Intelligent detection method for quality of overlying strata separation filling material

Abstract

The invention relates to the technical field of overlying strata separation layer filling material detection, in particular to an intelligent overlying strata separation layer filling material quality detection method which comprises the following steps of sample treatment preparation, slurry preparation and multi-parameter detection. The method comprises the following steps: acquiring corresponding data, analyzing and evaluating the data, judging and feeding back a result, judging whether a sample is qualified or not according to multiple detection results, feeding back the detection results, maintaining equipment and managing the data, and storing, backing up and sharing the detection data after the detection is completed. The sample type is recognized by image recognition and machine learning, a dynamic weighing compensation algorithm is combined, a weighing system is calibrated according to environmental parameters, and the high precision of + / -0.05 g is achieved. A high-precision weighing system in the intelligent overlying strata separation layer filling material quality detection device is connected with a signal conditioning circuit through a strain gauge type sensor, so that weight measurement accuracy is guaranteed, sample treatment errors are reduced, and a foundation is built for subsequent detection.

Description

Technical Field

[0001] The present invention relates to the technical field of overburden rock separation layer filling material detection technology, in particular to an intelligent detection method for the quality of overburden rock separation layer filling material. Background Art

[0002] In the field of coal mine backfill mining, the quality of backfill materials plays a vital role in the stability, safety and mining efficiency of the mine. Currently, traditional methods for testing the quality of backfill materials in overburden separation layers mainly rely on manual operation and simple instruments and equipment, which have exposed many drawbacks in actual application, as follows: First, there are significant flaws in sample handling. Manual weighing and sample placement is not only inefficient but also subject to human error, making it difficult to ensure weighing accuracy and consistent sample placement. Even the slightest oversight can lead to biased test results, making it impossible to provide a reliable sample basis for subsequent testing.

[0003] Second, the stirring effect of traditional stirring equipment is poor, and local agglomeration or insufficient mixing often occurs, further reducing the quality of the slurry.

[0004] Third, slurry performance testing methods are outdated. Traditional methods are mostly limited to testing a single parameter, such as specific gravity or fluidity, making it difficult to comprehensively assess multiple key slurry performance parameters. Furthermore, testing accuracy is low, with errors in specific gravity testing being large, making it difficult to meet increasingly stringent quality standards for overburden delamination filling materials. Furthermore, traditional testing equipment is complex to operate and requires specialized technicians, significantly increasing testing costs and time.

[0005] Fourth, data analysis and result interpretation lack scientific validity. Traditional methods rely primarily on manual analysis against standard data, which is highly subjective and prone to misjudgment. Furthermore, outdated data storage and management methods make it difficult to quickly query, share, and preserve data over the long term, hindering the effective use and in-depth mining of historical data.

[0006] Fifth, equipment maintenance is a weakness. Traditional intelligent testing devices for detecting the quality of overburden delamination filling materials lack automatic cleaning and intelligent maintenance functions. Manual cleaning of the equipment is required after each test. This not only consumes a lot of manpower and time, but incomplete cleaning may also adversely affect the next test results, reducing the equipment's service life and detection efficiency. Summary of the Invention

[0007] The present invention solves one of the above technical problems and adopts a technical solution: an intelligent detection method for the quality of overburden separation layer filling material, comprising the following steps: Sample preparation: Pre-process the samples of the overburden separation layer filling material to be inspected, and use intelligent control to accurately control and weigh the sample amount, and automatically discharge the excess sample; Slurry preparation: The weighed sample is mixed and stirred with water extracted according to a set ratio (the ratio is set based on the slurry ratio used in the current construction period and the characteristics of the material sample) in an intelligent quality detection device for overburden separation layer filling materials to form a uniform slurry; Multi-parameter detection: Detect key performance parameters of the slurry, such as specific gravity, stone rate, water separation rate and fluidity, and obtain relevant data; Data analysis and evaluation: Compare and analyze the detected data with the preset database standard indicators, calculate the deviation rate, and automatically trigger the re-inspection mechanism if the deviation exceeds the preset threshold; Result determination and feedback: Determine whether the sample is qualified based on multiple test results and provide feedback on the test results; Equipment maintenance and data management: After the test is completed, the test data will be stored, backed up and shared.

[0008] In any of the above schemes, it is preferred that in the sample processing preparation step, the appearance characteristics of the sample are analyzed by image recognition technology, and the color, particle shape and size distribution characteristics of the obtained sample are compared with historical data and material standard libraries to automatically identify the sample type; for the different types of samples identified, dynamic weighing is performed and the weighing accuracy is ensured to reach ±0.05g, so as to accurately determine the preset weight and amount of the sample.

[0009] In any of the above schemes, it is preferred that, in the slurry preparation step, the water pumping volume is monitored in real time by a high-precision flow sensor, and the pressure in the pumping pipe is monitored by a pressure sensor, and the feedback is sent to the PLC control cabinet; when the sample characteristics change and the water demand changes, the speed of the variable frequency pump and the opening of the intelligent flow control valve are dynamically adjusted to control the error of the predetermined water weight within ±0.3%; by online monitoring of the viscosity and conductivity parameters of the slurry, the stirring speed is adjusted in real time between 80-200 r / min, and the stirring time is adaptively changed within 3-12 minutes to ensure the uniformity and stability of the slurry.

[0010] In any of the above schemes, it is preferred that in the multi-parameter detection step, the specific gravity detection utilizes a high-precision density sensor, and the high-precision density sensor is pre-integrated in the sample pool. After the robotic arm injects the slurry sample into the sample pool, the high-precision density sensor accurately measures the slurry density by detecting the change in its own resonant frequency in the slurry; and simultaneously measures the volume and mass and achieves a volume measurement accuracy of ±0.01ml and a mass measurement accuracy of ±0.001g.

[0011] In any of the above schemes, it is preferred that, in the multi-parameter detection step, the stone rate detection relies on X-ray CT equipment to perform tomographic scanning on the slurry in the test tube to obtain the three-dimensional structural information of the stones inside the slurry at different times; at the same time, by tracking the displacement changes of the characteristic points inside the stone, the growth rate of the stone and the final stone rate are accurately calculated to achieve comprehensive and high-precision detection of the stone rate; wherein, the measurement accuracy is ±0.02mm.

[0012] Preferably, in any of the above schemes, in the multi-parameter detection step, an SPR optical fiber sensor with a water-sensitive nanomaterial on its surface is inserted into the slurry for water desorption rate detection. When the moisture content in the slurry changes, the plasma resonance characteristics of the optical fiber surface change. By detecting the intensity and phase changes of the reflected light, the water desorption height of the slurry is measured in real time and with high precision, with a measurement accuracy of ±0.01 mm. According to the real-time changes in the water desorption rate, the time interval of data collection is dynamically adjusted to optimize the detection process.

