Ore conveying system capable of removing impurities based on automatic control

Abstract

The invention relates to the technical field of ore processing, and discloses an impurity-removable ore conveying system based on automatic control, which comprises an impurity content identification module, a statistics module, a flow velocity detection module, an operation module and a processing module, the flow velocity detection module detects the medium flow velocity; an operation module obtains an impurity content mean value, a reference impurity content and a mass offset; the processing module sets the initial conveying speed and the conveying interval of the ore separation equipment according to the mass deviation, corrects the conveying interval according to the medium flow speed and sets the conveying acceleration of each conveying interval according to the conveying interval. According to the system, through multi-module cooperation and an intelligent algorithm, accurate impurity detection and dynamic adjustment of conveying parameters are achieved, the ore conveying efficiency and the impurity treatment effect are improved, and the system is suitable for the field of ore processing.

Description

Technical Field

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

[0002] In the field of ore mining and processing, the impurity treatment and conveying efficiency optimization during the ore conveying process have always been the key concerns of the industry. Conventional ore conveying systems generally suffer from problems such as low automation level, insufficient accuracy in impurity treatment, and lag in conveying parameter regulation, resulting in low ore sorting efficiency and serious resource waste, and it is difficult to meet the requirements of high-efficiency production in modern mines.

[0003] From the perspective of impurity identification, conventional systems mostly rely on manual visual inspection or simple physical screening, and cannot obtain dynamic data on the amount of impurities attached to the ore surface in real time and accurately. Manual inspection is greatly affected by subjective factors, with a high error rate, and cannot cover large batches of conveyed ore; physical screening can only perform rough separation based on particle size, and it is difficult to effectively identify fine impurities attached to the ore surface, resulting in a lack of reliable impurity data support for subsequent sorting processes and affecting sorting accuracy.

[0004] In terms of conveying parameter control, parameters such as conveying speed and conveying interval in conventional systems are usually set fixedly and cannot be dynamically adjusted according to the quality change of ore batches and the medium flow rate in the conveying pipeline. For example, when the impurity content of ore fluctuates, a fixed conveying speed may cause the sorting equipment to be unable to process ore batches with a higher impurity content in time, resulting in incomplete impurity removal or ore accumulation and blockage; and if the change in medium flow rate (such as the flow rate of pulp in the pipeline fluctuates due to changes in ore sources or equipment operating conditions) is not responded to in time, it will further exacerbate the instability of the conveying process and reduce the overall operating efficiency of the system.

[0005] In addition, conventional systems lack the ability to globally optimize the conveying process. The data interaction and collaborative effect between each functional module (such as detection module, control module, sorting module, etc.) are insufficient, and a closed-loop control system cannot be formed. For example, the impurity detection data fails to be linked with the conveying parameter regulation in real time, resulting in the system being unable to adjust the conveying strategy in time according to the detection results, making it difficult to achieve the best balance between impurity removal effect and conveying efficiency.

[0006] With the continuous expansion of the scale of mine exploitation and the increasing requirement for ore quality, the limitations of traditional ore conveying systems have become increasingly prominent. How to achieve real-time and accurate detection of the impurity content of ore, dynamic adaptive adjustment of conveying parameters, and collaborative optimization control of each module of the system has become a technical problem to be solved urgently. The present invention is proposed under such a background, aiming to construct an efficient and accurate ore conveying system capable of removing impurities through the combination of automatic control technology and intelligent algorithms, so as to improve the automation level and production efficiency of the ore processing process. Summary of the Invention

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

[0008] To achieve the above purpose, the present invention provides the following technical solutions: An ore conveying system capable of removing impurities based on automatic control, the system includes: An impurity content identification module, a statistics module, a flow velocity detection module, an operation module, and a processing module: The impurity content identification module is used to detect the amount of impurities attached to the surface of the conveyed ore, the statistics module is used to calculate the number of ores in the conveyed ore batch, and the flow velocity detection module is used to detect the medium flow velocity of the ore conveying pipeline; The operation module is used to obtain the average impurity content of the conveyed ore batch according to the correlation between the number of ores and the amount of impurities attached to the surface of each conveyed ore; the operation module is also used to substitute the medium flow velocity into the conveying model pre-constructed by the operation module to obtain the reference impurity content, and determine the quality deviation of the conveyed ore batch according to the deviation between the average impurity content and the reference impurity content; The processing module is used to set the initial conveying speed of the ore sorting equipment according to the quality deviation of the ore batch, and determine the conveying interval of the ore sorting equipment according to the quality deviation of the ore batch; The processing module is also used to correct the conveying interval according to the medium flow velocity, and set the conveying acceleration of each conveying interval according to the conveying interval.

[0009] Preferably, the conveying model pre-constructed by the operation module includes: Obtain each conveying parameter of the standard conveying period, and filter redundant data in each conveying parameter, wherein the conveying parameters include: medium flow velocity and average impurity content of ore; According to the standard conveying period, obtain the flow velocity distribution characteristics of different medium flow velocities when the average impurity content is the same among the conveying parameters after filtering redundant data; According to the standard conveying period, obtain the impurity distribution characteristics of the average values of different impurity contents at the same medium flow rate among the conveying parameters after filtering redundant data; Construct the conveying model according to the flow rate distribution characteristics and the impurity distribution characteristics.

[0010] Preferably, setting the initial conveying speed of the ore sorting device includes: The processing module is further configured to obtain the impurity content difference between the average impurity content and the reference impurity content, and set the initial conveying speed according to the comparison between the impurity content difference and the preset impurity difference threshold and the 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 device 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 a rate correction factor according to 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.

[0011] 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: The processing module is further configured to obtain the absolute ratio of the impurity content difference to the intermediate value, and select the rate correction factor according to the corresponding relationship between the absolute ratio and the primary ratio range and the 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; Wherein, the upper limit value of the primary ratio range is less than the lower limit value of the secondary ratio range, and R1 < R2 < R3.

[0012] Preferably, determining the conveying interval of the ore sorting device according to the mass deviation of the ore batch includes: The processing module is further configured to obtain the conveying cycle of the ore sorting device, and set the conveying interval based on the conveying cycle, the initial conveying speed, the single conveying volume of the ore sorting device, and an operation relationship, where the operation relationship obtains a time parameter by dividing the product of the conveying cycle and the single conveying volume by the square of the initial conveying speed.

[0013] Preferably, the correcting of the conveying interval according to the medium flow velocity includes: The processing module is further configured to determine whether to trigger the correction condition of the conveying interval according to the change trend between the current flow velocity of the conveying medium and the historical adjacent flow velocity: When the current flow velocity and the historical adjacent flow velocity remain constant, the processing module maintains the original conveying interval; When there is a change between the current flow velocity and the historical adjacent flow velocity, the processing module calculates an interval adjustment factor according to the combined characteristics of the flow velocity change direction and the change amplitude, and updates the conveying interval based on the interval adjustment factor.

