An intelligent safety control method and system for mine hoists

Abstract

The present invention relates to the field of lift control, and particularly to an intelligent safety control method and system for a mine hoist. The method includes the steps of: obtaining the regulation sensitivity at each historical moment, performing segmentation processing on the sequence composed of the regulation sensitivities at all historical moments to obtain a number of regulation sensitivity data segments, and obtaining a reference data segment from all the regulation sensitivity data segments; setting the proportional gain coefficient at the current moment according to the stability of the regulation sensitivity in the reference data segment, predicting the regulation sensitivity at the current moment according to the regulation sensitivity of the reference data segment, setting the integral gain coefficient at the current moment according to the difference between the regulation sensitivity at the current moment and the average value of the regulation sensitivities of the reference data segment, and setting the differential gain coefficient according to the prediction deviation of the regulation sensitivity at the current moment; setting the parameters of the PID algorithm according to the proportional gain coefficient, the integral gain coefficient, and the differential gain coefficient to achieve the control of the mine hoist. Provide the stability of the hoist control.

Description

Technical Field

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

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

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

[0004] The patent application document with the publication number CN118270626A discloses a mine hoist control system and a mine hoist. The method in this patent application document mainly introduces the module composition of the control system and the connection method between modules, and does not involve the PID control algorithm. Therefore, the method in this patent application document cannot solve the technical problems 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, adopting the following technical solution:

[0007] An intelligent safety control method for a mine hoist includes the steps of:

[0008] Obtain the actual speed and regulation value of the mine hoist at each historical moment;

[0009] Record the ratio of the actual speed at each historical moment to the regulation value at the previous moment as the regulation sensitivity at each historical moment, perform segmentation processing on the sequence composed of the regulation sensitivities at all historical moments to obtain several regulation sensitivity data segments, and obtain a reference data segment from all the regulation sensitivity data segments;

[0010] Set the proportional gain coefficient at the current moment according to the stability of the regulation sensitivity in the reference data segment, and the proportional gain coefficient is positively correlated with the stability;

[0011] Predict the regulation sensitivity at the current moment according to the regulation sensitivity in the reference data segment, and set the integral gain coefficient at the current moment according to the difference between the regulation sensitivity at the current moment and the average value of the regulation sensitivity in the reference data segment, and the integral gain coefficient is positively correlated with the difference;

[0012] Set the differential gain coefficient according to the prediction deviation of the regulation sensitivity at the current moment, and the differential gain coefficient is positively correlated with the prediction deviation;

[0013] Set the parameters of the PID algorithm according to the proportional gain coefficient, integral gain coefficient and differential gain coefficient to realize the control of the mine hoist.

[0014] The present invention adapts the parameters of the PID algorithm to improve the accuracy of the PID algorithm regulation, and further realizes the stability of the operation of the mine hoist; further, when adapting the parameters of the PID algorithm, considering that the collected historical data may contain data of multiple control stages, and the linearity degrees of different control stages are different, the parameters of the PID algorithm are related to the regulation linearity degree, so by segmenting the historical data, a basis is provided for accurately setting the parameters of the PID algorithm in the follow-up; further, when adapting the parameters of the PID algorithm, considering that the proportional gain parameter in the PID algorithm is related to the regulation linearity degree, and the stability of the regulation sensitivity can reflect the regulation linearity degree, so a relatively accurate proportional gain parameter is set for the PID algorithm in combination with the stability of the regulation sensitivity; further, when adapting the parameters of the PID algorithm, considering that the integral link of the PID algorithm is a non-linear compensation for the regulation system, and the linear deviation of the regulation system is related to the linear deviation of the regulation sensitivity, so the linear deviation of the regulation sensitivity is used to set a relatively accurate integral gain coefficient for the PID algorithm; further, when adapting the parameters of the PID algorithm, considering that the differential link of the PID algorithm is an adjustment of the non-linear compensation deviation, and a main factor causing the non-linear compensation deviation is the prediction deviation of the regulation sensitivity at the current moment, so a relatively accurate differential gain coefficient is set for the PID algorithm according to the prediction deviation of the regulation sensitivity.

