Intelligent mining monitoring system and method for coal mine

Abstract

The invention relates to the technical field of mining monitoring, in particular to an intelligent mining monitoring system and method for a coal mine, and the system comprises a propulsion recognition module, a pressure work imbalance recognition module, a rotating speed cooling linkage module, a cooling efficiency delay module and a monitoring output module. According to the method, the abnormal response in the propelling process is disclosed step by step by collecting the propelling displacement and resistance change sequence of the tunneling oil cylinder, calculating the gradient change of propelling and resistance, comparing propelling and load mismatching sections and further analyzing the asynchronous behavior of the pressure intensity and the motor power, the coupling relation between a hydraulic system and a power system is extracted layer by layer, and the abnormal response in the propelling process is disclosed step by step. For coupling of start and stop of a cooling pump and the rotating speed of a main drive motor, a water temperature change curve is combined, a dislocation segment with the cooling response lagging behind mechanical restability is positioned, and it is determined that response delay exists when equipment deals with temperature rise. Segments with reduced thermal conduction efficiency are screened to identify cooling efficiency issues due to a mismatch of hydrodynamic force and thermal conduction response.

Description

Technical Field

[0001] The present invention relates to the technical field of mining monitoring, and in particular to a coal mine intelligent mining monitoring system and method. Background Art

[0002] The field of mining monitoring technology includes systems and methods for real-time collection, processing and feedback of various operating environments, equipment operating status, safety risks and other information during the mining process. The core content of this technical field is to achieve comprehensive monitoring and management of mining operations, covering key links such as personnel positioning, environmental parameter perception, mechanical operating status detection, data transmission and storage.

[0003] Among them, the intelligent coal mine mining monitoring system refers to the coal resource mining link, which aims at technical matters such as working environment perception, equipment operation information acquisition, production data analysis and personnel safety management. It realizes the collection of various working condition information of the mine by deploying multi-point gas concentration sensors, dust concentration sampling elements, underground temperature and humidity monitoring devices and electrical fire detection components.

[0004] Existing technologies mainly rely on sensors to monitor single-point indicators such as underground gas concentration, dust concentration, temperature and humidity, and electrical fires. Although information collection is arranged at multiple points, the connection between them is weak, and it can only provide environmental data in a static or isolated state, and lacks the ability to link and analyze the dynamic behavior of equipment operation. During the advancement of tunneling equipment, the existing system cannot effectively reflect the correlation between the change in propulsion load and the response of the hydraulic system and the output capacity of the motor, and it is easy to miss the key state nodes of unstable propulsion or abnormal hydraulic output. At the same time, the monitoring of the cooling system is limited to recording the rising and falling trends of water temperature, ignoring the temporal relationship between the water pump speed, liquid flow changes and temperature response, and cannot accurately identify potential hidden dangers such as cooling response lag or reduced cooling efficiency. In addition, the existing technology has not established an effective data labeling mechanism, nor has it formed a mapping table between equipment behavior and status numbers, resulting in fault behavior that can only be inferred manually, lacking structured output and automatic recognition capabilities. The above deficiencies may cause abnormal hidden dangers to remain undetected for a long time in actual operation. For example, a short-term and drastic fluctuation in the hydraulic system pressure may not be identified as a risk signal, or the delayed response of the cooling system may be misjudged as an external environmental fluctuation, ultimately leading to a series of problems such as equipment shutdown, increased energy consumption, and increased maintenance costs. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a coal mine intelligent mining monitoring system and method.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A coal mine intelligent mining monitoring system comprises: The propulsion recognition module obtains the propulsion displacement sequence of the tunneling cylinder and the records of the resistance sensor, calculates the propulsion rate and the change gradient coefficient of the resistance, screens the sections where the load increases but the propulsion does not match, and generates the propulsion load offset sections; The pressure-work imbalance recognition module extracts the changes in the hydraulic cylinder pressure and the fluctuations in the motor output power within the propulsion load offset sections, compares them with the pressure-work stability reference value section by section, identifies the areas where the pressure and power are imbalanced, and obtains the pressure-power imbalance sections; The rotation speed-cooling linkage module obtains the rotation speed of the cooling pump and the water temperature change at the time point when the cooling pump is turned on corresponding to the pressure-power imbalance sections, identifies the misaligned sections where the cooling response lags behind the rotation speed stabilization, and obtains the cooling rotation speed response misaligned sections; The cooling efficiency delay module, based on the cooling rotation speed response misaligned sections, collects the temperature drop change and the coolant flow rate, determines whether the temperature-flow ratio sequence is less than the temperature-flow conduction response reference ratio, screens the section numbers that do not reach the benchmark, and obtains the cooling conduction efficiency delay sections; The monitoring output module, based on the numbers of the cooling conduction efficiency delay sections, extracts the corresponding unique device identification codes, assigns corresponding device abnormal behavior labels to the identification codes, and generates the abnormal operation monitoring results of the roadheader;

[0007] As a further solution of the present invention, the propulsion load offset sections include the propulsion stable section numbers, the continuous increasing trend of the resistance, and the propulsion resistance mismatch ratio; the pressure-power imbalance sections include the pressure increase amplitude sequence, the motor power fluctuation ratio, and the pressure-work synchronization deviation sections; the cooling rotation speed response misaligned sections include the rotation speed stabilization time period, the cooling response duration deviation, and the early stabilization number; the cooling conduction efficiency delay sections include the temperature drop rate sequence, the coolant flow rate increase amplitude, and the temperature-flow ratio insufficient number; the abnormal operation monitoring results of the roadheader include the unique device identification code, the device operation mapping field, and the summary of abnormal behavior labels.

[0008] As a further solution of the present invention, the propulsion recognition module includes: The propulsion data acquisition sub-module obtains the propulsion displacement sequence of the tunneling cylinder and the records of the resistance sensor, extracts the displacement data and the resistance data corresponding to the time period in the record sequence, and plots the resistance data as a resistance curve graph according to the propulsion time axis to obtain the resistance time curve data; The rate calculation sub-module, based on the resistance time curve data, extracts the propulsion displacement changes per second to form a propulsion rate sequence, and extracts the resistance changes per second to form a resistance rate sequence, and uses the formula: , ; Calculate the propulsion rate change gradient coefficient at the th second and the resistance change gradient coefficient at the , an integrated gradient change coefficient list is obtained; Among them, represents the propulsion rate at the th second, represents the resistance at the th second, represents the time at the th second, represents the change amount of adjacent propulsion rates, represents the change amount of adjacent resistances, represents the adjacent time interval, is the total number of data points of the propulsion rate, represents the summation variable index; Based on the gradient change coefficient list, the offset screening sub-module judges according to the set propulsion rate change threshold and resistance rate change threshold, calculates the increase ratio of the propulsion displacement and the resistance within the section in the identified number section, calls the average increase ratio in the continuous section for comparison, identifies the record number with the increase ratio greater than the average value, screens the section number where the propulsion rate is lower than the propulsion rate change threshold and the resistance exceeds the resistance rate change threshold, and generates a propulsion load offset section.

