Ore conveying system based on automatic control to remove impurities

Abstract

The present invention relates to the field of ore processing technology, and discloses an automatically controlled ore conveying system capable of removing impurities. The system comprises an impurity content identification module, a statistics module, a flow rate detection module, a calculation module, and a processing module. The impurity content identification module detects the amount of impurities attached to the ore surface, the statistics module calculates the amount of ore, and the flow rate detection module detects the medium flow rate. The calculation module obtains the mean impurity content, the baseline impurity content, and the mass offset based on these values. The processing module sets the initial conveying speed and conveying interval of the ore sorting equipment based on the mass offset, corrects the conveying interval based on the medium flow rate, and sets the conveying acceleration for each conveying interval accordingly. Through multi-module collaboration and intelligent algorithms, the system achieves accurate impurity detection and dynamic adjustment of conveying parameters, improving ore conveying efficiency and impurity treatment effectiveness, and is suitable for the field of ore processing.

Description

Technical Field

[0001] The present invention relates to the technical field of ore processing, in particular to an ore conveying system capable of removing impurities based on automatic control. Background Art

[0002] In the field of ore mining and processing, impurity handling and efficiency optimization during ore transportation have always been key industry concerns. Traditional ore conveying systems generally suffer from low automation, insufficient impurity handling accuracy, and lagging conveying parameter control. These issues lead to low ore sorting efficiency, severe resource waste, and difficulty meeting the requirements of efficient production in modern mines.

[0003] From the perspective of impurity identification, traditional systems rely heavily on manual visual inspection or simple physical screening, which is unable to accurately and accurately obtain dynamic data on the amount of impurities adhering to the ore surface in real time. Manual inspection is subject to significant subjective factors, has a high error rate, and cannot cover large-scale ore batches. Physical screening can only roughly separate particles by size, making it difficult to effectively identify fine impurities adhering to the ore surface. This results in a lack of reliable impurity data support for subsequent sorting processes, affecting sorting accuracy.

[0004] In terms of conveying parameter control, traditional systems typically use fixed settings for parameters such as conveying speed and conveying intervals, and are unable to dynamically adjust to changes in ore batch quality or the flow rate of the medium within the conveying pipeline. For example, when the ore's impurity content fluctuates, a fixed conveying speed may prevent the sorting equipment from promptly processing batches with high impurity content, resulting in incomplete impurity removal or ore accumulation and blockage. Furthermore, if changes in medium flow rate (such as fluctuations in slurry flow rate within the pipeline due to changes in ore source or equipment operating status) are not promptly addressed, they will further exacerbate the instability of the conveying process and reduce the overall operating efficiency of the system.

[0005] Furthermore, traditional systems lack the ability to globally optimize the conveying process. Data interaction and synergy between functional modules (such as detection, control, and sorting) are insufficient, preventing the formation of a closed-loop control system. For example, impurity detection data is not linked to conveying parameter control in real time, resulting in the system's inability to adjust conveying strategies based on detection results. This makes it difficult to achieve an optimal balance between impurity removal and conveying efficiency.

[0006] With the continuous expansion of mining operations and increasing demands for ore quality, the limitations of traditional ore conveying systems have become increasingly prominent. Real-time and accurate detection of ore impurity content, dynamic and adaptive adjustment of conveying parameters, and coordinated optimization and control of system modules have become urgent technical challenges. This invention, proposed against this backdrop, aims to construct an efficient and precise ore conveying system capable of removing impurities by combining automatic control technology with intelligent algorithms, thereby improving the automation level and production efficiency of the ore processing process. Summary of the Invention

[0007] The object of the present invention is to provide an ore conveying system capable of removing impurities based on automatic control, so as to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an automatic control-based impurity-removable ore conveying system, the system comprising:

[0009] Impurity content identification module, statistics module, flow rate detection module, calculation module and processing module:

[0010] The impurity content identification module is used to detect the amount of impurities attached to the surface of the transported ore, the statistical module is used to calculate the number of ores in the transported ore batch, and the flow rate detection module is used to detect the medium flow rate of the ore transport pipeline;

[0011] The calculation module is used to obtain a mean impurity content of the batch of conveyed ore based on a correlation between the amount of ore and the amount of impurities attached to the surface of each conveyed ore; the calculation module is also used to substitute the medium flow rate into a conveying model pre-constructed by the calculation module to obtain a baseline impurity content, and determine a mass deviation of the batch of conveyed ore based on a deviation between the mean impurity content and the baseline impurity content;

[0012] The processing module is used to set the initial conveying speed of the ore sorting equipment according to the mass deviation of the ore batch, and determine the conveying interval of the ore sorting equipment according to the mass deviation of the ore batch;

[0013] The processing module is further configured to correct the conveying interval according to the medium flow rate, and set a conveying acceleration of each conveying interval according to the conveying interval.

[0014] Preferably, the transport model pre-built by the computing module includes:

[0015] Acquire various transport parameters during a standard transport period, and filter redundant data in the transport parameters, wherein the transport parameters include: medium flow rate and mean impurity content of the ore;

[0016] According to the standard transport period, obtaining flow rate distribution characteristics of different medium flow rates at the same impurity content mean value between the transport parameters after filtering redundant data;

[0017] According to the standard transport period, obtaining impurity distribution characteristics of different impurity content means at the same medium flow rate between the transport parameters after filtering redundant data;

[0018] The transport model is constructed according to the flow velocity distribution characteristics and the impurity distribution characteristics.

[0019] Preferably, the setting of the initial conveying speed of the ore sorting equipment includes:

[0020] The processing module is further configured to obtain an impurity content difference between the impurity content mean and the reference impurity content, and set the initial conveying speed based on a comparison between the impurity content difference and a preset impurity difference threshold and an auxiliary impurity difference threshold configured by the processing module:

[0021] When the impurity content difference does not exceed the auxiliary impurity difference threshold, or the impurity content difference exceeds the preset impurity difference threshold, the processing module sets the reference conveying speed of the ore sorting equipment as the initial conveying speed;

[0022] When the impurity content difference is between the auxiliary impurity difference threshold and the preset impurity difference threshold, the processing module obtains the intermediate value of the impurity content difference between the preset impurity difference threshold and the auxiliary impurity difference threshold, calculates the rate correction factor based on the proportional relationship between the impurity content difference and the intermediate value, and sets the reference conveying speed corrected based on the rate correction factor as the initial conveying speed.

[0023] Preferably, when the processing module calculates the rate correction factor according to the proportional relationship between the impurity content difference and the intermediate value, it includes:

[0024] The processing module is further configured to obtain an absolute ratio of the impurity content difference to the intermediate value, and select the rate correction factor based on a correspondence between the absolute ratio and a primary ratio range and a secondary ratio range configured by the processing module:

[0025] When the absolute ratio falls within the primary ratio range, the processing module selects the rate correction factor as R1;

[0026] When the absolute ratio falls within the secondary ratio range, the processing module selects the rate correction factor as R2;

[0027] When the absolute ratio exceeds the secondary ratio range, the processing module selects the rate correction factor as R3;

[0028] The upper limit of the primary ratio range is smaller than the lower limit of the secondary ratio range, and R1<R2<R3.