[0013] In any of the above schemes, it is preferred that, in the multi-parameter detection step, fluorescent tracer particles are added to the clean slurry during fluidity detection, and after the robotic arm injects the clean slurry into the truncated cone mold, the fluorescent particles are excited by laser, and a high-speed camera captures the movement images of the fluorescent particles during the flow of the clean slurry from multiple angles; the image is processed using the PIV algorithm to accurately measure the flow velocity field and shear rate distribution parameters of the clean slurry, and combined with the traditional flow diameter measurement, more comprehensive and in-depth information is provided for evaluating the fluidity of the slurry.

[0014] In any of the above schemes, it is preferred to add fluorescent tracer particles to the clean slurry, so that the fluorescent tracer particles added to the clean slurry emit fluorescence under laser excitation, so that the high-speed camera can capture its movement trajectory; by analyzing the multiple images taken, the displacement of the fluorescent particles is calculated, so as to obtain parameters such as the flow velocity field and shear rate distribution of the clean slurry. Combined with the traditional flow diameter measurement, this method evaluates the slurry fluidity from multiple dimensions. Compared with a single measurement method, it can reflect the flow characteristics of the slurry more comprehensively and deeply, and provide rich data support for accurately judging the fluidity of the overburden delamination filling material.

[0015] In any of the above schemes, it is preferred that, in the data analysis and evaluation step, the standard indicator data in the database is maintained and updated to ensure the authenticity, reliability and security of the data and automatically identify potential regularities and abnormal patterns in the data.

[0016] If the deviation exceeds the preset threshold, select the most discriminatory detection method or equipment for re-inspection to improve the reliability of the test results.

[0017] In any of the above schemes, it is preferred that, in the result determination and feedback step, the quality risk level of the sample is comprehensively evaluated in combination with the material usage scenario, historical test data and multiple indicators of this test.

[0018] In the equipment maintenance and data management steps, the automatic cleaning program uses intelligent cleaning assisted by a cleaning robot. During cleaning, the machine vision system identifies the degree of dirt and wear of each component. Based on the preset cleaning knowledge base, it automatically plans the cleaning path and adjusts the cleaning time, cleaning agent concentration and water flow rate parameters. During the cleaning process, the cleaning effect is evaluated and optimized in real time to continuously improve cleaning efficiency and quality; the detection data is stored in local solid-state drives and cloud servers based on a distributed storage architecture, and quantum encryption technology is used to ensure data security; at the same time, correlation analysis is performed on historical detection data.

[0019] The intelligent detection device for the quality of the overburden separation layer filling material used in the above method includes a sample processing component, a pumping system, a pulping module, a detection module, an operating platform and a PLC control cabinet; The sample processing component includes a funnel-shaped hopper, the bottom of which is movably connected to an intelligent control baffle by a hinge. The intelligent control baffle is driven by a PLC control cabinet through a servo motor. The encoder of the servo motor feeds back the baffle position information to the PLC control cabinet to achieve precise opening and closing control. A high-precision weighing system is also installed below the hopper. The high-precision strain gauge weighing sensor on the high-precision weighing system contacts the hopper. The weighing sensor converts the weight signal into an electrical signal, which is amplified and filtered by the signal conditioning circuit and transmitted to the signal input end of the PLC control cabinet for accurate measurement of the sample weight. An inclined material pipe is connected below the hopper, and a vibrator is provided inside the material pipe. The vibrator is controlled by the PLC control cabinet and started regularly to ensure smooth discharge of excess samples and prevent blockage.

[0020] In any of the above solutions, preferably, the pumping system includes a variable-frequency pump, a wear-resistant pumping pipe, and an intelligent flow control valve; the variable-frequency pump is driven by a permanent magnet synchronous motor, the pump's water inlet is connected to a water source via a quick-connect connector, and its outlet is connected to the pulping module's water inlet via a wear-resistant rubber composite pumping pipe; the intelligent flow control valve is installed on the pumping pipe and located between the variable-frequency pump and the pulping module. The variable-frequency pump and intelligent flow control valve are respectively equipped with current sensors, pressure sensors, and flow sensors to monitor operating status parameters in real time and provide feedback to the PLC control cabinet, enabling precise pumping and flow control.

[0021] In any of the above schemes, it is preferred that the pulping module includes a stirring motor and a stirring barrel with a jacket; the stirring motor is fixed to the top of the stirring barrel through a shock-absorbing mounting bracket, and the output shaft of the stirring motor is fixedly connected to two groups of stirring paddles in the stirring barrel, and the two groups of stirring paddles are distributed at an angle of 45 degrees; the feed port of the stirring barrel is connected to the discharge end of the sample processing component and the water pumping pipe of the pumping system through a pipe with an electromagnetic valve, and the discharge port is connected to the detection module through a pipe with a flow sensor and a pressure sensor.

[0022] In any of the above schemes, preferably, the detection module includes a six-degree-of-freedom robotic arm, a multi-functional detection platform and a multi-sensor detection system; the six-degree-of-freedom robotic arm is installed on a high-strength aluminum alloy bracket on one side of the multi-functional detection platform, and the six-degree-of-freedom robotic arm realizes precise motion control through the high-precision harmonic reducer and servo motor; a quickly replaceable gripper is provided at the end of the six-degree-of-freedom robotic arm for grabbing slurry samples to various detection instruments; a test tube table, a high-precision test tube weighing table and a net slurry fluidity test mold are provided on the multi-functional detection platform; the test tube table is made of polytetrafluoroethylene and is located in the central area of ​​the detection platform, which has good chemical stability and low friction coefficient; the high-precision test tube weighing table is installed under the test tube table, contacts the test tube table through a high-precision pressure sensor and is used to weigh the slurry in the test tube; the signal received by the high-precision pressure sensor is amplified and filtered and then transmitted to the PLC control cabinet; The slurry fluidity test mold is located on one side of the test tube table and is made of stainless steel of standard size; The multi-sensor detection system includes an infrared detection device, an image sensor, an X-ray CT device, an SPR fiber optic sensor, and a laser-induced fluorescence device; the infrared detection device is installed on an adjustable bracket above the detection platform and is used to detect the position and fluidity parameters of the slurry; The image sensor uses a high-speed, high-resolution camera with an optical zoom lens to capture images of the slurry flow and sedimentation process; X-ray CT equipment, SPR fiber optic sensors, and laser-induced fluorescence devices work together to achieve multi-parameter detection functions.

[0023] In any of the above solutions, preferably, the intelligent detection device for the quality of the overburden separation layer filling material further includes an operating platform and a PLC control cabinet; the operating platform adopts an industrial-grade tablet computer and is equipped with a touch screen and physical buttons to facilitate the operator to set parameters, monitor the process and view data; The operating platform is electrically connected to the PLC control cabinet via a high-speed data transmission line. The data transmission line uses a shielded twisted pair cable to ensure the stability and anti-interference of data transmission. The PLC control cabinet is equipped with a controller with a multi-core processor; It also includes a large-capacity data storage module for storing test data and preset standard indicator information; It also includes a 5G communication module to achieve high-speed data transmission with external systems.

