CN111401629B - Production management method for intelligent knitting factory warp knitting workshop - Google Patents
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Abstract
The invention belongs to the technical field of intelligent knitting factory production, and particularly relates to a production management algorithm and a production management method for a warp knitting workshop of an intelligent knitting factory, wherein the production management algorithm for the warp knitting workshop of the intelligent knitting factory comprises the following steps: acquiring data; establishing a corresponding vector according to the data and the historical data; constructing a warp knitting machine running state model according to the corresponding vectors; setting a set rotating speed of the warp knitting machine according to the running state model of the warp knitting machine; and generating a working strategy according to the rotating speed and the constraint conditions of the warp knitting machine, so that the highest production flow efficiency of the production orders in the warp knitting workshop is realized.
Description
Technical Field
The invention belongs to the technical field of intelligent knitting factory production, and particularly relates to a production management algorithm and a production management method for a warp knitting workshop of an intelligent knitting factory.
Background
The textile industry is the traditional industry, has the industrial characteristics of long supply chain, multiple links, various products, quick update, various specifications and the like, seriously influences the development and management of the textile industry and hinders the smooth circulation of product production. At present, the collection and monitoring of production data and equipment operation data in a warp knitting workshop are mostly based on an industrial personal computer, a report box, manual output statistics and the like. The monitoring of whole workshop production data often realizes through the billboard of artifical hand drawing, has the real-time poor, is difficult for centralized management, to the not deep scheduling problem of data analysis. At present, most of traditional warp knitting workshops of knitting factories cannot monitor production data smoothly and efficiently, and cannot analyze and mine the data better.
Therefore, based on the technical problems, a new production management algorithm and a new production management method for the warp knitting workshop of the intelligent knitting factory need to be designed.
Disclosure of Invention
The invention aims to provide a production management algorithm and a production management method for a warp knitting workshop of a knitting intelligent factory.
In order to solve the technical problem, the invention provides a production management algorithm for a warp knitting workshop of a knitting intelligent factory, which comprises the following steps:
acquiring data;
establishing a corresponding vector according to the data and the historical data;
constructing a warp knitting machine running state model according to the corresponding vectors;
setting a set rotating speed of the warp knitting machine according to the running state model of the warp knitting machine; and
and generating an operating strategy according to the rotating speed and the constraint conditions of the warp knitting machine.
Further, the method for acquiring data comprises the following steps: obtaining a vibration signal mean value according to the vibration signal, obtaining a temperature mean value according to the temperature, and obtaining the total working time of the warp knitting machine;
the data includes: the vibration signal mean value, the temperature mean value and the total working time of the warp knitting machine.
Further, the method for establishing the corresponding vector according to the data and the historical data comprises the following steps: constructing a fault parameter vector and a coefficient vector of the warp knitting machine;
the historical data includes: historical vibration signal mean value, historical temperature mean value and historical total working time of the warp knitting machine;
the warp knitting machine fault parameter vector is as follows: x ═ x (1) ,x (2) ,x (3) ,1)；
Wherein x is (1) As the mean value of the vibration signal, x (2) Is the mean value of temperature, x (3) The total working time of the warp knitting machine;
the coefficient vector is: w ═ w (w) (1) ,w (2) ,w (3) ,b)；
Wherein, w (1) Is the mean coefficient of the vibration signal, w (2) Is a temperature mean coefficient, w (3) B is the offset of the total working time coefficient of the warp knitting machine.
Further, the method for constructing the running state model of the warp knitting machine according to the corresponding vectors comprises the following steps:
the warp knitting machine running state model comprises:
wherein, w T Is the transposition of w; e is a natural constant;
the loss function is then:
wherein m is the number of data set samples; y is a category label which indicates two states of fault and no fault, and when y is 1, it indicates fault, and when y is 0, it indicates no fault; y is (i) A category label for the ith data;
iterating the loss function to obtain the best value of w when the loss function is minimized, the iterative function is:
where α represents the step size.
