Automatic operation control method and system for loading building

Abstract

The present application relates to the field of loading building control technology, and discloses a method and system for automatic operation control of a loading building. The method includes: generating a vibration characteristic curve of a loading mechanism based on operation status data analysis, calculating conveying control parameters through wavelet transform; processing a loading task list to obtain an operation scheduling sequence, and calculating conveying partition data according to dynamic planning; calculating conveying partition data in real time to obtain the operating rate of the feeding mechanism and the speed parameters of the conveyor belt, and outputting an execution instruction set after verification; evaluating the execution instruction set to calculate the conveying mechanism load, loading platform status, and material accumulation data, and generating control instructions after hierarchical processing; collecting control instruction execution data to obtain loading time, loading mechanism energy consumption, and equipment utilization data, and determining the target control strategy after correlation analysis. The present application realizes precise control of the automatic operation process of the loading building, thereby ensuring the stability and safety of the loading process.

Description

Technical Field

[0001] The present application relates to the field of loading building control, and in particular to a loading building automatic operation control method and system. Background Art

[0002] With the rapid development of industrial automation, loading bays have become widely used as key equipment for loading and unloading large bulk materials. Existing automated control systems for loading bays typically employ fixed-parameter control schemes, primarily encompassing basic functions such as silo pressure monitoring, feeder temperature control, and conveyor belt operating parameter adjustment. These systems utilize PLC controllers for simple open-loop control and are equipped with basic safety mechanisms to maintain basic loading functionality during material transportation. At the same time, some advanced loading bay control systems are beginning to incorporate digital technologies, employing sensor networks for data acquisition and computer systems for simple data processing and status monitoring.

[0003] However, existing automatic operation control methods for loading buildings have many shortcomings: first, traditional fixed parameter control schemes are difficult to adapt to complex and changeable loading conditions, especially when material properties change and equipment status fluctuates, and precise process control cannot be achieved; second, existing control systems lack in-depth analysis of the vibration characteristics of the loading mechanism, which may lead to mechanical resonance during high-speed operation, affecting the service life of the equipment; third, traditional control methods have not established a complete safety assessment system, and are slow to respond when dealing with sudden working conditions, posing a safety hazard; finally, existing systems generally lack energy consumption analysis and optimization mechanisms for the loading process, resulting in energy waste and high operating costs. Summary of the Invention

[0004] The present application provides a method and system for controlling the automatic operation of a loading building, which is used to achieve precise control of the automatic operation process of the loading building by establishing a multi-level data collection and analysis system, combined with dynamic planning and safety assessment mechanisms, thereby ensuring the stability and safety of the loading process.

[0005] In the first aspect, the present application provides an automatic operation control method for a loading building, which includes: collecting loading operation area data to generate silo pressure, feeding mechanism temperature and conveyor belt operating parameters, and normalizing the silo pressure, feeding mechanism temperature and conveyor belt operating parameters to generate operation status data; performing spectrum analysis based on the operation status data to generate a loading mechanism vibration characteristic curve, and calculating the loading mechanism vibration characteristic curve through wavelet transform to obtain conveying control parameters; calculating and processing the loading task list to generate an operation scheduling sequence, and calculating the operation scheduling sequence and the conveying control parameters through dynamic programming to output a conveying partition data; real-time calculation of the conveying partition data to generate the feeding mechanism operating rate and the conveyor belt speed parameters, and processing the feeding mechanism operating rate and the conveyor belt speed parameters through verification to output an execution instruction set; multi-level security evaluation of the execution instruction set to generate the conveying mechanism load, loading platform status and material accumulation data, and hierarchically processing the conveying mechanism load, the loading platform status and the material accumulation data to output control instructions; collecting the execution process data of the control instructions to generate loading time, loading mechanism energy consumption and equipment utilization data, and outputting the target control strategy through correlation analysis of the loading time, the loading mechanism energy consumption and the equipment utilization data.

[0006] In a second aspect, the present application provides an automatic operation control system for a loading building, the automatic operation control system for a loading building comprising:

[0007] an acquisition module, configured to acquire data from the loading operation area to generate silo pressure, feeding mechanism temperature, and conveyor belt operating parameters, and to generate operation status data by normalizing the silo pressure, feeding mechanism temperature, and conveyor belt operating parameters;

[0008] an analysis module, configured to perform spectrum analysis based on the operation status data to generate a vibration characteristic curve of a loading mechanism, and calculate the vibration characteristic curve of the loading mechanism through wavelet transform to obtain a conveying control parameter;

[0009] a processing module, configured to calculate and process a loading task list to generate an operation scheduling sequence, and calculate the operation scheduling sequence and the transportation control parameters through dynamic programming to output transportation partition data;

[0010] a calculation module for calculating the conveying partition data in real time to generate a feeding mechanism operating rate and a conveyor belt speed parameter, and outputting an execution instruction set by verifying and processing the feeding mechanism operating rate and the conveyor belt speed parameter;

[0011] a hierarchical module, configured to generate conveyor load, loading platform status, and material accumulation data by performing a multi-level safety evaluation on the execution instruction set, and output a control instruction by hierarchically processing the conveyor load, the loading platform status, and the material accumulation data;

[0012] The association module is used to collect the execution process data of the control instruction to generate loading time, loading mechanism energy consumption and equipment utilization data, and output the target control strategy by associating and analyzing the loading time, the loading mechanism energy consumption and the equipment utilization data.

[0013] In the technical solution provided by the present application, by collecting sensor data in the loading operation area, real-time monitoring of the silo pressure, feeding mechanism temperature and conveyor belt operating parameters is achieved, and unified operation status data is obtained through normalization processing, which lays a data foundation for the precise control of the loading operation. At the same time, the operation status data is subjected to spectrum analysis and wavelet transform to accurately obtain the vibration characteristic curve of the loading mechanism and the conveying control parameters, effectively avoiding the adverse effects of equipment vibration on the loading process. Moreover, by performing calculation processing and dynamic planning calculations on the loading task list, the operation scheduling sequence is reasonably arranged and the conveying partition data is generated, thereby improving the scheduling efficiency of the loading operation. rate, and performs real-time calculations on the conveying partition data, accurately controls the feeding mechanism operation rate and conveyor belt speed parameters, and ensures the smoothness and continuity of the loading process. By conducting multi-level safety assessments on the execution instruction set, it monitors the conveying mechanism load, loading platform status and material accumulation data in real time, and establishes a complete safety early warning mechanism. Finally, through data collection and correlation analysis of the control instruction execution process, it obtains loading time, loading mechanism energy consumption and equipment utilization data, and generates target control strategies based on this, realizing energy efficiency optimization and equipment utilization improvement in the loading operation process, and significantly improving the safety, efficiency and reliability of the loading building's automatic operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 This is a schematic diagram of an embodiment of the automatic operation control method of the loading building in the embodiment of the present application;

[0016] Figure 2 This is a schematic diagram of an embodiment of the automatic operation control system of the loading building in the embodiment of the present application. DETAILED DESCRIPTION

[0017] The embodiments of the present application provide a method and system for controlling the automatic operation of a loading building. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way are interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, an automatic operation control method for a loading building includes:

