Automatic operation control method and system for loading building

Abstract

The invention relates to the technical field of loading building control, and discloses a loading building automatic operation control method and system. The method comprises the following steps: analyzing and generating a vibration characteristic curve of a loading mechanism based on operation state data, and calculating transmission control parameters through wavelet transform; processing the loading task list to obtain a job scheduling sequence, and calculating and conveying partition data according to dynamic planning; conveying partition data are calculated in real time to obtain feeding mechanism operation speed and conveying belt speed parameters, and an execution instruction set is output after verification; the evaluation execution instruction set calculates conveying mechanism load, loading platform state and material accumulation data, and generates a control instruction after grading processing; and acquiring control instruction execution data to obtain loading time, loading mechanism energy consumption and equipment utilization rate data, and determining a target control strategy after correlation analysis. Accurate control over the automatic operation process of the loading building is achieved, and therefore the stability and safety of the loading process are guaranteed.

Description

Technical Field

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

[0002] With the rapid development of industrial automation, loading buildings have been widely used as key equipment for loading and unloading large bulk materials. Existing loading building automatic operation control systems usually adopt fixed parameter control schemes, mainly including basic functions such as silo pressure monitoring, feeding mechanism temperature control and conveyor belt operation parameter adjustment. These systems implement simple open-loop control through PLC controllers and are equipped with basic safety protection mechanisms to maintain basic loading functions during material transportation. At the same time, some advanced loading building control systems have begun to introduce digital technology, using sensor networks to realize data collection, and performing simple data processing and status monitoring through computer systems.

[0003] However, the existing automatic operation control methods of loading buildings have many shortcomings: first, the traditional fixed parameter control scheme is difficult to adapt to the complex and changeable loading conditions, especially when the material properties change and the equipment status fluctuates, it is impossible to achieve precise process control; second, the existing control system lacks in-depth analysis of the vibration characteristics of the loading mechanism, which may lead to mechanical resonance at high-speed operation, affecting the service life of the equipment; third, the traditional control method has not established a complete safety assessment system, and is slow to respond when dealing with sudden conditions, posing a safety hazard; finally, the existing system generally lacks energy consumption analysis and optimization mechanism 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, the automatic operation control method for a loading building comprising: collecting loading operation area data to generate silo pressure, feeding mechanism temperature and conveyor belt operating parameters, and normalizing the silo pressure, the feeding mechanism temperature and the 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 a job scheduling sequence, and calculating the job 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 operation rate and the conveyor belt speed parameters, and output the execution instruction set by verifying and processing the feeding mechanism operation rate and the conveyor belt speed parameters; multi-level safety evaluation of the execution instruction set to generate the conveying mechanism load, loading platform status and material accumulation data, and hierarchically process the conveying mechanism load, the loading platform status and the material accumulation data to output the control instruction; collecting the execution process data of the control instruction to generate the loading time, the loading mechanism energy consumption and the 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.

[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: A collection module, used for collecting data of the loading operation area to generate silo pressure, feeding mechanism temperature and conveyor belt operation parameters, and generating operation status data by normalizing the silo pressure, feeding mechanism temperature and conveyor belt operation 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, used for calculating and processing the loading task list to generate a job scheduling sequence, and calculating the job scheduling sequence and the transportation control parameters through dynamic programming to output transportation partition data; A calculation module, used for calculating the conveying partition data in real time to generate a feeding mechanism operation rate and a conveyor belt speed parameter, and outputting an execution instruction set by checking and processing the feeding mechanism operation rate and the conveyor belt speed parameter; A hierarchical module, for generating a conveying mechanism load, a loading platform status and a material accumulation data by performing a multi-level safety evaluation on the execution instruction set, and outputting a control instruction by hierarchically processing the conveying mechanism 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.

[0007] 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, spectrum analysis and wavelet transform are performed on the operation status data to accurately obtain the vibration characteristic curve and conveying control parameters of the loading mechanism, effectively avoiding the adverse effects of equipment vibration on the loading process, and 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, thereby improving the scheduling efficiency of the loading operation. rate, and perform real-time calculations on the conveying partition data, accurately control the feeding mechanism operation rate and conveyor belt speed parameters, and ensure the stability and continuity of the loading process. By performing multi-level safety assessments on the execution instruction set, the conveying mechanism load, loading platform status and material accumulation data are monitored in real time, and a complete safety early warning mechanism is established. Finally, through data collection and correlation analysis of the control instruction execution process, the loading time, loading mechanism energy consumption and equipment utilization data are obtained, and the target control strategy is generated accordingly, which realizes energy efficiency optimization and equipment utilization improvement in the loading operation process, and significantly improves the safety, efficiency and reliability of the loading building's automatic operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0009] Figure 1 This is a schematic diagram of an embodiment of the automatic operation control method of a loading building in an embodiment of the present application; 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

[0010] 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 the present 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 data used in this way can be interchanged 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 that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the automatic operation control method of the loading building in the embodiment of the present application includes: Step S101, collecting loading operation area data to generate silo pressure, feeding mechanism temperature and conveyor belt operation parameters, and generating operation status data by normalizing the silo pressure, feeding mechanism temperature and conveyor belt operation parameters; 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; Step S103, calculating and processing the loading task list to generate a job scheduling sequence, and calculating the job scheduling sequence and transportation control parameters through dynamic programming to output transportation partition data; Step S104, real-time calculation of the conveying partition data to generate the feeding mechanism running speed and the conveyor belt speed parameters, and output the execution instruction set by checking and processing the feeding mechanism running speed and the conveyor belt speed parameters; Step S105, executing the instruction set through multi-level safety assessment to generate the conveying mechanism load, loading platform status and material accumulation data, and outputting the control instruction through hierarchical processing of the conveying mechanism load, loading platform status and material accumulation data; Step S106, collecting the execution process data of the control instruction to generate the loading time, loading mechanism energy consumption and equipment utilization data, and outputting the target control strategy through correlation analysis of the loading time, loading mechanism energy consumption and equipment utilization data.

