CN111476466B - Digital workshop electric energy management research method based on context awareness - Google Patents

Digital workshop electric energy management research method based on context awareness Download PDF

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CN111476466B
CN111476466B CN202010219118.9A CN202010219118A CN111476466B CN 111476466 B CN111476466 B CN 111476466B CN 202010219118 A CN202010219118 A CN 202010219118A CN 111476466 B CN111476466 B CN 111476466B
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魏旻
李彩芹
王平
杨旭
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Chongqing Ruanjiang Turing Artificial Intelligence Technology Co ltd
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Abstract

The invention relates to a digital workshop electric energy management research method based on context awareness, and belongs to the field of Internet of things. The method comprises the following steps: s1: establishing a digital workshop electric energy management architecture based on context awareness; s2: classifying the order; s3: monitoring electric energy; s4: voting decision; s5: and (6) production scheduling. The invention obtains the physical information and the production demand information related to the production line through the context awareness, so that the working state or the running power grade of a certain device on the production line is voted and determined by other devices on the production line according to the information, and the electric energy consumption of the whole production line is reduced in a flexible, automatic and intelligent manner.

Description

Digital workshop electric energy management research method based on context awareness
Technical Field
The invention belongs to the field of Internet of things, and relates to a digital workshop electric energy management research method based on context awareness.
Background
With the evolution of industry 4.0, the problems of "backward production mode", "low management efficiency", "excess or insufficient capacity", "uncontrollable power consumption in factory" and the like in the conventional industrial production environment are increasingly prominent. However, due to the rapid development of the Internet of Things (IoT) in recent years, researchers in information technology and technology have focused on applying IoT to Industrial production environment, and have proposed Industrial Internet of Things (IIoT) to solve the problems related to Industrial production. On the one hand, as the country is advancing the green and healthy development concept of the industry, high-energy-consumption enterprises are avoided being eliminated by the market, and the reduction of the electric energy consumption by the enterprises is an important guarantee for keeping the market competitiveness. Therefore, it is becoming more and more important to control the electric energy consumed by the enterprise production and to consider flexible scheduling of production tasks.
At present, most industrial production enterprises have the problem of lacking an electric energy management mechanism in the aspect of electric energy consumption of industrial production. Firstly, most factories pay attention to monitoring of equipment power conditions and monitoring of production conditions, but how to change the states of the factory equipment is not considered in combination with monitoring data to reduce power consumption; secondly, the electric energy management of the workshop mostly depends on the human experience and lacks scientific energy-saving indexes, so that an electric energy management mechanism suitable for industrial production is urgently needed.
Disclosure of Invention
In view of this, the present invention provides a method for researching digital workshop power management based on context awareness.
In order to achieve the purpose, the invention provides the following technical scheme:
the digital workshop electric energy management research method based on context awareness comprises the following steps:
s1: establishing a digital workshop electric energy management architecture based on context awareness;
s2: classifying the order;
s3: monitoring electric energy;
s4: voting decision;
s5: and (6) production scheduling.
Optionally, the digital workshop electric energy management architecture based on context awareness includes:
the digital workshop connects workshop personnel, workshop field equipment information and production information by using a network information technology to implement a production task; in the process of executing the production task, factory personnel pay attention to the state of the production task, including whether the production task is finished or not, whether the yield reaches the standard or not, the running state of production line equipment and the electric energy consumption of a production line;
three production facilities are defined: the method comprises the following steps that a non-closeable device NCE, a variable power device VPE and a short-term closeable device STSE are built, based on the three production devices, a digital workshop electric energy management framework based on context awareness is built, and the framework content comprises an energy management system EMS, an industrial network and a workshop production line consisting of production line elements;
the production process information, the customer order information and the equipment information are used as the input of a network voting system, the working state of equipment and the running power level of the equipment on a production line are flexibly scheduled through a voting mechanism, a production line model with energy priority is automatically switched according to a network voting result, and production is arranged with low energy consumption.