[0015] Preferably, segment the sequence composed of the regulation sensitivities at all historical moments to obtain several regulation sensitivity data segments, including:

[0016] Denote the sequence of regulation sensitivities at all historical moments as the regulation sensitivity sequence. With a sliding step of 1, use a window of a preset size to slide on the regulation sensitivity sequence, and obtain the autocorrelation of the data within the window, which is denoted as the degree of regularity. According to the degree of regularity, cluster the data in the regulation sensitivity sequence into two categories, and denote the data in the category with a smaller mean autocorrelation degree as the suspected segmentation points;

[0017] Form continuous segments from consecutive suspected segmentation points. If there is only one suspected segmentation point in a continuous segment, then take this suspected segmentation point as the segmentation point; if the number of suspected segmentation points in a continuous segment is greater than 1, take the suspected segmentation point corresponding to the minimum autocorrelation in the continuous segment as the segmentation point;

[0018] Based on the segmentation points, divide the regulation sensitivity sequence into several regulation sensitivity data segments.

[0019] The present invention reflects the regularity of the data within the window through autocorrelation, so as to screen out points with less regularity as the segmentation points for regular changes, thereby clustering the regulation sensitivity data with the same change regularity together and separating the regulation sensitivity data with different change regularities, providing a data basis for subsequent accurate parameter setting.

[0020] Preferably, obtaining the reference data segment includes:

[0021] Take the regulation sensitivity data segment with the smallest time interval from the current moment as the first reference data segment; obtain the target speed at each historical moment, and denote the sequence composed of the target speeds at all historical moments as the target speed sequence; obtain the data corresponding to the segmentation point moment in the target speed sequence and denote it as the corresponding segmentation point; based on the corresponding segmentation point, divide the target speed sequence into several target speed data segments; take the target speed data segment with the smallest time interval from the current moment as the analysis data segment, and denote the target speed data segments other than the analysis data segment as the candidate speed data segments. According to the length of the candidate speed data segments, supplement several target speeds at future moments after the analysis data segment, and take the supplemented analysis data segment as the comparison data segment for the candidate speed data segments; divide the cosine similarity between the candidate speed data segment and the comparison data segment by the mean of the differences between the data in the candidate speed data segment and the data in the comparison data segment to obtain the degree of similarity, and take the regulation sensitivity data segment corresponding to the candidate speed data segment with the greatest degree of similarity as the second reference data segment, and collectively call the first reference data segment and the second reference data segment the reference data segment.

[0022] The present invention screens out the regulation sensitivity data in the same regulation stage as the current moment through variation similarity and time interval, thereby providing a basis for subsequent analysis of the regulation linear situation, etc. at the current moment.

[0023] Preferably, the method for obtaining the stability includes:

[0024] Denote the reciprocal of the variance of the first reference data segment as the first stability, and the reciprocal of the variance of the second reference data segment as the second stability;

[0025] Denote the ratio of the quantity of the first reference data segment to the quantity of the second reference data segment as the first credibility, denote the similarity degree of the second reference data segment as the second credibility, divide the first credibility by the cumulative sum of the first credibility and the second credibility to obtain the first weight, divide the second credibility by the cumulative sum of the first credibility and the second credibility to obtain the second weight, use the first weight as the weight of the first stability, use the second weight as the weight of the second stability, and perform weighted summation on the first stability and the second stability to obtain the stability.

[0026] The present invention combines the regulation sensitivity data in the first reference data segment and the second reference data segment to analyze the stability of the regulation sensitivity at the current moment, thereby preventing the problem of poor calculation accuracy of stability caused by a small amount of data in the first reference data segment or a difference in the variation law between the second reference data segment and the current moment, and more accurately reflecting the stability situation of the regulation sensitivity at the current moment.