[0029] Preferably, determining the conveying interval of the ore sorting equipment according to the mass deviation of the ore batch includes:

[0030] The processing module is also used to obtain the conveying cycle of the ore sorting equipment, and set the conveying interval based on the conveying cycle, the initial conveying speed, the single conveying volume of the ore sorting equipment and the operation relationship, wherein the operation relationship obtains the time parameter by multiplying the product of the conveying cycle and the single conveying volume by the square of the initial conveying speed.

[0031] Preferably, the correction of the delivery interval according to the medium flow rate includes:

[0032] The processing module is further configured to determine whether to trigger the correction condition of the delivery interval based on a change trend between the current flow rate of the delivery medium and the historical adjacent flow rates:

[0033] When the current flow rate and the historical adjacent flow rate remain constant, the processing module maintains the original delivery interval;

[0034] When there is a change between the current flow rate and the historical adjacent flow rate, the processing module calculates an interval adjustment factor according to a combination of flow rate change direction and change amplitude, and updates the delivery interval based on the interval adjustment factor.

[0035] Preferably, when the processing module calculates the interval adjustment factor according to the combined characteristics of the flow velocity change direction and change amplitude, it includes:

[0036] The processing module is further configured to obtain a flow rate change rate of the current flow rate relative to a historical adjacent flow rate, and determine the interval adjustment factor based on a comparison result between the flow rate change rate and a positive change threshold and a negative change threshold configured by the processing module;

[0037] When the flow rate change rate exceeds the negative change threshold, the processing module determines that the interval adjustment factor is S1;

[0038] When the flow velocity change rate is within the positive change threshold range, the processing module determines that the interval adjustment factor is S2;

[0039] When the flow velocity change rate exceeds the positive change threshold, the processing module determines that the interval adjustment factor is S3;

[0040] Among them, S1>S2>S3.

[0041] Preferably, the step of setting the conveying acceleration of each conveying interval according to the conveying interval includes:

[0042] Determine the corresponding conveying acceleration adjustment mode based on the fluctuation characteristics of the current conveying interval and the historical adjacent conveying intervals;

[0043] When the current conveying interval and the historical adjacent conveying intervals form a continuous increasing sequence, the processing module sets the conveying acceleration in a step-by-step increasing manner;

[0044] When the current conveying interval and the historical adjacent conveying intervals form an alternating fluctuation sequence, the processing module calculates an acceleration compensation value according to the interval adjustment factor, and sets the conveying acceleration optimized based on the acceleration compensation value.

[0045] Preferably, when the processing module calculates the acceleration compensation value according to the interval adjustment factor, it includes:

[0046] The processing module is further configured to determine a compensation intensity level according to a mapping relationship between the interval adjustment factor and a baseline adjustment coefficient and an extended adjustment coefficient configured by the processing module:

[0047] When the interval adjustment factor is less than the reference adjustment coefficient, the processing module selects the compensation intensity level as U3;

[0048] When the interval adjustment factor is equal to or greater than the reference adjustment coefficient and less than the expansion adjustment coefficient, the processing module selects the compensation intensity level as U2;

[0049] When the interval adjustment factor is equal to or greater than the expansion adjustment coefficient, the processing module selects the compensation intensity level as U1;

[0050] The reference adjustment coefficient is smaller than the extended adjustment coefficient, and U1>U2>U3.

[0051] Preferably, the present invention further includes an ore conveying method applicable to the above-mentioned automatic control-based impurity-removable ore conveying system, the method comprising:

[0052] Detect the amount of impurities attached to the surface of the transported ore and calculate the amount of ore in the transported ore batch;

[0053] Obtaining an average impurity content of the batch of transported ore based on a correlation between the amount of ore and the amount of impurities attached to the surface of each transported ore;

[0054] Substituting the medium flow rate into a pre-built transport model to obtain a baseline impurity content, and determining the mass deviation of the ore batch based on the deviation between the mean impurity content and the baseline impurity content;

[0055] setting an initial conveying speed and conveying interval of the ore sorting equipment according to the mass deviation of the ore batch;

[0056] The medium flow rate of the ore conveying pipeline is detected, the conveying interval is corrected according to the medium flow rate, and the conveying acceleration of each conveying interval is set according to the corrected conveying interval.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] During impurity content detection and quality assessment, the impurity content identification module can detect the amount of impurities attached to the ore surface in real time. Combined with the ore quantity calculation by the statistics module and the conveying model constructed by the calculation module, it can accurately obtain the mean impurity content and the baseline impurity content, thereby determining the quality deviation of the ore batch. This process automates the entire process from impurity data collection to quality assessment, avoiding the subjectivity and lag of manual inspection, providing a reliable quality basis for subsequent sorting operations, and enabling the system to dynamically adjust the processing strategy based on the actual quality of the ore, significantly improving the targeted and effective impurity removal.

[0059] In terms of initial conveying speed setting, the processing module compares the impurity content difference with the preset threshold, calculates the rate correction factor based on the proportional relationship, and realizes graded dynamic adjustment of the initial conveying speed. When the impurity content difference is in different ranges, the system can automatically match the corresponding speed adjustment strategy: when the difference is small, maintain the reference speed to ensure efficiency; when the difference is in the middle range, fine-tune the speed proportionally to balance quality and efficiency; when the difference is too large, return to the reference speed to avoid system fluctuations caused by over-adjustment. This differentiated speed control mode enables the sorting equipment to better adapt to ore batches with different impurity contents, while ensuring the impurity removal effect, minimizing the problems of ore accumulation or incomplete sorting caused by unreasonable speed.

[0060] The setting and correction mechanism of the conveying interval is another major innovation of the present invention. The system sets the basic conveying interval based on the calculation relationship between the conveying cycle, initial speed and single conveying volume, and triggers the correction mechanism by monitoring the changing trend of the medium flow rate. When the flow rate is constant or changes, the interval parameters are updated by maintaining the original interval or calculating the interval adjustment factor based on the flow rate change rate. This dynamic correction mechanism can respond to the fluid dynamics changes in the conveying pipeline in real time. For example, when the flow rate suddenly increases, the conveying interval is shortened to speed up the ore conveying rhythm and avoid pipeline blockage; when the flow rate decreases, the interval is extended to reduce energy consumption, thereby ensuring that the conveying process is always in an efficient and stable operating state, effectively improving the system's adaptability to complex conveying environments.

[0061] Optimizing conveyor acceleration further enhances system operational stability and energy efficiency. The processing module selects different acceleration adjustment modes based on the fluctuating characteristics of the conveyor interval (continuous increase or alternating fluctuation). For continuous increase, a step-by-step approach is used to gradually increase acceleration, preventing sudden shocks to the equipment caused by excessive acceleration. For alternating fluctuation, an acceleration compensation value is calculated using an interval adjustment factor to achieve dynamic acceleration compensation optimization. This refined acceleration control strategy not only reduces equipment wear and extends service life, but also reduces overall system operating costs by rationally allocating energy consumption, embodying a green and energy-saving design philosophy.