Optionally, the S2 includes the following steps:
s21: building a hierarchical structure
The uppermost layer is a target layer, and aims to select one order from a batch of customer orders to produce; the middle layer is a criterion layer and comprises various factors influencing the action of selecting an order, the delivery time of a product, the quantity of the product required by the order, the delay punishment to be suffered by the delay delivery of the product and the relationship between a client and a manufacturing enterprise; the lowest layer is a scheme layer, namely, the scheme layer indicates the existing alternative customer orders;
the factory personnel numbers the received n customer orders according to the order of order receiving time, namely { order1，order2，order3，…ordern}；
S22: constructing a contrast matrix
In order to determine the influence weight of each factor of the criterion layer on the order selection behavior of the target layer and the influence weight of each customer order of the scheme layer on the product delivery, the product quantity, the delay punishment and the customer importance of the criterion layer, a ratio scale of 1-9 is introduced;
judgment matrix A of the criterion layer to the target layer:
Figure BDA0002425457100000021
the matrix represents relative importance comparison of factors such as a total target of order selection, product delivery date, product quantity, delay punishment and customer importance;
the judgment matrixes of the scheme layer alignment rule layer are 4 in total1、B2、B3、B4
Figure BDA0002425457100000031
Figure BDA0002425457100000032
The matrixes respectively represent relative importance comparison of n customer orders for 'product delivery date', 'product quantity', 'delay penalty', 'customer importance';
s23: calculating each matrix eigenvector, eigenvalue and consistency check index
Calculating a feature vector according to a root finding method;
calculation of B1、B2、B3、B4And determining a consistency requirement;
according to the calculation, the influence weight of the criterion layer 'product delivery date', 'product quantity', 'delay penalty', 'customer importance' on the target layer 'order selection' is W ═ (W ═1，...w4)T；
Scheme layer { order1，order2，order3，…ordernThe weight of the influence on the product delivery period is W1＝(α1，…αn)T；
Scheme layer { order1，order2，order3，…ordernThe weight of the influence on the "product quantity" is W2＝(β1，…βn)T；
Scheme layer { order1，order2，order3，…ordernThe weight of the influence on the 'deferral penalty' is W3＝(γ1，…γn)T；
Scheme layer { order1，order2，order3，…ordernThe weight of the impact on "customer importance" is W4＝(δ1，…δn)T；
S24: hierarchical gross ordering and decision making
Calculating the weight vector of the lowest scheme layer to the uppermost target layer, wherein the weight vector is the weight of each customer order, and order selection is made according to the result; the comprehensive weight calculation formula is as follows:
ordernthe comprehensive weight of (a) is: w'n＝αn*w1+βn*w2+γn*w3+δn*w4；
S25: w 'of the order'iAre sorted, w'iThe larger orders are sorted in the front, and orders with the same comprehensive weight value are sorted according to the time sequence of receiving the orders;
and sorting the comprehensive weight of the n customer orders, and classifying the sorted n customer orders.
Optionally, the S3 specifically includes:
the electric energy information of the equipment in the workshop production layer is uploaded to the EMS through the industrial network, so that the EMS can master the electric energy consumption condition of the equipment in real time; the equipment power consumption information is used as one of the inputs of the voting decision system to change the running state of the production equipment of the workshop production layer.