Further, the method for setting the set rotating speed of the warp knitting machine according to the running state model of the warp knitting machine comprises the following steps:
wherein m is 0 The number of samples of which the category y is 0; m is 1 The number of samples in the category y is 1; mu.s 1 The mean value of data obtained after the fault data are projected on a w vector axis; mu.s 0 The mean value of data obtained after projection of the fault-free data on the w vector axis is obtained; delta 0 The standard deviation of data obtained after the projection of the fault-free data on the w vector axis is obtained; delta 1 The standard deviation of the data obtained after the projection of the fault data on the w vector axis is obtained;
x ci is the current state of the ith warp knitting machine v i The set rotating speed of the ith warp knitting machine is as follows:
when wx ci ≤μ 0 +δ 0 At the set rotation speed v of the warp knitting machine h I.e. v i ＝v h ；
When wx ci ≥μ 1 -δ 1 At the set rotation speed v of the warp knitting machine L I.e. v i ＝v L ；
When mu is 0 +δ 0 ≤wx ci ≤μ 1 -δ 1 In the meantime, the set rotation speed of the warp knitting machine is as follows:
wherein v is h The upper limit of the highest rotating speed of the warp knitting machine; v. of L The lowest rotating speed lower limit of the warp knitting machine;
and when wx ci ＞μ 1 -2δ 1 And early warning is carried out to prompt maintenance of the warp knitting machine.
Further, the method for generating the working strategy according to the rotating speed and the constraint conditions of the warp knitting machine comprises the following steps:
wherein n is the total number of warp knitting machines; l is the total number of orders; v. of i Setting the rotating speed of the ith warp knitting machine; t is t ki Production hours on the ith warp knitting machine for the kth order, k ∈ [1, l [ ]]；P ks Selling a unit price for the kth order; p kr The raw material unit price for the kth order; a is the loss value/unit yield of the warp knitting machine; t is t r The total working hours for operators; p r Pay per hour for the operating workers;
the constraint conditions include: constraint conditions of working time of an operator, constraint conditions of total amount of each order and constraint conditions of working time of each warp knitting machine;
the constraint conditions of the working time of the operator comprise:
wherein d is the number of warp knitting machines for each worker;
the constraint conditions of the total amount of each order comprise:
wherein q is 1 Amount of contract order for order 1; q. q.s k The volume of contract orders for the kth order; q. q of l The contract order amount for the first order; t is t 1i Man-hours on the ith warp knitting machine for the 1 st order; t is t li The labor hour for the ith order on the ith warp knitting machine;
the constraint conditions of the working time of each warp knitting machine comprise:
wherein, TG i For the maximum allowable working time of the ith warp knitting machine in the production planning period, i e [1, n ]]；
The working strategy comprises the following steps: the set rotating speed of each warp knitting machine, the optimal distribution working hour and the total working hour of operators, wherein each order is produced on the corresponding warp knitting machine;
obtaining the best distribution working hours t of different orders on different warp knitting machines according to the simplex method ki And total man-hours t of operators r And Z is minimized.
On the other hand, the invention also provides a production management method for the warp knitting workshop of the intelligent knitting factory, which comprises the following steps:
collecting data and sending the data to a server;
and the server generates a working strategy according to the data.
Further, the server is suitable for generating the working strategy by adopting the production management algorithm of the warp knitting workshop of the intelligent knitting factory.
Further, the method for collecting and sending data to the server comprises the following steps:
the vibration sensor node is suitable for detecting vibration signals of the warp knitting machine and sending the vibration signals to the server;
the temperature sensor is suitable for detecting the temperature data of the warp knitting machine and sending the temperature data to the server;
the server obtains the vibration signal mean value through the vibration signal, and the server obtains the temperature mean value through the temperature data.
The method has the advantages that the data are acquired; establishing a corresponding vector according to the data and the historical data; constructing a warp knitting machine running state model according to the corresponding vectors; setting a set rotating speed of the warp knitting machine according to the running state model of the warp knitting machine; and generating a working strategy according to the rotating speed and the constraint conditions of the warp knitting machine, so that the highest production flow efficiency of the production orders in the warp knitting workshop is realized.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a knitted intelligent factory warp knitting shop production management algorithm in accordance with the present invention;
FIG. 2 is a flow chart of a production management method for a warp knitting shop of a knitting intelligent factory according to the present invention;
FIG. 3 is a functional block diagram of a vibration sensor node in accordance with the present invention;
fig. 4 is a functional block diagram of a temperature sensor node according to the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
FIG. 1 is a flow chart of a knitted intelligent factory warp knitting shop production management algorithm in accordance with the present invention.
