CN114112819A - Method and device for measuring ore grinding granularity - Google Patents
Method and device for measuring ore grinding granularity Download PDFInfo
- Publication number
- CN114112819A CN114112819A CN202210083020.4A CN202210083020A CN114112819A CN 114112819 A CN114112819 A CN 114112819A CN 202210083020 A CN202210083020 A CN 202210083020A CN 114112819 A CN114112819 A CN 114112819A
- Authority
- CN
- China
- Prior art keywords
- mill
- value
- particle size
- algorithm
- current moment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000227 grinding Methods 0.000 title claims abstract description 105
- 238000000034 method Methods 0.000 title claims abstract description 49
- 239000002245 particle Substances 0.000 claims abstract description 208
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 131
- 238000005259 measurement Methods 0.000 claims abstract description 118
- 230000004069 differentiation Effects 0.000 claims abstract description 64
- 238000012549 training Methods 0.000 claims abstract description 56
- 238000012545 processing Methods 0.000 claims description 25
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 11
- 230000015654 memory Effects 0.000 claims description 10
- 238000003860 storage Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 description 12
- 238000005070 sampling Methods 0.000 description 7
- 238000012544 monitoring process Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000012417 linear regression Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- Analytical Chemistry (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- Dispersion Chemistry (AREA)
- Medical Informatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Disintegrating Or Milling (AREA)
- Crushing And Grinding (AREA)
Abstract
The application provides a method and a device for measuring ore grinding granularity. The method comprises the following steps: determining standard parameters of the mill according to mill working parameters of the mill in at least one preset time period; determining a differentiation coefficient corresponding to the mill according to mill working parameters and standard parameters of the mill at the current moment; when the dissimilarity coefficient is within a preset range, taking the particle size data and the particle size data of the ore grinding particles at the current moment as sample data to be trained; for each algorithm in a preset algorithm database, inputting sample data to be trained into a data training model corresponding to the algorithm for training to obtain a measurement model corresponding to the algorithm; and determining a modeling error value with the minimum error value from the modeling error values of the measurement models corresponding to all the algorithms, taking the measurement model corresponding to the modeling error value with the minimum error value as a target measurement model, and inputting the particle size data of the ore grinding particles into the target measurement model to obtain the measurement result of the particle size of the ore grinding particles, so that the measurement accuracy is improved.
Description
Technical Field
The application relates to the technical field of ore dressing and grinding granularity measurement, in particular to a method and a device for measuring ore grinding granularity.
Background
The ore grinding is an important processing procedure in the ore dressing production process and plays a role in starting and stopping, the ore grinding process mainly comprises the step of crushing ore raw materials to a proper granularity size, wherein the ore grinding granularity is a key operation index for representing the quality of a produced product in the ore grinding process, so that the real-time monitoring of the ore grinding granularity has important practical significance, a traditional sampling rule is mainly a fixed or approximate time span during granularity modeling, for example, ore pulp is intercepted from a process pipeline at fixed time of each shift, sample data selected out can be concentrated, the application range is small, and therefore the problem of low measurement accuracy rate exists in the process of monitoring the ore grinding granularity of a regression model established based on a traditional sampling mode.
Disclosure of Invention
In view of this, the embodiment of the present application provides a method and an apparatus for measuring an ore grinding particle size, so as to solve the problem in the prior art that an accuracy rate of a regression model established based on a traditional sampling rule is low when the ore grinding particle size is monitored.
In a first aspect, the present application provides a method for measuring ore grinding granularity, which includes:
determining standard parameters of the mill according to mill working parameters of the mill within a preset time period;
determining a differentiation coefficient corresponding to the mill according to mill working parameters of the mill at the current moment and the standard parameters; when the differentiation coefficient is within a preset range, taking the particle size data and the particle size data of the ore grinding particles at the current moment as sample data to be trained;
inputting the sample data to be trained into a data training model corresponding to the algorithm for training aiming at each algorithm in a preset algorithm database to obtain a measurement model corresponding to the algorithm;
determining a modeling error value with the minimum error value from the modeling error values of the measurement models corresponding to all the algorithms respectively, and taking the measurement model corresponding to the modeling error value with the minimum error value as a target measurement model;
and inputting the particle size data of the grinding particles into the target measurement model to obtain the measurement result of the particle size of the grinding particles.
Optionally, the mill operating parameter of the mill is an instantaneous power value of the mill or a particle size value of particles output by the mill, and the standard parameter of the mill is determined according to the mill operating parameter of the mill within a preset time period, including:
if the working parameters of the mill are the instantaneous power values of the mill, the maximum value, the minimum value, the mean value and the variance value of the instantaneous power values obtained by calculation according to the instantaneous power values of the mill in a preset time period are used as standard parameters of the mill;
if the mill working parameters of the mill are the particle size values of the particles output by the mill, the maximum value, the minimum value, the mean value and the variance value of the particle size values of the particles obtained by calculation according to the particle size values of the particles output by the mill in the preset time period are used as the standard parameters of the mill.