[0009] As a further solution of the present invention, the pressure-work imbalance identification module includes: The pressure-work data extraction sub-module extracts the hydraulic cylinder pressure change records and the motor output power fluctuation data within the propulsion load offset section, pairs the output sequence of the pressure sensor and the output sequence of the motor power in a time-synchronized manner to obtain a raw pressure-power sequence set; Based on the raw pressure-power sequence set, the pressure-work ratio calculation sub-module divides the section at a fixed time interval, calculates the mean value of the pressure sequence in each section, records the mean difference between the current section and the previous section as the pressure increase, calculates the mean difference of the motor output power as the power increase, and represents the pressure-work synchronization relationship in the corresponding time period by the ratio between the pressure increase value and the power increase value to obtain a pressure-work synchronization ratio sequence; The imbalance section screening sub-module compares the pressure-work ratio in each time period with the set stable reference ratio of the pressure-work load section by section, marks the section number where the ratio deviates from the stable reference ratio interval, extracts the continuous numbered time period, and obtains the pressure-power imbalance section.

[0010] As a further solution of the present invention, the rotational speed-cooling linkage module includes: The cooling speed extraction submodule obtains the cooling pump start-up time point corresponding to the pressure-power imbalance section, extracts the speed data sequence of the main drive motor before and after the corresponding cooling pump is started, compares the absolute value of the change of adjacent speed points in a continuous time period with the set speed fluctuation threshold, marks the time period exceeding the threshold as a fluctuation state, and the time period below or equal to the threshold and continuously meeting the conditions as a stable state, and obtains the speed fluctuation recovery section; The stabilization response recording submodule records the time period from the start of the fluctuation state to the continuous stable state in each section according to the speed fluctuation recovery section, which is defined as the stabilization time period, and simultaneously extracts the time length between the temperature drop starting point and the stable point in the cooling water temperature sequence as the cooling response time length to generate a stabilization response time set; The response misalignment identification submodule adopts the formula based on the return-to-stabilization response time set: ; Operation to obtain the Normalized stabilization response deviation for each time period , classify the time period numbers where the deviation is greater than zero and the stabilization normalized value is less than the cooling normalized value into the response lag classification, and obtain the cooling speed response misalignment section; in, Indicates The normalized stabilization response deviation of the time period is Indicates Speed stabilization time, Indicates Segment cooling response time, It represents the maximum value of all stabilization times. Indicates the maximum value of all cooling response times.

[0011] As a further solution of the present invention, the cooling efficiency delay module includes: The temperature-flow ratio extraction submodule collects the continuous temperature drop change sequence and the corresponding coolant flow rate data sequence recorded by the temperature guide sensor in the cooling pipeline based on the cooling speed response dislocation section, extracts the temperature drop rate and the coolant flow rate growth range within each cooling section time range, calculates the temperature difference and the flow rate difference within the specified time, and generates a temperature-flow ratio sequence; The delay section screening submodule compares each section ratio with the set temperature flow conduction response reference ratio based on the temperature flow ratio sequence, marks the numbered sections that are less than the reference ratio, collects the consecutive numbers that do not reach the reference value, screens and summarizes the corresponding time period range, and obtains the cooling conduction efficiency delay section.

[0012] As a further solution of the present invention, the monitoring output module includes: The identification mapping sub-module extracts the device unique identification code corresponding to the number based on the number of the cooling conduction efficiency delay section, calls the operation identification field in the device operation status record data, establishes a mapping set from the section number to the device identification according to the corresponding manner of the number and the device code, and obtains the device mapping relationship table; The label collection sub-module calls the behavior label items in the propulsion load offset section, the pressure-power imbalance section, and the cooling speed response misalignment section according to the number set defined by the device mapping relationship table, screens the behavior identification information that has been determined to be propulsion imbalance, pressure-power deviation, and cooling response delay, and incorporates it into the mapping field under the corresponding device identification code to obtain the device abnormal behavior label; The anomaly summary sub-module performs itemized calculations on the number of behavior label types and the number of sections corresponding to the device identification code based on the device abnormal behavior label, sets the total frequency of label types and the number of mapped sections as independent indicators, and uses the formula: ; Calculate the behavior anomaly index of device z, compare it with the abnormal benchmark index of the roadheader, and obtain the abnormal operation monitoring result of the roadheader; Wherein, represents the number of behavior labels recorded by device z in section u, represents the number of identifications of device z in behavior type v, is the total number of sections mapped by the device, is the total number of types of behavior labels.

