CN110142803B - Method and device for detecting working state of mobile welding robot system - Google Patents

Method and device for detecting working state of mobile welding robot system Download PDF

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CN110142803B
CN110142803B CN201910452500.1A CN201910452500A CN110142803B CN 110142803 B CN110142803 B CN 110142803B CN 201910452500 A CN201910452500 A CN 201910452500A CN 110142803 B CN110142803 B CN 110142803B
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吕学勤
陈超
王培松
孟令政
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Shanghai University of Electric Power
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
    • B23K37/00—Auxiliary devices or processes, not specially adapted to a procedure covered by only one of the preceding main groups
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
    • B23K37/00—Auxiliary devices or processes, not specially adapted to a procedure covered by only one of the preceding main groups
    • B23K37/02—Carriages for supporting the welding or cutting element
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
    • B25J19/0095—Means or methods for testing manipulators
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Abstract

The invention relates to a method and a device for detecting the working state of a mobile welding robot system, wherein the method comprises the following steps: s1: constructing a working state grade standard to obtain a working index state grade standard corresponding to each working index; s2: collecting measured data of each working index of the mobile welding robot system in a set time period, and carrying out forward processing on the measured data; s3: acquiring the comprehensive weight of each working index; s4: converting the working index state grade standard and the measured data into corresponding evaluation standard cloud and evaluation cloud respectively; s5: respectively carrying out cloud aggregation on the evaluation standard cloud and the evaluation cloud based on the comprehensive weight to obtain a comprehensive evaluation standard cloud and a comprehensive evaluation cloud; s6: and calculating the correlation between the comprehensive evaluation standard cloud and the comprehensive evaluation cloud, and taking the grade corresponding to the maximum correlation as the working state grade of the system. Compared with the prior art, the method has the advantages of high reliability, convenience, real-time performance and the like.

Description

Method and device for detecting working state of mobile welding robot system
Technical Field
The invention relates to the technical field of online comprehensive diagnosis of complex systems, in particular to a method and a device for detecting the working state of a mobile welding robot system.
Background
The performance parameters of the mobile welding robot driven by the fuel cell hybrid power system have the characteristics of nonlinearity, strong coupling, multiple dimensions and the like. The scientific and reasonable working state detection is beneficial to the online optimization of the energy efficiency and the control performance of the system.
The fuel cell hybrid power system has the states of stable load, sudden increase, energy feedback and the like under the conditions of robot starting, stable running, acceleration braking and the like, and the nonlinear control process is very complicated. In order to improve the dynamic response capability of the fuel cell, reasonably distribute the energy of the hybrid power supply and optimize the dynamic characteristics of the power system and the performance of a weld tracking system, the control is critical and difficult. In order to optimize the control strategy, it is necessary to fully evaluate the system parameters and control performance to ensure optimal operation of the system and establish a numerical relationship between the system parameters to optimize the operating conditions of the fuel cell hybrid system and the robot. The real-time motion state and energy optimization evaluation of the system can fully reflect the overall performance change of the robot, help the robot system to realize rapid optimal control under different task environments, and realize the maximization of performance.
A mobile welding robot system driven by a fuel cell hybrid power system is a complex dynamic system, how to use real-time operation parameters of the system, visualize dynamic characteristics of the robot hybrid power system and working performance of a welding seam tracking system, comprehensively know the state of the system part and the whole, provide scientific and effective basis for optimizing the system performance on line according to operation conditions, realize the evaluation of the real-time state of the system, and is a premise and key in the optimization of the on-line performance. However, no better method for achieving the above purpose exists in the prior art.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method and a device for detecting the working state of a mobile welding robot system, which can detect and obtain the real-time working state of the mobile welding robot system and evaluate the comprehensive performance of the system, thereby conveniently optimizing the performance of the mobile welding robot system in real time.