Optionally, the mill operating parameter of the mill at the current moment is an instantaneous power value of the mill, the standard parameter of the mill is a maximum value, a minimum value, a mean value, and a variance value of the instantaneous power value of the mill, and the differentiation coefficient corresponding to the mill is determined according to the mill operating parameter of the mill at the current moment and the standard parameter, including:
if the instantaneous power value of the grinder at the current moment is larger than the maximum value of the instantaneous power value or smaller than the minimum value of the instantaneous power value, determining a differentiation coefficient corresponding to the grinder at the current moment as 1;
if the instantaneous power value of the mill at the current moment is between the maximum value and the minimum value of the instantaneous power value, calculating a differentiation coefficient corresponding to the mill at the current moment according to the following formula:
wherein f ispAs a coefficient of differentiation, pmFor instantaneous power value, p, of mill at presentaIs the mean value of the instantaneous power values, psIs the variance value of the instantaneous power value.
Optionally, mill operating parameters of the mill at the current moment are particle size values output by the mill, standard parameters of the mill are maximum values, minimum values, mean values, and variance values of the particle size values output by the mill, and the differentiation coefficient corresponding to the mill is determined according to the mill operating parameters of the mill at the current moment and the standard parameters, and includes:
if the particle size value of the particles output by the mill at the current moment is larger than the maximum value of the particle size value or smaller than the minimum value of the particle size value, determining the differentiation coefficient corresponding to the mill at the current moment as 1;
if the particle size value of the particles output by the mill at the current moment is between the maximum value and the minimum value of the particle size value, calculating a differentiation coefficient corresponding to the mill at the current moment according to the following formula:
wherein f issAs a coefficient of differentiation, smThe particle size value, s, of the particles produced by the mill at the current momentaIs the mean value of the particle size values, ssIs the variance value of the particle size values.
Optionally, the inputting, for each algorithm in a preset algorithm database, the sample data to be trained into a data training model corresponding to the algorithm for training to obtain a measurement model corresponding to the algorithm includes:
performing vacancy value processing and outlier processing on the sample data to be trained, and taking the processed sample data to be trained as target sample data to be trained;
and inputting the target sample data to be trained into a data training model corresponding to the algorithm for training aiming at each algorithm in a preset algorithm database to obtain a measurement model corresponding to the algorithm.
In a second aspect, the present application provides an apparatus for measuring a grain size of ore grinding, comprising:
the first determining module is used for determining standard parameters of the mill according to mill working parameters of the mill within a preset time period;
the second determining module is used for determining a differentiation coefficient corresponding to the mill according to the mill working parameter of the mill at the current moment and the standard parameter; when the differentiation coefficient is within a preset range, taking the particle size data and the particle size data of the ore grinding particles at the current moment as sample data to be trained;
the training module is used for inputting the sample data to be trained into a data training model corresponding to the algorithm for training aiming at each algorithm in a preset algorithm database to obtain a measurement model corresponding to the algorithm;
the third determining module is used for determining a modeling error value with the minimum error value from the modeling error values of the measurement models corresponding to all the algorithms respectively, and taking the measurement model corresponding to the modeling error value with the minimum error value as a target measurement model;
and the measurement module is used for inputting the particle size data of the ore grinding particles into the target measurement model to obtain the measurement result of the particle size of the ore grinding particles.
Optionally, the first determining module includes:
the first calculation unit is used for calculating the maximum value, the minimum value, the mean value and the variance value of the instantaneous power value according to the instantaneous power value of the grinding machine in a preset time period as standard parameters of the grinding machine if the working parameters of the grinding machine are the instantaneous power value of the grinding machine;
and the second calculation unit is used for calculating the maximum value, the minimum value, the mean value and the variance value of the particle size value of the particles, which are obtained according to the particle size value output by the grinder in a preset time period, as standard parameters of the grinder if the working parameters of the grinder are the particle size value output by the grinder.
Optionally, the mill operating parameter of the mill at the current moment is an instantaneous power value of the mill, the standard parameter of the mill is a maximum value, a minimum value, a mean value, and a variance value of the instantaneous power value of the mill, respectively, and the second determining module includes:
the first calculation unit is used for determining a differentiation coefficient corresponding to the grinder at the current moment as 1 if the instantaneous power value of the grinder at the current moment is larger than the maximum value of the instantaneous power value or smaller than the minimum value of the instantaneous power value;
the second calculating unit is used for calculating a differentiation coefficient corresponding to the mill at the current moment according to the following formula if the instantaneous power value of the mill at the current moment is between the maximum value and the minimum value of the instantaneous power value:
wherein f ispAs a coefficient of differentiation, pmFor instantaneous power value, p, of mill at presentaIs the mean value of the instantaneous power values, psFor said instantaneous power valueAnd (4) variance value.
Optionally, mill operating parameters of the mill at the current moment are particle size values output by the mill, standard parameters of the mill are maximum values, minimum values, mean values and variance values of the particle size values output by the mill, respectively, and the second determining module further includes:
the third calculation unit is used for determining the differentiation coefficient corresponding to the mill at the current moment as 1 if the particle size value of the particles output by the mill at the current moment is larger than the maximum value of the particle size values or smaller than the minimum value of the particle size values;
a fourth calculating unit, configured to calculate a differentiation coefficient corresponding to the mill at the current time according to the following formula if the particle size value of the particles output by the mill at the current time is between the maximum value and the minimum value of the particle size value:
wherein f issAs a coefficient of differentiation, smThe particle size value, s, of the particles produced by the mill at the current momentaIs the mean value of the particle size values, ssIs the variance value of the particle size values.