[0013] A coal mine intelligent mining monitoring method, which is executed based on the above coal mine intelligent mining monitoring system, includes the following steps: S1: Obtain the propulsion displacement sequence of the propulsion cylinder and the records of the resistance sensor, calculate the change gradient coefficients of the propulsion rate and the resistance, screen the sections where the load increases but the propulsion does not match, and generate the propulsion load offset section; S2: Extract the hydraulic cylinder pressure change and the motor output power fluctuation in the propulsion load offset section, compare the pressure-power stability reference value section by section, identify the area where the pressure and power are imbalanced, and obtain the pressure-power imbalance section; S3: Obtain the cooling pump speed and the water temperature change at the cooling pump start time point corresponding to the pressure-power imbalance section, identify the misalignment section where the cooling response lags behind the speed recovery, and obtain the cooling speed response misalignment section; S4: Based on the cooling speed response misalignment section, collect the temperature drop change and the coolant flow rate, judge whether the temperature-flow ratio sequence is less than the temperature-conduction response reference ratio, screen the section numbers that do not reach the benchmark, and obtain the cooling conduction efficiency delay section; S5: Based on the numbers of the cooling conduction efficiency delay sections, extract the corresponding unique device identification codes, assign corresponding device abnormal behavior labels to the identification codes, and generate the abnormal operation monitoring results of the roadheader.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by collecting the sequences of the advancing displacement and resistance change of the roadheader cylinders, calculating the gradient changes of the advancement and resistance, comparing the sections where the advancement and load do not match, and further analyzing the asynchronous behavior of the pressure and motor power, the coupling relationship between the hydraulic system and the power system is extracted layer by layer, and the abnormal responses during the advancement process are gradually revealed. For the coupling of the start and stop of the cooling pump and the rotation speed of the main drive motor, combined with the water temperature change curve, the misaligned segments where the cooling response lags behind the mechanical return to stability are located, and it is clear that there is a response delay in the equipment when dealing with temperature rise. At the same time, using the ratio of the temperature drop rate to the coolant flow rate, the sections with a decrease in heat conduction efficiency are further screened, so as to identify the cooling efficiency problems caused by the mismatch between the fluid dynamics and the heat conduction response. During the output process of abnormal behaviors, a multi-dimensional mapping relationship between the device numbers and the abnormal labels is constructed, the behavior frequencies and the section coverage degrees shown by the device in each dimension are extracted, and they are converted into behavior abnormal indicators to realize the quantitative expression of the operating health status of the roadheader equipment. Through the cross-analysis of four dimensions: the advancing load, the pressure power, the rotation speed cooling, and the temperature-flow response, an abnormal identification path covering the complete link of the advancing power matching, the energy efficiency transfer stability, and the cooling efficiency is formed. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is the system flow chart of the present invention; Figure 2 is the flow chart of the advancement identification module of the present invention; Figure 3 is the flow chart of the pressure-power imbalance identification module of the present invention; Figure 4 is the flow chart of the rotation speed-cooling linkage module of the present invention; Figure 5 is the flow chart of the cooling efficiency delay module of the present invention; Figure 6 is the flow chart of the monitoring output module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0017] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0018] Please refer to Figure 1 , the present invention provides a technical solution: an intelligent coal mine mining monitoring system includes: The propulsion identification module obtains the propulsion displacement sequence of the tunneling cylinder and the records of the resistance sensor, calculates the propulsion rate and the change gradient coefficient of the resistance, screens the sections where the load increases but the propulsion does not match, and generates the propulsion load offset section. The pressure-work imbalance identification module extracts the changes in the hydraulic cylinder pressure and the fluctuations in the motor output power within the propulsion load offset section, compares the pressure-work stability reference value section by section, identifies the areas where the pressure and power are imbalanced, and obtains the pressure-power imbalance section. The rotation speed-cooling linkage module obtains the rotation speed of the cooling pump and the water temperature change at the time point when the cooling pump is turned on corresponding to the pressure-power imbalance section, identifies the misaligned section where the cooling response lags behind the rotation speed stabilization, and obtains the cooling-rotation speed response misalignment section. The cooling efficiency delay module, based on the cooling-rotation speed response misalignment section, collects the temperature drop change and the coolant flow rate, determines whether the temperature-flow ratio sequence is less than the temperature-conduction response reference ratio, screens the section numbers that do not reach the benchmark, and obtains the cooling conduction efficiency delay section. The monitoring output module, based on the numbers of the cooling conduction efficiency delay sections, extracts the corresponding unique device identification codes, assigns corresponding device abnormal behavior labels to the identification codes, and generates the abnormal monitoring results of the roadheader operation. The propulsion load offset section includes the propulsion stable section number, the continuously increasing resistance trend, and the propulsion resistance mismatch ratio. The pressure-power imbalance section includes the pressure increase amplitude sequence, the motor power fluctuation ratio, and the pressure-work synchronization deviation section. The cooling-rotation speed response misalignment section includes the rotation speed stabilization time period, the cooling response duration deviation, and the early stabilization number. The cooling conduction efficiency delay section includes the temperature drop rate sequence, the coolant flow rate increase amplitude, and the temperature-flow ratio insufficient number. The abnormal monitoring results of the roadheader operation include the unique device identification code, the device operation mapping field, and the summary of abnormal behavior labels.

[0019] Please refer to Figure 2 , the propulsion identification module includes: The propulsion data acquisition sub-module obtains the propulsion displacement sequence of the tunneling cylinder and the records of the resistance sensor, extracts the displacement data and resistance data corresponding to the corresponding time period in the record sequence, and plots the resistance data according to the propulsion time axis to obtain the resistance time curve data; First, it is necessary to rely on the layout of the sensor system of the tunneling equipment. The displacement sensor is installed at the end of the axis of the hydraulic propulsion cylinder, and the resistance sensor is set at the docking position of the tunneling head or the cutter head. The PLC acquisition system is used to achieve the second-level data acquisition of the propulsion process. The obtained propulsion displacement data is the elongation stroke data of the hydraulic cylinder per second, with the unit of mm, and the resistance data is the instantaneous reaction force in the hydraulic system, with the unit of kN. In the implementation scenario, such as a tunneling machine is arranged in a coal mine working face, the data sequence recorded by the propulsion sensor is as follows: displacement sequence , resistance sequence , and the corresponding time node is . After real-time data recording, the system outputs the resistance data according to the time sequence and plots the resistance curve, that is, a line graph with time as the horizontal axis and resistance as the vertical axis, which is used to reflect the force change process per unit time during the propulsion process. In this process, no numerical operation or function operation is introduced, and the coordinate system curve is established only based on the horizontal and vertical values corresponding to the recording points, and the graph is drawn by linear connection. This operation can be regarded as the data preparation stage during the propulsion process to obtain the resistance time curve data.

[0020] The rate calculation sub-module extracts the propulsion rate sequence composed of the change in propulsion displacement per second and the resistance rate sequence composed of the change in resistance per second based on the resistance time curve data, and uses the formula: , ; Calculate the propulsion rate change gradient coefficient at the th second and the resistance change gradient coefficient at the th second respectively, and integrate to obtain the gradient change coefficient list; Among them, represents the propulsion rate change gradient coefficient at the th second. The superscript indicates that this gradient coefficient corresponds to the propulsion rate, and the subscript represents the th time point; represents the resistance change gradient coefficient at the th second. The superscript indicates that this gradient coefficient corresponds to the resistance change, and the subscript represents the th time point; represents the propulsion rate at the th second; represents the The resistance in seconds; Indicates the Time in seconds; Indicates the change in the propulsion rate between two adjacent seconds; Indicates the change in resistance between two adjacent seconds; Indicates the time interval between two adjacent seconds; Indicates from the 1st second to the Sum of the absolute gradient values of the change in the propulsion rate; Indicates from the 1st second to the Sum of the squared gradients of the change in resistance; Indicates the summation variable index for iteratively calculating the gradient change within a time period; Indicates the number of samples in the propulsion rate sequence, i.e., the total number of time periods; Indicates the current time point index for locating the time period corresponding to the current gradient coefficient.