[0027] Preferably, setting the proportional gain coefficient at the current moment according to the stability of the regulation sensitivity in the reference data segment includes:

[0028] Multiply the stability by a preset proportional adjustment parameter to obtain the proportional gain coefficient.

[0029] Preferably, setting the integral gain coefficient at the current moment according to the difference between the regulation sensitivity at the current moment and the average value of the regulation sensitivity of the reference data segment includes:

[0030] Denote the mean value of all data in the first reference data segment as the first average value; denote the mean value of all data in the second reference data segment as the second average value; use the first weight as the weight of the first average value, use the second weight as the weight of the second average value, and perform weighted summation on the first average value and the second average value to obtain the average value of the regulation sensitivity of the reference data segment;

[0031] Multiply the difference between the average value of the regulation sensitivity of the reference data segment and the regulation sensitivity at the current moment by a preset integral gain parameter to obtain the integral gain coefficient.

[0032] The present invention combines the mean value of the first reference data segment and the mean value of the second reference data segment, thereby preventing the problem of inaccurate mean value calculation caused by a small amount of data in the first parameter data segment and a difference in the variation law between the second reference data segment and the current moment, and providing a basis for accurately calculating the linear deviation of the regulation sensitivity subsequently.

[0033] Preferably, the method for obtaining the prediction deviation of the regulation sensitivity at the current moment includes:

[0034] Performing polynomial fitting on the first reference data segment, calculating the fitting deviation of the polynomial of the first reference data segment and recording it as the first prediction deviation; performing polynomial fitting on the second reference data segment, calculating the difference between each data in the second reference data segment and the corresponding matching data in the first reference data segment, and taking 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 as the second prediction deviation; taking the first weight as the weight of the first prediction deviation, taking the second weight as the weight of the second prediction deviation, and performing weighted summation on the first prediction deviation and the second prediction deviation to obtain the prediction deviation of the regulation sensitivity at the current moment.

[0035] Preferably, the method for obtaining the differential gain coefficient includes:

[0036] Multiplying the prediction deviation by a preset differential gain parameter to obtain the differential gain coefficient.

[0037] Preferably, the implementation of the control of the mine hoist includes:

[0038] Using the PID algorithm after setting parameters to calculate the regulation value at the current moment, and using the regulation value to control the mine hoist.

[0039] In a second aspect, the present invention provides an intelligent safety control system for a mine hoist, adopting the following technical solution:

[0040] An intelligent safety control system for a mine hoist includes: a processor and a memory, and the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned intelligent safety control method for a mine hoist is implemented.

[0041] 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 the memory, so as to be loaded and executed by the processor, and thus a terminal device is manufactured according to the memory and the processor, which is convenient to use.

[0042] The present invention has the following technical effects:

[0043] The present invention adapts the parameters of the PID algorithm, improves the accuracy of the PID algorithm regulation, and further realizes the stability of the operation of the mine hoist;

[0044] Furthermore, when adapting the parameters of the PID algorithm, considering that the collected historical data may contain data of multiple control stages, the linear degrees of different control stages are different, and the parameters of the PID algorithm are related to the regulation linear degree. Therefore, by segmenting the historical data, a basis is provided for accurately setting the parameters of the PID algorithm in the follow-up.

[0045] Furthermore, when adapting the parameters of the adaptive PID algorithm, considering that the proportional gain parameter in the PID algorithm is related to the degree of regulation linearity, and the stability of the regulation sensitivity can reflect the degree of regulation linearity, a relatively accurate proportional gain parameter is set for the PID algorithm in combination with the stability of the regulation sensitivity.

[0046] Furthermore, when adapting the parameters of the adaptive PID algorithm, considering that the integral link of the PID algorithm is a non-linear compensation for the regulation system, the linear deviation of the regulation system is related to the linear deviation of the regulation sensitivity, so the linear deviation of the regulation sensitivity is used to set a relatively accurate integral gain coefficient for the PID algorithm.