As shown in fig. 1, this embodiment 1 provides a production management algorithm for a knitting intelligent factory warp knitting shop, which includes: acquiring data; establishing a corresponding vector according to the data and the historical data; constructing a warp knitting machine running state model according to the corresponding vectors; setting a set rotating speed of the warp knitting machine according to the running state model of the warp knitting machine; and generating a working strategy according to the rotating speed and the constraint conditions of the warp knitting machine, so that intelligent production management of a warp knitting workshop is realized, and the production flow efficiency of the production order is highest.
In this embodiment, the method for acquiring data includes: obtaining a vibration signal mean value according to the vibration signal, obtaining a temperature mean value according to the temperature, and obtaining the total working time of the warp knitting machine; the data includes: the vibration signal mean value, the temperature mean value and the total working time of the warp knitting machine.
In this embodiment, the method for establishing the corresponding vector according to the data and the historical data includes: constructing a fault parameter vector and a coefficient vector of the warp knitting machine; the history numberThe method comprises the following steps: historical vibration signal mean value, historical temperature mean value and historical total working time of the warp knitting machine; the warp knitting machine fault parameter vector is as follows: x ═ x (1) ,x (2) ,x (3) 1); wherein x is (1) As the mean value of the vibration signal, x (2) Is the mean value of temperature, x (3) The total working time of the warp knitting machine; the coefficient vector is: w ═ w (w) (1) ,w (2) ,w (3) B); wherein, w (1) Is the mean coefficient of the vibration signal, w (2) Is a temperature mean coefficient, w (3) B is the offset of the total working time coefficient of the warp knitting machine.
In this embodiment, the method for constructing the running state model of the warp knitting machine according to the corresponding vector comprises the following steps:
the warp knitting machine running state model comprises:
let the function be:
wherein, w T Is the transposition of w; e is a natural constant, is a constant in mathematics, is an infinite acyclic decimal number, is an transcendental number, and has a value of about 2.71828;
the loss function is then:
wherein m is the number of data set samples; y is a category label which indicates two states of fault and no fault, and when y is equal to 1, it indicates fault, and when y is equal to 0, it indicates no fault; y is (i) A category label for the ith data;
iterating the loss function by using a gradient descent method to obtain an optimal value of w when the loss function J (w) is minimum, wherein the iteration function is as follows:
wherein α represents a step size, and α is 0.1, the step size keeps a moderate iteration speed, and neither too fast iteration and possibly missing an optimal solution nor too slow iteration speed causing that the iteration cannot be finished occur; setting the initial value of w as: (1, 1.5, 2, 1), the above preset initial value of the coefficient in iteration is beneficial to improving the chance of obtaining the global optimal solution; the iterative calculation number R is 800.
In this embodiment, the method for setting the set rotating speed of the warp knitting machine according to the warp knitting machine running state model comprises the following steps: the running state model of the warp knitting machine can be used for fault prediction, and the set rotating speed of the warp knitting machine can be calculated on the basis of the running state model of the warp knitting machine; for the warp knitting machine with better working state, the upper limit of the set maximum rotating speed is determined to be v h (ii) a For the warp knitting machine with poor working state, the lower limit of the set minimum rotating speed is determined to be v L ,
Wherein m is 0 The number of samples of which the category y is 0; m is 1 The number of samples in the category y is 1; mu.s 1 The mean value of the data obtained after the projection of the fault data on the w vector axis is obtained; mu.s 0 The mean value of data obtained after projection of the fault-free data on the w vector axis is obtained; delta 0 The standard deviation of data obtained after the projection of the fault-free data on the w vector axis is obtained; delta 1 For faulty data inStandard deviation of data obtained after w vector axis projection;
x ci is the current state of the ith warp knitting machine v i The set rotating speed of the ith warp knitting machine is as follows:
when wx ci ≤μ 0 +δ 0 At the time, the set rotational speed of the warp knitting machine is v, i.e., v i ＝v h ；
When wx ci ≥μ 1 -δ 1 At the set rotation speed v of the warp knitting machine L I.e. v i ＝v L ；
When mu is 0 +δ 0 ≤wx ci ≤μ 1 -δ 1 In the meantime, the set rotation speed of the warp knitting machine is as follows:
wherein v is h The upper limit of the highest rotating speed of the warp knitting machine; v. of L The lowest rotating speed lower limit of the warp knitting machine; exp is an exponential function with e as the base;
and when wx ci ＞μ 1 -2δ 1 Early warning is carried out to prompt maintenance of the warp knitting machine; according to different running states of the warp knitting machines, the set rotating speed suitable for each warp knitting machine is set, so that the total loss of the warp knitting machines is reduced to the maximum extent, and the total maintenance cost of a warp knitting workshop is reduced.