[0012] It is understandable that the execution subject of the present application may be a loading building automatic operation control system, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0013] Specifically, the sensor network is used to monitor the loading operation area in all directions. The silo pressure is collected using a distributed pressure sensor array. Multi-point pressure sensors are arranged at the top, middle and bottom of the silo to monitor the material accumulation pressure in real time. The feeding mechanism temperature is monitored using thermocouple sensors, and temperature collection points are installed at key positions of the drive motor, bearings and frame. The conveyor belt operation parameter monitoring includes tension sensors, speed encoders and displacement sensors to obtain belt speed, tension and offset data in real time. The original data is converted to a unified numerical range after normalization processing, which is convenient for subsequent processing and analysis. The vibration characteristic analysis of the loading mechanism adopts the spectrum analysis method to decompose the operation status data in the time domain to obtain the vibration basic data, and convert the time domain signal to the frequency domain through Fourier transform to obtain the frequency domain characteristic data. The bandpass filter processes the frequency domain characteristic data and extracts the main frequency component data. The vibration characteristic curve of the loading mechanism is generated by data reconstruction and curve fitting, and then the characteristic curve is decomposed using the Haar wavelet basis to obtain the characteristic parameters required for conveying control.

[0014] During the loading task scheduling process, the loading task list is first grouped by vehicle type and material category, basic loading standards are established, and timing matching is performed in combination with the loading building capacity. The loading equipment is subjected to linkage timing analysis, including hoist capacity assessment and feeder connection analysis, to generate loading equipment linkage data. The loading guidance trajectory is determined based on the material accumulation characteristics analysis, and the operation scheduling sequence and conveying control parameters are dynamically planned and calculated to generate the final conveying partition data. The real-time calculation of the conveying partition data involves the calculation of the loading floor height parameters, the determination of the inter-layer conveying spacing, and the generation of the material distribution trajectory parameters through the distributor swing angle analysis. The feeder mechanism operating rate is calculated based on the material level setting and the material unloading speed, and the conveyor belt speed parameters are determined through load power analysis and speed matching calculation. The operating parameters are subjected to mechanical stress verification and safety margin calculation, and finally an execution instruction set is generated. The multi-level safety assessment link conducts a comprehensive analysis of the execution instruction set, obtains the operating data of the loading building operation mechanism through equipment operation status detection, and performs load analysis to obtain the conveying mechanism load data. The loading platform status is obtained through force distribution measurement and platform inclination calculation, and the 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.

[0015] During the execution of control instructions, the loading equipment operation data is sampled in time series to obtain loading time series data. The power consumption of each component of the loading mechanism is collected to generate an energy consumption distribution map, and the equipment operation status data is obtained through Markov chain state migration analysis. The correlation between loading time and loading mechanism energy consumption is calculated, and gray correlation analysis is performed in combination with equipment utilization data. The target control strategy is generated through multi-objective constraint modeling and optimization calculation.

[0016] For example, when a loading building is loading coal, the pressure sensor readings at the top of the silo are 0.8MPa, 1.2MPa in the middle, and 1.5MPa at the bottom. The complete pressure distribution curve is obtained through linear interpolation. The temperature of the feeding mechanism drive motor is 45℃, the bearing temperature is 38℃, and the frame temperature is 32℃. The weighted average gives a comprehensive temperature of the feeding mechanism of 41℃. The conveyor belt tension is 15kN, the belt speed is 2m / s, and the offset is ±5mm. These data are mapped to the [0,1] interval after normalization to generate standardized operation status data. Through spectrum analysis, it is found that the loading mechanism has obvious vibration peaks at 10Hz, 20Hz and 35Hz, of which 20Hz is the main frequency component. After wavelet decomposition of the vibration curve, the coefficient sequence of three scales is obtained, and the optimized control parameters are reconstructed after denoising. According to the task list, 20 carriages need to be loaded, and the rated load of each carriage is 70 tons. The loading operation is divided into 5 continuous operation intervals through dynamic programming calculation.

[0017] The initial operating rate of the feeding mechanism was set at 80t / h. After load verification and safety margin calculation, the actual operating rate was determined to be 75t / h. The conveyor belt speed was calculated by load power to obtain the optimal operating speed of 2.2m / s. During the operation of the system, the loading time was an average of 18 minutes / section, the total energy consumption of the loading mechanism was 85kWh, and the equipment utilization rate reached 92%. Through the correlation analysis of these data, the target control strategy suitable for the working condition was finally obtained, realizing the efficient and stable operation of the loading operation.