[0024] 1. High-Precision Sample Processing: Image recognition and machine learning are used to identify sample types, combined with a dynamic weighing compensation algorithm. The weighing system is calibrated based on environmental parameters, achieving a high accuracy of ±0.05g. The high-precision weighing system in this intelligent device for testing the quality of overburden delamination filling materials utilizes strain gauge sensors and signal conditioning circuitry to ensure accurate weight measurement, reduce sample processing errors, and lay a solid foundation for subsequent testing.

[0025] 2. Precise and Intelligent Slurrying: The pumping system utilizes a flow-pressure dual closed-loop and fuzzy control algorithm based on sample characteristics to maintain a predetermined water-weight error within ±0.3%. The stirring system uses an adaptive fuzzy-PID algorithm to adjust the stirring speed from 80-200 rpm and the stirring time from 3-12 minutes according to the slurry's real-time parameters. The intelligent quality detection device for overburden delamination filling materials features a dual-shaft motor in the slurrying module, paired with a jacketed stirring barrel. Agitators and guide vanes are positioned at a 45-degree angle to optimize stirring, ensure stable and uniform slurry quality, and enhance sample reliability.

[0026] 3. Comprehensive Multi-Parameter Testing: This method integrates cutting-edge technologies such as MEMS, CT-DIC, SPR fiber optic sensing, and LIF-PIV to comprehensively capture key slurry performance parameters. The intelligent quality testing device for overburden separation layer filling materials incorporates a multi-sensor system, including infrared, imaging, and X-ray CT sensors, working collaboratively to achieve multi-parameter testing. This provides rich and accurate material performance information for engineering applications, facilitating precise assessments.

[0027] 4. Efficient and Reliable Analysis: Data is managed using a distributed ledger combined with quantum encryption, and deep belief network algorithms are used to mine data and identify potential patterns and anomalies. Bayesian reasoning is used to recheck when deviations exceed thresholds. Intelligent chips within the PLC control cabinet run algorithms, and large-capacity storage modules store data, enhancing the scientificity and accuracy of analysis, reducing misjudgments, and providing strong support for quality assessments.

[0028] 5. Accurate Risk Assessment: A Monte Carlo simulation risk assessment model is constructed to assess sample risk levels, integrating material usage scenarios, test data, and geological and environmental factors. An intelligent quality detection device for overburden separation layer filling materials transmits results and reports in real time via 5G, providing comprehensive and accurate information for coal mining decision-making and proactively preventing safety hazards caused by quality issues.

[0029] 6. Intelligent Equipment Maintenance: Robots assist with intelligent cleaning, using machine vision to identify dirt and wear. Cleaning routes and parameter adjustments are planned based on a knowledge base, leveraging big data to optimize results. Inspection data is distributed and encrypted, and data mining provides a reference for equipment maintenance and upgrades, ensuring cleanliness, extending service life, and improving stability.

[0030] 7. Convenient Interaction and Remote Control: The operating platform utilizes an industrial tablet with a touchscreen display and physical buttons, facilitating parameter setting, process monitoring, and data viewing. High-speed transmission lines connect to the PLC control cabinet to ensure stable data transmission. A 5G module enables remote data interaction, breaking down space limitations, reducing operational complexity, and improving efficiency. This enhances the intelligence of on-site testing, moving from manual, extensive operation to intelligent testing.

[0031] 8. Improve overall testing efficiency: From sample processing to equipment maintenance, all processes are automated and intelligently coordinated. A six-degree-of-freedom robotic arm quickly grabs samples, while testing technology operates efficiently and data is transmitted and processed in real time, reducing manual intervention and waiting time, meeting rapid testing requirements and reducing testing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or components are generally identified by similar reference numerals throughout the drawings. Elements or components in the drawings are not necessarily drawn to scale.

[0033] Figure 1 It is a schematic diagram of the overall structure of the intelligent detection device for overburden separation layer filling material quality of the present invention.

[0034] Figure 2 This is a schematic diagram of the main structure of the intelligent detection device of the present invention.

[0035] Figure 3 This is a schematic structural diagram of the high-precision flow sensor of the present invention.

[0036] Figure 4 It is a schematic diagram of the structure of the stirring motor of the present invention.

[0037] Figure 5 It is a schematic structural diagram of the shock-absorbing mounting seat of the present invention.

[0038] Figure 6 Schematic diagram of the stirring paddle structure of the present invention.

[0039] Figure 7 It is a schematic diagram of the structure of the electromagnetic valve of the present invention.

[0040] Figure 8It is a schematic diagram of the servo motor structure of the present invention.

[0041] Figure 9 It is a structural schematic diagram of the multifunctional detection platform of the present invention.

[0042] Figure 10 It is a schematic structural diagram of the pumping system of the present invention.

[0043] Figure 11 Schematic diagram of the six-degree-of-freedom robotic arm structure of the present invention.

[0044] Parts list: 1. Hopper; 2. High-precision flow sensor; 3. Pressure sensor; 4. PLC control cabinet; 5. Variable frequency water pump; 6. Intelligent flow control valve; 7. High-precision density sensor; 8. Intelligent control baffle; 9. Servo motor; 10. High-precision strain gauge weighing sensor; 11. Material pipe; 12. Permanent magnet synchronous motor; 13. Quick plug connector; 14. Wear-resistant water pipe; 15. Stirring motor; 16. Shock-absorbing mounting bracket; 17. Stirring barrel; 18. Stirring paddle; 19. Solenoid valve; 20. Six-degree-of-freedom robotic arm; 21. Multi-function detection platform; 22. High-strength aluminum alloy bracket; 23. Test tube table; 24. High-precision test tube weighing table; 25. Net slurry fluidity test mold; 26. Infrared detection device; 27. Operating platform. DETAILED DESCRIPTION

[0045] The following embodiments of the technical solution of the present invention are described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are therefore only used as examples and are not intended to limit the scope of protection of the present invention. Figures 1-11 As shown in .

[0046] An intelligent detection method for the quality of overburden separation layer filling materials includes the following steps: Sample preparation: Pre-process the samples of the overburden separation layer filling material to be inspected, and use intelligent control to accurately control and weigh the sample amount, and automatically discharge the excess sample; Slurry preparation: The weighed sample is mixed and stirred with water extracted according to a set ratio (the ratio is set based on the slurry ratio used in the current construction period and the characteristics of the material sample) in an intelligent quality detection device for overburden separation layer filling materials to form a uniform slurry; Multi-parameter detection: Detect key performance parameters of the slurry, such as specific gravity, stone rate, water separation rate and fluidity, and obtain relevant data; Data analysis and evaluation: Compare and analyze the detected data with the preset database standard indicators, calculate the deviation rate, and automatically trigger the re-inspection mechanism if the deviation exceeds the preset threshold; Result determination and feedback: Determine whether the sample is qualified based on multiple test results and provide feedback on the test results; Equipment maintenance and data management: After the test is completed, the test data will be stored, backed up and shared.

[0047] Before formal testing, multiple groups of qualified samples are selected for testing, and the performance index results are stored in the material standard library as the basis for judging whether the samples are qualified. When preparing the slurry, the water-cement ratio must be strictly controlled, and the amount of water added cannot be adjusted due to changes in sample characteristics.

