CN117967307B - Data processing method for remotely controlling rotation adjustment mining of coal mining machine - Google Patents
Data processing method for remotely controlling rotation adjustment mining of coal mining machine Download PDFInfo
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Abstract
The invention relates to the technical field of coal cutter rotation adjustment and extraction attitude control, in particular to a data processing method for remotely controlling rotation adjustment and extraction of a coal cutter. The method comprises the steps of obtaining state measurement data and distance measurement data of a cutting head of a coal cutter; acquiring the complexity of the coal wall interface according to the data distribution characteristics; obtaining the time lag degree of the state measurement data through the correlation between the complexity of the coal wall interface and the state data of the cutting head; obtaining a logic consumption factor according to the data difference between the cutting head state measurement data and the distance measurement data and the time lag degree; and obtaining a regulating and controlling inertia factor according to the speed change of the cutting head, so as to obtain a speed regulating coefficient of the cutting head, and further obtain the speed regulating quantity in a front speed regulator of the coal mining machine. According to the invention, the front speed regulator reduces the regulating and controlling range of the coal mining machine in the regulating and controlling posture, reduces the logic instruction output quantity in the PLC system, and improves the regulating and controlling efficiency of the cutting head of the coal mining machine.
Description
Technical Field
The invention relates to the technical field of coal cutter rotation adjustment and extraction attitude control, in particular to a data processing method for remotely controlling rotation adjustment and extraction of a coal cutter.
Background
The design of the coal face is affected by faults, coal pillar protection and the like, so that two lanes of the coal face are often irregularly arranged for achieving the maximum coal recovery rate, and the head (or tail) of the coal mining machine is required to be subjected to large-scale rotation adjustment and mining. The intelligent cutting system of the coal cutter can remotely control the coal cutter to cut and pull the coal wall, and under the automatic cutting mode of the coal cutter, the software of the automatic cutting system automatically collects and screens all relevant operation data and state variables in the production process in a system memory, and the adjustment and extraction posture of the coal cutter is adjusted through the remote control system.
In the prior art, a PLC system is often used for controlling the coal mining machine, but because the environment in an underground mine environment is complex, the state variable and the operation data of the coal mining machine are greatly changed, and at the moment, the requirement of the coal mining machine on the number of logic output instructions of the PLC system for the coal mining machine to the control of the coal mining posture is higher, the CPU consumption is huge, and the regulation and control efficiency of a cutting head of the coal mining machine is reduced.
Disclosure of Invention
In order to solve the technical problem that the CPU consumption is huge and the regulation and control efficiency of a cutting head of the coal mining machine is reduced due to the fact that the state variable and the operation data of the coal mining machine are greatly changed in the complex environment of an underground mine, at the moment, the requirement of the coal mining machine on the logic output instruction number of a PLC system is higher in the regulation and control gesture control, the invention aims to provide a data processing method for remotely controlling the rotation regulation and the extraction of the coal mining machine, and the adopted technical scheme is as follows:
A data processing method for remotely controlling rotational modulation of a shearer, the method comprising:
Acquiring all kinds of state measurement data of a cutting head of the coal cutter and all distance measurement data between the cutting head of the coal cutter and a coal wall in each preset unit time during the preheating operation of the coal cutter; each state measurement data is a sequence composed of sensor data of corresponding types at different positions of a cutting head of the coal mining machine;
Acquiring the coal wall interface complexity of each preset unit time coal wall according to the data distribution characteristics of all the state measurement data and all the distance measurement data of the cutting head of the coal mining machine during the preheating operation; obtaining the time lag degree of each state measurement data according to the data correlation degree between the complexity of all the coal wall interfaces and each state measurement data in the preheating operation period;
Obtaining logic consumption factors in a PLC system of the coal mining machine according to the data difference degree between each state measurement data and the distance measurement data and the time lag degree of each state measurement data in the preheating operation period of the cutting head of the coal mining machine; obtaining a regulating and controlling inertia factor of the cutting head of the coal mining machine according to the speed change degree of the cutting head of the coal mining machine in the preheating operation period;
Obtaining a speed regulation coefficient of the cutting head of the coal mining machine according to the logic consumption factor and the regulation inertia factor; and obtaining the speed adjustment quantity in a front speed regulator of the coal mining machine according to the speed adjustment coefficient.
Further, the calculation formula of the coal wall interface complexity is as follows:
; in the/> Represents the/>The coal wall interface complexity of the coal wall in a preset unit time is set; /(I)The state measurement data type number of the cutting head of the coal mining machine is represented; /(I)Represents the/>First/>, in a preset unit timeVariance of all sensor data in the seed status measurement data; /(I)Represents the/>The variance of all distance measurement data in a preset unit time; /(I)Represents the/>Average value of all distance measurement data in preset unit time; Representing the normalization function.
