CN107942873B - A kind of the operation cost intelligence accounting and monitoring method of Furniture manufacture production line - Google Patents
A kind of the operation cost intelligence accounting and monitoring method of Furniture manufacture production line Download PDFInfo
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Abstract
The invention discloses a kind of operation cost intelligence accounting of Furniture manufacture production line and monitoring method, all kinds of production lines that Furniture manufacture produces are identified and are classified first by this method；It is designed then according to process characteristic and implements to produce real-time status intelligent data collection system, acquire the real-time status data of each object in production line；All kinds of status monitoring models, and the relational model based on itself and production and operation cost of model foundation are obtained with modeling and analysis method based on mass data；The overall cost of operation model to production line is realized based on the above cost model, production real-time status data, finally combines the process of intelligent optimization algorithm and intelligent decision output and its execution, monitors all kinds of production statuses with information-based, automation mode.Production process can be made to keep efficient, stable and energy conservation and environmental protection using the present invention, reduce production cost under the premise of guaranteeing economic goal and environmental goals.
Description
Technical Field
The invention relates to the field of production line cost accounting and monitoring, in particular to an intelligent operation cost accounting and monitoring method for a furniture manufacturing production line.
Background
The manufacturing industry is an industry which reasonably utilizes various resource costs and converts the resource costs into products, wherein the furniture manufacturing industry production line generally has the problems of labor waste, material waste, unstable product quality, lag in production real-time information, high manufacturing process cost, difficulty in control and the like due to the characteristics of various products, complex manufacturing process and the like. The effective monitoring of the operation cost of the production line is an important guarantee for ensuring the efficient operation of enterprises and maintaining the competitiveness of the enterprises in the industry. Under the background that the manufacturing industry in China faces overall upgrading, domestic furniture manufacturing enterprises urgently need to implement great reform to meet the needs of new forms, and the operating cost of the manufacturing industry such as resources, energy, manpower and the like is gradually increased, the intelligent accounting and monitoring of the operating cost of the furniture manufacturing production line can help the enterprises to effectively solve the crisis problems.
The traditional furniture manufacturing line generally adopts modes such as manual meter reading, paper report record, simple calculation to solve its running cost accounting problem, mainly has following several aspects not enough: 1) manual recording is inefficient and prone to errors; 2) the data collection and arrangement mode is single, and the data cooperativity is poor due to the fact that the data collection and arrangement mode cannot adapt to the characteristics of various complex production lines; 3) the real-time performance of data is poor, and the problem of abnormal production line tracking is not facilitated; 4) the paper file is easy to damage and has fuzzy handwriting, thus influencing the development of data analysis and processing; 5) the cost accounting has limited related range and incomplete information, and is easy to ignore various factors of equipment, quality, energy consumption and logistics cost which need to be controlled; 6) the data analysis and processing method is simple, has poor precision and single target, and causes great difficulty in implementing improvement measures based on data processing results and poor effect; 7) the production line and management layer have low information interactivity and cannot effectively utilize the whole resources to implement comprehensive improvement.
Generally speaking, an operation cost intelligent accounting and monitoring method of a furniture manufacturing production line is lacked at present to improve the overall efficiency of the furniture production line and keep the furniture production line efficient, stable, energy-saving and environment-friendly in real time, so that the production cost is reduced on the premise of ensuring economic targets and environmental targets.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an intelligent operation cost accounting and monitoring method for a furniture manufacturing production line, so that the production process is efficient, stable, energy-saving and environment-friendly, and the production cost is reduced on the premise of ensuring economic targets and environmental targets.
An intelligent operation cost accounting and monitoring method for a furniture manufacturing production line comprises the following steps:
step 1, dividing various production lines for furniture manufacturing production into discrete type, continuous type or mixed type according to a production organization mode;
step 2, designing and implementing a production real-time state intelligent data acquisition system scheme according to the process flow characteristics of three production lines, and acquiring real-time state data of each object in the production lines;
step 3, for the collected real-time state data, a modeling and analyzing method is used to obtain a real-time state monitoring model of each object in the production line;
step 4, establishing a production operation cost model of each object according to the real-time state monitoring model;
step 5, establishing an integral operation cost model of the production line according to the production operation cost model and the real-time state data of each object;
step 6, intelligently detecting various states of the production line according to the whole operation cost model of the production line and the real-time state data of each object, and outputting an optimization measure scheme based on a specific target by using an intelligent algorithm;
and 7, implementing based on the optimization measure scheme, and monitoring various production states.
