Pneumatic feeding dust cleaning equipment control method and system

Abstract

The present invention relates to the field of data processing technology, and the present invention relates to a control method for pneumatic feeding and cleaning equipment, comprising: first, obtaining torque data of multiple sawtooths in the pneumatic feeding and cleaning equipment at different times, and combining the torque data of multiple sawtooths at the same time into a torque sequence, thereby forming a first torque sequence group; then, calculating the correlation between the current torque sequence and the next torque sequence, and analyzing it using the product of the Pearson correlation coefficient and the maximum and minimum values ​​of the torque; if the correlation is lower than the set correlation threshold, then eliminating the corresponding torque sequence to obtain a second torque sequence group; then, calculating the abnormal value for the second torque sequence group, when the abnormal value exceeds the abnormal threshold, the system will automatically trigger the maximum pneumatic power for cleaning. The present invention solves the problem of poor cleaning effect in complex operating environments.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically, to a control method and system for pneumatic feeding and dust cleaning equipment. Background Art

[0002] In modern industrial production, the application of pneumatic feeding and cleaning equipment is becoming more and more extensive, covering many fields such as mining, metallurgy, chemical industry, and electricity. This type of equipment mainly relies on the pneumatic principle to achieve efficient material transportation and cleaning through air pressure. Its core function is to quickly transport various materials to the designated location, while effectively removing dust, dirt and other residues inside and around the equipment, thereby ensuring unimpeded production processes and normal operation of equipment.

[0003] However, with the continuous use of the equipment, its performance is often affected by many factors. For example, torque changes, equipment wear, material blockage and other problems may lead to unstable operation of the cleaning equipment, thereby affecting the overall production efficiency. Especially in high-intensity and high-frequency working environments, the accumulation of dust and impurities inside the equipment is becoming more and more serious. If it is not cleaned in time, it will not only have a negative impact on the operation of the equipment, but may also cause equipment failure and even cause serious safety accidents. In actual operation, operators are often unable to monitor and adjust the working status of the equipment in time, resulting in failure to respond in time when the torque data is abnormal, increasing the risk of equipment failure. In addition, the existing control system is difficult to achieve efficient adaptability when faced with complex operating environments and changing working conditions, which often leads to waste of resources and poor cleaning effects.

[0004] The patent application document with application publication number CN108205260A discloses an intelligent control method for an industrial silicon waste heat boiler cleaning device. The patent application document includes: by inputting samples and target vector data, the created BP neural network is offline trained according to the set training times and training errors to establish a BP neural network controller; the expected braking distance, cleaning frequency, and braking signal issuance time of the cleaning device are input into the BP neural network controller to calculate the braking signal issuance time compensation coefficient; the mechanical transmission mechanism of the cleaning device calculates the actual braking signal issuance time according to the braking signal issuance time compensation coefficient, and brakes according to the actual braking signal issuance time: the online network cable training mechanism adjusts the training data samples according to the error between the actual braking distance of the mechanical transmission mechanism and the expected braking distance of the cleaning device, and trains the network controller online. However, although the above technical solution realizes the normal operation of the cleaning device, the cleaning effect is still poor in the face of a complex operating environment, resulting in the problem of ineffective removal of dust accumulation. Summary of the invention

[0005] In order to solve the problem of being unable to effectively remove dust accumulation raised in the above background technology, the present invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a pneumatic feeding dust cleaning equipment control method, comprising:

[0007] The torque data of multiple saw teeth in the pneumatic feeding dust cleaning equipment at different times are obtained, and the torque data of multiple saw teeth at the same time are combined into a torque sequence to obtain a first torque sequence group at different times; the correlation degree of the current torque sequence in the first torque sequence group is calculated, and the correlation degree is positively correlated with the Pearson correlation coefficient between the current torque sequence and the next torque sequence, and the product of the maximum torque data and the minimum torque data in the current torque sequence, and is negatively correlated with the product of the maximum torque data and the minimum torque data in the next torque sequence; when the correlation degree is less than the correlation degree threshold, the torque sequence corresponding to the correlation degree is eliminated to obtain a second torque sequence group, and the abnormal value of the second torque sequence group is calculated:

[0008] ;

[0009] In the formula, Indicates the first torque sequence in the second torque sequence group The outliers of the torque series, Indicates The average value of torque data under torque sequences, represents the maximum value function, represents an empirical constant, and In response to the abnormal value being greater than the abnormal threshold, maximum pneumatic power is used for cleaning.