[0018] 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 and conveying control parameters of the loading mechanism, effectively avoiding the adverse effects of equipment vibration on the loading process, and 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, 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. It also conducts multi-level safety assessments on the execution instruction set, 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, thus achieving 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.

[0019] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (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; (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; (3) Real-time detection of the conveyor belt's tension, speed and offset parameters, acquisition of the conveyor belt's basic operating data, and standardized processing of the conveyor belt's basic operating data to generate the conveyor belt's operating parameters; (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 value judgment of the feeding system status data; (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 system linkage parameters to calculate the correction coefficient; (6) The correction coefficient is converted into an interval mapping value to generate a normalized reference value. The silo pressure, feeding mechanism temperature and conveyor belt operating parameters are quantified according to the normalized reference value to output the operation status data.

[0020] Specifically, pressure sensor arrays are arranged at the top, middle and bottom of the silo, with 4 pressure sensors installed at the top, 6 pressure sensors installed in the middle and 8 pressure sensors installed at the bottom, generating a multi-point pressure monitoring network. The pressure sensor adopts 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 temperature monitoring of the feeding mechanism adopts a K-type thermocouple sensor, and temperature sensors are installed at key positions such as the stator winding of the drive motor, the motor bearings, and the support points of the frame. The temperature monitoring points of the drive motor are set at the surface of the stator winding and the bearing seat, and the temperature monitoring points of the frame 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 weight of the bearing temperature is 0.3, and the weight of the frame temperature is 0.2.

[0021] The monitoring of conveyor belt operation parameters includes three aspects: tension monitoring uses a tension sensor installed at the beginning and end of the conveyor belt, with a measurement range of 0-50kN; speed monitoring uses a photoelectric encoder with a resolution of 0.1m / s; offset monitoring uses a laser displacement sensor with a measurement range of ±50mm. The basic operation data of the conveyor belt is standardized, and the parameters of different dimensions are converted to a unified standard interval [0,1] to facilitate subsequent analysis and processing. The comparison analysis of the silo pressure and the feeding mechanism temperature uses the correlation analysis method to calculate the correlation coefficient between the two sets of data and generate the feeding system status data. The feeding system status data is judged by thresholds, and the pressure threshold is set to 1.5MPa and the temperature threshold is set to 60℃. When the monitoring data exceeds the threshold, the early warning mechanism is triggered. The threshold judgment result is used as a reference value for the feeding system to guide feeding control.

[0022] The correlation calculation between the conveyor belt operating parameters and the reference values ​​of the feeding system is based on fuzzy correlation analysis, and the mapping relationship between the parameters is established to generate the system linkage parameters. The linkage parameters are numerically corrected to remove abnormal fluctuations and obtain the correction coefficient. The numerical correction adopts the sliding average method, with a calculation time window of 10 minutes to smooth abnormal data points. The correction coefficient is converted to a normalized reference value through interval mapping, and the mapping interval is [0,1]. The silo pressure, feeding mechanism temperature and conveyor belt operating parameters are uniformly quantified according to the normalized reference value, and the linear normalization method is used to convert each parameter to a unified numerical interval, which is convenient for comprehensive analysis and decision-making control.

[0023] For example: In the silo pressure monitoring, the readings of the four pressure sensors at the top are 0.82MPa, 0.85MPa, 0.79MPa, and 0.83MPa, respectively; the readings of the six pressure sensors in the middle are 1.15MPa, 1.18MPa, 1.21MPa, 1.19MPa, 1.16MPa, and 1.20MPa; the readings of the eight pressure sensors at the bottom are 1.45MPa, 1.48MPa, 1.52MPa, 1.49MPa, 1.47MPa, 1.51MPa, 1.46MPa, and 1.50MPa. The pressure distribution data between the 18 monitoring points is obtained by linear interpolation calculation to generate a pressure distribution curve. In the feeding mechanism temperature monitoring, the stator winding temperature of the drive motor is 48°C, the bearing temperature is 42°C, and the frame temperature is 35°C. The weighted average calculation is performed according to the preset weight coefficient: comprehensive temperature = 48×0.5 +42×0.3 + 35×0.2 = 43.4°C. The conveyor belt operation parameter monitoring shows that the belt speed is 2.5m / s, the tension is 18kN, and the offset is 3.5mm. These data are mapped to the [0,1] interval after normalization.

[0024] The correlation analysis of the silo pressure and the feeding mechanism temperature showed a correlation coefficient of 0.85, indicating that the two have a strong correlation. None of the monitoring data exceeded the preset threshold, and the feeding system was in normal condition. The conveyor belt operating parameters and the reference values ​​of the feeding system were analyzed by fuzzy correlation, and the system linkage parameter was calculated to be 0.78. After the sliding average processing of the 10-minute time window, the correction coefficient of 0.82 was obtained. Finally, through the interval mapping conversion, the correction coefficient of 0.82 was mapped to the normalized reference value of 0.75. According to the reference value, the silo pressure, feeding mechanism temperature and conveyor belt operating parameters were uniformly quantified to generate standardized operation status data.