[0014] Preferably, 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 the flow velocity change rate of the current flow velocity relative to the historical adjacent flow velocity, and determine the interval adjustment factor according to the comparison result between the flow velocity change rate and the positive change threshold and the negative change threshold configured by the processing module; When the flow velocity 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; Wherein, S1 > S2 > S3.

[0015] Preferably, the setting of the conveying acceleration for each of the conveying intervals according to the conveying interval includes: Determine the adjustment mode of the corresponding conveying acceleration according to the fluctuation characteristics of the current conveying interval and the historical adjacent conveying interval; When the current conveying interval and the historical adjacent conveying interval form a continuously increasing sequence, the processing module sets the conveying acceleration in a stepped 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.

[0016] Preferably, 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 the mapping relationship between the interval adjustment factor and the reference adjustment coefficient and the 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 extended adjustment coefficient, the processing module selects the compensation intensity level as U2; When the interval adjustment factor is equal to or greater than the extended adjustment coefficient, the processing module selects the compensation intensity level as U1; Wherein, the reference adjustment coefficient is less than the extended adjustment coefficient, and U1 > U2 > U3.

[0017] Preferably, the present invention further includes an ore conveying method, which is applicable to the above-mentioned impurity-removable ore conveying system based on automatic control. The method includes: Detect the impurity adhesion amount on the surface of the conveyed ore and calculate the number of ores in the conveyed ore batch; Obtain the average impurity content of the conveyed ore batch according to the correlation between the number of ores and the impurity adhesion amount on the surface of each conveyed ore; Substitute the medium flow rate into a pre-constructed conveying model to obtain a reference impurity content, and determine the mass deviation of the ore batch according to the deviation between the average impurity content and the reference impurity content; Set the initial conveying speed and conveying interval of the ore sorting device according to the mass deviation of the ore batch; Detect the medium flow rate of the ore conveying pipeline, correct the conveying interval according to the medium flow rate, and set the conveying acceleration of each conveying interval according to the corrected conveying interval.

[0018] Compared with the prior art, the beneficial effects of the present invention are: In the impurity content detection and quality assessment process, the impurity content identification module can detect the amount of impurities attached to the ore surface in real time. By combining the calculation of the ore quantity by the statistics module and the transportation model constructed by the operation module, it can accurately obtain the average impurity content and the reference impurity content, and then determine the quality deviation of the ore batch. This process realizes the full-process automation from impurity data collection to quality assessment, avoiding the subjectivity and lag of manual detection, providing a reliable quality basis for the subsequent sorting process, enabling the system to dynamically adjust the processing strategy according to the actual quality of the ore, and significantly improving the pertinence and effectiveness of impurity removal.

[0019] Regarding the initial conveying speed setting, the processing module calculates the rate correction factor by comparing the impurity content difference with the preset threshold and combining the proportional relationship, realizing the hierarchical dynamic adjustment of the initial conveying speed. When the impurity content difference is in different intervals, the system can automatically match the corresponding speed adjustment strategies: maintaining the reference speed to ensure efficiency when the difference is small, finely adjusting the speed proportionally to balance quality and efficiency when the difference is in the middle range, and returning to the reference speed when the difference is too large to avoid system fluctuations caused by excessive adjustment. This differential speed control mode enables the sorting equipment to better adapt to ore batches with different impurity contents, ensuring the impurity removal effect while minimizing problems such as ore accumulation or incomplete sorting caused by unreasonable speeds.

[0020] 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 operational relationship among the conveying cycle, initial speed, and single conveying volume, and triggers the correction mechanism by monitoring the change trend of the medium flow rate. When the flow rate is constant or changing, the interval parameters are updated by maintaining the original interval or calculating the interval adjustment factor based on the flow rate change rate respectively. This dynamic correction mechanism can respond in real time to the hydrodynamic changes in the conveying pipeline. 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, thus ensuring that the conveying process is always in an efficient and stable operating state and effectively enhancing the system's adaptability to complex conveying environments.

[0021] The optimized setting of the conveying acceleration further enhances the operating stability and energy conservation of the system. The processing module selects different acceleration adjustment modes according to the fluctuation characteristics of the conveying interval (continuous increase or alternating fluctuation): adopting a stepped increase method to gradually increase the acceleration when continuously increasing to avoid excessive instantaneous acceleration impacting the equipment; calculating the acceleration compensation value by combining the interval adjustment factor when alternatingly fluctuating to achieve dynamic compensation and optimization of the acceleration. This refined acceleration control strategy can not only reduce equipment wear and extend the service life but also reduce the overall operating cost of the system by reasonably distributing energy consumption, embodying the design concept of green energy conservation.

[0022] From the perspective of the overall system coordination, a closed-loop control system is formed through real-time data interaction among modules. The impurity detection data drives the operation module to conduct quality assessment, and the assessment results guide the processing module to adjust the conveying parameters. Meanwhile, the flow velocity detection data dynamically corrects the conveying interval and acceleration, forming a complete control chain of "detection - assessment - control - feedback". This highly coordinated working mode enables the system to optimize the operation parameters in real time according to the changes in ore characteristics and conveying environment, achieving the best balance between impurity removal and conveying efficiency, significantly enhancing the automation level and production efficiency of ore processing, and providing important technical support for the intelligent upgrade of the mining industry. Brief Description of the Drawings

[0023] Figure 1 It is the working principle diagram of the ore conveying system capable of removing impurities based on automatic control according to the present invention; Figure 2 It is the design diagram for constructing the conveying model; Figure 3 It is the design diagram for setting the initial conveying speed; Figure 4 It is the design diagram for calculating the rate correction factor; Figure 5 It is the design diagram for calculating the interval adjustment factor. Specific Embodiments

[0024] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0025] Please refer to Figures 1 - 5 , the ore conveying system capable of removing impurities based on automatic control involved in the present invention, which system includes an impurity content identification module, a statistics module, a flow velocity detection module, an operation module, and a processing module. Specifically, it includes the following steps: When the system operates, the impurity content identification module detects the amount of impurity adhesion on the surface of the conveyed ore. This module can adopt image recognition technology or spectral analysis technology. For example, it collects the ore surface image through a high-resolution camera, and uses image processing algorithms to identify and calculate the proportion of the impurity area, so as to quantify the amount of impurity adhesion; meanwhile, the statistics module calculates the number of ores in the conveyed ore batches through devices such as photoelectric sensors or weighing devices. For example, a through-beam photoelectric sensor is set at a specific position of the conveying pipeline, and when the ore passes, it triggers counting, thereby accumulating the number of ores in the batch.