In this embodiment, the method for generating the working strategy according to the rotating speed and the constraint condition of the warp knitting machine comprises the following steps:
wherein minZ is the minimum value of the target function Z; n is the total number of warp knitting machines; l is the total number of orders; v. of i Setting the rotation speed i E [1, n ] of the ith warp knitting machine]；t ki Production on ith warp knitting machine for kth order, k ∈ [1, l ]]；P ks Selling a unit price for the kth order; p kr The raw material unit price for the kth order; a is the loss value/unit yield of the warp knitting machine and can be set according to experience; t is t r The total working hours for operators; p r Pay per hour for the operating workers; wherein t is ki And t r For decision variables to be found, t ki ≥0，t r ≥0；
The constraint conditions include: constraint conditions of working time of an operator, constraint conditions of total amount of each order and constraint conditions of working time of each warp knitting machine;
the constraint conditions of the working time of the operator comprise:
wherein d is the number of warp knitting machines responsible for each operator, can be set according to experience and adjusted according to actual conditions, and if d is set to be 5, 6 or 7;
the constraint conditions of the total amount of each order comprise:
wherein q is 1 Amount of contract order for order 1; q. q.s k The volume of contract orders for the kth order; q. q.s l The volume of contract orders for the first order; t is t 1i Man-hours on the ith warp knitting machine for the 1 st order; t is t li The labor hour for the ith order on the ith warp knitting machine;
the constraint conditions of the working time of each warp knitting machine comprise:
wherein, TG i For the maximum allowable working time of the ith warp knitting machine in the production planning period, i E [1, n ]]；
The working strategy comprises the following steps: the set rotating speed of each warp knitting machine, the optimal distribution working hour and the total working hour of operators, wherein each order is produced on the corresponding warp knitting machine; obtaining the best distribution working hours t of different orders on different warp knitting machines according to the simplex method ki And total man-hours t of operators r Minimizing Z; solving the linear programming problem by using a simplex method, firstly finding out a basic feasible solution, and identifying the basic feasible solution to see whether the solution is the optimal solution; if not, switching to another improved basic feasible solution and then identifying; if not, the conversion is carried out again, and the conversion is carried out repeatedly according to the above steps, so that the optimal solution of the problem can be obtained through the limited conversion because the number of the basically feasible solutions is limited; according to the optimal set rotating speed which should be set currently by the warp knitting machine, the data such as intelligent orders, sales, raw materials, human capital and the like are combined to carry out linear programming, and the optimal allocation working hours t of different orders on different warp knitting machines are given ki And total man-hours t of operators r And the highest production flow efficiency of the production orders in the warp knitting workshop is realized.
Example 2
FIG. 2 is a flow chart of the production management method of the intelligent knitting factory warp knitting workshop according to the invention.
As shown in fig. 2, based on embodiment 1, this embodiment 2 further provides a production management method for a warp knitting shop of a knitting intelligent factory, including: collecting data and sending the data to a server; and the server generates a working strategy according to the data.
In this embodiment, the server is adapted to generate the working strategy by using the warp knitting shop production management algorithm of the intelligent knitting factory described in embodiment 1.
In this embodiment, the method for acquiring and sending data to the server includes: the vibration sensor node is suitable for detecting vibration signals of the warp knitting machine and sending the vibration signals to the server; the temperature sensor is suitable for detecting the temperature data of the warp knitting machine and sending the temperature data to the server; the server obtains the vibration signal mean value through the vibration signal, and the server obtains the temperature mean value through the temperature data.
Fig. 3 is a functional block diagram of a vibration sensor node according to the present invention.
As shown in fig. 3, in the present embodiment, the vibration sensor node includes: the device comprises a vibration sensor, a signal amplification circuit, an AD conversion module circuit, a first vibration microprocessor, a second vibration microprocessor and a vibration communication unit; the first vibration microprocessor may be, but is not limited to, a DSP from TI corporation: TMS320C 6748; the second vibratory microprocessor may be, but is not limited to, CC2530 from TI, Inc.; the vibration communication unit may be, but is not limited to, a ZigBee module; the vibration sensor is suitable for detecting a vibration signal of the warp knitting machine; the vibration signal detected by the vibration sensor is amplified by the signal amplifying circuit and then is input into the first vibration microprocessor through the AD conversion module circuit, and the signal converted by the AD conversion module circuit is sent to the second vibration microprocessor by the first vibration microprocessor and is sent to the server by the second vibration microprocessor through the vibration communication unit; the server is suitable for obtaining a vibration signal mean value through the vibration signal, and the vibration signal mean value is used as one of important characteristics of training data to train and obtain the running state model of the warp knitting machine.