[0025] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (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; (2) Use a bandpass filter to process the frequency domain characteristic data to obtain the main frequency component data, and derive the vibration characteristic data of the loading mechanism based on the amplitude statistical analysis of the main frequency component data; (3) constructing a vibration time series curve based on the vibration characteristic data of the loading mechanism, and processing the vibration time series curve according to the curve fitting algorithm to calculate the vibration characteristic curve of the loading mechanism; (4) Decompose and calculate the vibration characteristic curve of the loading mechanism according to the Haar wavelet basis to obtain the wavelet coefficient sequence, and output the characteristic coefficient data by threshold denoising of the wavelet coefficient sequence; (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.

[0026] Specifically, the operation status data is decomposed in the time domain, and the vibration signals at different positions of the loading mechanism are synchronously collected using a multi-channel data sampling device, 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 basic vibration data obtained after time domain decomposition contains vibration amplitude, phase and timestamp information. The basic vibration 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 and corresponding amplitude information in the range of 0-500Hz, reflecting the vibration characteristics of the loading mechanism at different frequencies.

[0027] The frequency domain characteristic data is processed by a bandpass filter, and the passband range of the filter is 10-100Hz, which corresponds to the main working 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 to calculate the amplitude mean and standard deviation of each frequency point to generate the vibration characteristic data of the loading mechanism. After the data reconstruction link, the vibration characteristic data of the loading mechanism is converted back to the time domain by inverse Fourier transform to obtain time series data reflecting the main vibration characteristics. The least squares curve fitting is performed on the reconstructed vibration time series data, and the fitting adopts 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.

[0028] The vibration characteristic curve of the loading mechanism is decomposed at multiple scales by using the Haar wavelet basis. The Haar wavelet is the simplest orthogonal wavelet basis function with good time-frequency localization characteristics. The wavelet decomposition adopts a three-layer decomposition structure to obtain wavelet coefficient sequences of different scales. The wavelet coefficient sequence is subjected to soft threshold denoising. The threshold setting adopts the donoho criterion to remove the influence of noise and obtain the characteristic coefficient data. The characteristic coefficient data is reconstructed by wavelet to obtain the denoised vibration trend data. The reconstruction process adopts the inverse wavelet transform to recombine the characteristic coefficients of different scales. The vibration trend data is converted into actual conveying control parameters through parameter mapping, including operating parameters such as feeding rate and conveyor belt speed.

[0029] For example: during the operation of the loading mechanism, the amplitude range of the original vibration signal collected is ±5mm, the sampling time is 60 seconds, and a total of 60,000 sampling points are obtained. After time domain decomposition, the data is divided into 60 time windows, each containing 1,000 data points. FFT analysis is performed on the data of the first time window, and the spectrum diagram shows that there are obvious vibration peaks at 15Hz, 35Hz and 50Hz, with peak amplitudes of 3.2mm, 2.8mm and 1.5mm respectively. After bandpass filtering, the frequency components in the range of 10-100Hz are retained. The amplitude of the main frequency component data at 15Hz is 3.1mm, 35Hz is 2.7mm, and 50Hz is 1.4mm. Statistical analysis of the main frequency components of 60 time windows showed that the average amplitude of 15 Hz frequency was 3.0 mm, with a standard deviation of 0.2 mm; the average amplitude of 35 Hz frequency was 2.6 mm, with a standard deviation of 0.15 mm; and the average amplitude of 50 Hz frequency was 1.3 mm, with a standard deviation of 0.1 mm.

[0030] During the data reconstruction process, the frequency components with amplitudes greater than 1 mm were selected for inverse transformation to obtain time series data reflecting the main vibration characteristics. A smooth vibration characteristic curve was obtained by fitting the cubic spline function, and the fitting error was less than 0.1 mm. The vibration characteristic curve was decomposed by 3-layer Haar wavelet to obtain the scale coefficient and detail coefficient sequence. Soft threshold denoising was used, and the threshold was set to 0.5 mm to filter out high-frequency noise components with amplitudes less than the threshold.

[0031] The vibration trend data obtained through wavelet reconstruction showed that the main vibration frequencies of the loading mechanism were stable at 15Hz and 35Hz, with amplitudes of 2.9mm and 2.5mm respectively. According to the vibration trend characteristics, the operating rate of the feeding mechanism was determined to be 70t / h and the conveyor belt speed was 1.8m / s. These parameter settings ensured the smooth operation of the loading process.

[0032] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (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; (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; (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; (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; (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 load allocation principle to output the transportation partition data.

[0033] Specifically, the loading task list is classified and sorted, and the vehicles are divided into three standard models according to the model: C60, C70, and C80. At the same time, the material categories are statistically classified, including different material types such as coal, ore, and grain. The calculation of the basic loading standard number takes into account the rated load of the vehicle model and the material density characteristics, and establishes a standard loading volume for each vehicle model and material combination. The basic loading standard number is arranged in chronological order and matched with the design capacity of the loading building. The hourly capacity of the loading building is 1,000 tons / hour, and a task loading sequence containing loading time, vehicle model, and material type is generated. The task loading sequence enters the equipment capacity evaluation link. The hoist capacity evaluation includes three parameters: lifting height, lifting speed, and maximum lifting amount. The feeder connection timing analysis considers the feeder start-stop characteristics and material transportation delay. The loading equipment linkage data records the operating status and switching timing of each equipment, and determines the equipment start-stop time and operating parameters through scheduling constraint calibration. The loading path set contains all feasible paths from the silo to the loading point, and each path contains equipment combinations and operating parameters.

