A coal mine intelligent mining monitoring system and method

Abstract

The present invention relates to the field of mining monitoring technology, specifically to a coal mine intelligent mining monitoring system and method, the system comprising: a propulsion identification module, a pressure-work imbalance identification module, a speed-cooling linkage module, a cooling efficiency hysteresis module, and a monitoring output module. In the present invention, by collecting the propulsion displacement and resistance change sequence of the tunneling cylinder, calculating the gradient change of propulsion and resistance, comparing the propulsion and load mismatch sections, and further analyzing the different steps of pressure and motor power, the coupling relationship between the hydraulic system and the power system is extracted layer by layer, and the abnormal response in the propulsion process is gradually revealed. In view of the coupling between the start and stop of the cooling pump and the speed of the main drive motor, combined with the water temperature change curve, the dislocation segment where the cooling response lags behind the mechanical stabilization is located, and it is clear that the equipment has a response delay when responding to temperature rise. The sections where the heat conduction efficiency decreases are screened to identify the cooling efficiency problems caused by the mismatch between the fluid dynamics and the heat 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 encompasses systems and methods for real-time collection, processing, and feedback of information on the operating environment, equipment operating status, and safety risks during the mining process. The core of this technology lies in achieving comprehensive monitoring and management of mining operations, encompassing key aspects such as personnel positioning, environmental parameter sensing, machinery operating status monitoring, and data transmission and storage.

[0003] Among them, the intelligent coal mining monitoring system refers to the coal resource mining link, focusing on 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 primarily 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 deployed at multiple points, the connections between them are weak, and they can only provide environmental data in a static or isolated state. They lack the ability to conduct a coordinated analysis of the dynamic behavior of equipment operation. During the advancement of tunneling equipment, existing systems are unable to effectively reflect the correlation between changes in propulsion load and the response of the hydraulic system and the output capacity of the motor, and are prone to missing key state nodes where propulsion is unstable or hydraulic output is abnormal. At the same time, monitoring of the cooling system is limited to recording the rising and falling trends of water temperature, ignoring the temporal relationship between water pump speed, liquid flow changes, and temperature response, and is unable to accurately identify potential hidden dangers such as delayed cooling response or decreased cooling efficiency. In addition, existing technologies have not established an effective data labeling mechanism, nor have they formed a mapping table between equipment behavior and status numbers. As a result, fault behavior can only be inferred manually, lacking structured output and automated identification capabilities. The above deficiencies may lead to abnormal hidden dangers remaining undetected for a long time during actual operation. For example, a short-term and drastic fluctuation in the hydraulic system pressure may not be identified as a risk signal, or a delayed response of the cooling system may be misjudged as a fluctuation in the external environment, 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 solution: A coal mine intelligent mining monitoring system includes:

[0007] The propulsion identification module obtains the propulsion displacement sequence of the tunneling cylinder and the resistance sensor records, calculates the change gradient coefficient of the propulsion rate and resistance, screens the sections where the load increases but the propulsion does not match, and generates the propulsion load offset section;

[0008] The pressure-power imbalance identification module extracts the hydraulic cylinder pressure change and the motor output power fluctuation in the propulsion load offset section, compares the pressure-power stability reference value section by section, identifies the area where the pressure and power are unbalanced, and obtains the pressure-power imbalance section;

[0009] The speed-cooling linkage module obtains the cooling pump speed and water temperature changes at the cooling pump start-up time point corresponding to the pressure-power imbalance section, identifies the dislocation section where the cooling response lags behind the speed stabilization, and obtains the cooling speed response dislocation section;

[0010] The cooling efficiency delay module collects temperature drop changes and coolant flow rates based on the cooling speed response dislocation segment, determines whether the temperature-flow ratio sequence is less than the temperature-flow conduction response benchmark ratio, selects the segment number that does not reach the benchmark, and obtains the cooling conduction efficiency delay segment;

[0011] The monitoring output module extracts the corresponding device unique identification code based on the number of the cooling conduction efficiency delay section, assigns the corresponding device abnormal behavior label to the identification code, and generates the tunnel boring machine operation abnormality monitoring result.

[0012] As a further solution of the present invention, the propulsion load offset section includes the propulsion stability section number, the resistance continuous enhancement trend, 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 speed response misalignment section includes the speed stabilization time period, the cooling response time deviation, and the stabilization advance 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 tunnel boring machine operation abnormality monitoring results include the equipment unique identification code, the equipment operation mapping field, and the abnormal behavior label summary.

[0013] As a further solution of the present invention, the propulsion identification module includes:

[0014] The propulsion data acquisition submodule obtains the propulsion displacement sequence of the tunneling cylinder and the resistance sensor records, extracts the displacement data and resistance data of the corresponding time period in the record sequence, and plots the resistance data into a resistance curve according to the propulsion time axis to obtain the resistance time curve data;

[0015] The rate calculation submodule extracts the propulsion displacement change per second to form a propulsion rate sequence based on the resistance time curve data, and extracts the resistance change per second to form a resistance rate sequence, using the formula:

[0016] , ;

[0017] Calculate the first Second propulsion rate change gradient coefficient Hedi Second resistance change gradient coefficient , integrate to get the list of gradient change coefficients;

[0018] in, Indicates the The propulsion rate in seconds, Indicates the Seconds of resistance, Indicates the Seconds of time, represents the change in the adjacent advancement rate, represents the change in adjacent resistances, represents the adjacent time intervals, is the total number of propulsion rate data points, represents the sum variable index;

[0019] The offset screening submodule makes a judgment based on the gradient change coefficient list and the set propulsion rate change threshold and resistance rate change threshold, calculates the increase ratio of the propulsion displacement to the resistance in the segment in the identified number segment, calls the average increase ratio in the continuous segments for comparison, identifies the record numbers with an increase ratio greater than the average value, screens the segment numbers 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 segment.

