each source of difference is divided and classified according to the preset difference class standard to generate the corresponding target difference class;

[0120] S203. Based on the correlation coefficient between each target difference category and the difference in operation parameters, set the primary processing priority corresponding to the target difference category. The correlation coefficient is directly proportional to the primary processing priority.

[0121] S204. If there are multiple difference influencing factors in the target difference class, then according to the sensitivity of each difference influencing factor in the corresponding target difference class, set the secondary processing priority corresponding to the difference influencing factor. The sensitivity is proportional to the secondary processing priority, and the primary priority is higher than the secondary priority.

[0122] S205. Match and execute the error reduction strategy corresponding to the difference in job parameters based on the primary processing priority and the secondary processing priority.

[0123] In step S201, if the difference between the theoretical operating parameters and the actual operating parameters does not exceed the preset operating parameter threshold, it indicates that the emulsion delivery parameters are well controlled and the gap between the actual operation results and the expected results is within an acceptable range, but there is still a small difference between the theoretical operating parameters and the actual operating parameters.

[0124] Furthermore, in order to improve the control effect of the emulsion on the relevant mining functional modules during the transportation process, the current sources of difference are classified into major categories, namely target difference categories, and minor categories, namely difference influencing factors. Then, based on the above analysis, the corresponding anomaly handling sequence is formulated.

[0125] Among them, the source of difference refers to the inducing factors that cause a small difference between the theoretical operating parameters and the actual operating parameters. For example, the source of difference is environmental factors: the mine environment is complex, and changes in environmental factors such as temperature, humidity, and air pressure affect the actual operating parameters.

[0126] In step S202, if there are multiple sources of difference, in order to facilitate the analysis and processing of these multiple sources of difference, the sources of difference are divided and classified according to a preset difference classification standard to generate corresponding target difference classes.

[0127] Among these, pre-defined differentiation criteria are a method for classifying and managing sources of difference. These criteria are pre-set based on factors such as the nature, degree of impact, and controllability of the sources of difference. For example, based on the nature of the sources of difference, they can be classified as differences in equipment performance, operator differences, environmental differences, material differences, measurement error differences, and control strategy differences. Furthermore, based on the controllability of the sources of difference, they can be classified as controllable differences, partially controllable differences, and uncontrollable differences.

[0128] Secondly, the target difference category is a category generated by classifying and categorizing various difference sources according to preset difference classification criteria. Each target difference category represents a type of difference source with similar properties, degree of influence, or controllability.

[0129] For example, the category of "Equipment Performance Difference" includes all sources of difference caused by equipment performance, such as performance degradation due to aging or wear. The category of "Environmental Difference" includes all sources of difference caused by environmental factors, such as changes in temperature, humidity, and air pressure. The category of "Measurement Error Difference" includes all sources of difference caused by measurement errors, such as errors caused by the accuracy of the measuring equipment or the measurement method.

[0130] In step S203, the correlation coefficient is used to measure the relationship between the target difference class and the operational parameter difference. If the correlation coefficient is high, it indicates that the target difference class has a greater impact on the operational parameter difference; if the correlation coefficient is low, it indicates that the target difference class has a smaller impact on the operational parameter difference.

[0131] The primary processing priority is set based on the correlation coefficient, which determines the order in which to process each target difference class. If a target difference class has a high correlation coefficient, its primary processing priority is high, and it should be processed first; if a target difference class has a low correlation coefficient, its primary processing priority is low, and it can be processed later. This setting ensures that target difference classes that have a greater impact on job parameter differences are processed first, thereby more effectively improving job performance. At the same time, it also avoids wasting too much time and resources processing target difference classes with less impact.

[0132] For example, the aforementioned target difference categories are equipment performance difference, operator difference, environmental difference, and material difference. Their correlation coefficients with operational parameter differences are 0.7, 0.5, 0.3, and 0.1, respectively. Therefore, equipment performance difference has the highest correlation coefficient, making it the highest priority for primary processing; operator difference has the second highest, making it the next highest priority; environmental difference has the next lowest, making it the next lowest priority; and material difference has the lowest correlation coefficient, making it the lowest priority. The statistical calculation of these correlation coefficients can be performed using the Pearson correlation coefficient method.

[0133] In steps S204 and S205, the difference influencing factors refer to various factors that affect the target difference class. These factors may come from multiple different areas, including but not limited to equipment, personnel, environment, and management. Each difference influencing factor may affect the target difference class, thereby causing changes in the target difference class. For example, in the equipment performance difference class, difference influencing factors include equipment age, equipment maintenance frequency, and equipment usage frequency.