[0048] The test slurry mix ratio should be set according to the designed fill slurry mix ratio. Different mix ratios are preset and numbered within the test system. The system selects a slurry mix number based on the sample characteristics and the current fill slurry mix ratio. The system controls the amount of fly ash and water added based on the selected mix ratio. Precise control of both fly ash and water addition is crucial. Weighing and flow meters are used to control the accuracy of material and water addition. Mix ratio accuracy is a key factor influencing performance indicators and directly determines test results.

[0049] This method establishes an intelligent system for the entire process, from sample processing to result feedback and equipment maintenance. By integrating the various functions of an intelligent quality detection device for overburden delamination filling materials, the testing process is automated and precise. The intelligent control and weighing system operates based on preset programs and provides real-time feedback, ensuring accurate sample processing. The multi-parameter detection system integrates various advanced testing technologies to comprehensively capture slurry performance data. The entire process is coherent and efficient, reducing human intervention and improving detection accuracy and efficiency.

[0050] In any of the above schemes, it is preferred that in the sample processing preparation step, the appearance characteristics of the sample are analyzed by image recognition technology, and the color, particle shape and size distribution characteristics of the obtained sample are compared with historical data and material standard libraries to automatically identify the sample type; for the different types of samples identified, dynamic weighing is performed and the weighing accuracy is ensured to reach ±0.05g, so as to accurately determine the preset weight and amount of the sample.

[0051] Image recognition technology is used to achieve the ability to visually and autonomously identify sample types; by comparing historical data and material standard libraries, sample characteristics can be matched quickly and accurately; dynamic correction of weight measurement significantly improves weighing accuracy, making sample processing more scientific and reasonable, and avoiding detection errors caused by sample differences.

[0052] In any of the above schemes, it is preferred that, in the slurry preparation step, the water pumping volume is monitored in real time by a high-precision flow sensor, and the pressure in the pumping pipe is monitored by a pressure sensor, and the feedback is sent to the PLC control cabinet; when the sample characteristics change and the water demand changes, the speed of the variable frequency pump and the opening of the intelligent flow control valve are dynamically adjusted to control the error of the predetermined water weight within ±0.3%; by online monitoring of the viscosity and conductivity parameters of the slurry, the stirring speed is adjusted in real time between 80-200 r / min, and the stirring time is adaptively changed within 3-12 minutes to ensure the uniformity and stability of the slurry.

[0053] The use of flow and pressure dual closed-loop control technology can sense the flow and pressure changes during the pumping process in real time and make precise adjustments through the PLC control cabinet; this control method can quickly respond to the water demand caused by changes in sample characteristics and ensure the accuracy of the pumping volume; according to the slurry physical parameters monitored online, the stirring speed and time are dynamically adjusted, and the stirring process can be optimized in real time according to the actual state of the slurry, effectively improving the uniformity and stability of the slurry, and providing more reliable samples for subsequent testing.

[0054] In any of the above schemes, it is preferred that in the multi-parameter detection step, the specific gravity detection utilizes a high-precision density sensor, and the high-precision density sensor is pre-integrated in the sample pool. After the robotic arm injects the slurry sample into the sample pool, the high-precision density sensor accurately measures the slurry density by detecting the change in its own resonant frequency in the slurry; and simultaneously measures the volume and mass and achieves a volume measurement accuracy of ±0.01ml and a mass measurement accuracy of ±0.001g.

[0055] The high-precision density sensor utilizes the principle that the resonant frequency of an object changes when it is exposed to different media. Integrating it into the sample cell allows it to precisely sense the effect of the slurry on its resonant frequency, thereby accurately measuring the slurry's density. Furthermore, combined with a volume measurement module, it enables highly accurate acquisition of the parameters required for calculating the slurry's specific gravity. Compared to traditional detection methods, this approach significantly improves the accuracy of specific gravity detection, reduces measurement errors, and increases detection efficiency, providing more reliable specific gravity data for subsequent data analysis.

[0056] In any of the above schemes, it is preferred that, in the multi-parameter detection step, the stone rate detection relies on X-ray CT equipment to perform tomographic scanning on the slurry in the test tube to obtain the three-dimensional structural information of the stones inside the slurry at different times; at the same time, by tracking the displacement changes of the characteristic points inside the stone, the growth rate of the stone and the final stone rate are accurately calculated to achieve comprehensive and high-precision detection of the stone rate; wherein, the measurement accuracy is ±0.02mm.

[0057] X-ray CT equipment can penetrate the slurry within the test tube, obtaining three-dimensional structural information about the stones within, comprehensively displaying their morphology at different times. By tracking the characteristic points of the stones in the CT images, it can accurately calculate their displacement changes, thereby deriving the stone growth rate and final stone formation rate. This combined approach overcomes the limitations of traditional stone formation rate detection methods, which can only obtain surface or two-dimensional information. It enables comprehensive, high-precision monitoring of the stone formation process, greatly improving the accuracy and reliability of stone formation rate detection and contributing to a deeper understanding of the stone formation characteristics of overburden delamination filling materials.

[0058] Preferably, in any of the above schemes, in the multi-parameter detection step, an SPR optical fiber sensor with a water-sensitive nanomaterial on its surface is inserted into the slurry for water desorption rate detection. When the moisture content in the slurry changes, the plasma resonance characteristics of the optical fiber surface change. By detecting the intensity and phase changes of the reflected light, the water desorption height of the slurry is measured in real time and with high precision, with a measurement accuracy of ±0.01 mm. According to the real-time changes in the water desorption rate, the time interval of data collection is dynamically adjusted to optimize the detection process.

[0059] When the water content in the slurry changes, it alters the surface plasmon resonance characteristics of the SPR fiber sensor, causing changes in the intensity and phase of the reflected light. By accurately detecting these changes, the water extraction height can be measured in real time with high precision. The data collection interval is adjusted according to the dynamic changes in the water extraction rate, increasing the sampling frequency when the water extraction rate changes rapidly and reducing it when the change is slow. This ensures data accuracy and improves detection efficiency, optimizing the entire water extraction rate detection process and more accurately reflecting the water extraction characteristics of the overburden delamination filling material.

[0060] In any of the above schemes, it is preferred that, in the multi-parameter detection step, fluorescent tracer particles are added to the clean slurry during fluidity detection, and after the robotic arm injects the clean slurry into the truncated cone mold, the fluorescent particles are excited by laser, and a high-speed camera captures the movement images of the fluorescent particles during the flow of the clean slurry from multiple angles; the image is processed using the PIV algorithm to accurately measure the flow velocity field and shear rate distribution parameters of the clean slurry, and combined with the traditional flow diameter measurement, more comprehensive and in-depth information is provided for evaluating the fluidity of the slurry.