[0026] The flow rate detection module uses devices such as electromagnetic flow meters or ultrasonic flow meters to detect the medium flow rate in the ore conveying pipeline in real time, and obtains the flow velocity data of the mixture of ore and conveying medium in the pipeline.

[0027] The operation module performs the following operations: On the one hand, according to the ore quantity obtained by the statistics module and the impurity adhesion amount on the surface of each conveyed ore detected by the impurity content identification module, the average impurity content of the conveyed ore batch is obtained through algorithms such as arithmetic mean or weighted mean. 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. This 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 average impurity content, and the impurity distribution characteristics of different average impurity contents under the same medium flow rate, the mapping relationship between the medium flow rate and the reference impurity content is fitted, so as to calculate the reference impurity content. Further, according to the deviation value between the average impurity content and the reference impurity content, through the preset deviation calculation formula or threshold comparison method, the quality deviation degree of the conveyed ore batch is determined. For example, the deviation value is the absolute value of the difference between the average impurity content and the reference impurity content. When the difference exceeds the preset threshold, it is determined that the quality deviation is large.

[0028] The processing module performs the following control operations according to the quality deviation of the ore batch determined by the operation module: First, set the initial conveying speed of the ore sorting equipment. Specifically, by obtaining the impurity content difference between the average impurity content and the reference impurity content, this difference is compared 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, directly set 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, calculate the intermediate value between the two thresholds, and according to the proportional relationship between the impurity content difference and the intermediate value, calculate the rate correction factor through algorithms such as linear interpolation or piecewise function, and then multiply the reference conveying speed by the rate correction factor to obtain the corrected initial conveying speed.

[0029] The processing module determines the conveying interval of the ore sorting equipment according to the quality deviation of the ore batch. Specifically, by obtaining the conveying cycle of the ore sorting equipment, based on the conveying cycle, the initial conveying speed, and the single conveying volume of the ore sorting equipment, the time parameter is calculated using the operation relationship (the product of the conveying cycle and the single conveying volume divided by the square of the initial conveying speed), and this time parameter is used as the initial setting value of the conveying interval.

[0030] The processing module corrects the conveying interval according to 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 conveyed medium and the historical adjacent flow rates, it is determined whether the correction condition of the conveying interval is triggered. When the current flow rate and the historical adjacent flow rates remain constant, the original conveying 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 according to the comparison result between the flow rate change rate and the positive change threshold and the negative change threshold configured by the processing module, the interval adjustment factor is determined. 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, and the conveying interval is updated by multiplying the initial conveying interval by the interval adjustment factor.

[0031] The processing module sets the conveying acceleration of each conveying interval according to the corrected conveying interval. Specifically, by analyzing the fluctuation characteristics of the current conveying interval and the historical adjacent conveying intervals, the adjustment mode is determined. When a continuous increasing sequence is formed, the conveying acceleration is set in a stepwise increasing manner, for example, increasing by a fixed acceleration value each time; when an alternating fluctuation sequence is formed, according to the mapping relationship between the interval adjustment factor and the reference adjustment coefficient and the extended adjustment coefficient configured by the processing module, the compensation intensity level (U1, U2, U3, and U1 > U2 > U3) is determined, and the acceleration compensation value is calculated through the compensation intensity level to optimize the setting of the conveying acceleration.

[0032] The present invention will be further described below in conjunction with Embodiments 1 to 5: Embodiment 1: When the operation module constructs the conveying model, it first enters the data acquisition stage. In this stage, the system will determine the selection principle of the standard conveying period. Considering the operating characteristics of the ore conveying system, the standard conveying period should cover the operating data of different time periods to ensure the comprehensiveness and representativeness of the data. For example, three time periods in the morning, afternoon, and evening on weekdays and some time periods on weekends will be selected. The factors such as the source, type, and conveying volume of the ore may vary during these time periods, which can reflect the operating conditions of the system under different working conditions.

[0033] After determining the standard conveying period, the system will obtain various conveying parameters during this period through a variety of sensors and detection devices. For the medium flow rate, a high-precision electromagnetic flowmeter is used for real-time monitoring. This flowmeter can accurately measure the flow rate of the fluid in the pipeline, and its measurement principle is based on Faraday's law of electromagnetic induction. When a conductive fluid passes through a magnetic field, an induced electromotive force proportional to the flow rate will be generated, and the fluid flow rate can be obtained by detecting this electromotive force. For the average impurity content of the ore, image recognition technology and chemical analysis methods are combined for detection. In the image recognition part, a high-resolution industrial camera is used to continuously capture the ore on the conveyor belt, and then the impurity distribution on the ore surface is analyzed through image processing algorithms to calculate the proportion of the impurity area; in the chemical analysis part, an online spectral analyzer is used to analyze the composition of the ore in real time to determine the types and contents of impurities, and finally the average impurity content is obtained by integrating the two methods.

[0034] During the data acquisition process, the system will synchronously record the timestamps of each parameter to ensure the time consistency of the data and provide an accurate data basis for subsequent analysis. At the same time, to ensure the reliability of the data, multiple sensors are used to measure each parameter, and then the data fusion algorithm is used to process multiple measurement values to obtain the final parameter value.

[0035] The data filtering stage is a key link in constructing the conveying model, directly affecting the accuracy of subsequent analysis. The system first preprocesses the obtained conveying parameters, including data cleaning and standardization. During the data cleaning process, mainly the outliers and missing values in the data are processed. For outliers, the Z-score method based on statistics is used for detection. This method determines whether a data point is an outlier by calculating the deviation degree of the data point from the mean. For data points with an absolute Z-score greater than 3, they are considered outliers and replaced by the interpolation method. The interpolation method estimates the outliers by using the values of adjacent data points according to the time series characteristics of the data.

[0036] For missing values, the system will select a suitable processing method according to the quantity and distribution of the missing values. If the number of missing values is small, the linear interpolation method is used for filling; if the number of missing values is large and concentrated in a certain time period, the data in this time period will be removed as a whole to avoid having a greater impact on subsequent analysis.

[0037] In terms of data standardization, the system adopts the Min-Max standardization method to map the values of each conveying parameter to the interval [0,1], eliminating the influence of different parameter dimensions and orders of magnitude, making the data comparable. The standardization formula is: x'=(x - min) / (max - min), where x is the original data, min and max are the minimum and maximum values of this parameter respectively, and x' is the standardized data.

[0038] After completing data cleaning and standardization, the system will perform feature extraction on the data to uncover the hidden information in the data. For example, it calculates the statistical features of each parameter, such as mean, variance, skewness, kurtosis, etc., to analyze the distribution characteristics of the data; at the same time, it extracts the time series features of the data, such as autocorrelation coefficient, cross-correlation coefficient, etc., to analyze the time-dependent relationship between parameters.