Further, the method for acquiring the data correlation degree comprises the following steps:
Fitting the complexity of the coal wall interface in all unit time during the preheating operation of the coal mining machine into a complexity curve;
Taking the average value of the sensor data in each state measurement data of each preset unit time as the independent state data of the corresponding type of state measurement data under each preset unit time;
fitting independent state data of each state measurement data in all preset unit time during preheating operation of the coal mining machine into each state data curve;
And calculating a cross-correlation function between the complexity curve and the state data curve as a data correlation degree between the complexity of all coal wall interfaces and each state measurement data in the preheating operation period.
Further, the method for obtaining the time lag degree comprises the following steps:
delaying the acquisition time of each independent state data in each state data curve for the same time to obtain initial delay state curves of different delay times of each state data curve;
And when the data correlation degree between the initial delay state curve and the complexity curve reaches the maximum value, taking the delay time corresponding to the initial delay state curve as the time lag degree of each state measurement data.
Further, the method for acquiring the data difference degree comprises the following steps:
Obtaining probability density distribution functions of all distance measurement data during preheating operation of the coal mining machine as distance probability density functions;
Acquiring probability density distribution functions of average values of all sensor data in state measurement data of each type during the preheating operation of the coal mining machine as probability density functions of each state;
aligning the distance probability density function with the state probability density function to obtain all alignment items;
The data difference degree is obtained according to a data difference degree calculation formula, wherein the data difference degree calculation formula is as follows:
; in the/> Represents the/>Degree of data difference between the seed status measurement data and all the distance measurement data; /(I)Representing the number of alignment terms of the distance probability density function and each state probability density function; /(I)Represents the/>The/>, in the state probability density function corresponding to the seed state measurement dataProbability density of the individual alignment items; Representing the/>, in the distance probability density function Probability density of individual alignment terms.
Further, the method for acquiring the logic consumption factor comprises the following steps:
And normalizing and averaging the product of the degree of data difference between each type of state measurement data and the distance measurement data and the degree of time lag of each type of state measurement data to obtain a logic consumption factor in the PLC system of the coal mining machine.
Further, the method for acquiring the speed variation degree comprises the following steps:
acquiring a travelling speed change curve of a cutting head of the coal mining machine during preheating operation;
Obtaining a spectrogram of the travelling speed change curve; the horizontal axis of the spectrogram is the travelling speed change frequency, and the vertical axis is the travelling speed change duration;
normalizing the running speed change duration to obtain a speed duration;
calculating an average value of all the speed durations as a first average value;
Calculating the square of the corresponding speed duration of each travelling speed change frequency as the frequency importance degree;
And acquiring a speed change degree, wherein the speed change degree and the frequency importance degree are in positive correlation and in negative correlation with the first average value.
Further, the method for acquiring the regulation and control inertia factor comprises the following steps:
Taking all travelling speed change frequencies with the travelling speed duration not being 0 as effective speed change frequencies; accumulating and summing the speed change degrees corresponding to all the effective speed change frequencies to obtain an overall speed change degree;
Taking a frequency range between a minimum travel speed change frequency and a maximum travel speed change frequency, the travel speed duration of which is not 0, as an overall frequency difference;
And taking the ratio of the integral speed variation degree to the integral frequency difference as a regulating and controlling inertia factor of the cutting head of the coal mining machine.
Further, the method for acquiring the speed adjustment coefficient comprises the following steps:
And taking the ratio of the logic consumption factor to the regulation inertia factor as the speed regulation coefficient of the coal mining machine.
Further, obtaining a speed adjustment amount in a front speed regulator of the coal mining machine according to the adjustment coefficient, including:
taking the difference value between the default travelling speed and the actual travelling speed of each preset unit time of the cutting head of the coal mining machine as the speed error amount of each preset unit time;
And taking the product of the speed adjustment coefficient and the speed error amount as the speed adjustment amount of the cutting head of the coal mining machine.