[0062] From the perspective of overall system synergy, the modules interact with each other in real-time, forming a closed-loop control system. Impurity detection data drives the computational module to perform quality assessments, and the assessment results guide the processing module in adjusting conveying parameters. Flow rate detection data dynamically corrects conveying intervals and acceleration, forming a complete control chain of "detection-assessment-control-feedback." This highly collaborative working model enables the system to optimize operating parameters in real time based on changes in ore characteristics and the conveying environment, achieving an optimal balance between impurity removal and conveying efficiency. This significantly improves the automation level and production efficiency of ore processing, providing important technical support for the intelligent upgrade of the mining industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a working principle diagram of the automatic control-based impurity-removable ore conveying system of the present invention;

[0064] Figure 2 Design drawings constructed for the conveying model;

[0065] Figure 3 Design drawing for initial conveying speed setting;

[0066] Figure 4 Design diagram for rate correction factor calculation;

[0067] Figure 5 Design diagram for interval adjustment factor calculation. DETAILED DESCRIPTION

[0068] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0069] See also Figure 1-Figure 5 The present invention relates to an automatic control-based impurity removal ore conveying system, which includes an impurity content identification module, a statistics module, a flow rate detection module, a calculation module, and a processing module. Specifically, the system includes the following steps:

[0070] When the system is running, the amount of impurities attached to the surface of the conveyed ore is detected through the impurity content identification module. This module can use image recognition technology or spectral analysis technology. For example, a high-resolution camera is used to capture images of the ore surface, and an image processing algorithm is used to identify and calculate the area ratio of impurities to quantify the amount of impurity attachment. At the same time, the statistical module calculates the number of ores in the conveyed ore batch through equipment such as photoelectric sensors or weighing devices. For example, a through-beam photoelectric sensor is set at a specific position in the conveying pipeline. When the ore passes through, the count is triggered, thereby accumulating the number of ores in the batch.

[0071] The flow rate detection module uses equipment such as electromagnetic flowmeters or ultrasonic flowmeters to detect the medium flow rate in the ore conveying pipeline in real time, and obtains the flow rate data of the ore and conveying medium mixed in the pipeline.

[0072] The operation module performs the following operations: on the one hand, based on the ore quantity obtained by the statistical module and the amount of impurity attachment on the surface of each conveyed ore detected by the impurity content identification module, the impurity content mean of the conveyed ore batch is obtained by arithmetic average or weighted average and other algorithms; on the other hand, the medium flow rate detected by the flow rate detection module is substituted into the conveying model pre-constructed by the operation module. The conveying model is established based on the sample data of the historical standard conveying period. By analyzing the flow rate distribution characteristics of different medium flow rates under the same impurity content mean, and the impurity distribution characteristics of different impurity contents under the same medium flow rate, the mapping relationship between the medium flow rate and the benchmark impurity content is fitted, thereby calculating the benchmark impurity content, and further according to the deviation value between the impurity content mean and the benchmark impurity content, the quality deviation degree of the conveyed ore batch is determined by a preset deviation calculation formula or threshold comparison method. For example, the deviation value is the absolute value of the difference between the impurity content mean and the benchmark impurity content. When the difference exceeds the preset threshold, it is determined that the quality deviation is large.

[0073] The processing module performs the following control operations based on the ore batch quality offset determined by the calculation module: First, the initial conveying speed of the ore sorting equipment is set by obtaining the impurity content difference between the mean impurity content and the baseline impurity content, and comparing the difference with the preset impurity difference threshold and the auxiliary impurity difference threshold configured in the processing module. When the impurity content difference does not exceed the auxiliary impurity difference threshold, or exceeds the preset impurity difference threshold, the baseline conveying speed of the ore sorting equipment is directly set as the initial conveying speed. When the impurity content difference is between the auxiliary impurity difference threshold and the preset impurity difference threshold, the intermediate value between the two thresholds is calculated. Based on the proportional relationship between the impurity content difference and the intermediate value, a rate correction factor is calculated using an algorithm such as linear interpolation or piecewise function. The baseline conveying speed is then multiplied by the rate correction factor to obtain the corrected initial conveying speed.

[0074] The processing module determines the conveying interval of the ore sorting equipment according to the mass deviation of the ore batch. Specifically, it obtains the conveying cycle of the ore sorting equipment, and based on the conveying cycle, the initial conveying speed, and the single conveying volume of the ore sorting equipment, uses the operation relationship (the product of the conveying cycle and the single conveying volume divided by the square of the initial conveying speed) to calculate the time parameter, and uses the time parameter as the initial setting value of the conveying interval.

[0075] The processing module corrects the delivery interval based on the medium flow rate detected in real time by the flow rate detection module. Specifically, by analyzing the change trend between the current flow rate of the conveying medium and the historical adjacent flow rate, it is determined whether the correction condition of the delivery interval is triggered. When the current flow rate and the historical adjacent flow rate remain constant, the original delivery interval is maintained; when there is a change, the flow rate change rate of the current flow rate relative to the historical adjacent flow rate is calculated, and the interval adjustment factor is determined based on the comparison result of the flow rate change rate with the positive change threshold and negative change threshold configured by the processing module. For example, when the flow rate change rate exceeds the negative change threshold, the interval adjustment factor is S1; when it is within the positive change threshold range, it is S2; when it exceeds the positive change threshold, it is S3, where S1>S2>S3. The delivery interval is updated by multiplying the initial delivery interval by the interval adjustment factor.

[0076] The processing module sets the conveying acceleration for each conveying interval based on the corrected conveying interval. Specifically, the adjustment mode is determined by analyzing the fluctuation characteristics of the current conveying interval and historical adjacent conveying intervals. When a continuously increasing sequence is formed, the conveying acceleration is set using a step-by-step increment method, for example, each increment uses a fixed acceleration value. When an alternating fluctuating sequence is formed, the compensation intensity level (U1, U2, U3, with U1>U2>U3) is determined based on the mapping relationship between the interval adjustment factor and the baseline adjustment coefficient and extended adjustment coefficient configured by the processing module. The acceleration compensation value is calculated based on the compensation intensity level to optimize the conveying acceleration setting.

[0077] The present invention will be further described below in conjunction with Examples 1 to 5:

[0078] Example 1: When the computational module constructs the conveying model, it first enters the data acquisition phase. During this phase, the system determines the principles for selecting standard conveying time periods. Taking into account the operational characteristics of the ore conveying system, standard conveying time periods should encompass operational data from different time periods to ensure comprehensiveness and representativeness. For example, the system may select three time periods: morning, noon, and evening on weekdays, as well as portions of weekend time periods. Factors such as ore source, type, and conveying volume may vary during these time periods, thus reflecting the system's operational performance under different operating conditions.

[0079] After determining the standard conveying period, the system uses various sensors and detection equipment to obtain various conveying parameters within that period. The medium flow rate is monitored in real time using a high-precision electromagnetic flowmeter. This flowmeter accurately measures the flow rate of fluids within the pipeline. Its measurement principle is based on Faraday's law of electromagnetic induction: when a conductive fluid passes through a magnetic field, it generates an induced electromotive force proportional to the flow rate. By detecting this electromotive force, the fluid flow rate can be determined. The average impurity content of the ore is determined by combining image recognition technology and chemical analysis methods. The image recognition component uses a high-resolution industrial camera to continuously capture the ore on the conveyor belt. Image processing algorithms are then used to analyze the impurity distribution on the ore surface and calculate the impurity area percentage. The chemical analysis component uses an online spectrometer to analyze the ore's composition in real time, determining the type and content of impurities. The combined approach ultimately determines the average impurity content.

[0080] During data acquisition, the system synchronously records the timestamps of each parameter to ensure temporal consistency and provide an accurate data foundation for subsequent analysis. To ensure data reliability, each parameter is measured using multiple sensors, and then processed using a data fusion algorithm to obtain the final parameter value.