[0047] Furthermore, when adapting the parameters of the adaptive PID algorithm, considering that the differential link of the PID algorithm is an adjustment of the non-linear compensation deviation, and a main factor causing the non-linear compensation deviation is the predicted deviation of the regulation sensitivity at the current moment, a relatively accurate differential gain coefficient is set for the PID algorithm according to the predicted deviation of the regulation sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] By referring to the accompanying drawings and reading the detailed description below, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0049] Figure 1 is a flowchart of the method in an intelligent safety control method for a mine hoist according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] It should be understood that when the claims, specifications, and drawings of the present invention use terms such as "first" and "second", they are only used to distinguish different objects and not to describe a specific order. The terms "including" and "comprising" used in the specifications 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 combinations.

[0052] An embodiment of the present invention discloses an intelligent safety control method for a mine hoist. Refer to Figure 1 , which includes steps S1 - S6:

[0053] S1: Obtain the actual speed and regulation value of the mine hoist at each historical moment.

[0054] Specifically, use the PID algorithm to control the mine hoist, and use sensors to collect the moving speeds of the mine hoist at several historical moments before the current moment, denoted as the actual speed; obtain the regulation values at several historical moments before the current moment.

[0055] S2: Denote the ratio of the actual speed at each historical moment to the regulation value at the previous moment as the regulation sensitivity at each historical moment, perform segmentation processing on the sequence composed of the regulation sensitivities at all historical moments to obtain several regulation sensitivity data segments, and obtain a reference data segment among all the regulation sensitivity data segments.

[0056] It should be noted that the PID algorithm is a control algorithm that combines the three links of proportional, integral, and differential. In this algorithm, the proportional link performs linear control on the regulation system. If the regulation sensitivity of the regulation system is stable, the parameters of this proportional 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, affected by factors such as wind resistance, the regulation sensitivity of the regulation system decreases. At this time, the parameters of the integral link need to be set larger to compensate for more regulation values. The differential link is an adjustment to the supplement. Since there may sometimes be over - supplementation or under - supplementation, etc., the coefficient of the position differential link needs to be set according to the accurate situation of the supplement.

[0057] S20: Denote the ratio of the actual speed at each historical moment to the regulation value at the previous moment as the regulation sensitivity at each historical moment.

[0058] It should be noted that through the above analysis, the coefficients of each link have a great relationship with the regulation sensitivity. Therefore, it is necessary to first analyze the regulation sensitivity of the mine hoist.

[0059] It should be further noted that the response of the mine hoist to each regulation value can be reflected by the speed at the corresponding next moment. Therefore, the regulation sensitivity can be calculated through the proportional relationship between each regulation value and the speed at the corresponding next moment.

[0060] S21: Perform segmentation processing on the sequence composed of the regulation sensitivities at all historical moments to obtain several regulation sensitivity data segments.

[0061] It should be noted that since there are multiple control links in the mine hoist, for example, the mine hoist transports minerals to the ground or transports people to the underground mine. The variation laws of the control sensitivities corresponding to different control links are different, and the corresponding control parameters should also be different. Therefore, it is necessary to segment the data according to the variation law of the control sensitivity first. For example, the control sensitivities during the ascending or descending process of the mine hoist are different. Therefore, it is necessary to set appropriate parameters for the PID algorithm at the current moment according to the control sensitivity situation at the current stage.

[0062] Preferably, as an example, segment the sequence composed of the control sensitivities at all historical moments to obtain several control sensitivity data segments, including:

[0063] Denote the sequence composed of the control sensitivities at all historical moments as the control sensitivity sequence. With a sliding step of 1, use a window with a preset size to slide on the control sensitivity sequence, and obtain the autocorrelation of the data within the window as the degree of regularity. According to the degree of regularity, cluster the data in the control sensitivity sequence into two categories, and denote the data in the category with a smaller average autocorrelation degree as the suspected segmentation points;

[0064] Form continuous segments from the continuous suspected segmentation points. If there is only one suspected segmentation point in the continuous segment, then use this suspected segmentation point as the segmentation point; if the number of suspected segmentation points in the continuous segment is greater than 1, use the suspected segmentation point corresponding to the minimum autocorrelation in the continuous segment as the segmentation point;

[0065] Based on the segmentation points, divide the control sensitivity sequence into several control sensitivity data segments.