The invention has the following beneficial effects:
The method comprises the steps of acquiring state measurement data of a cutting head of a coal cutter and distance measurement data between the cutting head of the coal cutter and a coal wall; the complexity of the coal wall interface is related to the state of the cutting head and the concave-convex degree of the coal wall surface, so that the coal wall interface complexity of each preset unit time coal wall is obtained according to the data distribution characteristics of all state measurement data and all distance measurement data of the cutting head of the coal mining machine during the preheating operation; in actual conditions, a certain time delay exists from the condition of touching the coal wall to the condition of the cutting head, so that the most relevant state measurement data of the complexity of the coal wall interface is required to be obtained, and the time lag degree of the state measurement data is further obtained; because the distance measurement data and the various state measurement data of the cutting head can reflect the complexity of the coal wall interface when the cutting head regulates the coal wall, and the more complex coal wall interface is more required to be provided with more logic instructions by the PLC system to regulate the cutting head, the logic cost of the PLC system needs to be analyzed in order to reduce the logic instruction output of the PLC system subsequently, and the logic cost factor in the PLC system of the coal mining machine is obtained according to the data difference degree between the state measurement data and the distance measurement data of the cutting head of the coal mining machine in the preheating operation period and the time lag degree of each state measurement data; because the PLC system has regulation inertia when regulating the travelling speed of the cutting head, a regulation error can occur, and in order to reduce the regulation error, a regulation inertia factor of the cutting head of the coal mining machine is obtained according to the speed change degree of the cutting head of the coal mining machine in the preheating operation period; obtaining a speed regulation coefficient of the cutting head of the coal mining machine according to the logic consumption factor and the regulation inertia factor; and obtaining the speed adjustment quantity in the front speed regulator of the coal mining machine according to the adjustment coefficient. According to the invention, the adjusting and mining posture adjusting and controlling range of the coal cutter can be reduced through the front speed regulator, the logic instruction output quantity in the PLC system is reduced, and the adjusting and controlling efficiency of the cutting head of the coal cutter is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a data processing method for remotely controlling rotary mining of a coal mining machine according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a data processing method for remotely controlling rotation adjustment and mining of a coal mining machine according to the invention, which is provided by combining the accompanying drawings and the preferred embodiment, and the specific implementation, structure, characteristics and effects thereof are described in detail below. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of a data processing method for remotely controlling rotation adjustment and mining of a coal mining machine.
Referring to fig. 1, a flowchart of a data processing method for remotely controlling rotary mining of a coal mining machine according to an embodiment of the present invention is shown, where the method includes:
Step S1: during preheating operation of the coal cutter, acquiring all kinds of state measurement data of the cutting head of the coal cutter and distance measurement data between the cutting head of the coal cutter and a coal wall in each preset unit time; each state measurement data is a sequence consisting of sensor data of a corresponding kind at different positions of the shearer cutting head.
The embodiment of the invention provides a data processing method for remotely controlling rotary mining adjustment of a coal cutter, which aims at controlling the mining adjustment posture of the coal cutter, and because the main working part of the coal cutter is a cutting head of the coal cutter, state data of the cutting head of the coal cutter and concave-convex conditions of any position of a coal wall are firstly obtained and analyzed, and when the state of the cutting head of the coal cutter is changed or the concave-convex conditions of any position of the coal wall are changed, the mining adjustment parameters of the coal cutter are also changed. In the embodiment of the invention, the mining adjustment parameter is expressed as the travelling speed of the cutter head of the coal cutter. During the preheating operation of the coal cutter, the coal cutter can perform coal mining on the coal wall according to default mining adjustment parameters, and at the moment, the cutting head of the coal cutter and the coal wall are in initial states, namely, the state data of the cutting head of the coal cutter and concave-convex conditions of the coal wall in the coal mining adjustment process are more representative. Therefore, in the embodiment of the invention, during the preheating operation of the coal cutter, the state measurement data of the cutting head of the coal cutter and the distance measurement data between the cutting head of the coal cutter and the coal wall in each preset unit time are obtained. Since there are a plurality of sensors for each type of sensor on the cutter head, each state measurement data is a sequence of sensor data of a corresponding type at different positions of the cutter head of the shearer.
In one embodiment of the present invention, the warm-up time is set to 10 minutes and the preset unit time is set to 1 second. In other embodiments of the present invention, the preheating operation time and the unit time may be set by the operator, which is not limited herein.
In one embodiment of the invention, sensor equipment such as a vibration sensor, a displacement sensor, a power sensor, a temperature sensor and the like are loaded on a cutting head of the coal cutter to measure the state of the cutting head of the coal cutter, and the automatic cutting system software is used for automatically collecting and screening all relevant operation data and state variables in the production process and storing all relevant operation data and state variables in a system memory to obtain all continuous variables such as vibration signals of the cutting head, power signals, displacement data, temperature signals and the like in the preheating operation process, and respectively perform the processes of module electricity conversion, amplification and denoising; since the sensor data are continuous variable signals in time sequence, the average transmitting frequency of the laser radar is also taken as the sampling frequency of the sensor signals, and a plurality of frames of measured values exist in the sensor signals of each second, so that the state measurement data of each sensor are obtained, wherein each state measurement data comprises a plurality of sensor data of the same kind.