The purpose of the invention can be realized by the following technical scheme:
a method for detecting the working state of a mobile welding robot system comprises the following steps:
s1: constructing a working state grade standard to obtain a working index state grade standard corresponding to each working index;
s2: collecting measured data of each working index of the mobile welding robot system in a set time period, and carrying out forward processing on the measured data;
s3: acquiring the comprehensive weight of each working index;
s4: converting the working index state grade standard and the measured data into corresponding evaluation standard cloud and evaluation cloud respectively;
s5: respectively carrying out cloud aggregation on the evaluation standard cloud and the evaluation cloud based on the comprehensive weight to obtain a comprehensive evaluation standard cloud and a comprehensive evaluation cloud;
s6: and calculating the correlation between the comprehensive evaluation standard cloud and the comprehensive evaluation cloud, and taking the grade corresponding to the maximum correlation as the working state grade of the system.
Further, the mobile welding robot system is a mobile welding robot system driven by a fuel cell and a battery hybrid power, and a robot system of a similar structure.
Further, in the forward processing of the measured data, a forward calculation formula for a reverse index is as follows:
x′ i ＝|x max -x i |
in formula (II), x' i Is the ith index normalized value, x max Is the upper and lower limit values of the working index, x i The measured value of the ith index;
the forward calculation formula for the moderate index is as follows:
Figure BDA0002075590780000021
in the formula, x e Is the operating target rating.
Further, in step S3, the comprehensive weight is a combination of an objective weight of the work index and a subjective weight of the work index, and the combination formula is:
Figure BDA0002075590780000022
in the formula, W i Is the integrated weight of the ith work index, W RAHP (i) Is the subjective weight of the ith work index, W e (i) Is the objective weight of the ith working index, and n is the number of the working indexes.
Further, the objective weight is obtained by an entropy weight method, and the subjective weight is obtained by a rough set analytic hierarchy process.
Further, in step S4, the conversion formula of the evaluation standard cloud is:
C IEC (Ex,En,He)＝((C min +C max )/2,(C max -C min )/6,En/5)
in the formula, C IEC (Ex, en, he) is evaluation standard cloud, C min Is the lower limit of the working index state grade standard, C max As the state grade of the working indexThe upper limit of the standard, ex is desired, en is entropy and He is super entropy.
Further, in step S5, the formula of the cloud aggregation is:
Figure BDA0002075590780000031
in the formula, C WAIC (Ex, en, he) is the synthetic cloud, W i Is the integrated weight of the ith work index, ex i For the cloud expectation, en, corresponding to the ith work index i The cloud entropy, he corresponding to the ith working index i The cloud super entropy corresponding to the ith working index is obtained, and n is the number of the working indexes.
Further, in step S6, the calculation formula of the correlation is:
Figure BDA0002075590780000032
wherein d is a correlation value,
Figure BDA0002075590780000033
in order to comprehensively evaluate the standard cloud,
Figure BDA0002075590780000034
for comprehensive evaluation of clouds, ex 1 、En 1 And He 1 Expectation, entropy and hyper-entropy, ex, respectively, of the comprehensive evaluation standard cloud 2 、En 2 And He 2 Respectively, the expectation, entropy and hyper-entropy of the comprehensive evaluation cloud.
Further, the method further comprises:
and carrying out visual graphic display on the system working state grade according to the time sequence.
Further, the method further comprises:
and controlling the working parameters of the mobile welding robot system according to the system working state grade.
The invention also provides a device for detecting the working state of the mobile welding robot system, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the method.
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention diagnoses the working state of the mobile welding robot system according to the real-time collected working index actual measurement data of the mobile welding robot system, thereby evaluating the comprehensive performance of the system in real time and providing a basis for improving the system performance.
(2) The invention carries out forward transformation on the real-time parameters of the working indexes, and solves the problem of mutual reasonable transformation between qualitative concepts and quantitative values of the working indexes.
(3) The invention adopts an entropy weight method to calculate the objective weight of each working index, effectively eliminates the human interference factor and ensures that the research result is more reasonable and fair.