[0066] It can be understood that the control sensitivity of each control link will have a certain pattern. Therefore, segment the control sensitivity according to the pattern of the control sensitivity.

[0067] S22: Obtain the reference data segment among all the control sensitivity data segments.

[0068] It should be noted that in order to set appropriate parameters at the current moment, it is necessary to obtain numbers with a variation law similar to the control sensitivity at the current moment.

[0069] Preferably, as an example, obtain the reference data segment among all the control sensitivity data segments, including:

[0070] Take the regulation sensitivity data segment with the smallest time interval from the current moment as the first reference data segment; obtain the target speed at each historical moment, and denote the sequence composed of the target speeds at all historical moments as the target speed sequence; obtain the data corresponding to the segmentation point in the target speed sequence as the corresponding segmentation point; based on the corresponding segmentation point, divide the target speed sequence into several target speed data segments; obtain the target speed data segment with the smallest time interval from the current moment as the analysis data segment, and obtain the target speed data segments other than the analysis data segment as the candidate speed data segments. According to the length of the candidate speed data segments, supplement several target speeds at future moments after the analysis data segment, and use the supplemented analysis data segment as the comparison data segment for the candidate speed data segments; divide the cosine similarity between the candidate speed data segment and the comparison data segment by the mean value of the difference between the data in the candidate speed data segment and the data in the comparison data segment to obtain the similarity degree, and take the regulation sensitivity data segment corresponding to the candidate speed data segment with the largest similarity degree as the second reference data segment. Collectively refer to the first reference data segment and the second reference data segment as the reference data segments.

[0071] It should be noted that since the amount of data in the same stage as the current moment is small, and a small amount of data is not conducive to analyzing the linear situation of the regulation system, this solution screens out the data segments with similar variation laws to the current moment from historical data as the reference segments through the similarity of variation laws.

[0072] S3: Set the proportional gain coefficient at the current moment according to the stability of the regulation sensitivity in the reference data segment, and the proportional gain coefficient is positively correlated with the stability.

[0073] It should be noted that the proportional gain coefficient is the parameter of the proportional link in the PID algorithm. Only when the linear degree of the regulation system is large, the regulation value calculated through the proportional link can be accurate, and the set parameter should be large. And the stability of the regulation sensitivity can reflect the linear degree of the regulation system, so the parameter of the proportional link can be set according to the stability of the regulation sensitivity.

[0074] Preferably, as an example, setting the proportional gain coefficient at the current moment according to the stability of the regulation sensitivity in the reference data segment includes:

[0075] Denote the ratio of the number of the first reference data segments to the number of the second reference data segments as the first credibility, denote the similarity degree of the second reference data segment as the second credibility, divide the first credibility by the cumulative sum of the first credibility and the second credibility to obtain the first weight, divide the second credibility by the cumulative sum of the first credibility and the second credibility to obtain the second weight, take the first weight as the weight of the first stability, take the second weight as the weight of the second stability, and perform weighted summation on the first stability and the second stability to obtain the stability.

[0076] Multiply the stability by a preset proportional adjustment parameter to obtain a proportional gain coefficient.

[0077] It should be noted that by combining the stability of the regulation sensitivity of the first reference data segment and the second reference data segment to determine the proportional gain coefficient at the current moment, it is possible to prevent the problem of inaccurate analysis of the stability of the regulation sensitivity at the current stage caused by a small amount of data in the reference data segment or a difference from the variation law of the regulation sensitivity at the current moment, thereby improving the accuracy of the calculation of the proportional gain coefficient.