[0034] Material stacking characteristic analysis considers the angle of repose, fluidity and stacking density of different materials. The angle of repose determines the geometric shape of material stacking, fluidity affects the feed rate control, and stacking density is related to the loading capacity calculation. The loading guide track is designed according to the material characteristics and the size of the carriage to ensure uniform distribution of materials. When matching the loading guide track and the conveying control parameters, the following formula is used: 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 of the 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.

[0035] The job scheduling sequence is spatially partitioned and mapped using a dynamic programming algorithm, dividing the continuous loading process into multiple discrete operation intervals. The loading area boundary parameters define the spatial range and loading capacity limit of each interval. The material flow analysis determines the flow characteristics and distribution law of the material in each interval and generates the loading point data. The loading point data reflects the specific location and loading capacity of the material loading, and the carriage coverage trajectory planning ensures that the material is evenly distributed in the carriage. The feeding node sequence specifies the feeding order and feeding amount of each loading point, and finally generates the conveying partition data to guide the execution of the entire loading process.

[0036] For example, the loading task list includes 30 C70 carriages, each with a rated load of 70 tons. The material is bituminous coal with a density of 1.4 tons / cubic meter. After grouping the vehicle types and counting the materials, the basic loading standard is calculated to be 66 tons per car (considering a 95% loading factor). According to the capacity of the loading building of 1,000 tons / hour, the task loading sequence is obtained after sorting, and the total loading time is expected to be 2 hours. The rated lifting capacity of the elevator is 150 tons / hour, the start time is 30 seconds, and the braking time is 20 seconds. The rated feeding capacity of the feeder is 120 tons / hour, and the start delay is 15 seconds. Through equipment capacity matching analysis, the optimal loading path is determined: silo 1-elevator A-feeder 2-loading point. The angle of repose of bituminous coal is 38 degrees, the fluidity index is 65, and the maximum stacking height of a single point is calculated to be 1.2 meters. The 30 carriages are divided into 5 loading intervals, with 6 carriages in each interval. The dynamic programming algorithm calculates the loading parameters for each section: section length 72 meters, loading point spacing 12 meters, single point loading capacity 11 tons. The final generated conveying partition data specifies the specific parameters of each loading point: feed rate 90 tons / hour, conveyor belt speed 1.5 meters / second, loading time 4.4 minutes / point. The control parameters of the entire loading process are dynamically adjusted to ensure the uniformity and efficiency of the loading process.

[0037] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Calculate the loading floor height parameters of the conveying partition data to obtain the inter-layer conveying spacing data. Analyze the inter-layer conveying spacing data according to the swing angle of the material distributor to determine the material distribution trajectory parameters; (2) Calculate the feeding bin position of the material distribution trajectory parameters, output the material level setting data, and calculate the feeding mechanism operation rate based on the estimated material level setting data according to the material discharge speed; (3) Calculate the conveyor belt load corresponding to the running speed of the feeding mechanism to generate the belt load power parameter, and use speed matching to calculate the belt load power parameter to obtain the conveyor belt speed parameter; (4) Verify the mechanical stress of the feeding mechanism running rate and conveyor belt speed parameters, output the transmission load data, and derive the operation verification results by calculating the transmission load data according to the safety margin; (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 schedule.

[0038] Specifically, the real-time operation link in the automatic operation control method of the loading building first calculates the loading floor height parameters of the transportation partition data, calculates the vertical distance and horizontal offset between each floor, and obtains the inter-layer 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.

[0039] 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 jth feed port, is the material level height, is the feed port area, is the friction coefficient of the material, is the time attenuation coefficient, p is the number of feeding ports, It is the unloading time.

[0040] The following formula is used to calculate the conveyor belt load at the feed mechanism operating rate: in: is the load power, is the power conversion factor, is the load tension of the rth section, is the friction resistance, is the belt speed, is the inclination angle, is the resistance correction factor, is the wrap angle, and q is the number of transmission belt segments.

[0041] For example: A five-story loading building is carrying out coal loading operations. The calculation of the floor height parameters shows that the vertical spacing between each floor is 8 meters and the horizontal offset is 2 meters. The results of the distributor swing angle analysis show that the swing angle range is ±15 degrees at a height of 8 meters. Considering the material flow characteristics, the distribution trajectory is parabolic and the trajectory width is 3 meters. The feeding bin position calculation is based on a silo volume of 200 cubic meters, a material level height set at 6 meters, and a feeding port area of ​​0.8 square meters. The material unloading speed estimation takes into account the material friction coefficient of 0.6 and the flow coefficient of 0.85, and the calculated feeding mechanism operating rate is 90 tons / hour. The conveyor belt load calculation shows that at this feeding rate, the conveyor belt working load is 75% and the load power demand is 55 kilowatts. The belt speed matching calculation determines that the conveyor belt speed is 1.8 meters / second.

[0042] Mechanical stress verification analyzes the force of the feeding mechanism and conveyor belt, and calculates the bearing load, tension and motor torque. Transmission load data shows that the maximum bearing load is 12 kN, the belt tension is 18 kN, and the drive motor torque is 280 Nm. The safety margin calculation uses a safety factor of 1.5 to verify that the stress of all components is within the allowable range. The operation verification results are converted into binary instruction codes, including the equipment start and stop sequence, operating parameters and monitoring thresholds, with a coding length of 32 bits. The loading operation instructions are arranged in a time sequence, and the start interval of each equipment is determined as follows: feeder start → 3 seconds → elevator start → 5 seconds → conveyor belt start, generating a complete execution instruction set.