[0019] Step S101: Collect loading operation area data to generate silo pressure, feeding mechanism temperature and conveyor belt operating parameters, and generate operation status data by normalizing the silo pressure, feeding mechanism temperature and conveyor belt operating parameters;

[0020] Step S102: performing spectrum analysis based on the operation status data to generate a vibration characteristic curve of the loading mechanism, and calculating the vibration characteristic curve of the loading mechanism through wavelet transform to obtain a conveying control parameter;

[0021] Step S103: Calculate and process the loading task list to generate a job scheduling sequence, and calculate the job scheduling sequence and transportation control parameters through dynamic programming to output transportation partition data;

[0022] Step S104: Real-time calculation of the conveying partition data to generate the feeding mechanism operating rate and the conveyor belt speed parameters, and the feeding mechanism operating rate and the conveyor belt speed parameters are processed by verification to output an execution instruction set;

[0023] Step S105: Execute the instruction set through multi-level safety assessment to generate conveyor load, loading platform status and material accumulation data, and output control instructions by hierarchically processing the conveyor load, loading platform status and material accumulation data;

[0024] Step S106: Collect execution process data of the control instruction to generate loading time, loading mechanism energy consumption and equipment utilization data, and output the target control strategy through correlation analysis of the loading time, loading mechanism energy consumption and equipment utilization data.

[0025] It is understandable that the execution subject of this application can be the automatic operation control system of the loading building, or it can be a terminal or a server, and the specific details are not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0026] Specifically, a sensor network provides comprehensive monitoring of the loading area. A distributed pressure sensor array is used to collect silo pressure. Multiple pressure sensors are deployed at the top, middle, and bottom of the silo to provide real-time monitoring of material accumulation pressure. Thermocouple sensors are used to monitor the feeding mechanism's temperature, with temperature collection points installed at key locations on the drive motor, bearings, and frame. Conveyor belt operating parameter monitoring includes a tension sensor, speed encoder, and displacement sensor to obtain real-time belt speed, tension, and offset data. Raw data is normalized and converted to a uniform numerical range for subsequent processing and analysis. Spectral analysis is used to analyze the vibration characteristics of the loading mechanism. Time-domain decomposition of the operating status data is performed to obtain basic vibration data. Fourier transforms are used to convert the time-domain signals into the frequency domain to obtain frequency-domain characteristic data. Bandpass filters are used to process the frequency-domain characteristic data and extract the dominant frequency component data. The loading mechanism vibration characteristic curve is generated through data reconstruction and curve fitting. The characteristic curve is then decomposed using a Haar wavelet basis to obtain the characteristic parameters required for conveying control.

[0027] During loading task scheduling, the loading task list is first grouped by vehicle type and material type, basic loading standards are established, and time matching is performed based on the loading building's capacity. A timing analysis of the loading equipment is performed, including an assessment of elevator capacity and feeder connection analysis, to generate loading equipment linkage data. The loading guidance trajectory is determined based on material accumulation characteristics. Dynamic planning is performed to combine the operation scheduling sequence with conveyor control parameters to generate final conveyor zone data. Real-time calculation of conveyor zone data involves calculating loading floor height parameters, determining inter-floor conveyor spacing, and generating material distribution trajectory parameters through distributor swing angle analysis. The feeder operating rate is calculated based on the material level setting and material discharge speed, while the conveyor belt speed parameters are determined through load power analysis and speed matching calculations. Mechanical stress verification and safety margin calculations are performed on the operating parameters to generate the execution instruction set. A multi-level safety assessment process comprehensively analyzes the execution instruction set. Equipment operating status monitoring is used to obtain operating data for the loading building's operating mechanism, and load analysis is performed to generate conveyor load data. The loading platform status is determined through force distribution measurement and platform inclination calculation, while material accumulation data is generated based on flow calibration and accumulation height calculation. Safety level classification and hazard source identification provide the basis for the generation of control instructions.

[0028] During control command execution, loading equipment operating data is sampled in time series to obtain loading time series data. The power consumption of each loading mechanism component is collected to generate an energy consumption distribution map. Markov chain state transition analysis is used to obtain equipment operating status data. The correlation between loading time and loading mechanism energy consumption is calculated, and gray correlation analysis is performed on this data combined with equipment utilization data. Multi-objective constraint modeling and optimization calculations are used to generate a target control strategy.

[0029] For example, during coal loading operations at a loading building, the pressure sensor readings at the top of the silo were 0.8 MPa, 1.2 MPa in the middle, and 1.5 MPa at the bottom. Linear interpolation yielded a complete pressure distribution curve. The feeder drive motor temperature was 45°C, the bearing temperature was 38°C, and the frame temperature was 32°C. Weighted average yielded a combined feeder temperature of 41°C. The conveyor belt tension was 15 kN, the belt speed was 2 m / s, and the offset was ±5 mm. These data were normalized and mapped to the [0, 1] interval to generate standardized operating status data. Spectral analysis revealed significant vibration peaks at 10 Hz, 20 Hz, and 35 Hz for the loading mechanism, with 20 Hz being the dominant frequency component. Wavelet decomposition of the vibration curve yielded coefficient sequences at three scales. After denoising, the optimized control parameters were reconstructed. The task list indicated that 20 railway cars needed to be loaded, each with a rated load of 70 tons. Dynamic programming was used to divide the loading operation into five consecutive operating intervals.

[0030] The initial operating rate of the feeding mechanism was set at 80 t / h. After load verification and safety margin calculations, the actual operating rate was determined to be 75 t / h. The conveyor belt speed was calculated based on load power, resulting in an optimal operating speed of 2.2 m / s. During system operation, loading time averaged 18 minutes per section, total energy consumption of the loading mechanism was 85 kWh, and equipment utilization reached 92%. Correlation analysis of these data ultimately led to a target control strategy tailored to the operating conditions, achieving efficient and stable loading operations.

[0031] In the embodiment of the present application, by collecting sensor data in the loading operation area, real-time monitoring of the silo pressure, feeding mechanism temperature and conveyor belt operating parameters is achieved, and unified operation status data is obtained through normalization processing, which lays a data foundation for the precise control of the loading operation. At the same time, spectrum analysis and wavelet transform are performed on the operation status data to accurately obtain the vibration characteristic curve of the loading mechanism and the conveying control parameters, effectively avoiding the adverse effects of equipment vibration on the loading process. Moreover, by performing calculation processing and dynamic planning calculation on the loading task list, the operation scheduling sequence is reasonably arranged and the conveying partition data is generated, thereby improving the scheduling efficiency of the loading operation. It also performs real-time calculations on the conveying partition data, accurately controls the feeding mechanism operating rate and conveyor belt speed parameters, and ensures the smoothness and continuity of the loading process. By conducting multi-level safety assessments on the execution instruction set, it monitors the conveying mechanism load, loading platform status and material accumulation data in real time, and establishes a complete safety early warning mechanism. Finally, through data collection and correlation analysis of the control instruction execution process, it obtains loading time, loading mechanism energy consumption and equipment utilization data, and generates target control strategies based on this, realizing energy efficiency optimization and equipment utilization improvement in the loading operation process, and significantly improving the safety, efficiency and reliability of the loading building's automatic operation.