Optionally, the S4 specifically includes:
the voting decision system is used for receiving the voting information and controlling the working state and the power level of equipment in a production workshop after voting calculation;
(1) the input of the voting system comprises order information, production process information and equipment information;
order information Order _ Info:
after the EMS classifies all orders, Order information is obtained, namely Order _ Info belongs to { urgent Order, conventional Order and schedulable Order }; the electric quantity consumed by producing three orders is different, and the order demand is used as one of indexes of a dispatching production line;
production process information Mid _ Info:
the production process information is the current margin Vm _ now of the intermediate product in the buffer area of the workshop production line, and the Vm _ now is not more than the saturated limited capacity Vm; the buffer area sensing node NH detects the current allowance of the intermediate product in the buffer area through a sensing technology, takes the sensing result as voting information, and votes the power level and the running state of the production line equipment;
device information Equipment _ Info:
the method comprises the steps that the NF acquires information of production Equipment by using a perception technology, namely Equipment _ Info, wherein the information comprises Equipment type EquipmentTypeAnd Equipment power consumption EquipmentPowerWherein EquipmentTypeIndicating that the type of production Equipment belongs to one of { NCE, VPE, STSE }, EquipmentPowerIndicating the overall energy consumption of the STSE equipment during a certain period of operation; the composite node NF takes the sensing result as voting information to vote the working state of the production line equipment and the running power of the equipment;
(2) output of the voting system:
the voting decision system divides the contents of the 'Order _ Info', 'Mid _ Info' and 'Equipment _ Info' voting information into a limiting factor Order _ Info, an Equipment _ qType, a variable factor Vm _ now, a variable factor V (m +1) _ now and an Equipment _ qPower, wherein the limiting factor influences the operating power of the production Equipment and determines the number of variable factors, and the variable factors influence the working state of the production Equipment;
constructing a mapping relation between variable factors and the working state of production equipment:
the variable factors generate logic voting according to actually acquired data values, namely, putting 'on', 'off' and 'uncertain' on production equipment; stipulate if the logical ticket is "open", then the mathematical expression is "1"; if the logic ticket is 'off', the mathematical expression is '0'; if the logic ticket is 'uncertain', the mathematical expression is '0.5', namely a 'variable factor and production equipment working state mapping table' is constructed;
calculate the logical voting result for "different types of production equipment":
setting the number of variable factors according to the limiting factor Equipment _ qType, designing weight values for the variable factors, and calculating the logic voting result of different types of production Equipment by integrating the mapping relation and the weight of each variable factor and the working state of the production Equipment;
generating a corresponding table of the working state and the operating power grade of the production equipment:
the voting decision system integrates the limiting factor Order _ Info, the mapping relation between the variable factors and the working states of the production equipment and the logical voting results of the production equipment of different types to generate a corresponding table of the working states and the operating power levels of the production equipment, wherein the working states comprise an operating state, a shutdown state and an operating power which are divided into a high level, a medium level and a low level.
Optionally, the S5 specifically includes:
the EMS obtains the operation power level voting result and the working state voting result of the workshop production line equipment through the voting decision system, and the production scheduling module issues a corresponding control instruction according to the voting result to complete the change of the working state of the workshop production line equipment so as to schedule the production task.
The invention has the beneficial effects that: the invention obtains the physical information and the production demand information related to the production line through the context awareness, so that the working state or the running power grade of a certain device on the production line is voted and determined by other devices on the production line according to the information, and the electric energy consumption of the whole production line is reduced in a flexible, automatic and intelligent manner.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a conventional operation of a factory production line;
FIG. 2 is a digital workshop power management architecture based on context awareness;
FIG. 3 is a hierarchy;
FIG. 4 is a timing diagram of voting;
fig. 5 is an example voting decision.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and embodiments may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
Considering that the working state (operation/shutdown) and the operation power level of production equipment in a factory can be directly controlled, the production equipment is prevented from continuously operating at unnecessary high power for a long time, and the electric energy consumption of the production equipment is reduced, the scheme provides an electric energy management method facing a digital workshop: the workshop production line equipment is used as a research object, the working state or the running power of certain production equipment on the production line is influenced by the working states of other production equipment on the production line, namely, physical information and production demand information related to the production line are acquired through context awareness, so that the working state or the running power grade of the certain equipment on the production line is voted and determined by other equipment on the production line according to the information, and the electric energy consumption of the whole production line is reduced in a flexible, automatic and intelligent mode. The "physical information" mainly refers to production process information and production equipment information, and the "production demand information" refers to order information received by a factory. The order information is associated with the energy consumption of the production line, and the factory realizes the energy consumption priority production model through the method, namely, the order information is automatically switched to the energy resource priority production model when the order delivery is not urgent. The classification of orders into urgent orders, regular orders and schedulable orders can thus be achieved by introducing different decision-making methods.