Optionally, the training module further includes:
the first processing module is used for performing vacancy value processing and outlier processing on the sample data to be trained and taking the processed sample data to be trained as target sample data to be trained;
and the second training module is used for inputting the target sample data to be trained into the data training model corresponding to the algorithm for training aiming at each algorithm in a preset algorithm database to obtain the measurement model corresponding to the algorithm.
In a third aspect, the present application provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above method.
The application provides a method and a device for measuring ore grinding granularity, wherein the method comprises the following steps: determining standard parameters of the mill according to mill working parameters of the mill within a preset time period; determining a differentiation coefficient corresponding to the mill according to mill working parameters of the mill at the current moment and the standard parameters; when the differentiation coefficient is within a preset range, taking the particle size data and the particle size data of the ore grinding particles at the current moment as sample data to be trained; inputting the sample data to be trained into a data training model corresponding to the algorithm for training aiming at each algorithm in a preset algorithm database to obtain a measurement model corresponding to the algorithm; determining a modeling error value with the minimum error value from the modeling error values of the measurement models corresponding to all the algorithms respectively, and taking the measurement model corresponding to the modeling error value with the minimum error value as a target measurement model; and finally, inputting the particle size data of the grinding particles into a target measurement model to obtain the measurement result of the particle size of the grinding particles. The obtained granularity measurement result of the grinding particles is more accurate, and the accuracy of measuring the granularity of the module is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a schematic flow chart of a method for measuring ore grinding grain size according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart illustrating step S103 of a method for measuring a grain size of ore grinding according to an embodiment of the present disclosure;
FIG. 3 is a schematic structural view of an apparatus for measuring a grain size of ore grinding provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
The ore grinding is an important processing procedure in the ore dressing production process and plays a role in starting and stopping, the ore grinding process mainly comprises the step of crushing ore raw materials to a proper granularity size, wherein the ore grinding granularity is a key operation index for representing the quality of a produced product in the ore grinding process, so that the real-time monitoring of the ore grinding granularity has important practical significance, a traditional sampling rule is mainly a fixed or approximate time span during granularity modeling, for example, ore pulp is intercepted from a process pipeline at fixed time of each shift, sample data selected out can be concentrated, the application range is small, and therefore the problem of low measurement accuracy rate exists in the process of monitoring the ore grinding granularity of a regression model established based on a traditional sampling mode.
The embodiment of the application provides a method for measuring ore grinding granularity, as shown in fig. 1, comprising the following steps:
s101, determining standard parameters of the mill according to mill working parameters of the mill in at least one preset time period;
s102, determining a differentiation coefficient corresponding to the mill according to mill working parameters of the mill at the current moment and the standard parameters; when the differentiation coefficient is within a preset range, taking the particle size data and the particle size data of the ore grinding particles at the current moment as sample data to be trained;
s103, aiming at each algorithm in a preset algorithm database, inputting the sample data to be trained into a data training model corresponding to the algorithm for training to obtain a measurement model corresponding to the algorithm;
s104, determining a modeling error value with the minimum error value from the modeling error values of the measurement models corresponding to all the algorithms respectively, and taking the measurement model corresponding to the modeling error value with the minimum error value as a target measurement model;
and S105, inputting the particle size data of the ore grinding particles into the target measurement model to obtain the measurement result of the particle size of the ore grinding particles.
In the step S101, the mill operating parameter represents a real-time operating parameter of the mill during the process of grinding and crushing the ore grinding particles, and is used to determine at which moment the standard parameter of the mill obtains the ore grinding particles; the standard parameters of the mill are basic representative values which can represent the working parameter values of various mills and are comprehensively calculated according to the working parameter values of each mill, and the basic representative values are used for determining the corresponding differentiation coefficients of the mill;
specifically, mill working parameters corresponding to the mills at each moment in at least one preset time period are obtained, and standard parameters of the mills are determined according to the mill working parameters corresponding to the mills at each moment in at least one preset time period; the preset time period may be every two hours, or every fifteen minutes, or every minute, and the time of each preset time period may be the same or different, which is not specifically limited in the embodiment of the present application.
In the step S102, the differentiation coefficient is used to represent a difference between the working parameter of the mill and the standard parameter of the mill, and the particle size data of the ore grinding particles is a particle size value of the ore grinding particles measured by the online analyzer at the current time and a standard difference value corresponding to the particle size value; the granularity data of the grinding particles is the size of the grinding particles obtained by manually weighing, filtering, drying and the like the grinding particles.
Specifically, a differentiation coefficient of the mill is determined according to mill working parameters of the mill at the current moment and standard parameters of the mill, whether the differentiation coefficient is within a preset range or not is judged, and when the differentiation coefficient is within the preset range, particle size data and particle size data of the ore grinding particles at the current moment are used as sample data to be trained.
In step S103, the algorithms included in the preset algorithm database typically include: a linear regression algorithm, a ridge regression algorithm, a Lasso regression algorithm, a support vector machine linear regression algorithm, a support vector machine regression algorithm, a neural network regression algorithm, etc., which are not specifically limited in this embodiment of the present application.
Specifically, according to each algorithm in a preset algorithm database, sample data to be trained is respectively input into a data training model corresponding to each algorithm for training, and a measurement model corresponding to each algorithm is obtained.