[0039] In the parameter relationship analysis stage, the system will deeply analyze the distribution of different medium flow rates under the condition of the same mean impurity content. First, the obtained mean impurity contents are grouped. For example, the mean impurity contents are divided into multiple levels according to a certain interval range. For each level of mean impurity content, the corresponding medium flow rate data are statistically analyzed, and a flow rate distribution histogram is drawn to visually display the distribution of the flow rate.

[0040] Then, it calculates the statistical quantities such as the occurrence frequency, mean, variance, etc. of different flow rates to analyze the central tendency and dispersion degree of the flow rate. For example, by calculating the mean, the average level of the medium flow rate under a specific mean impurity content can be understood; by calculating the variance, the dispersion degree of the flow rate data can be understood. The larger the variance, the greater the fluctuation of the flow rate.

[0041] The system will also analyze the probability density function of the flow rate distribution to determine whether it conforms to a certain common probability distribution, such as normal distribution, lognormal distribution, etc. If it conforms to a certain distribution, the characteristics of this distribution can be used for more in-depth analysis and prediction.

[0042] Under the condition of the same medium flow rate, the system will statistically analyze the distribution of different mean impurity contents. Similarly, the medium flow rates are grouped, and for each flow rate group, the corresponding mean impurity content data are statistically analyzed, and a mean impurity content distribution histogram is drawn.

[0043] It calculates the statistical quantities such as the frequency distribution, median, range, etc. of the mean impurity content to analyze the distribution characteristics of the impurity content. The median can reflect the middle level of the data, and the range can reflect the fluctuation range of the data. The system will analyze the skewness of the impurity content distribution to determine whether the impurity content is biased towards high values or low values, which is of great significance for understanding the quality distribution of the ore.

[0044] The system will combine the flow rate distribution characteristics and the impurity distribution characteristics to analyze the internal relationship between the two. For example, by calculating the correlation coefficient between the medium flow rate and the mean impurity content, it is judged whether there is a linear correlation between the two. If the correlation coefficient is large, it indicates that there is a strong linear relationship between the two, and a linear regression model can be further established for prediction.

[0045] In the stage of constructing the transportation model, based on the flow velocity distribution characteristics and impurity distribution characteristics obtained from the above analysis, the system uses machine learning algorithms to construct the mapping relationship between the medium flow velocity and the reference impurity content. First, the system will select a suitable machine learning algorithm. Considering the possible complex non-linear relationship between the medium flow velocity and the reference impurity content, it is decided to use the neural network algorithm for modeling.

[0046] Neural networks have powerful non-linear mapping capabilities and can handle complex input-output relationships. The neural network model constructed by the system includes an input layer, a hidden layer, and an output layer. The input layer receives the medium flow velocity and other relevant parameters, such as ore type, transportation volume, etc.; the hidden layer contains multiple neurons that process and transform the input information through non-linear activation functions; the output layer outputs the predicted value of the reference impurity content.

[0047] During the model training process, the system divides the data obtained during the standard transportation period into a training set, a validation set, and a test set. The training set is used for learning and optimizing the model parameters, the validation set is used to adjust the hyperparameters of the model, such as the number of neurons in the hidden layer, the learning rate, etc., and the test set is used to evaluate the performance of the model.

[0048] The system uses the backpropagation algorithm for model training. By continuously adjusting the weights and biases of the neural network, it minimizes the error between the model predicted value and the actual value. During the training process, to prevent the model from overfitting, the system will use regularization methods, such as L1 and L2 regularization, to constrain the complexity of the model.

[0049] To improve the generalization ability of the model, the system will also use data augmentation techniques to expand the training data. For example, adding random noise to the original data, performing data transformation, etc., to generate more training samples, so that the model can learn more abundant features.

[0050] In terms of model evaluation, the system uses a variety of evaluation metrics to comprehensively evaluate the performance of the model. Commonly used evaluation metrics include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), etc. Through these metrics, the prediction accuracy and stability of the model can be comprehensively evaluated.

[0051] The system will perform cross-validation on the model, divide the dataset into multiple subsets, and take turns using different subsets as the training set and the test set, train and evaluate the model multiple times, and take the average performance as the final evaluation result to reduce the randomness of the evaluation result.

[0052] After completing model training and evaluation, the system deploys the trained neural network model to the actual production environment. During actual application, the system continuously collects new data to online update and optimize the model to adapt to the changes in the operating conditions of the ore conveying system and improve the prediction accuracy and adaptability of the model.

[0053] Example 2: When the processing module sets the initial conveying speed of the ore sorting equipment, it first enters the difference calculation stage. In this stage, through precise algorithms, the difference between the average impurity content and the reference impurity content is calculated. This process involves multi-step data processing and analysis to ensure the accuracy of the difference. The system preprocesses the average impurity content and the reference impurity content, including unified conversion of data formats, standardization of units, etc. For example, if the average impurity content is presented in percentage form while the reference impurity content is stored in decimal form, the system will convert them into the same representation for subsequent calculations.

[0054] When calculating the difference, the system adopts high-precision numerical calculation methods to avoid result deviations caused by floating-point operation errors. For complex ore compositions, there may be multiple impurities, and the content of each impurity has a different influence weight on the overall impurity content. The system assigns corresponding weights to each impurity according to the type and properties of the impurity, then calculates the weighted average impurity content, and compares it with the reference impurity content.

[0055] After the difference calculation is completed, the system verifies the calculation result. By comparing and analyzing with historical data, it judges whether the difference is within a reasonable range. If the difference is abnormal, the system will recheck the data source and calculation process to ensure the reliability of the difference.

[0056] After completing the difference calculation, the system enters the threshold comparison stage. In this stage, the calculated impurity content difference is compared with the preset auxiliary impurity difference threshold and the preset impurity difference threshold in the processing module. These two thresholds are important parameters set by the system according to long-term operating experience and ore sorting process requirements, directly affecting the subsequent speed adjustment strategy.

[0057] The auxiliary impurity difference threshold is a relatively small value used to judge whether the deviation of the impurity content belongs to minor fluctuations. The preset impurity difference threshold is a relatively large value used to judge whether the deviation of the impurity content exceeds the normal range and significant adjustments are required. The system will precisely compare the impurity content difference with these two thresholds to determine the interval where the difference is located.

[0058] During the threshold comparison process, the system takes into account the dynamics of the threshold. Due to changes in factors such as ore source and production process, the preset threshold may need to be adjusted in a timely manner. The system regularly collects historical data, analyzes the distribution of impurity content differences, and optimizes and updates the threshold based on the analysis results to ensure the rationality and effectiveness of the threshold.