[0134] Sensitivity refers to the degree or influence of a difference factor on the target difference class. If a change in a difference factor leads to a significant change in the target difference class, then the difference factor is highly sensitive to the target difference class; conversely, if a change in a difference factor has a small impact on the target difference class, then the difference factor is less sensitive to the target difference class.

[0135] For example, there are three factors influencing the difference in equipment performance: equipment age, equipment maintenance frequency, and equipment usage frequency. Using partial derivatives or differentials, the sensitivities of these three factors to differences in equipment performance are found to be 0.6, 0.3, and 0.1, respectively.

[0136] Furthermore, secondary processing priorities for each differential influencing factor are set based on the aforementioned sensitivity. Among them, equipment age has the highest sensitivity, so it has the highest secondary processing priority; equipment maintenance frequency has the second highest sensitivity, so it has the second highest secondary processing priority; and equipment usage frequency has the lowest sensitivity, so it has the lowest secondary processing priority.

[0137] It is important to note that the secondary processing priorities described above are set based on the primary processing priorities. That is, the priority order for processing the target difference class is first determined according to the primary processing priorities, and then the priority order for processing each difference influencing factor within that target difference class is determined according to the secondary processing priorities. This ensures that the corresponding processing strategy considers both the importance of the target difference class and the importance of the difference influencing factors.

[0138] Furthermore, based on the aforementioned primary and secondary processing priorities, error mitigation strategies corresponding to the differences in operational parameters are matched and executed. The goal of these error mitigation strategies is to reduce or eliminate the differences between various factors influencing the outcome. Such strategies are typically developed after analyzing the causes and influencing factors of the problem, in order to address the issue in a targeted manner.

[0139] The mining emulsion transportation method provided in this embodiment classifies minor sources of difference in the emulsion during transportation into various target difference categories based on a preset differentiation standard. This facilitates the analysis and processing of anomalies during the transportation process. Then, a primary processing priority is set based on the correlation coefficient between each target difference category and the differences in operating parameters. Next, a secondary processing priority is set based on the influence factors of each difference category and their sensitivity within that category. Finally, the anomalies are eliminated based on the primary and secondary processing priorities, thereby improving the efficiency of anomaly detection and handling during emulsion transportation.

[0140] In one embodiment of this example, such as Figure 3 As shown, step S106, which involves obtaining the corresponding abnormal control item and executing the abnormal elimination strategy corresponding to the abnormal control item to generate the corresponding process elimination process feedback information, includes the following steps:

[0141] S301. If the process control detection result is abnormal, then obtain the damage indication item corresponding to the abnormal control item;

[0142] S302. If there are multiple damage indicators, an anomaly distribution table for the corresponding anomaly control items shall be generated based on the damage parameters of each damage indicator.

[0143] S303. Match the anomaly elimination strategy corresponding to the anomaly distribution table;

[0144] S304. Execute the exception elimination strategy and generate process exception elimination process feedback information corresponding to the exception control item.

[0145] In steps S301 and S302, damage indicators are parameters or indices used to describe the potential damage or impact of abnormal control items. If a single abnormal control item corresponds to multiple damage indicators, the system generates an anomaly distribution table based on the damage parameters of each indicator. This anomaly distribution table provides a more convenient understanding of the likelihood and severity of various damages that abnormal control items may cause.

[0146] For example, if the abnormal control item is excessively high equipment temperature, the corresponding damage indicators include equipment damage, decreased production efficiency, etc. The system will generate an anomaly distribution table based on the damage parameters of these damage indicators (such as the probability of equipment damage, the degree of decreased production efficiency, etc.) in order to understand the probability and severity of various damages that may be caused by excessively high equipment temperature.

[0147] In steps S303 to S304, the system matches the corresponding anomaly elimination strategy according to the aforementioned anomaly distribution table. This strategy is formulated based on the specific damage content of the damage indication items recorded in the anomaly distribution table, with the aim of eliminating or reducing the damage that anomaly control items may cause.

[0148] Then, the system will execute this anomaly elimination strategy. The execution process may include adjusting equipment parameters, replacing equipment, etc., depending on the content of the anomaly elimination strategy. Finally, the system will generate process elimination progress feedback information corresponding to the anomaly control item. This feedback information reflects the result of executing the anomaly elimination strategy, helping to understand the effectiveness of the strategy in real time, so as to adjust or optimize the strategy. For example, if the feedback information shows that the equipment temperature has returned to normal after adjusting the equipment parameters, it indicates that the anomaly elimination strategy is effective.