[0081] The data filtering stage is a critical step in building the transport model and directly impacts the accuracy of subsequent analysis. The system first preprocesses the acquired transport parameters, including data cleaning and standardization. During data cleaning, outliers and missing values are primarily addressed. Outliers are detected using the statistically based Z-score method, which determines whether a data point is an outlier by calculating the degree of deviation from the mean. Data points with an absolute Z-score greater than 3 are considered outliers and replaced using interpolation. Interpolation uses the values of adjacent data points to estimate outliers based on the time series characteristics of the data.

[0082] For missing values, the system will select an appropriate handling method based on the number and distribution of missing values. If the number of missing values is small, linear interpolation will be used to fill them in. If the number of missing values is large and concentrated in a certain time period, the data for that time period will be removed entirely to avoid significant impact on subsequent analysis.

[0083] For data standardization, the system uses the Min-Max normalization method, mapping the values of each transport parameter to the interval [0, 1]. This eliminates the impact of different parameter dimensions and orders of magnitude, making the data comparable. The normalization formula is: x' = (x-min) / (max-min), where x is the original data, min and max are the minimum and maximum values of the parameter, respectively, and x' is the normalized data.

[0084] After data cleaning and standardization, the system extracts features from the data to uncover hidden information. For example, it calculates the statistical characteristics of each parameter, such as mean, variance, skewness, and kurtosis, to analyze the data's distribution characteristics. It also extracts temporal characteristics of the data, such as autocorrelation coefficients and cross-correlation coefficients, to analyze the temporal dependencies between parameters.

[0085] During the parameter relationship analysis phase, the system conducts an in-depth analysis of the flow rate distribution of different media under the same mean impurity content conditions. First, the obtained mean impurity content values are grouped, for example, into multiple levels within a specific range. For each level of mean impurity content, the corresponding media flow rate data is collected and a flow rate distribution histogram is plotted to visually display the flow rate distribution.

[0086] Then, calculate statistics such as the frequency, mean, and variance of different flow rates to analyze the central tendency and dispersion of the flow rates. For example, by calculating the mean, we can understand the average level of medium flow rate under a specific mean impurity content; by calculating the variance, we can understand the dispersion of flow rate data; a larger variance indicates greater fluctuation in flow rate.

[0087] The system also analyzes the probability density function of the velocity distribution to determine whether it conforms to a common probability distribution, such as normal distribution, lognormal distribution, etc. If it does, the characteristics of that distribution can be used for more in-depth analysis and prediction.

[0088] Under the same medium flow rate conditions, the system will calculate the distribution of different impurity content means. Similarly, the medium flow rate is grouped, and for each flow rate group, the corresponding impurity content mean data is calculated to draw an impurity content distribution histogram.

[0089] Calculate statistics such as the frequency distribution, median, and range of the impurity content mean to analyze the distribution characteristics of the impurity content. The median reflects the median level of the data, while the range reflects the fluctuation range of the data. The system analyzes the skewness of the impurity content distribution and determines whether the impurity content tends to be high or low, which is important for understanding the quality distribution of the ore.

[0090] The system combines flow rate and impurity distribution characteristics to analyze the inherent relationship between them. For example, by calculating the correlation coefficient between the medium flow rate and the mean impurity content, it determines whether there is a linear correlation between the two. If the correlation coefficient is large, it indicates a strong linear relationship between the two, and a linear regression model can be established for prediction.

[0091] During the transport modeling phase, the system uses a machine learning algorithm to map the flow rate of the medium to the baseline impurity content based on the velocity and impurity distribution characteristics analyzed above. The system first selects an appropriate machine learning algorithm. Considering the potentially complex, nonlinear relationship between the flow rate and the baseline impurity content, the system decides to use a neural network algorithm for modeling.

[0092] Neural networks possess powerful nonlinear mapping capabilities, capable of handling complex input-output relationships. The neural network model constructed by the system consists of an input layer, a hidden layer, and an output layer. The input layer receives medium flow rate and other relevant parameters, such as ore type and conveying volume. The hidden layer, comprised of multiple neurons, processes and transforms this input information through nonlinear activation functions. The output layer outputs the predicted baseline impurity content.

[0093] During model training, the system divides the data acquired during the standard delivery period into training, validation, and test sets. The training set is used to learn and optimize model parameters, the validation set is used to adjust model hyperparameters such as the number of hidden layer neurons and learning rate, and the test set is used to evaluate model performance.

[0094] The system uses a backpropagation algorithm for model training, continuously adjusting the weights and biases of the neural network to minimize the error between the model's predicted values and the actual values. During training, to prevent overfitting, the system uses regularization methods such as L1 and L2 regularization to constrain the model's complexity.

[0095] To improve the model's generalization capabilities, the system also uses data augmentation techniques to expand the training data. For example, random noise is added to the original data, data transformations are performed, and more training samples are generated, allowing the model to learn richer features.

[0096] In terms of model evaluation, the system uses a variety of metrics to comprehensively assess model performance. Commonly used evaluation metrics include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). These metrics can be used to comprehensively evaluate the model's prediction accuracy and stability.

[0097] The system will cross-validate the model, divide the dataset into multiple subsets, use different subsets as training sets and test sets in turn, train and evaluate the model multiple times, and take the average performance as the final evaluation result to reduce the randomness of the evaluation results.

[0098] After model training and evaluation, the system deploys the trained neural network model into the actual production environment. During actual application, the system continuously collects new data and performs online updates and optimizations on the model to adapt to changes in the operating conditions of the ore conveyor system, improving the model's predictive accuracy and adaptability.

[0099] Example 2: When the processing module sets the initial conveying speed of the ore sorting equipment, it first enters the difference calculation phase. This phase uses a precise algorithm to calculate the difference between the mean impurity content and the baseline impurity content. This process involves multiple steps of data processing and analysis to ensure the accuracy of the difference. The system preprocesses the mean impurity content and the baseline impurity content, including unified data format conversion and unit standardization. For example, if the mean impurity content is presented as a percentage and the baseline impurity content is stored as a decimal, the system will convert them to the same representation to facilitate subsequent calculations.

[0100] When performing difference calculations, the system uses high-precision numerical methods to avoid biased results due to floating-point errors. Complex ore compositions may contain multiple impurities, each with a different impact on the overall impurity content. The system assigns a weight to each impurity based on its type and nature, then calculates the weighted mean impurity content and compares it to the baseline impurity content.

[0101] After the difference calculation is completed, the system will verify the calculation results and determine whether the difference is within a reasonable range by comparing and analyzing with historical data. If the difference is abnormal, the system will re-check the data source and calculation process to ensure the reliability of the difference.

[0102] After the difference calculation is complete, the system enters the threshold comparison phase. Here, the calculated impurity content difference is compared with the auxiliary impurity difference threshold and the preset impurity difference threshold preset within the processing module. These two thresholds are important parameters set by the system based on long-term operating experience and ore sorting process requirements, and directly influence subsequent speed adjustment strategies.

[0103] The auxiliary impurity difference threshold is a relatively small value used to determine whether a deviation in impurity content is a minor fluctuation. The preset impurity difference threshold is a larger value used to determine whether the impurity content deviation exceeds the normal range and requires significant adjustment. The system accurately compares the impurity content difference with these two thresholds to determine the range in which the difference falls.