The power level, operation, shutdown and other actions of the production equipment are determined by the network node by using a voting mechanism, the input of the voting mechanism comprises order information, production process information acquired by a sensing technology and production equipment information, and a reasonable scheduling task of the production equipment is completed through the output of the voting mechanism, so that the electric energy consumption required by production is reduced.
3.1 digital workshop electric energy management architecture based on context awareness
The digital workshop utilizes a network information technology to connect workshop personnel, workshop field equipment information, production information and the like to implement production tasks. In the process of executing the production task, factory personnel pay attention to the state of the production task, such as whether the production task is completed or not, whether the yield reaches the standard or not, the running state of production line equipment, the electric energy consumption of the production line and the like.
The method divides the existing production equipment into 3 types:
TABLE 1 production facility types
Figure BDA0002425457100000061
Figure BDA0002425457100000071
NCE equipment is defined as special equipment in a factory that cannot be powered down or is powered down as little as possible. Such as certain high-power equipment in a plant, the equipment is not shut down and the operational status of such equipment is not scheduled as much as possible since starting the equipment consumes a lot of power.
VPE devices have different operating power levels, and therefore, when different VPE power levels are set, the overall power consumption of different lines will be different.
The STSE device is a device with fixed power consumption and can be turned off in a short time, and the device can be turned off in a certain running period exceeding the set fixed power consumption.
The factory production line operates conventionally as follows:
as shown in FIG. 1, each manufacturing facility corresponds to a unique power consumption type [ NCE, VPE, STSE ]. Once the material is put into the production line, all 3 types of production equipment will start and continue to run at maximum power until the material is consumed and the product is manufactured. The conventional production line working mode of a factory causes high electric energy consumption of the production line due to the lack of flexible scheduling of the power grade and the working state of production line equipment.
The method takes production process information, customer order information and equipment information as input of a network voting system, flexibly schedules the working state (running/shutdown) of equipment and the running power level of the equipment on a production line through a voting mechanism, realizes automatic switching of a production line model with energy priority according to a network voting result, and arranges production with low energy consumption. The digital workshop power management architecture based on context awareness is shown in fig. 2.
(1) Energy management system EMS
EMS is used as a collaborator of MES, and the functions of EMS mainly comprise order classification, electric energy monitoring, voting decision making and production scheduling.
(2) Gateway
Responsible for forming and configuring the network.
(3) Routing
And is responsible for forwarding data information.
(4) Node point
The method divides the node types into buffer sensing Nodes (NH) and compound Nodes (NF). The nodes complete different tasks according to different types, and the tasks are shown in table 2.
TABLE 2 node types
Figure BDA0002425457100000072
Figure BDA0002425457100000081
(5) Workshop production line
The production line shown in fig. 2 comprises production raw materials, production equipment, a buffer area, intermediate products of the buffer area and final products. The type of equipment in the production line, the power level of the equipment, the number of the buffers, the maximum capacity of the buffers and the limit saturation capacity are different according to the actual conditions of different factories. The in-line elements are shown in table 3.
Table 3 production line element table
Figure BDA0002425457100000082
3.2 EMS function Module
3.2.1 order Classification
When a factory personnel receives a batch of customer orders, the factory personnel need to decide which order to produce in priority, since the importance of each order varies. The EMS needs to classify all orders, and the customer orders can be subjected to importance sequencing and classification by introducing methods such as an analytic hierarchy process and the like. The method adopts an analytic hierarchy process to determine an order processing sequence, and arranges the order production sequence as follows: order of high importance > order of higher importance > order of low importance. Since the orders with different importance levels affect the power consumption of the production line, the EMS schedules the production line by using the classified order information as one of the inputs of the voting decision system.
The method has the advantages that the order demand classification is influenced by a plurality of factors, and four factors including product delivery time, product quantity, delay punishment and customer importance are selected in the scheme. And obtaining the comprehensive weight of each order by adopting an analytic hierarchy process, wherein the weight represents the importance degree of the order, the weight value is positioned in an interval (0,1), and the higher the numerical value is, the higher the importance degree of the order is.
The analytic hierarchy process is used as a decision making method and decomposes factors related to decision making into a target layer, a criterion layer and a scheme layer. The procedure is shown in figure 3.