In this embodiment, the vibration sensor node further includes: and the SD card is electrically connected with the first vibration microprocessor and used for storing vibration signals.
Fig. 4 is a functional block diagram of a temperature sensor node according to the present invention.
As shown in fig. 4, in the present embodiment, the temperature sensor node includes: the temperature sensor, the temperature microprocessor and the temperature communication unit; the temperature microprocessor can adopt but not limited to STM32 series single-chip microcomputer; the temperature communication unit can be but is not limited to adopt a ZigBee module; the temperature sensor is suitable for detecting temperature data of the warp knitting machine and sending the temperature data to the temperature microprocessor; the temperature microprocessor is suitable for sending temperature data to the server through the temperature communication unit; the server is suitable for obtaining the temperature mean value through the temperature data.
In this embodiment, a gateway and a display module connected to the gateway may be further provided between each node and the server; the display module can be but is not limited to a display screen; when the vibration sensor node and the temperature sensor node are communicated by adopting the ZigBee module, the gateway can adopt a ZigBee gateway; the gateway is suitable for forwarding vibration signals sent by the vibration sensor nodes and temperature data sent by the temperature sensor nodes to the server; and the gateway is suitable for receiving the working strategy sent by the server and displaying the working strategy through the display module.
In summary, the present invention obtains data; establishing a corresponding vector according to the data and the historical data; constructing a warp knitting machine running state model according to the corresponding vectors; setting a set rotating speed of the warp knitting machine according to the running state model of the warp knitting machine; and generating a working strategy according to the rotating speed and the constraint conditions of the warp knitting machine, so that the highest production flow efficiency of the production orders in the warp knitting workshop is realized.
In the embodiments provided in the present application, it should be understood that the disclosed method can be implemented in other ways. The apparatus embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules 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 invention may be embodied in the form of a software product, which is stored in a storage medium and includes 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 invention. 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.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (5)
1. A production management method for a warp knitting workshop of a knitting intelligent factory is characterized by comprising the following steps:
acquiring data;
establishing a corresponding vector according to the data and the historical data;
constructing a warp knitting machine running state model according to the corresponding vectors;
setting a set rotating speed of the warp knitting machine according to the running state model of the warp knitting machine; and
generating a working strategy according to the rotating speed and the constraint conditions of the warp knitting machine;
the method for acquiring data comprises the following steps: obtaining a vibration signal mean value according to the vibration signal, obtaining a temperature mean value according to the temperature, and obtaining the total working time of the warp knitting machine;
the data includes: the average value of the vibration signal, the average value of the temperature and the total working time of the warp knitting machine;
the method for establishing the corresponding vector according to the data and the historical data comprises the following steps: constructing a fault parameter vector and a coefficient vector of the warp knitting machine;
the historical data includes: historical vibration signal mean value, historical temperature mean value and historical total working time of the warp knitting machine;
the warp knitting machine fault parameter vector is as follows: x ═ x (1) ,x (2) ,x (3) ,1)；
Wherein x is (1) As the mean value of the vibration signal, x (2) Is the mean value of temperature, x (3) The total working time of the warp knitting machine;
the coefficient vector is: w ═ w (w) (1) ,w (2) ,w (3) ,b)；
Wherein w (1) Is a vibration signal is allValue coefficient, w (2) Is a temperature mean coefficient, w (3) B is the total working time coefficient of the warp knitting machine, and b is offset;
the method for constructing the running state model of the warp knitting machine according to the corresponding vectors comprises the following steps:
the warp knitting machine running state model comprises:
wherein, w T Is the transposition of w; e is a natural constant;
the loss function is then:
wherein m is the number of data set samples; y is a category label which indicates two states of fault and no fault, and when y is 1, it indicates fault, and when y is 0, it indicates no fault; y is (i) A category label for the ith data;
iterating the loss function to obtain the best value of w when the loss function is minimized, the iterative function is:
where α represents the step size.