In the step 1, various production lines of furniture manufacturing production are identified according to a production organization mode and are mainly classified into a discrete type, a continuous type or a mixed type, wherein the continuous type production refers to continuous product production, and the process flow of the continuous type production often adopts large-scale equipment or continuous production lines, such as spraying, automatic welding, automatic cutting production lines and the like; discrete production is the production of a single project, various elements input into the production process are intermittently input, production equipment and a transportation device are required to be suitable for the processing requirements of various products, certain product storage is required between the working procedures, and the manual participation degree is high, such as hardware processing, sewing, assembly and packaging production lines and the like. The mixed production line has the characteristics of the former two, such as die-casting, polishing, injection molding production lines and the like.
In step 2, according to the process flow characteristics of various production lines, a production real-time state intelligent data acquisition system scheme comprising various state information such as equipment, energy consumption, logistics, quality and the like is designed and implemented, and a monitored parameter variable XMA、YMA、ZMAThe system has the characteristics of controllability and easiness in measurement so as to facilitate subsequent work such as modeling, optimal solution and implementation of the system. The communication transmission mode comprises a wireless mode, a twisted pair mode, a network cable mode, an optical fiber mode and the like, and a duplex or half-duplex method can be adopted to adapt to the requirements of monitoring and control; the communication transmission protocol CAN adopt the universal bus standards such as Modbus, Fins and CAN; the system server database can adopt software systems such as SQL server, oracle and the like.
The data acquisition principle in step 2 is as follows: suppose that at the whole sampling period tsThe number of sampling samples is nsAnd at a certain time tiThe sample of the collected data corresponding to the physical quantity of the state is Smac(ti) The corresponding physical quantity data samples of energy consumption, logistics and quality states are respectively Sener(ti)、Slog(ti)、Squa(ti)(i＝1,2,3,...,ns). For continuous type production lines, tiNamely, the data acquisition sampling period is obtained by the following calculation formula:
ts＝ns·ti
the continuous production line adopts large-scale equipment or a continuous production line in large quantity, so that the corresponding intelligent data acquisition scheme is mainly focused on methods of using intelligent instruments, sensors, chemical test tests and the like.
Accordingly, for a discrete production line, tiI.e. the data signal trigger instant, nsAre indeterminate integer values. Due to the characteristics of high manual participation degree, uncertain data acquisition granularity and the like, the discrete production line focuses on methods of using manual input equipment, RFID tags, smart phones, accessing related webpages and the like.
The specific modeling and analyzing methods in step 3 include linear regression, logistic regression, polynomial regression, variance analysis, factor analysis, gray correlation analysis, and the like. E.g. data acquisition sample period ti(i＝1,2,3,...,ns) In the method, the monitoring models of equipment, energy consumption, logistics and quality states corresponding to a certain monitored object are S respectivelymac(XMA,YMA,ZMA...)、Sener(XE,YE,ZE...)、Slog(XL,YL,ZL...)、Squa(XQ,YQ,ZQ...), wherein XMA、YMA、ZMAThe parameters represent corresponding controllable and easily-measured parameter variables in each monitoring model, and the specific meanings of the parameters are determined according to the characteristics of specific scenes and process flow objects, for example, for an energy consumption monitoring model of a die casting machine process, the method determines the variables as factors such as production rate, production quality stability, raw material category and the like. The parameter variables corresponding to different state models can be overlapped, and therefore, various state monitoring models can be used as the theoretical calculation basis of multi-objective balance optimization.
The selection of the specific regression analysis method needs to be optimized and selected according to data and model characteristics and a performance evaluation method based on the regression analysis method, and specific evaluation indexes comprise R-square, residual standard deviation s, F test and the like. Various types of relational models can be verified and finalized by measurement of actual data. The test formula is as follows:
determining the coefficient R2And (4) checking:
and (5) testing the residual standard deviation s:
and F, testing:
wherein SSE is the sum of the squares of the residuals, SST is the sum of the total squares, SSR is the sum of the regression squares, y is the true value of the sample,the value is calculated for the model,is the sample mean. It is obvious thatA large case indicates that the observed value is closer to the fitted value. That means that n is viewed as a wholesThe spread of the dots is closer to the curve. Thus selecting R2A large equation is good. s can be viewed as an estimate of mean square error in a one-dimensional linear regression equation, whichGenerally, smaller values are better. The larger the F value, the better.