[0149] The mining emulsion transportation method provided in this embodiment can promptly identify the distribution of anomalies by generating a corresponding anomaly distribution table. At the same time, by matching the anomaly elimination strategy corresponding to the anomaly distribution table, anomalies can be handled in a targeted manner, thereby improving the efficiency of anomaly handling.

[0150] In one embodiment of this example, such as Figure 4 As shown, step S302, which involves generating an anomaly distribution table for the corresponding anomaly control item based on the damage parameters of each damage indicator if there are multiple damage indicators, includes the following steps:

[0151] S401. If there are multiple damage indicators, the damage parameters are analyzed according to the preset damage judgment criteria to determine the abnormal target stage of the corresponding damage indicator;

[0152] S402. Combine each damage indicator and the corresponding abnormal target stage to generate an anomaly distribution table for the corresponding anomaly control item.

[0153] In steps S401 to S402, if an abnormal control item corresponds to multiple damage indicators, the system will analyze these damage parameters according to preset damage judgment criteria. The purpose of this analysis is to determine the abnormal target stage of each damage indicator, that is, to determine the extent to which the potential damage of each damage indicator would be considered abnormal.

[0154] Among them, the preset damage judgment standard is a pre-set standard or rule used to assess and judge the degree of damage corresponding to the damage parameter. For example, temperature is an important parameter because it can affect the stability and quality of the emulsion. If the temperature is too high or too low, it may cause the emulsion to separate, solidify, or have other quality problems, thereby affecting the conveying efficiency and product quality. If the set temperature exceeds 60 degrees, it enters the "high temperature abnormality target stage"; if the temperature is below 10 degrees, it enters the "low temperature abnormality target stage"; the damage parameter corresponding to the temperature is the current temperature value.

[0155] Furthermore, the system combines all damage indicators and their corresponding abnormal target stages to generate an anomaly distribution table. This anomaly distribution table helps to understand the likelihood and severity of various damages that abnormal control items may cause.

[0156] For example, if the abnormal control item is excessively high equipment temperature, then its corresponding damage indicators include equipment damage and decreased production efficiency. The system will determine, based on preset damage assessment criteria, whether the current temperature is in the high-temperature abnormality target stage, and identify the damage indicators that would cause equipment damage and decreased production efficiency during this stage. Then, it will combine this information to generate a corresponding abnormality distribution table.

[0157] The mining emulsion transportation method provided in this embodiment, by combining various damage indicators and corresponding abnormal target stages, generates an anomaly distribution table that can provide more comprehensive anomaly information, thereby providing more reference information for subsequent anomaly handling and improving anomaly handling efficiency.

[0158] In one embodiment of this example, such as Figure 5 As shown, step S108, which involves obtaining the corresponding fluid anomaly item if the fluid property detection result is abnormal, and performing anomaly factor analysis on the fluid anomaly item to generate the corresponding anomaly analysis result, includes the following steps:

[0159] S501. If the fluid property test results are abnormal, perform anomaly factor analysis on the fluid anomaly items and generate corresponding fluid property analysis data;

[0160] S502. If the fluid property analysis data meets the preset response property standard, the generated fluid anomaly item is an anomaly analysis result with internal and external correlation.

[0161] In steps S501 and S502, fluid property detection refers to the process of measuring and analyzing various physical and chemical properties of the emulsion fluid, such as temperature, pressure, viscosity, density, flow rate, and chemical composition, to understand and control the fluid's state and behavior. This detection is typically performed using various instruments and equipment, such as thermometers, pressure gauges, viscometers, densitometers, flow meters, and spectrometers. The detection results can be used to assess fluid quality and predict and control fluid behavior.

[0162] For example, by measuring and analyzing the temperature and pressure of a fluid, we can understand its thermodynamic state; by measuring and analyzing its viscosity and density, we can understand its fluidity; and by measuring and analyzing its chemical composition, we can understand its chemical properties and reactivity.

[0163] Furthermore, if the above test results are abnormal, it indicates that there are some abnormalities in the current emulsion. The corresponding fluid anomalies are then identified. Fluid anomalies refer to property indicators that deviate from preset standards or normal values ​​when the properties of the emulsion are tested. These anomalies may include abnormal changes in any property of the fluid, such as temperature, pressure, viscosity, density, flow rate, and chemical composition. For example, if the fluid temperature exceeds the preset normal range, then temperature is an anomaly. Similarly, if the fluid viscosity or pressure differs significantly from the preset normal value, then viscosity or pressure is also an anomaly.

