CN115456220B - Intelligent factory architecture method and system based on digital model - Google Patents
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Abstract
The invention relates to the field of intelligent factories and discloses an intelligent factory architecture method and system based on a digital model. Based on the characteristics of the chemical fiber production line, different links are divided into proportional links and probability links, so that the production line control function is simplified, the calculation efficiency is improved, and the calculation error is reduced; the invention also determines the production line control function, takes all links as the calculation items of the production line control function, reflects the calculation items on the specific expression of the production line control function when any one or more links fail, and completes control adjustment based on the updated control function by a control layer (control computer), thereby simplifying the model construction of each link and having higher universality.
Description
Technical Field
The invention belongs to the field of intelligent factories, and particularly relates to an intelligent factory architecture method and system based on a digital model.
Background
Intelligent manufacturing (Intelligent Manufacturing, IM) is a human-machine integrated intelligent system consisting of intelligent machines and human experts that can conduct intelligent activities such as analysis, reasoning, judgment, conception and decision-making during manufacturing. Through the cooperation of the human and the intelligent machine, the brain work of human expert in the manufacturing process is enlarged, extended and partially replaced. It extends the concept of manufacturing automation to flexibility, intelligence and high integration. Intelligent manufacturing compared to traditional manufacturing, intelligent manufacturing systems have the following features:
(1) Autonomic capability; namely, the capability of collecting and understanding the environment information and the information of the self and analyzing, judging and planning the behavior of the self is achieved;
(2) Man-machine integration: IMS (Intelligent Manufacturing System ) is not purely an "artificial intelligence" system, but a man-machine integrated intelligent system, which is a hybrid intelligent;
(3) Virtual reality technology: by means of various audio-visual and sensing devices, various processes, objects and the like in real life are virtually displayed, so that the manufacturing process and future products can be simulated, and people can feel completely as real feeling in sense and vision;
(4) Self-organizing superflexibility: each component unit in the intelligent manufacturing system can automatically form an optimal structure according to the requirements of work tasks;
(5) Self-learning and maintenance; the intelligent manufacturing system can continuously enrich a knowledge base in practice and has a self-learning function; meanwhile, the system has the functions of self fault diagnosis in the running process, self fault elimination and self maintenance.
In the prior art, there are improvements to digital intelligent factories:
(1) CN110109425a discloses an intelligent control platform driven by a process digital model based on an expert knowledge base, in particular to an intelligent digital control platform driven by a process digital model based on a general intelligent manufacturing execution process in a wider industry field, which provides dynamic knowledge configuration functions driven by an expert knowledge base and a process digital model in a business layer, and provides services based on various knowledge such as industry, standard, expert knowledge, practical experience and the like for various business layers.
(2) CN109298685a discloses a digital factory implementation method, a digital factory implementation system and a digital factory, and specifically discloses that characteristics related to physical production data, operation and maintenance states of the physical production data, manufacturing process flow and production line logistics simulation related to an actual production process in an entity production factory are simulated and digitized through a model, so that corresponding virtual simulation can be performed to reflect the actual production process and states in the entity production factory.
(3) CN109003038A discloses a system architecture of a digital factory in textile printing and dyeing industry, specifically discloses a system architecture comprising an equipment layer, a sensing layer, a network layer and an application layer, which strengthens construction in aspects of digital workshops and industrial internet of things, and realizes synchronous acquisition, interconnection, sharing and calling of various element information in production and manufacture.
(4) CN111401629a discloses a production management algorithm and a production management method for a warp knitting workshop of a knitting intelligent factory, and specifically discloses that the highest production flow efficiency of a production order of the warp knitting workshop is realized by establishing corresponding vectors, constructing a running state model of a warp knitting machine, setting a set rotating speed of the warp knitting machine, and generating a working strategy according to the rotating speed and constraint conditions of the warp knitting machine.
The above technical solution proposes an improvement for a digital intelligent factory, however, the following problems still exist in the prior art:
1. the universality is high, but the specificity is insufficient; taking the prior art (1) as an example, the practical improvement is that an expert knowledge base unit is added for the digital platform, and a conceptual architecture is provided, wherein the conceptual architecture comprises an interface unit, a process resource configuration module and the like; taking the prior art (2) as an example, the improvement is that the digital factory architecture is divided into six layers according to types, and hierarchical control is carried out; the above prior art has only theoretical guiding effect on specific industrial architecture, such as the digital architecture of chemical fiber factory, but cannot combine the industry characteristics to provide a substantial architecture scheme, that is, based on the above prior art, those skilled in the art still need to perform detailed design based on specific industry, but cannot directly complete the architecture of the digital factory of the specific industry, such as chemical fiber industry.