In step 4, the production operation cost models of the objects are respectively Cmac(Tci)、Cener(Tci)、Clog(Tci)、Cqua(Tci) And the calculation formula is as follows:
wherein, Cmac(Tci)、Cener(Tci)、Clog(Tci)、Cqua(Tci) Respectively representing equipment cost, energy consumption cost, logistics cost and quality cost; f. ofmac、fener、flog、fquaRespectively representing a conversion method of a real-time state monitoring model corresponding to a production operation cost model aiming at equipment, energy consumption, logistics and quality within a certain time t; xMA(t)、YMA(t)、ZMA(t) are specific data measured values of various parameter variables at the moment t respectively; t issExpressed as a simplified calculation, will be in the monitoring period TciMinimum sampling period, T, for conversion of inner integral calculation into summation calculationsIs greater than the sampling period ti(ii) a N represents TciDecompose to maximumThe number of small cycles satisfies Tci＝N·Ts。
In step 5, the concrete method of the whole operation cost in the whole operation cost model of the production line is that,
Ctotal(Tci)＝Cmac(Tci)+Cener(Tci)+Clog(Tci)+Cqua(Tci)
wherein, Ctotal(Tci) As a whole operating cost, Cmac(Tci)、Cener(Tci)、Clog(Tci)、Cqua(Tci) Respectively equipment cost, energy consumption cost, logistics cost and quality cost.
In step 6, the specific steps of outputting the multi-target equilibrium optimization solution and the corresponding optimization implementation scheme by using the intelligent algorithm are as follows:
step 6-1: in a certain state monitoring period TciIf the manager finds that the overall production cost in the monitoring period is high through the monitoring system, the manager tracks the production state cost C which correspondingly causes the overall cost to be highOBJ1(Tci)、COBJ2(Tci) And the like. Wherein C isOBJ1(Tci)、COBJ2(Tci) .., one of the cost models for equipment, energy consumption, logistics and quality status, respectively, is expressed by the following expression, where M represents the total number of targets to be monitored:
COBJj(Tci)∈{Cmac(Tci),Cener(Tci),Clog(Tci),Cqua(Tci)...}(i,j＝1,2,3...,M)
step 6-2: identifying and analyzing a production state cost model C that results in a high overall costOBJ1(Tci)、COBJ2(Tci) Equal corresponding production state model SOBJ1(X1,Y1,Z1...)、SOBJ2(X2,Y2,Z2...), etc. The two models do not generally correspond to each other, but because the control variable of a certain production state model is overlapped with the control variable of other state models, the unified consideration is needed, namely, a single cost model may correspond to a plurality of production state models. However, S can be determinedOBJ1(X1,Y1,Z1...)、SOBJ2(X2,Y2,Z2...) et al are one of the production state models of equipment, energy consumption, logistics and quality status, respectively, M represents the total number of targets to be monitored, and is expressed by the following expression:
step 6-3: according to the M descending rule, namely the sequence of M from the maximum value M to M which is reduced to 1, the production state model S to be monitored is sequentially identified, classified and analyzedOBJ1(X1,Y1,Z1...)、SOBJ2(X2,Y2,Z2...). the parameter set of each dimension multi-objective optimization problem inWherein m represents the dimension of the multi-objective optimization problem, and the meaning is as follows: aiming at the balanced optimization problem of m state targets,i.e., the coincidence control variables of each state target model in the m-dimensional multi-target optimization problem, thereforeThe subset of the parameter variables in each production state model is defined, and the intersection of the subsets is an empty set to ensure the overall optimization effect, and the following expression is used for expressing:
where n represents the sequence number of the M-dimensional multi-objective optimization problem for the total number of M targets, it is obvious that the maximum value n of nmaxThe calculation formula is as follows:
step 6-4: sequentially solving n of M-dimensional multi-target equilibrium optimization solution according to M descending rule, namely the sequence of M from the maximum value M to M which is reduced to 1maxTo each specific question, obtaining each specific questionOptimal solution algorithm ofAnd final pareto solution setAlgorithms which can be used for solving comprise genetic algorithms, deep search algorithms, particle swarm algorithms, annealing algorithms, artificial neural networks, NSGA-2, MOEA-D and other popular general intelligent algorithms. For each specific problemUsing an index of the efficiency of the operationPareto integrity indicatorRobustness indexEqual-index comprehensive evaluation of performance of various intelligent algorithms to obtain corresponding optimal solving algorithmAnd final pareto solution setThe calculation formula of various evaluation indexes is as follows:
wherein,to address specific problemsThe number of times an algorithm is run to evaluate the efficiency of the algorithm, which value may be determined by the particular problem situation,the time to obtain a final convergence or pareto result for each run of the algorithm;to address specific problemsRunning an algorithm to obtain the number of solutions in the most complete pareto solution set;obtaining a number of final converged or pareto solutions in a solution set for each run of the algorithm;to address specific problemsThe number of times an algorithm is run to evaluate its robustness, which may be determined by the particular problem case,is thatThe number of times a converged or more complete pareto solution set is obtained in the secondary run.