[0164] Secondly, to conduct a deeper analysis of the aforementioned fluid anomaly, anomaly factor analysis is performed. Anomaly factor analysis involves analyzing various possible factors that could cause this anomaly, such as temperature, pressure, and flow rate. Through this analysis, corresponding fluid property analysis data can be generated.

[0165] Fluid property analysis data is obtained through property testing and anomaly factor analysis of fluids. This data reflects various physical and chemical properties of the fluid, such as temperature, pressure, viscosity, density, and chemical composition. This data helps in understanding the state and behavior of fluids; for example, temperature and pressure can affect fluid viscosity and flowability, while chemical composition can affect fluid reactivity and stability.

[0166] Furthermore, preset response property standards refer to pre-defined criteria used to assess whether the properties of a fluid are normal. If the fluid property analysis data meets these preset response property standards, then the anomaly is considered to be caused by factors related to both internal and external factors. These factors may include the fluid's internal properties (e.g., chemical composition, viscosity) and the external environment (e.g., temperature, pressure). For example, if the system detects an abnormal viscosity in an emulsion fluid, anomaly factor analysis can be performed on the viscosity of the emulsion fluid to determine that the anomaly is caused by excessively high temperature.

[0167] The mining emulsion transportation method provided in this embodiment performs anomaly factor analysis on fluid anomalies, which can more accurately identify the causes of anomalies. At the same time, based on fluid property analysis data, detailed information showing the anomaly can be quickly obtained, thereby improving the accuracy and efficiency of anomaly handling.

[0168] In one embodiment of this example, such as Figure 6 As shown, step S109, which involves generating an anomaly factor distribution indicator set corresponding to the internal property anomaly and the external factor anomaly of the fluid anomaly item if the anomaly analysis result is an internal-external correlation, includes the following steps:

[0169] S601. If the anomaly analysis result is an internal-external correlation, then obtain the anomaly interference factors of the internal property anomaly and the external factor anomaly corresponding to the fluid anomaly item;

[0170] S602. If there is co-directional interference among the abnormal interference factors, determine the initiating factor and the inducing factor among the abnormal interference factors, and generate the corresponding abnormal factor distribution indicator set by combining the correlation coefficient between the initiating factor and the inducing factor.

[0171] S603. If there is reverse interference among the abnormal interference factors, then determine the increasing and decreasing factors among the abnormal interference factors, and generate the corresponding abnormal factor distribution indicator set by combining the offsetting factors between the increasing and decreasing factors.

[0172] In step S601, the abnormal interference factor refers to the factors that affect the fluid properties and cause abnormalities, including abnormalities in intrinsic properties and abnormalities in external factors. For example, if the fluid temperature is abnormal, a possible abnormal interference factor in intrinsic properties might be a change in the fluid's chemical composition, while a possible abnormal interference factor in external factors might be a change in ambient temperature. Obtaining the abnormal interference factors in intrinsic property abnormalities and external factor abnormalities corresponding to fluid anomalies is to more accurately identify the causes of fluid anomalies, thereby solving the problem more effectively.

[0173] In step S602, co-directional interference refers to a relationship of mutual influence or reinforcement between abnormal interference factors. For example, the temperature and pressure of a fluid may exhibit co-directional interference; when the temperature increases, the pressure may also increase. The initiating factor is the factor that first exhibits an abnormality, while the inducing factor is another abnormal factor triggered by the abnormality of the initiating factor. For example, if the chemical composition of a fluid first becomes abnormal, leading to abnormalities in the fluid's temperature and pressure, then the chemical composition is the initiating factor, and the temperature and pressure are the inducing factors.

[0174] The correlation coefficient refers to the degree of correlation between two factors, which can be calculated through statistical analysis and other methods. A larger correlation coefficient indicates a higher degree of correlation between the two factors. The anomalous factor distribution indicator set is a set of indicators representing the distribution of various anomalous factors, generated based on the correlation coefficients between the initiating and precipitating factors. For example, if chemical composition is the initiating factor, and temperature and pressure are the precipitating factors, and the correlation coefficient between chemical composition and temperature is greater than that between chemical composition and pressure, then the anomalous factor distribution indicator set indicates that chemical composition is mainly distributed at temperature and less so at pressure.