[0021] Extract the change value of the propulsion displacement per second And the change value of the resistance , the propulsion rate sequence is mm / s, the resistance change sequence is kN / s, respectively calculate the gradient coefficient of the change in the propulsion rate per second and the gradient coefficient of the change in the resistance. The formula for the gradient coefficient of the change in the propulsion rate is ; Substitute the propulsion rate, assuming the time step is 1 second, and calculate as follows: ; The formula for calculating the gradient coefficient of the change in the resistance is ; Let , with the unit of kN / s, calculate : ; Then calculate : ; Establish a list of gradient change coefficients through the above two sets of gradient coefficients for the next step of identifying the stable propulsion and resistance increase sections. The innovation points of the two formulas are to enhance the sensitivity of change judgment and trend evaluation by combining the instantaneous change term with the cumulative average / squared term, and then establish a gradient state stability evaluation system.

[0022] The advantage of the formula is that by jointly introducing the average term of the propulsion rate change and the sum of squares of the resistance change, the system can not only capture short-term mutations but also consider the cumulative trend, effectively distinguishing the composite state of abnormally stable propulsion and abnormally increased load.

[0023] Based on the gradient change coefficient list, the offset screening sub-module makes judgments according to the set propulsion rate change threshold and resistance rate change threshold. It calculates the ratio of the increase in propulsion displacement to the resistance within the section in the identified numbered section, and calls the historical average increase ratio in the continuous section for comparison. It identifies the record numbers with an increase ratio greater than the average value, screens the section numbers where the propulsion rate is lower than the propulsion rate change threshold and the resistance exceeds the resistance rate change threshold, and generates the propulsion load offset section. The propulsion rate change gradient is compared with the resistance change gradient and a preset threshold. The propulsion rate change threshold is set to 0.1 mm / s², and the resistance change threshold is set to 10 kN / s². The data paragraph numbers that meet and are screened. For example, when , , , they meet the conditions. The number 2 is marked as the section with stable propulsion rate but continuously increasing resistance. Subsequently, the increase values of the propulsion displacement data and the resistance data are collected within the numbered section. Suppose the displacement at the 2nd second is 8.2 mm, and at the 3rd second is 12.4 mm, and the resistance increases from 32.1 kN to 36.4 kN. The increase ratios are: the increase in propulsion displacement is 4.2 mm, the increase in resistance is 4.3 kN, and the increase ratio is 1.02. Comparing with the average increase ratio of 1.00 in five consecutive cycle sections, it is judged that the current section is a section with a deviated increase ratio. Further, it is judged whether it is a section with unmatched propulsion. Based on the condition that the increase ratio is greater than the average value and the propulsion rate is lower than 2 mm / s, and the current propulsion rate is 4.2 mm / s, it is judged that it does not belong to the section with unmatched propulsion; if the propulsion rate is changed to 1.8 mm / s, it is judged as a match and included in the final result to obtain the propulsion load offset section.

[0024] Please refer to Figure 3 , the pressure-work imbalance identification module includes: The pressure-work data extraction sub-module extracts the records of the hydraulic cylinder pressure change and the motor output power fluctuation data within the propulsion load offset section, pairs the output sequences of the pressure sensor and the motor power output sequence in a time-synchronized manner to obtain the original pressure-power sequence set. First, establish a corresponding time index structure within the propulsion load offset section. The time period of this section can be set to one cycle every 30 seconds. For example, if the set paragraph is 10 minutes, it can be divided into 20 sub-sections. In each sub-section, collect the output value of the pressure sensor on the hydraulic cylinder and the power data at the output end of the motor. The pressure value is measured in MPa, and the power value is measured in kW. For example, the pressure of the hydraulic cylinder recorded at the initial moment of a certain section is 12.4 MPa, and at the end it is 13.7 MPa, then the pressure change in this section is 1.3 MPa. At the same time, the motor power in this section rises from 41.2 kW to 43.5 kW, and the power fluctuation is 2.3 kW. When collecting data, records with three or more consecutive missing data points need to be excluded. After screening according to this condition, retain the valid data sequence, and number and identify each section of data. The unified format is T1, T2... In this way, the pressure change and power fluctuation data synchronization alignment processing at the data level of each propulsion load offset section can be realized, and a data structure foundation for subsequent calculations can be laid. The obtained result is the original sequence set of pressure and power.

[0025] Based on the original sequence set of pressure and power, the pressure-work ratio calculation sub-module divides sections at a fixed time interval, calculates the mean value of the pressure sequence in each section, records the mean difference between the current section and the previous section to form the pressure increase, calculates the mean difference of the motor output power to form the power increase, and represents the pressure-work synchronization relationship in the corresponding time period by the ratio between the pressure increase value and the power increase value, obtaining the pressure-work synchronization ratio sequence; After calling the original sequence set of pressure and power, calculate the data in each divided time period. Taking 10 seconds for each section as an example, if the sampling frequency is set to 10 Hz, then each section contains 100 data points. First, calculate the mean value of the hydraulic cylinder pressure data in each section. For example, the pressure data of a certain section is {12.3, 12.4, 12.4, 12.5...} with a total of 100 points, and the mean value is 12.45 MPa. Similarly, for the motor power sequence such as {42.1, 42.3, 42.4, 42.5...}, take its mean value as 42.40 kW. Then calculate the mean difference between this section and the previous section. If the pressure mean value of the previous section is 12.15 MPa, then the increase is 0.30 MPa. If the power mean value is 42.00 kW, then the power increase is 0.40 kW. Furthermore, take their ratio as 0.30÷0.40 = 0.75 as the pressure-work synchronization ratio of this section. Calculate the ratios of all sections in turn to form a ratio sequence in the form of a one-dimensional array, such as {0.75, 0.82, 0.60, 0.95...}. Each ratio represents the change consistency characteristics of pressure and power in each time period, and the obtained result is the pressure-work synchronization ratio sequence.

[0026] The imbalance section screening sub-module compares the pressure-power ratio of each time period with the set stable reference ratio of pressure-power load one by one according to the pressure-power synchronization ratio sequence, marks the section numbers where the ratio deviates from the stable reference ratio range, extracts consecutive numbered time periods, and obtains the pressure-power imbalance section; According to the pressure-power synchronization ratio sequence, judge the ratio of each time period with the set stable reference ratio of pressure-power load. Set the stable reference ratio to 0.85, and the deviation limit for the differentiation range is ±0.10. Then the stable interval is [0.75, 0.95]. If the ratio is within this interval, it is judged as normal, otherwise it is a deviation. For example, in the ratio sequence {0.73, 0.82, 0.98, 0.94, 0.66, 0.87}, 0.73, 0.98, and 0.66 are deviation values. Record the corresponding time period numbers such as T2, T3, T5, and form a set of these numbers. If the numbers are consecutive, such as T2, T3, they can be merged into a continuous deviation section. Organize the continuous deviation sections in this way as: section A (T2 - T3), section B (T5), output all the deviation section numbers as the record of abnormal pressure-power matching sections, and further output the result as the pressure-power imbalance section.