Step 6-5: according to the M descending rule, namely the sequence of M starting from the maximum value M to the M descending to 1, the M descending rule is sequentially according to each specific problemFinal pareto solution set ofAnd corresponding control variablesTo output the corresponding optimization implementation planThe higher the corresponding value of m, the higher the priority of its implementation.
Data acquisition established in step 7 based on the above stepsA state dynamic monitoring system, an optimal state model and cost model, an optimization measure implementation scheme and the like, wherein the optimization measure implementation scheme is sequentially performed according to an M descending rule, namely the sequence of M from a maximum value M to M which is reduced to 1The details of (1).
The invention has the advantages that a manager can keep the production process efficient, stable, energy-saving and environment-friendly as much as possible by implementing the solution of system output so as to improve the overall production efficiency, thereby reducing the production cost on the premise of ensuring economic targets and environmental targets.
The invention balances and effectively solves the production cost monitoring problems caused by a plurality of contradictions such as process state information lag, complex management, less profit, unstable product quality, low effective utilization rate of equipment and the like by an intelligent means.
Drawings
FIG. 1 is a basic flow diagram of an embodiment of the present invention;
FIG. 2 is a diagram of an energy consumption modeling case;
FIG. 3 is a solution using four algorithmsProblem the pareto front plot obtained.
Detailed Description
The invention is further described with reference to the accompanying drawings and specific examples.
FIG. 1 shows the steps of the present invention, including:
step 1, dividing various production lines for furniture manufacturing production into discrete type, continuous type or mixed type according to a production organization mode;
step 2, designing and implementing a production real-time state intelligent data acquisition system scheme according to the process flow characteristics of three production lines, and acquiring real-time state data of each object in the production lines;
step 3, for the collected real-time state data, a modeling and analyzing method is used to obtain a real-time state monitoring model of each object in the production line;
step 4, establishing a production operation cost model of each object according to the real-time state monitoring model;
step 5, establishing an integral operation cost model of the production line according to the production operation cost model and the real-time state data of each object;
step 6, intelligently detecting various states of the production line according to the whole operation cost model of the production line and the real-time state data of each object, and outputting an optimization measure scheme based on a specific target by using an intelligent algorithm;
and 7, implementing based on the optimization measure scheme, and monitoring various production states.
Because the production line of the die-casting workshop belongs to a mixed production line and has the characteristics of continuity and dispersion, namely, part of the production process flow belongs to continuity, such as melting, soup feeding, mold closing and opening, pressure maintaining and forming, demolding, conveying and the like, and the other part of the production process flow belongs to dispersion, such as raw material input, finished product polishing, quality inspection, transportation and the like; on the other hand, the production line of the die-casting workshop has the characteristics of complex process, high cost, high energy consumption, high pollution, unstable quality, easy equipment loss and the like, and has typical and instructive significance for the intelligent accounting and monitoring of the operation cost of the production line, so that the invention takes the relevant production line of the die-casting workshop as a case of a specific implementation mode for explanation.
According to the process flow characteristics of the die-casting workshop production line, a production real-time state intelligent data acquisition system scheme aiming at various state information of acquisition objects including equipment, energy consumption, logistics, quality and the like is designed and implemented.
According to the step 2, the development of the die-casting workshop production line data acquisition and analysis system is realized based on four functional modules. The first module consists of a digital meter and a sensor for acquiring data. Specifically, the voltage and current sensors are used for measuring electric energy, the gas flow sensor is used for measuring natural gas consumption, the weighbridge is used for measuring raw material input amount, the infrared sensor is used for measuring finished product production rate and the like, and the mobile phone APP and related webpages are used for acquiring information such as equipment and product combination information, quality inspection qualified product production rate and defective product production rate. Once the real-time energy consumption data is collected by the first module, the data is transmitted to the second module through the RS232/RS485 communication line and the modulebus standard serial communication protocol. The second module is an integrated control panel provided with data acquisition and receiving software, the function of the second module is to receive original data, convert the original data into HTML format and transmit the HTML format to a system server, and the communication transmission mode comprises wireless, twisted pair, network cable and other modes, and a duplex method can be adopted to adapt to the requirements of monitoring and control. The third module consists of a system server and an energy consumption database, the system server is used for receiving and storing data and responding to analysis requests, and the database adopts SQLserver as a development platform. The fourth module is an analysis module which has the functions of monitoring the energy consumption state of the production process, analyzing the energy efficiency and outputting a production scheduling improvement decision to a user. By integrating all four levels of functionality, the real-time status of the energy consumption efficiency of the production process can be monitored, and management personnel can thereby regulate the production rate or reschedule the production plan accordingly.