And loading a range radar on the cutting head of the coal mining machine, and acquiring distance measurement data of the cutting head at any position on the coal wall by utilizing a lattice structure of the range radar. In order to control the adjustment and the mining of the coal mining machine subsequently, the acquired state measurement data and distance measurement data are transmitted to a remote PLC control system of the coal mining machine.
In one embodiment of the invention, before the PLC system carries out logic regulation, a front regulator is arranged for priority regulation, so that the travel speed of the PLC system is reduced to be regulated, the regulation range of the coal mining machine regulation posture is reduced, the programming quantity, the judgment times, the regulation frequency and the CPU consumption of the control logic of the PLC are reduced, and the PLC system only needs to regulate and control in a smaller error range each time when regulating and controlling the travel speed of the cutting head of the coal mining machine.
Step S2: acquiring the coal wall interface complexity of each preset unit time coal wall according to the data distribution characteristics of all state measurement data and all distance measurement data of the cutting head of the coal mining machine during the preheating operation; and obtaining the time lag degree of each state measurement data according to the data correlation degree between the interface complexity of all the coal walls and each state measurement data in the preheating operation period.
The state of the cutter head of the coal mining machine during the preheating work can cause various sensors to generate different response performances, if the cutter head of the coal mining machine encounters a coal wall with hard texture, rugged and higher complexity, the travelling speed of the cutter head can be reduced, at the moment, the PLC system needs to adjust the travelling speed of the cutter head of the coal mining machine, the adjustment process is that state measurement data on the surface of the cutter head are generally input into the PLC system for analysis, and the rugged degree of the surface of the coal wall can be obtained from the distance measurement data. Therefore, in the embodiment of the invention, the coal wall interface complexity of each preset unit time coal wall is obtained according to the data distribution characteristics of all state measurement data and all distance measurement data of the cutting head of the coal mining machine during the preheating operation.
Preferably, in one embodiment of the present invention, the calculation formula of the complexity of the coal wall interface is as follows:
In the method, in the process of the invention, Represents the/>The coal wall interface complexity of the second coal wall; /(I)The state measurement data type number of the cutting head of the coal mining machine is represented; /(I)Represents the/>Second/>Variance of all sensor data in the seed status measurement data; /(I)Represents the/>Variance of all distance measurement data in seconds; /(I)Represents the/>Average value of all distance measurement data in seconds; /(I)Representing the normalization function.
In the calculation formula of the complexity of the coal wall interface,The larger the description of the first/>Second/>The more discrete the sensor data distribution in the seed state measurement data is, the greater the resistance of the cutting head to the coal wall is at the moment; /(I)The smaller the distance between the cutting head and the coal wall is, the more uniform the distance is, namely the lower the concave-convex degree of the surface of the coal wall is; at/>The larger the molecule and the smaller the denominator are, the larger the resistance of the cutting head to the coal wall is under the condition that the surface roughness of the coal wall is lower, which means that the higher the hardness of the coal wall is, the higher the interface complexity of the coal wall is; /(I)The higher is described in the/>The larger the distance between the whole cutting head and the coal wall is, the lower the concave-convex degree of the coal wall surface is, at this time, if the/>Second/>The more discrete the distribution of the seed state measurement data, the higher the complexity in the coal wall interface is, the more difficult the acquisition is; the states of all kinds of state measurement data are summed up for the performance of the coal wall interface complexity, and the obtained coal wall interface complexity can be confirmed from all the state data of the cutting head, so that the method has higher reliability.
When the cutting head encounters a coal wall interface with higher complexity, the traveling speed is reduced, and at the moment, the PLC controller increases the power of the motor and adjusts the traveling speed to the default traveling speed. After encountering a coal wall with higher complexity, all state data of the cutting head can be changed, but a certain time delay is needed, a sensor on the cutting head can respond, and then the PLC is used for controlling the travelling speed of the cutting head. Therefore, in the embodiment of the invention, the time lag degree of each state measurement data is obtained according to the data correlation degree between the complexity of the coal wall interface and the corresponding state measurement data in each preset unit time in the preheating operation period.
Preferably, in one embodiment of the present invention, the method for acquiring the data correlation degree includes:
The coal wall interface complexity of each unit time in the preheating operation period can correspondingly change various state measurement data of the cutting head after a certain delay due to the fact that a certain distance exists between the cutting head and the coal wall interface, so that the coal wall interface complexity in all unit time in the preheating operation period of the coal mining machine can be fitted into a complexity curve; taking the average value of the sensor data in each state measurement data of each preset unit time as the independent state data of the corresponding type of state measurement data under each preset unit time; fitting independent state data of each state measurement data in all preset unit time during preheating operation of the coal mining machine into each state data curve; the cross correlation function between the computational complexity curve and the state data curve is used as a data correlation between the complexity of all coal wall interfaces and each state measurement data during the warm-up operation. In one embodiment of the present invention, the cross-correlation function between the complexity curve and the state data curve is calculated as follows:
In the method, in the process of the invention, Represents the/>A degree of data correlation between the individual state data curves and the complexity curves; /(I)Representing the length of time during the warm-up operation; /(I)The representation input is/>Complexity curve of (2); /(I)The representation input is/>Is the first of (2)A status data curve.