1. Building a hierarchical structure
The top layer is a target layer which is used as a decision-making behavior and aims to select one order from a batch of customer orders to produce; the middle layer is a criterion layer and comprises various factors influencing the behavior of selecting orders, such as delivery time of products, the quantity of the products required by the orders, delay punishment to be suffered by delay delivery of the products and the relationship between a client and a manufacturing enterprise; the lowest level is the solution level, indicating that there are customer orders available for selection.
The factory personnel numbers the received n customer orders according to the order of order receiving time, namely { order1，order2，order3，…ordern}。
2. Constructing a contrast matrix
In order to determine the influence weight of each factor of the criterion layer on the order selection behavior of the target layer and the influence weight of each customer order of the scheme layer on the product delivery, the product quantity, the postponing penalty and the customer importance of the criterion layer, a ratio scale of 1-9 is introduced. The scale is shown in table 4.
TABLE 4 Scale description
Scale Means of
1 Factor i is equally important compared to factor j, both factors being equally important
3 Factor i is slightly more important than factor j
5 Factor i is significantly more important than factor j
7 Factor i is more important than factor j
9 Factor i is extremely important than factor j
2,4,6,8 Median value of the above two adjacent judgments
Reciprocal of the Factor i has a value a over factor jijThe value of factor j to factor i is then aji＝1/aij
Judgment matrix A of the criterion layer to the target layer:
Figure BDA0002425457100000091
the matrix represents relative importance comparison of factors such as product delivery, product quantity, delay penalty and customer importance for the total objective of order selection.
The judgment matrixes of the scheme layer alignment rule layer are 4 in total1、B2、B3、B4
Figure BDA0002425457100000092
Figure BDA0002425457100000101
The above matrices represent relative importance comparisons for n customer orders for "product delivery", "product quantity", "deferral penalty", "customer importance", respectively.
3. Calculating each matrix eigenvector, eigenvalue and consistency check index
The eigenvectors are calculated according to the root-finding method (taking the calculation matrix a as an example below):
1) the 4 th root of the product of the elements of each row of matrix a is calculated,
Figure BDA0002425457100000102
2) normalization
Figure BDA0002425457100000107
W＝(w1，...w4)TWhich is the eigenvector approximation for a. Maximum eigenvalue:
Figure BDA0002425457100000104
3) the consistency test indexes are as follows:
Figure BDA0002425457100000105
computing
Figure BDA0002425457100000106
The RI value lookup table is shown in Table 5:
TABLE 5 RI value corresponding table
Order of the order 3 4 5 6 7 8 9 10 11 12 13 14
RI 0.58 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58
Consistency judgment requirement: generally, when CI is less than 0.1 and CR is less than 0.1, the consistency of the matrix is judged to be acceptable, otherwise, pairwise comparison is carried out again.
4) Repeat the above steps to calculate B1、B2、B3、B4And determining a consistency requirement.
According to the calculation, the influence weight of the criterion layer 'product delivery date', 'product quantity', 'delay penalty', 'customer importance' on the target layer 'order selection' is W ═ (W is1，...w4)T；
Scheme layer { order1，order2，order3，…ordernThe weight of the influence on the product delivery period is W1＝(α1，…αn)T；
Scheme layer { order1，order2，order3，…ordernThe weight of influence on the product quantity is W2＝(β1，…βn)T；
Scheme layer { order1，order2，order3，…ordernThe weight of the influence on the 'deferral penalty' is W3＝(γ1，…γn)T；
Scheme layer { order1，order2，order3，…ordernThe weight of the influence on the client importance is W4＝(δ1，…δn)T。
4. Hierarchical gross ordering and decision making
And calculating the weight vector of the lowest scheme layer to the uppermost target layer, wherein the weight vector is the weight of each customer order, and order selection is made according to the result. The integrated weight calculation formula is as follows:
ordernthe comprehensive weight of (a) is: w'n＝αn*w1+βn*w2+γn*w3+δn*w4。
5. W 'of the above order'iAre sorted, w'iThe larger orders are sorted in the front, and orders with the same comprehensive weight value are sorted according to the time sequence of receiving the orders.