[0043] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Detect the equipment operation status corresponding to the execution instruction set, generate the operation data of the loading building operation mechanism, and calculate the load of the conveying mechanism based on the load analysis of the loading building operation mechanism operation data; (2) Calculate the force distribution of the conveying mechanism load, output the bearing capacity data, and calculate the bearing capacity data based on the platform inclination angle to obtain the loading platform status; (3) Verify the feeding speed of the loading platform status, generate flow calibration data, and calculate the flow calibration data according to the stacking height to determine the material stacking data; (4) Classify the conveying mechanism load, loading platform status and material accumulation data into safety levels, output the graded assessment results, identify the hazard sources of the graded assessment results and derive early warning identification data; (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.

[0044] Specifically, the equipment operation status is detected for the execution instruction set, and the operation status of each operating mechanism of the loading building is monitored in real time using a multi-point sensor network. The loading building operating mechanism includes a feeding mechanism, a lifting mechanism, a conveyor belt and a distributor. Each mechanism is equipped with a current sensor, a speed sensor and a temperature sensor to collect key operating parameters. The current sensor measures the operating current of the equipment to determine the load level; the speed sensor monitors the actual operating speed of the rotating parts; and the temperature sensor detects the temperature of key parts such as bearings and motors. These data are aggregated to generate the operation data of the loading building operating mechanism, and the load of the conveying mechanism is obtained through load analysis and calculation. The force distribution calculation of the conveying mechanism load takes into account static loads and dynamic impact forces, and strain gauges and pressure sensors are arranged at key structural nodes. The bearing capacity data is obtained through a comprehensive analysis of multi-point measurement values, reflecting the stress state of the entire conveying system. The platform inclination calculation uses an inclination sensor array, and high-precision inclination sensors are installed at the four corners of the loading platform to monitor the spatial posture of the platform in real time. The loading platform status data includes the platform inclination angle, deformation and vibration amplitude, which are used to evaluate the working condition of the platform.

[0045] The loading platform status data is used to verify the feeding speed, and the actual material conveying flow is calculated by combining the measurement results of the material level sensor and the flow meter. The flow calibration data is calculated after the stacking height, and the material stacking profile is measured by 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 hierarchical evaluation adopts a fuzzy comprehensive evaluation method, taking into account the weight and correlation of various indicators. The hierarchical evaluation 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.

[0046] The operation status verification of the early warning identification data adopts the state matrix analysis method to establish the correlation model of equipment status, platform status and material status. The control constraint parameters include equipment operation limit, platform load limit and stacking height limit. The instruction encoding process converts the constraint parameters into a standard control instruction format, including operation code, parameter field and check bit.

[0047] For example: During a certain ore loading operation, the current reading of the feeding mechanism is 32A, the speed is 980rpm, and the bearing temperature is 42℃; the current of the lifting mechanism is 28A, the lifting speed is 1.2m / s, and the reducer temperature is 38℃; the conveyor belt tension is 15kN, the running speed is 1.8m / s, and the motor temperature is 45℃. After load analysis, these data are calculated to have a comprehensive load of 75% for the conveying mechanism. The force distribution measurement shows that the maximum strain of the main frame is 235 microstrains, the pressure of the supporting structure is 180kPa, and the dynamic impact coefficient is 1.15. The inclination angles of the four corners of the loading platform are 0.3°, 0.4°, 0.3°, and 0.2°, respectively. The maximum deformation of the platform is 2.8mm, and the vibration amplitude is 0.5mm. The feeding speed verification shows that the actual flow rate is 85t / h, and the deviation from the set value of 90t / h is within the allowable range.

[0048] The material accumulation measurement shows that the maximum accumulation height is 1.2m, the accumulation density is 1.6t / m³, and the accumulation shape is uniformly distributed in a trapezoidal shape. In the safety level assessment, the conveying mechanism load is in the warning area (level II), the loading platform status is in the safe operation area (level I), and the material accumulation data is in the safe operation area (level I). Hazard source identification found that the bearing temperature of the feeding mechanism was close to the warning value, and a temperature warning mark was generated. The operating status verification results show that the feeding speed needs to be appropriately adjusted. The control constraint parameters set the upper limit of the feeding speed to 80t / h, and at the same time strengthen the bearing temperature monitoring frequency. The control instructions finally generated include: adjusting the feeding speed to 80t / h, maintaining the conveyor belt speed at 1.8m / s, and shortening the bearing temperature monitoring interval from 10 minutes to 5 minutes. The entire evaluation process realizes real-time monitoring and early warning control of the safety status of loading operations.

[0049] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Sample the loading equipment operation data during the execution of the control instructions, generate loading time series data, and calculate the loading time by segmented statistical processing of the loading time series data; (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; (3) Use Markov chain to analyze the state transition of the energy consumption of the loading mechanism, generate equipment operation status data, and divide the equipment operation status data according to the time window to determine the equipment utilization data; (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. (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; (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.