[0020] As a further solution of the present invention, the pressure-work imbalance identification module includes:

[0021] The pressure work data extraction submodule extracts the hydraulic cylinder pressure change record and the motor output power fluctuation data in the propulsion load offset section, and pairs the pressure sensor output sequence with the motor power output sequence in a time-synchronous manner to obtain a pressure power original sequence set;

[0022] The pressure-power ratio calculation submodule divides the pressure-power original sequence set into segments at fixed time intervals, calculates the mean of the pressure sequence in each segment, records the mean difference between the current segment and the previous segment 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;

[0023] The imbalance section screening submodule compares the pressure-power ratio of each time period with the set pressure-power load stability 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 imbalance section.

[0024] As a further solution of the present invention, the speed-cooling linkage module includes:

[0025] 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 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 period exceeding the threshold as a fluctuation state, and the period below or equal to the threshold and continuously meeting the condition as a stable state, thereby obtaining the speed fluctuation recovery section;

[0026] 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. The time length between the temperature drop starting point and the stable point in the cooling water temperature sequence is simultaneously extracted as the cooling response duration to generate a stabilization response time set.

[0027] The response misalignment identification submodule adopts the formula based on the stabilization response time set:

[0028] ;

[0029] Operation to obtain the Normalized stabilization response deviation for each time period , the time period numbers of which the deviation is greater than zero and the stabilization normalized value is less than the cooling normalized value are classified into the response lag classification, and the cooling speed response misalignment section is obtained;

[0030] in, Indicates the The normalized stabilization response deviation of each time period is Indicates the Speed ​​stabilization time, Indicates the Segment cooling response time, Indicates the maximum value of all stabilization times. Indicates the maximum value of all cooling response times.

[0031] As a further solution of the present invention, the cooling efficiency hysteresis module includes:

[0032] 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 coolant flow rate increase within the time range of each cooling section, calculates the temperature difference and flow rate difference within the specified time, and generates a temperature-flow ratio sequence;

[0033] The delay segment screening submodule compares each segment ratio with a set temperature-flow conduction response benchmark ratio based on the temperature-flow ratio sequence, marks the numbered segments that are less than the benchmark ratio, collects the consecutive numbers that do not reach the benchmark value, screens and summarizes the corresponding time period range, and obtains the cooling conduction efficiency delay segment.

[0034] As a further solution of the present invention, the monitoring output module includes:

[0035] The identification mapping submodule extracts the unique device identification code corresponding to the cooling conduction efficiency delay segment number based on the segment number, calls the operation identification field in the device operation status record data, establishes a mapping set from the segment number to the device identification according to the correspondence between the segment number and the device code, and obtains a device mapping relationship table;

[0036] 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 misalignment section according to the number set defined in the device mapping relationship table, filters 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 item to obtain the device abnormal behavior label;

[0037] The abnormality summary submodule calculates the number of behavior tag types and segments corresponding to the device identification code based on the abnormal behavior tag of the device, sets the total frequency of tag types and the number of mapped segments as independent indicators, and uses the formula:

[0038] ;

[0039] Calculate the abnormal behavior index of device z and compare it with the abnormal benchmark index of the tunnel boring machine to obtain the abnormal operation monitoring result of the tunnel boring machine;

[0040] in, Represents the number of behavior tags recorded by device z under segment u, Represents the number of times device z is identified under behavior type v, The total number of segments mapped for the device, The total number of behavior label types.

[0041] A method for intelligent coal mining monitoring is provided, which is based on the above-mentioned intelligent coal mining monitoring system and includes the following steps:

[0042] S1: Obtain the propulsion displacement sequence of the tunneling cylinder and the resistance sensor records, calculate the change gradient coefficient of the propulsion rate and resistance, screen the sections where the load increases but the propulsion does not match, and generate the propulsion load offset section;

[0043] S2: extracting the hydraulic cylinder pressure change and motor output power fluctuation in the propulsion load offset section, comparing the pressure-power stability reference value section by section, identifying the area where the pressure and power are unbalanced, and obtaining the pressure-power imbalance section;

[0044] S3: Obtaining the cooling pump speed and water temperature changes at the cooling pump start-up time point corresponding to the pressure-power imbalance section, identifying the dislocation section in which the cooling response lags behind the speed stabilization, and obtaining the cooling speed response dislocation section;

[0045] S4: Based on the cooling speed response dislocation section, collecting temperature drop changes and coolant flow rate, determining whether the temperature-flow ratio sequence is less than the temperature-flow conduction response benchmark ratio, screening the section number that does not reach the benchmark, and obtaining the cooling conduction efficiency delay section;

[0046] S5: Based on the number of the cooling conduction efficiency delay section, extract the corresponding device unique identification code, assign the corresponding device abnormal behavior label to the identification code, and generate the roadheader operation abnormality monitoring result.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by collecting the displacement and resistance change sequence of the tunneling cylinder, calculating the gradient change of propulsion and resistance, comparing the propulsion and load mismatch sections, and further analyzing the different behaviors of pressure and motor power, the coupling relationship between the hydraulic system and the power system is extracted layer by layer, and the abnormal response in the propulsion process is gradually revealed. In view of the coupling between the start and stop of the cooling pump and the speed of the main drive motor, combined with the water temperature change curve, the dislocation segment where the cooling response lags behind the mechanical stabilization is located, and it is clear that the equipment has a response delay when responding to temperature rise. At the same time, the ratio of the temperature drop rate to the coolant flow rate is used to further screen the sections with reduced heat conduction efficiency, thereby identifying cooling efficiency problems caused by the mismatch between fluid dynamics and heat conduction response. In the process of abnormal behavior output, a multi-dimensional mapping relationship between the equipment number and the abnormal label is constructed, and the behavior frequency and section coverage of the equipment in each dimension are extracted and converted into behavior abnormality indicators to achieve a quantitative expression of the health status of the tunneling equipment. Through cross-analysis of four dimensions: propulsion load, pressure power, speed cooling, and temperature flow response, an abnormality identification path is formed that covers the entire link of propulsion power matching, energy efficiency transmission stability, and cooling efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1is a system flow chart of the present invention;