[0032] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0033] (1) Collect data from the pressure sensors at the top, middle and bottom of the silo to generate multi-point silo pressure data, and calculate the silo pressure based on the linear interpolation operation of the multi-point silo pressure data;

[0034] (2) Measure the temperature values at the feeding mechanism drive motor, bearing and frame positions to obtain the temperature data of each point, and calculate the feeding mechanism temperature based on the weighted average of the temperature data of each point;

[0035] (3) Real-time detection of the conveyor belt's tension, speed, and offset parameters, obtaining the conveyor belt's basic operating data, and generating the conveyor belt's operating parameters by standardizing the conveyor belt's basic operating data;

[0036] (4) Compare the silo pressure and the feeding mechanism temperature to generate feeding system status data, and derive the feeding system reference value based on the threshold judgment of the feeding system status data;

[0037] (5) Extract the correlation characteristics between the conveyor belt operating parameters and the reference values of the feeding system, generate the system linkage parameters, and use the numerical correction to calculate the correction coefficient of the system linkage parameters;

[0038] (6) Convert the correction coefficient into an interval mapping value to generate a normalized reference value. According to the normalized reference value, perform quantitative calculations on the silo pressure, feeding mechanism temperature, and conveyor belt operating parameters, and output the operation status data.

[0039] Specifically, pressure sensor arrays are arranged at the top, middle and bottom of the silo, with 4 pressure sensors installed at the top, 6 at the middle and 8 at the bottom, to generate a multi-point pressure monitoring network. The pressure sensor uses a strain gauge pressure sensor with a range of 0-2MPa and an accuracy of 0.1%. The frequency of multi-point silo pressure data acquisition is 10Hz, and a linear interpolation algorithm is used to continuously process discrete pressure data points to generate a complete silo pressure distribution curve. The linear interpolation operation is based on the pressure values of adjacent measuring points, and the interpolation function is used to calculate the pressure data at the intermediate position. The feeding mechanism temperature monitoring uses a K-type thermocouple sensor, and temperature sensors are installed at key locations such as the drive motor stator winding, motor bearings, and frame support points. The drive motor temperature monitoring points are set at the stator winding surface and the bearing seat, and the frame temperature monitoring points are distributed in the stress concentration area of the support structure. The temperature data collection interval is 1 minute, and the weighted average algorithm is used to process the temperature data of each point. The weight coefficient is determined according to the importance of the monitoring point. The weight of the drive motor temperature is 0.5, the bearing temperature weight is 0.3, and the frame temperature weight is 0.2.

[0040] Conveyor belt operating parameter monitoring encompasses three key aspects: tension monitoring utilizes tension sensors installed at the conveyor belt's front and rear sections, with a measurement range of 0-50 kN; speed monitoring utilizes a photoelectric encoder with a resolution of 0.1 m / s; and offset monitoring utilizes a laser displacement sensor with a measurement range of ±50 mm. Basic conveyor belt operating data undergoes standardization, converting parameters of varying dimensions to a unified standard interval of [0, 1] to facilitate subsequent analysis. Correlation analysis is used to compare silo pressure and feeder mechanism temperature, calculating the correlation coefficient between the two data sets and generating feeder system status data. Threshold determination is performed on the feeder system status data, with a pressure threshold of 1.5 MPa and a temperature threshold of 60°C. When the monitored data exceeds these thresholds, an early warning mechanism is triggered. The threshold determination results serve as reference values for the feeder system, guiding feeder control.

[0041] The correlation calculation between conveyor belt operating parameters and feeder system reference values is based on fuzzy correlation analysis, establishing a mapping relationship between the parameters and generating system linkage parameters. The linkage parameters undergo numerical correction to eliminate abnormal fluctuations and obtain correction coefficients. This correction utilizes a sliding average method with a 10-minute calculation window to smooth out abnormal data points. The correction coefficients are converted to normalized reference values using interval mapping within the [0, 1] range. Silo pressure, feeder mechanism temperature, and conveyor belt operating parameters are uniformly quantified based on the normalized reference values. Linear normalization is used to convert each parameter to a uniform numerical range, facilitating comprehensive analysis and decision-making control.

[0042] For example, in silo pressure monitoring, the readings of the four top pressure sensors were 0.82 MPa, 0.85 MPa, 0.79 MPa, and 0.83 MPa, respectively; the readings of the six middle pressure sensors were 1.15 MPa, 1.18 MPa, 1.21 MPa, 1.19 MPa, 1.16 MPa, and 1.20 MPa; and the readings of the eight bottom pressure sensors were 1.45 MPa, 1.48 MPa, 1.52 MPa, 1.49 MPa, 1.47 MPa, 1.51 MPa, 1.46 MPa, and 1.50 MPa. Linear interpolation was used to calculate the pressure distribution data between the 18 monitoring points and generate a pressure distribution curve. In feeding mechanism temperature monitoring, the drive motor stator winding temperature was 48°C, the bearing temperature was 42°C, and the frame temperature was 35°C. A weighted average calculation is performed based on the preset weight coefficients: Comprehensive temperature = 48 × 0.5 + 42 × 0.3 + 35 × 0.2 = 43.4°C. Conveyor belt operating parameter monitoring shows a belt speed of 2.5 m / s, a tension of 18 kN, and an offset of 3.5 mm. These data are normalized and mapped to the [0, 1] interval.

[0043] Correlation analysis between silo pressure and feeder temperature showed a correlation coefficient of 0.85, indicating a strong correlation between the two. None of the monitored data exceeded the preset threshold, indicating the feeding system was operating normally. Fuzzy correlation analysis of the conveyor belt operating parameters and the feeder system reference values resulted in a calculated system linkage parameter of 0.78. A sliding average over a 10-minute window yielded a correction factor of 0.82. Finally, through interval mapping, this correction factor of 0.82 was mapped to a normalized baseline value of 0.75. Based on this baseline value, the silo pressure, feeder temperature, and conveyor belt operating parameters were uniformly quantified to generate standardized operating status data.

[0044] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0045] (1) Perform time domain decomposition operation on the operation status data to generate vibration basic data, and use Fourier transform to process the vibration basic data to generate frequency domain feature data;

[0046] (2) Using a bandpass filter to process the frequency domain characteristic data, the main frequency component data is obtained, and the vibration characteristic data of the loading mechanism is derived based on the amplitude statistical analysis of the main frequency component data;

[0047] (3) Construct a vibration time series curve based on the vibration characteristic data of the loading mechanism, and calculate the vibration characteristic curve of the loading mechanism by processing the vibration time series curve according to the curve fitting algorithm;

[0048] (4) Decompose the vibration characteristic curve of the loading mechanism according to the Haar wavelet basis to obtain the wavelet coefficient sequence, and then output the characteristic coefficient data by threshold denoising.

[0049] (5) Reconstruct the coefficients based on the characteristic coefficient data to generate vibration trend data, and process the vibration trend data according to the parameter conversion rules to determine the conveying control parameters.