[0175] In step S603, reverse interference refers to a relationship where abnormal interference factors cancel each other out or weaken each other. For example, fluid temperature and pressure may have reverse interference; when temperature increases, pressure may decrease. An increasing factor is a factor whose value or influence is increasing among abnormal interference factors. A decreasing factor is a factor whose value or influence is decreasing among abnormal interference factors. For example, if fluid temperature is increasing while pressure is decreasing, then temperature is an increasing factor and pressure is a decreasing factor.

[0176] The offsetting factor refers to the degree of mutual influence between the increasing and decreasing factors, which can be calculated through statistical analysis and other methods. The larger the offsetting factor, the higher the degree of mutual offsetting between the increasing and decreasing factors.

[0177] Secondly, the anomaly factor distribution indicator set refers to an indicator set representing the distribution of each anomaly factor, generated based on the offsetting factors between increasing and decreasing factors. For example, if temperature is an increasing factor and pressure is a decreasing factor, and the offsetting factors between temperature and pressure are large, then the anomaly factor distribution indicator set may indicate that the increase in temperature is mainly offset by the decrease in pressure.

[0178] The mining emulsion transportation method provided in this embodiment obtains the abnormal interference factors of the internal property abnormalities and external factor abnormalities corresponding to the fluid abnormalities. Regardless of whether there is unidirectional or reverse interference between the abnormal interference factors, the corresponding abnormal factor distribution indicator set can be generated by determining the corresponding initiating factor, inducing factor, increasing factor and decreasing factor. This allows for more accurate location of the source of the abnormality and improves the accuracy of abnormality handling.

[0179] In one embodiment of this example, such as Figure 7 As shown, after step S110, which executes the fluid anomaly elimination strategy corresponding to the anomaly factor distribution indicator set and generates the corresponding fluid anomaly elimination process feedback information, the following steps are also included:

[0180] S701. Based on the feedback information of the fluid anomaly elimination process, generate dynamic anomaly elimination indication data corresponding to the abnormal interference factors in the anomaly factor distribution indication set;

[0181] S702. Generate the elimination equilibrium curve corresponding to the emulsion based on the dynamic elimination indicator data.

[0182] In steps S701 and S702, the dynamic anomaly elimination indicator data refers to data updated in real time based on feedback information from the fluid anomaly elimination process, representing changes in abnormal interference factors. The anomaly elimination equilibrium curve corresponding to the emulsion is generated based on the dynamic anomaly elimination indicator data, indicating the changes in the equilibrium state of the emulsion during the anomaly elimination process. The anomaly elimination equilibrium curve can indicate the stability of the emulsion and when the optimal anomaly elimination effect is reached.

[0183] For example, if dynamic anti-aliasing indicators show that both increasing temperature and decreasing pressure improve the anti-aliasing effect of the emulsion, then the anti-aliasing equilibrium curve may show that the emulsion achieves the best anti-aliasing effect within a certain temperature and pressure range. By observing the anti-aliasing equilibrium curve, the optimal temperature and pressure conditions can be found to achieve the best anti-aliasing effect.

[0184] The mining emulsion transportation method provided in this embodiment can more accurately analyze and control the process of eliminating abnormalities in the emulsion based on the corresponding abnormality elimination equilibrium curve, thereby improving the accuracy and efficiency of abnormality handling.

[0185] This application also discloses a mining emulsion delivery system, such as... Figure 8 As shown, it includes:

[0186] Operation parameter acquisition module 1 is used to acquire the operation requirement parameters corresponding to the mining function modules;

[0187] Parameter matching module 2 is used to match the emulsion delivery parameters corresponding to the operational requirements parameters;

[0188] Parameter execution module 3 executes the emulsion delivery parameters and obtains the corresponding theoretical and actual operating parameters;

[0189] If the difference between the theoretical operation parameters and the actual operation parameters exceeds the preset operation parameter threshold, the difference detection module 4 generates a corresponding difference detection instruction.

[0190] Process detection module 5 is used to execute difference detection instructions and obtain process control detection results corresponding to emulsion delivery parameters;

[0191] If the process control detection result is abnormal, the process elimination module 6 is used to obtain the corresponding abnormal control item, execute the abnormal elimination strategy corresponding to the abnormal control item, and generate the corresponding process elimination process feedback information.