Preferably, in one embodiment of the present invention, the method for obtaining the time lag degree includes:
Delaying the acquisition time of each independent state data in each state data curve for the same time to obtain initial delay state curves of different delay times of each state data curve; when the data correlation degree between the initial delay state curve and the complexity curve reaches the maximum value, the delay time corresponding to the initial delay state curve is taken as the time lag degree of each state measurement data. In one embodiment of the present invention, when the formula for calculating the cross-correlation function between the complexity curve and the state data curve is When it reaches maximum,/>Represents the/>The degree of time lag of the state measurement data. In the actual situation, the sensor acquires the state measurement data after the cutting head contacts the coal wall interface, so the time lag degree is not less than 0.
To this end, the degree of time lag of each state measurement data is obtained.
Step S3: obtaining a logic consumption factor in a PLC system of the coal mining machine according to the data difference degree between each state measurement data and the distance measurement data of the cutting head of the coal mining machine in the preheating operation period and the time lag degree of each state measurement data; and obtaining the regulating and controlling inertia factor of the cutting head of the coal mining machine according to the speed change degree of the cutting head of the coal mining machine in the preheating operation period.
Because the traditional PLC can process input signals according to a series of binarization logics, when the state measurement data and the distance measurement data of the cutting head of the coal mining machine are greatly changed, the mining adjustment parameters of the coal mining machine are also greatly changed, at the moment, the PLC needs more logic judgment to adjust and control the coal mining machine, and during the operation of the cutting head of the coal mining machine, the PLC needs to frequently output a plurality of control instructions, so that a large amount of CPU is wasted, and the quantity of the instructions to be controlled of the cutting head of the coal mining machine needs to be reduced so as to avoid redundant CPU consumption. The distance measurement data and the various state measurement data of the cutting head can reflect the complexity of the coal wall interface when the cutting head regulates the coal wall, and the more complex coal wall interface is, the more logic instructions are required to be output by the PLC system to regulate the cutting head, so that the logic instruction output of the PLC system is required to be reduced subsequently, and the logic consumption of the PLC system is required to be analyzed. Therefore, in the embodiment of the invention, the logic consumption factor in the PLC system of the coal cutter is obtained according to the data difference degree between the state measurement data and the distance measurement data of the cutting head of the coal cutter in the preheating operation period and the time lag degree of each state measurement data.
Preferably, in one embodiment of the present invention, the method for acquiring the degree of data difference includes:
Because the probability density function can reflect the characteristic distribution and the change trend of the data, the probability density function of all distance measurement data during the preheating operation of the coal mining machine is obtained as a distance probability density function; acquiring probability density functions of average values of all sensor data in state measurement data of each type during the preheating operation of the coal mining machine as each state probability density function; aligning the distance probability density function with the state probability density function to obtain all alignment items; obtaining the data difference degree according to a data difference degree calculation formula, wherein the data difference degree calculation formula is as follows:
In the method, in the process of the invention, Represents the/>Degree of data difference between the seed status measurement data and all the distance measurement data; /(I)Representing the number of alignment terms of the distance probability density function and each state probability density function; /(I)Represents the/>The/>, in the state probability density function corresponding to the seed state measurement dataProbability density of the individual alignment items; /(I)Representing the/>, in the distance probability density functionProbability density of individual alignment terms.
In the data difference degree calculation formula,Represents the/>The/>, of the individual state probability density functionsInformation contained in the alignment item,/>Representing the/>, in the distance probability density functionInformation contained in the alignment term, when the probability density of the distance measurement data is equal to the/>When the probability density of the class state measurement data is replaced, the larger the generated information difference is, the first/>The greater the relative entropy between all alignment items of the class state measurement data and all distance measurement data, the greater the degree of data difference, from the first/>The higher the additional cost of acquiring logic in the class state measurement data.
Because the time lag degree of each state measurement data also causes additional logic loss, in the embodiment of the invention, the regulating inertia factor of the cutting head of the coal mining machine is obtained according to the speed change degree of the cutting head of the coal mining machine in the preheating operation period.