[0050] Specifically, the operating status data is decomposed in the time domain, and a multi-channel data sampling device is used to synchronously collect vibration signals at different positions of the loading mechanism, with the sampling frequency set to 1000Hz. Time domain decomposition divides the continuous vibration signal into several time windows, each with a length of 1 second, and the continuity of the data is ensured by overlapping sampling. The vibration basic data obtained after time domain decomposition contains vibration amplitude, phase and timestamp information. The vibration basic data is processed by fast Fourier transform (FFT) to convert the time domain vibration signal into frequency domain space. The Fourier transform processing uses a 512-point FFT algorithm to transform the data of each time window to obtain frequency domain feature data. The frequency domain feature data contains frequency components in the range of 0-500Hz and the corresponding amplitude information, reflecting the vibration characteristics of the loading mechanism at different frequencies.

[0051] The frequency domain characteristic data is processed by a bandpass filter with a passband range of 10-100Hz, corresponding to the main operating frequency range of the loading mechanism. The bandpass filter uses a Butterworth filter with a filter order of 4 and cutoff frequencies of 10Hz and 100Hz respectively. After filtering, the main frequency component data is obtained, and the amplitude statistical analysis of the main frequency component data is performed. The amplitude mean and standard deviation of each frequency point are calculated to generate the vibration characteristic data of the loading mechanism. The vibration characteristic data of the loading mechanism undergoes data reconstruction and uses an inverse Fourier transform to convert the main frequency components back to the time domain to obtain time series data reflecting the main vibration characteristics. The reconstructed vibration time series data is subjected to least squares curve fitting, and the fitting uses a cubic spline function to reflect the vibration change trend while ensuring the continuity of the curve, and obtain the vibration characteristic curve of the loading mechanism.

[0052] The vibration characteristic curve of the loading mechanism undergoes multi-scale decomposition using the Haar wavelet basis. The Haar wavelet is the simplest orthogonal wavelet basis function with excellent time-frequency localization characteristics. Wavelet decomposition employs a three-layer decomposition structure to produce wavelet coefficient sequences of different scales. Soft threshold denoising is performed on the wavelet coefficient sequence, using the Donoho criterion for threshold setting to remove the influence of noise and obtain characteristic coefficient data. This characteristic coefficient data is then reconstructed using wavelet transforms to obtain denoised vibration trend data. The reconstruction process utilizes an inverse wavelet transform to recombine the characteristic coefficients of different scales. Vibration trend data is converted into actual conveying control parameters, including feed rate, conveyor belt speed, and other operating parameters, through parameter mapping.

[0053] For example, during the operation of the loading mechanism, the amplitude range of the raw vibration signal collected was ±5 mm, the sampling time was 60 seconds, and a total of 60,000 sampling points were obtained. After time domain decomposition, the data was divided into 60 time windows, each containing 1,000 data points. FFT analysis of the data in the first time window revealed a spectrum diagram showing significant vibration peaks at 15 Hz, 35 Hz, and 50 Hz, with peak amplitudes of 3.2 mm, 2.8 mm, and 1.5 mm, respectively. After bandpass filtering, frequency components in the 10-100 Hz range were retained. The amplitudes of the main frequency component data were 3.1 mm at 15 Hz, 2.7 mm at 35 Hz, and 1.4 mm at 50 Hz. Statistical analysis of the main frequency components of 60 time windows showed that the average amplitude of the 15Hz frequency was 3.0mm, with a standard deviation of 0.2mm; the average amplitude of the 35Hz frequency was 2.6mm, with a standard deviation of 0.15mm; and the average amplitude of the 50Hz frequency was 1.3mm, with a standard deviation of 0.1mm.

[0054] During data reconstruction, frequency components with amplitudes greater than 1 mm were selected for inverse transformation, yielding time series data reflecting the primary vibration characteristics. A smooth vibration characteristic curve was obtained using a cubic spline function fit, with a fitting error of less than 0.1 mm. A three-layer Haar wavelet decomposition was performed on the vibration characteristic curve to obtain a sequence of scale coefficients and detail coefficients. Soft threshold denoising was performed, with a threshold of 0.5 mm, to filter out high-frequency noise components with amplitudes below the threshold.

[0055] Vibration trend data obtained through wavelet reconstruction showed that the loading mechanism's primary vibration frequencies remained stable at 15Hz and 35Hz, with amplitudes of 2.9mm and 2.5mm, respectively. Based on these vibration trend characteristics, the feeder's operating rate was determined to be 70t / h, and the conveyor belt speed was set to 1.8m / s. These parameter settings ensured a smooth loading process.

[0056] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0057] (1) Classify and count the vehicle models and material categories in the loading task list to generate the basic loading standard number. Process the basic loading standard number according to the time sorting and loading building capacity matching rules to output the task loading sequence;

[0058] (2) Evaluate the elevator capacity of the task loading sequence and analyze the feeder connection timing to obtain the loading equipment linkage data. Calibrate the loading equipment linkage data according to the scheduling constraints and calculate the loading path set;

[0059] (3) Analyze the material accumulation characteristics of the loading path set, derive the loading guidance trajectory, and combine the loading guidance trajectory with the transportation control parameter matching operation to obtain the operation scheduling sequence;

[0060] (4) Use dynamic programming algorithm to map the job scheduling sequence to spatial partitions, calculate the loading area boundary parameters, and determine the loading point data based on the loading area boundary parameters according to the material flow analysis;

[0061] (5) Plan the carriage coverage trajectory of the loading point data, generate the feeding node sequence, and process the feeding node sequence according to the loading capacity allocation principle to output the transportation partition data.

[0062] Specifically, the loading task list is categorized and organized, with vehicles divided into three standard vehicle types: C60, C70, and C80. Material types are also statistically classified, including coal, ore, and grain. The calculation of the basic loading standard number takes into account the vehicle's rated load capacity and material density, establishing a standard loading capacity for each vehicle type and material combination. The basic loading standard numbers are arranged chronologically and matched to the loading building's designed capacity of 1,000 tons per hour. This generates a task loading sequence that includes loading time, vehicle type, and material type. The task loading sequence then enters the equipment capacity assessment phase. The hoist capacity assessment includes three parameters: lifting height, lifting speed, and maximum lifting capacity. The feeder connection timing analysis considers feeder start-stop characteristics and material delivery delays. Loading equipment linkage data records the operating status and switching sequence of each piece of equipment. Scheduling constraint calibration determines equipment start-stop times and operating parameters. The loading path set contains all feasible paths from the silo to the loading point, each path including the equipment combination and operating parameters.

[0063] Material stacking characteristics analysis considers the angle of repose, fluidity, and bulk density of different materials. The angle of repose determines the geometric shape of the material stack, fluidity affects feed rate control, and bulk density is related to load capacity calculation. The loading guide track is designed based on the material characteristics and car dimensions to ensure uniform material distribution. The following formula is used to match the loading guide track with the conveying control parameters: Where: M(t) is the load control quantity at time t, α is the path matching coefficient, is the efficiency coefficient of the i-th section conveying equipment, is the conveying speed of the i-th section, is the material density correction factor, is the equipment linkage coefficient, is the time attenuation factor, and n is the number of conveying equipment sections.