[0010] The above technical solution collects the torque data of multiple saw teeth in the pneumatic feeding and cleaning equipment at different times, combines the torque data at the same time into a sequence, calculates the correlation to evaluate the correlation between torque sequences, and effectively identifies abnormal conditions. By eliminating low-correlation sequences and calculating abnormal values, accurate analysis of torque sequences is achieved, so that maximum pneumatic power cleaning measures can be taken in time when abnormal values ​​exceed the threshold, solving the problem of poor cleaning effect in complex operating environments, improving equipment operation efficiency and reliability while reducing the risk of failure.

[0011] Furthermore, the degree of association is:

[0012] ;

[0013] In the formula, Indicates The correlation degree of the torque series, Indicates The maximum torque data in the torque sequence, Indicates The minimum torque data in the torque sequence, Indicates The maximum torque data in the torque sequence, Indicates The minimum torque data in the torque sequence, Indicates The torque sequence and Pearson correlation coefficient of the torque series.

[0014] The above technical solution provides a method for comprehensively evaluating the relationship between torque sequences by defining the correlation of torque sequences, combining the maximum and minimum torque data of torque sequences, and the Pearson correlation coefficient between torque sequences. This method can accurately reflect the relative changes and consistency between torque sequences, help to detect abnormal behaviors in a timely manner, ensure the stability and efficiency of equipment operation, and provide a scientific basis for subsequent fault detection and maintenance decisions.

[0015] Furthermore, a torque sensor is used to obtain torque data of multiple saw teeth in the pneumatic feeding and cleaning equipment at different times.

[0016] The above technical solution solves the problem of real-time monitoring of equipment performance and operating status by using torque sensors to obtain torque data of multiple saw teeth at different times in pneumatic feed cleaning equipment. This method not only improves the ability to observe the torque changes of each saw tooth and promotes the analysis of the overall efficiency of the equipment, but also can timely identify potential failure risks, providing important data support for equipment maintenance and optimization, and ensuring the efficiency and reliability of the cleaning process.

[0017] Furthermore, the torque sensor includes: a flange-type torque sensor, a shaft-type torque sensor or a dual-range torque sensor.

[0018] Furthermore, a multi-point measurement technique is used to simultaneously monitor the torque data of multiple saw teeth.

[0019] The above technical solution adopts multi-point measurement technology to enable the torque sensor to monitor the torque data of multiple saw teeth at the same time, thereby realizing comprehensive real-time monitoring of the equipment's operating status. This method effectively improves the efficiency and accuracy of data collection, can timely identify the performance differences between different saw teeth, reduces the risk of blind spots that may be caused by single-point measurement, and thus enhances the equipment's fault warning capability and the scientific nature of maintenance management.

[0020] Furthermore, the torque sequence is preprocessed, including data cleaning and data normalization.

[0021] Furthermore, the data cleaning is specifically: using the standard score method to identify and eliminate outliers, and then filling missing values; the data normalization is specifically: using maximum and minimum normalization to scale the data to the same range.

[0022] The above technical solution uses the standard score method to identify and remove outliers, and then fills in missing values ​​to ensure the integrity and accuracy of the data set, and uses maximum and minimum normalization to scale the data to the same range, improving the consistency and comparability of data processing. The above data cleaning and normalization methods effectively reduce the impact of noise on the analysis results, providing a more reliable foundation for subsequent data analysis and model training, thereby enhancing the accuracy and effectiveness of overall data processing.