[0050] Figure 2 A flow chart for advancing the identification module of the present invention;

[0051] Figure 3 This is a flow chart of the pressure-work imbalance identification module of the present invention;

[0052] Figure 4 This is a flow chart of the speed-cooling linkage module of the present invention;

[0053] Figure 5 This is a flow chart of the cooling efficiency delay module of the present invention;

[0054] Figure 6 This is a flow chart of the monitoring output module of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0056] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0057] See also Figure 1 The present invention provides a technical solution: a coal mine intelligent mining monitoring system comprising:

[0058] The propulsion identification module obtains the propulsion displacement sequence of the tunneling cylinder and the resistance sensor records, calculates the change gradient coefficient of the propulsion rate and resistance, screens the sections where the load increases but the propulsion does not match, and generates the propulsion load offset section;

[0059] The pressure-power imbalance identification module extracts the hydraulic cylinder pressure changes and motor output power fluctuations within the propulsion load offset section, compares the pressure-power stability benchmark value section by section, identifies the area where pressure and power are unbalanced, and obtains the pressure-power imbalance section;

[0060] The speed-cooling linkage module obtains the cooling pump speed and water temperature changes at the cooling pump start-up time point corresponding to the pressure-power imbalance section, identifies the dislocation section where the cooling response lags behind the speed stabilization, and obtains the cooling speed response dislocation section;

[0061] The cooling efficiency hysteresis module collects temperature drop changes and coolant flow rates based on the cooling speed response dislocation segment, determines whether the temperature-flow ratio sequence is less than the temperature-flow conduction response benchmark ratio, selects the segment number that does not reach the benchmark, and obtains the cooling conduction efficiency delay segment;

[0062] The monitoring output module extracts the unique identification code of the corresponding equipment based on the number of the cooling conduction efficiency delay segment, assigns the corresponding equipment abnormal behavior label to the identification code, and generates the abnormal operation monitoring results of the roadheader;

[0063] The propulsion load offset section includes the propulsion stability section number, the continuous increase trend of 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 speed response misalignment section includes the speed stabilization time period, the cooling response time deviation, and the stabilization advance number; the cooling conduction efficiency delay section includes the temperature drop rate sequence, the coolant flow rate increase, and the temperature-flow ratio deficiency number; the abnormal operation monitoring results of the tunnel boring machine include the equipment unique identification code, the equipment operation mapping field, and the abnormal behavior label summary.

[0064] See also Figure 2 , the advancement recognition module includes:

[0065] The propulsion data acquisition submodule obtains the propulsion displacement sequence of the tunneling cylinder and the resistance sensor records, extracts the displacement data and resistance data of the corresponding time period in the record sequence, and plots the resistance data into a resistance curve according to the propulsion time axis to obtain the resistance time curve data;

[0066] First, it is necessary to rely on the sensor system layout of the tunneling equipment. The displacement sensor is installed at the end of the hydraulic propulsion cylinder axis, and the resistance sensor is set at the tunneling head or cutterhead docking position. The PLC acquisition system is used to collect second-level data of the propulsion process. The acquired propulsion displacement data is the extension stroke data of the hydraulic cylinder per second, in mm, and the resistance data is the instantaneous reaction force in the hydraulic system, in kN. In the implementation scenario, if a tunneling machine is arranged on the mining face of a coal mine, the propulsion sensor records data sequences such as: displacement sequence , resistance series , the corresponding time node is After real-time data recording, the system outputs the resistance data in time series and draws a resistance curve, which is a broken line graph with time as the horizontal axis and resistance as the vertical axis. It is used to reflect the force change process per unit time during the propulsion process. In this process, no numerical calculation or function operation is introduced. Only the coordinate system curve is established according to the horizontal and vertical values ​​corresponding to the recording points, and the line drawing is completed through linear connection. This operation can be regarded as the data preparation stage in the propulsion process to obtain the resistance time curve data.

[0067] The rate calculation submodule extracts the propulsion displacement change per second to form a propulsion rate sequence based on the resistance time curve data, and extracts the resistance change per second to form a resistance rate sequence, using the formula:

[0068] , ;

[0069] Calculate the first Second propulsion rate change gradient coefficient Hedi Second resistance change gradient coefficient , integrate to get the list of gradient change coefficients;

[0070] in, Indicates the Second propulsion rate change gradient coefficient, where the superscript Indicates that the gradient coefficient corresponds to the propulsion rate, the subscript Indicates the time points; Indicates the Second resistance change gradient coefficient, where the superscript Indicates that the gradient coefficient corresponds to the resistance change, the subscript Indicates the time points; Indicates the Advance rate in seconds; Indicates the Seconds of resistance; Indicates the Seconds of time; Indicates the change in 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 The sum of the absolute gradient values ​​of the change in the second propulsion rate; Indicates from the 1st second to the The sum of squared gradients of resistance changes per second; Represents the sum variable index, which is used to iteratively calculate the gradient change within the time period; Represents the number of samples in the advancement rate series, that is, the total number of time periods; Represents the current time point index, which is used to locate the time period corresponding to the current gradient coefficient.

