Intelligent safety control method and system for mine hoist

Abstract

The invention relates to the field of elevator control, in particular to an intelligent safety control method and system for a mine elevator. The method comprises the following steps: acquiring regulation sensitivity at each historical moment, performing segmentation processing on a sequence formed by the regulation sensitivity at all historical moments to obtain a plurality of regulation sensitivity data segments, and acquiring a reference data segment from all the regulation sensitivity data segments; setting a proportional gain coefficient at the current moment according to the stability of the regulation and control sensitivity in the reference data segment, predicting the regulation and control sensitivity at the current moment according to the regulation and control sensitivity of the reference data segment, and setting an integral gain coefficient at the current moment according to a difference value between the regulation and control sensitivity at the current moment and an average value of the regulation and control sensitivity of the reference data segment, setting a differential gain coefficient according to the prediction deviation of the regulation sensitivity at the current moment; and setting parameters of a PID algorithm according to the proportional gain coefficient, the integral gain coefficient and the differential gain coefficient so as to control the mine hoist. And the control stability of the elevator is provided.

Description

Technical Field

[0001] The present invention relates to the field of elevator control, and in particular to an intelligent safety control method and system for a mine hoist. Background Art

[0002] As an important material handling equipment, elevators have been widely used in industrial production, construction, warehousing and logistics. Its main function is to realize the vertical transportation of goods between different heights or floors. However, with the continuous improvement of modern industry's requirements for production efficiency, automation and safety, traditional elevator control methods have gradually exposed some limitations and cannot meet the increasingly complex usage scenarios.

[0003] As a commonly used control algorithm, the PID algorithm can be adjusted in real time according to the control deviation, so it has a good control effect. However, the accuracy of the algorithm is highly correlated with the parameter setting. If the parameter setting is inappropriate, the detection accuracy will be low. Traditionally, the parameters of the PID algorithm are set based on experience or experiments. This method of setting parameters is not only difficult, but also the parameters set in this way are constant parameters. They cannot be adjusted according to the actual changes in the data, resulting in reduced control accuracy. Therefore, how to set appropriate parameters for the PID algorithm has become the research focus of this program.

[0004] A mine hoist control system and a mine hoist are disclosed in a patent application document with publication number CN118270626A. The method in the patent application document mainly introduces the module composition of the control system and the connection method between the modules, and does not involve the PID control algorithm. Therefore, the method in the patent application document cannot solve the technical problem of this solution. Summary of the invention

[0005] In order to solve the problem of how to set appropriate parameters for the PID algorithm, the present invention provides an intelligent safety control method and system for a mine hoist.

[0006] In a first aspect, the present invention provides an intelligent safety control method for a mine hoist, which adopts the following technical solution: A mine hoist intelligent safety control method, comprising the steps of: Obtain the actual speed and control value of the mine hoist at each historical moment; The ratio of the actual speed at each historical moment to the control value at the previous moment is recorded as the control sensitivity at each historical moment, the sequence of the control sensitivities at all historical moments is segmented to obtain a number of control sensitivity data segments, and a reference data segment is obtained from all the control sensitivity data segments; Setting a proportional gain coefficient at the current moment according to the stability of the control sensitivity in the reference data segment, wherein the proportional gain coefficient is positively correlated with the stability; Predicting the control sensitivity at the current moment according to the control sensitivity of the reference data segment, and setting the integral gain coefficient at the current moment according to the difference between the control sensitivity at the current moment and the average value of the control sensitivity of the reference data segment, wherein the integral gain coefficient is positively correlated with the difference; Setting a differential gain coefficient according to the predicted deviation of the control sensitivity at the current moment, wherein the differential gain coefficient is positively correlated with the predicted deviation; The parameters of the PID algorithm are set according to the proportional gain coefficient, integral gain coefficient and differential gain coefficient to realize the control of the mine hoist.