[0064] The job scheduling sequence is spatially mapped using a dynamic programming algorithm, dividing the continuous loading process into multiple discrete work intervals. Loading area boundary parameters define the spatial extent and load capacity limits of each interval. Material flow analysis determines the flow characteristics and distribution patterns of materials within each interval, generating loading point data. This loading point data reflects the specific location and load capacity of material loading, and carriage coverage trajectory planning ensures even material distribution within the carriage. The feeding node sequence specifies the feeding order and feed capacity of each loading point, ultimately generating conveying zone data to guide the execution of the entire loading process.

[0065] For example, a loading task list includes 30 C70 railcars, each with a rated load of 70 tons. The material is bituminous coal with a density of 1.4 tons per cubic meter. After grouping the railcars and counting the materials, the basic loading standard is calculated to be 66 tons per railcar (assuming a 95% load factor). Based on the loading building's 1,000-ton-per-hour capacity, the task loading sequence is sorted and the total loading time is estimated to be 2 hours. The elevator has a rated lifting capacity of 150 tons per hour, a start-up time of 30 seconds, and a braking time of 20 seconds. The feeder has a rated feed rate of 120 tons per hour and a start-up delay of 15 seconds. Equipment capacity matching analysis determines the optimal loading path: silo 1 - elevator A - feeder 2 - loading point. The bituminous coal has a repose angle of 38 degrees and a flowability index of 65, resulting in a maximum single-point stacking height of 1.2 meters. The 30 railcars are divided into five loading zones, each with six railcars. A dynamic programming algorithm calculated the loading parameters for each section: section length 72 meters, loading point spacing 12 meters, and a single-point load capacity of 11 tons. The resulting conveyor zone data specified specific parameters for each loading point: a feed rate of 90 tons / hour, a conveyor speed of 1.5 meters / second, and a loading time of 4.4 minutes per point. Dynamic adjustment of control parameters throughout the loading process ensured uniformity and efficiency.

[0066] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0067] (1) Calculate the loading floor height parameters of the transport partition data to obtain the inter-layer transport spacing data. Analyze the inter-layer transport spacing data based on the swing angle of the distributor to determine the distribution trajectory parameters.

[0068] (2) Calculate the feeding position of the material trajectory parameters, output the material level setting data, and calculate the feeding mechanism operation rate based on the material level setting data estimated by the material unloading speed;

[0069] (3) Calculate the conveyor belt load corresponding to the feeding mechanism operating speed, generate the load power parameter, and use speed matching to calculate the load power parameter to obtain the conveyor belt speed parameter;

[0070] (4) Verify the mechanical stress of the feeding mechanism operating rate and conveyor belt speed parameters, output the transmission load data, and derive the operation verification results based on the transmission load data calculated according to the safety margin;

[0071] (5) Convert the operation verification results into instruction codes, generate loading operation instructions, and arrange the loading operation instructions to output the execution instruction set according to the operation sequence.

[0072] Specifically, the real-time calculation process in the loading floor automatic operation control method first calculates the loading floor height parameters based on the transportation partition data, calculates the vertical distance and horizontal offset between each floor, and obtains the inter-floor transportation spacing data. The swing angle analysis of the distributor is based on the following formula: in: is the swing angle of the distributor at height h, is the swing frequency of the distributor, is the correction coefficient of the kth layer, is the relative height of the kth layer, is the acceleration due to gravity, is the material flow correction factor, is the inter-layer offset angle, and m is the number of loading floors.

[0073] The feeding bin position is calculated based on the material trajectory parameters, and the material feeding speed estimation formula is adopted in combination with the material level setting data: in: is the feeding speed, is the flow coefficient, is the opening coefficient of the j-th feed port, is the material level height, is the feed port area, is the material friction coefficient, is the time attenuation coefficient, p is the number of feeding ports, It is the unloading time.

[0074] The following formula is used to calculate the conveyor belt load based on the feed mechanism operating rate: in: is the load power, is the power conversion coefficient, is the loaded tension of the rth section, is the friction resistance, is the belt speed, is the inclination angle, is the resistance correction coefficient, is the wrap angle, and q is the number of transmission belt segments.

[0075] For example, a five-story loading building is used for coal loading. Calculation of floor height parameters indicates a vertical spacing of 8 meters and a horizontal offset of 2 meters between floors. Analysis of the distributor's swing angle indicates a swing range of ±15 degrees at an 8-meter height. Taking into account the material flow characteristics, the distribution path is parabolic with a width of 3 meters. The feed silo position is calculated based on a 200-cubic-meter silo volume, a material level height set at 6 meters, and a feed opening area of 0.8 square meters. The feed rate is estimated based on a material friction coefficient of 0.6 and a flow coefficient of 0.85, resulting in a feeder operating rate of 90 tons / hour. Conveyor belt load calculations indicate that at this feed rate, the conveyor belt is operating at 75% capacity, with a load power requirement of 55 kilowatts. Belt speed matching calculations determine a conveyor belt speed of 1.8 meters / second.

[0076] Mechanical stress verification analyzes the forces acting on the feeding mechanism and conveyor belt, calculating bearing loads, tension, and motor torque. Transmission load data indicates a maximum bearing load of 12 kN, belt tension of 18 kN, and a drive motor torque of 280 Nm. A safety factor of 1.5 is used in safety margin calculations to verify that all component stresses are within the permissible range. The operational verification results are converted into binary instruction codes, containing equipment start / stop sequences, operating parameters, and monitoring thresholds, with a coding length of 32 bits. Loading operation instructions are arranged in a time sequence, determining the start intervals for each device as follows: feeder start → 3 seconds → elevator start → 5 seconds → conveyor start, generating a complete set of execution instructions.

[0077] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0078] (1) Detect the equipment operating status corresponding to the execution instruction set, generate the loading building operation mechanism operating data, and calculate the conveying mechanism load based on the load analysis of the loading building operation mechanism operating data;

[0079] (2) Calculate the force distribution of the conveying mechanism load, output the load-bearing capacity data, and calculate the load-bearing capacity data based on the platform inclination angle to obtain the loading platform status;

[0080] (3) Verify the feeding speed of the loading platform state, generate flow calibration data, and calculate the flow calibration data according to the stacking height to determine the material stacking data;

[0081] (4) Classify the load of the conveying mechanism, the status of the loading platform and the material accumulation data into safety levels, output the graded assessment results, identify the hazard sources of the graded assessment results and derive the early warning identification data;

[0082] (5) Verify the operating status of the early warning identification data, calculate the control constraint parameters, and convert the control constraint parameters into instruction codes to output control instructions.

[0083] Specifically, the execution instruction set is used to detect the equipment's operating status. A multi-point sensor network is used to monitor the operating status of each operating mechanism in the loading building in real time. The loading building's operating mechanisms include the feeder, hoist, conveyor belt, and distributor. Each mechanism is equipped with current, speed, and temperature sensors to collect key operating parameters. Current sensors measure the equipment's operating current to determine load levels; speed sensors monitor the actual operating speed of rotating components; and temperature sensors detect the temperatures of key components such as bearings and motors. This data is aggregated to generate loading building operating mechanism operational data, which is then used to calculate the conveyor load through load analysis. The force distribution of the conveyor load is calculated by taking into account static and dynamic impact forces, using strain gauges and pressure sensors placed at key structural nodes. Loading capacity data is derived through a comprehensive analysis of multi-point measurements and reflects the stress state of the entire conveyor system. Platform inclination is calculated using a tilt sensor array. High-precision tilt sensors are installed at the four corners of the loading platform to monitor the platform's spatial posture in real time. Loading platform status data, including platform tilt angle, deformation, and vibration amplitude, is used to assess the platform's operating condition.