[0078] S4: Predict the regulation sensitivity at the current moment according to the regulation sensitivity of the reference data segment, and set the integral gain coefficient at the current moment according to the difference between the regulation sensitivity at the current moment and the average value of the regulation sensitivity of the reference data segment. The integral gain coefficient is positively correlated with the difference.

[0079] It should be noted that the integral link is a compensation for the non-linear regulation deviation in the proportional link, and the greater the non-linear regulation deviation, the greater the compensation amount. The difference between the regulation sensitivity at the current moment and the average regulation sensitivity can reflect the non-linear deviation situation. Therefore, the integral gain coefficient can be set according to the difference between the regulation sensitivity at the current moment and the average regulation sensitivity. For example, during linear regulation, when the regulation amount is 10, it can cause the speed of the mine hoist to increase by 4. Due to the decrease in regulation sensitivity, when the regulation amount at the current moment is 10, the speed of the mine hoist can only increase by 3.5. Therefore, in the integral gain link, a part of the regulation amount needs to be compensated to make the speed increase by 4. Since the decrease in speed is caused by the decrease in regulation sensitivity, the compensation amount can be set according to the decrease in regulation sensitivity. Therefore, the compensation amount is related to the difference in regulation sensitivity, and the integral gain parameter can be set according to the decrease in regulation sensitivity.

[0080] S40: Predict the regulation sensitivity at the current moment according to the regulation sensitivity of the reference data segment.

[0081] It should be noted that at the same time, since the regulation sensitivity at the current moment cannot be calculated, it is necessary to predict the regulation sensitivity at the current moment.

[0082] Preferably, as an example, predicting the regulation sensitivity at the current moment according to the regulation sensitivity of the reference data segment includes:

[0083] Using the first reference data segment to fit polynomial processing, and using the polynomial to fit the regulation sensitivity at the current moment, which is recorded as the first predicted value at the current moment; matching the first reference data segment with the second reference data segment, and obtaining the next data of the matching data of the last data in the first reference data segment in the first reference data segment as the second predicted value at the current moment;

[0084] Take the first weight as the weight of the first predicted value, take the second weight as the weight of the second predicted value, and perform weighted summation on the first predicted value and the second predicted value to obtain the regulation sensitivity at the current moment.

[0085] It can be understood that a data at the current moment can be predicted through the first reference data segment. However, it is very likely that there is less data in the first reference data segment, resulting in inaccurate predicted data. Therefore, it is necessary to combine the second reference data segment for prediction; the second reference data segment is a data segment with a variation law similar to that of the current moment. There are data in the second reference data segment with values similar to those at the current moment. Therefore, the predicted data of the regulation 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.

[0086] S41: Set the integral gain coefficient at the current moment according to the difference between the regulation sensitivity at the current moment and the average value of the regulation sensitivities of the reference data segments.

[0087] Preferably, as an example, setting the integral gain coefficient at the current moment according to the difference between the regulation sensitivity at the current moment and the average value of the regulation sensitivities of the reference data segments includes:

[0088] Denote the average value of all data in the first reference data segment as the first average value; denote the average value of all data in the second reference data segment as the second average value; take the first weight as the weight of the first average value, take the second weight as the weight of the second average value, and perform weighted summation on the first average value and the second average value to obtain the average value of the regulation sensitivities of the reference data segments;

[0089] Multiply the difference between the average value of the regulation sensitivities of the reference data segments and the regulation sensitivity at the current moment by a preset integral gain parameter to obtain the integral gain coefficient.

[0090] It should be noted that the average value of the regulation sensitivities of the reference data segments reflects the regulation sensitivity under linear regulation. By analyzing the deviation between the regulation sensitivity at the current moment and the linear regulation sensitivity, the deviation of the regulation sensitivity caused by non-linear regulation is reflected. Furthermore, the integral gain coefficient is set based on the regulation sensitivity deviation, so that the integral link can compensate for the regulation deviation phenomenon caused by non-linear regulation.