[0061] In any of the above schemes, it is preferred to add fluorescent tracer particles to the clean slurry, so that the fluorescent tracer particles added to the clean slurry emit fluorescence under laser excitation, so that the high-speed camera can capture its movement trajectory; by analyzing the multiple images taken, the displacement of the fluorescent particles is calculated, so as to obtain parameters such as the flow velocity field and shear rate distribution of the clean slurry. Combined with the traditional flow diameter measurement, this method evaluates the slurry fluidity from multiple dimensions. Compared with a single measurement method, it can reflect the flow characteristics of the slurry more comprehensively and deeply, and provide rich data support for accurately judging the fluidity of the overburden delamination filling material.

[0062] In any of the above schemes, it is preferred that in the data analysis and evaluation step, the standard indicator data in the database is maintained and updated. Once the test results of the previous set of samples are judged to be qualified, they are automatically entered into the standard library and used as the basis for subsequent sample judgments, ensuring the authenticity, reliability and security of the data, and automatically identifying potential regularities and abnormal patterns in the data.

[0063] If the deviation exceeds the preset threshold, select the most discriminatory detection method or equipment for re-inspection to improve the reliability of the test results.

[0064] In any of the above schemes, it is preferred that, in the result determination and feedback step, the quality risk level of the sample is comprehensively evaluated in combination with the material usage scenario, historical test data and multiple indicators of this test.

[0065] In the equipment maintenance and data management steps, the automatic cleaning program uses intelligent cleaning assisted by a cleaning robot. During cleaning, the machine vision system identifies the degree of dirt and wear of each component. Based on the preset cleaning knowledge base, it automatically plans the cleaning path and adjusts the cleaning time, cleaning agent concentration and water flow rate parameters. During the cleaning process, the cleaning effect is evaluated and optimized in real time to continuously improve cleaning efficiency and quality; the detection data is stored in local solid-state drives and cloud servers based on a distributed storage architecture, and quantum encryption technology is used to ensure data security; at the same time, correlation analysis is performed on historical detection data.

[0066] The robot-assisted intelligent cleaning strategy utilizes a machine vision system to monitor equipment components in real time. Based on a pre-set cleaning knowledge base, it uses existing algorithms to plan the optimal cleaning path and adjust cleaning parameters. Data from the cleaning process is continuously collected and analyzed to continuously optimize the cleaning strategy and improve cleaning efficiency and quality. Inspection data is stored using a distributed architecture on local solid-state drives and cloud servers. Quantum encryption technology ensures data security during storage and transmission. Data mining technology analyzes historical inspection data through correlations, identifying potential connections between equipment operation and inspection data. This provides a scientific basis for equipment maintenance and upgrades, extending equipment life and improving performance.

[0067] The intelligent detection device for the quality of the overburden separation layer filling material used in the above method includes a sample processing component, a pumping system, a pulping module, a detection module, an operating platform and a PLC control cabinet; The sample processing component includes a funnel-shaped hopper, the bottom of which is movably connected to an intelligent control baffle by a hinge. The intelligent control baffle is driven by a PLC control cabinet through a servo motor. The encoder of the servo motor feeds back the baffle position information to the PLC control cabinet to achieve precise opening and closing control. A high-precision weighing system is also installed below the hopper. The high-precision strain gauge weighing sensor on the high-precision weighing system contacts the hopper. The weighing sensor converts the weight signal into an electrical signal, which is amplified and filtered by the signal conditioning circuit and transmitted to the signal input end of the PLC control cabinet for accurate measurement of the sample weight. An inclined material pipe is connected below the hopper, and a vibrator is provided inside the material pipe. The vibrator is controlled by the PLC control cabinet and started regularly to ensure smooth discharge of excess samples and prevent blockage.

[0068] The funnel-shaped hopper design facilitates centralized sample discharge. Its inner surface is made of a high-strength, anti-adhesion nanocomposite material to prevent sample adhesion and ensure smooth discharge. The intelligently controlled baffle is precisely opened and closed through the PLC control cabinet and servo motor. The position information fed back by the servo motor encoder ensures control accuracy. The high-precision weighing system uses a strain gauge weighing sensor to convert the sample weight into an electrical signal, which is then processed by the signal conditioning circuit and transmitted to the PLC control cabinet to achieve high-precision weighing. The automatically cleaned, tilted material pipe and the internal vibrating overburden separation layer filling material quality intelligent detection device are controlled by the PLC control cabinet. Regular activation of the vibrating overburden separation layer filling material quality intelligent detection device can effectively prevent the material pipe from clogging, ensure the stable operation of the sample processing component, and provide reliable sample input for the entire detection process.

[0069] In any of the above solutions, preferably, the pumping system includes a variable-frequency pump, a wear-resistant pumping pipe, and an intelligent flow control valve; the variable-frequency pump is driven by a permanent magnet synchronous motor, the pump's water inlet is connected to a water source via a quick-connect connector, and its outlet is connected to the pulping module's water inlet via a wear-resistant rubber composite pumping pipe; the intelligent flow control valve is installed on the pumping pipe and located between the variable-frequency pump and the pulping module. The variable-frequency pump and intelligent flow control valve are respectively equipped with current sensors, pressure sensors, and flow sensors to monitor operating status parameters in real time and provide feedback to the PLC control cabinet, enabling precise pumping and flow control.

[0070] The variable frequency pump is driven by a permanent magnet synchronous motor, offering high efficiency and energy conservation, and can adjust its speed based on actual needs. Quick-connect connectors facilitate connection to a water source, while the wear-resistant rubber composite pump hose extends its service life. The intelligent flow control valve, controlled by a PLC control cabinet, precisely adjusts the pumping volume. Current sensors, pressure sensors, and flow sensors collect real-time operating status parameters of the variable frequency pump and intelligent flow control valve, feeding them back to the PLC control cabinet. This allows the control cabinet to dynamically adjust the pumping system's operation based on actual conditions, ensuring a stable and accurate pumping process and providing the required water volume for the pulping module.

[0071] In any of the above schemes, it is preferred that the pulping module includes a stirring motor and a stirring barrel with a jacket; the stirring motor is fixed to the top of the stirring barrel through a shock-absorbing mounting bracket, and the output shaft of the stirring motor is fixedly connected to two groups of stirring paddles in the stirring barrel, and the two groups of stirring paddles are distributed at an angle of 45 degrees; the feed port of the stirring barrel is connected to the discharge end of the sample processing component and the water pumping pipe of the pumping system through a pipe with an electromagnetic valve, and the discharge port is connected to the detection module through a pipe with a flow sensor and a pressure sensor.

[0072] The stirring motor is fixed to the top of the mixing barrel through a shock-absorbing mount to reduce the impact of vibration during motor operation on the equipment, especially on precision sensor components, to avoid affecting measurement accuracy. The two sets of stirring paddles are distributed at a 45-degree angle, which expands the stirring range. The stirring paddles made of high-strength alloy steel with wear-resistant surface treatment improve stirring efficiency and service life. The mixing barrel is made of stainless steel to ensure corrosion resistance. The internal guide plate optimizes the slurry flow path for more uniform mixing. The electromagnetic valve at the feeding port of the mixing barrel is controlled by the PLC control cabinet to ensure that the sample and water enter the mixing barrel in an accurate proportion. The flow sensor and pressure sensor at the discharge port monitor the slurry output status in real time, providing a stable sample flow for subsequent testing, and ensuring the efficient and stable operation of the pulping module.