[0071] Extract the displacement change per second and resistance change value , the propulsion rate sequence is mm / s, the resistance change sequence is kN / s, calculate the propulsion rate change gradient coefficient and resistance change gradient coefficient per second respectively, where the propulsion rate change gradient coefficient formula is:

[0072] ;

[0073] Substituting the propulsion rate and setting the time step to 1 second, the calculation is as follows:

[0074] ;

[0075] The calculation formula of resistance change gradient coefficient is:

[0076] ;

[0077] set up , unit is kN / s, calculate :

[0078] ;

[0079] Then calculate :

[0080] ;

[0081] The above two sets of gradient coefficients are used to establish a list of gradient change coefficients for the next step of identifying the sections of stable propulsion and increased resistance. The innovation of the two formulas is to enhance the sensitivity of change judgment and trend assessment by combining the immediate change terms and the cumulative average / square terms, and then establish a gradient state stability evaluation system.

[0082] The benefit of the formula is that by jointly introducing the average term of the propulsion rate change and the sum of the squares of the resistance change, the system can capture short-term mutations while taking into account the cumulative trend, and effectively distinguish between the complex states of abnormal propulsion stability and abnormal load increase.

[0083] The offset screening submodule makes judgments based on the gradient change coefficient list and the set propulsion rate change threshold and resistance rate change threshold. It calculates the increase ratio of propulsion displacement to resistance in the identified numbered segment, calls the historical average increase ratio in consecutive segments for comparison, identifies the record numbers with an increase ratio greater than the average, and screens the segment numbers where the propulsion rate is lower than the propulsion rate change threshold and the resistance exceeds the resistance rate change threshold, thus generating the propulsion load offset segment.

[0084] The propulsion rate gradient With resistance gradient Compared with the preset thresholds, the propulsion rate change threshold is set to 0.1mm / s², and the resistance change threshold is set to 10kN / s², and the and For example, when hour, 、 , it meets the conditions, and number 2 is marked as a section with stable propulsion rate but continuously increasing resistance. Then, the increase values ​​of propulsion displacement data and resistance data are collected in the numbered section. Assume that the displacement in the 2nd second is 8.2mm, the displacement in the 3rd second is 12.4mm, and the resistance increases from 32.1kN to 36.4kN. The increase ratios are: propulsion displacement increase of 4.2mm, resistance increase of 4.3kN, and the increase ratio is 1.02. Compared with the average increase ratio of 1.00 of five consecutive period sections, the current section is judged to be an increase deviation section, and further judged whether it is a propulsion mismatch section. According to the conditions, the increase ratio is greater than the average value, and the propulsion rate is lower than 2mm / s. The current propulsion rate is 4.2mm / s, which is judged not to be a propulsion mismatch section; if the propulsion rate is changed to 1.8mm / s, it is judged to be matched, and it is included in the final result to obtain the propulsion load offset section.

[0085] See also Figure 3 , the pressure-work imbalance identification module includes:

[0086] The pressure work data extraction submodule extracts the hydraulic cylinder pressure change records and motor output power fluctuation data in the propulsion load offset section, and pairs the pressure sensor output sequence with the motor power output sequence in a time-synchronous manner to obtain the pressure power original sequence set;

[0087] First, a corresponding time index structure is established within the propulsion load offset segment. This segment can be set to a 30-second period. For example, if a 10-minute segment is set, it can be divided into 20 subsegments. In each subsegment, the output value of the pressure sensor on the hydraulic cylinder and the power data of the motor output are collected. The pressure value is measured in MPa, and the power value is measured in kW. For example, if the hydraulic cylinder pressure recorded at the beginning of a segment is 12.4 MPa and at the end is 13.7 MPa, the pressure change in this segment is 1.3 MPa. At the same time, the motor power in this segment increases from 41.2 kW to 43.5 kW, with a power fluctuation of 2.3 kW. The collected data must exclude recording segments with three or more consecutive missing data points. After filtering according to this condition, valid data sequences are retained and each data segment is numbered and identified in a unified format of T1, T2, etc. In this way, the pressure change and power fluctuation data of each propulsion load offset segment can be synchronized and processed at the data level, laying the data structure foundation for subsequent calculations. The result is a set of original pressure and power sequences.

[0088] The pressure-power ratio calculation submodule is based on the original pressure-power sequence set, divides the segments into fixed time intervals, calculates the mean value of the pressure sequence in each segment, records the mean difference between the current segment and the previous segment 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 the pressure-power synchronization ratio sequence;

[0089] After calling the original sequence set of pressure and power, the data in each divided time period is calculated. Taking each section of 10 seconds as an example, the sampling frequency is set to 10Hz, and each section contains 100 data points. First, the mean of the hydraulic cylinder pressure data in each section is calculated. For example, a section of pressure data is {12.3, 12.4, 12.4, 12.5...} with a total of 100 points, and the mean is 12.45MPa. Similarly, for the motor power sequence such as {42.1, 42.3, 42.4, 42.5...}, the mean is 42.40kW, and then the section is calculated. The difference from the mean value of the previous section, if the mean pressure value of the previous section is 12.15MPa, then the increase is 0.30MPa, if the mean power value is 42.00kW, then the power increase is 0.40kW, and then the ratio is taken as 0.30÷0.40=0.75, which is used as the pressure-power synchronization ratio of this section. The ratios of all sections are calculated in turn to form a ratio sequence in the form of a one-dimensional array, for example {0.75, 0.82, 0.60, 0.95...}. Each ratio represents the consistency of the pressure and power changes in each time period, and the result is a pressure-power synchronization ratio sequence.