(4) The invention adopts a rough analytic hierarchy process to calculate the subjective weight of the judgment of each work index expert, not only maintains the objectivity of the original data, but also considers the opinions of other experts, so that the weight is more comprehensive.
(5) The invention adopts a weighted average algorithm to unify objective weight and subjective weight to obtain comprehensive weight, combines the advantages of a rough analytic hierarchy process and an entropy weight method, not only maintains the objectivity of original data, but also considers the opinions of other experts, and obtains more scientific and reliable work index weight.
(6) The invention adopts a cloud conversion mode to detect the working state, can effectively convert the real-time parameters of each working index of the system, solves the problem of mutual reasonable conversion between the qualitative concept and the quantitative value of the working index, correctly reflects the dynamic characteristic of a hybrid power driving system and the operation state of a welding seam tracking system in a mobile welding robot system driven by a fuel cell hybrid power system, improves the reliability of the weight of the working index, avoids the ambiguity of the description of the system state, provides scientific decision basis for the performance of an online optimization system, and can help the robot system to realize rapid optimal control under different working conditions and realize the maximization of the performance.
(7) According to the invention, the real-time working state grade of the system is determined by calculating the correlation between the comprehensive evaluation cloud and the comprehensive evaluation standard cloud, so that a basis is provided for improving the system performance, and the real-time performance optimization is facilitated.
(8) The real-time working state grade of the system obtained by the method can realize the dynamic characteristic of the visual robot hybrid power system and the working performance of the welding seam tracking system, comprehensively know the state of the system part and the whole, and provide scientific and effective basis for optimizing the system performance on line according to the operating condition.
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FIG. 1 is a flow chart of the present invention;
fig. 2 is a cloud diagram of a comprehensive evaluation standard cloud obtained in an embodiment of the present invention;
fig. 3 is a relational diagram of a comprehensive evaluation standard cloud and a comprehensive evaluation cloud in a first evaluation period according to an embodiment of the present invention;
fig. 4 is a graph illustrating the visualization of the system operating condition level according to an embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
The invention provides a method for detecting the working state of a mobile welding robot system, which can conveniently obtain the performance evaluation result of the mobile welding robot system, thereby providing a basis for the real-time control of the mobile welding robot system. The method is applicable to mobile welding robot systems driven by fuel cell and storage battery hybrid power and robot systems with similar structures, and the mobile welding robot can be configured with a hybrid power driving system and a welding seam tracking system.
As shown in fig. 1, the method comprises the steps of:
s1: and constructing a working state grade standard to obtain a working index state grade standard corresponding to each working index.
The inventionDetermining the state standard intervals of five levels of the system according to the golden section rate alpha, wherein the five state level intervals are respectively as follows from low to high: i0, alpha 4 ],II[α 4 ,1-α],III[1-α,α],IV[α,1-α 4 ],V[1-α 4 ,1]。
The invention classifies the working indexes according to reliability, stability and economy, and determines the working index state grade standard as I0, alpha according to the upper and lower limit values of each working index 4 *x max ],II[α 4 *x max ,(1-α)*x max ],III[(1-α)*x max α*x max ],IV[α*x max ,(1-α 4 )*x max ],V[(1-α 4 )*x max ,x max ]Wherein x is max The working index is an upper limit value and a lower limit value.
S2: and acquiring the measured data of each working index of the mobile welding robot system in a set time period, and carrying out forward processing on the measured data. The period is set as an evaluation period.
In the multi-index comprehensive evaluation, some indexes are better evaluated as the index value is larger, and are called as forward indexes (also called as benefit indexes or expected large indexes); some of the indexes are those whose smaller index value is better in evaluation, and are called reverse indexes (also called cost-type indexes or expectation-miniaturization indexes), and some of the indexes are those whose index value is closer to a certain value, and are better, and are called moderate indexes. The method firstly trends the indexes in the same way when acquiring the measured data, generally converts the reverse indexes and the moderate indexes into the forward indexes, so that the indexes with better performance when the index value is smaller and the indexes with better performance when the index value is closer to a median value are all converted into the indexes with better performance when the index value is larger, and the indexes are also referred to as the forward indexes.