[0192] If the process control detection result is normal, the fluid detection module 7 is used to obtain the fluid property detection result corresponding to the emulsion delivery parameters;

[0193] Anomaly analysis module 8: If the fluid property detection result is abnormal, the anomaly analysis module 8 is used to obtain the corresponding fluid anomaly item, perform anomaly factor analysis on the fluid anomaly item, and generate the corresponding anomaly analysis result.

[0194] Anomaly Factor Indication Module 9: If the anomaly analysis result is an internal-external correlation, then the anomaly factor indication module 9 is used to generate anomaly factor distribution indication set corresponding to the internal property anomaly and external factor anomaly of the fluid anomaly item.

[0195] The fluid anomaly elimination module 10 is used to execute the fluid anomaly elimination strategy corresponding to the anomaly factor distribution indicator set and generate corresponding fluid anomaly elimination process feedback information.

[0196] The mining emulsion conveying system provided in this embodiment compares the theoretical and actual operating parameters provided by the emulsion conveying system to the corresponding operating equipment during coal mine production operations using the difference detection module 4. If the parameter difference exceeds the preset operating parameter threshold, it indicates that the current emulsion conveying status does not fully support the normal operation of the mining equipment. Then, the process detection module 5 executes the corresponding difference detection command. Since the control parameters during the emulsion conveying process have a significant impact on the operation of the coal mine equipment, process control detection is performed first. If any abnormalities are detected, the process anomaly elimination module 6 executes the corresponding elimination strategy and generates real-time feedback information on the process anomaly elimination process between the emulsion conveying system and the corresponding coal mine equipment. If the process control detection result is normal, further processing is performed via the flow... The volume detection module 7 performs relevant safety checks on the fluid properties of the emulsion. If the detection result is abnormal and there is an internal and external correlation, in order to accurately analyze and characterize the relevant abnormal factors during the transportation of the emulsion, and thus improve the elimination effect of fluid abnormalities, the abnormal factor indication module 9 performs a specific analysis on the internal property abnormalities and external factor abnormalities of the current fluid abnormality item, and generates an abnormal factor distribution indication set corresponding to the fluid abnormality item. Then, the fluid anomaly elimination module 10 executes the corresponding fluid anomaly elimination strategy and records the fluid anomaly elimination process feedback information in real time. Because the actual abnormal situation that occurs during the transportation of the emulsion is analyzed and processed in depth, the anomaly can be eliminated in a timely and effective manner and feedback can be provided in real time, thereby improving the production efficiency of the coal mine.

[0197] It should be noted that the mining emulsion conveying system provided in this application embodiment also includes each module and / or corresponding sub-module corresponding to the logical function or logical step of any of the above-mentioned mining emulsion conveying methods, to achieve the same effect as each logical function or logical step, which will not be elaborated here.

[0198] This application also discloses a terminal device, including a memory, a processor, and computer instructions stored in the memory and capable of running on the processor, wherein when the processor executes the computer instructions, it employs any of the mining emulsion delivery methods described in the above embodiments.

[0199] The terminal device can be a computer device such as a desktop computer, a laptop computer, or a cloud server. The terminal device includes, but is not limited to, a processor and a memory. For example, the terminal device may also include input / output devices, network access devices, and buses.

[0200] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it.

[0201] The memory can be an internal storage unit of the terminal device, such as a hard disk or RAM of the terminal device, or an external storage device of the terminal device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) equipped on the terminal device. Furthermore, the memory can be a combination of internal storage units and external storage devices of the terminal device. The memory is used to store computer instructions and other instructions and data required by the terminal device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0202] In this terminal device, any one of the mining emulsion delivery methods in the above embodiments can be stored in the memory of the terminal device and loaded and executed on the processor of the terminal device for convenient use.

[0203] This application also discloses a computer-readable storage medium, which stores computer instructions, wherein when the computer instructions are executed by a processor, any of the mining emulsion delivery methods described in the above embodiments are employed.

[0204] The computer instructions can be stored in a computer-readable medium. The computer instructions include computer instruction code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer instruction code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0205] In this computer-readable storage medium, any one of the mining emulsion delivery methods in the above embodiments can be stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the above methods.