[0104] During the threshold comparison process, the system takes into account the dynamic nature of thresholds. Due to changes in factors such as ore source and production process, preset thresholds may need to be adjusted. The system regularly collects historical data and analyzes the distribution of impurity content differences. Based on the analysis results, the thresholds are optimized and updated to ensure their rationality and effectiveness.

[0105] The system adopts different processing strategies when the impurity content difference falls within different ranges. If the impurity content difference is less than or equal to the auxiliary impurity difference threshold, the system determines that the impurity content deviation is small and does not require adjustment to the initial conveying speed, directly using the reference conveying speed as the initial conveying speed. This is because in this case, fluctuations in impurity content have little impact on the ore sorting effect, and maintaining the reference conveying speed ensures stable operation of the equipment.

[0106] If the impurity content difference exceeds the preset impurity difference threshold, the system deems the impurity content to be excessively deviated. To ensure ore sorting effectiveness, the reference conveying speed is also used. This is because when impurity content is too high, a too fast conveying speed may result in incomplete sorting, while a too slow conveying speed may affect production efficiency. The reference conveying speed is the optimal speed determined after comprehensive consideration of various factors, and it can achieve a certain balance between sorting effectiveness and production efficiency.

[0107] When the auxiliary impurity difference threshold < impurity content difference ≤ preset impurity difference threshold, the system enters the rate correction process. This process is the core link of Example 2, which aims to accurately correct the initial conveying speed according to the specific situation of the impurity content difference.

[0108] The system first calculates the median between the preset impurity difference threshold and the auxiliary impurity difference threshold. This calculation must be precise to ensure the accuracy of subsequent proportional relationships. The formula for calculating the median is: (preset impurity difference threshold + auxiliary impurity difference threshold) / 2. By calculating the median, the system further subdivides the impurity content difference range into two subranges, facilitating more precise adjustment of the conveying speed.

[0109] Next, the system calculates the proportional relationship between the impurity content difference and the midpoint. This proportional relationship reflects the relative position of the impurity content difference on either side of the midpoint and is an important basis for determining the rate correction factor. The proportional coefficient k is calculated as follows: k = (impurity content difference - auxiliary impurity difference threshold) / (preset impurity difference threshold - auxiliary impurity difference threshold). The value range of k is (0, 1).

[0110] After calculating the proportionality factor k, the system compares it with the primary and secondary ratio ranges configured for the processing module. The primary and secondary ratio ranges are set according to the characteristics and requirements of the ore sorting process and are used to divide different correction levels.

[0111] When the proportionality factor k falls within the primary ratio range (e.g., 0 < k ≤ 0.3), the impurity content difference is relatively small, approaching the auxiliary impurity difference threshold. In this case, the system selects a smaller correction factor R1 (e.g., 0.9) and slightly decreases the baseline conveying speed. This is because, in this case, while the increase in impurity content requires an adjustment in conveying speed, the adjustment should not be too large to avoid affecting production efficiency.

[0112] When the proportional coefficient k falls within the secondary ratio range (e.g., 0.3 < k < 0.7), the impurity content difference is in the middle and requires appropriate adjustment. In this case, the system selects a medium correction factor R2 (e.g., 1.1) and adjusts the base conveying speed moderately upward. This is because, in this case, the increase in impurity content has already had a certain impact on the ore sorting effect, and the conveying speed needs to be appropriately increased to ensure the sorting effect.

[0113] When the proportionality factor k ≥ 0.7, the impurity content difference is approaching the preset impurity difference threshold and requires significant adjustment. In this case, the system selects a larger correction factor R3 (e.g., 1.3) and significantly increases the baseline conveying speed. This is because in this case, the increase in impurity content is already significant, and a significant increase in conveying speed is required to ensure the ore sorting effect.

[0114] After determining the correction factor, the system multiplies the baseline conveying speed by the correction factor to obtain the corrected initial conveying speed. This calculation process is simple and direct, but crucial, and directly determines the initial operating status of the ore sorting equipment.

[0115] To ensure the rationality and effectiveness of the revised initial conveying speed, the system performs a secondary calibration. This calibration process includes checking whether the revised initial conveying speed is within the equipment's safe operating range and meets production process requirements. If any issues are found with the revised initial conveying speed, the system reassesses the impurity content difference and correction factor, making further adjustments until a reasonable initial conveying speed is achieved.

[0116] The system also records relevant information for each speed adjustment, including the difference in impurity content, correction factor, and the corrected initial conveying speed. This recorded information will be used for subsequent data analysis and process optimization, providing strong support for the long-term stable operation of the system. Through the above detailed implementation method, the processing module can accurately set the initial conveying speed of the ore sorting equipment based on changes in the ore's impurity content, while ensuring ore sorting results, improving production efficiency, and realizing intelligent operation of the equipment.

[0117] Example 3: When the processing module determines the conveying interval of the ore sorting equipment, it is necessary to comprehensively consider multiple parameters such as the equipment operation cycle, conveying speed, and single conveying volume, and achieve accurate setting through rigorous logical deduction and calculation. The specific implementation method is as follows:

[0118] First, the system uses sensors to obtain the conveying cycle of the ore sorting equipment in real time . Conveying cycle This refers to the time it takes for a piece of equipment to complete a complete conveying cycle (e.g., from loading ore to sorting and discharge). Its measurement method depends on the mechanical characteristics of the equipment. For example, for reciprocating conveyors, angle encoders can be installed on transmission components (such as screws and gears) to record the time it takes for the components to complete a complete motion cycle. For continuous conveyors, the cycle time can be determined by measuring the time interval between material passages through specific locations. To ensure data accuracy, the system averages the measured values over multiple consecutive cycles to eliminate the influence of random errors.

[0119] Secondly, obtain the initial conveying speed Initial conveying speed The processing module determines this value based on the comparison of the impurity content difference value described in Example 2 with the threshold value. Its physical meaning is the initial operating speed of the ore sorting equipment when processing the current batch of ore, expressed in meters per second (m / s). This speed value must be within the equipment's safe operating speed range and compatible with the ore's physical properties (e.g., particle size and density) to avoid ore accumulation due to excessive speed or processing efficiency being affected by excessive speed.

[0120] Then, determine the single conveying capacity of the ore sorting equipment . Single delivery volume Indicates the volume or mass of ore processed by the equipment each time it completes a conveying action. Its calibration method depends on the type of equipment. For volumetric hoppers, the volume can be calculated by measuring the internal geometric dimensions of the hopper (length, width, height), and then converted into mass based on the bulk density of the ore; for weighing equipment, the mass of the ore loaded each time can be directly measured by a high-precision weighing sensor. In order to meet the processing requirements of different types of ores, the single conveying volume The operator can make adjustments based on actual conditions through the human-machine interface, and the system will automatically record and save the adjusted parameters.

[0121] In getting 、 、 After three key parameters, the processing module calculates the time parameter based on a specific operation relationship , the time parameter This is the initial setting value of the conveying interval of the ore sorting equipment. The calculation relationship is expressed by the formula:

[0122]

[0123] The meaning of each character in the formula is as follows: : The initial setting value of the conveying interval, in seconds (s), which indicates the time interval between two adjacent conveying actions; : The conveying cycle of the ore sorting equipment, in seconds (s); : The single conveying capacity of the ore sorting equipment, in cubic meters (m³) or kilograms (kg), depending on the parameter calibration method; : The initial conveying speed of the ore sorting equipment, in meters per second (m / s).