[0007] The present invention adopts adaptive PID algorithm parameters to improve the accuracy of PID algorithm regulation, thereby achieving the stability of mine hoist operation; further, when the PID algorithm parameters are adapted, it is considered that the collected historical data may contain data of multiple control stages, and the linearity of different control stages is different. The parameters of the PID algorithm are related to the linearity of regulation, and thus the historical data is processed in sections to provide a basis for subsequent accurate setting of the parameters of the PID algorithm; further, when the PID algorithm parameters are adapted, it is considered that the proportional gain parameter in the PID algorithm is related to the linearity of regulation, and the stability of the regulation sensitivity can reflect the linearity of regulation, and thus a relatively accurate proportional gain parameter is set for the PID algorithm in combination with the stability of the regulation sensitivity; further, when the PID algorithm parameters are adapted, it is considered that the integral link of the PID algorithm is a nonlinear compensation for the regulation system, and the linear deviation of the regulation system is related to the linear deviation of the regulation sensitivity, and thus the linear deviation of the regulation sensitivity sets a relatively accurate integral gain coefficient for the PID algorithm; further, when the PID algorithm parameters are adapted, it is considered that the differential link of the PID algorithm is an adjustment of the nonlinear compensation deviation, and a major factor causing the nonlinear compensation deviation is the prediction deviation of the regulation sensitivity at the current moment, and thus a relatively accurate differential gain coefficient is set for the PID algorithm according to the prediction deviation of the regulation sensitivity.

[0008] Preferably, the sequence of regulation sensitivities at all historical moments is segmented to obtain a number of regulation sensitivity data segments, including: The sequence of regulatory sensitivities at all historical moments is recorded as the regulatory sensitivity sequence. With a sliding step of 1, a window of a preset size is used to slide on the regulatory sensitivity sequence. The autocorrelation of the data in the window is obtained and recorded as the regularity. The data in the regulatory sensitivity sequence are clustered into two categories according to the regularity, and the data in the category with a small mean of autocorrelation is recorded as a suspected segmentation point. The continuous suspected segmentation points are used to form a continuous segment. If there is only one suspected segmentation point in the continuous segment, the suspected segmentation point is used as the segmentation point. If the number of suspected segmentation points in the continuous segment is greater than 1, the suspected segmentation point corresponding to the minimum autocorrelation value in the continuous segment is used as the segmentation point. Based on the segmentation points, the regulatory sensitivity sequence is divided into several regulatory sensitivity data segments.

[0009] The present invention reflects the regularity of the data in the window through autocorrelation, thereby screening out points with less regularity as segmentation points of regular changes, thereby segmenting the control sensitivity data of the same change law together, and segmenting the control sensitivity data of different change laws, providing a data basis for subsequent accurate parameter setting.

[0010] Preferably, obtaining the reference data segment includes: The regulation sensitivity data segment with the smallest time interval with the current moment is taken as the first reference data segment; the target speed of each historical moment is obtained, and the sequence composed of the target speeds of all historical moments is recorded as the target speed sequence; the data at the moment corresponding to the segmentation point in the target speed sequence is obtained and recorded as the corresponding segmentation point; the target speed sequence is divided into a plurality of target speed data segments based on the corresponding segmentation points; the target speed data segment with the smallest time interval with the current moment is obtained as the analysis data segment, and the target speed data segments other than the analysis data segment are obtained and recorded as the speed data segments to be selected; according to the length of the speed data segments to be selected, the target speeds of several future moments are supplemented after the analysis data segment, and the supplemented analysis data segments are used as the comparison data segments of the speed data segments to be selected; the cosine similarity between the speed data segments to be selected and the comparison data segments is divided by the average value of the difference between the data in the speed data segments to be selected and the data in the comparison data segments to be selected to obtain the similarity, and the regulation sensitivity data segment corresponding to the speed data segments to be selected with the largest similarity is taken as the second reference data segment, and the first reference data segment and the second reference data segment are collectively referred to as reference data segments.

[0011] The present invention screens out the control sensitivity data that is in the same control stage as the current moment through change similarity and time interval, thereby providing a basis for subsequent analysis of the control linearity of the current moment.

[0012] Preferably, the method for obtaining stability comprises: The inverse of the variance of the first reference data segment is recorded as the first stability, and the inverse of the variance of the second reference data segment is recorded as the second stability; The ratio of the number of first reference data segments to the number of second reference data segments is recorded as the first credibility, the similarity of the second reference data segments is recorded as the second credibility, the first credibility is divided by the cumulative sum of the first credibility and the second credibility to obtain the first weight, the second credibility is divided by the cumulative sum of the first credibility and the second credibility to obtain the second weight, the first weight is used as the weight of the first stability, the second weight is used as the weight of the second stability, and the first stability and the second stability are weightedly summed to obtain stability.