[0206] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for conveying mining emulsion, characterized in that, Includes the following steps: Obtain the operational requirements parameters corresponding to the mining functional modules; Match the emulsion delivery parameters corresponding to the operational requirements parameters. The emulsion delivery parameters include flow rate, pressure, temperature, viscosity, density, surface tension, and pH value. The emulsion delivery parameters are executed to obtain the corresponding theoretical operating parameters and actual operating parameters. The theoretical operating parameters refer to the expected operating parameters of the mining functional module obtained by theoretical calculation based on the emulsion delivery parameters. The actual operating parameters refer to the actual operating parameters of the mining functional module obtained by measurement during actual operation. If the difference between the theoretical operation parameters and the actual operation parameters exceeds a preset operation parameter threshold, a corresponding difference detection instruction is generated. Execute the difference detection command to obtain the process control detection results corresponding to the emulsion delivery parameters; If the process control detection result is abnormal, the corresponding abnormal control item is obtained, and the abnormal elimination strategy corresponding to the abnormal control item is executed to generate the corresponding process elimination process feedback information. If the process control detection result is normal, then obtain the fluid property detection result corresponding to the emulsion delivery parameters; If the fluid property detection result is abnormal, the corresponding fluid anomaly item is obtained, and anomaly factor analysis is performed on the fluid anomaly item to generate the corresponding anomaly analysis result; If the anomaly analysis results are correlated internally and externally, then an anomaly factor distribution indicator set corresponding to the fluid anomaly item and the anomaly of the internal property and external factors is generated; Execute the fluid anomaly elimination strategy corresponding to the abnormal factor distribution indicator set, and generate corresponding fluid anomaly elimination process feedback information; If the process control detection result is abnormal, the corresponding abnormal control item is obtained, and the abnormal elimination strategy corresponding to the abnormal control item is executed to generate the corresponding process elimination process feedback information, including the following steps: If the process control detection result is abnormal, then the damage indicator corresponding to the abnormal control item is obtained. The damage indicator is a parameter or index used to describe the damage or impact that the abnormal control item may cause. If there are multiple damage indicators, an anomaly distribution table corresponding to the anomaly control item is generated based on the damage parameters of each damage indicator. Match the anomaly elimination strategy corresponding to the anomaly distribution table; Execute the anomaly elimination strategy to generate the process anomaly elimination process feedback information corresponding to the anomaly control item; If there are multiple damage indicators, generating an anomaly distribution table corresponding to the anomaly control item based on the damage parameters of each damage indicator includes the following steps: If there are multiple damage indicators, the damage parameters are analyzed according to the preset damage judgment criteria to determine the abnormal target stage corresponding to the damage indicator; By combining each of the damage indicators and the corresponding abnormal target stage, an abnormal distribution table corresponding to the abnormal control item is generated.

2. The method for conveying mining emulsion according to claim 1, characterized in that, After executing the emulsion delivery parameters and obtaining the corresponding theoretical and actual operating parameters, the following steps are also included: If the difference between the theoretical operation parameters and the actual operation parameters does not exceed the preset operation parameter threshold, then the source of the difference corresponding to the difference in operation parameters is obtained; If there are multiple sources of difference, then each source of difference is divided and classified according to a preset difference class standard to generate a corresponding target difference class. Based on the correlation coefficient between each target difference class and the difference in the operation parameters, a primary processing priority corresponding to the target difference class is set, and the correlation coefficient is proportional to the primary processing priority. If there are multiple difference influencing factors in the target difference class, then according to the sensitivity of each difference influencing factor in the corresponding target difference class, the secondary processing priority corresponding to the difference influencing factor is set, the sensitivity is proportional to the secondary processing priority, and the primary processing priority is higher than the secondary processing priority; Based on the primary processing priority and the secondary processing priority, the error reduction strategy corresponding to the difference in the job parameters is matched and executed.

3. The method for conveying mining emulsion according to claim 1, characterized in that, If the fluid property detection result is abnormal, the corresponding fluid anomaly item is obtained, and anomaly factor analysis is performed on the fluid anomaly item to generate the corresponding anomaly analysis result, including the following steps: If the fluid property detection result is abnormal, then anomaly factor analysis is performed on the fluid anomaly item, and corresponding fluid property analysis data is generated; If the fluid property analysis data meets the preset response property criteria, then the fluid anomaly item is generated as the anomaly analysis result of the internal and external correlation.

4. A method for conveying mining emulsion according to claim 1, characterized in that, If the anomaly analysis results are correlated internally and externally, then generating the anomaly factor distribution indicator set corresponding to the fluid anomaly item's intrinsic property anomaly and external factor anomaly includes the following steps: If the anomaly analysis result is an internal-external correlation, then obtain the anomaly interference factor corresponding to the internal property anomaly and the external factor anomaly of the fluid anomaly item; If there is co-directional interference among the abnormal interference factors, then the initiating factor and the inducing factor among the abnormal interference factors are determined, and the corresponding abnormal factor distribution indicator set is generated by combining the correlation coefficient between the initiating factor and the inducing factor. If there is reverse interference among the abnormal interference factors, then the increasing and decreasing factors among the abnormal interference factors are determined, and the corresponding abnormal factor distribution indicator set is generated by combining the offsetting factors between the increasing and decreasing factors.