And sorting the comprehensive weight of the n customer orders, and classifying the sorted n customer orders. The order classification table is shown in table 6, and the order information table is shown in table 7.
TABLE 6 order Classification Table
Figure BDA0002425457100000111
Note: when n is 1, the order is an urgent order; when n is 2, the two orders are both urgent orders, and the order with higher comprehensive weight is preferentially arranged for production.
Table 7 order information table
Figure BDA0002425457100000112
3.2.2 monitoring of Electrical energy
The electric energy information of the equipment in the workshop production layer is uploaded to the EMS through the industrial network, so that the EMS can master the electric energy consumption condition of the equipment in real time. The equipment power consumption information is used as one of the inputs of the voting decision system to change the running state of the production equipment of the workshop production layer.
3.2.3 voting decision system
The voting decision system is used for receiving the voting information and controlling the working state (operation/shutdown) and the power level of equipment in the production workshop after voting calculation.
(1) The inputs to the voting system include order information, production process information, and equipment information.
Order information (Order _ Info)
After the EMS classifies all orders, Order information is obtained, namely Order _ Info is E { urgent Order, regular Order, schedulable Order }. The three orders are produced with different power consumption, so the order demand can be used as one of the indexes for dispatching the production line.
Production Process information (Mid _ Info)
The production process information is the current margin (Vm _ now) of the intermediate product in the buffer area of the workshop production line. And the buffer area sensing node NH detects the current allowance of the intermediate product in the buffer area through a sensing technology, takes a sensing result as voting information, and votes the power level and the running state of the equipment in the production line.
Table 8 buffer information table
Figure BDA0002425457100000121
Equipment information (Equipment _ Info)
The method comprises the steps that the NF acquires information of production Equipment by using a perception technology, namely Equipment _ Info, and the informationIncluding device type (Equipment)Type) And Equipment power consumption (Equipment)Power) The device information element table is shown in table 9. And the composite node NF takes the sensing result as voting information to vote the working state of the production line equipment and the running power of the equipment.
Table 9 device information elements table
Figure BDA0002425457100000122
(2) Output of the voting system:
by integrating voting information such as 'Order _ Info', 'Mid _ Info', 'Equipment _ Info', and the like, the voting decision system outputs the running power level and the working state of the production line Equipment.
3.2.4 production scheduling
The EMS obtains the operation power level voting result and the working state voting result of the workshop production line equipment through the voting decision system, and the production scheduling module issues a corresponding control instruction according to the voting result to complete the change of the working state of the workshop production line equipment so as to schedule the production task.
3.3 general procedure
The specific process is as follows:
1. and constructing a digital workshop electric energy management architecture based on context awareness. Configuring a production line according to the actual production equipment type, the actual equipment quantity, the actual buffer zone quantity and the like of a factory; and (4) building an industrial network according to the actual production line condition of a factory.
2. And presetting voting decision configuration. The factory personnel enter into the EMS the actual buffer limit saturation capacity Vm of the factory, a number of power levels of the VPE device, the maximum fixed power consumption MaxPower of the STSE device.
And 3, classifying the order by the EMS. Factory personnel input judgment matrix content in the EMS according to the table 4, the EMS finishes classification work of each Order type in the process link, calculates which Order is preferentially produced, and obtains Order _ Info.
4. And (4) carrying out order production by factory personnel according to the result of the previous step, and starting the production line.
5. And starting the industrial network, and detecting and controlling the VPE equipment, the STSE equipment and the nodes on the buffer area to start working.
6. The NF nodes bound by a certain production equipment are voted according to the sequence of the production line production process, and the elements involved in the voting decision process are shown in table 10.