[0013] The present invention combines the control sensitivity data in the first reference data segment and the second reference data segment to analyze the stability of the control sensitivity of the current stage, thereby preventing the problem of poor stability calculation accuracy caused by the small amount of data in the first reference data segment or the difference in the change law between the second reference data segment and the current stage, and more accurately reflects the stability of the control sensitivity of the current stage.

[0014] Preferably, the step of setting the proportional gain coefficient at the current moment according to the stability of the control sensitivity in the reference data segment includes: The stability is multiplied by the preset proportional adjustment parameter to obtain the proportional gain factor.

[0015] Preferably, the step of setting the integral gain coefficient at the current moment according to the difference between the control sensitivity at the current moment and the average value of the control sensitivity of the reference data segment includes: The mean of all data in the first reference data segment is recorded as the first average value; the mean of all data in the second reference data segment is recorded as the second average value; the first weight is used as the weight of the first average value, the second weight is used as the weight of the second average value, and the first average value and the second average value are weighted and summed to obtain the average value of the control sensitivity of the reference data segment; The difference between the average value of the control sensitivity of the reference data segment and the control sensitivity at the current moment is multiplied by a preset integral gain parameter to obtain an integral gain coefficient.

[0016] The present invention combines the mean of the first reference data segment and the mean of the second reference data segment to prevent the problem of inaccurate mean calculation caused by less data in the first parameter data segment and the difference between the change law of the second reference data segment and the change law of the current stage, thereby providing a basis for the subsequent accurate calculation of the linear deviation of the control sensitivity.

[0017] Preferably, the method for obtaining the predicted deviation of the control sensitivity at the current moment includes: A polynomial is fitted to the first reference data segment, and the fitting deviation of the polynomial of the first reference data segment is calculated and recorded as the first prediction deviation; a polynomial is fitted to the second reference data segment, and the difference between each data in the second reference data segment and the corresponding matching data in the first reference data segment is calculated, and the average of the differences between all data in the second reference data segment and the corresponding matching data in the first reference data segment is taken as the second prediction deviation; the first weight is taken as the weight of the first prediction deviation, and the second weight is taken as the weight of the second prediction deviation, and the first prediction deviation and the second prediction deviation are weightedly summed to obtain the prediction deviation of the control sensitivity at the current moment.

[0018] Preferably, the method for obtaining the differential gain coefficient includes: The predicted deviation is multiplied by the preset differential gain parameter to obtain the differential gain coefficient.

[0019] Preferably, the control of the mine hoist is realized, including: The PID algorithm after setting the parameters is used to calculate the control value at the current moment, and the control value is used to control the mine hoist.

[0020] In a second aspect, the present invention provides an intelligent safety control system for a mine hoist, which adopts the following technical solution: A mine hoist intelligent safety control system comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned mine hoist intelligent safety control method is implemented.

[0021] By adopting the above technical solution, the above-mentioned intelligent safety control method for a mine hoist is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a terminal device is made according to the memory and the processor for easy use.

[0022] The present invention has the following technical effects: The present invention adapts PID algorithm parameters to improve the accuracy of PID algorithm control, thereby achieving the stability of mine hoist operation; Furthermore, when the parameters of the adaptive PID algorithm are adjusted, it is considered that the collected historical data may contain data from multiple control stages, and the linearity of different control stages is different. The parameters of the PID algorithm are related to the linearity of the control. Therefore, by processing the historical data in segments, a basis is provided for the subsequent accurate setting of the parameters of the PID algorithm. Furthermore, when the PID algorithm parameters are adapted, it is considered that the proportional gain parameter in the PID algorithm is related to the degree of control linearity, and the stability of the control sensitivity can reflect the degree of control linearity. Therefore, a relatively accurate proportional gain parameter is set for the PID algorithm in combination with the stability of the control sensitivity. Furthermore, when the PID algorithm parameters are adapted, it is considered that the integral link of the PID algorithm is a nonlinear compensation for the control system, and the linear deviation of the control system is related to the linear deviation of the control sensitivity. Therefore, the linear deviation of the control sensitivity sets a relatively accurate integral gain coefficient for the PID algorithm. Furthermore, when adapting the PID algorithm parameters, it is taken into account that the differential link of the PID algorithm is an adjustment of the nonlinear compensation deviation. A major factor causing the nonlinear compensation deviation is the predicted deviation of the control sensitivity at the current moment. Therefore, a relatively accurate differential gain coefficient is set for the PID algorithm according to the predicted deviation of the control sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, 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-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0024] Figure 1 It is a flow chart of a method in a mine hoist intelligent safety control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] 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.