5. A method for conveying mining emulsion according to claim 4, characterized in that, After executing the fluid anomaly elimination strategy corresponding to the anomaly factor distribution indicator set and generating the corresponding fluid anomaly elimination process feedback information, the following steps are also included: Based on the feedback information of the fluid elimination process, dynamic elimination indication data corresponding to the abnormal interference factors in the abnormal factor distribution indication set is generated; Based on the dynamic anomaly elimination indicator data, an anomaly elimination equilibrium curve corresponding to the emulsion is generated.

6. A mining emulsion conveying system, characterized in that, include: The operation parameter acquisition module (1) is used to acquire the operation requirement parameters corresponding to the mining function modules; The parameter matching module (2) is used to match the emulsion delivery parameters corresponding to the operation requirements parameters. The emulsion delivery parameters include flow rate, pressure, temperature, viscosity, density, surface tension, and pH value. The parameter execution module (3) is used to execute the emulsion delivery parameters and obtain the corresponding theoretical operation parameters and actual operation parameters. The theoretical operation parameters refer to the expected operation parameters of the mining functional module obtained by theoretical calculation based on the emulsion delivery parameters. The actual operation parameters refer to the actual operation parameters of the mining functional module obtained by measurement during actual operation. If the difference between the theoretical operation parameters and the actual operation parameters exceeds the preset operation parameter threshold, the difference detection module (4) generates a corresponding difference detection instruction. The process detection module (5) is used to execute the difference detection instruction and obtain the process control detection results corresponding to the emulsion delivery parameters; If the process control detection result is abnormal, the process elimination module (6) is used to obtain the corresponding abnormal control item, execute the abnormal elimination strategy corresponding to the abnormal control item, and generate the corresponding process elimination process feedback information. The fluid detection module (7) is used to obtain the fluid property detection result corresponding to the emulsion delivery parameters if the process control detection result is normal. Anomaly analysis module (8): If the fluid property detection result is abnormal, the anomaly analysis module (8) is used to obtain the corresponding fluid anomaly item, and perform anomaly factor analysis on the fluid anomaly item to generate the corresponding anomaly analysis result. Anomaly indicator module (9): If the anomaly analysis result is related to internal and external factors, then the anomaly indicator module (9) is used to generate anomaly factor distribution indicator set corresponding to the internal property anomaly and external factor anomaly of the fluid anomaly item. The fluid anomaly elimination module (10) is used to execute the fluid anomaly elimination strategy corresponding to the anomaly factor distribution indication set and generate corresponding fluid anomaly elimination process feedback information. If the process control detection result is abnormal, the corresponding abnormal control item is obtained, and the abnormal elimination strategy corresponding to the abnormal control item is executed to generate the corresponding process elimination process feedback information, including the following steps: If the process control detection result is abnormal, then the damage indicator corresponding to the abnormal control item is obtained. The damage indicator is a parameter or index used to describe the damage or impact that the abnormal control item may cause. If there are multiple damage indicators, an anomaly distribution table corresponding to the anomaly control item is generated based on the damage parameters of each damage indicator. Match the anomaly elimination strategy corresponding to the anomaly distribution table; Execute the anomaly elimination strategy to generate the process anomaly elimination process feedback information corresponding to the anomaly control item; If there are multiple damage indicators, generating an anomaly distribution table corresponding to the anomaly control item based on the damage parameters of each damage indicator includes the following steps: If there are multiple damage indicators, the damage parameters are analyzed according to the preset damage judgment criteria to determine the abnormal target stage corresponding to the damage indicator; By combining each of the damage indicators and the corresponding abnormal target stage, an abnormal distribution table corresponding to the abnormal control item is generated.

7. A terminal device, comprising a memory and a processor, characterized in that, The memory stores computer instructions that can run on the processor. When the processor loads and executes the computer instructions, it employs a mining emulsion delivery method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing computer instructions, characterized in that, When the computer instructions are loaded and executed by the processor, a mining emulsion delivery method as described in any one of claims 1 to 5 is employed.

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