[0124] The physical meaning of this formula is to convert the periodic operation characteristics of the equipment (delivery cycle ), single processing capacity (single delivery volume ) and operating speed (initial conveying speed ) to quantify the correlation. The denominator is The square term is because the change in conveying speed has a nonlinear effect on the conveying interval - when the speed increases, the conveying volume per unit time increases. In order to avoid excessive accumulation of ore on the conveying path, the conveying interval needs to be shortened by a square multiple; conversely, when the speed decreases, the conveying interval needs to be extended by a square multiple to ensure the continuity and stability of equipment operation.

[0125] During the actual calculation process, the system will automatically convert the units of each parameter to ensure the dimensional consistency of the calculation. For example, if the single delivery volume In kilograms (kg), the system will calculate the weight of the ore based on its density. (Unit: kg / m³) Convert it to volume unit cubic meter (m³), the conversion formula is , so that the unit of the numerator in the formula is cubic meter second (m³·s) or kilogram second (kg·s), and the unit of the denominator is square meter per second² (m² / s²). The final time parameter is The unit is seconds (s).

[0126] Completion time parameter After the calculation, the system needs to check the rationality of the value. The verification rules include:

[0127] Minimum limit: delivery interval It shall not be less than the shortest time for the equipment to complete a conveying action, that is, ,in The mechanical limit cycle of the equipment is provided by the equipment manufacturer;

[0128] Maximum limit: Delivery interval The ore must not stagnate or settle in the conveying pipeline. The maximum allowable interval must be calculated using fluid mechanics formulas based on parameters such as the pipeline inner diameter and ore particle size. ;

[0129] Integer multiple constraint: To facilitate the execution of the equipment control system, the transmission interval Need to be taken as the device control cycle An integer multiple of ,in is a positive integer, It is usually a fixed value in milliseconds (such as 100ms).

[0130] If the calculated If the above verification rules are not met, the system will automatically adjust the parameter values. For example, if , then take and prompt the operator to adjust the initial conveying speed through the alarm or single delivery volume ;like , then press Set and trigger the pipeline blockage warning mechanism and increase the sampling frequency of the flow rate detection module.

[0131] In addition, the system will establish a conveying interval database based on historical operation data, recording the optimal conveying interval values under different ore types, impurity content and equipment working conditions. When processing a new batch of ore, the system can first retrieve similar historical records from the database based on the ore type to obtain the initial conveying interval reference value, and then combine it with the currently calculated value to obtain the initial conveying interval reference value. The weighted average of the values is used to obtain the final transmission interval setting value. The weighting coefficient is automatically adjusted based on the similarity between historical records and current working conditions. The higher the similarity, the greater the weight of historical data, thereby improving setting efficiency and accuracy.

[0132] During the conveying process, the system monitors the equipment's operating status and the ore delivery process in real time. If it detects an abnormal load on the equipment (such as current fluctuations exceeding a preset threshold) or a change in pipeline pressure, it automatically triggers a dynamic fine-tuning mechanism for the conveying interval. The fine-tuning range is determined by the severity of the abnormality, typically within ±5% to ±10% of the initial setting. During the fine-tuning process, it is necessary to ensure that the adjusted conveying interval still meets the aforementioned verification rules. The purpose of dynamic fine-tuning is to enable the system to quickly adapt to sudden changes in operating conditions, preventing equipment failure or reduced production efficiency caused by fixed interval settings.

[0133] Through this complete implementation process, the processing module achieves scientific setting of the conveyor intervals for ore sorting equipment based on precise parameter measurement, rigorous formula calculations, and a comprehensive verification mechanism. This process not only considers the mechanical characteristics of the equipment and the physical parameters of the ore, but also incorporates the accumulated experience of historical data and dynamic feedback from real-time operating conditions. This ensures that the conveyor intervals meet production efficiency requirements while maintaining the stability and reliability of ore sorting, providing key support for the automated control of the entire ore conveying system.

[0134] Example 4: When the processing module corrects the delivery interval according to the medium flow rate, it is necessary to dynamically adjust the delivery interval by monitoring the flow rate change trend in real time and combining it with preset rules. The following describes the implementation process in detail with reference to specific scenarios:

[0135] Assume that during the operation of a certain ore conveying system, the flow rate detection module collects the flow rate data of the medium in the pipeline at a frequency of once per second. At a certain moment, the system obtains the current flow rate for , the historical adjacent flow rate of the previous sampling period for , at this time, the current flow rate and the historical adjacent flow rate remain constant, the system determines that there is no need to correct the delivery interval and maintains the original set value (for example )constant.

[0136] When the system runs to another period, the current flow rate is detected for , historical adjacent flow rate for First, calculate the flow rate change rate: the change is , the rate of change is The processing module compares the change rate with the preset negative change threshold (such as ) compared and found Exceeding the negative change threshold indicates that the flow rate has dropped significantly. At this time, the system determines the interval adjustment factor according to the preset rules as (For example, the value ), the original conveying interval Updated to The logic behind this adjustment is that a decrease in flow rate may be caused by ore accumulation or increased concentration in the pipeline. Extending the transportation interval can reduce the amount of ore transported per unit time, reduce the pipeline load, and avoid the risk of blockage.

[0137] For example, the system detects the current flow rate in subsequent operation for , historical adjacent flow velocity for Calculate the flow rate change rate as , the value exceeds the preset positive change threshold (such as ), indicating a significant increase in flow rate. Based on this, the system determines the interval adjustment factor to be (For example, the value ), adjust the delivery interval to The reason for the adjustment is that the increase in flow rate may be due to the increase in conveying power or the decrease in ore concentration. Shortening the conveying interval can increase the conveying volume per unit time, fully utilize the conveying capacity under the current working conditions, and improve production efficiency.

[0138] If the system detects the current flow rate for , historical adjacent flow velocity for , calculate the rate of change At this time, the rate of change is at the negative change threshold ( ) and the positive change threshold ( ) (i.e. within the normal fluctuation range), the system determines that the interval adjustment factor is (For example, the value ), the delivery interval is maintained This is because small flow rate fluctuations may be caused by normal vibration of equipment operation or uneven distribution of ore particles, and there is no need to adjust the conveying interval to maintain the stability of system operation.

[0139] In actual applications, the flow rate changes are more complex. For example, the flow rate shows a continuous downward trend within a certain period of time: , , The system calculates the adjacent flow rate change rates in sequence: 、 The first rate of change is equal to the negative change threshold, and the system maintains the interval unchanged; the second rate of change exceeds the negative change threshold, triggering the interval adjustment factor , change the delivery interval from the initial Adjust to Then, if the flow rate continues to drop to , the rate of change is , the system applies again , adjust the interval to , until the flow rate stabilizes or reaches the preset maximum interval limit.

[0140] In another scenario, the flow rate fluctuates by first increasing and then decreasing: , , . Calculate the rate of change: (Exceeding the positive change threshold, apply , the interval from Adjust to ); (Exceeding the negative change threshold, apply , the interval is adjusted to This alternating flow rate trend reflects the instability of the conveying conditions. The system dynamically responds to flow rate fluctuations by continuously applying different interval adjustment factors to avoid ore accumulation or loss of conveying efficiency caused by fixed intervals.