In the invention, in the forward processing of the measured data, a forward calculation formula for a reverse index is as follows:
x′ i ＝|x max -x i |
in formula (II), x' i Is the i index normalized value, x max Is the upper and lower limit values of the working index, x i The measured value of the ith index;
the forward calculation formula for the moderate index is as follows:
Figure BDA0002075590780000051
in the formula, x e The rated value is the working index.
S3: and acquiring the comprehensive weight of each working index.
In the invention, the comprehensive weight is the combination of the objective weight of the working index and the subjective weight of the working index, so as to obtain reliable comprehensive weight, and the evaluation result is more reliable, and the combination formula is as follows:
Figure BDA0002075590780000061
W＝(W 1 ,W 2 ,...,W i )
in the formula, W i Is the integrated weight of the ith work index, W RAHP (i) Is the subjective weight of the ith work index, W e (i) Is the objective weight of the ith working index, and n is the number of the working indexes.
The combination of the subjective evaluation method and the objective evaluation method can obtain more scientific and reliable working index weight. In the invention, an entropy weight method is adopted to calculate the objective weight of the working index, a rough analytic hierarchy process is adopted to calculate the subjective weight of the working index, and a correction formula is adopted to calculate two weighting methods to obtain the accurate, reliable and objective comprehensive weight of the working index.
3.1 entropy weight method
The entropy weight method is an objective evaluation method. The weight calculation criterion of the entropy weight method is determined according to the numerical value discrete degree among all indexes, so that the man-made interference factor can be effectively eliminated, and the research result is more reasonable and fair. The specific process is as follows:
1) Constructing a work index data matrix R
Figure BDA0002075590780000062
In the formula, n is the number of the working indexes, and m is the data number of each working index.
2) Calculating an entropy value e of a work index j
Figure BDA0002075590780000063
3) Calculating a work index weight W e
g j ＝1-e j
Figure BDA0002075590780000064
Figure BDA0002075590780000065
3.2 coarse analytic hierarchy Process
The rough analytic hierarchy process utilizes an expert scoring method to complete index weight calculation to form a judgment matrix. And then, the rough number is used for characterizing and processing the judgment matrix to construct a rough comparison matrix. And finally, obtaining the weight of each index through mathematical calculation. Therefore, the objectivity of the original data is kept, and the opinions of other experts are considered, so that the weight is more comprehensive.
1) Investigating expert opinions to obtain a comparison and judgment matrix A k
Supposing that n experts give weight opinions to x working indexes according to a 1-9 scaling method, a judgment matrix is as follows:
Figure BDA0002075590780000071
where k =1,2.. N represents different experts, i, j =1,2.. X represents different work indexes.
1-9 Scale method for judging importance degree of operation index, as shown in Table 1:
TABLE 1
Figure BDA0002075590780000072
Judgment matrix A of all experts k A consistency check is required. CR<And when the consistency of the judgment matrix is 0.1, the next operation can be carried out, otherwise, the corresponding judgment matrix needs to be adjusted until the consistency passes the consistency test. The consistency check procedure is as follows:
calculation of A k The maximum eigenvalue of (a) is substituted into the following formula for consistency verification:
Figure BDA0002075590780000073
where n is the decision matrix A k The RI values are shown in Table 2.
TABLE 2
Figure BDA0002075590780000074
2) Constructing a coarse decision matrix A * Determining the corresponding roughness
Figure BDA0002075590780000075
A coarse comparison matrix a is obtained.