[0059] When the impurity content difference is in different ranges, the system will adopt different processing strategies. If the impurity content difference ≤ the auxiliary impurity difference threshold, the system determines that the impurity content deviation is small and there is no need to adjust the initial conveying speed. Instead, the reference conveying speed is directly used as the initial conveying speed. This is because in this case, the fluctuation of the impurity content has a small impact on the ore sorting effect, and maintaining the reference conveying speed can ensure the stable operation of the equipment.

[0060] If the impurity content difference > the preset impurity difference threshold, the system believes that the impurity content deviation is too large. To ensure the ore sorting effect, the reference conveying speed is also adopted. This is because when the impurity content is too high, too fast a conveying speed may lead to incomplete sorting, while too slow a conveying speed will affect production efficiency. The reference conveying speed is the optimal speed determined after comprehensively considering various factors and can balance the sorting effect and production efficiency to a certain extent.

[0061] When the auxiliary impurity difference threshold < the impurity content difference ≤ the preset impurity difference threshold, the system enters the rate correction process. This process is the core link of Embodiment 2 and aims to accurately correct the initial conveying speed according to the specific situation of the impurity content difference.

[0062] The system first calculates the intermediate value between the preset impurity difference threshold and the auxiliary impurity difference threshold. This calculation process needs to be accurate to ensure the accuracy of the subsequent proportional relationship. The formula for the intermediate value is: (preset impurity difference threshold + auxiliary impurity difference threshold) / 2. By calculating the intermediate value, the system further divides the range of the impurity content difference into two sub-ranges, facilitating more accurate adjustment of the conveying speed.

[0063] Next, the system calculates the proportional relationship between the impurity content difference and the intermediate value. This proportional relationship reflects the relative position of the impurity content difference on both sides of the intermediate value and is an important basis for determining the rate correction factor. The formula for the proportional coefficient k is: k = (impurity content difference - auxiliary impurity difference threshold) / (preset impurity difference threshold - auxiliary impurity difference threshold), and the value range of k is (0, 1).

[0064] After calculating the proportional coefficient k, the system compares it with the primary ratio range and the secondary ratio range configured by the processing module. The primary ratio range and the secondary ratio range are set according to the characteristics and requirements of the ore sorting process and are used to divide different correction levels.

[0065] When the proportionality coefficient k falls within the primary ratio range (e.g., 0 < k ≤ 0.3), it indicates that the difference in impurity content is relatively small and close to the auxiliary impurity difference threshold. At this time, the system selects a relatively small correction factor R1 (e.g., 0.9) to slightly lower the reference conveying speed. This is because in this case, although an increase in impurity content requires an adjustment of the conveying speed, the adjustment amplitude should not be too large to avoid affecting production efficiency.

[0066] When the proportionality coefficient k falls within the secondary ratio range (e.g., 0.3 < k < 0.7), it indicates that the difference in impurity content is in the middle position and requires a moderate adjustment. At this time, the system selects a medium correction factor R2 (e.g., 1.1) to moderately increase the reference conveying speed. This is because in this case, the increase in impurity content has already had a certain impact on the ore sorting effect, and it is necessary to appropriately increase the conveying speed to ensure the sorting effect.

[0067] When the proportionality coefficient k ≥ 0.7, it indicates that the difference in impurity content is close to the preset impurity difference threshold and requires a significant adjustment. At this time, the system selects a relatively large correction factor R3 (e.g., 1.3) to significantly increase the reference conveying speed. This is because in this case, the increase in impurity content is already relatively obvious, and it is necessary to greatly increase the conveying speed to ensure the ore sorting effect.

[0068] After determining the correction factor, the system multiplies the reference conveying speed by the correction factor to obtain the corrected initial conveying speed. This calculation process is simple and direct, but crucial, directly determining the initial operating state of the ore sorting equipment.

[0069] To ensure the rationality and effectiveness of the corrected initial conveying speed, the system will perform a secondary verification on it. The verification process includes checking whether the corrected initial conveying speed is within the safe operating range of the equipment and whether it meets the requirements of the production process, etc. If it is found that there are problems with the corrected initial conveying speed, the system will re-evaluate the difference in impurity content and the correction factor and make another adjustment until a reasonable initial conveying speed is obtained.

[0070] The system will also record the relevant information of each speed adjustment, including the difference in impurity content, the correction factor, the corrected initial conveying speed, etc. These 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 methods, the processing module can accurately set the initial conveying speed of the ore sorting equipment according to the change in ore impurity content, improve production efficiency while ensuring the ore sorting effect, and realize the intelligent operation of the equipment.

[0071] Example 3: When determining the conveying interval of the ore separation equipment, the processing module needs to comprehensively consider multi-dimensional 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: First, the system obtains the conveying cycle of the ore separation equipment in real time through sensors . The conveying cycle refers to the time required for the equipment to complete a full conveying action (such as the entire process from loading ore to completing separation and discharging). Its measurement method depends on the mechanical structure characteristics of the equipment. For example, for reciprocating conveying equipment, an angle encoder can be installed on the transmission components (such as lead screws, gears) to record the time for the components to complete a full movement cycle; for continuous conveying equipment, the cycle can be determined by detecting the time interval for the material to pass through a specific position. To ensure data accuracy, the system takes the average value of the measurement values of multiple consecutive cycles to eliminate the influence of accidental errors.

[0072] Secondly, obtain the initial conveying speed . The initial conveying speed is determined by the processing module according to the comparison result of the impurity content difference and the threshold value described in Example 2. Its physical meaning is the initial operating rate of the ore separation equipment during the current batch of ore processing, with the unit of meters per second (m / s). This speed value needs to be within the safe operating speed range of the equipment and match the physical properties of the ore (such as particle size, density) to avoid ore accumulation caused by too fast speed or affecting the processing efficiency due to too slow speed.

[0073] Then, determine the single conveying volume of the ore separation equipment . The single conveying volume represents the volume or mass of ore processed by the equipment each time it completes a conveying action. Its calibration method depends on the equipment type. For volumetric hoppers, the volume can be calculated by measuring the internal geometric dimensions (length, width, height) of the hopper, and then converted to mass in combination with 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. To meet the processing requirements of different types of ore, the single conveying volume can be adjusted by the operator according to the actual situation through the human-machine interface, and the system will automatically record and save the adjusted parameters.

[0074] After obtaining , , the three key parameters, the processing module calculates the time parameter based on a specific operation relationship. This time parameter is used as the initial setting value of the conveying interval of the ore separation equipment. The operation relationship is expressed by the formula:

[0075] The meanings of each character in the formula are as follows: : The initial set value of the conveying interval, in seconds (s), representing the time interval between two adjacent conveying operations; : The conveying cycle of the ore sorting equipment, in seconds (s); : The single - time conveying volume 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).