[0141] Before executing corrections, the system filters the flow rate data to eliminate sudden noise interference. For example, a sliding average filter algorithm is used to average the flow rate data from the last five sampling cycles as the effective value of the current flow rate, preventing a single abnormal data point from triggering an erroneous correction instruction. Furthermore, to prevent frequent adjustments from impacting the equipment, the system sets a minimum adjustment interval (e.g., 10 seconds). This means that even if the flow rate fluctuates multiple times within a short period of time, the delivery interval will not be frequently adjusted, ensuring smooth operation of the equipment.

[0142] In terms of parameter configuration, positive change threshold, negative change threshold and interval adjustment factor 、 、 The specific value of can be adjusted by the operator according to the actual working conditions through the human-machine interface. For example, for the transportation of high-viscosity ore slurry, the negative change threshold can be set to , in order to identify the velocity decrease trend earlier; for low resistance pipeline systems, the positive change threshold can be set to , allowing for greater flow rate fluctuations. The system automatically saves parameter settings and loads them the next time it is started, eliminating the need for repeated configuration.

[0143] The entire correction process achieves closed-loop control through real-time data acquisition, rate-of-change calculation, threshold comparison, and logical judgment. The processing module completes data updates and calculations once per second, ensuring that the delivery interval can promptly respond to flow rate changes. The system also records the time of each correction, flow rate change rate, adjustment factor, and adjusted interval value, creating a historical log for operators to query and analyze, enabling them to optimize parameter configurations or troubleshoot equipment failures.

[0144] As demonstrated in the examples above, the processing module implements intelligent corrections to conveying intervals based on the direction and magnitude of flow rate changes, using a pre-set set of rules. This mechanism can both handle significant changes in flow rate to ensure system safety and tolerate fluctuations within a normal range to maintain stable operation. This demonstrates the precision and adaptability of automatic control in ore conveying systems, providing critical support for the efficient and stable operation of ore sorting.

[0145] Example 5: When the processing module sets the conveying acceleration for each conveying interval, it is necessary to analyze the fluctuation characteristics of the conveying interval and dynamically adjust the acceleration value in accordance with the preset rules to adapt to the conveying requirements under different working conditions. The following describes the implementation process in detail with reference to specific scenario examples:

[0146] Assume that in the initial stage of operation of a ore conveying system, the conveying intervals of three consecutive sampling cycles are 、 、 , showing an increasing trend. The processing module first identifies the fluctuation characteristics and determines whether it forms a continuous increasing sequence ( ). According to the preset rules, when a continuous increasing sequence is detected, the system sets the conveying acceleration in a step-by-step increasing manner. The initial acceleration is set to , each increment Therefore, the accelerations corresponding to the three conveying intervals are set as 、 、 The logic behind this setting is that a gradual increase in the conveying interval may cause the ore flow rate in the pipeline to decrease. By gradually increasing the acceleration, the power loss caused by the increase in the interval can be compensated, and the ore conveying speed can be maintained stable.

[0147] If the transmission interval fluctuates during subsequent operation of the system, for example, the interval value of five consecutive sampling cycles is 、 、 、 、 , forming an alternating wave sequence ( After detecting this fluctuation feature, the system adjusts the interval according to the interval adjustment factor determined in Example 4. Benchmark adjustment coefficient , expansion adjustment coefficient The comparison results determine the compensation intensity level. Assume that in a certain adjustment, the interval adjustment factor , benchmark adjustment coefficient , expansion adjustment coefficient ,at this time , the system selects the medium compensation intensity level .correspond The acceleration compensation value of the level is , the system will transfer the current delivery acceleration (for example ) plus the compensation value to obtain the optimized acceleration The purpose of this compensation mechanism is to suppress the impact of conveying interval fluctuations on system stability, ensure uniform ore conveying speed in the pipeline, and reduce the risk of pipeline wear and ore deposition caused by speed fluctuations.

[0148] In practical applications, the identification of fluctuation characteristics and acceleration adjustment is a dynamic cycle. For example, the system continuously monitors the conveying interval of 、 、 、 , showing a continuous increasing trend. The system increases the acceleration from the initial Adjust to 、 、 Then, if the delivery interval suddenly becomes , forming an alternating wave sequence ( ), the system immediately switches the compensation strategy. Assume that the interval adjustment factor , exceeding the benchmark adjustment coefficient , but did not reach the expansion adjustment coefficient , the system selects the compensation intensity level , adjust the acceleration to This rapid response mechanism enables the system to flexibly respond to changes in working conditions and maintain the stability of the transportation process.

[0149] When the system processes different types of ores, the fluctuation characteristics and compensation strategies will be adjusted accordingly. For example, when processing high viscosity ores, the conveying interval may be more prone to fluctuations, and the fluctuation range may be larger. Assume that during the conveying of high viscosity ores, the interval value of three consecutive sampling cycles is 、 、 , the fluctuation range is After the system detects this drastic fluctuation, if the interval adjustment factor (Exceeding the expansion adjustment coefficient ), the highest compensation intensity level is selected , corresponding compensation value By significantly increasing the acceleration, the flow resistance of high viscosity ores is overcome and the ore is prevented from being retained in the pipeline. On the contrary, when processing low viscosity ores, the conveying interval fluctuation is small. For example, the interval value of five consecutive sampling cycles is to If the acceleration fluctuates between 0 and 1, the system may determine that no additional compensation is needed and maintain the current acceleration unchanged.

[0150] When setting the acceleration, the system must also consider the physical limitations and safety margins of the device. For example, when the acceleration is adjusted to If the acceleration value is increased further, it may cause the equipment motor to overload or the pipeline vibration to intensify. At this time, the system will trigger the acceleration upper limit protection mechanism and will no longer increase the acceleration value regardless of the fluctuation characteristics. At the same time, in order to avoid frequent adjustments causing impact on the equipment, the system sets a minimum adjustment range (such as ) and adjustment frequency limit (such as a maximum of 3 adjustments per minute).

[0151] In terms of parameter configuration, the benchmark adjustment coefficient , expansion adjustment coefficient The compensation value corresponding to each compensation intensity level can be adjusted by the operator through the human-machine interface according to the ore characteristics and equipment performance. For example, for ores prone to scaling, the baseline adjustment coefficient can be reduced. The value of ), so that the system triggers high-intensity compensation earlier; for pipes with poor wear resistance, the compensation value can be reduced (such as The compensation value of the level is from Adjust to ), reducing the wear rate of pipes. The system will automatically save the parameter settings and load them at the next startup.

[0152] The entire acceleration setting process achieves closed-loop control through real-time data acquisition, fluctuation characteristic analysis, and logical judgment. The processing module completes data updates and calculations every second, ensuring that the acceleration can promptly respond to changes in the conveying interval. The system also records the time, fluctuation characteristics, adjustment factor, and adjusted value of each acceleration adjustment in a historical log. This log allows operators to analyze the acceleration adjustment patterns under different operating conditions, further optimize parameter configuration, and improve system efficiency.

[0153] As demonstrated in the examples above, the processing module intelligently sets conveyor acceleration based on the fluctuating characteristics of the conveyor intervals using a pre-set set of rules. This mechanism not only manages continuously increasing interval changes to maintain conveyor speed, but also suppresses the impact of alternating fluctuations on system stability, while also ensuring equipment safety and longevity. This demonstrates the precision and adaptability of automatic control in ore conveyor systems, providing a strong foundation for the efficient and stable operation of the ore sorting process.