According to the data acquisition principle and the die-casting process cost characteristics, the monitoring period T is mainly monitoredciCarrying out data acquisition and modeling work on indexes such as internal energy consumption, raw material consumption, quality qualified product and defective product quantity, wherein corresponding physical quantity data samples are S respectivelyener(ti)、Slog(ti)、Squa(ti)(i＝1,2,3,...,ns). Regarding the data acquisition period and frequency setting, the data are collected, processed and monitored according to 8 hours, so that the actual scheduling situation on site is met, and the balance of processing complexity, accuracy and processing efficiency can be achieved as much as possible, so that the whole sampling period t is sets8h and monitor period Tci1h (i ═ 1,2, 3.); for the continuous production process, a data acquisition sampling period t is seti1s, the number of samples is
Accordingly, for a discrete production process, tiI.e. the data signal trigger instant, nsAre indeterminate integer values.
According to the steps of the modeling analysis method in the step 3, energy consumption, logistics and quality state monitoring objects corresponding to the die-casting process are specified to be data acquisition sampling periods t respectivelyi(1,2,3,...,ns) Internal energy consumption (kwh), raw material consumption (kg), quality qualification quantity, and respectively using Sener(XE,YE,ZE...)、Slog(XL,YL,ZL...)、Squa(XQ,YQ,ZQ...) in which X, Y, Z, etc. represent the corresponding parameter variables that can affect the monitored object in each monitoring model, and have the characteristics of controllability and easiness in measurement, wherein the lower case letters represent continuous variables generally corresponding to signals generated by a continuous production line, and the upper case letters represent discrete variables generally corresponding to signals generated by a discrete production line, and the specific steps are as follows:
step 3-1: for energy consumption Sener(r,tFG, I, O, M):
r: tact time in [ piece/s]；tF: the temperature of the furnace is measured in degrees centigrade;
g: the equipment and the products are combined, and 3 products are produced in the case, and are numbered as a-c; therefore, the value range of G is [1, 3], and the value range of G corresponds to the product numbers in sequence.
I: the material flow state of the raw materials in the workshop, namely the sufficient degree of raw material conveying, can be divided into sufficient, more sufficient, normal, insufficient and serious less than 5 grades in the present case, so that the value range of I is [1, 5], and the value ranges correspond to the grades in sequence.
O: the logistics state of the finished products in the workshop, namely the smooth degree of the finished product transportation, can be divided into 5 grades of smooth, normal, crowded and seriously crowded in the present case, so that the value range of O is [1, 5], and the values correspond to the grades in sequence.
M: the maintenance state of the equipment can be divided into 6 grades in the maintenance and repair states of just maintained (within 1 day), recently maintained (within 3 days), recently maintained (within 1 week), earlier maintained (within 1 month), untimely maintenance (beyond one month without maintenance), and the maintenance and repair states, so that the value range of M is [1, 6], and the values correspond to the grades in sequence.
Step 3-2: for raw material consumption Slog(r,tFG, I, O):
r: the production beat; t is tF: the temperature of the furnace; g: the equipment and the product are combined; i and O: and (5) the logistics state of the workshop.
Step 3-3: for quality qualified product quantity Squa(r,tF,tMG, I, O, M, F, H):
r: the production beat; t is tF: the temperature of the furnace; t is tM: preheating the die at the temperature of centigrade; g: the equipment and the product are combined; i and O: the logistics state of the workshop; m: the device maintains the state.
F: the slag removal state of the smelting furnace can be divided into just cleaned (within 1 h), recently cleaned (within 3 h), qualified cleaned (within 1 day), early cleaned (within 3 days) and untimely cleaned (beyond 1 day) in the present caseUnworked after 3 days), in maintenance and repair state 6 grades, so FQThe value range is [1, 6]]And correspond to the above levels in turn.
H: the skill level of the worker can be divided into 4 grades in this case, namely, high-grade skilled workers with abundant experience, middle-grade skilled workers with abundant experience, qualified primary skilled workers and newly-entered workers, so that the Z level is determined by the skill level of the workerQThe value range is [1, 4 ]]And correspond to the above levels in turn.