For example, the sample data to be trained is input into the data training model corresponding to the algorithm a to obtain the measurement model corresponding to the algorithm a, and the sample data to be trained is input into the data training model corresponding to the algorithm B to obtain the measurement model corresponding to the algorithm B, until the sample data to be trained is input into the data training model corresponding to the last algorithm to obtain the measurement model corresponding to the algorithm.
In the above step S104, the modeling error value is a degree that the test result obtained when the data measurement is performed using each measurement model is inconsistent with the actual test result corresponding to the data; the target measurement model is used for processing the particle size data of the grinding particles to obtain the measurement result of the particle size of the grinding particles.
Specifically, a modeling error value of the measurement model corresponding to each algorithm in a preset algorithm database is obtained, a modeling error value with the minimum error value is determined from all the modeling error values, and the measurement model corresponding to the modeling error value is used as a target measurement model.
For example, if the preset algorithm database has algorithm a, algorithm B, algorithm C, algorithm D, and algorithm E, the measurement result obtained by the measurement model corresponding to each algorithm may have a certain difference from the real measurement result, that is, the accuracy of the measurement result obtained by the measurement model corresponding to each algorithm may not be 100% accurate, and therefore a modeling error value is required to determine the measurement model, if the modeling error value of the measurement model corresponding to algorithm a is q, the measurement model corresponding to algorithm a is determined as the modeling error value q1The modeling error value of the measurement model corresponding to the B algorithm is q2The modeling error value of the measurement model corresponding to the C algorithm is q3And the modeling error value of the measurement model corresponding to the D algorithm is q4The modeling error value of the measurement model corresponding to the E algorithm is q5Comparison q1、q2、q3、q4、q5If the error value is the smallest, q is3And finding out that the corresponding measurement model is the measurement model corresponding to the C algorithm according to the modeling error value q, namely, the measurement accuracy is the highest when the measurement model corresponding to the C algorithm is used, and taking the measurement model corresponding to the C algorithm as a target measurement model.
In step S105, specifically, the particle size data of the ore grinding particles acquired at any one time is input to the target measurement model, and the result of measuring the particle size of the ore grinding particles at that time is obtained.
According to the method for measuring the ore grinding granularity, through the five steps, firstly, standard parameters of the grinding machine are determined according to the working parameters of the grinding machine in at least one preset time period; determining a differentiation coefficient corresponding to the mill according to mill working parameters of the mill at the current moment and the standard parameters; when the differentiation coefficient is within a preset range, taking the particle size data and the particle size data of the ore grinding particles at the current moment as sample data to be trained; and then inputting the sample data to be trained into a data training model corresponding to each algorithm in a preset algorithm database for processing to obtain a measurement model corresponding to each algorithm, and taking the measurement model corresponding to the modeling error value with the minimum error value as a target measurement model, so that the obtained particle size measurement result of the ore grinding particles is more accurate, and the accuracy of the particle size of the measurement module is improved.
Because the grinding machine has many variables in the working process, the grinding machine plays an important role in the grinding and crushing process of the ground ore particles, based on the above situation, in the application, in the step S101, the grinding machine working parameter of the grinding machine is the instantaneous power value of the grinding machine or the particle size value output by the grinding machine, the standard parameter of the grinding machine is determined according to the grinding machine working parameter of the grinding machine in at least one preset time period, and the method further comprises the following steps:
step S1011, if the mill working parameter of the mill is the instantaneous power value of the mill, taking the maximum value, the minimum value, the mean value and the variance value of the instantaneous power value calculated according to the instantaneous power value of the mill in the at least one preset time period as the standard parameters of the mill;
step S1012, if the mill operating parameter of the mill is the particle size value of the particles output from the mill, taking the maximum value, the minimum value, the mean value, and the variance value of the particle size value calculated according to the particle size value of the particles output from the mill within the at least one preset time period as the standard parameter of the mill.
In the step S1011, when the mill operating parameter of the mill is the instantaneous power value of the mill, the instantaneous power value of the mill at each time in at least one preset time period is obtained, the maximum value, the minimum value, the mean value and the variance value of the instantaneous power value are calculated according to the instantaneous power values of the mills at all times, and the four parameters are used as the standard parameters of the mill.
For example, if there are five moments in at least one preset time period, the instant power values of the mills at the five moments are 1, 2, 3, 4 and 5, respectively, and if the maximum value of the instant power value is 5, the minimum value of the instant power value is 1, the mean value is 3, and the variance value is 2, then the four parameters of the maximum value 5 of the instant power value, the minimum value 1 of the instant power value, the mean value 3 of the instant power value, and the variance value 2 of the instant power value are taken as the standard parameters of the mills.
In step S1012, when the mill operating parameter of the mill is the particle size value of the particles output from the mill, all the particle size values output from the mill within at least one preset time period are obtained, the maximum value, the minimum value, the mean value, and the variance value of the particle size values are calculated according to all the particle size values, and the four parameters are used as the standard parameters of the mill.