[0026] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0027] The embodiment of the present invention discloses an intelligent safety control method for a mine hoist, referring to Figure 1 , comprising steps S1 to S6: S1: Obtain the actual speed and control value of the mine hoist at each historical moment.

[0028] Specifically, the PID algorithm is used to control the mining machine hoist, and the moving speed of the mining machine hoist at several historical moments before the current moment is collected by a sensor and recorded as the actual speed; the control values ​​at several historical moments before the current moment are obtained.

[0029] S2: The ratio of the actual speed at each historical moment to the control value at the previous moment is recorded as the control sensitivity of each historical moment, and the sequence consisting of the control sensitivities of all historical moments is segmented to obtain several control sensitivity data segments, and a reference data segment is obtained from all the control sensitivity data segments.

[0030] It should be noted that the PID algorithm is a control algorithm that combines the three links of proportion, integration and differentiation. The proportion link in the algorithm is a linear control of the control system. If the control sensitivity of the control system is stable, the parameters of the proportion link should be set larger; the integral link is a supplement to the linear control. For example, as the speed of the mine hoist increases, the control sensitivity of the control system is reduced due to factors such as wind resistance. At this time, the integral link parameters need to be set larger to compensate for more control values. The differential link is an adjustment to the supplement. Since sometimes there will be over-supplementation or under-supplementation, the coefficient of the position differential link needs to be set according to the accurate situation of the supplement.

[0031] S20: The ratio of the actual speed at each historical moment to the control value at the previous moment is recorded as the control sensitivity at each historical moment.

[0032] It should be noted that, through the above analysis, the coefficient of each link has a great relationship with the control sensitivity, so it is necessary to analyze the control sensitivity of the mine hoist first.

[0033] It should be further explained that the response of the mine hoist to each control value can be reflected by the speed corresponding to the next moment, and thus the control sensitivity can be calculated by the proportional relationship between each control value and the speed corresponding to the next moment.

[0034] S21: Segment the sequence consisting of the regulatory sensitivities of all historical moments to obtain a number of regulatory sensitivity data segments.

[0035] It should be noted that there are many control links in the mine hoist, such as the mine hoist that delivers minerals to the surface or transports people to the underground mine. The change rules of the control sensitivity corresponding to different control links are different, and the corresponding control parameters should also be different. Therefore, the data needs to be segmented according to the change rules of the control sensitivity. For example, the control sensitivity of the mine hoist during the ascent or descent process is different, so it is necessary to set appropriate parameters for the PID algorithm at the current moment according to the control sensitivity of the stage at the current moment.

[0036] Preferably, as an example, the sequence of regulation sensitivities at all historical moments is segmented to obtain a number of regulation sensitivity data segments, including: The sequence of regulatory sensitivities at all historical moments is recorded as the regulatory sensitivity sequence. With a sliding step of 1, a window of a preset size is used to slide on the regulatory sensitivity sequence. The autocorrelation of the data in the window is obtained and recorded as the regularity. The data in the regulatory sensitivity sequence are clustered into two categories according to the regularity, and the data in the category with a small mean of autocorrelation is recorded as a suspected segmentation point. The continuous suspected segmentation points are used to form a continuous segment. If there is only one suspected segmentation point in the continuous segment, the suspected segmentation point is used as the segmentation point. If the number of suspected segmentation points in the continuous segment is greater than 1, the suspected segmentation point corresponding to the minimum autocorrelation value in the continuous segment is used as the segmentation point. Based on the segmentation points, the regulatory sensitivity sequence is divided into several regulatory sensitivity data segments.

[0037] It is understandable that the regulatory sensitivity of each regulatory link will have certain regularities, and therefore the regulatory sensitivity is segmented according to the regularity of the regulatory sensitivity.

[0038] S22: Obtain a reference data segment from all regulation-sensitive data segments.

[0039] It should be noted that in order to set appropriate parameters for the current moment, it is necessary to obtain numbers with similar variation patterns to the control sensitivity at the current moment.