2. For the construction of chemical fiber factories, the prior art only completes a conceptual framework, taking the prior art (3) as an example, a device layer, a perception layer, a network layer and an application layer are arranged, and the organization relation framework between the layers is completed, however, regarding how the devices at the same layer coordinate, a quantized control scheme cannot be given, and the device control cannot be completed based on numerical calculation from the technical point of view, namely, the technical scheme still belongs to the category of overall design; in the prior art (4), although a technical means for performing coordinated control of a warp knitting machine by using an operation state model is provided, the whole factory, that is, the process equipment such as yarn winding, two-for-one twisting, shaping and the like, cannot be considered, and no coordinated control is provided.
In summary, the architecture schemes of the digital plants at present are often conceptual-like architectures or plant-level architecture schemes for single-process or equipment control, but lack numerical model-based architecture schemes.
Disclosure of Invention
An intelligent factory architecture method based on a digital model, wherein the architecture comprises a control layer, a parameter layer and a device layer;
the equipment layer comprises a winding link; a double twisting step; setting; a rewinding step; warping; a pulp silk link; weaving;
the parameter layer comprises a production line transfer function and a link control function;
the line transfer function satisfies a rational formula:
G(s)＝Y(s)/X(s)＝(b m S m +b m-1 S m-1 +…+b 1 +b 0 )/(a m S m +a m-1 S m-1 +…+a 1 +a 0 )；
wherein a and b are term coefficients, and m is the term order;
where X(s) is the Laplace transform of the input quantity and Y(s) is the Laplace transform of the output quantity.
G(s) is a transfer function;
setting a yarn winding link, a shaping link, a rewinding link, a warping link and a sizing link as proportion links, and setting a two-for-one twisting link and a weaving link as probability links;
the link control function is as follows:
(A) For the proportion link, the filament outlet diameter of a single device meets the normal distribution: li to N (u, sigma) i 2 ) The filament diameters per unit time of all the winders satisfy the normal distribution:
wherein Li is the equipment number, u is the set filament diameter,for distribution variance, v i The yarn feeding device comprises a yarn feeding device, a yarn feeding device and a yarn feeding device, wherein the yarn feeding device is used for feeding yarn at a yarn feeding speed, and the yarn feeding speed is the yarn feeding speed for a winding link, a rewinding link, a warping link and a sizing link of continuous yarn feeding;
(B) For the probability link, the broken wire probability of a single device satisfies Bernoulli distribution:
P(X＝1)＝p，P(X＝0)＝1-p；
wherein x=1 indicates that yarn breakage occurs, and x=0 indicates that yarn breakage does not occur;
based on the sampling data Di, the p value of a single device adopting a log likelihood function probability link is as follows:
logP(D ij )＝log∏ j P(D ij )＝∑ j (D ij logp+(1-D ij )log(1-p))；
obtain transfer function G(s) =e -τs Wherein τ is a maintenance delay calculated based on the wire breakage probability p, and the maintenance delay satisfies: τ= NpT; n is the number of the devices in the link, T is the average maintenance time of a single device, i represents the ith device, j represents the jth sample of the ith device;
substituting the link control functions of the proportional links and the probability links into the rational division to obtain the transfer function of the production line.
Further, for the proportion link, based on a normal distribution test, determining whether the proportion link accords with a normal distribution, and determining expected mu and varianceWhen a certain device meets any one of the following conditions, the device is in offline maintenance, and the production line transfer function is redetermined:
a. the filament outlet diameter of the device does not meet normal distribution;
b. the device satisfies a normal distribution, but the difference between the desired μ and the set wire diameter is greater than a predetermined value;
c. the device satisfies normal distribution, but the period varianceGreater than a predetermined value.
Further, the normal distribution test is one of a t test, an analysis of variance, a person test and an SW test.
An intelligent factory architecture system based on a digital model, wherein the architecture system is constructed based on the method, and comprises a control layer, a parameter layer and a device layer; the control layer, the parameter layer and the equipment layer are connected through buses.
Further, the shaping link is steam shaping.
Furthermore, the weaving link adopts a water-jet loom
Further, the production line transfer function is constructed based on closed loop control.
Advantageous effects
Compared with the prior art, the invention provides an intelligent factory architecture method and system based on a digital model, which have the following beneficial effects:
1. the invention divides different links into a proportion link and a probability link based on the characteristics of chemical fiber production lines, in particular a yarn winding link, a shaping link, a rewinding link, a warping link and a sizing link, and the yarn breakage probability is lower, the output efficiency is basically in direct proportion to the linear speed of a yarn drum, and the diameter of the produced yarn generally accords with normal distribution, so the yarn is determined as the proportion link; the probability of broken wires and knotting in the two-for-one twisting link and the weaving link is higher, once broken wires or knotting occurs, the machine is stopped to remove faults, and the broken wires or knotting occurs as a probabilistic event; by classifying links, the production line control function is simplified, the calculation efficiency is improved, and the calculation error is reduced.