[0076] The physical meaning of this formula is to quantitatively relate the periodic operation characteristics of the equipment (conveying cycle ), the single - time processing capacity (single - time conveying volume ), and the operating speed (initial conveying speed ). The denominator uses the square term of because the influence of the change in conveying speed on the conveying interval is non - linear - when the speed increases, the conveying volume per unit time increases, and 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 the equipment operation.

[0077] In the actual calculation process, the system will automatically convert the units of each parameter to ensure the dimensional consistency of the operation. For example, if the single - time conveying volume is in kilograms (kg), the system will convert it to the volume unit of cubic meters (m³) according to the density of the ore (unit: kg / m³), and the conversion formula is , so as to ensure that the unit of the numerator part in the formula is cubic meters·second (m³·s) or kilograms·second (kg·s), the unit of the denominator is square meters per second² (m² / s²), and the finally obtained time parameter is in seconds (s).

[0078] After completing the calculation of the time parameter , the system needs to perform a rationality check on this value. The check rules include: Minimum value limit: The conveying interval shall not be less than the shortest time for the equipment to complete one conveying operation, that is , where is the mechanical limit cycle of the equipment, provided by the equipment manufacturer; Maximum value limit: The conveying interval shall not cause the ore to stagnate or deposit in the conveying pipeline, and the allowable maximum interval needs to be calculated through hydrodynamic formulas according to parameters such as the inner diameter of the pipeline and the particle size of the ore ; Integer multiple constraint: For the convenience of the device control system to execute, the conveying interval needs to be taken as an integer multiple of the device control period , that is , where is a positive integer, usually a fixed value in milliseconds (such as 100 ms).

[0079] If the calculated does not meet the above verification rules, the system will automatically adjust the parameter value. For example, if , then take , and prompt the operator to adjust the initial conveying speed or the single - time conveying volume through an alarm; if , then set according to , and trigger the pipeline blockage warning mechanism to increase the sampling frequency of the flow rate detection module.

[0080] 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 contents and equipment working conditions. When processing a new batch of ore, the system can first retrieve similar historical records from the database according to the ore type to obtain the initial conveying interval reference value, and then perform weighted averaging in combination with the currently calculated value to obtain the final conveying interval setting value. The weighting coefficient is automatically adjusted according to the similarity between the historical record and the current working condition. The higher the similarity, the greater the weight of the historical data, thereby improving the setting efficiency and accuracy.

[0081] During the conveying process, the system will continuously monitor the operation status of the equipment and the conveying situation of the ore. If it detects abnormal equipment load (such as current fluctuation exceeding the preset threshold) or changes in the conveying pipeline pressure, it will automatically trigger the dynamic fine - tuning mechanism of the conveying interval. The fine - tuning amplitude is determined according to the degree of abnormality, usually ±5% - ±10% of the initial setting value. During the fine - tuning process, it is necessary to ensure that the adjusted conveying interval still meets the above verification rules. The purpose of dynamic fine - tuning is to enable the system to quickly adapt to sudden changes in working conditions and avoid equipment failures or production efficiency declines caused by fixed interval settings.

[0082] Through the above complete implementation process, the processing module, based on accurate parameter measurement, rigorous formula operation and perfect verification mechanism, realizes the scientific setting of the conveying interval of the ore sorting equipment. This process not only considers the mechanical characteristics of the equipment and the physical parameters of the ore, but also incorporates the experience accumulation of historical data and the dynamic feedback of the real - time working condition, ensuring that the conveying interval can not only meet the production efficiency requirements, but also guarantee the stability and reliability of ore sorting, providing key support for the automatic control of the entire ore conveying system.

[0083] Example 4: When the processing module corrects the conveying interval according to the medium flow rate, it is necessary to dynamically adjust the conveying interval by monitoring the change trend of the flow rate in real time and combining with the preset rules. The implementation process is described in detail below with specific scenario examples: Suppose that in the operation of an ore conveying system, the flow rate detection module collects the medium flow rate data in the pipeline at a frequency of once per second. At a certain moment, the system obtains the current flow rate as , and the historical adjacent flow rate in the previous sampling period is . At this time, the current flow rate and the historical adjacent flow rate remain constant, and the system determines that there is no need to correct the conveying interval and maintains the original set value (for example ) unchanged.

[0084] When the system runs to another period, it is detected that the current flow rate is , and the historical adjacent flow rate is . First, calculate the flow rate change rate: the change amount is , and the change rate is . The processing module compares this change rate with the preset negative change threshold (such as ) and finds that exceeds the negative change threshold, indicating that the flow rate has decreased significantly. At this time, the system determines the interval adjustment factor to be (for example, taking the value ), and updates the original conveying interval to . The logic of this adjustment is that the decrease in flow rate may be caused by the accumulation of ore or the increase in concentration in the pipeline. Extending the conveying interval can reduce the ore conveying volume per unit time, reduce the pipeline load, and avoid the risk of blockage.

[0085] For another example, during the subsequent operation of the system, it is detected that the current flow rate is , and the historical adjacent flow rate is . Calculate the flow rate change rate to be , and this value exceeds the preset positive change threshold (such as ), indicating that the flow rate has increased significantly. Based on this, the system determines the interval adjustment factor to be (for example, taking the value ), and adjusts the conveying interval to . The reason for the adjustment is that the increase in flow rate may be due to the enhancement of conveying power or the decrease in ore concentration. Shortening the conveying interval can increase the conveying volume per unit time, make full use of the conveying capacity under the current working conditions, and improve production efficiency.

[0086] If the system detects the current flow rate is , the historical adjacent flow rate is , and the calculated change rate is . At this time, the change rate is between the negative change threshold ( ) and the positive change threshold ( ) (i.e., within the normal fluctuation range), and the system determines that the interval adjustment factor is (for example, taking the value ), and the conveying interval remains unchanged. This is because small fluctuations in the flow rate may be caused by the normal tremors of the equipment or uneven distribution of ore particles, and there is no need to adjust the conveying interval to maintain the stability of the system operation.

[0087] In practical applications, the scenarios of flow rate changes are more complex. For example, within a certain period, the flow rate shows a continuous downward trend: , , . The system calculates the adjacent flow rate change rates in sequence: , . The first change rate is equal to the negative change threshold, and the system maintains the interval unchanged; the second change rate exceeds the negative change threshold, triggering the interval adjustment factor , and the conveying interval is adjusted from the initial to . Subsequently, if the flow rate continues to drop to , the change rate is , and the system applies again, adjusting the interval to until the flow rate stabilizes or reaches the preset maximum interval limit.