[0040] Preferably, as an example, obtaining a reference data segment from all regulation sensitivity data segments includes: The regulation sensitivity data segment with the smallest time interval with the current moment is taken as the first reference data segment; the target speed of each historical moment is obtained, and the sequence composed of the target speeds of all historical moments is recorded as the target speed sequence; the data at the moment corresponding to the segmentation point in the target speed sequence is obtained and recorded as the corresponding segmentation point; the target speed sequence is divided into a plurality of target speed data segments based on the corresponding segmentation points; the target speed data segment with the smallest time interval with the current moment is obtained as the analysis data segment, and the target speed data segments other than the analysis data segment are obtained and recorded as the speed data segments to be selected; according to the length of the speed data segments to be selected, the target speeds of several future moments are supplemented after the analysis data segment, and the supplemented analysis data segments are used as the comparison data segments of the speed data segments to be selected; the cosine similarity between the speed data segments to be selected and the comparison data segments is divided by the average value of the difference between the data in the speed data segments to be selected and the data in the comparison data segments to be selected to obtain the similarity, and the regulation sensitivity data segment corresponding to the speed data segments to be selected with the largest similarity is taken as the second reference data segment, and the first reference data segment and the second reference data segment are collectively referred to as reference data segments.

[0041] It should be noted that since the amount of data at the same stage as the current moment is small, and the small amount of data is not conducive to analyzing the linear situation of the control system, this scheme uses the similarity of change laws to filter out data segments with similar change laws as reference segments from historical data.

[0042] S3: setting the proportional gain coefficient at the current moment according to the stability of the control sensitivity in the reference data segment, wherein the proportional gain coefficient is positively correlated with the stability.

[0043] It should be noted that the proportional gain coefficient is a parameter of the proportional link in the PID algorithm. Only when the linearity of the control system is large, the control value calculated by the proportional link can be accurate, and the parameter should be set large. The stability of the control sensitivity can reflect the linearity of the control system, so the parameters of the proportional link can be set according to the stability of the control sensitivity.

[0044] Preferably, as an example, setting the proportional gain coefficient at the current moment according to the stability of the control sensitivity in the reference data segment includes: The ratio of the number of first reference data segments to the number of second reference data segments is recorded as the first credibility, the similarity of the second reference data segments is recorded as the second credibility, the first credibility is divided by the cumulative sum of the first credibility and the second credibility to obtain the first weight, the second credibility is divided by the cumulative sum of the first credibility and the second credibility to obtain the second weight, the first weight is used as the weight of the first stability, the second weight is used as the weight of the second stability, and the first stability and the second stability are weightedly summed to obtain stability.

[0045] The stability is multiplied by the preset proportional adjustment parameter to obtain the proportional gain factor.

[0046] It should be noted that the proportional gain coefficient at the current moment is determined by combining the stability of the control sensitivity of the first reference data segment and the second reference data segment, so as to prevent the inaccurate analysis of the control sensitivity stability of the current stage caused by the small amount of data in the reference data segment or the difference in the control sensitivity change law at the current moment, thereby improving the accuracy of the proportional gain coefficient calculation.

[0047] S4: predicting the control sensitivity at the current moment according to the control sensitivity of the reference data segment, and setting the integral gain coefficient at the current moment according to the difference between the control sensitivity at the current moment and the average value of the control sensitivity of the reference data segment, wherein the integral gain coefficient is positively correlated with the difference.

[0048] It should be noted that the integral link is a compensation for the nonlinear control deviation in the proportional link, wherein the larger the nonlinear control deviation, the larger the compensation amount. The difference between the control sensitivity at the current moment and the average control sensitivity can reflect the nonlinear deviation, so the integral gain coefficient can be set by the difference between the control sensitivity at the current moment and the average control sensitivity. For example, in linear control, the control amount is 10, which can cause the mine hoist speed to increase by 4. Due to the reduction in control sensitivity, when the control amount at the current moment is 10, the mine hoist speed can only increase by 3.5. Therefore, in the integral gain link, a part of the control amount needs to be compensated to increase the speed by 4. Since the speed reduction is caused by the reduction in control sensitivity, the compensation amount can be set according to the reduction in control sensitivity. Therefore, the compensation amount is related to the difference in control sensitivity, so the integral gain parameter can be set according to the reduction in control sensitivity.