2. The invention determines the production line control function, takes all links as calculation items of the production line control function, reflects the calculation items on a specific expression of the production line control function when any one or more links fail, and completes control adjustment based on the updated control function by a control layer (control computer).
3. The invention utilizes normal distribution to generate the control function of the proportional link, utilizes Bernoulli distribution to determine the control function of the probability link, simplifies the model construction of each link, has higher universality, namely, for the production line using different signal equipment, the normal function and the Bernoulli probability can be obtained only by samples based on debugging and trial generation stages, and further the production line control function is obtained.
Drawings
FIG. 1 is a schematic diagram of a hierarchical structure of the present invention;
FIG. 2 is a graph showing the yield of a portion of the process according to the present invention;
fig. 3 is a feedback control logic diagram of the present invention.
In the figure 1, the equipment layer comprises a silk winder, a two-for-one twister, a shaping rewinding machine, a warping machine and a sizing machine from left to right in sequence; weaving machine; the abscissa in fig. 2 represents time.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
an intelligent factory architecture method based on a digital model, wherein the architecture comprises a control layer, a parameter layer and a device layer;
the equipment layer comprises a winding link; a double twisting step; setting; a rewinding step; warping; a pulp silk link; weaving;
the parameter layer comprises a production line transfer function and a link control function;
in chemical fiber production lines, because the output speed and the output stability of the prior process are affected, the production line transfer function is set based on closed loop control, and the rational and separate formulas are satisfied:
G(s)＝Y(s)/X(s)＝(b m S m +b m-1 S m-1 +…+b 1 +b 0 )/(a m S m +a m-1 S m-1 +…+a 1 +a 0 )；
wherein X(s) is Laplacian transformation of input quantity, Y(s) is Laplacian transformation of output quantity; g(s) is a transfer function;
setting a yarn winding link, a shaping link, a rewinding link, a warping link and a sizing link as proportion links, and setting a two-for-one twisting link and a weaving link as probability links;
the link control function is as follows:
(A) For the proportion link, the filament outlet diameter of a single device meets the normal distribution: li to N (u, sigma) i 2 ) The filament diameters per unit time of all the winders satisfy the normal distribution:
wherein Li is the equipment number, u is the set filament diameter,for distribution variance, v i The yarn feeding device comprises a yarn feeding device, a yarn feeding device and a yarn feeding device, wherein the yarn feeding device is used for feeding yarn at a yarn feeding speed, and the yarn feeding speed is the yarn feeding speed for a winding link, a rewinding link, a warping link and a sizing link of continuous yarn feeding;
(B) For the probability link, the broken wire probability of a single device satisfies Bernoulli distribution:
P(X＝1)＝p，P(X＝0)＝1-p；
wherein x=1 indicates that yarn breakage occurs, and x=0 indicates that yarn breakage does not occur;
based on the sampling data Di, the p value of a single device adopting a log likelihood function probability link is as follows:
logP(D ij )＝log∏ j P(D ij )＝∑ j (D ij logp+(1-D ij )log(1-p))；
obtain transfer function G(s) =e -τs Wherein τ is a maintenance delay calculated based on the wire breakage probability p, and the maintenance delay satisfies: τ= NpT; n is the number of the devices in the link, T is the average maintenance time of a single device, i represents the ith device, j represents the jth sample of the ith device;
substituting the link control functions of the proportional links and the probability links into the rational division to obtain the transfer function of the production line. For the proportion links, based on a normal distribution test, determining whether the proportion links conform to normal distribution, and determining expected mu and varianceWhen a certain device meets any one of the following conditions, the device is in offline maintenance, and the production line transfer function is redetermined:
a. the filament outlet diameter of the device does not meet normal distribution;
b. the device satisfies a normal distribution, but the difference between the desired μ and the set wire diameter is greater than a predetermined value;
c. the device satisfies normal distribution, but the period varianceGreater than a predetermined value.
The normal distribution test is one of t test, variance analysis, person test and SW test.