[0090] The imbalance section screening submodule compares the pressure-power ratio of each time period with the set pressure-power load stability reference ratio according to the pressure-power synchronization ratio sequence, marks the segment numbers where the ratio deviates from the stable reference ratio interval, extracts the consecutive numbered time periods, and obtains the pressure-power imbalance section;

[0091] According to the pressure-power synchronization ratio sequence, the ratio of each time period is judged against the set pressure-power load stability benchmark ratio. The stability benchmark ratio is set to 0.85, and the deviation limit is ±0.10. The stability interval is [0.75, 0.95]. If the ratio is within this interval, it is judged to be normal, otherwise it is a deviation. For example, the ratio sequence contains {0.73, 0.82, 0.98, 0.94, 0.66, 0.87}, of which 0.73, 0.98, and 0.66 are deviation values. The corresponding time period numbers such as T2, T3, and T5 are recorded and these numbers are grouped together. If the numbers are continuous, such as T2 and T3, they can be merged into a continuous deviation segment. In this way, the continuous deviation segments are organized as: segment A (T2-T3) and segment B (T5). All deviation segment numbers are output as pressure-power matching abnormal segment records, and the output result is further output as the pressure-power imbalance segment.

[0092] See also Figure 4 , the speed cooling linkage module includes:

[0093] 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 cooling pump is started, and compares the absolute value of the change of adjacent speed points in a continuous time period with the set speed fluctuation threshold. The period exceeding the threshold is marked as a fluctuating state, and the period below or equal to the threshold and continuously meeting the conditions is marked as a stable state, thus obtaining the speed fluctuation recovery section;

[0094] To obtain the cooling pump start-up time point corresponding to the pressure-power imbalance section, it is necessary to call the cooling pump control signal record from the equipment operation log and extract the start-up time information. For example, the cooling pump start-up time marked in the record is 14:05:10. Then, the main drive motor speed sequence of 60 seconds before and after the cooling pump is started is extracted, and the speed value per second is continuously analyzed to determine whether the motor is in a fluctuating or stable state. For this purpose, the speed fluctuation judgment threshold is set to ±3rpm. If the change in adjacent speed values ​​is greater than 3rpm within 5 consecutive seconds, the section is classified as a fluctuating state. If the change is less than or equal to 3rpm within 10 consecutive seconds, it is considered that the speed has reached stability. For example, in the section before the cooling pump is started, if the motor speed is between 14:04:50 and 14:04:55 They are 1482rpm, 1487rpm, 1485rpm, 1489rpm, 1484rpm, and 1486rpm respectively. The fluctuation amplitude between consecutive values ​​is more than 3rpm, so it is determined to be a fluctuating state. Continue to observe the time period from 14:05:00 to 14:05:10. If the speed is 1483rpm, 1481rpm, 1480rpm, 1481rpm, 1480rpm, and 1479rpm respectively, which are all within the fluctuation threshold, it is determined to be a stable state. Then, the continuous time period 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-up action to clarify whether the speed response overlaps with the cooling behavior, and finally obtain the speed fluctuation recovery section.

[0095] 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. This is defined as the stabilization time period. The time length between the temperature drop starting point and the stable point in the cooling water temperature sequence is simultaneously extracted as the cooling response duration to generate the stabilization response time set.

[0096] The complete time it takes for the speed to fluctuate and then stabilize in each section is recorded as the stabilization period. The cooling water temperature data is then collected on the same time axis. The start time of the cooling pump is used as the starting reference point for the temperature response. The interval between the starting value of the temperature drop and the starting point of the stable section is identified in the temperature sequence as the cooling response duration. The criterion for temperature stability is defined as a temperature fluctuation of less than 0.1°C within 10 consecutive seconds. If the temperature starts to drop from 43.2°C after the cooling pump is started at 14:05:10, and at 14:0 The temperature dropped to 41.0°C at 5:50 and stabilized at 40.9°C after 14:06:00. The starting point of the cooling response record is 14:05:10, the ending point is 14:06:00, and the response duration is 50 seconds. If the stabilization time period of the corresponding segment is from 14:05:15 to 14:05:45, then its duration is 30 seconds. Repeat this process for other segments, and obtain the stabilization time and cooling response time in multiple time periods from the data records. Then, correspond them with the segment numbers to construct a stabilization response time set.

[0097] The response dislocation identification submodule is based on the set of stabilization response times and uses the formula:

[0098] ;

[0099] Operation to obtain the Normalized stabilization response deviation for each time period , the time period numbers of which the deviation is greater than zero and the stabilization normalized value is less than the cooling normalized value are classified into the response lag classification, and the cooling speed response misalignment section is obtained;

[0100] in, Indicates the The normalized stabilization response deviation of each time period is Indicates the Speed ​​stabilization time, Indicates the Segment cooling response time, Indicates the maximum value of all stabilization times. Indicates the maximum value of all cooling response times.