Figure BDA0002075590780000076
Roughness number
Figure BDA0002075590780000081
The calculation formula of (a) is as follows:
Figure BDA0002075590780000082
Figure BDA0002075590780000083
Figure BDA0002075590780000084
calculating all the rough numbers to obtain a rough comparison matrix A
Figure BDA0002075590780000085
3) Calculating the subjective judgment weight W of the working index RAHP
Splitting rough comparison matrix A into A-and A +
Figure BDA0002075590780000086
Figure BDA0002075590780000087
Respectively calculate A - And A + Corresponding eigenvector w for the largest eigenvalue - ,w +
Figure BDA0002075590780000088
Figure BDA0002075590780000089
w - ＝(w - (1),w - (2),...,w - (i))
w + ＝(w + (1),w + (2),...,w + (i))
Calculating the subjective judgment weight W of the working index RAHP
Figure BDA00020755907800000810
S4: and respectively converting the working index state grade standard and the measured data into corresponding evaluation standard cloud and evaluation cloud.
The working index state grade standard and the actually measured data are converted into an evaluation standard cloud and an evaluation cloud by using the forward cloud generator, the working index state grade standard and the actually measured data are converted into the evaluation standard cloud and the evaluation cloud by using the forward cloud generator, the conversion from the setting concept of an interval type language value (standard interval) to quantitative numerical value description is realized, and the evaluation standard cloud C of each working index state grade standard IEC The formula for the conversion (Ex, en, he) is:
C IEC (Ex,En,He)＝((C min +C max )/2,(C max -C min )/6,En/5)
wherein C is min And C max And the upper limit and the lower limit of the working index state grade standard.
Evaluation cloud C of actually measured data of working index state REC The formula for the conversion (Ex, en, he) is:
Figure BDA0002075590780000091
wherein x is i Is the value of the sample(s),
Figure BDA0002075590780000092
is the mean of the index samples, μ is the first order absolute central moment of the samples, and σ is the variance of the samples.
S5: and respectively carrying out cloud aggregation on the evaluation standard cloud and the evaluation cloud based on the comprehensive weight to obtain a comprehensive evaluation standard cloud and a comprehensive evaluation cloud.
The formula for cloud aggregation is:
Figure BDA0002075590780000093
in the formula, C WAIC (Ex, en, he) is a synthetic cloud。
The comprehensive evaluation standard cloud generated by the above formula is shown in fig. 2. The number of the comprehensive evaluation standard clouds corresponds to the level number of the working state level standard.
S6: and calculating the correlation between the comprehensive evaluation standard cloud and the comprehensive evaluation cloud, and taking the grade corresponding to the maximum correlation as the working state grade of the system according to the maximum membership rule.
The calculation formula of the correlation is as follows:
Figure BDA0002075590780000094
wherein d is a correlation value,
Figure BDA0002075590780000095
in order to comprehensively evaluate the standard cloud,
Figure BDA0002075590780000096
for comprehensive evaluation of clouds, ex 1 、En 1 And He 1 Expectation, entropy and hyper-entropy, ex, respectively, of the comprehensive evaluation standard cloud 2 、En 2 And He 2 Respectively, the expectation, entropy and hyper-entropy of the comprehensive evaluation cloud.
In certain embodiments, the method further comprises: and carrying out visual graphic display on the system working state grade according to the time sequence. As shown in fig. 3, the cloud distribution diagram is a cloud distribution diagram of the present embodiment in which correlation calculation is performed on the comprehensive evaluation cloud and the comprehensive evaluation criterion in the first evaluation period. As shown in fig. 4, the present embodiment is an evaluation graph of the system state level throughout the evaluation period, which includes a start-up process, a smoothing process, a steering process, and a re-smoothing process.
In certain embodiments, the method further comprises: and controlling the working parameters of the mobile welding robot system according to the system working state grade.