[0049] S40: Predicting the control sensitivity at the current moment according to the control sensitivity of the reference data segment.

[0050] It should be noted that, since the regulation sensitivity at the current moment cannot be calculated, the regulation sensitivity at the current moment needs to be predicted.

[0051] Preferably, as an example, predicting the regulation sensitivity at the current moment according to the regulation sensitivity of the reference data segment includes: The first reference data segment is used to fit a polynomial, and the control sensitivity at the current moment is fitted by the polynomial and recorded as the first prediction value at the current moment; the first reference data segment is matched with the second reference data segment, and the next data of the matching data of the last data of the first reference data segment in the first reference data segment is obtained as the second prediction value at the current moment; The first weight is used as the weight of the first predicted value, the second weight is used as the weight of the second predicted value, and the first predicted value and the second predicted value are weighted and summed to obtain the control sensitivity at the current moment.

[0052] It is understandable that the data at the current moment can be predicted through the first reference data segment, but it is likely that there is less data in the first reference data segment, resulting in the predicted data being inaccurate, and therefore it is necessary to combine the second reference data segment for prediction; the second reference data segment is a data segment with a similar change pattern to the stage at the current moment, and the second reference data segment contains data with similar values ​​to the current moment, so the predicted data of the control sensitivity at the current moment can be calculated based on the data at the corresponding moment in the second reference data segment at the current moment.

[0053] S41: setting the integral gain coefficient at the current moment according to the difference between the control sensitivity at the current moment and the average value of the control sensitivity of the reference data segment.

[0054] Preferably, as an example, the integral gain coefficient at the current moment is set according to the difference between the control sensitivity at the current moment and the average value of the control sensitivity of the reference data segment, including: The mean of all data in the first reference data segment is recorded as the first average value; the mean of all data in the second reference data segment is recorded as the second average value; the first weight is used as the weight of the first average value, the second weight is used as the weight of the second average value, and the first average value and the second average value are weighted and summed to obtain the average value of the control sensitivity of the reference data segment; The difference between the average value of the control sensitivity of the reference data segment and the control sensitivity at the current moment is multiplied by a preset integral gain parameter to obtain an integral gain coefficient.

[0055] It should be noted that the control sensitivity under linear control is reflected by the average value of the control sensitivity of the reference data segment, and the control sensitivity deviation caused by nonlinear control is reflected by analyzing the deviation between the control sensitivity at the current moment and the linear control sensitivity. Then, the integral gain coefficient is set based on the control sensitivity deviation, so that the integral link can compensate for the control deviation caused by nonlinear control.

[0056] S5: setting a differential gain coefficient according to the predicted deviation of the control sensitivity at the current moment, wherein the differential gain coefficient is positively correlated with the predicted deviation.

[0057] It should be noted that the differential link is the regulation of the compensation deviation of the integral link, and therefore the differential gain coefficient of the differential link should be related to the compensation deviation; since the control sensitivity at the current moment is obtained through prediction, the predicted control sensitivity will be inaccurate, which will lead to deviations in the compensation of the integral link, and therefore the differential gain coefficient can be set by analyzing the inaccuracy of the control sensitivity prediction at the current moment.

[0058] Preferably, as an example, the differential gain coefficient is set according to the predicted deviation of the control sensitivity at the current moment, including: A polynomial is fitted to the first reference data segment, and the fitting deviation of the polynomial of the first reference data segment is calculated and recorded as the first prediction deviation; a polynomial is fitted to the second reference data segment, and the difference between each data in the second reference data segment and the corresponding matching data in the first reference data segment is calculated, and the average of the differences between all data in the second reference data segment and the corresponding matching data in the first reference data segment is taken as the second prediction deviation; the first weight is taken as the weight of the first prediction deviation, and the second weight is taken as the weight of the second prediction deviation, and the first prediction deviation and the second prediction deviation are weightedly summed to obtain the prediction deviation of the control sensitivity at the current moment.

[0059] The predicted deviation is multiplied by the preset differential gain parameter to obtain the differential gain coefficient.

[0060] It should be added that the polynomial fitted by the first reference data segment is used to fit the fitting value at each moment, and the mean of the difference between all data in the first reference data segment and the corresponding fitting value is used as the fitting deviation of the polynomial of the first reference data segment.