[0101] To eliminate the inconsistency caused by the difference in time units and magnitudes, the constructed set of stabilization response time is called. Each stabilization time and cooling response time is divided by the maximum value of the sequence to be normalized. In the segment, the stabilization time is 30 seconds, the cooling response time is 50 seconds, and the maximum stabilization time in all samples is 45 seconds, and the maximum cooling response time is 65 seconds. After normalization, they are and , and then according to the formula

[0102] ;

[0103] The normalized deviation of this segment is , Repeat the same calculation for multiple sections. For example, the stabilization time of the second section 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 section 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 section 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 time of the fifth section are both 36 seconds. After normalization, they are all 0.8000, and the deviation is 0. The normalized deviation values ​​of all sections are counted. When the normalized stabilization time is less than the cooling response time and the deviation value is greater than 0, the corresponding section number is marked as cooling response lag, and finally the cooling speed response dislocation section is obtained. The benefit of the formula is that by introducing normalization operation and difference operation, a unified comparable standard is constructed, and the time discrimination ability when the cross-variable dimensions are not synchronized is enhanced. The result shows that in multiple sections where the normalized stabilization time is less than the cooling response time, there is a response lag phenomenon, which is confirmed as a dislocated response section.

[0104] See also Figure 5 , the cooling efficiency hysteresis module includes:

[0105] 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. It extracts the temperature drop rate and coolant flow rate increase within each cooling section time range, calculates the temperature difference and flow rate difference within the specified time, and generates a temperature-flow ratio sequence.

[0106] Based on the cooling speed response dislocation section, it is necessary to extract the cooling pipe temperature diversion sensor records and coolant flow rate data corresponding to the response section time range. First, the start and end time points of the dislocation section must be clarified. For example, the dislocation section time is from 202 seconds to 234 seconds. During this time period, the temperature data collected from the coolant pipe temperature diversion sensor is sampled once per second to form a sequence The unit is degrees Celsius. The coolant flow rate is obtained using a turbine flow meter and recorded once per second to form a sequence. , in L / min. Secondly, the average drop rate of the continuous drop section in the temperature sequence needs to be calculated. The difference between the current point and the previous point is used as the instantaneous drop rate and the average of the entire section is taken. For example, from 202 seconds to 212 seconds, the temperature drops from 75.4 to 73.0, and 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 the increase is The temperature-flow ratio is calculated by taking the ratio of the temperature drop rate to the flow rate increase as After performing this operation on each cooling response offset section, a temperature-flow ratio sequence is formed. It should be noted that during the acquisition of the temperature drop rate, if there is a short-term abnormality in the temperature sensor or data is missing, the gap should be filled by linear interpolation of the valid points on both sides. 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 for 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.

[0107] The delay segment screening submodule compares each segment ratio with the set temperature-flow conduction response benchmark ratio based on the temperature-flow ratio sequence, marks the numbered segments with a ratio less than the benchmark ratio, collects the consecutive numbers that do not reach the benchmark ratio, screens and summarizes the corresponding time period range, and obtains the cooling conduction efficiency delay segment;

[0108] 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 dislocation 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, and the record number set is , where continuous numbers can be merged into an overall number segment. If each time interval is 10 seconds, the actual time range represented by the number 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 the temperature-flow ratio value of a certain segment in the data source is missing or zero, the segment is automatically eliminated and does not participate in the screening. After the judgment is completed, all number segments that meet the screening conditions are merged and sorted, and finally all number 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.

[0109] See also Figure 6 , monitoring output modules include:

[0110] The identification mapping submodule extracts the unique device identification code corresponding to the cooling conduction efficiency delay segment number based on the segment number, calls the operation identification field contained in the device operation status record data, establishes a mapping set from the segment number to the device identification based on the correspondence between the segment number and the device code, and obtains a device mapping relationship table;

[0111] First, the temperature response value of the temperature control sensor on the cooling pipe of each section of the roadheader needs to be collected. Each response data is sampled and numbered through the set time window, such as Z001, Z002, etc., which represent the cooling response delay sections under different time periods or spatial positions. The temperature change delay exceeds the normal response average value by more than 2.5 seconds and is identified as a delay section. Then, the unique device identification code associated with each delay number is called from the device data set. This identification code can be obtained through the roadheader master control record table. For example, the device D_3185 corresponds to the Z001 section number. Then, the label in the device operation record log is read. Identification fields, such as the current power of the equipment, temperature change curve, speed and other operating indicators, are compared one by one with the segment numbers to construct a mapping structure between the numbers and the equipment codes, and finally form a mapping set from the segment numbers to the equipment identification codes, which is recorded as a segment-equipment mapping relationship table, such as Z001→D_3185, Z002→D_3190, and Z003→D_3221. In this example, the sensor temperature response delay of the Z001 segment is 3.1 seconds, which is greater than the set reference value of 2.5 seconds. It is determined to be a delayed segment, and its corresponding equipment code is D_3185. The mapping relationship is recorded as Z001-D_3185.

[0112] The label collection submodule uses the number set defined in the device mapping table to call the behavior label items in the propulsion load offset section, the pressure power imbalance section, and the cooling speed response misalignment section. It selects 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 item to obtain the device abnormal behavior label;

[0113] The data items in the three sections of propulsion load offset, pressure power imbalance, and cooling speed response dislocation are called to extract the labels that have been identified as abnormal behaviors, such as "propulsion force suddenly increased by more than 10kN" identified in propulsion load offset, "pressure fluctuation exceeds 15%" identified in pressure power imbalance, and other behavioral events. If these behavioral events match the section numbers in the mapping table, the behavior label is included in the corresponding device mapping field, and the label aggregation operation is performed on each device. The aggregation logic is to extract all matching codes under the same device identification code. The segment labels of the device D_3185 are classified by behavior type and their frequencies are counted. For example, if mapping segment Z001 of device D_3185 contains two propulsion offset behaviors and one cooling response delay behavior, the behavioral label distribution under D_3185 is: propulsion offset = 2, cooling delay = 1, and pressure deviation = 0. The aggregation process is judged based on the matching accuracy. Only label items with completely corresponding segment numbers are counted. If some labels cross segments, they are not recorded. After completing label aggregation, the frequency data of identified abnormal behavior labels for each device is obtained.