[0023] In a second aspect, the present invention provides a pneumatic feeding and cleaning equipment control system, comprising a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, any one of the above-described pneumatic feeding and cleaning equipment control methods is implemented.

[0024] The beneficial effects of the present invention are:

[0025] The present invention effectively solves the problem that pneumatic feeding and dust cleaning equipment cannot effectively remove dust accumulation. Utilizing multi-point measurement technology, the present invention can simultaneously obtain torque data of multiple sawtooths, providing reliable basic data for analyzing equipment performance. By calculating the correlation between torque sequences and detecting outliers, it is possible to comprehensively analyze torque data of different time periods and locations, avoiding accidental errors that may be caused by a single data point, thereby improving the accuracy of detection. At the same time, the implementation of scientific data cleaning and normalization processing improves the accuracy and comparability of torque data and enhances the reliability of subsequent analysis. This series of measures not only solves the problem of pneumatic feeding and dust cleaning equipment effectively removing dust accumulation, but also provides effective guarantees for the safe and stable operation of the equipment, and promotes the continuous improvement of industrial production and the intelligent management of equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0027] Figure 1 is a flow chart schematically showing a method for controlling a pneumatic feeding and cleaning equipment according to an embodiment of the present invention;

[0028] Figure 2 is a schematic grayscale diagram schematically illustrating the operation of a pneumatic feeding and cleaning process according to an embodiment of the present invention;

[0029] Figure 3 Schematically shows a structural block diagram of a pneumatic feeding and cleaning equipment control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0031] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0032] An embodiment of a control method for pneumatic feeding and cleaning equipment.

[0033] like Figure 1 As shown, the flow chart of the pneumatic feeding and dust cleaning equipment control method of the embodiment of the present invention comprises the following steps:

[0034] S101: Obtain torque data of multiple saw teeth in a dust cleaning device at different times, and combine the torque data of multiple saw teeth at the same time into a torque sequence to obtain a first torque sequence group at different times.

[0035] In one embodiment, torque data of multiple saw teeth in a pneumatic feeding and cleaning device at different times are collected by a torque sensor. The torque sensor adopts multi-point measurement technology and can monitor the torque conditions of multiple saw teeth at the same time. Based on the torque data of multiple saw teeth collected at different time points, the torque data of multiple saw teeth at the same time point are combined into a torque sequence. Subsequently, the torque sequence is preprocessed, and the preprocessing steps include data cleaning and data normalization. The specific operation of data cleaning is: first, the standard score method is used to identify and eliminate the outliers in the sequence, and then the missing values ​​are filled. The missing value filling method can use the mean, mode or median of the sequence. Data normalization scales the data to the same range through the maximum and minimum normalization method, and finally generates the first torque sequence group at different times. The torque sensor used for data collection may include: a flange torque sensor, a shaft torque sensor or a dual-range torque sensor. Through the above processing flow, the torque data at different time points are guaranteed to be consistent and comparable, thereby providing more accurate basic data for subsequent analysis.

[0036] like Figure 2As shown, a grayscale diagram of the operation of the pneumatic feeding and cleaning process of an embodiment of the present invention includes: ser and dust, wherein ser represents saw teeth, dust represents dust, and the saw teeth are used to clean dust and reciprocate in a vertical direction.

[0037] Furthermore, taking the dual-range torque sensor as an example, in the pneumatic feeding and cleaning equipment, it is assumed that there are multiple saw teeth, numbered Y1, Y2, Y3, ..., YN, where YN represents the Nth saw tooth; the dual-range torque sensor is used to collect the torque data corresponding to the N saw teeth at the same time, and the torque data of the N saw teeth at the same time are combined into a torque sequence, and finally the torque sequence at different times is obtained to obtain the first torque sequence group at different times. In this process, the dual-range torque sensor can automatically switch the range according to different load conditions, ensuring accurate measurement of torque data under different loads during the acquisition process, and avoiding data loss or reduced accuracy due to torque exceeding the range. In this way, the acquired torque sequence not only has good time continuity, but also can effectively adapt to complex working conditions with large torque changes.