[0061] It should be noted that the first prediction value is obtained by fitting the polynomial of the first reference data segment, and thus the prediction deviation of the first prediction value can be reflected by the fitting deviation of the polynomial; the second prediction value is the data at the current moment corresponding to the moment in the second reference data segment. Although the second reference data segment has a similar changing pattern to the first reference data segment, it is not necessarily exactly the same. Therefore, the prediction deviation of the second prediction value can be reflected by the difference between the corresponding data in the first reference data segment and the second reference data segment.

[0062] S6: Parameters of the PID algorithm are set according to the proportional gain coefficient, the integral gain coefficient and the differential gain coefficient to realize the control of the mine hoist.

[0063] Preferably, as an example, the parameters of the PID algorithm are set according to the proportional gain coefficient, the integral gain coefficient and the differential gain coefficient to realize the control of the mine hoist, including: The proportional gain coefficient, integral gain coefficient and differential gain coefficient are used to set the parameters of the proportional link, integral link and differential link in the PID algorithm respectively. The PID algorithm after setting the parameters is used to calculate the control value at the current moment, and the control value is used to control the mine hoist.

[0064] In particular, when adaptively adjusting the proportional gain coefficient, integral gain coefficient and differential gain coefficient at the current moment, it is necessary to collect the control amount and speed at several historical moments before the current moment. If there is no data volume that meets the requirements before the current moment, it is only necessary to use the PID algorithm with fixed parameters to control the mine hoist. That is, a constant gain coefficient is set for the PID algorithm based on experience until the data volume before the current moment meets the requirements, and then the parameters of the PID algorithm are adaptively adjusted.

[0065] An embodiment of the present invention further discloses an intelligent safety control system for a mine hoist, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an intelligent safety control method for a mine hoist according to the present invention is implemented.

[0066] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0067] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory, a dynamic random access memory, a static random access memory, an enhanced dynamic random access memory, a high bandwidth memory, a hybrid storage 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 the device or accessible or connectable to the device.

[0068] 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.

[0069] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A mine hoist intelligent safety control method, characterized in that: Includes steps: Obtain the actual speed and control value of the mine hoist at each historical moment; The ratio of the actual speed at each historical moment to the control value at the previous moment is recorded as the control sensitivity at each historical moment, the sequence of the control sensitivities at all historical moments is segmented to obtain a number of control sensitivity data segments, and a reference data segment is obtained from all the control sensitivity data segments; Setting a proportional gain coefficient at the current moment according to the stability of the control sensitivity in the reference data segment, wherein the proportional gain coefficient is positively correlated with the stability; Predicting the control sensitivity at the current moment according to the control sensitivity of the reference data segment, and setting the integral gain coefficient at the current moment according to the difference between the control sensitivity at the current moment and the average value of the control sensitivity of the reference data segment, wherein the integral gain coefficient is positively correlated with the difference; Setting a differential gain coefficient according to the predicted deviation of the control sensitivity at the current moment, wherein the differential gain coefficient is positively correlated with the predicted deviation; The parameters of the PID algorithm are set according to the proportional gain coefficient, integral gain coefficient and differential gain coefficient to realize the control of the mine hoist.

2. The intelligent safety control method for a mine hoist according to claim 1 is characterized in that: The sequence of regulation sensitivities at all historical moments is segmented to obtain a number of regulation sensitivity data segments, including: The sequence of regulatory sensitivities at all historical moments is recorded as the regulatory sensitivity sequence. With a sliding step of 1, a window of a preset size is used to slide on the regulatory sensitivity sequence. The autocorrelation of the data in the window is obtained and recorded as the regularity. The data in the regulatory sensitivity sequence are clustered into two categories according to the regularity, and the data in the category with a small mean of autocorrelation is recorded as a suspected segmentation point. The continuous suspected segmentation points are used to form a continuous segment. If there is only one suspected segmentation point in the continuous segment, the suspected segmentation point is used as the segmentation point. If the number of suspected segmentation points in the continuous segment is greater than 1, the suspected segmentation point corresponding to the minimum autocorrelation value in the continuous segment is used as the segmentation point. Based on the segmentation points, the regulatory sensitivity sequence is divided into several regulatory sensitivity data segments.