[0073] In any of the above schemes, preferably, the detection module includes a six-degree-of-freedom robotic arm, a multi-functional detection platform and a multi-sensor detection system; the six-degree-of-freedom robotic arm is installed on a high-strength aluminum alloy bracket on one side of the multi-functional detection platform, and the six-degree-of-freedom robotic arm realizes precise motion control through the high-precision harmonic reducer and servo motor; a quickly replaceable gripper is provided at the end of the six-degree-of-freedom robotic arm for grabbing slurry samples to various detection instruments; a test tube table, a high-precision test tube weighing table and a net slurry fluidity test mold are provided on the multi-functional detection platform; the test tube table is made of polytetrafluoroethylene and is located in the central area of ​​the detection platform, which has good chemical stability and low friction coefficient; the high-precision test tube weighing table is installed under the test tube table, contacts the test tube table through a high-precision pressure sensor and is used to weigh the slurry in the test tube; the signal received by the high-precision pressure sensor is amplified and filtered and then transmitted to the PLC control cabinet; The slurry fluidity test mold is located on one side of the test tube table and is made of stainless steel of standard size; The multi-sensor detection system includes an infrared detection device, an image sensor, an X-ray CT device, an SPR fiber optic sensor, and a laser-induced fluorescence device; the infrared detection device is installed on an adjustable bracket above the detection platform and is used to detect the position and fluidity parameters of the slurry; The image sensor uses a high-speed, high-resolution camera with an optical zoom lens to capture images of the slurry flow and sedimentation process; X-ray CT equipment, SPR fiber optic sensors, and laser-induced fluorescence devices work together to achieve multi-parameter detection functions.

[0074] The six-degree-of-freedom robotic arm, through a harmonic reducer and servo motor, can achieve high-precision spatial motion control, quickly and accurately deliver samples to various testing instruments, and improve testing efficiency; its quickly replaceable gripper design adapts to the needs of different testing scenarios. The various components on the multi-functional testing platform are rationally arranged, and the polytetrafluoroethylene material of the test tube table can prevent sample adhesion and chemical reactions, ensuring the accuracy of testing. The high-precision test tube weighing table uses pressure sensors to accurately weigh the slurry in the test tube. The multi-sensor detection system integrates a variety of detection technologies. Each sensor can flexibly adjust its position and parameters according to the detection requirements, and work together to achieve comprehensive and high-precision detection of multiple parameters of the slurry, providing a rich and accurate data foundation for the entire detection process.

[0075] In any of the above solutions, preferably, the intelligent detection device for the quality of the overburden separation layer filling material further includes an operating platform and a PLC control cabinet; the operating platform adopts an industrial-grade tablet computer and is equipped with a touch screen and physical buttons to facilitate the operator to set parameters, monitor the process and view data; The operating platform is electrically connected to the PLC control cabinet via a high-speed data transmission line. The data transmission line uses a shielded twisted pair cable to ensure the stability and anti-interference of data transmission. The PLC control cabinet is equipped with a controller with a multi-core processor; It also includes a large-capacity data storage module for storing test data and preset standard indicator information; It also includes a 5G communication module to achieve high-speed data transmission with external systems.

[0076] The operating platform utilizes an industrial-grade tablet computer, combined with a touch screen and physical buttons, providing a convenient human-machine interface, allowing operators to operate according to actual needs. It is connected to the PLC control cabinet via shielded twisted-pair cables, effectively reducing electromagnetic interference and ensuring stable and reliable data transmission. The multi-core processor high-performance controller within the PLC control cabinet is capable of rapidly processing large amounts of data and complex logic, and precisely controlling the various components of the intelligent detection device for the quality of overburden delamination filling materials. The large-capacity solid-state hard drive storage module ensures the secure storage and rapid access of information such as test data and standard indicators. The 5G communication module enables high-speed data interaction between the intelligent detection device for the quality of overburden delamination filling materials and external systems, facilitating real-time sharing of test results and receiving remote commands. The intelligent algorithm processing chip integrates multiple intelligent algorithms, enabling the intelligent detection device for the quality of overburden delamination filling materials to automatically complete operations such as intelligent control, data analysis, and result determination based on test data and preset rules, greatly improving the intelligence level and detection efficiency of the intelligent detection device for the quality of overburden delamination filling materials.

[0077] From the above, it can be seen that the present invention optimizes the sample processing link as a whole: in the intelligent detection process of the quality of the overburden delamination filling material, from the method dimension, it innovatively uses image recognition technology and machine learning algorithms to accurately analyze the appearance characteristics of the sample, automatically determine the sample type, and then provide a scientific basis for the preset sample weight. At the same time, combined with the dynamic weighing compensation algorithm, the environmental parameters and the state of the intelligent detection device for the quality of the overburden delamination filling material are comprehensively considered, and the weighing system is calibrated in real time to achieve high-precision weighing of ±0.05g. At the level of the intelligent detection device for the quality of the overburden delamination filling material, the high-precision weighing system in the sample processing component senses the sample weight through a strain gauge weighing sensor, transmits the data after fine processing by the signal conditioning circuit, and ensures accurate weighing. This all-round optimization greatly reduces the error in the sample processing link, lays a solid and reliable foundation for subsequent detection, and effectively improves the initial accuracy and stability of the entire detection process.

[0078] Comprehensively improving slurry preparation quality: From a methodological perspective, the pumping system utilizes flow-pressure dual closed-loop control technology, coupled with a fuzzy control algorithm. This intelligently adjusts the pumping volume and speed based on real-time changes in sample characteristics, ensuring a ±0.3% error in the predetermined water weight. The stirring system utilizes an adaptive fuzzy-PID control algorithm, flexibly adjusting the stirring speed (80-200 rpm) and stirring time (3-12 minutes) based on the slurry's real-time viscosity, conductivity, and other parameters. Regarding the intelligent quality detection device for overburden delamination filling materials, the slurry module's stirring motor is equipped with a jacketed mixing barrel. Two sets of stirring paddles are arranged at a 45-degree angle, and a guide vane is installed within the barrel to optimize the stirring path and effect. Overall, this innovative design, combined with the intelligent quality detection device for overburden delamination filling materials, ensures highly stable and uniform slurry quality, providing high-quality and reliable samples for subsequent multi-parameter testing, significantly enhancing the credibility and validity of the test results.