[0038] S102: Calculate the correlation degree of the current torque sequence in the first torque sequence group, and when the correlation degree is less than a set correlation degree threshold, remove the torque sequence corresponding to the correlation degree to obtain a second torque sequence group.

[0039] In one embodiment, the correlation is positively correlated with the Pearson correlation coefficient between the current torque sequence and the next torque sequence, and the product of the maximum torque data and the minimum torque data in the current torque sequence, and is negatively correlated with the product of the maximum torque data and the minimum torque data in the next torque sequence, specifically:

[0040] ;

[0041] In the formula, Indicates The correlation degree of the torque series, Indicates The maximum torque data in the torque sequence, Indicates The minimum torque data in the torque sequence, Indicates The maximum torque data in the torque sequence, Indicates The minimum torque data in the torque sequence, Indicates The torque sequence and Pearson correlation coefficient of the torque series.

[0042] For example, assuming that The torque sequence is [60,65,70,80,50], the The torque sequence is [55, 70, 75, 60, 45]. Substituting the above two torque sequences into the above formula, we get ≈0.92; In this embodiment, the set correlation threshold may be 0.9. Of course, the correlation threshold may also be set according to actual needs. The correlation of the torque sequences is greater than the set correlation threshold, indicating that the torque data at the current moment maintains a high degree of consistency and correlation with the torque data at the next moment, so the torque sequence is not eliminated. This result shows that the working state of the equipment remains relatively stable at different times and can be used as a reliable basis for judging the performance of the equipment. Through the above correlation calculation and threshold judgment process, the torque sequence that does not have stability can be effectively filtered out, ensuring that the torque data used in subsequent analysis has high accuracy and reliability.

[0043] S103: Calculate the abnormal value of the second torque sequence group.

[0044] In one embodiment, the calculation of the outlier value is specifically as follows:

[0045] ;

[0046] In the formula, Indicates the first torque sequence in the second torque sequence group The outliers of the torque series, Indicates The average value of torque data under torque sequences, represents the maximum value function, represents an empirical constant, and , in this embodiment, It can be 1, or it can be set according to actual conditions.

[0047] For example: assume that there are three adjacent torque sequences in the second torque sequence group, namely: [60, 62, 64, 63, 65], [61, 61, 61, 61, 61] and [66, 67, 68, 66, 65]; first, calculate the average values ​​of the above three torque sequences respectively, and then substitute the above data into the calculation formula to obtain the abnormal values ​​of each torque sequence. Through the calculation of the abnormal values, the changes between the torque sequences can be reflected.

[0048] By analyzing the average and abnormal values ​​of the torque sequence, it is possible to more comprehensively capture subtle changes in the equipment operation process and provide reliable data basis for continuous monitoring of equipment performance. This method not only improves the accuracy of data analysis, but also can detect early warning signs of equipment failure, which helps maintain the stable operation of the equipment.

[0049] S104: In response to the abnormal value being greater than the abnormal threshold, using maximum pneumatic power for dust cleaning.

[0050] The value of the above abnormal threshold may be 1, and of course, may also be determined according to actual conditions.

[0051] For example, if the abnormal value of a sequence is 0.9, which is less than the abnormal threshold value of 1, it is determined that there is no abnormality in the sequence and there is no need to clean the dust at the maximum power.

[0052] In this embodiment, when it is determined that the abnormal value is greater than the abnormal threshold, a reminder may be given, for example, by an alarm to remind the staff so that corresponding cleaning measures can be taken in time to prevent the cleaning effect from being affected.