2. The method for managing the production of a warp knitting shop of a knitting intelligent factory according to claim 1,
the method for setting the set rotating speed of the warp knitting machine according to the running state model of the warp knitting machine comprises the following steps:
wherein m is 0 The number of samples of which the category y is 0; m is 1 The number of samples in the category y is 1; mu.s 1 The mean value of the data obtained after the projection of the fault data on the w vector axis is obtained; mu.s 0 The mean value of data obtained after projection of the fault-free data on the w vector axis is obtained; delta 0 The standard deviation of data obtained after the projection of the fault-free data on the w vector axis is obtained; delta 1 The standard deviation of the data obtained after the projection of the fault data on the w vector axis is obtained;
x ci is the current state of the ith warp knitting machine v i The set rotating speed of the ith warp knitting machine is as follows:
when wx ci ≤μ 0 +δ 0 At a set rotation speed v of the warp knitting machine h I.e. v i ＝v h ；
When wx ci ≥μ 1 -δ 1 At the set rotation speed v of the warp knitting machine L I.e. v i ＝v L ；
When mu is 0 +δ 0 ≤wx ci ≤μ 1 -δ 1 In the meantime, the set rotation speed of the warp knitting machine is as follows:
wherein v is h The upper limit of the highest rotating speed of the warp knitting machine; v. of L The lowest rotation speed lower limit of the warp knitting machine;
and when wx ci ＞μ 1 -2δ 1 And early warning is carried out to prompt maintenance of the warp knitting machine.
3. The method for managing the production of a warp knitting shop of a knitting intelligent factory according to claim 2,
the method for generating the working strategy according to the rotating speed and the constraint conditions of the warp knitting machine comprises the following steps:
wherein n is the total number of warp knitting machines; l is the total number of orders; v. of i Setting the rotating speed of the ith warp knitting machine; t is t ki Production hours on the ith warp knitting machine for the kth order, k ∈ [1, l [ ]]；P ks Selling a unit price for the kth order; p kr The raw material unit price for the kth order; a is the loss value/unit yield of the warp knitting machine; t is t r The total working hours for operators; p r Pay per hour for the operating workers;
the constraint conditions include: constraint conditions of the working time of an operator, constraint conditions of the total amount of each order and constraint conditions of the working time of each warp knitting machine;
the constraint conditions of the working time of the operator comprise:
wherein d is the number of warp knitting machines for each worker;
the constraint conditions of the total amount of each order comprise:
wherein q is 1 Amount of contract order for order 1; q. q.s k The volume of contract orders for the kth order; q. q.s l The volume of contract orders for the first order; t is t 1i Man-hours on the ith warp knitting machine for the 1 st order; t is t li The labor hour for the ith order on the ith warp knitting machine;
the constraint conditions of the working time of each warp knitting machine comprise:
wherein, TG i For the maximum allowable working time of the ith warp knitting machine in the production planning period, i e [1, n ]]；
The working strategy comprises the following steps: the set rotating speed of each warp knitting machine, the optimal distribution working hour and the total working hour of operators, wherein each order is produced on the corresponding warp knitting machine;
obtaining the best distribution working hours t of different orders on different warp knitting machines according to the simplex method ki And total man-hours t of operators r And Z is minimized.
4. A production management method for a warp knitting workshop of a knitting intelligent factory is characterized by comprising the following steps:
collecting data and sending the data to a server;
the server generates a working strategy according to the data;
the server is adapted to generate an operating strategy using the intelligent knitting factory warp knitting shop production management method of claim 1.
5. The method for managing the production of a warp knitting shop of a knitting intelligent factory according to claim 4,
the method for collecting data and sending the data to the server comprises the following steps:
the vibration sensor node is suitable for detecting vibration signals of the warp knitting machine and sending the vibration signals to the server;
the temperature sensor is suitable for detecting the temperature data of the warp knitting machine and sending the temperature data to the server;
the server obtains the vibration signal mean value through the vibration signal, and the server obtains the temperature mean value through the temperature data.
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| CN105843185A (en) * | 2016-03-25 | 2016-08-10 | 福州职业技术学院 | Warp knitting machine production data acquisition and management system |
| CN107168263A (en) * | 2017-06-16 | 2017-09-15 | 江南大学 | A kind of knitting MES Production-Plan and scheduling methods excavated based on big data |
| CN109801180A (en) * | 2019-01-23 | 2019-05-24 | 广州市天海花边有限公司 | The Internet of Things intelligent information management system and control method of tricot machine |
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