The specific modeling and analyzing methods in step 3 include linear regression, logistic regression, polynomial regression, variance analysis, factor analysis, gray correlation analysis, and the like. Energy consumption monitoring model S of die casting machine processener(r,tFG, I, O, M), at t, according to the production experienceFUnder the condition that variables such as G, I, O, M meet the general production requirements, the energy consumption modeling can perform polynomial regression analysis on a decreasing relation model between SEC and the production rate r, wherein SEC is defined as the required energy consumption in [ KWh/piece ] for producing or processing a certain unit quantity of a certain product]From SEC and the production rate r, S can be further calculatedener. At tF, as shown in FIG. 2, for example>And (3) the curve relation between SEC and production rate r under the conditions that the temperature is 670 ℃, G is 2, and O and M are less than or equal to 3. Based on the real relevant energy consumption data collected by the system, 5 different common mathematical curve relations (good regression analysis fitting effect, R is good) suitable for the type of mathematical curve relations are used2>0.95) was performed for each combination of equipment and product. The five regression analysis methods include: reciprocal functions, power functions, cubic curves, exponential functions, logarithmic functions, and different line types are used in fig. 2 to represent the regression fitting effect of the respective curves. The selection of the specific regression analysis method needs to be optimized and selected according to the data and model characteristics and the performance evaluation method based on the regression analysis method, and the specific evaluation index comprises R in the step 32S, F, etc., the evaluation index and the best model are shown in Table 1, wherein the best model is indicated in bold.
TABLE 1
Various optimum production state models Sener、Slog、SquaCan be verified and finalized by measurement of actual data, with respect to the final modeling result, e.g. with respect to energy consumption Sener(r,tFG, I, O, M), the best model can be obtained by clustering analysis and polynomial regression analysis, as follows:
2≤Sener≤3(tF＜670)
1.5≤Sener≤1.8(tF＞＝670M＝6)
when t isFWhen the number is more than or equal to 670M ≠ 6G ≠ 2I ≤ 3, SenerThe calculated optimal model is shown in table 2:
TABLE 2
Establishing a relation model of various production state information and production operation cost based on the state model in the step 4, wherein the relation model is expressed as follows: in a certain state monitoring period TciAnd the relation models of energy consumption, logistics, quality states and production operation cost corresponding to certain monitored objects are respectively Cener(Tci)、Clog(Tci)、Cqua(Tci) And f is determined within a certain time t according to a cost accounting methodener、flog、fquaThe calculation formula of (a) is as follows:
fener(Sener)＝Pener·Sener
flog(Slog)＝Plog·Slog
fqua(Squa)＝Pqua·Squa+Punqua·(r·t-Squa)
determining a monitoring period TciMinimum sampling period T for converting internal integral calculation into summation calculations60s, therefore TciNumber of cycles decomposed into minimumFurther obtaining C according to the correlation formula in the step 4ener(Tci)、Clog(Tci)、Cqua(Tci) The specific calculation method of (1) is as follows:
obtaining a monitoring period T in a certain state according to the formula of the intelligent accounting method for the whole operation cost of the production line in the step 5, namely the whole operation cost is the energy consumption cost, the logistics cost, the quality cost and the likeciInner (1, 2, 3.) and overall operating cost Ctotal(Tci) The calculation method is as follows. And the overall operation cost C can be known according to historical accumulated data and production experiencetotal(Tci) At TciShould be maintained under the condition of 1hWithin a certain range.
Ctotal(Tci)＝Cener(Tci)+Clog(Tci)+Cqua(Tci)
According to the concrete steps in the step 6, when the monitoring system finds that the overall production cost in the monitoring period is higher, the intelligent algorithm is applied to output a multi-target balance optimization solution and a corresponding optimization implementation scheme:
step 6-1: while in a certain state monitoring period TciIn the monitoring period, if the manager finds that the overall production cost is higher through the monitoring system, the manager tracks the corresponding production state cost which causes higher overall cost, and finds that the production state cost which causes higher overall cost is Cqua(Tci) I.e. COBJ1(Tci)＝Cqua(Tci)。
Step 6-2: identifying and analyzing a production state cost model C that results in a high overall costqua(Tci) The directly corresponding production state model is Squa(Tci) And due to Squa(Tci) Control variable and Sener(Tci)、Slog(Tci) Has a coincidence and therefore needs to be considered uniformly, i.e. a production state cost model C which leads to a higher overall costqua(Tci) The corresponding production state model is Sener(Tci)、Slog(Tci)、Squa(Tci). Namely SOBJ1＝Sener(Tci)、SOBJ2＝Slog(Tci)、SOBJ3＝Squa(Tci). From this, the above analysis of this case can be seen to find that the total number M of targets to be monitored is 3.