[0027] Please refer to Figure 4 , the rotational speed-cooling linkage module includes: The cooling rotational speed extraction sub-module obtains the opening time points of the cooling pumps corresponding to the pressure-power imbalance section, extracts the rotational speed data sequence of the main drive motor before and after the start of the corresponding cooling pumps, compares the absolute value of the change between adjacent rotational speed points within a continuous time period with the set rotational speed fluctuation threshold, marks the time periods exceeding the threshold as the fluctuation state, and the time periods below or equal to the threshold and continuously meeting the conditions as the stable state, and obtains the rotational speed fluctuation recovery section; To obtain the starting time point of the cooling pump corresponding to the pressure-power imbalance section, it is necessary to call the control signal record of the cooling pump from the equipment operation log and extract the starting moment information. For example, the recorded starting time of the cooling pump is 14:05:10. Subsequently, the main drive motor speed sequences for 60 seconds before and after the start of the cooling pump are extracted, and the speed values per second are continuously analyzed to determine whether the motor is in a fluctuating or stable state. To this end, a speed fluctuation determination threshold of ±3 rpm is set. If the change in adjacent speed values is greater than 3 rpm within 5 consecutive seconds, this section is classified as a fluctuating state. If the change is less than or equal to 3 rpm within 10 consecutive seconds, the speed is considered to have reached stability. For example, in the section before the start of the cooling pump, if the motor speeds are 1482 rpm, 1487 rpm, 1485 rpm, 1489 rpm, 1484 rpm, and 1486 rpm from 14:04:50 to 14:04:55 respectively, it can be observed that the fluctuation amplitude between consecutive values exceeds 3 rpm, so it is determined to be in a fluctuating state. Continuing to observe the time period from 14:05:00 to 14:05:10, if the speeds are 1483 rpm, 1481 rpm, 1480 rpm, 1481 rpm, 1480 rpm, and 1479 rpm respectively, all within the fluctuation threshold, it is determined to be in a stable state. Then, the continuous time period spanning from fluctuation to stability is extracted as the fluctuation-to-stabilization section. At the same time, it is necessary to match the position of the cooling pump start action to clarify whether the speed response overlaps with the cooling behavior, and finally obtain the speed fluctuation recovery section.

[0028] The stabilization response recording sub-module records, according to the speed fluctuation recovery section, the time period from the start of the fluctuating state to continuously meeting the stable state within each section, which is defined as the stabilization time period, and synchronously extracts the time length between the starting point and the stable point of the temperature drop in the cooling water temperature sequence as the cooling response duration to generate a set of stabilization response times; Record the complete time experienced from the rotational speed fluctuation state to the stable state within each segment as the stabilization time period. Then, call the collected cooling water temperature data in the same time axis. Taking the cooling pump startup time as the starting reference point for temperature response, identify the interval time between the starting value of temperature drop and the starting point of the stable segment in the temperature sequence as the cooling response duration. The criterion for temperature stability is defined as the temperature fluctuation being less than 0.1 °C within 10 consecutive seconds. If the temperature starts to drop from 43.2 °C after the cooling pump starts at 14:05:10, drops to 41.0 °C at 14:05:50, and stabilizes at 40.9 °C after 14:06:00, then record the starting point of the cooling response as 14:05:10, the ending point as 14:06:00, and the response duration as 50 seconds. If the stabilization time period for the corresponding segment is from 14:05:15 to 14:05:45, then its duration is 30 seconds. Repeat the processing for other segments in this way, accumulate the stabilization time and cooling response time under multiple time periods from the data record, and correspond them with the segment numbers to construct a stabilization response time set.

[0029] Based on the stabilization response time set, the response misalignment identification sub-module uses the formula: ; Calculate to obtain the normalized stabilization response deviation for the th time period. Classify the time period numbers with the deviation greater than zero and the stabilization normalization value less than the cooling normalization value into the response lag category to obtain the cooling rotational speed response misalignment segments; Among them, represents the normalized stabilization response deviation for the th time period, represents the rotational speed stabilization time for the nd segment, represents the cooling response duration for the th segment, represents the maximum value among all the stabilization times, represents the maximum value among all the cooling response durations.

[0030] Call the constructed stabilization response time set. To eliminate the inconsistency caused by the differences in time units and magnitudes, divide the stabilization time and cooling response time of each segment by the maximum value in their respective sequences for normalization. Suppose in the th segment, the stabilization time is 30 seconds and the cooling response time is 50 seconds. The maximum stabilization time in all samples is 45 seconds, and the maximum cooling response time is 65 seconds. Then, after normalization, they are and respectively. Furthermore, according to the formula ; it can be obtained that the normalized deviation for this segment is ​​​​, Repeat the same calculation for multiple paragraphs. For example, the stabilization time of the second paragraph is 40 seconds, and the cooling response is 55 seconds. After normalization, they are 0.8889 and 0.8462 respectively, and the deviation is 0.0427. The stabilization time of the third paragraph is 45 seconds, and the cooling response is 40 seconds. After normalization, they are 1.0000 and 0.6154 respectively, and the deviation is 0.3846. The stabilization time of the fourth paragraph is 20 seconds, and the cooling response is 35 seconds. After normalization, they are 0.4444 and 0.5385 respectively, and the deviation is 0.0941. The stabilization and cooling response times of the fifth paragraph are both 36 seconds, so after normalization, they are both 0.8000 and the deviation is 0. Statistically analyze the normalized deviation values of all paragraphs. When the normalized stabilization time is less than the cooling response time and the deviation value is greater than 0, mark the corresponding paragraph number as a cooling response lag. Finally, obtain the misaligned section of the cooling speed response. The benefit of the formula is that by introducing normalization operations and difference calculations, a unified comparable standard is constructed, enhancing the time discrimination ability when the cross-variable dimensions are out of sync. The result shows that in multiple paragraphs where the normalized stabilization time is less than the cooling response duration, there is a response lag phenomenon, which is confirmed as a misaligned response section.