[0084] The loading platform status data is used to verify the feeding speed, and the actual material conveying flow rate is calculated in combination with the measurement results of the material level sensor and flow meter. The flow calibration data is calculated after the stacking height, and the material stacking profile is measured using a laser rangefinder array to generate a three-dimensional stacking model. The material stacking data includes the stacking height distribution, stacking density and stacking shape characteristics, which are used to evaluate the uniformity of the loading process. The safety level classification is based on multi-dimensional evaluation indicators, and the conveying mechanism load, loading platform status and material stacking data are divided into four levels according to the degree of danger: safe operation area, warning area, danger area and emergency stop area. The graded assessment adopts a fuzzy comprehensive evaluation method, taking into account the weight and correlation of various indicators. The graded assessment results are deeply analyzed through the hazard source identification algorithm to identify potential risks such as equipment failure, structural overload and abnormal material stacking, and generate early warning identification data.

[0085] The operational status verification of early warning identification data utilizes a state matrix analysis method to establish a correlation model between equipment, platform, and material status. Control constraint parameters include equipment operating limits, platform load limits, and stacking height limits. Instruction encoding converts these constraint parameters into a standard control instruction format, consisting of an operation code, parameter fields, and a check bit.

[0086] For example, during a certain ore loading operation, the feed mechanism current reading was 32A, the speed was 980 rpm, and the bearing temperature was 42°C; the hoist mechanism current was 28A, the hoist speed was 1.2 m / s, and the reducer temperature was 38°C; the conveyor belt tension was 15 kN, the operating speed was 1.8 m / s, and the motor temperature was 45°C. Load analysis of these data determined that the combined load of the conveyor mechanism was 75%. Force distribution calculations showed a maximum strain of 235 microstrains on the main frame, a pressure of 180 kPa on the support structure, and a dynamic impact coefficient of 1.15. The loading platform had inclination angles of 0.3°, 0.4°, 0.3°, and 0.2° at its four corners, with a maximum platform deformation of 2.8 mm and a vibration amplitude of 0.5 mm. Feed rate verification indicated an actual flow rate of 85 t / h, within the allowable deviation from the set value of 90 t / h.

[0087] Material accumulation measurements showed a maximum accumulation height of 1.2m, a density of 1.6t / m³, and a uniform trapezoidal distribution. During the safety level assessment, the conveyor load was in the warning zone (Level II), the loading platform was in the safe operating zone (Level I), and the material accumulation data was in the safe operating zone (Level I). Hazard identification revealed that the bearing temperature of the feeder was approaching the warning value, generating a temperature warning indicator. Operational status verification results indicated the need for appropriate adjustment of the feed rate. Control constraint parameters set the upper limit of the feed rate to 80t / h, while also increasing the frequency of bearing temperature monitoring. The resulting control instructions included adjusting the feed rate to 80t / h, maintaining the conveyor belt speed at 1.8m / s, and reducing the bearing temperature monitoring interval from 10 minutes to 5 minutes. The entire assessment process enabled real-time monitoring and early warning control of the loading operation's safety status.

[0088] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0089] (1) Sample the loading equipment operation data during the execution of the control instructions to generate loading time series data, and calculate the loading time by segmented statistical processing of the loading time series data;

[0090] (2) Collect the power consumption of each component of the loading mechanism during loading time, output the component energy consumption distribution map, and calculate the energy consumption of the loading mechanism based on the cumulative quantitative calculation of the component energy consumption distribution map;

[0091] (3) Use Markov chain to analyze the state transition of the loading mechanism energy consumption, generate equipment operation status data, and divide the equipment operation status data according to the time window to determine the equipment utilization data;

[0092] (4) Calculate the correlation between loading time and loading mechanism energy consumption to obtain loading efficiency parameters. Use grey correlation analysis to process loading efficiency parameters and equipment utilization data to output system efficiency indicators.

[0093] (5) Extract the quantitative characteristics of the system performance indicators, generate a control reference sequence, and map the control reference sequence to the parameter interval to calculate the control factor data;

[0094] (6) Establish a multi-objective constraint model for control factor data, output the operating constraints, and derive the target control strategy by converting the operating constraints according to the KKT conditions.

[0095] Specifically, loading equipment operating data during control command execution is sampled in time series at a 10Hz sampling frequency. Key timing information, including feeder start and stop times, conveyor belt operating time, and loading completion time, is recorded. The loading time series data is processed using a segmented statistical method, dividing the continuous operation process into independent time periods for each carriage. The loading start and end times, actual loading duration, and loading intervals for each section are calculated, ultimately generating standardized loading time data. Loading time serves as a baseline parameter for collecting power consumption data for various components of the loading mechanism, including the feeder motor, elevator, conveyor belt drive, and auxiliary equipment. Power consumption data is collected using a power quality analyzer, which independently measures active power, reactive power, and apparent power for each component. Component energy consumption distribution maps are generated through data visualization, reflecting the energy consumption trends of each component over different time periods. Cumulative quantitative calculations employ the time integration method to calculate the total energy consumption of each component throughout the loading process and convert it into standard kilowatt-hours.

[0096] The energy consumption data of the loading mechanism was analyzed for state transitions using a Markov chain, dividing the equipment's operating status into five states: standby, startup, stable operation, deceleration, and shutdown. The Markov chain model calculates the transition probability matrix between each state and predicts the pattern of equipment state changes. The equipment operating status data was divided into time windows with a window length of 1 hour. The duration of each state was calculated to determine the equipment utilization data. The correlation between loading time and loading mechanism energy consumption was calculated using the Pearson correlation coefficient method. The linear correlation between the two sets of data was analyzed to generate loading efficiency parameters. Grey correlation analysis was performed on the loading efficiency parameters and the equipment utilization data to calculate the correlation coefficient and assess the overall efficiency level of the loading process. Grey correlation analysis considers the geometric similarity of data sequences and derives system efficiency indicators through normalization and correlation calculation.

[0097] System performance indicators are quantitatively characterized using principal component analysis, and dimensionality reduction is performed to identify the main influencing factors. A control reference sequence is sorted by impact and includes equipment operating parameters, energy consumption control targets, and efficiency optimization indicators. Parameter interval mapping converts each indicator to a standard interval, generating factor data that can be directly used for control. This control factor data is then used in the multi-objective constraint modeling phase to establish a multi-objective function encompassing energy consumption minimization, loading efficiency maximization, and equipment utilization optimization. The operating constraints are converted into a mathematical optimization problem using KKT (Knowledge-in-Training) conditions, and the optimal control strategy is obtained by solving them.