Preferably, in one embodiment of the present invention, the method for acquiring the logic consumption factor includes:
And normalizing and averaging the product of the data difference degree between each type of state measurement data and the distance measurement data and the time lag degree of each type of state measurement data to obtain a logic consumption factor in the PLC system of the coal mining machine. In one embodiment of the present invention, the logic cost factor calculation formula is as follows:
In the method, in the process of the invention, Representing logic consumption factors in a PLC system of the coal mining machine; /(I)The state measurement data type number of the cutting head of the coal mining machine is represented; /(I)Represents the/>The degree of time lag of the seed status measurement data; /(I)Represents the/>Degree of data difference between the seed status measurement data and all the distance measurement data; /(I)The hyperbolic tangent function is shown for normalizing the content in brackets.
In the logic cost factor calculation formula, the first isDegree of time lag and/>, of seed status measurement dataThe product of the degree of data difference between class state measurement data and all distance measurement data is taken as the/>The logic consumption of the seed state measurement data; first/>The greater the time lag degree of the seed state measurement data is, the greater the additional logic loss is caused to the PLC system, and the greater the logic consumption factor in the PLC system of the coal mining machine is at the moment; first/>The greater the degree of data difference between the seed status measurement data and all the distance measurement data, the/>The greater the information difference between the seed status measurement data and the distance measurement data, the more/>The greater the extra logic loss caused by the seed state measurement data; the logic consumption amount of the state measurement data of each kind is averaged, and the larger the average value is, the larger the logic consumption factor in the PLC system is.
So far, the logic consumption factor in the PLC system of the coal mining machine is obtained.
When the cutting head encounters cutting resistance in the advancing process, the advancing speed is reduced, so that the PLC system can directly increase the power of a motor and adjust the advancing speed to a default advancing speed, but due to inertia influence, the advancing speed can not be completely compensated to the default advancing speed during each adjustment, but can float up and down at the default advancing speed, namely, a regulating error is generated, the error can reflect the inertia influence of the cutting head of the coal mining machine, and the inertia influence needs to be compensated in advance during PLC regulation so as to reduce the regulating error. Therefore, in the embodiment of the invention, the regulating and controlling inertia factor of the cutting head of the coal mining machine is obtained according to the speed change degree of the cutting head of the coal mining machine in the preheating operation period.
Preferably, in one embodiment of the present invention, the method for acquiring the speed variation degree includes:
Acquiring a travelling speed change curve of a cutting head of the coal mining machine during preheating operation, wherein the travelling speed of the cutting head of the coal mining machine is frequently regulated and controlled by a PLC system, and the cutting head of the coal mining machine has dense turning points; in order to visually represent the duration of the different travel speed changes, a spectrogram of the travel speed change curve is obtained by utilizing Fourier transformation; the horizontal axis of the spectrogram is the travelling speed change frequency, and the vertical axis of the spectrogram is the travelling speed change duration; normalizing the running speed change duration to obtain a speed duration; calculating an average value of all the speed durations as a first average value; calculating the square of the speed duration corresponding to each travelling speed change frequency as the frequency importance degree, wherein the larger the frequency importance degree is, the longer the travelling speed change duration corresponding to the frequency is; the speed change degree is obtained, and the speed change degree and the frequency importance degree are in positive correlation and negative correlation with the first average value.
Preferably, in one embodiment of the present invention, the method for acquiring the regulatory inertia factor includes:
Taking all travelling speed change frequencies with the travelling speed duration not being 0 as effective speed change frequencies; accumulating and summing the speed change degrees corresponding to all the effective speed change frequencies to obtain the overall speed change degree; taking a frequency range between a minimum travel speed change frequency and a maximum travel speed change frequency, the travel speed duration of which is not 0, as an overall frequency difference; and taking the ratio of the overall speed variation degree to the overall frequency difference as a regulating and controlling inertia factor of the cutting head of the coal mining machine. In one embodiment of the invention, the process of obtaining the regulation inertia factor calculation formula is as follows:
In the method, in the process of the invention, Representing the regulation and control inertia factors of the cutting head of the coal mining machine; /(I)Represents the/>A speed duration corresponding to the effective speed change frequency; /(I)Representing the average of all speed durations; /(I)Representing the overall frequency difference in the travel speed variation curve; /(I)Representing the number of effective speed change frequencies; /(I)The effective speed change frequency number in the travel speed change curve is shown.
In the method, in the process of the invention,Represents the/>A speed duration corresponding to the effective speed change frequency; /(I)Represents the/>A phase value of the frequency of the effective velocity change; the method adjusts the phase value corresponding to each effective speed change frequency to be between 0 and 1 through a cosine function.