[0114] The abnormality summary submodule calculates the number of behavior tag types and segments corresponding to the device identification code based on the device abnormal behavior tag, sets the total frequency of tag types and the number of mapped segments as independent indicators, and uses the formula:

[0115] ;

[0116] Calculate the abnormal behavior index of device z and compare it with the abnormal benchmark index of the tunnel boring machine to obtain the abnormal operation monitoring result of the tunnel boring machine;

[0117] in, Represents the number of behavior tags recorded by device z under segment u, Represents the number of times device z is identified under behavior type v, The total number of segments mapped for the device, The total number of behavior label types.

[0118] Obtain the total number of behavior tags under each device identification code and the number of segments and tag types mapped to them. Normalize each value and calculate its average performance. Suppose device Z1 generates 7 abnormal tags in 3 segments, of which the behavior types are propulsion offset, pressure work deviation, and cooling response delay, with a total of 3 types, and the number of occurrences is 4 times, 2 times, and 1 time respectively. Then the behavior tag frequency array is ; = [4, 2, 1], the label type frequency array is = [1, 1, 1], number of segments a = 3, number of label types b = 3, substitute the above values ​​into the formula:

[0119] ;

[0120] 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 convergence in the equipment. Based on this, the abnormal operation monitoring results of the roadheader can be obtained.

[0121] The formula's benefit lies in its balanced integration of the frequency of abnormal device behavior across spatial segments and the richness of its behavior types, eliminating the risk of direct overlap between participating units and ensuring a reasonable representation of abnormal intensity within the device label distribution structure. This results in a more representative representation of abnormalities in systematic assessments. This result indicates that the operating status of device Z1 deviates from its normal operating range, necessitating further diagnosis of its operating process and device health.

[0122] A method for intelligent coal mining monitoring is provided. The method is based on the above-mentioned intelligent coal mining monitoring system and includes the following steps:

[0123] S1: Obtain the propulsion displacement sequence of the tunneling cylinder and the resistance sensor records, calculate the change gradient coefficient of the propulsion rate and resistance, screen the sections where the load increases but the propulsion does not match, and generate the propulsion load offset section;

[0124] S2: Extract the hydraulic cylinder pressure changes and motor output power fluctuations in the propulsion load offset section, compare them with the pressure-power stability benchmark value section by section, identify the area where pressure and power are unbalanced, and obtain the pressure-power imbalance section;

[0125] S3: Obtain the cooling pump speed and water temperature changes at the cooling pump start-up time point corresponding to the pressure-power imbalance section, identify the dislocation section where the cooling response lags behind the speed stabilization, and obtain the cooling speed response dislocation section;

[0126] S4: Based on the cooling speed response dislocation section, collect temperature drop changes and coolant flow rate, determine whether the temperature-flow ratio sequence is less than the temperature-flow conduction response benchmark ratio, filter the section number that does not reach the benchmark, and obtain the cooling conduction efficiency delay section;

[0127] S5: Based on the number of the cooling conduction efficiency delay segment, extract the corresponding device unique identification code, assign the corresponding device abnormal behavior label to the identification code, and generate the tunnel boring machine operation abnormality monitoring result.

[0128] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A coal mine intelligent mining monitoring system, characterized in that: The system comprises: The propulsion identification module obtains the propulsion displacement sequence of the tunneling cylinder and the resistance sensor records, calculates the change gradient coefficient of the propulsion rate and resistance, screens the sections where the load increases but the propulsion does not match, and generates the propulsion load offset section; The propulsion identification module includes: The propulsion data acquisition submodule obtains the propulsion displacement sequence of the tunneling cylinder and the resistance sensor records, extracts the displacement data and resistance data of the corresponding time period in the record sequence, and plots the resistance data into a resistance curve according to the propulsion time axis to obtain the resistance time curve data; The rate calculation submodule extracts the propulsion displacement change per second to form a propulsion rate sequence based on the resistance time curve data, and extracts the resistance change per second to form a resistance rate sequence, using the formula: Calculate the gradient coefficient of the propulsion rate change at the i-th second respectively and the resistance change gradient coefficient at the i-th second Integrate to obtain a list of gradient change coefficients; Among them, v i represents the propulsion rate in the i-th second, r i represents the resistance at the i-th second, t i represents the time of the i-th second, v i+1 -v i Represents the change in adjacent advancement rate, r i+1 -r i Indicates the change in adjacent resistance, t i+1 -t i represents the adjacent time interval, n is the total number of propulsion rate data points, and j represents the summation variable index; The offset screening submodule makes a judgment based on the gradient change coefficient list and the set propulsion rate change threshold and resistance rate change threshold, calculates the increase ratio of the propulsion displacement to the resistance in the identified number segment, calls the average increase ratio in the continuous segments for comparison, identifies the record numbers with an increase ratio greater than the average, screens the segment 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 segment; The pressure-power imbalance identification module extracts the hydraulic cylinder pressure change and the motor output power fluctuation in the propulsion load offset section, compares the pressure-power stability reference value section by section, identifies the area where the pressure and power are unbalanced, and obtains the pressure-power imbalance section; The speed-cooling linkage module obtains the cooling pump speed and water temperature changes at the cooling pump start-up time point corresponding to the pressure-power imbalance section, identifies the dislocation section where the cooling response lags behind the speed stabilization, and obtains the cooling speed response dislocation section; The cooling efficiency delay module collects temperature drop changes and coolant flow rates based on the cooling speed response dislocation segment, determines whether the temperature-flow ratio sequence is less than the temperature-flow conduction response benchmark ratio, selects the segment number that does not reach the benchmark, and obtains the cooling conduction efficiency delay segment; The monitoring output module extracts the corresponding device unique identification code based on the number of the cooling conduction efficiency delay section, assigns the corresponding device abnormal behavior label to the identification code, and generates the tunnel boring machine operation abnormality monitoring result.