In some embodiments, there is also provided an apparatus for detecting the working state of a mobile welding robot system, comprising a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the above method.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (4)

1. A method for detecting the working state of a mobile welding robot system is characterized by comprising the following steps:
s1: constructing a working state grade standard to obtain a working index state grade standard corresponding to each working index;
s2: acquiring measured data of each working index of the mobile welding robot system in a set time period, and carrying out forward processing on the measured data, wherein the set time period is an evaluation period, and the whole evaluation period comprises a starting process, a stabilizing process, a turning process and a restabilizing process;
s3: acquiring the comprehensive weight of each working index;
s4: converting the working index state grade standard and the measured data into corresponding evaluation standard cloud and evaluation cloud respectively;
s5: respectively carrying out cloud aggregation on the evaluation standard cloud and the evaluation cloud based on the comprehensive weight to obtain a comprehensive evaluation standard cloud and a comprehensive evaluation cloud;
s6: calculating the correlation between the comprehensive evaluation standard cloud and the comprehensive evaluation cloud, taking the grade corresponding to the maximum correlation as the working state grade of the system, and carrying out visual graphic display on the working state grade of the system according to the time sequence;
in the step S2 of performing forward processing on the measured data, a forward calculation formula for a reverse index is as follows:
x′ i ＝|x max -x i |
in formula (II), x' i Is the ith index normalized value, x max Is the upper and lower limit values of the working index, x i The measured value of the ith index;
the forward calculation formula for the moderate index is as follows:
Figure RE-FDA0003910710880000011
in the formula, x e Rated value is a working index;
in step S3, the comprehensive weight is a combination of an objective weight of the work index and a subjective weight of the work index, the objective weight is obtained by an entropy weight method, and the subjective weight is obtained by a rough set analytic hierarchy process, wherein the rough set analytic hierarchy process uses an expert scoring method to complete calculation of the index weight to form a judgment matrix, and then uses a rough number to characterize and process the judgment matrix to construct a rough comparison matrix, and finally obtains the weight of each index through mathematical calculation, and the combination formula is:
Figure RE-FDA0003910710880000012
in the formula, W i Is the integrated weight of the ith work index, W RAHP (i) Is the subjective weight of the ith work index, W e (i) The objective weight of the ith working index is obtained, and n is the number of the working indexes;
in step S4, evaluation standard cloud C of working index state grade standard IEC The formula for (Ex, en, he) conversion is:
C IEC (Ex,En,He)＝((C min +C max )/2,(C max -C min )/6,En/5)
wherein C min And C max Upper and lower limits of the working index state grade standard;
evaluation cloud C of actually measured data of working index state REC The formula for the conversion (Ex, en, he) is:
Figure RE-FDA0003910710880000021
wherein x is i Is the value of the sample(s),
Figure RE-FDA0003910710880000022
is the mean of the index samples, μ is the first-order absolute central moment of the samples, and σ is the variance of the samples;
in step S5, the formula of cloud aggregation is:
Figure RE-FDA0003910710880000023
in the formula, C WAIC (Ex, en, he) is the synthetic cloud, W i Is the integrated weight of the ith work index, ex i For the cloud expectation corresponding to the ith working index, en i The cloud entropy, he corresponding to the ith working index i The cloud super entropy corresponding to the ith working index is obtained, and n is the number of the working indexes.
2. The method for detecting the operating state of the mobile welding robot system according to claim 1, wherein in step S6, the correlation is calculated by the following formula:
Figure RE-FDA0003910710880000024
wherein d is a correlation value,
Figure RE-FDA0003910710880000025
in order to comprehensively evaluate the standard cloud,
Figure RE-FDA0003910710880000026
for comprehensive evaluation of clouds, ex 1 、En 1 And He 1 Expectation, entropy and hyper-entropy, ex, respectively, of the comprehensive evaluation standard cloud 2 、En 2 And He 2 Respectively, the expectation, entropy and hyper-entropy of the comprehensive evaluation cloud.
3. The method for detecting the operating state of the mobile welding robot system according to claim 1, further comprising:
and controlling the working parameters of the mobile welding robot system according to the system working state grade.
4. An apparatus for detecting an operating state of a mobile welding robot system, comprising a memory storing a computer program and a processor for executing the steps of the method according to any one of claims 1 to 3.
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