[0031] Please refer to Figure 5 , The cooling efficiency delay module includes: Based on the misaligned section of the cooling speed response, the temperature flow ratio extraction sub-module collects the continuous temperature drop change sequence recorded by the temperature diversion sensor in the cooling pipeline and the corresponding coolant flow rate data sequence, extracts the temperature drop rate and the growth amplitude of the coolant flow rate within each cooling section time range, calculates the temperature difference and the flow rate difference within the specified time respectively, and generates a temperature flow ratio sequence; Based on the misaligned section of the cooling speed response, it is necessary to extract the cooling pipeline temperature diversion sensor records and coolant flow rate data corresponding to the time range of this response section. First, it is necessary to clarify the start and end time points of the misaligned section. For example, the time of the misaligned section is from 202 seconds to 234 seconds. During this time period, the temperature data collected from the coolant pipeline temperature diversion sensor is sampled once per second to form a sequence The unit is degrees Celsius, and the coolant flow rate is obtained by a turbine flowmeter and recorded once per second to form a sequence , The unit is L / min. Secondly, it is necessary to calculate the average drop rate of the continuous drop section in the temperature sequence. Use the difference between the current point and the previous moment point as the instantaneous drop speed and take the average of the whole section. For example, from 202 seconds to 212 seconds, the temperature drops from 75.4 to 73.0, then the temperature drop rate is calculated as , Similarly, the coolant flow rate increases from 2.2 liters per minute to 3.8 liters per minute, and its growth amplitude is , Calculate the ratio of the temperature drop rate to the growth amplitude of the flow rate to obtain the temperature flow ratio value of , After performing this operation on each cooling response misaligned section, a temperature flow ratio sequence is formed It should be noted that during the acquisition of the temperature drop rate, when there is a short-term abnormality of the temperature sensor or data is missing, the linear interpolation method of the valid points on both sides should be used to fill the gap. For example, if there is no value at 204 seconds and 203 seconds is 74.9 and 205 seconds is 74.3, then the estimated value of 204 seconds is To ensure the continuity of the sequence, the temperature drop rate and the coolant flow rate growth value adopt a segment-by-segment division strategy. Usually the segment length is set to 10 seconds to form multiple short segment ratios and generate a temperature-flow ratio sequence.

[0032] The delay section screening submodule compares each section ratio with the set temperature flow conduction response reference ratio based on the temperature flow ratio sequence, marks the numbered sections that are less than the reference ratio, collects the consecutive numbers that do not reach the reference value, screens and summarizes the corresponding time period range, and obtains the cooling conduction efficiency delay section; According to the temperature-flow ratio sequence, it is necessary to determine whether each section meets the conduction efficiency requirements. First, the reference ratio of the temperature-flow conduction response is set. The reference value needs to be calculated based on the performance data of the cooling system in the initial commissioning stage or rated operating state. For example, in steady-state operation, the coolant temperature drop rate is stable at , the corresponding coolant flow rate increase is , so the reference ratio is , considering the equipment fluctuation tolerance, the ratio is lowered to As the benchmark ratio of the temperature flow conduction response, this value is fixed and used as the basis for subsequent judgment conditions. After that, each value in the temperature flow ratio sequence is traversed and compared segment by segment. For each item in the temperature flow ratio sequence ,implement The judgment logic is as follows: For example, the temperature-flow ratio sequence formed in a certain response misalignment section is , it can be seen that the first three items are less than the benchmark ratio, and the last two items do not meet the conditions. The corresponding time numbers are segment 1 to segment 5. Then segment 1, segment 2, and segment 3 all meet the screening conditions. The record number set is , where continuous numbers can be merged into an overall numbering segment. If each time interval is 10 seconds, the actual time range represented by the numbering segment is 202 seconds to 232 seconds, indicating a complete segment with a relatively delayed coolant temperature response. For non-continuous numbers, their original segment numbers are retained and recorded separately. During the judgment process, if a certain temperature-flow ratio value in the data source is missing or zero, the segment is automatically removed and does not participate in the screening. After the judgment is completed, all numbering segments that meet the screening conditions are merged and sorted, and finally all numbering segments that do not reach the temperature-flow conduction response benchmark ratio are obtained. The time period set is summarized and mapped to the original time axis of the cooling response misalignment to obtain the cooling conduction efficiency delay segment.

[0033] See also Figure 6 , the monitoring output module includes: The identification mapping sub-module extracts the unique device identification code corresponding to the number based on the number of the cooling conduction efficiency delay section, calls the operation identification field included in the device operation status record data, and establishes a mapping set from the section number to the device identification according to the corresponding manner between the number and the device code, and obtains the device mapping relation table; First, it is necessary to collect the temperature response values of the temperature control sensors on the cooling pipelines in each section of the roadheader. The response data is sampled and numbered through a set time window. For example, the numbers are Z001, Z002, etc., representing the cooling response delay sections under different time periods or spatial positions. If the temperature change delay exceeds the average value of the normal response by more than 2.5 seconds, it is identified as a delay section. Then, the unique device identification code related to each delay number is called from the device dataset. This identification code can be obtained from the main control record table of the roadheader. For example, device D_3185 corresponds to the Z001 section number. After that, the identification fields in the device operation record log are read, such as the current power, temperature change curve, rotation speed and other operation indicators of the device. By comparing them one by one with the section number, a mapping structure between the number and the device code is constructed. Finally, a mapping set from the section number to the device identification code is formed, denoted as the section-device mapping relation table, such as Z001→D_3185, Z002→D_3190, Z003→D_3221. In the example, the sensor temperature response delay in the Z001 section is 3.1 seconds, which is greater than the set reference value of 2.5 seconds, so it is determined as a delay section. Its corresponding device code is D_3185, and the mapping relation is recorded as Z001-D_3185.

[0034] The label aggregation sub-module calls the behavior label items in the propulsion load offset section, the pressure-power imbalance section, and the cooling rotation speed response misalignment section according to the number set defined by the device mapping relation table, screens the behavior identification information that has been determined to be propulsion imbalance, pressure-power deviation, and cooling response delay, and classifies it into the mapping field under the corresponding device identification code to obtain the device abnormal behavior label; Call data items in three types of sections: propulsion load offset, pressure-power imbalance, and cooling speed response misalignment, and extract the tags identified as abnormal behaviors. For example, behavior events such as "propulsion force suddenly increases by more than 10 kN" identified in propulsion load offset and "pressure fluctuation exceeds 15%" identified in pressure-power imbalance. If these behavior events match the section numbers in the mapping table, then record the behavior tags in the corresponding device mapping fields. Perform tag aggregation operations on each device. The aggregation logic is to extract all matching section tags under the same device identification code, classify them by behavior type, and count their frequencies. For example, in the mapping section Z001 of device D_3185, there are 2 propulsion offset behaviors and 1 cooling response delay behavior. Then the distribution of behavior tags under D_3185 is: propulsion offset = 2, cooling delay = 1, pressure-power deviation = 0. This aggregation process is judged according to the matching accuracy, and only the tag items with exactly corresponding section numbers can be recorded. If there are some tags across sections, they will not be recorded. After completing the tag aggregation, obtain the frequency data of the identified abnormal behavior tags for each device.