[0079] Comprehensive and accurate testing is achieved through the integration of multiple technologies: The method integrates a variety of cutting-edge technologies in the multi-parameter testing step. Specific gravity testing utilizes a high-precision density sensor fabricated using microelectromechanical systems (MEMS) technology. This sensor accurately measures slurry density by detecting changes in resonant frequency. Combined with a high-precision volume measurement module, this sensor achieves volume measurement accuracy of ±0.01ml and mass measurement accuracy of ±0.001g. Stone rate testing utilizes X-ray tomography (CT) to obtain three-dimensional structural information about stones, combined with digital image correlation (DIC) technology to track the displacement of characteristic points, achieving a measurement accuracy of ±0.02mm. Water effusion rate testing utilizes surface plasmon resonance (SPR) fiber optic sensing technology to detect changes in reflected light and measure water effusion height in real time with an accuracy of ±0.01mm. Fluidity testing combines laser-induced fluorescence (LIF) to excite fluorescent tracer particles, and particle image velocimetry (PIV) technology to obtain parameters such as the flow velocity field and shear rate distribution. The intelligent quality detection device for overburden separation layer filling materials demonstrates the multi-sensor detection system of the detection module, which includes an infrared intelligent quality detection device for overburden separation layer filling materials, an image sensor, and an X-ray CT device. Each sensor works in coordination with the support of corresponding protection and adjustment mechanisms. This multi-technical integration enables a more comprehensive and accurate performance evaluation of overburden separation layer filling materials, providing rich and accurate material performance information for engineering applications and strongly supporting the scientific selection and rational use of filling materials in coal mining.

[0080] Advanced algorithms ensure efficient and reliable data analysis: During the data analysis and evaluation phase, the method utilizes distributed ledger technology combined with quantum encryption algorithms to manage data. Multi-node storage and joint maintenance ensure data authenticity and reliability, and quantum encryption ensures secure data transmission and storage. The Deep Belief Network (DBN) algorithm constructs a multi-layer neural network to deeply mine test data and standard indicators, automatically identifying underlying patterns and anomalies. When test data deviations exceed a preset threshold, a re-inspection strategy is initiated based on Bayesian reasoning. By calculating the discriminability of different test methods or equipment for anomalies, the optimal re-inspection method is selected. Regarding the intelligent quality detection device for overburden delamination filling materials, the intelligent algorithm processing chip within the PLC control cabinet efficiently executes these complex algorithms, and the large-capacity data storage module securely stores data. This series of innovative measures significantly improves the scientific nature and accuracy of data analysis, effectively reduces the risk of misjudgment, provides a solid and powerful basis for quality assessment, and facilitates the timely identification and resolution of overburden delamination filling material quality issues.

[0081] Comprehensive factors achieve accurate quality risk assessment: In the result judgment and feedback steps, the method constructs a quality risk assessment model based on Monte Carlo simulation. This model comprehensively considers the material usage scenarios, historical test data, multiple indicators of this test, as well as the potential impact of geological conditions (such as formation pressure, rock properties) and environmental factors (such as groundwater pH and temperature) in coal mining on the quality of filling materials, and comprehensively evaluates the quality risk level of samples. The intelligent detection device for the quality of overburden separation filling materials uses a 5G communication module to feed back the test results containing detailed risk assessment content to the project grouting control system, quality supervision system and other related systems in real time. This comprehensive and accurate quality risk assessment method provides rich and accurate information for coal mining decision-making, helps to identify and prevent safety hazards caused by filling material quality problems in advance, and ensure the safety and quality of coal mining.

[0082] Intelligent strategies assist equipment maintenance and data management: In the equipment maintenance and data management steps, the method adopts a robot-assisted intelligent cleaning strategy. The cleaning robot in the intelligent detection device for the quality of overburden separation filling materials uses a machine vision system to accurately identify the degree of dirt and wear of each component. Based on the preset cleaning knowledge base, it uses an algorithm to automatically plan the optimal cleaning path and dynamically adjust parameters such as cleaning time, cleaning agent concentration, and water flow rate. During the cleaning process, big data analysis technology evaluates and optimizes the cleaning effect in real time. In terms of data management, the detection data is stored in local solid-state drives and cloud servers using a distributed storage architecture, and quantum encryption technology is used to ensure data security. At the same time, data mining technology performs correlation analysis on historical detection data to provide scientific reference for equipment maintenance and upgrades. This intelligent strategy effectively improves the efficiency and quality of equipment maintenance, extends the service life of equipment, ensures data security and reliability, and provides a strong guarantee for the long-term stable operation of equipment.

[0083] Innovative design enables convenient human-machine interaction and remote control: The intelligent device for detecting the quality of overburden and debonding filling materials utilizes an industrial-grade tablet computer with a touchscreen display and physical buttons, providing operators with a convenient human-machine interface. Operators can easily set parameters, monitor processes, and view data. The operating platform is connected to the PLC control cabinet via a high-speed data transmission line (shielded twisted pair), ensuring stable and interference-resistant data transmission. Furthermore, the intelligent device for detecting the quality of overburden and debonding filling materials is equipped with an industrial-grade 5G communication module, enabling high-speed data transmission with external systems. This not only facilitates local control of the detection process by operators, but also overcomes spatial limitations, enabling remote monitoring and management. This reduces the operator's workload and improves operational efficiency, meeting the demand for convenient operation of intelligent detection equipment in modern coal mine backfill mining.

[0084] Collaborative operation significantly improves overall testing efficiency: From a holistic perspective, the method is integrated with the intelligent quality testing equipment for overburden delamination filling materials. The entire testing process, from sample processing to result feedback and equipment maintenance, is tightly coordinated and highly automated and intelligent. A six-degree-of-freedom robotic arm, leveraging a high-precision harmonic reducer and servo motor, quickly and accurately grabs slurry samples and delivers them to the various testing instruments. Multi-parameter testing technology operates efficiently, rapidly acquiring various performance data. Data is transmitted to a PLC control cabinet in real time via high-speed data transmission lines and a 5G communication module for processing, reducing manual intervention and waiting time. Optionally, testing system data can be transmitted to the project production management system in real time, displaying results in real time. This allows all project members to view experimental results immediately, facilitating material scheduling and grouting control. Intelligent equipment maintenance strategies ensure that equipment is always in optimal operating condition, minimizing the impact of equipment failures on testing efficiency. This collaborative operation significantly improves testing efficiency, meeting the urgent need for rapid quality testing of filling materials in modern coal mines, while also reducing testing costs and enhancing the overall economic benefits of coal mine filling.

[0085] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some or all of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention. For those skilled in the art, any replacement improvements or changes made to the implementation methods of the present invention fall within the scope of protection of the present invention.

[0086] Any matters not described in detail in the present invention are well-known technologies to those skilled in the art.

Claims

1. An intelligent detection method for the quality of overburden separation layer filling materials, characterized by: The following steps are involved: Sample preparation: Pre-process the samples of the overburden separation layer filling material to be inspected, and use intelligent control to accurately control and weigh the sample amount, and automatically discharge the excess sample; Slurry preparation: The weighed sample is mixed with water extracted according to the set ratio in the intelligent detection device for the quality of overburden separation layer filling materials, and stirred to form a uniform slurry; Multi-parameter detection: Detect key performance parameters of the slurry, such as specific gravity, stone rate, water separation rate and fluidity, and obtain relevant data; Data analysis and evaluation: Compare and analyze the detected data with the preset database standard indicators, calculate the deviation rate, and automatically trigger the re-inspection mechanism if the deviation exceeds the preset threshold; Result determination and feedback: Determine whether the sample is qualified based on multiple test results and provide feedback on the test results; Equipment maintenance and data management: After the test is completed, the test data will be stored, backed up and shared.