According to fig. 3, the invention adopts a feedback control mechanism, a yarn winding link is a feedback node, a two-for-one twisting link is a feedback node, a shaping link, a rewinding link, a warping link and a sizing link (called as a fixed rewinding and sizing link for short) are feedback nodes, and a weaving link is a feedback node; the weaving link and the two-for-one twisting link negatively feed back to the yarn winding link according to the yarn breaking quantity, namely if the yarn breaking quantity of the weaving link and the two-for-one twisting link is increased, the yarn winding link should be slowed down, the fiber processing quality is improved, and the yarn breaking is reduced; the weaving link positively feeds back to the yarn winding link and the fixed-pouring pulp finishing link according to the weaving machine speed, namely if the weaving machine speed of the weaving link is increased, the yarn winding link is accelerated, and the downstream link is accelerated in response to the increase of the speed, so that the sufficient supply to the weaving link is ensured; the two-for-one twisting step is positively fed back to the fixed pouring and sizing step according to the two-for-one twisting speed and negatively fed back according to the yarn breaking amount, namely, when the two-for-one twisting speed is increased, the downstream fixed pouring and sizing speed should be correspondingly increased, and when the two-for-one twisting yarn breaking amount is increased, the yarn winding step is slowed down and the fixed pouring and sizing step is correspondingly slowed down.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. An intelligent factory architecture method based on a digital model is characterized in that the architecture comprises a control layer, a parameter layer and a device layer;
the equipment layer comprises a winding link; a double twisting step; setting; a rewinding step; warping; a pulp silk link; weaving;
the parameter layer comprises a production line transfer function and a link control function;
the line transfer function satisfies a rational formula:
G(s)＝Y(s)/X(s)＝
(b m S m +b m-1 S m-1 +…+b 1 +b 0 )/(a m S m +a m-1 S m-1 +…+a 1 +a 0 )；
where X(s) is the Laplace transform of the input quantity and Y(s) is the Laplace transform of the output quantity. G(s) is a transfer function; setting a yarn winding link, a shaping link, a rewinding link, a warping link and a sizing link as proportion links, and setting a two-for-one twisting link and a weaving link as probability links;
the link control function is as follows:
(A) For the proportion link, the filament outlet diameter of a single device meets the normal distribution: li to N (u, sigma) i 2 ) The filament diameters per unit time of all the winders satisfy the normal distribution:
wherein Li is the equipment number, u is the set filament diameter,for distribution variance, v i The yarn feeding device comprises a yarn feeding device, a yarn feeding device and a yarn feeding device, wherein the yarn feeding device is used for feeding yarn at a yarn feeding speed, and the yarn feeding speed is the yarn feeding speed for a winding link, a rewinding link, a warping link and a sizing link of continuous yarn feeding;
(B) For the probability link, the broken wire probability of a single device satisfies Bernoulli distribution:
P(X＝1)＝p，P(X＝0)＝1-p；
wherein x=1 indicates that yarn breakage occurs, and x=0 indicates that yarn breakage does not occur;
based on the sampling data Di, estimating the p value of a single device in the probability link by adopting a log likelihood function:
logP(D ij )＝log∏ j P(D ij )＝∑ j (D ij logp+(1-D ij )log(1-p))；
obtain transfer function G(s) =e -τs Wherein τ is a maintenance delay calculated based on the wire breakage probability p, and the maintenance delay satisfies: τ= NpT; n is the number of the devices in the link, T is the average maintenance time of a single device, i represents the ith device, j represents the jth sample of the ith device;
substituting the link control functions of the proportional links and the probability links into the rational division to obtain the transfer function of the production line.
2. The method of claim 1, wherein the method comprises the steps of: for the proportion links, based on a normal distribution test, determining whether the proportion links conform to normal distribution, and determining expected mu and varianceWhen a certain device meets any one of the following conditions, the device is in offline maintenance, and the production line transfer function is redetermined:
a. the filament outlet diameter of the device does not meet normal distribution;
b. the device satisfies a normal distribution, but the difference between the desired μ and the set wire diameter is greater than a predetermined value;
c. the device satisfies normal distribution, but the period varianceGreater than a predetermined value.
3. The method of intelligent plant architecture based on a digitized model of claim 2, wherein: the normal distribution test is one of t test, analysis of variance, person test and SW test.
4. An intelligent plant architecture system based on a digital model, the architecture system being constructed based on the method of any one of claims 1-3, wherein the architecture comprises a control layer, a parameter layer, a device layer; the control layer, the parameter layer and the equipment layer are connected through buses.
5. The intelligent plant architecture system based on a digitized model of claim 4 wherein said shaping element is steam shaping.
6. The intelligent factory architecture system based on a digital model of claim 5, wherein the weaving process employs a water jet loom.
7. The intelligent plant architecture system based on a digitized model of claim 6 wherein said production line transfer function is constructed based on closed loop control.
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