If the mill operating parameter of the mill at the current moment is the instantaneous power value of the mill, and the standard parameter of the mill is the maximum value, the minimum value, the mean value, and the variance value of the instantaneous power value of the mill, when step S102 is executed to determine the differentiation coefficient corresponding to the mill according to the mill operating parameter of the mill at the current moment and the standard parameter, the steps may be specifically executed according to the following steps:
step S1021, if the instantaneous power value of the grinder at the current moment is larger than the maximum value of the instantaneous power value or smaller than the minimum value of the instantaneous power value, determining a differentiation coefficient corresponding to the grinder at the current moment as 1;
step S1022, if the instantaneous power value of the mill at the current time is between the maximum value and the minimum value of the instantaneous power value, calculating a differentiation coefficient corresponding to the mill at the current time according to the following formula:
wherein f ispAs a coefficient of differentiation, pmFor instantaneous power value, p, of mill at presentaIs the mean value of the instantaneous power values, psIs the variance value of the instantaneous power value.
If the mill operating parameters of the mill at the current moment are the particle size values output by the mill, and the standard parameters of the mill are respectively the maximum value, the minimum value, the mean value and the variance value of the particle size values output by the mill, when the step S102 is executed to determine the differentiation coefficient corresponding to the mill according to the mill operating parameters of the mill at the current moment and the standard parameters, the steps may specifically be executed according to the following steps:
step S1023, if the particle size value of the particles output by the mill at the current moment is larger than the maximum value of the particle size value or smaller than the minimum value of the particle size value, determining a differentiation coefficient corresponding to the mill at the current moment as 1;
step S1024, if the particle size value of the particles output by the mill at the current moment is between the maximum value and the minimum value of the particle size value, calculating a differentiation coefficient corresponding to the mill at the current moment according to the following formula:
wherein f issAs a coefficient of differentiation, smThe particle size value, s, of the particles produced by the mill at the current momentaIs the mean value of the particle size values, ssIs the variance value of the particle size values.
As shown in fig. 2, in order to learn more specifically about each algorithm in the preset algorithm database, the sample data to be trained is input into the data training model corresponding to the algorithm for training to obtain the measurement model corresponding to the algorithm, and step S103 includes:
step S1031, carrying out vacancy value processing and outlier processing on the sample data to be trained, and taking the processed sample data to be trained as target sample data to be trained;
step S1032, aiming at each algorithm in a preset algorithm database, inputting the target data to be trained into a data training model corresponding to the algorithm for training to obtain a measurement model corresponding to the algorithm.
In the step S1031, the null value processing refers to a process of deleting some sample data that is lost due to human errors or damages of other machines when the data to be trained is collected, the outlier processing refers to deleting sample data that affects the model fitting accuracy in the sample data to be trained, and the outliers in the sample data to be trained may be processed by using a quartile method or a three-sigma method, which is not limited in the present application.
In the step S1032, the target sample data to be trained is input into the data training model corresponding to the algorithm a to obtain the measurement model corresponding to the algorithm a, and the target sample data to be trained is input into the data training model corresponding to the algorithm B to obtain the measurement model corresponding to the algorithm B, until the target sample data to be trained is input into the data training model corresponding to the last algorithm to obtain the measurement model corresponding to the algorithm.
The method for measuring the ore grinding granularity provided by the embodiment of the application comprises the following steps: firstly, determining standard parameters of a mill according to mill working parameters of the mill in at least one preset time period; determining a differentiation coefficient corresponding to the mill according to mill working parameters of the mill at the current moment and the standard parameters; when the differentiation coefficient is within a preset range, taking the particle size data and the particle size data of the ore grinding particles at the current moment as sample data to be trained; then inputting sample data to be trained into a data training model corresponding to each algorithm in a preset algorithm database for processing to obtain a measurement model corresponding to each algorithm, and taking the measurement model corresponding to the modeling error value with the minimum error value as a target measurement model; and finally, inputting the particle size data of the grinding particles into a target measurement model to obtain the measurement result of the particle size of the grinding particles. The obtained granularity measurement result of the grinding particles is more accurate, and the accuracy of measuring the granularity of the module is improved.
Referring to fig. 3, there is shown a schematic structural diagram of an apparatus 300 for measuring a grain size of an ore grinding according to an embodiment of the present disclosure, the apparatus including: a first determining module 301, a second determining module 302, a training module 303, a third determining module 304, and a measuring module 305, specifically:
the first determining module 301 is configured to determine a standard parameter of the mill according to a mill operating parameter of the mill within at least one preset time period;
a second determining module 302, configured to determine a differentiation coefficient corresponding to the mill according to a mill operating parameter of the mill at the current time and the standard parameter; when the differentiation coefficient is within a preset range, taking the particle size data and the particle size data of the ore grinding particles at the current moment as sample data to be trained;
the training module 303 is configured to, for each algorithm in a preset algorithm database, input the sample data to be trained into a data training model corresponding to the algorithm for training to obtain a measurement model corresponding to the algorithm;
a third determining module 304, configured to determine a modeling error value with a minimum error value from the modeling error values of the measurement models corresponding to all the algorithms, and use the measurement model corresponding to the modeling error value with the minimum error value as a target measurement model;
and the measuring module 305 is used for inputting the particle size data of the ore grinding particles into the target measurement model to obtain the measurement result of the particle size of the ore grinding particles.
Optionally, the first determining module includes:
the first calculation unit is used for calculating the maximum value, the minimum value, the mean value and the variance value of the instantaneous power value obtained according to the instantaneous power value of the mill in at least one preset time period as standard parameters of the mill if the mill working parameters of the mill are the instantaneous power value of the mill;
and the second calculation unit is used for taking the maximum value, the minimum value, the mean value and the variance value of the particle size value obtained by calculation according to the particle size value output by the mill in at least one preset time period as standard parameters of the mill if the mill working parameters of the mill are the particle size value output by the mill.