[0098] For example, during an 8-hour operation cycle, 20 C70 carriages were loaded. Time-series sampling records show that the average loading time for a single carriage was 18 minutes, including 15 minutes for feeding and 3 minutes for positioning and switching. Segmented statistical processing revealed that the total loading time was 420 minutes, the effective loading time was 360 minutes, and the loading efficiency was 85.7%. Component power consumption data showed that the average power of the feeder was 28kW, with a peak power of 42kW; the average power of the elevator was 35kW, with a peak power of 52kW; and the average power of the conveyor belt was 22kW, with a peak power of 33kW. Cumulative quantitative calculations showed that the total energy consumption for 8 hours was 680kWh, of which the feeder accounted for 35%, the elevator accounted for 42%, and the conveyor belt accounted for 23%.

[0099] Markov chain analysis showed that stable operation accounted for 75%, startup and deceleration transition states accounted for 15%, and standby state accounted for 10%. Time window analysis calculated an average equipment utilization rate of 82%. The correlation coefficient between loading time and energy consumption was 0.92, indicating a high correlation between the two. Grey correlation analysis determined an overall system efficiency index of 0.85. Quantitative feature extraction identified three key control factors: feed rate, belt speed matching, and start-stop frequency. The parameter ranges were mapped to [60, 100] t / h, [0.8, 1.2], and [0.1, 0.3] times / h, respectively. Multi-objective optimization resulted in the target control strategy: a feed rate of 85 t / h, a belt speed matching coefficient of 0.95, and a start-stop frequency of 0.2 times / h. This set of parameters ensures loading efficiency and optimizes energy consumption while maintaining high equipment utilization.

[0100] The above describes the automatic operation control method of the loading building in the embodiment of the present application. The following describes the automatic operation control system of the loading building in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the automatic operation control system of the loading building includes:

[0101] an acquisition module, configured to acquire data from the loading operation area to generate silo pressure, feeding mechanism temperature, and conveyor belt operating parameters, and to generate operation status data by normalizing the silo pressure, feeding mechanism temperature, and conveyor belt operating parameters;

[0102] an analysis module, configured to perform spectrum analysis based on the operation status data to generate a vibration characteristic curve of a loading mechanism, and calculate the vibration characteristic curve of the loading mechanism through wavelet transform to obtain a conveying control parameter;

[0103] a processing module, configured to calculate and process a loading task list to generate an operation scheduling sequence, and calculate the operation scheduling sequence and the transportation control parameters through dynamic programming to output transportation partition data;

[0104] a calculation module for calculating the conveying partition data in real time to generate a feeding mechanism operating rate and a conveyor belt speed parameter, and outputting an execution instruction set by verifying and processing the feeding mechanism operating rate and the conveyor belt speed parameter;

[0105] a hierarchical module, configured to generate conveyor load, loading platform status, and material accumulation data by performing a multi-level safety evaluation on the execution instruction set, and output a control instruction by hierarchically processing the conveyor load, the loading platform status, and the material accumulation data;

[0106] The association module is used to collect the execution process data of the control instruction to generate loading time, loading mechanism energy consumption and equipment utilization data, and output the target control strategy by associating and analyzing the loading time, the loading mechanism energy consumption and the equipment utilization data.

[0107] Through the coordinated cooperation of the above components and the collection of sensor data in the loading operation area, the real-time monitoring of the silo pressure, feeding mechanism temperature and conveyor belt operating parameters is realized, and unified operation status data is obtained through normalization processing, which lays a data foundation for the precise control of the loading operation. At the same time, the operation status data is subjected to spectrum analysis and wavelet transform to accurately obtain the vibration characteristic curve of the loading mechanism and the conveying control parameters, effectively avoiding the adverse effects of equipment vibration on the loading process. Moreover, by calculating and processing the loading task list and performing dynamic planning calculations, the operation scheduling sequence is reasonably arranged and the conveying partition data is generated, which improves the scheduling of the loading operation. It improves efficiency and performs real-time calculations on the conveying partition data, accurately controls the feeding mechanism operating rate and conveyor belt speed parameters, and ensures the smoothness and continuity of the loading process. By conducting multi-level safety assessments on the execution instruction set, it monitors the conveying mechanism load, loading platform status and material accumulation data in real time, and establishes a complete safety early warning mechanism. Finally, through data collection and correlation analysis of the control instruction execution process, it obtains loading time, loading mechanism energy consumption and equipment utilization data, and generates target control strategies based on this, realizing energy efficiency optimization and equipment utilization improvement in the loading operation process, and significantly improving the safety, efficiency and reliability of the loading building's automatic operation.

[0108] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for controlling automatic operation of a loading building, characterized in that: The automatic operation control method of the loading building includes: Collecting loading operation area data to generate silo pressure, feeding mechanism temperature, and conveyor belt operating parameters, and generating operation status data by normalizing the silo pressure, feeding mechanism temperature, and conveyor belt operating parameters; performing spectrum analysis based on the operation status data to generate a loading mechanism vibration characteristic curve, and calculating the loading mechanism vibration characteristic curve through wavelet transform to obtain a conveying control parameter; Calculating and processing the loading task list to generate an operation scheduling sequence, and calculating the operation scheduling sequence and the transportation control parameters through dynamic programming to output transportation partition data; Calculating the conveying partition data in real time to generate a feeding mechanism operating rate and a conveyor belt speed parameter, and outputting an execution instruction set by verifying and processing the feeding mechanism operating rate and the conveyor belt speed parameter; The execution instruction set is evaluated through multi-level security to generate conveying mechanism load, loading platform status and material accumulation data, and the conveying mechanism load, the loading platform status and the material accumulation data are processed hierarchically to output control instructions; The execution process data of the control instruction is collected to generate loading time, loading mechanism energy consumption and equipment utilization data, and the loading time, the loading mechanism energy consumption and the equipment utilization data are analyzed through correlation to output a target control strategy.

2. The automatic operation control method of a loading building according to claim 1, characterized in that: The collecting of loading operation area data to generate silo pressure, feeding mechanism temperature, and conveyor belt operating parameters, and normalizing the silo pressure, feeding mechanism temperature, and conveyor belt operating parameters to generate operation status data, includes: Collecting data from pressure sensors at the top, middle, and bottom of the silo to generate multi-point silo pressure data, and calculating the silo pressure based on linear interpolation of the multi-point silo pressure data; Measuring temperature values at the feeding mechanism drive motor, bearing and frame positions to obtain temperature data at each point, and generating the feeding mechanism temperature by weighted average calculation of the temperature data at each point; Real-time detection of conveyor belt tension, speed and offset parameters, acquisition of conveyor belt basic operation data, and standardization of the conveyor belt basic operation data to generate conveyor belt operation parameters; Performing a correlation analysis on the silo pressure and the feeding mechanism temperature to generate feeding system status data, and deriving a feeding system reference value based on the feeding system status data based on a threshold value; Extracting correlation features between the conveyor belt operating parameters and the feeding system reference values, generating system linkage parameters, and using numerical values to correct the system linkage parameters to calculate correction coefficients; The correction coefficient is converted into an interval mapping value to generate a normalized reference value, and the silo pressure, the feeding mechanism temperature and the conveyor belt operating parameters are quantitatively calculated according to the normalized reference value to output operation status data.