[0053] The solution of the present invention effectively solves the problem of poor cleaning effect of pneumatic feeding equipment in complex operating environments through a systematic control method. Utilizing multi-point measurement technology, the solution can simultaneously obtain torque data of multiple sawtooths, providing reliable basic data for analyzing equipment performance. By calculating the correlation between torque sequences, potential faults and performance problems can be identified in a timely manner to ensure that the equipment operates in the best condition. In addition, the technology of identifying and eliminating outliers using the standard score method can effectively filter noise data, thereby improving the accuracy and credibility of the data. The calculation of outliers further strengthens the monitoring of torque sequences, allowing the equipment to respond to changes in a timely manner during operation and prevent the occurrence of potential faults. This series of measures not only improves the operating efficiency of pneumatic feeding and cleaning equipment, but also provides effective guarantees for the safe and stable operation of the equipment, and promotes the continuous improvement and intelligent management of industrial production.

[0054] Pneumatic feeding dust cleaning equipment control system implementation example:

[0055] like Figure 3 As shown, a structural block diagram of a pneumatic feeding and cleaning equipment control system according to an embodiment of the present invention includes a processor and a memory.

[0056] The present invention also provides a pneumatic feeding and dust cleaning equipment control system. Figure 3 As shown, the system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the pneumatic feeding and cleaning equipment control method described above according to the present invention is implemented.

[0057] The pneumatic feeding and cleaning equipment control system also includes other components familiar to those skilled in the art, such as a communication interface, and the configuration and functions of the components are known in the art, so they will not be described in detail here.

[0058] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0059] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0060] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A pneumatic feeding and cleaning equipment control method, characterized in that: include: Obtaining torque data of multiple saw teeth in the pneumatic feeding dust cleaning equipment at different times, and combining the torque data of multiple saw teeth at the same time into a torque sequence to obtain a first torque sequence group at different times; Calculating a correlation degree of a current torque sequence in the first torque sequence group, wherein the correlation degree is positively correlated with a Pearson correlation coefficient between the current torque sequence and the next torque sequence and a product of a maximum torque data and a minimum torque data in the current torque sequence, and is negatively correlated with a product of a maximum torque data and a minimum torque data in the next torque sequence; When the correlation degree is less than the correlation degree threshold, the torque sequence corresponding to the correlation degree is eliminated to obtain a second torque sequence group, and the abnormal value of the second torque sequence group is calculated: ； In the formula, Indicates the first torque sequence in the second torque sequence group The outliers of the torque series, Indicates The average value of torque data under torque sequences, represents the maximum value function, represents an empirical constant, and ; In response to the abnormal value being greater than the abnormal threshold, maximum pneumatic power is used for cleaning.

2. The pneumatic feeding and cleaning equipment control method according to claim 1 is characterized in that: The degree of association is: ； In the formula, Indicates The correlation degree of the torque series, Indicates The maximum torque data in the torque sequence, Indicates The minimum torque data in the torque sequence, Indicates The maximum torque data in the torque sequence, Indicates The minimum torque data in the torque sequence, Indicates The torque sequence and Pearson correlation coefficient of the torque series.

3. The pneumatic feeding and cleaning equipment control method according to claim 1 is characterized in that: This includes using a torque sensor to obtain torque data of multiple saw teeth in a pneumatic feeding and cleaning equipment at different times.

4. The pneumatic feeding and cleaning equipment control method according to claim 3 is characterized in that: The torque sensor includes: a flange torque sensor, a shaft torque sensor or a dual-range torque sensor.

5. The pneumatic feeding and cleaning equipment control method according to claim 1, characterized in that: Multi-point measurement technology is used to monitor the torque data of multiple saw teeth simultaneously.

6. The pneumatic feeding and cleaning equipment control method according to claim 1, characterized in that: Preprocess the torque series, including data cleaning and data normalization.

7. The pneumatic feeding and cleaning equipment control method according to claim 6, characterized in that: The data cleaning is specifically: using the standard score method to identify and eliminate outliers, and then filling missing values; the data normalization is specifically: using maximum and minimum normalization to scale the data to the same range.

8. Pneumatic feeding and cleaning equipment control system, characterized in that: It comprises a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the pneumatic feeding and cleaning equipment control method according to any one of claims 1 to 7 is implemented.

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