[0154] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0155] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An automatic control-based impurity removal ore conveying system, characterized in that: It includes impurity content identification module, statistics module, flow rate detection module, calculation module and processing module: The impurity content identification module is used to detect the amount of impurities attached to the surface of the transported ore, the statistical module is used to calculate the number of ores in the transported ore batch, and the flow rate detection module is used to detect the medium flow rate of the ore transport pipeline; The calculation module is used to obtain a mean impurity content of the batch of conveyed ore based on a correlation between the amount of ore and the amount of impurities attached to the surface of each conveyed ore; the calculation module is also used to substitute the medium flow rate into a conveying model pre-constructed by the calculation module to obtain a baseline impurity content, and determine a mass deviation of the batch of conveyed ore based on a deviation between the mean impurity content and the baseline impurity content; The processing module is used to set the initial conveying speed of the ore sorting equipment according to the mass deviation of the ore batch, and determine the conveying interval of the ore sorting equipment according to the mass deviation of the ore batch; The processing module is further configured to modify the conveying interval according to the medium flow rate, and set the conveying acceleration of each conveying interval according to the conveying interval; The transport model pre-built by the computing module includes: Acquire various transport parameters during a standard transport period, and filter redundant data in the transport parameters, wherein the transport parameters include: medium flow rate and mean impurity content of the ore; According to the standard transport period, obtaining flow rate distribution characteristics of different medium flow rates at the same impurity content mean value between the transport parameters after filtering redundant data; According to the standard transport period, obtaining impurity distribution characteristics of different impurity content means at the same medium flow rate between the transport parameters after filtering redundant data; Constructing the transport model according to the flow velocity distribution characteristics and the impurity distribution characteristics; The setting of the initial conveying speed of the ore sorting equipment includes: The processing module is further configured to obtain an impurity content difference between the impurity content mean and the reference impurity content, and set the initial conveying speed based on a comparison between the impurity content difference and a preset impurity difference threshold and an auxiliary impurity difference threshold configured by the processing module: When the impurity content difference does not exceed the auxiliary impurity difference threshold, or the impurity content difference exceeds the preset impurity difference threshold, the processing module sets the reference conveying speed of the ore sorting equipment as the initial conveying speed; When the impurity content difference is between the auxiliary impurity difference threshold and the preset impurity difference threshold, the processing module obtains the intermediate value of the impurity content difference between the preset impurity difference threshold and the auxiliary impurity difference threshold, calculates the rate correction factor based on the proportional relationship between the impurity content difference and the intermediate value, and sets the reference conveying speed corrected based on the rate correction factor as the initial conveying speed.

2. The ore conveying system according to claim 1, characterized in that The processing module calculates the rate correction factor according to the proportional relationship between the impurity content difference and the intermediate value, including: The processing module is further configured to obtain an absolute ratio of the impurity content difference to the intermediate value, and select the rate correction factor based on a correspondence between the absolute ratio and a primary ratio range and a secondary ratio range configured by the processing module: When the absolute ratio falls within the primary ratio range, the processing module selects the rate correction factor as R1; When the absolute ratio falls within the secondary ratio range, the processing module selects the rate correction factor as R2; When the absolute ratio exceeds the secondary ratio range, the processing module selects the rate correction factor as R3; The upper limit of the primary ratio range is smaller than the lower limit of the secondary ratio range, and R1<R2<R3.

3. The ore conveying system according to claim 2, characterized in that The step of determining the conveying interval of the ore sorting equipment according to the mass deviation of the ore batch comprises: The processing module is also used to obtain the conveying cycle of the ore sorting equipment, and set the conveying interval based on the conveying cycle, the initial conveying speed, the single conveying volume of the ore sorting equipment and the operation relationship, wherein the operation relationship obtains the time parameter by multiplying the product of the conveying cycle and the single conveying volume by the square of the initial conveying speed.

4. The ore conveying system according to claim 3, characterized in that The step of correcting the delivery interval according to the medium flow rate includes: The processing module is further configured to determine whether to trigger the correction condition of the delivery interval based on a change trend between the current flow rate of the delivery medium and the historical adjacent flow rates: When the current flow rate and the historical adjacent flow rate remain constant, the processing module maintains the original delivery interval; When there is a change between the current flow rate and the historical adjacent flow rate, the processing module calculates an interval adjustment factor according to a combination of flow rate change direction and change amplitude, and updates the delivery interval based on the interval adjustment factor.

5. The ore conveying system according to claim 4, characterized in that When the processing module calculates the interval adjustment factor according to the combined characteristics of the flow velocity change direction and the change amplitude, it includes: The processing module is further configured to obtain a flow rate change rate of the current flow rate relative to a historical adjacent flow rate, and determine the interval adjustment factor based on a comparison result between the flow rate change rate and a positive change threshold and a negative change threshold configured by the processing module; When the flow rate change rate exceeds the negative change threshold, the processing module determines that the interval adjustment factor is S1; When the flow velocity change rate is within the positive change threshold range, the processing module determines that the interval adjustment factor is S2; When the flow velocity change rate exceeds the positive change threshold, the processing module determines that the interval adjustment factor is S3; Among them, S1>S2>S3.

6. The ore conveying system according to claim 5, characterized in that The step of setting the conveying acceleration of each conveying interval according to the conveying interval includes: Determine the corresponding conveying acceleration adjustment mode based on the fluctuation characteristics of the current conveying interval and the historical adjacent conveying intervals; When the current conveying interval and the historical adjacent conveying intervals form a continuous increasing sequence, the processing module sets the conveying acceleration in a step-by-step increasing manner; When the current conveying interval and the historical adjacent conveying intervals form an alternating fluctuation sequence, the processing module calculates an acceleration compensation value according to the interval adjustment factor, and sets the conveying acceleration optimized based on the acceleration compensation value.

7. The ore conveying system according to claim 6, characterized in that When the processing module calculates the acceleration compensation value according to the interval adjustment factor, it includes: The processing module is further configured to determine a compensation intensity level according to a mapping relationship between the interval adjustment factor and a baseline adjustment coefficient and an extended adjustment coefficient configured by the processing module: When the interval adjustment factor is less than the reference adjustment coefficient, the processing module selects the compensation intensity level as U3; When the interval adjustment factor is equal to or greater than the reference adjustment coefficient and less than the expansion adjustment coefficient, the processing module selects the compensation intensity level as U2; When the interval adjustment factor is equal to or greater than the expansion adjustment coefficient, the processing module selects the compensation intensity level as U1; The reference adjustment coefficient is smaller than the extended adjustment coefficient, and U1>U2>U3.

8. An ore conveying method, applicable to the automatic control-based impurity-removable ore conveying system according to any one of claims 1 to 7, characterized in that: include: Detect the amount of impurities attached to the surface of the transported ore and calculate the amount of ore in the transported ore batch; Obtaining an average impurity content of the batch of transported ore based on a correlation between the amount of ore and the amount of impurities attached to the surface of each transported ore; Substituting the medium flow rate into a pre-built transport model to obtain a baseline impurity content, and determining the mass deviation of the ore batch based on the deviation between the mean impurity content and the baseline impurity content; setting an initial conveying speed and conveying interval of the ore sorting equipment according to the mass deviation of the ore batch; The medium flow rate of the ore conveying pipeline is detected, the conveying interval is corrected according to the medium flow rate, and the conveying acceleration of each conveying interval is set according to the corrected conveying interval.

Images