[0050] Specifically, the operation data of the loading equipment during the execution of the control instructions is sampled in time series, and the sampling frequency is set to 10Hz. Key time series information including the start and stop time of the feeder, the running time of the conveyor belt, and the time point of loading completion are recorded. The loading time series data adopts the segmented statistical processing method to divide the continuous operation process into independent time periods according to each carriage, calculate the loading start and end time, actual loading time and loading interval time of each section, and finally obtain standardized loading time data. The loading time is used as a reference parameter for the power consumption collection of each component of the loading mechanism, including the feeder motor power consumption, the hoist power consumption, the conveyor belt drive power consumption and the auxiliary equipment power consumption. The power consumption data is collected using a power quality analyzer to measure each component independently and record the active power, reactive power and apparent power. The component energy consumption distribution map is generated through data visualization processing to reflect the energy consumption change trend of each component in different time periods. The cumulative quantitative calculation adopts the time integration method to calculate the total energy consumption of each component during the entire loading process and convert it into the standard unit kilowatt-hour.

[0051] The energy consumption data of the loading mechanism is analyzed through state transition through Markov chain, and the equipment operation state is divided into five states: standby, start-up, stable operation, deceleration and shutdown. The Markov chain model calculates the transition probability matrix between each state and predicts the law of equipment state change. The equipment operation state data is divided into time windows, and the window length is set to 1 hour. The duration of each state is counted to calculate the equipment utilization data. The correlation between loading time and loading mechanism energy consumption is calculated using the Pearson correlation coefficient method to analyze the linear correlation between the two sets of data and generate loading efficiency parameters. The loading efficiency parameters and equipment utilization data are subjected to gray correlation analysis, and the correlation coefficient is calculated to evaluate the overall efficiency level of the loading process. Grey correlation analysis takes into account the geometric similarity of the data sequence, and obtains the system efficiency index through normalization and correlation calculation.

[0052] The system performance index is quantitatively extracted through principal component analysis, and the main influencing factors are obtained after dimensionality reduction. The control reference sequence is sorted according to the degree of influence, including equipment operating parameters, energy consumption control targets and efficiency optimization indicators. Parameter interval mapping converts each indicator to a standard interval to generate factor data that can be directly used for control. The control factor data enters the multi-objective constraint modeling link to establish a multi-objective function including minimizing energy consumption, maximizing loading efficiency and optimizing equipment utilization. The operating constraints are converted into mathematical optimization problems through KKT conditions, and the optimal control strategy is obtained by solving them.

[0053] For example: in an 8-hour operation cycle, 20 C70 carriages are loaded. The time-series sampling records show that the average loading time of a single carriage is 18 minutes, including 15 minutes for feeding and 3 minutes for positioning and switching. Segmented statistical processing shows that the total loading time is 420 minutes, the effective loading time is 360 minutes, and the loading work rate is 85.7%. The power consumption data collected from the components show that the average power of the feeder is 28kW, and the peak power is 42kW; the average power of the hoist is 35kW, and the peak power is 52kW; the average power of the conveyor belt is 22kW, and the peak power is 33kW. The cumulative quantitative calculation shows that the total energy consumption in 8 hours is 680kWh, of which the feeder accounts for 35%, the hoist accounts for 42%, and the conveyor belt accounts for 23%.

[0054] Markov chain analysis shows that the stable operation state accounts for 75%, the start-up and deceleration transition state accounts for 15%, and the standby state accounts for 10%. The time window analysis calculated that the average equipment utilization rate was 82%. The correlation coefficient between loading time and energy consumption was 0.92, indicating that the two were highly correlated. Grey correlation analysis showed that the system comprehensive efficiency index was 0.85. Quantitative feature extraction determined three main control factors: feeding rate, belt speed matching and start-stop frequency, and the parameter intervals were mapped to [60,100]t / h, [0.8,1.2] and [0.1,0.3] times / hour respectively. Multi-objective optimization solution obtained the target control strategy: feeding rate 85t / h, belt speed matching coefficient 0.95, start-stop frequency 0.2 times / hour. This set of parameters not only ensures loading efficiency, but also achieves energy consumption optimization, while maintaining a high equipment utilization rate.

[0055] 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: A collection module, used for collecting data of the loading operation area to generate silo pressure, feeding mechanism temperature and conveyor belt operation parameters, and generating operation status data by normalizing the silo pressure, feeding mechanism temperature and conveyor belt operation 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, used for calculating and processing the loading task list to generate a job scheduling sequence, and calculating the job scheduling sequence and the transportation control parameters through dynamic programming to output transportation partition data; A calculation module, used for calculating the conveying partition data in real time to generate a feeding mechanism operation rate and a conveyor belt speed parameter, and outputting an execution instruction set by checking and processing the feeding mechanism operation rate and the conveyor belt speed parameter; A hierarchical module, for generating a conveying mechanism load, a loading platform status and a material accumulation data by performing a multi-level safety evaluation on the execution instruction set, and outputting a control instruction by hierarchically processing the conveying mechanism 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.

[0056] Through the coordinated cooperation of the above-mentioned components, by collecting sensor data in the loading operation area, real-time monitoring of 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 loading operations. 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. In addition, 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 loading operations. It improves efficiency 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 stability and continuity of the loading process. It conducts multi-level safety assessments on the execution instruction set, 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 the loading time, loading mechanism energy consumption and equipment utilization data, and generates target control strategies based on this, thus achieving 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.