[0035] The abnormal summary sub-module calculates item by item the number of behavior tag types and the number of sections corresponding to the device identification code based on the device abnormal behavior tags. Set the total frequency of tag types and the number of mapped sections as independent indicators, and use the formula: ; Calculate the behavior anomaly index of device z, compare it with the abnormal benchmark index of the roadheader, and obtain the abnormal operation monitoring result of the roadheader; Among them, represents the number of behavior tags recorded by device z under section u, represents the number of identifications of device z under behavior type v, is the total number of sections mapped by the device, is the total number of types of behavior tags.

[0036] Obtain the total number of behavior tags, the number of sections they are mapped to, and the number of tag types under each device identification code, and calculate their average performance by normalizing each value respectively. Suppose device Z1 has a total of 7 abnormal tags in 3 sections, and among them, there are 3 types of behavior types: propulsion offset, pressure-power deviation, and cooling response delay, with occurrence times of 4 times, 2 times, and 1 time respectively. Then the behavior tag frequency array is ; = [4, 2, 1], the tag type frequency array is ; = [1, 1, 1], the number of sections a = 3, the number of tag types b = 3. Substitute the above values into the formula: ; The abnormal behavior index of device Z1 is 1.665. If the abnormal benchmark index of the roadheader is 1.5, this value is higher than the benchmark, indicating that there is a trend of abnormal behavior aggregation in the device. Based on this, the abnormal operation monitoring result of the roadheader can be obtained.

[0037] The benefit of the formula is that by evenly integrating the distribution frequency of device abnormal behavior in the spatial section and the richness of behavior types, the direct superposition risk between different unit participation items is eliminated, ensuring the reasonable expression of the abnormal intensity in the device label distribution structure, so as to obtain a more representative abnormal performance degree in the systematic judgment. This result indicates that the operating state of device Z1 deviates from the normal operating range, and its operating process and device health status need to be further diagnosed.

[0038] A coal mine intelligent mining monitoring method, which is executed based on the above coal mine intelligent mining monitoring system, includes the following steps: S1: Obtain the propulsion displacement sequence of the tunneling cylinder and the records of the resistance sensor, calculate the change gradient coefficients of the propulsion rate and the resistance, screen the sections where the load increases but the propulsion does not match, and generate the propulsion load offset section; S2: Extract the changes in the hydraulic cylinder pressure and the fluctuations in the motor output power within the propulsion load offset section, compare the pressure-work stability reference value section by section, identify the areas where the pressure and power are unbalanced, and obtain the pressure-power imbalance section; S3: Obtain the cooling pump speed and the water temperature change at the time point when the cooling pump is turned on corresponding to the pressure-power imbalance section, identify the misaligned section where the cooling response lags behind the speed stabilization, and obtain the cooling speed response misaligned section; S4: Based on the cooling speed response misaligned section, collect the temperature drop change and the coolant flow rate, judge whether the temperature-flow ratio sequence is less than the temperature-conduction response reference ratio, screen the section numbers that do not reach the benchmark, and obtain the cooling conduction efficiency delay section; S5: Based on the numbers of the cooling conduction efficiency delay sections, extract the corresponding device unique identification codes, assign the corresponding device abnormal behavior labels to the identification codes, and generate the abnormal operation monitoring result of the roadheader.

[0039] The above is only the preferred embodiment of the present invention, and it is not intended to limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical content of the technical solution of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A coal mine intelligent mining monitoring system, characterized in that, The system includes: The propulsion recognition module obtains the propulsion displacement sequence of the tunneling cylinder and the records of the resistance sensor, calculates the propulsion rate and the change gradient coefficient of the resistance, screens the sections where the load increases but the propulsion does not match, and generates the propulsion load offset section; The pressure-work imbalance recognition module extracts the changes in the hydraulic cylinder pressure and the fluctuations in the motor output power within the propulsion load offset section, compares the pressure-work stability reference value section by section, identifies the areas where the pressure and power are imbalanced, and obtains the pressure-power imbalance section; The rotational speed-cooling linkage module obtains the rotational speed of the cooling pump and the change in the water temperature at the time point when the cooling pump is turned on corresponding to the pressure-power imbalance section, identifies the misaligned section where the cooling response lags behind the rotational speed stabilization, and obtains the cooling-rotational speed response misalignment section; The cooling efficiency delay module, based on the cooling-rotational speed response misalignment section, collects the temperature drop change and the coolant flow rate, determines whether the temperature-flow ratio sequence is less than the temperature-conduction response reference ratio, screens the section numbers that do not reach the benchmark, and obtains the cooling conduction efficiency delay section; The monitoring output module, based on the numbers of the cooling conduction efficiency delay sections, extracts the corresponding unique device identification codes, assigns corresponding device abnormal behavior labels to the identification codes, and generates the abnormal operation monitoring results of the roadheader; 2. The coal mine intelligent mining monitoring system according to claim 1, characterized in that The propulsion load offset section includes the propulsion stable section number, the continuously increasing trend of the resistance, and the propulsion resistance mismatch ratio. The pressure-power imbalance section includes the pressure increase sequence, the motor power fluctuation ratio, and the pressure-work synchronization deviation section. The cooling-rotational speed response misalignment section includes the rotational speed stabilization time period, the cooling response duration deviation, and the early stabilization number. The cooling conduction efficiency delay section includes the temperature drop rate sequence, the coolant flow rate increase, and the temperature-flow ratio insufficient number. The abnormal operation monitoring results of the roadheader include the unique device identification code, the device operation mapping field, and the summary of abnormal behavior labels; 3. The coal mine intelligent mining monitoring system according to claim 1, wherein The propulsion recognition module includes: The propulsion data acquisition sub-module obtains the propulsion displacement sequence of the tunneling cylinder and the records of the resistance sensor, extracts the displacement data and the resistance data corresponding to the corresponding time period in the record sequence, and plots the resistance data as a resistance curve graph according to the propulsion time axis to obtain the resistance time curve data; The rate calculation sub-module, based on the resistance time curve data, extracts the change in the propulsion displacement per second to form a propulsion rate sequence, and extracts the change in the resistance per second to form a resistance rate sequence, using the formula: ， ； Calculate the gradient coefficient of the propulsion rate change per second and the gradient coefficient of the drag force change per second respectively, and integrate them to obtain a list of gradient change coefficients; Among them, represents the propulsion rate at the th second, represents the resistance at the th second, represents the time at the th second, represents the change in adjacent propulsion rates, represents the change in adjacent resistances, represents the adjacent time interval, is the total number of data points of the propulsion rate, represents the summation variable index; The offset screening sub-module, based on the gradient change coefficient list, makes a judgment according to the set propulsion rate change threshold and the resistance rate change threshold, calculates the increase ratio of the propulsion displacement and the resistance within the section in the identified numbered section, calls the average increase ratio in the continuous section for comparison, identifies the record numbers with an increase ratio greater than the average value, screens the section numbers where the propulsion rate is lower than the propulsion rate change threshold and the resistance exceeds the resistance rate change threshold, and generates the propulsion load offset section; 4. The coal mine intelligent mining monitoring system according to claim 3, wherein, The pressure-work imbalance recognition module includes: The pressure-work data extraction sub-module extracts the records of the changes in the hydraulic cylinder pressure and the fluctuations in the motor output power within the propulsion load offset section, pairs the pressure sensor output sequence and the motor power output sequence in a time-synchronized manner to obtain the pressure-power original sequence set; The pressure-power ratio calculation submodule is based on the pressure-power original sequence set, divides the sections into fixed time intervals, performs mean calculation on the pressure sequence in each section, records the mean difference between the current section and the previous section to form the pressure increase, calculates the mean difference of the motor output power to form the power increase, and uses the ratio between the pressure increase value and the power increase value to represent the pressure-power synchronization relationship in the corresponding time period, thereby obtaining a pressure-power synchronization ratio sequence; The unbalanced section screening submodule compares the pressure-power ratio of each time period with the set pressure-power load stable reference ratio section by section according to the pressure-power synchronization ratio sequence, marks the section number where the ratio deviates from the stable reference ratio interval, extracts the consecutive numbered time periods, and obtains the pressure-power unbalanced section.