Optionally, the mill operating parameter of the mill at the current moment is an instantaneous power value of the mill, the standard parameter of the mill is a maximum value, a minimum value, a mean value, and a variance value of the instantaneous power value of the mill, respectively, and the second determining module includes:
the first determining unit is used for determining a differentiation coefficient corresponding to the grinder at the current moment as 1 if the instantaneous power value of the grinder at the current moment is larger than the maximum value of the instantaneous power value or smaller than the minimum value of the instantaneous power value;
a second determining unit, configured to, if the instantaneous power value of the mill at the current time is between the maximum value and the minimum value of the instantaneous power values, calculate a differentiation coefficient corresponding to the mill at the current time according to the following formula:
wherein f ispAs a coefficient of differentiation, pmFor instantaneous power value, p, of mill at presentaIs the mean value of the instantaneous power values, psIs the variance value of the instantaneous power value.
Optionally, mill operating parameters of the mill at the current moment are particle size values output by the mill, standard parameters of the mill are maximum values, minimum values, mean values and variance values of the particle size values output by the mill, respectively, and the second determining module further includes:
a third determining unit, configured to determine a differentiation coefficient corresponding to the mill at the current time as 1 if a particle size value output by the mill at the current time is greater than a maximum value of the particle size values or smaller than a minimum value of the particle size values;
a fourth determining unit, configured to, if the particle size value of the particles output by the mill at the current time is between the maximum value and the minimum value of the particle size value, calculate a differentiation coefficient corresponding to the mill at the current time according to the following formula:
wherein f issAs a coefficient of differentiation, smThe particle size value, s, of the particles produced by the mill at the current momentaIs the mean value of the particle size values, ssIs the variance value of the particle size values.
Optionally, the training module further includes:
the first processing module is used for performing vacancy value processing and outlier processing on the sample data to be trained and taking the processed sample data to be trained as target sample data to be trained;
and the second training module is used for inputting the target sample data to be trained into the data training model corresponding to the algorithm for training aiming at each algorithm in a preset algorithm database to obtain the measurement model corresponding to the algorithm.
For the method of measuring the ore grinding particle size in fig. 1, the embodiment of the present application further provides a computer device 400, as shown in fig. 4, the device includes a memory 401, a processor 402 and a computer program stored in the memory 401 and operable on the processor 402, wherein the processor 402 implements the steps of the method of measuring the ore grinding particle size when executing the computer program.
Specifically, the memory 401 and the processor 402 can be general memories and processors, which are not limited in particular, and when the processor 402 runs a computer program stored in the memory 401, the method for measuring the ore grinding particle size can be executed, so that the problem of low accuracy in monitoring the ore grinding particle size of a regression model established based on a traditional sampling rule in the prior art is solved. Firstly, determining standard parameters of a mill according to mill working parameters of the mill in at least one preset time period; determining a differentiation coefficient corresponding to the mill according to mill working parameters of the mill at the current moment and the standard parameters; when the differentiation coefficient is within a preset range, taking the particle size data and the particle size data of the ore grinding particles at the current moment as sample data to be trained; and then inputting the sample data to be trained into a data training model corresponding to each algorithm in a preset algorithm database for processing to obtain a measurement model corresponding to each algorithm, and taking the measurement model corresponding to the modeling error value with the minimum error value as a target measurement model, so that the obtained particle size measurement result of the ore grinding particles is more accurate, and the accuracy of the particle size of the measurement module is improved.
Corresponding to the method of measuring a grain size of ore grinding in fig. 1, the present application also provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor performs the steps of the above method of measuring a grain size of ore grinding.
Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk and the like, when a computer program on the storage medium is run, the method for measuring the ore grinding granularity can be executed, the problem that in the prior art, the accuracy of a regression model established based on a traditional sampling rule is low when the ore grinding granularity is monitored is solved, and first, standard parameters of a grinding machine are determined according to the working parameters of the grinding machine in at least one preset time period; determining a differentiation coefficient corresponding to the mill according to mill working parameters of the mill at the current moment and the standard parameters; when the differentiation coefficient is within a preset range, taking the particle size data and the particle size data of the ore grinding particles at the current moment as sample data to be trained; and then inputting the sample data to be trained into a data training model corresponding to each algorithm in a preset algorithm database for processing to obtain a measurement model corresponding to each algorithm, and taking the measurement model corresponding to the modeling error value with the minimum error value as a target measurement model, so that the obtained particle size measurement result of the ore grinding particles is more accurate, and the accuracy of the particle size of the measurement module is improved.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the present disclosure, which should be construed in light of the above teachings. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of measuring ore grinding grain size, comprising:
determining standard parameters of the mill according to mill working parameters of the mill in at least one preset time period;
determining a differentiation coefficient corresponding to the mill according to mill working parameters of the mill at the current moment and the standard parameters; when the differentiation coefficient is within a preset range, taking the particle size data and the particle size data of the ore grinding particles at the current moment as sample data to be trained;
inputting the sample data to be trained into a data training model corresponding to the algorithm for training aiming at each algorithm in a preset algorithm database to obtain a measurement model corresponding to the algorithm;
determining a modeling error value with the minimum error value from the modeling error values of the measurement models corresponding to all the algorithms respectively, and taking the measurement model corresponding to the modeling error value with the minimum error value as a target measurement model;
and inputting the particle size data of the grinding particles into the target measurement model to obtain the measurement result of the particle size of the grinding particles.