[0091] S5: Set the differential gain coefficient according to the prediction deviation of the regulation sensitivity at the current moment, and the differential gain coefficient is positively correlated with the prediction deviation.

[0092] It should be noted that the differential link is a regulation for compensating the deviation of the integral link. Therefore, the differential gain coefficient of the differential link should be related to the compensation deviation. Since the regulation sensitivity at the current moment is obtained through prediction, the predicted regulation sensitivity will be inaccurate, resulting in a deviation in the compensation of the integral link. Therefore, the differential gain coefficient can be set by analyzing the inaccuracy of the predicted regulation sensitivity at the current moment.

[0093] Preferably, as an example, setting the differential gain coefficient according to the prediction deviation of the regulation sensitivity at the current moment includes:

[0094] Performing polynomial fitting processing on the first reference data segment, and calculating the fitting deviation of the polynomial of the first reference data segment, which is denoted as the first prediction deviation; performing polynomial fitting processing on the second reference data segment, calculating the difference between each data in the second reference data segment and the corresponding matching data in the first reference data segment, and taking the average value of the differences between all data in the second reference data segment and the corresponding matching data in the first reference data segment as the second prediction deviation; taking the first weight as the weight of the first prediction deviation, taking the second weight as the weight of the second prediction deviation, and performing weighted summation on the first prediction deviation and the second prediction deviation to obtain the prediction deviation of the regulation sensitivity at the current moment.

[0095] Multiplying the prediction deviation by a preset differential gain parameter to obtain the differential gain coefficient.

[0096] It should be added that the fitting values at each moment are fitted by the polynomial fitted from the first reference data segment, and the average value of the differences between all data in the first reference data segment and the corresponding fitting values is taken as the fitting deviation of the polynomial of the first reference data segment.

[0097] It should be noted that the first predicted value is obtained by polynomial fitting of the first reference data segment. Therefore, the prediction deviation of the first predicted value can be reflected by the fitting deviation of the polynomial. The second predicted value is the data at the corresponding moment of the current moment in the second reference data segment. Since although the variation law of the second reference data segment is similar to that of the first reference data segment, they are not necessarily exactly the same. Therefore, the prediction deviation of the second predicted value can be reflected by the difference between the corresponding data in the first reference data segment and the second reference data segment.

[0098] S6: Setting the parameters of the PID algorithm according to the proportional gain coefficient, integral gain coefficient and differential gain coefficient to achieve the control of the mine hoist.

[0099] Preferably, as an example, setting the parameters of the PID algorithm according to the proportional gain coefficient, integral gain coefficient and differential gain coefficient to achieve the control of the mine hoist includes:

[0100] Set the parameters of the proportional link, integral link, and derivative link in the PID algorithm by using the proportional gain coefficient, integral gain coefficient, and derivative gain coefficient respectively. Use the PID algorithm with the set parameters to calculate the control value at the current moment, and use the control value to control the mine hoist.

[0101] Specifically, when adaptively adjusting the proportional gain coefficient, integral gain coefficient, and derivative gain coefficient at the current moment, it is necessary to collect the control amounts and speeds at several historical moments before the current moment. If there is not enough data volume before the current moment, only use the PID algorithm with fixed parameters to control the mine hoist. That is, set constant gain coefficients for the PID algorithm according to experience until the data volume before the current moment meets the requirements, and then start to adaptively adjust the parameters of the PID algorithm.

[0102] The embodiment of the present invention also discloses an intelligent safety control system for a mine hoist, including a processor and a memory. 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.

[0103] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be described in detail here.

[0104] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. For example, the computer-readable storage medium can 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 program, a module, or both. Any such computer storage medium can be a part of the device or accessible or connectable to the device.

[0105] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and idea of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted in the process of practicing the present invention.

[0106] The above are all preferred embodiments of the present invention. The protection scope of the present invention is not limited by this. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention should be covered within 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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