5. The coal mine intelligent mining monitoring system according to claim 4, wherein, The speed cooling linkage module includes: The cooling speed extraction submodule obtains the cooling pump start-up time point corresponding to the pressure-power imbalance section, extracts the speed data sequence of the main drive motor before and after the corresponding cooling pump is started, compares the absolute value of the change of adjacent speed points in a continuous time period with the set speed fluctuation threshold, marks the time period exceeding the threshold as a fluctuation state, and the time period below or equal to the threshold and continuously meeting the conditions as a stable state, and obtains the speed fluctuation recovery section; The stabilization response recording submodule records the time period from the start of the fluctuation state to the continuous stable state in each section according to the speed fluctuation recovery section, which is defined as the stabilization time period, and simultaneously extracts the time length between the temperature drop starting point and the stable point in the cooling water temperature sequence as the cooling response time length to generate a stabilization response time set; The response misalignment identification submodule adopts the formula based on the return-to-stabilization response time set: ； Operation to obtain the normalized steady-state response deviation in the th time period, classify the time period numbers with deviation greater than zero and steady-state normalization value less than cooling normalization value into the response lag classification, and obtain the cooling speed response misalignment section; Among them, represents the stabilized response deviation after normalization in the th time period, represents the rotational speed stabilization time in the th segment, represents the cooling response duration in the th segment, represents the maximum value among all the stabilization times, represents the maximum value among all the cooling response durations.

6. The coal mine intelligent mining monitoring system according to claim 5, characterized in that The cooling efficiency delay module includes: The temperature-flow ratio extraction submodule collects the continuous temperature drop change sequence and the corresponding coolant flow rate data sequence recorded by the temperature guide sensor in the cooling pipeline based on the cooling speed response dislocation section, extracts the temperature drop rate and the coolant flow rate growth range within each cooling section time range, calculates the temperature difference and the flow rate difference within the specified time, and generates a temperature-flow ratio sequence; The delay section screening submodule compares each section ratio with the set temperature flow conduction response reference ratio based on the temperature flow ratio sequence, marks the numbered sections that are less than the reference ratio, collects the consecutive numbers that do not reach the reference value, screens and summarizes the corresponding time period range, and obtains the cooling conduction efficiency delay section.

7. The coal mine intelligent mining monitoring system according to claim 6, wherein The monitoring output module comprises: The identification mapping submodule extracts the unique device identification code corresponding to the number based on the number of the cooling conduction efficiency delay section, calls the operation identification field in the equipment operation status record data, establishes a mapping set from the section number to the equipment identification according to the correspondence between the number and the equipment code, and obtains the equipment mapping relationship table; The label collection submodule calls the behavior label items in the propulsion load offset section, the pressure power imbalance section, and the cooling speed response dislocation section according to the number set defined in the equipment mapping relationship table, and screens the behavior identification information that has been determined to be propulsion imbalance, pressure power deviation, and cooling response delay, and classifies it into the mapping field under the corresponding equipment identification code item to obtain the equipment abnormal behavior label; The anomaly summary sub-module calculates item by item the number of behavior label types and the number of sections corresponding to the device identification code based on the device anomaly behavior label, sets the total frequency of label types and the number of mapped sections as independent indicators, and uses the formula: ； Calculate the behavior anomaly index of equipment z, compare it with the benchmark anomaly index of the roadheader, and obtain the monitoring result of the abnormal operation of the roadheader; Among them, represents the number of behavior labels recorded by device z under section u, represents the number of identifications of device z under behavior type v, is the total number of sections mapped by the device, is the total number of types of behavior labels.

8. A method for monitoring intelligent coal mine exploitation, characterized in that, Execute according to the coal mine intelligent mining monitoring system described in any one of claims 1-7, including the following steps: S1: Obtain the propulsion displacement sequence of the tunneling cylinder and the records of the resistance sensor, calculate the change gradient coefficients of the propulsion rate and the resistance, screen the sections where the load increases but the propulsion does not match, and generate the propulsion load offset section; S2: Extract the changes in the hydraulic cylinder pressure and the fluctuations in the motor output power within the propulsion load offset section, compare the pressure-power stability reference value section by section, identify the areas where the pressure and power are unbalanced, and obtain the pressure-power imbalance section; S3: Obtain the cooling pump speed and the water temperature change at the time point when the cooling pump is turned on corresponding to the pressure-power imbalance section, identify the misaligned section where the cooling response lags behind the speed recovery, and obtain the cooling speed response misaligned section; S4: Based on the cooling speed response misaligned section, collect the temperature drop change and the coolant flow rate, judge whether the temperature-flow ratio sequence is less than the temperature conduction response reference ratio, screen the section numbers that do not reach the benchmark, and obtain the cooling conduction efficiency delay section; S5: Based on the numbers of the cooling conduction efficiency delay sections, extract the corresponding device unique identification codes, assign the corresponding device anomaly behavior labels to the identification codes, and generate the monitoring result of the abnormal operation of the roadheader.

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