[0088] In another scenario, the flow rate shows a fluctuating trend of rising first and then falling: , , . Calculate the change rate: (exceeds the positive change threshold, applying , and the interval is adjusted from to ); (exceeds the negative change threshold, applying , and the interval is adjusted to ). This alternating change trend of the flow rate reflects the instability of the conveying working condition. The system dynamically responds to the flow rate fluctuations by continuously applying different interval adjustment factors, avoiding ore accumulation or loss of conveying efficiency caused by a fixed interval.

[0089] Before the system performs the correction operation, it filters the flow rate data to exclude sudden noise interference. For example, the moving average filtering algorithm is used to calculate the average value of the flow rate data in the last 5 sampling periods as the effective value of the current flow rate, avoiding triggering incorrect correction instructions due to a single abnormal data point. At the same time, to prevent frequent adjustments from impacting the equipment, the system sets a minimum adjustment interval (such as 10 seconds), that is, even if the flow rate changes multiple times in a short period, the conveying interval will not be frequently modified, ensuring the smooth operation of the equipment.

[0090] In terms of parameter configuration, the positive change threshold, negative change threshold, and interval adjustment factor 、 、 The specific values of can be adjusted by the operator through the human-machine interface according to the actual working conditions. For example, for the transportation of high-viscosity ore slurries, the negative change threshold can be set to to identify the downward trend of the flow rate earlier; for a low-resistance pipeline system, the positive change threshold can be set to to allow a larger range of flow rate fluctuations. The system will automatically save the parameter settings and load them when starting up next time, without the need for repeated configuration.

[0091] The entire correction process realizes closed-loop control through real-time data acquisition, change rate calculation, threshold comparison, and logical judgment. The processing module completes data update and calculation once per second to ensure that the conveying interval can respond to the flow rate change in a timely manner. At the same time, the system records the time, flow rate change rate, adjustment factor, and adjusted interval value of each correction, forming a historical log for the operator to query and analyze, so as to optimize parameter configuration or troubleshoot equipment failures.

[0092] From the above specific examples, it can be seen that the processing module realizes the intelligent correction of the conveying interval based on the direction and amplitude of the flow rate change through a preset rule set. This mechanism can not only cope with significant changes in the flow rate to ensure system safety but also tolerate fluctuations within the normal range to maintain stable operation, reflecting the accuracy and adaptability of automatic control in the ore conveying system and providing key support for the efficient and stable progress of the ore sorting process.

[0093] Example 5: When the processing module sets the conveying acceleration of each conveying interval, it is necessary to dynamically adjust the acceleration value by analyzing the fluctuation characteristics of the conveying interval and combining preset rules to meet the conveying requirements under different working conditions. The following details the implementation process through specific scenario examples: Suppose that in the initial stage of operation of an ore conveying system, the conveying intervals in three consecutive sampling periods are respectively 、 、 , showing an increasing trend. The processing module first identifies this fluctuation characteristic and determines that 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 stepped increasing manner. The initial acceleration is set to , and each increment is . Therefore, the accelerations corresponding to these three conveying intervals are set to , , respectively. The logic of this setting method is as follows: The gradually increasing conveying interval may cause the flow rate of the ore 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 conveying speed of the ore can be maintained stable.

[0094] If during the subsequent operation of the system, the conveying interval shows alternating fluctuations, for example, the interval values for five consecutive sampling periods are , , , , , forming an alternating fluctuation sequence ( ). After the system detects this fluctuation feature, according to the comparison results of the interval adjustment factor determined in Example 4 with the reference adjustment coefficient and the extended adjustment coefficient , the compensation intensity level is determined. Suppose in a certain adjustment, the interval adjustment factor , the reference adjustment coefficient , the extended adjustment coefficient . At this time , the system selects the medium compensation intensity level . The acceleration compensation value corresponding to the level is . The system adds the compensation value to the current conveying acceleration (for example ) to obtain the optimized acceleration . The purpose of this compensation mechanism is to suppress the influence of the conveying interval fluctuation on the system stability, ensure the uniform conveying speed of the ore in the pipeline, and reduce the risks of pipeline wear and ore deposition caused by speed fluctuations.

[0095] In practical applications, the identification of the fluctuation feature and the acceleration adjustment is a dynamic cyclic process. For example, the system continuously monitors the conveying interval as , , , during a certain period, showing a continuous increasing trend. The system adjusts the acceleration from the initial to , , in a stepped increasing manner. Subsequently, if the conveying interval suddenly becomes , forming an alternating fluctuation sequence ( ), the system immediately switches the compensation strategy. Suppose the interval adjustment factor at this time , exceeding the reference adjustment coefficient , but not reaching the extended adjustment coefficient , the system selects the compensation intensity level , and adjusts the acceleration to . This fast response mechanism enables the system to flexibly respond to changes in working conditions and maintain the stability of the conveying process.

[0096] 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 is more likely to fluctuate, and the fluctuation amplitude is larger. Suppose during the conveying of high-viscosity ores, the interval values for three consecutive sampling periods are , , , and the fluctuation amplitude reaches . After the system detects such a large fluctuation, if the interval adjustment factor (exceeding the extended adjustment coefficient ), it selects the highest compensation intensity level , corresponding to the compensation value . By significantly increasing the acceleration, the flow resistance of the high-viscosity ore is overcome, preventing the ore from staying in the pipeline. On the contrary, when processing low-viscosity ores, the fluctuation of the conveying interval is small. For example, the interval values for five consecutive sampling periods fluctuate between and , and the system may determine that no additional compensation is required and maintain the current acceleration unchanged.

[0097] When setting the acceleration, the system also needs to consider the physical limitations and safety margins of the equipment. For example, when the acceleration is adjusted to , if it continues to increase, it may cause the equipment motor to be overloaded or the pipeline vibration to intensify. At this time, the system will trigger the acceleration upper limit protection mechanism, and no longer increase the acceleration value regardless of the fluctuation characteristics. At the same time, to avoid impact on the equipment caused by frequent adjustment, the system sets a minimum adjustment amplitude (such as ) and an adjustment frequency limit (such as at most 3 adjustments per minute).

[0098] In terms of parameter configuration, the reference adjustment coefficient , the extended adjustment coefficient , and the compensation values 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 fouling, the value of the reference adjustment coefficient can be reduced (such as ), so that the system triggers high-intensity compensation earlier; for pipelines with poor wear resistance, the compensation value can be reduced (such as reducing the compensation value of the level from Adjust to ), reducing the pipeline wear rate. The system will automatically save the parameter settings and load them when starting up next time.