2. The intelligent quality detection method for overburden separation layer filling material according to claim 1, characterized in that: During the sample processing preparation step, the appearance characteristics of the sample are analyzed through image recognition technology. The color, particle shape and size distribution characteristics of the sample obtained are compared with historical data and the material standard library to automatically identify the sample type. For the different types of samples identified, dynamic weighing is performed to ensure a weighing accuracy of ±0.05g, accurately determining the preset weight and amount of the sample.

3. The intelligent quality detection method for overburden separation layer filling material according to claim 2, characterized in that: During the slurry preparation step, a high-precision flow sensor is used to monitor the pumping volume in real time, and a pressure sensor monitors the pressure in the pumping pipe, which is then fed back to the PLC control cabinet. The speed of the variable-frequency pump and the opening of the intelligent flow regulating valve are dynamically adjusted as needed to keep the error of the predetermined water weight within ±0.3%. By online monitoring of the slurry's viscosity and conductivity parameters, the stirring speed is adjusted in real time between 80-200 r / min, and the stirring time is adaptively changed within 3-12 minutes to ensure the uniformity and stability of the slurry.

4. The intelligent quality detection method for overburden separation layer filling material according to claim 3 is characterized by: In the multi-parameter detection step, the specific gravity detection uses a high-precision density sensor, and the high-precision density sensor is pre-integrated in the sample pool. After the robotic arm injects the slurry sample into the sample pool, the high-precision density sensor accurately measures the slurry density by detecting the change in its own resonant frequency in the slurry; at the same time, it measures the volume and mass and achieves a volume measurement accuracy of ±0.01ml and a mass measurement accuracy of ±0.001g.

5. The intelligent quality detection method for overburden separation layer filling material according to claim 4 is characterized by: In the multi-parameter detection step, fluorescent tracer particles are added to the clean slurry during fluidity testing. After the robotic arm injects the clean slurry into the truncated cone mold, the fluorescent particles are excited by laser, and a high-speed camera captures the movement of the fluorescent particles during the flow of the clean slurry from multiple angles; the image is processed using the PIV algorithm to accurately measure the flow velocity field and shear rate distribution parameters of the clean slurry. Combined with traditional flow diameter measurement, it provides more comprehensive and in-depth information for evaluating the fluidity of the slurry.

6. The intelligent quality detection method for overburden separation layer filling material according to claim 5, characterized in that: In the equipment maintenance and data management steps, the automatic cleaning program uses intelligent cleaning assisted by a cleaning robot. During cleaning, the machine vision system identifies the degree of dirt and wear of each component. Based on the preset cleaning knowledge base, it automatically plans the cleaning path and adjusts the cleaning time, cleaning agent concentration and water flow rate parameters. During the cleaning process, the cleaning effect is evaluated and optimized in real time to continuously improve cleaning efficiency and quality; the detection data is stored in local solid-state drives and cloud servers based on a distributed storage architecture, and quantum encryption technology is used to ensure data security; at the same time, correlation analysis is performed on historical detection data.

7. The intelligent quality detection device for overburden separation layer filling material used in the method according to any one of claims 1 to 6, characterized in that: It includes sample processing components, pumping system, pulping module, detection module, operating platform and PLC control cabinet; The sample processing component includes a funnel-shaped hopper, the bottom of which is movably connected to an intelligent control baffle by a hinge. The intelligent control baffle is driven by a PLC control cabinet through a servo motor. The encoder of the servo motor feeds back the baffle position information to the PLC control cabinet to achieve precise opening and closing control. A high-precision weighing system is also installed below the hopper. The high-precision strain gauge weighing sensor on the high-precision weighing system contacts the hopper. The weighing sensor converts the weight signal into an electrical signal, which is amplified and filtered by the signal conditioning circuit and transmitted to the signal input end of the PLC control cabinet for accurate measurement of the sample weight. An inclined material pipe is connected below the hopper, and a vibrator is provided inside the material pipe. The vibrator is controlled by the PLC control cabinet and started regularly to ensure smooth discharge of excess samples and prevent blockage.

8. The intelligent detection device for overburden separation layer filling material quality according to claim 7 is characterized by: The pumping system includes a variable frequency water pump, a wear-resistant water pumping pipe and an intelligent flow regulating valve; the variable frequency water pump is driven by a permanent magnet synchronous motor, the water inlet of the variable frequency water pump is connected to the water source through a quick-plug connector, and the water outlet is connected to the water inlet of the pulping module through a wear-resistant rubber composite water pumping pipe; the intelligent flow regulating valve is installed on the water pumping pipe and is located between the variable frequency water pump and the pulping module.

9. The intelligent detection device for overburden separation layer filling material quality according to claim 8, characterized in that: The detection module includes a six-degree-of-freedom robotic arm, a multifunctional detection platform, and a multi-sensor detection system; the six-degree-of-freedom robotic arm is mounted on a high-strength aluminum alloy bracket on one side of the multifunctional detection platform, and the six-degree-of-freedom robotic arm achieves precise motion control through a high-precision harmonic reducer and servo motor; a quickly replaceable gripper is provided at the end of the six-degree-of-freedom robotic arm for grabbing slurry samples to various detection instruments; the multifunctional detection platform is equipped with a test tube table, a high-precision test tube weighing table, and a net slurry fluidity test mold; The test tube table is made of polytetrafluoroethylene and is located in the center of the testing platform. It has good chemical stability and a low friction coefficient. The high-precision test tube weighing table is installed below the test tube table and contacts the test tube table through a high-precision pressure sensor to weigh the slurry in the test tube. The signal received by the high-precision pressure sensor is amplified and filtered before being transmitted to the PLC control cabinet. The slurry fluidity test mold is located on one side of the test tube table and is made of stainless steel of standard size; The multi-sensor detection system includes an infrared detection device, an image sensor, an X-ray CT device, an SPR fiber optic sensor, and a laser-induced fluorescence device; the infrared detection device is installed on an adjustable bracket above the detection platform and is used to detect the position and fluidity parameters of the slurry; The image sensor uses a high-speed, high-resolution camera with an optical zoom lens to capture images of the slurry flow and sedimentation process; X-ray CT equipment, SPR fiber optic sensors, and laser-induced fluorescence devices work together to achieve multi-parameter detection functions.

10. The intelligent quality detection device for overburden separation layer filling material according to claim 9, characterized in that: It also includes an operating platform and a PLC control cabinet; the operating platform uses an industrial-grade tablet computer and is equipped with a touch screen and physical buttons, making it convenient for operators to set parameters, monitor processes, and view data; The operating platform is electrically connected to the PLC control cabinet via a high-speed data transmission line. The data transmission line uses a shielded twisted pair cable to ensure the stability and anti-interference of data transmission. The PLC control cabinet is equipped with a controller with a multi-core processor; It also includes a large-capacity data storage module for storing test data and preset standard indicator information; It also includes a 5G communication module to achieve high-speed data transmission with external systems.

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