3. The automatic operation control method of a loading building according to claim 1, characterized in that: The performing of spectrum analysis based on the operation status data to generate a vibration characteristic curve of the loading mechanism, and calculating the vibration characteristic curve of the loading mechanism by wavelet transform to obtain a conveying control parameter, includes: Performing a time domain decomposition operation on the operation status data to generate vibration basic data, and processing the vibration basic data using Fourier transform to generate frequency domain feature data; Processing the frequency domain characteristic data using a bandpass filter to obtain main frequency component data, and deriving the loading mechanism vibration characteristic data based on amplitude statistical analysis of the main frequency component data; constructing a vibration time series curve according to the vibration characteristic data of the loading mechanism, and processing the vibration time series curve according to a curve fitting algorithm to calculate the vibration characteristic curve of the loading mechanism; Decomposing and calculating the vibration characteristic curve of the loading mechanism according to the Haar wavelet basis to obtain a wavelet coefficient sequence, and performing threshold denoising on the wavelet coefficient sequence to output characteristic coefficient data; The coefficients are reconstructed based on the characteristic coefficient data to generate vibration trend data, and the vibration trend data is processed according to parameter conversion rules to determine the conveying control parameters.

4. The automatic operation control method of a loading building according to claim 1, characterized in that: The calculating and processing the loading task list to generate a job scheduling sequence, and calculating the job scheduling sequence and the transport control parameters through dynamic programming to output transport partition data, includes: Classify and count vehicle models and material categories in the loading task list to generate a basic loading standard number. Process the basic loading standard number according to the time sorting and loading building capacity matching rules to output the task loading sequence. Evaluate the hoist capacity of the task loading sequence and analyze the feeder connection timing to obtain loading equipment linkage data, and calculate the loading path set by calibrating the loading equipment linkage data according to the scheduling constraints; Analyze the material accumulation characteristics of the loading path set, derive the loading guide trajectory, and combine the loading guide trajectory with the transportation control parameter matching operation to obtain the operation scheduling sequence; A dynamic programming algorithm is used to map the job scheduling sequence to a spatial partition, to calculate loading area boundary parameters, and to determine loading point location data based on the loading area boundary parameters according to material flow analysis; Plan the carriage coverage trajectory of the loading point data, generate a feeding node sequence, and process the feeding node sequence according to the loading amount distribution principle to output the transportation partition data.

5. The automatic operation control method of a loading building according to claim 1, characterized in that: The real-time operation of the conveying partition data to generate a feeding mechanism operating rate and a conveyor belt speed parameter, and the feeding mechanism operating rate and the conveyor belt speed parameter are processed by verification to output an execution instruction set, including: Calculate the loading floor height parameter of the transport partition data to obtain inter-layer transport spacing data, and determine the material distribution trajectory parameter based on the inter-layer transport spacing data analyzed according to the swing angle of the material distributor; Calculate the feeding bin position of the material distribution trajectory parameter, output the material level setting data, and calculate the feeding mechanism operation speed based on the material level setting data estimated according to the material discharge speed; Calculating the conveyor belt load corresponding to the operating speed of the feeding mechanism to generate a belt load power parameter, and calculating the belt load power parameter using speed matching to obtain a conveyor belt speed parameter; Verify the mechanical stress of the feeding mechanism operating rate and the conveyor belt speed parameters, output transmission load data, and calculate the transmission load data according to the safety margin to derive the operation verification result; The operation verification result is converted into an instruction code to generate a loading operation instruction, and the loading operation instruction output execution instruction set is arranged according to the operation schedule.

6. The automatic operation control method of a loading building according to claim 1, characterized in that: The executing instruction set is evaluated through a multi-level safety process to generate conveying mechanism load, loading platform status, and material accumulation data, and the conveying mechanism load, the loading platform status, and the material accumulation data are processed hierarchically to output control instructions, including: Detecting the operating status of the equipment corresponding to the execution instruction set, generating the operating data of the loading building operation mechanism, and calculating the load of the conveying mechanism based on the load analysis of the operating data of the loading building operation mechanism; Calculating the force distribution of the conveying mechanism load, outputting bearing capacity data, and calculating the bearing capacity data according to the platform inclination angle to obtain the loading platform state; Verify the feeding speed of the loading platform state, generate flow calibration data, and calculate the material accumulation data according to the flow calibration data according to the accumulation height; Classify the conveying mechanism load, the loading platform status and the material accumulation data into safety levels, output a graded assessment result, identify the hazard source of the graded assessment result and derive early warning identification data; The running status of the early warning identification data is verified, the control constraint parameters are calculated, and the control constraint parameters are converted into instruction codes to output control instructions.

7. The automatic operation control method of a loading building according to claim 1, characterized in that: The collecting of the execution process data of the control instruction to generate loading time, loading mechanism energy consumption and equipment utilization data, and outputting a target control strategy by correlating and analyzing the loading time, the loading mechanism energy consumption and the equipment utilization data, includes: Sampling loading equipment operation data during the execution of the control instruction to generate loading time series data, and calculating the loading time by segmented statistical processing of the loading time series data; Collecting the power consumption of each component of the loading mechanism during the loading time, outputting a component energy consumption distribution map, and calculating the energy consumption of the loading mechanism based on the cumulative quantitative calculation of the component energy consumption distribution map; Analyzing the state transition of the energy consumption of the loading mechanism using a Markov chain to generate equipment operation state data, and dividing the equipment operation state data according to time windows to determine equipment utilization data; Calculating the correlation between the loading time and the energy consumption of the loading mechanism to obtain a loading efficiency parameter, and processing the loading efficiency parameter and the equipment utilization rate data using grey correlation analysis to output a system efficiency index; Extracting quantitative features of the system performance indicators, generating a control reference sequence, and mapping the control reference sequence to a parameter interval to calculate control factor data; A multi-objective constraint model of the control factor data is established, operation constraint conditions are output, and the operation constraint conditions are converted according to KKT conditions to derive the target control strategy.

8. An automatic operation control system for a loading building, used to implement the automatic operation control method for a loading building as claimed in any one of claims 1 to 7, characterized in that: The automatic operation control system of the loading building includes: an acquisition module, configured to acquire data from the loading operation area to generate silo pressure, feeding mechanism temperature, and conveyor belt operating parameters, and to generate operation status data by normalizing the silo pressure, feeding mechanism temperature, and conveyor belt operating parameters; an analysis module, configured to perform spectrum analysis based on the operation status data to generate a vibration characteristic curve of a loading mechanism, and calculate the vibration characteristic curve of the loading mechanism through wavelet transform to obtain a conveying control parameter; a processing module, configured to calculate and process a loading task list to generate an operation scheduling sequence, and calculate the operation scheduling sequence and the transportation control parameters through dynamic programming to output transportation partition data; a calculation module for calculating the conveying partition data in real time to generate a feeding mechanism operating rate and a conveyor belt speed parameter, and outputting an execution instruction set by verifying and processing the feeding mechanism operating rate and the conveyor belt speed parameter; a hierarchical module, configured to generate conveyor load, loading platform status, and material accumulation data by performing a multi-level safety evaluation on the execution instruction set, and output a control instruction by hierarchically processing the conveyor load, the loading platform status, and the material accumulation data; The association module is used to collect the execution process data of the control instruction to generate loading time, loading mechanism energy consumption and equipment utilization data, and output the target control strategy by associating and analyzing the loading time, the loading mechanism energy consumption and the equipment utilization data.

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