[0099] The entire acceleration setting process realizes closed-loop control through real-time data acquisition, fluctuation feature analysis, and logical judgment. The processing module completes data update and calculation once per second to ensure that the acceleration can respond to the changes in the conveying interval in a timely manner. At the same time, the system records the time, fluctuation features, adjustment factors, and adjusted values of each acceleration adjustment to form a historical log. The operator can view the log to analyze the acceleration adjustment rules under different working conditions, further optimize the parameter configuration, and improve the system operation efficiency.

[0100] From the above specific examples, it can be seen that the processing module realizes the intelligent setting of the conveying acceleration based on the fluctuation features of the conveying interval through a preset rule set. This mechanism can not only cope with continuously increasing interval changes to maintain the conveying speed, but also suppress the impact of alternating fluctuations on the system stability, while taking into account the equipment safety and service life, reflecting the accuracy and adaptability of automatic control in the ore conveying system, and providing a strong guarantee for the efficient and stable progress of the ore sorting process.

[0101] It should be noted that in this article, relational terms such as first and second are only used 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 term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0102] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An impurity-removable ore conveying system based on automatic control, characterized in that, It includes an impurity content identification module, a statistics module, a flow rate detection module, an operation module, and a processing module: The impurity content identification module is used to detect the impurity adhesion amount on the surface of the conveyed ore. The statistics module is used to calculate the number of ores in the conveyed ore batch. The flow rate detection module is used to detect the medium flow rate of the ore conveying pipeline; The operation module is used to obtain the average impurity content of the conveyed ore batch according to the correlation between the number of ores and the impurity adhesion amount on the surface of each conveyed ore. The operation module is also used to substitute the medium flow rate into the conveying model pre-constructed by the operation module to obtain the reference impurity content, and determine the quality deviation of the conveyed ore batch according to the deviation between the average impurity content and the reference impurity content; The processing module is used to set the initial conveying speed of the ore sorting equipment according to the quality deviation of the ore batch, and determine the conveying interval of the ore sorting equipment according to the quality deviation of the ore batch; The processing module is also used to correct the conveying interval according to the medium flow rate, and set the conveying acceleration of each conveying interval according to the conveying interval.

2. The ore conveying system according to claim 1, characterized in that, The conveying model pre-constructed by the operation module includes: Obtain each conveying parameter of the standard conveying period, and filter redundant data in each conveying parameter. Among them, the conveying parameters include: medium flow rate and average impurity content of the ore; According to the standard conveying period, obtain the flow rate distribution characteristics of different medium flow rates when the average impurity content is the same among the conveying parameters after filtering redundant data; According to the standard conveying period, obtain the impurity distribution characteristics of different average impurity contents when the medium flow rate is the same among the conveying parameters after filtering redundant data; Construct the conveying model according to the flow rate distribution characteristics and impurity distribution characteristics.

3. The ore conveying system according to claim 1, wherein, The setting of the initial conveying speed of the ore sorting equipment includes: The processing module is also used to obtain the impurity content difference between the average impurity content and the reference impurity content, and set the initial conveying speed according to the comparison between the impurity content difference and the preset impurity difference threshold and the 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 according to 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.

4. The ore conveying system according to claim 3, characterized in that, 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: The processing module is further configured to obtain the absolute ratio of the impurity content difference to the intermediate value, and select the rate correction factor according to the correspondence between the absolute ratio and the primary ratio range and the 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; Wherein, the upper limit value of the primary ratio range is less than the lower limit value of the secondary ratio range, and R1 < R2 < R3.

5. The ore conveying system according to claim 4, wherein The determining the conveying interval of the ore sorting device according to the quality deviation of the ore batch includes: The processing module is further configured to obtain the conveying cycle of the ore sorting device, and set the conveying interval based on the conveying cycle, the initial conveying speed, the single conveying amount of the ore sorting device, and an operation relationship, wherein the operation relationship obtains a time parameter by multiplying the conveying cycle by the single conveying amount and dividing by the square of the initial conveying speed.

6. The ore conveying system according to claim 5, wherein, The correcting the conveying interval according to the medium flow velocity includes: The processing module is further configured to determine whether to trigger the correction condition of the conveying interval according to the change trend between the current flow velocity of the conveying medium and the historical adjacent flow velocity: When the current flow velocity and the historical adjacent flow velocity remain constant, the processing module maintains the original conveying interval; When there is a change between the current flow velocity and the historical adjacent flow velocity, the processing module calculates an interval adjustment factor according to the combined characteristics of the flow velocity change direction and the change amplitude, and updates the conveying interval based on the interval adjustment factor.

7. The ore conveying system according to claim 6, 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 the flow velocity change rate of the current flow velocity relative to the historical adjacent flow velocity, and determine the interval adjustment factor according to the comparison result between the flow velocity change rate and the positive change threshold and the negative change threshold configured by the processing module; When the flow velocity change rate exceeds the negative change threshold, the processing module determines the interval adjustment factor as S1; When the flow velocity change rate is within the positive change threshold range, the processing module determines the interval adjustment factor as S2; When the flow velocity change rate exceeds the positive change threshold, the processing module determines the interval adjustment factor as S3; Wherein, S1 > S2 > S3.

8. The ore conveying system according to claim 7, wherein, The setting the conveying acceleration of each of the conveying intervals according to the conveying interval includes: Determining an adjustment mode of the corresponding conveying acceleration according to the fluctuation characteristics of the current conveying interval and the historical adjacent conveying interval; When the current conveying interval and the historical adjacent conveying interval form a continuously increasing sequence, the processing module sets the conveying acceleration in a stepwise 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.

9. The ore conveying system according to claim 8, wherein, 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 the mapping relationship between the interval adjustment factor and the reference adjustment coefficient and the 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 extended adjustment coefficient, the processing module selects the compensation intensity level as U2; When the interval adjustment factor is equal to or greater than the extended adjustment coefficient, the processing module selects the compensation intensity level as U1; Wherein, the reference adjustment coefficient is less than the extended adjustment coefficient, and U1 > U2 > U3.

10. A method for transporting ore, applicable to the ore transporting system capable of removing impurities based on automatic control according to any one of claims 1-9, characterized in that, It includes: Detect the impurity adhesion amount on the surface of the conveyed ore, and calculate the number of ores in the conveyed ore batch; Obtain the average impurity content of the conveyed ore batch according to the correlation between the number of ores and the impurity adhesion amount on the surface of each conveyed ore; Substitute the medium flow rate into a pre-constructed conveying model to obtain a reference impurity content, and determine the mass offset of the ore batch according to the deviation between the average impurity content and the reference impurity content; Set the initial conveying speed and conveying interval of the ore sorting equipment according to the mass offset of the ore batch; Detect the medium flow rate of the ore conveying pipeline, correct the conveying interval according to the medium flow rate, and set the conveying acceleration of each conveying interval according to the corrected conveying interval.

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