2. The method of claim 1, wherein the mill operating parameter of the mill is an instantaneous power value of the mill or a particle size value of the output of the mill, and the determining the standard parameter of the mill according to the mill operating parameter of the mill for at least one preset time period comprises:
if the mill working parameter of the mill is the instantaneous power value of the mill, the maximum value, the minimum value, the mean value and the variance value of the instantaneous power value obtained by calculation according to the instantaneous power value of the mill in the at least one preset time period are used as standard parameters of the mill;
and if the mill working parameters of the mill are the particle size values output by the mill, taking the maximum value, the minimum value, the mean value and the variance value of the particle size values calculated according to the particle size values output by the mill in at least one preset time period as standard parameters of the mill.
3. The method of claim 2, wherein the mill operating parameters of the mill at the current moment are instantaneous power values of the mill, the standard parameters of the mill are maximum values, minimum values, mean values and variance values of the instantaneous power values of the mill, and the determining the corresponding differentiation coefficients of the mill according to the mill operating parameters of the mill at the current moment and the standard parameters comprises:
if the instantaneous power value of the grinder at the current moment is larger than the maximum value of the instantaneous power value or smaller than the minimum value of the instantaneous power value, determining a differentiation coefficient corresponding to the grinder at the current moment as 1;
if the instantaneous power value of the mill at the current moment is between the maximum value and the minimum value of the instantaneous power value, calculating a differentiation coefficient corresponding to the mill at the current moment according to the following formula:
wherein f ispAs a coefficient of differentiation, pmFor instantaneous power value, p, of mill at presentaIs the mean value of the instantaneous power values, psIs the variance value of the instantaneous power value.
4. The method of claim 2, wherein the mill operating parameters of the mill at the current moment are particle size values output by the mill, the standard parameters of the mill are a maximum value, a minimum value, a mean value and a variance value of the particle size values output by the mill, and the determining the corresponding differentiation coefficient of the mill according to the mill operating parameters of the mill at the current moment and the standard parameters comprises:
if the particle size value of the particles output by the mill at the current moment is larger than the maximum value of the particle size value or smaller than the minimum value of the particle size value, determining the differentiation coefficient corresponding to the mill at the current moment as 1;
if the particle size value of the particles output by the mill at the current moment is between the maximum value and the minimum value of the particle size value, calculating a differentiation coefficient corresponding to the mill at the current moment according to the following formula:
wherein f issAs a coefficient of differentiation, smThe particle size value, s, of the particles produced by the mill at the current momentaIs the mean value of the particle size values, ssIs the variance value of the particle size values.
5. The method according to claim 1, wherein for each algorithm in a preset algorithm database, inputting the sample data to be trained into a data training model corresponding to the algorithm for training to obtain a measurement model corresponding to the algorithm, and the method comprises:
performing vacancy value processing and outlier processing on the sample data to be trained, and taking the processed sample data to be trained as target sample data to be trained;
and inputting the target sample data to be trained into a data training model corresponding to the algorithm for training aiming at each algorithm in a preset algorithm database to obtain a measurement model corresponding to the algorithm.
6. An apparatus for measuring a grain size of an ore grinding, comprising:
the first determining module is used for determining standard parameters of the mill according to mill working parameters of the mill in at least one preset time period;
the second determining module is used for determining a differentiation coefficient corresponding to the mill according to the mill working parameter of the mill at the current moment and the standard parameter; when the differentiation coefficient is within a preset range, taking the particle size data and the particle size data of the ore grinding particles at the current moment as sample data to be trained;
the training module is used for inputting the sample data to be trained into a data training model corresponding to the algorithm for training aiming at each algorithm in a preset algorithm database to obtain a measurement model corresponding to the algorithm;
the third determining module is used for determining a modeling error value with the minimum error value from the modeling error values of the measurement models corresponding to all the algorithms respectively, and taking the measurement model corresponding to the modeling error value with the minimum error value as a target measurement model;
and the measurement module is used for inputting the particle size data of the ore grinding particles into the target measurement model to obtain the measurement result of the particle size of the ore grinding particles.
7. The apparatus of claim 6, wherein the first determining module comprises:
the first calculation unit is used for calculating the maximum value, the minimum value, the mean value and the variance value of the instantaneous power value obtained according to the instantaneous power value of the mill in at least one preset time period as standard parameters of the mill if the mill working parameters of the mill are the instantaneous power value of the mill;
and the second calculation unit is used for taking the maximum value, the minimum value, the mean value and the variance value of the particle size value obtained by calculation according to the particle size value output by the mill in at least one preset time period as standard parameters of the mill if the mill working parameters of the mill are the particle size value output by the mill.