Step 6-3: identifying and analyzing and classifying production state models S to be monitoredOBJ1、SOBJ2、SOBJ3Parameter set of each dimension multi-objective optimization problem inAnd obtaining corresponding m and n according to the calculation formula in the step 6-3maxValues, results are shown below:
when M is 3,the following parameter sets are thus available:
in the same way, when m is 2,the following 3 sets of parameters are available:
when S isOBJ1And SOBJ2When combined, the corresponding numbers m-2, n-1:
when S isOBJ1And SOBJ3When combined, the corresponding numbers m-2, n-2:
when S isOBJ1And SOBJ3When combined, the corresponding numbers m-2, n-3:
for the same reason, when m is 1,the following 3 sets of parameters are available:
when aiming at SOBJ1For the single target problem, the corresponding serial number m is 1, n is 1:
when aiming at SOBJ2For the single target problem, the corresponding sequence number m is 1, n is 2:
when aiming at SOBJ3For the single target problem, the corresponding sequence number m is 1, n is 3:
according to the step 6-4, sequentially solving the n of the M-dimensional multi-target equilibrium optimization solution according to the M descending rule, namely the sequence of M from the maximum value M to M which is reduced to 1maxTo each specific question, obtaining each specific questionOptimal solution algorithm ofAnd final pareto solution setThe algorithm for solving the problem selection comprises VEGA, particle swarm algorithm, NSGA-2 and MOEA-And D, and the like. For example, FIG. 3 shows solving with the above four algorithmsProblem-derived pareto frontier map, where SOBJ1'、SOBJ2'、SOBJ3' represents S after normalizationOBJ1、SOBJ2、SOBJ3The normalization formula is shown below, where f (x) represents a primitive function or variable, and f (x)' represents a normalized function or variable.
As shown in FIG. 3, for each particular questionThe execution process of the algorithm is repeatedly implemented, and the related adopted operation efficiency index is calculated according to the related formula in the step 6-4Pareto integrity indicatorRobustness indexEqual-index comprehensive evaluation of performance of various intelligent algorithms to obtain corresponding optimal solving algorithmAnd final pareto solution setThe evaluation results are shown in table 3.
TABLE 3
Step 6-5: according to the M descending rule, namely the sequence of M starting from the maximum value M to the M descending to 1, the M descending rule is sequentially according to each specific problemFinal pareto solution set ofAnd corresponding control variablesTo output the corresponding optimization implementation planThe higher the corresponding value of m, the higher the priority of its implementation. Each one ofIs shown in table 4 according to the priority order:
TABLE 4
According to the content in step 7, optimizing the measure implementation scheme in sequence according to the M descending rule, namely the sequence of M from the maximum value M to M falling to 1The details of (1).
In turn carry outAccording to the method, the whole production cost is gradually reduced and returns to a normal level in a plurality of continuous periods after the monitoring measures are implemented, and the method is proved to be capable of effectively applying an intelligent means, balancing and effectively solving the production cost monitoring problems caused by a plurality of contradictions such as process state information lag, complex management, less profits, unstable product quality, low effective utilization rate of equipment and the like.
Claims (8)
1. An intelligent operation cost accounting and monitoring method for a furniture manufacturing production line is characterized by comprising the following steps:
step 1, dividing various production lines for furniture manufacturing production into discrete type, continuous type or mixed type according to a production organization mode;
step 2, designing and implementing a production real-time state intelligent data acquisition system scheme according to the process flow characteristics of three production lines, and acquiring real-time state data of each object in the production lines; each object is equipment, energy consumption, logistics or quality;
step 3, for the collected real-time state data, a modeling and analyzing method is used to obtain a real-time state monitoring model of each object in the production line;
step 4, establishing a production operation cost model of each object according to the real-time state monitoring model; the production operation cost models of all the objects are respectively Cmac(Tci)、Cener(Tci)、Clog(Tci)、Cqua(Tci) And the calculation formula is as follows:
wherein, Cmac(Tci)、Cener(Tci)、Clog(Tci)、Cqua(Tci) Respectively representing equipment cost, energy consumption cost, logistics cost and quality cost; f. ofmac、fener、flog、fquaRespectively representing a conversion method of a real-time state monitoring model corresponding to a production operation cost model aiming at equipment, energy consumption, logistics and quality within a certain time t; xMA(t)、YMA(t)、ZMA(t) are specific data measured values of various parameter variables at the moment t respectively; t issExpressed as a simplified calculation, will be in the monitoring period TciInner integral calculationMinimum sampling period, T, converted to summation calculationsIs greater than the sampling period ti(ii) a N represents TciIs divided into the number of minimum periods to satisfy Tci＝N·Ts；
Step 5, establishing an integral operation cost model of the production line according to the production operation cost model and the real-time state data of each object;
step 6, intelligently detecting various states of the production line according to the whole operation cost model of the production line and the real-time state data of each object, and outputting an optimization measure scheme based on a specific target by using an intelligent algorithm;
and 7, implementing based on the optimization measure scheme, and monitoring various production states.