In the calculation formula of the regulating and controlling inertia factors,Represents the/>A degree of speed change corresponding to the frequency of effective speed change, wherein/(The larger the duration of the cutting head at the travelling speed, the more frequently the travelling speed occurs, the more/>The greater the frequency importance of the frequency of the effective velocity change, the greater the frequency importance of the frequency of the effective velocity changeThe greater the degree of speed change corresponding to the effective speed change frequency, the greater the adjustment inertia at that travel speed; the ratio between the overall speed variation degree and the overall frequency difference can reflect the distribution density of different advancing speeds, and the distribution density is used as a regulating inertia factor of the overall advancing speed of the cutting head during the preheating operation.
Step S4: obtaining a speed regulation coefficient of the cutting head of the coal mining machine according to the logic consumption factor and the regulation inertia factor; and obtaining the speed adjustment quantity in the front speed regulator of the coal mining machine according to the adjustment coefficient.
Preferably, in one embodiment of the present invention, a ratio of a logic consumption factor to a regulation inertia factor is used as a speed adjustment coefficient of the coal mining machine, wherein the larger the logic consumption factor is, the larger the number of output instructions of the PLC system is, the larger the travelling speed adjustment amount is; the larger the regulating inertia factor is, the larger the regulating inertia is, and the more the PLC system needs to be constrained, so that the PLC system cannot output larger travelling speed regulating quantity.
Preferably, in one embodiment of the present invention, obtaining the speed adjustment amount in the front speed regulator of the shearer according to the adjustment coefficient includes:
Taking the difference value between the default travelling speed and the actual travelling speed of the cutting head of the coal mining machine in each second as the speed error amount in each second; taking the product of the speed adjustment coefficient and the speed error amount as the speed adjustment amount of the cutting head of the coal mining machine; in one embodiment of the present invention, the speed adjustment amount calculation formula is as follows:
In the method, in the process of the invention, Indicating the speed adjustment amount of the front-end regulator; /(I)Representing the logic consumption factor of the PLC system of the coal mining machine; representing the regulation and control inertia factors of the cutting head of the coal mining machine; /(I) Representing a default travel speed of a shearer cutting head; /(I)Representing the actual travelling speed of the cutter head of the coal cutter; /(I)Representing the speed adjustment coefficient of the cutter head of the coal cutter.
Thus, the speed adjustment amount in the front speed adjuster of the coal mining machine is obtained.
In summary, the invention acquires state measurement data of the cutting head of the coal cutter and distance measurement data between the cutting head of the coal cutter and a coal wall; acquiring the coal wall interface complexity of each preset unit time coal wall according to the data distribution characteristics of all state measurement data and all distance measurement data of the cutting head of the coal mining machine during the preheating operation; acquiring state measurement data most relevant to the complexity of the coal wall interface in each unit time, and further solving the time lag degree of the state measurement data; obtaining a logic consumption factor in a PLC system of the coal cutter according to the data difference degree between state measurement data and distance measurement data of the cutting head of the coal cutter in the preheating operation period and the time lag degree of each state measurement data; obtaining a regulating and controlling inertia factor of the cutting head of the coal mining machine according to the speed change degree of the cutting head of the coal mining machine in the preheating operation period; obtaining a speed regulation coefficient of the cutting head of the coal mining machine according to the logic consumption factor and the regulation inertia factor; and obtaining the speed adjustment quantity in the front speed regulator of the coal mining machine according to the adjustment coefficient. According to the invention, the adjusting and mining posture adjusting and controlling range of the coal cutter can be reduced through the front speed regulator, the logic instruction output quantity in the PLC system is reduced, and the adjusting and controlling efficiency of the cutting head of the coal cutter is improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (3)
1. A data processing method for remotely controlling rotary mining of a coal mining machine, the method comprising:
Acquiring all kinds of state measurement data of a cutting head of the coal cutter and all distance measurement data between the cutting head of the coal cutter and a coal wall in each preset unit time during the preheating operation of the coal cutter; each state measurement data is a sequence composed of sensor data of corresponding types at different positions of a cutting head of the coal mining machine;
Acquiring the coal wall interface complexity of each preset unit time coal wall according to the data distribution characteristics of all the state measurement data and all the distance measurement data of the cutting head of the coal mining machine during the preheating operation; obtaining the time lag degree of each state measurement data according to the data correlation degree between the complexity of all the coal wall interfaces and each state measurement data in the preheating operation period;
Obtaining logic consumption factors in a PLC system of the coal mining machine according to the data difference degree between each state measurement data and the distance measurement data and the time lag degree of each state measurement data in the preheating operation period of the cutting head of the coal mining machine; obtaining a regulating and controlling inertia factor of the cutting head of the coal mining machine according to the speed change degree of the cutting head of the coal mining machine in the preheating operation period;