3. The intelligent safety control method for a mine hoist according to claim 1 is characterized in that: Get the reference data segment, including: The regulation sensitivity data segment with the smallest time interval with the current moment is taken as the first reference data segment; the target speed of each historical moment is obtained, and the sequence composed of the target speeds of all historical moments is recorded as the target speed sequence; the data at the moment corresponding to the segmentation point in the target speed sequence is obtained and recorded as the corresponding segmentation point; the target speed sequence is divided into a plurality of target speed data segments based on the corresponding segmentation points; the target speed data segment with the smallest time interval with the current moment is obtained as the analysis data segment, and the target speed data segments other than the analysis data segment are obtained and recorded as the speed data segments to be selected; according to the length of the speed data segments to be selected, the target speeds of several future moments are supplemented after the analysis data segment, and the supplemented analysis data segments are used as the comparison data segments of the speed data segments to be selected; the cosine similarity between the speed data segments to be selected and the comparison data segments is divided by the average value of the difference between the data in the speed data segments to be selected and the data in the comparison data segments to be selected to obtain the similarity, and the regulation sensitivity data segment corresponding to the speed data segments to be selected with the largest similarity is taken as the second reference data segment, and the first reference data segment and the second reference data segment are collectively referred to as reference data segments.

4. The intelligent safety control method for a mine hoist according to claim 3 is characterized in that: The method for obtaining the stability comprises: The inverse of the variance of the first reference data segment is recorded as the first stability, and the inverse of the variance of the second reference data segment is recorded as the second stability; The ratio of the number of first reference data segments to the number of second reference data segments is recorded as the first credibility, the similarity of the second reference data segments is recorded as the second credibility, the first credibility is divided by the cumulative sum of the first credibility and the second credibility to obtain the first weight, the second credibility is divided by the cumulative sum of the first credibility and the second credibility to obtain the second weight, the first weight is used as the weight of the first stability, the second weight is used as the weight of the second stability, and the first stability and the second stability are weightedly summed to obtain stability.

5. The intelligent safety control method for a mine hoist according to claim 1 is characterized in that: The step of setting the proportional gain coefficient at the current moment according to the stability of the control sensitivity in the reference data segment includes: The stability is multiplied by the preset proportional adjustment parameter to obtain the proportional gain factor.

6. The intelligent safety control method for a mine hoist according to claim 4 is characterized in that: The step of setting the integral gain coefficient at the current moment according to the difference between the control sensitivity at the current moment and the average value of the control sensitivity of the reference data segment includes: The mean of all data in the first reference data segment is recorded as the first average value; the mean of all data in the second reference data segment is recorded as the second average value; the first weight is used as the weight of the first average value, the second weight is used as the weight of the second average value, and the first average value and the second average value are weighted and summed to obtain the average value of the control sensitivity of the reference data segment; The difference between the average value of the control sensitivity of the reference data segment and the control sensitivity at the current moment is multiplied by a preset integral gain parameter to obtain an integral gain coefficient.

7. The intelligent safety control method for a mine hoist according to claim 4 is characterized in that: The method for obtaining the predicted deviation of the control sensitivity at the current moment includes: A polynomial is fitted to the first reference data segment, and the fitting deviation of the polynomial of the first reference data segment is calculated and recorded as the first prediction deviation; a polynomial is fitted to the second reference data segment, and the difference between each data in the second reference data segment and the corresponding matching data in the first reference data segment is calculated, and the average of the differences between all data in the second reference data segment and the corresponding matching data in the first reference data segment is taken as the second prediction deviation; the first weight is taken as the weight of the first prediction deviation, and the second weight is taken as the weight of the second prediction deviation, and the first prediction deviation and the second prediction deviation are weightedly summed to obtain the prediction deviation of the control sensitivity at the current moment.

8. The intelligent safety control method for a mine hoist according to claim 1, characterized in that: The method for obtaining the differential gain coefficient comprises: The predicted deviation is multiplied by the preset differential gain parameter to obtain the differential gain coefficient.

9. The intelligent safety control method for a mine hoist according to claim 1, characterized in that: The control of the mine hoist is realized by: The PID algorithm after setting the parameters is used to calculate the control value at the current moment, and the control value is used to control the mine hoist.

10. An intelligent safety control system for a mine hoist, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an intelligent safety control method for a mine hoist according to any one of claims 1 to 9 is implemented.

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