2. The intelligent operation cost accounting and monitoring method for the furniture manufacturing production line according to claim 1, wherein: in step 3, the modeling and analyzing method is linear regression, logistic regression, polynomial regression, variance analysis, factor analysis or grey correlation analysis.
3. The intelligent operation cost accounting and monitoring method for the furniture manufacturing production line according to claim 1, wherein: in step 5, the specific method of the overall operation cost in the overall operation cost model of the production line is as follows:
Ctotal(Tci)＝Cmac(Tci)+Cener(Tci)+Clog(Tci)+Cqua(Tci)
wherein, Ctotal(Tci) As a whole operating cost, Cmac(Tci)、Cener(Tci)、Clog(Tci)、Cqua(Tci) Respectively equipment cost, energy consumption cost, logistics cost and quality cost.
4. The intelligent operation cost accounting and monitoring method for the furniture manufacturing production line according to claim 1, wherein: in step 6, the specific steps of outputting the optimization measure scheme based on the specific target by using an intelligent algorithm are as follows:
step 6-1: in a state monitoring period TciWherein i 1,2,3, if the manager finds the overall production cost is increased by the monitoring system, the production state cost C causing the overall cost to be increased is trackedmac(Tci)、Cener(Tci)...；
Step 6-2: identification and analysis of Cmac(Tci)、Cener(Tci) .. real-time production state monitoring model S of corresponding objectmac(X1,Y1,Z1...)、Sener(X2,Y2,Z2...)...；
Step 6-3: sequentially identifying, classifying and analyzing S according to the sequence of M from the maximum value M to the value of M falling to 1mac(X1,Y1,Z1...)、Sener(X2,Y2,Z2...). the parameter set of each dimension multi-objective optimization problem in
Step 6-4: sequentially solving n of M-dimensional multi-target equilibrium optimization solution according to the sequence of M from the maximum value M to the value reduced to 1maxA specific problem, for each specific problemUsing an index of the efficiency of the operationPareto integrity indicatorRobustness indexIndex comprehensive evaluation performance of various intelligent algorithms to obtain corresponding optimal solving algorithmAnd final pareto solution set
Step 6-5: according to the sequence of M from the maximum value M to the value M which is reduced to 1, according to each specific problem in turnFinal pareto solution set ofAnd corresponding control variablesTo output the corresponding optimization implementation plan
5. The intelligent operation cost accounting and monitoring method for the furniture manufacturing production line according to claim 4, wherein: in step 6-1, the production state cost, which leads to an increase in the overall cost, is represented by the following expression:
COBJj(Tci)∈{Cmac(Tci),Cener(Tci),Clog(Tci),Cqua(Tci)...}(i,j＝1,2,3...,M)
wherein M represents the total number of objects to be monitored, Cmac(Tci)、Cener(Tci)、Clog(Tci)、Cqua(Tci) Respectively equipment cost, energy consumption cost, logistics cost and quality cost.
6. The intelligent operation cost accounting and monitoring method for the furniture manufacturing production line according to claim 4, wherein: in step 6-2, the real-time production status monitoring model of the object is expressed by the following expression:
wherein M represents the total number of objects to be monitored, Smac(XMA,YMA,ZMA...) is a real-time status monitoring model of the device, Sener(XE,YE,ZE...) is a model for monitoring the real-time status of energy consumption, Slog(XL,YL,ZL...) is a real-time state monitoring model of the logistics, Squa(XQ,YQ,ZQ...) is a real-time status monitoring model of quality.
7. The intelligent operation cost accounting and monitoring method for the furniture manufacturing production line according to claim 4, wherein: in step 6-3, theExpressed by the following expression:
wherein M represents the dimension of the multi-objective optimization problem, n represents the serial number of the M-dimensional multi-objective optimization problem under the total number of M targets, and the maximum value n of nmaxThe calculation formula is as follows:
8. the intelligent operation cost accounting and monitoring method for the furniture manufacturing production line according to claim 4, wherein: in step 6-4, the operation efficiency indexThe formula of (1) is:
the pareto integrity indicatorThe formula of (1) is:
the robustness indexThe formula of (1) is:
wherein,to address specific problemsThe number of times an algorithm is run to evaluate the efficiency of the algorithm, which value may be determined by the particular problem situation,the time to obtain a final convergence or pareto result for each run of the algorithm;to address specific problemsRunning an algorithm to obtain the number of solutions in the most complete pareto solution set;obtaining a number of final converged or pareto solutions in a solution set for each run of the algorithm;to address specific problemsThe number of times an algorithm is run to evaluate its robustness, which may be determined by the particular problem case,is thatThe number of times a converged or more complete pareto solution set is obtained in the secondary run.
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