Obtaining a speed regulation coefficient of the cutting head of the coal mining machine according to the logic consumption factor and the regulation inertia factor; obtaining the speed regulation quantity in a front speed regulator of the coal mining machine according to the speed regulation coefficient;
the calculation formula of the coal wall interface complexity is as follows:
; in the/> Represents the/>The coal wall interface complexity of the coal wall in a preset unit time is set; /(I)The state measurement data type number of the cutting head of the coal mining machine is represented; /(I)Represents the/>First/>, in a preset unit timeVariance of all sensor data in the seed status measurement data; /(I)Represents the/>The variance of all distance measurement data in a preset unit time; /(I)Represents the/>Average value of all distance measurement data in preset unit time; /(I)Representing a normalization function;
The method for acquiring the data correlation degree comprises the following steps:
Fitting the complexity of the coal wall interface in all unit time during the preheating operation of the coal mining machine into a complexity curve;
Taking the average value of the sensor data in each state measurement data of each preset unit time as the independent state data of the corresponding type of state measurement data under each preset unit time;
fitting independent state data of each state measurement data in all preset unit time during preheating operation of the coal mining machine into each state data curve;
calculating a cross-correlation function between the complexity curve and the state data curve as a data correlation degree between the complexity of all coal wall interfaces and each state measurement data in the preheating operation period;
The time lag degree obtaining method comprises the following steps:
delaying the acquisition time of each independent state data in each state data curve for the same time to obtain initial delay state curves of different delay times of each state data curve;
When the data correlation degree between the initial delay state curve and the complexity curve reaches the maximum value, taking the delay time corresponding to the initial delay state curve as the time lag degree of each state measurement data;
The method for acquiring the data difference degree comprises the following steps:
Obtaining probability density distribution functions of all distance measurement data during preheating operation of the coal mining machine as distance probability density functions;
Acquiring probability density distribution functions of average values of all sensor data in state measurement data of each type during the preheating operation of the coal mining machine as probability density functions of each state;
aligning the distance probability density function with the state probability density function to obtain all alignment items;
The data difference degree is obtained according to a data difference degree calculation formula, wherein the data difference degree calculation formula is as follows:
; in the/> Represents the/>Degree of data difference between the seed status measurement data and all the distance measurement data; /(I)Representing the number of alignment terms of the distance probability density function and each state probability density function; /(I)Represents the/>The/>, in the state probability density function corresponding to the seed state measurement dataProbability density of the individual alignment items; /(I)Representing the/>, in the distance probability density functionProbability density of the individual alignment items;
The method for acquiring the logic consumption factor comprises the following steps:
Normalizing and averaging the product of the data difference degree between each type of state measurement data and the distance measurement data and the time lag degree of each type of state measurement data to obtain a logic consumption factor in a PLC system of the coal mining machine;
The method for acquiring the speed variation degree comprises the following steps:
acquiring a travelling speed change curve of a cutting head of the coal mining machine during preheating operation;
Obtaining a spectrogram of the travelling speed change curve; the horizontal axis of the spectrogram is the travelling speed change frequency, and the vertical axis is the travelling speed change duration;
normalizing the running speed change duration to obtain a speed duration;
calculating an average value of all the speed durations as a first average value;
Calculating the square of the corresponding speed duration of each travelling speed change frequency as the frequency importance degree;
acquiring a speed change degree, wherein the speed change degree and the frequency importance degree are in positive correlation and in negative correlation with the first average value;
The method for acquiring the regulation and control inertia factor comprises the following steps:
Taking all travelling speed change frequencies with the travelling speed duration not being 0 as effective speed change frequencies; accumulating and summing the speed change degrees corresponding to all the effective speed change frequencies to obtain an overall speed change degree;
Taking a frequency range between a minimum travel speed change frequency and a maximum travel speed change frequency, the travel speed duration of which is not 0, as an overall frequency difference;
And taking the ratio of the integral speed variation degree to the integral frequency difference as a regulating and controlling inertia factor of the cutting head of the coal mining machine.
2. The data processing method for remotely controlling rotary mining of a coal mining machine according to claim 1, wherein the speed adjustment coefficient obtaining method comprises the following steps:
And taking the ratio of the logic consumption factor to the regulation inertia factor as the speed regulation coefficient of the coal mining machine.
3. A data processing method for remotely controlling rotational modulation of a shearer according to claim 1, wherein obtaining the speed adjustment in the front speed regulator of the shearer based on the adjustment coefficients comprises:
taking the difference value between the default travelling speed and the actual travelling speed of each preset unit time of the cutting head of the coal mining machine as the speed error amount of each preset unit time;
And taking the product of the speed adjustment coefficient and the speed error amount as the speed adjustment amount of the cutting head of the coal mining machine.
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