[0057] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned 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 comprises: Collecting loading operation area data to generate silo pressure, feeding mechanism temperature and conveyor belt operation parameters, and generating operation status data by normalizing the silo pressure, feeding mechanism temperature and conveyor belt operation 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 by wavelet transform to obtain a conveying control parameter; Calculate and process the loading task list to generate a job scheduling sequence, and calculate the job 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 operation rate and a conveyor belt speed parameter, and outputting an execution instruction set by verifying and processing the feeding mechanism operation rate and the conveyor belt speed parameter; The execution instruction set is evaluated through multi-level safety to generate the conveying mechanism load, the loading platform status and the material accumulation data, and the conveying mechanism load, the loading platform status and the material accumulation data are processed in a hierarchical manner to output the control instruction; 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 operation parameters, and normalizing the silo pressure, feeding mechanism temperature and conveyor belt operation parameters to generate operation status data, includes: Collect data from 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 linear interpolation calculation of the multi-point silo pressure data; Measuring temperature values ​​at the feeding mechanism driving motor, bearing and frame positions to obtain temperature data at each point, and generating the feeding mechanism temperature based on weighted average calculation of the temperature data at each point; Real-time detection of the tension, speed and offset parameters of the conveyor belt, acquisition of basic conveyor belt operation data, and standardization of the conveyor belt basic operation data to generate conveyor belt operation parameters; Comparing the silo pressure with the feeding mechanism temperature to generate feeding system status data, and deriving a feeding system reference value based on the feeding system status data by judging the threshold value; Extracting the correlation characteristics between the conveyor belt operation parameters and the reference values ​​of the feeding system, generating system linkage parameters, and using numerical values ​​to correct the system linkage parameters to calculate the correction coefficient; 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 operation 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 a loading mechanism, and calculating the vibration characteristic curve of the loading mechanism by wavelet transform to obtain a conveying control parameter, comprises: Performing time domain decomposition operation on the operation status data to generate vibration basic data, and processing the vibration basic data by Fourier transform to generate frequency domain feature data; Processing the frequency domain characteristic data by using a bandpass filter to obtain main frequency component data, and deriving the vibration characteristic data of the loading mechanism 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, obtaining a wavelet coefficient sequence, and processing the wavelet coefficient sequence through threshold denoising 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 computing and processing of the loading task list to generate a job scheduling sequence, and computing the job scheduling sequence and the transport control parameters through dynamic programming to output transport partition data, includes: Classify and count the vehicle models and material categories of the loading task list to generate the basic loading standard number, and 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 the space partition, and the loading area boundary parameters are calculated, and the loading point data is determined according to the material flow analysis of the loading area boundary parameters; The carriage coverage trajectory of the loading point data is planned, a feeding node sequence is generated, and the feeding node sequence is processed 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 operation rate and a conveyor belt speed parameter, and the feeding mechanism operation 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 the inter-layer transport spacing data, and determine the material distribution trajectory parameter according to 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 running speed based on the material level setting data estimated according to the material discharge speed; Calculate the conveyor belt load corresponding to the running speed of the feeding mechanism to generate a belt load power parameter, and calculate the belt load power parameter by speed matching to obtain a conveyor belt speed parameter; Verify the mechanical stress of the feeding mechanism running speed and the conveyor belt speed parameters, output the 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 multi-level safety to generate the conveying mechanism load, the loading platform status and the material accumulation data, and the conveying mechanism load, the loading platform status and the material accumulation data are processed in stages to output the control instruction, including: Detecting the equipment operation status corresponding to the execution instruction set, generating operation data of the loading building operation mechanism, and calculating the load of the conveying mechanism according to the load analysis of the operation data of the loading building operation mechanism; Calculate the force distribution of the conveying mechanism load, output the bearing capacity data, and calculate 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 flow calibration data according to the stacking height to determine the material stacking data; 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 associating and analyzing the loading time, the loading mechanism energy consumption and the equipment utilization data, includes: Sampling the operation data of the loading equipment 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 under the loading time, outputting a component energy consumption distribution map, and obtaining 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 by using a Markov chain to generate equipment operation state data, and dividing the equipment operation state data according to a time window to determine equipment utilization rate data; Calculating the correlation between the loading time and the energy consumption of the loading mechanism to obtain a loading efficiency parameter, and using grey correlation analysis to process the loading efficiency parameter and the equipment utilization rate data to output a system efficiency index; Extracting the quantitative characteristics of the system performance index, 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 constraints are output, and the operation constraints 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: A collection module, used for collecting data of the loading operation area to generate silo pressure, feeding mechanism temperature and conveyor belt operation parameters, and generating operation status data by normalizing the silo pressure, feeding mechanism temperature and conveyor belt operation 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, used for calculating and processing the loading task list to generate a job scheduling sequence, and calculating the job scheduling sequence and the transportation control parameters through dynamic programming to output transportation partition data; A calculation module, used for calculating the conveying partition data in real time to generate a feeding mechanism operation rate and a conveyor belt speed parameter, and outputting an execution instruction set by checking and processing the feeding mechanism operation rate and the conveyor belt speed parameter; A hierarchical module, for generating a conveying mechanism load, a loading platform status and a material accumulation data by performing a multi-level safety evaluation on the execution instruction set, and outputting a control instruction by hierarchically processing the conveying mechanism 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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