2. The coal mine intelligent mining monitoring system according to claim 1, characterized in that: The propulsion load offset section includes the propulsion stability section number, the resistance continuous enhancement trend, 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 speed response misalignment section includes the speed stabilization time period, the cooling response time deviation, and the stabilization advance number; the cooling conduction efficiency delay section includes the temperature drop rate sequence, the coolant flow rate increase, and the temperature-flow ratio deficiency number; the tunnel boring machine operation abnormality monitoring results include the equipment unique identification code, the equipment operation mapping field, and the abnormal behavior label summary.

3. The coal mine intelligent mining monitoring system according to claim 1, characterized in that: The pressure work imbalance identification module includes: The pressure work data extraction submodule extracts the hydraulic cylinder pressure change record and the motor output power fluctuation data in the propulsion load offset section, and pairs the pressure sensor output sequence with the motor power output sequence in a time-synchronous manner to obtain a pressure power original sequence set; The pressure-power ratio calculation submodule divides the pressure-power original sequence set into segments at fixed time intervals, calculates the mean of the pressure sequence in each segment, records the mean difference between the current segment and the previous segment 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 imbalance section screening submodule compares the pressure-power ratio of each time period with the set pressure-power load stability 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 imbalance section.

4. The intelligent coal mining monitoring system according to claim 3, characterized in that: 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 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 period exceeding the threshold as a fluctuation state, and the period below or equal to the threshold and continuously meeting the condition as a stable state, thereby obtaining 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. The time length between the temperature drop starting point and the stable point in the cooling water temperature sequence is simultaneously extracted as the cooling response duration to generate a stabilization response time set. The response misalignment identification submodule adopts the formula based on the stabilization response time set: Calculate and obtain the normalized stabilization response deviation of the i1th time period The time period numbers in which the deviation is greater than zero and the stabilization normalized value is less than the cooling normalized value are classified into the response lag classification to obtain the cooling speed response misalignment section; in, represents the normalized stabilization response deviation of the i1th time period, Indicates the time for the speed to stabilize in the i1 segment. Indicates the cooling response time of the i1th segment, max(T r ) represents the maximum value of all stabilization times, max(T c ) represents the maximum value of all cooling response times.

5. The intelligent coal mining monitoring system according to claim 4, characterized in that: The cooling efficiency hysteresis 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 coolant flow rate increase within the time range of each cooling section, calculates the temperature difference and flow rate difference within the specified time, and generates a temperature-flow ratio sequence; The delay segment screening submodule compares each segment ratio with a set temperature-flow conduction response benchmark ratio based on the temperature-flow ratio sequence, marks the numbered segments that are less than the benchmark ratio, collects the consecutive numbers that do not reach the benchmark value, screens and summarizes the corresponding time period range, and obtains the cooling conduction efficiency delay segment.

6. The coal mine intelligent mining monitoring system according to claim 5, characterized in that: The monitoring output module includes: The identification mapping submodule extracts the unique device identification code corresponding to the cooling conduction efficiency delay segment number based on the segment number, calls the operation identification field in the device operation status record data, establishes a mapping set from the segment number to the device identification according to the correspondence between the segment number and the device code, and obtains a device 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 misalignment section according to the number set defined in the device mapping relationship table, filters 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 item to obtain the device abnormal behavior label; The abnormality summary submodule calculates the number of behavior tag types and segments corresponding to the device identification code based on the abnormal behavior tag of the device, sets the total frequency of tag types and the number of mapped segments as independent indicators, and uses the formula: Calculate the abnormal behavior index of device z and compare it with the abnormal benchmark index of the tunnel boring machine to obtain the abnormal operation monitoring result of the tunnel boring machine; Among them, M zu Represents the number of behavior tags recorded by device z under segment u, N zv represents the number of times device z is identified under behavior type v, a is the total number of segments mapped to the device, and b is the total number of types of behavior tags.

7. A coal mine intelligent mining monitoring method, characterized in that: The intelligent coal mining monitoring system according to any one of claims 1 to 6 comprises the following steps: S1: Obtain the propulsion displacement sequence of the tunneling cylinder and the resistance sensor records, calculate the change gradient coefficient of the propulsion rate and resistance, screen the sections where the load increases but the propulsion does not match, and generate the propulsion load offset section; S2: extracting the hydraulic cylinder pressure change and motor output power fluctuation in the propulsion load offset section, comparing the pressure-power stability reference value section by section, identifying the area where the pressure and power are unbalanced, and obtaining the pressure-power imbalance section; S3: Obtaining the cooling pump speed and water temperature changes at the cooling pump start-up time point corresponding to the pressure-power imbalance section, identifying the dislocation section in which the cooling response lags behind the speed stabilization, and obtaining the cooling speed response dislocation section; S4: Based on the cooling speed response dislocation section, collecting temperature drop changes and coolant flow rate, determining whether the temperature-flow ratio sequence is less than the temperature-flow conduction response benchmark ratio, screening the section number that does not reach the benchmark, and obtaining the cooling conduction efficiency delay section; S5: Based on the number of the cooling conduction efficiency delay section, extract the corresponding device unique identification code, assign the corresponding device abnormal behavior label to the identification code, and generate the roadheader operation abnormality monitoring result.

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