8. The apparatus of claim 6, wherein the training module further comprises:
the first processing module is used for performing vacancy value processing and outlier processing on the sample data to be trained and taking the processed sample data to be trained as target sample data to be trained;
and the second training module is used for inputting the target sample data to be trained into the data training model corresponding to the algorithm for training aiming at each algorithm in a preset algorithm database to obtain the measurement model corresponding to the algorithm.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of the preceding claims 1-5 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210083020.4A CN114112819B (en) | 2022-01-25 | 2022-01-25 | Method and device for measuring ore grinding granularity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210083020.4A CN114112819B (en) | 2022-01-25 | 2022-01-25 | Method and device for measuring ore grinding granularity |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114112819A true CN114112819A (en) | 2022-03-01 |
| CN114112819B CN114112819B (en) | 2022-05-10 |
Family
ID=80361041
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210083020.4A Active CN114112819B (en) | 2022-01-25 | 2022-01-25 | Method and device for measuring ore grinding granularity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114112819B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114619334A (en) * | 2022-03-17 | 2022-06-14 | 西门子（中国）有限公司 | Mill control method and mill control device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101869860A (en) * | 2010-06-04 | 2010-10-27 | 中南大学 | Granularity distribution predicting method for ore milling product of ball mill |
| US20150347817A1 (en) * | 2012-12-19 | 2015-12-03 | Koninklijke Philips N.V. | System and method for classification of particles in a fluid sample |
| CN107506862A (en) * | 2017-08-28 | 2017-12-22 | 东北大学 | A kind of online real-time estimate system and method for grinding particle size based on Internet of Things |
| CN109190226A (en) * | 2018-08-24 | 2019-01-11 | 东北大学 | A kind of grinding system overflow granularity index flexible measurement method |
| CN109446236A (en) * | 2018-10-18 | 2019-03-08 | 太原理工大学 | Cement-particle size distribution forecasting method based on random distribution |
-
2022
- 2022-01-25 CN CN202210083020.4A patent/CN114112819B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101869860A (en) * | 2010-06-04 | 2010-10-27 | 中南大学 | Granularity distribution predicting method for ore milling product of ball mill |
| US20150347817A1 (en) * | 2012-12-19 | 2015-12-03 | Koninklijke Philips N.V. | System and method for classification of particles in a fluid sample |
| CN107506862A (en) * | 2017-08-28 | 2017-12-22 | 东北大学 | A kind of online real-time estimate system and method for grinding particle size based on Internet of Things |
| CN109190226A (en) * | 2018-08-24 | 2019-01-11 | 东北大学 | A kind of grinding system overflow granularity index flexible measurement method |
| CN109446236A (en) * | 2018-10-18 | 2019-03-08 | 太原理工大学 | Cement-particle size distribution forecasting method based on random distribution |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114619334A (en) * | 2022-03-17 | 2022-06-14 | 西门子（中国）有限公司 | Mill control method and mill control device |
| CN114619334B (en) * | 2022-03-17 | 2023-03-10 | 西门子（中国）有限公司 | Mill control method and mill control device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114112819B (en) | 2022-05-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112735094B (en) | Geological disaster prediction method and device based on machine learning and electronic equipment | |
| JP6835098B2 (en) | Factor analysis method, factor analyzer and factor analysis program | |
| JP7102941B2 (en) | Information processing methods, information processing devices, and programs | |
| CN105593864B (en) | Analytical device degradation for maintenance device | |
| JP2002525757A (en) | Multidimensional method and system for statistical process management | |
| JP2006318263A (en) | Information analysis system, information analysis method and program | |
| CN114112819B (en) | Method and device for measuring ore grinding granularity | |
| JP2012226511A (en) | Yield prediction system and yield prediction program | |
| JP2020027342A (en) | Information processing apparatus, monitoring device and information processing method | |
| CN113988325A (en) | Power system fault early warning method and device, terminal equipment and storage medium | |
| GB2603740A (en) | Alerting to model degradation based on distribution analysis using risk tolerance ratings | |
| CN117029968A (en) | Traffic data diagnosis method, system, storage medium and electronic equipment | |
| CN117556366B (en) | Data abnormality detection system and method based on data screening | |
| CN113868948A (en) | User-oriented dynamic threshold model training system and method | |
| JP2020052460A (en) | Abnormality detection system and abnormality detection program | |
| CN113205230A (en) | Data prediction method, device and equipment based on model set and storage medium | |
| CN112561333A (en) | Assessment data processing method and device, electronic equipment and storage medium | |
| JP6885321B2 (en) | Process status diagnosis method and status diagnosis device | |
| US11429909B2 (en) | Information-technology utilization evaluation device and information-technology utilization evaluation method | |
| CN113052256B (en) | Wind direction clustering method and device and electronic equipment | |
| CN113296990B (en) | Method and device for recognizing abnormity of time sequence data | |
| CN110263811B (en) | Equipment running state monitoring method and system based on data fusion | |
| CN115809404B (en) | Detection threshold calculation method and device, storage medium and electronic equipment | |
| CN111080446A (en) | Data processing method and device | |
| CN117972314B (en) | Cloud platform monitoring method and system based on digital twinning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: No.15 metallurgical Avenue, Guixi City, Yingtan City, Jiangxi Province, 335400 Patentee after: Jiangxi Copper Co.,Ltd. Patentee after: Beikuang Zhiyun Technology (Beijing) Co., Ltd Address before: 319, 3rd floor North block, building 2, No.1 yard, East Automobile Museum Road, Fengtai District, Beijing 100071 (Park) Patentee before: BEIKUANG ZHIYUN TECHNOLOGY (BEIJING) Co.,Ltd. Patentee before: Jiangxi Copper Co., Ltd |
|
| CP03 | Change of name, title or address |