TABLE 10 voting decision flow elements
Figure BDA0002425457100000131
(1) EMS is used as a voting initiator to initiate voting on the object NF to be cast, and Order _ Info is used as one of the inputs of a voting decision system;
(2) the NH votes for the cast object NF as a voter. NH at time interval Tmid_InfoThe inner completion acquires and sends Mid _ Info to EMS, which is the decider. Because a plurality of buffers may exist, a plurality of NH nodes may exist as voters to vote on the NF;
(3) the NF then acts as a voting party, voting on itself that is also the subject of the cast. NF in time interval TequipmentAnd internally completing the acquisition of Equipment _ Info and the transmission of Equipment _ Info to the EMS, wherein all the information is voting information of the NF and is transmitted to a voting decision system in the EMS.
7. And entering a voting decision system.
(1) The voting decision system in the EMS receives input:
{Order_Info，Equipment_qType，Equipment_qPower，Vm_now，V(m+1)_now}
note: according to the location relationship between the buffers and the production equipment in fig. 3, the two buffers affect the working state or power of the production equipment between the two buffers, i.e., Vm _ now, V (m +1) _ now.
(2) And (3) voting calculation:
a. in (1), all input information is divided into a limiting factor and a variable factor, wherein Order _ Info and Equipment _ qTypeTo defineFactors, Vm _ now, V (m +1) _ now, and Equipment _ qPowerIs a variable factor. The specific value of the variable factor changes along with the change of time, so that the variable factor influences the working state of the production equipment and votes for the working state of the production equipment. The mapping table of the variable factors and the working state of the production equipment is as follows:
TABLE 11 variable factor and production equipment operating condition mapping table
Figure BDA0002425457100000141
Vm _ now and V (m +1) _ now generate logical votes, Equipment _ q, from the actual capacity valuesPowerAnd generating logic voting according to the actually collected electric energy value, namely, switching on, switching off and uncertainty on production equipment. Stipulate if the logical ticket is "open", then the mathematical expression is "1"; if the logic ticket is 'off', the mathematical expression is '0'; if the logical ticket is "uncertain," the mathematical expression is "0.5".
b. For a VPE device, there are 2 variables, namely Vm _ now, V (m +1) _ now, and the logical ticket weights are all set to 0.5. The logical voting table for the VPE device is as follows:
TABLE 12 logic Voting (VPE)
Figure BDA0002425457100000142
Figure BDA0002425457100000151
If the logic voting weighting output result is less than 0.5 and less than 0, the VPE equipment enters a shutdown state; if the weighted output result of the logical voting is more than or equal to 0.5 and less than or equal to 1, the VPE equipment enters the running state and sets the power level according to the Order _ Info.
For STSE devices, there are 3 variables, namely Vm _ now, V (m +1) _ now, Equipment _ qPowerLet Vm be_nowSum of V (m +1)_nowThe logical ticket weights of (a) and (b) are all x, Equipment _ qPowerThe logical ticket weight of (1) is y, and the logical ticket weight satisfies the following formula:
Figure BDA0002425457100000152
the weight value is taken according to the formula: vm_nowSum of V (m +1)_nowThe logical ticket weights of (1) are all 0.3, Equipment _ qPowerThe logical ticket weight of (2) is 0.4. The logical voting table for the STSE device is as follows:
table 13 logical voting (STSE)
Figure BDA0002425457100000153
Figure BDA0002425457100000161
If the output result of the logic voting weighting is more than or equal to 0 and less than or equal to 0.55, the STSE equipment enters a shutdown state;
if the logic voting weighting output result is more than 0.55 and less than or equal to 1, the STSE equipment enters the running state.
The limiting factor Order _ Info influences the running power of the production Equipment, and the limiting factor Equipment _ qTypeDetermining the number of variables, Vm _ now, V (m +1) _ now and Equipment _ qPowerAffecting the working state of the production equipment. The working state comprises a running state and a shutdown state. The operating power is classified into a "high" level, a "medium" level, and a "low" level. The corresponding table of the working state and the operating power grade of the production equipment is as follows:
table 14 device control correspondence table
Figure BDA0002425457100000162
Figure BDA0002425457100000171
Figure BDA0002425457100000181
(3) And (3) outputting: and the EMS outputs the working state of the equipment and the running power level of the equipment after passing through the voting decision system.
And 8, the EMS issues a scheduling instruction to the NF node according to the output of the previous step, and the NF node controls the working state of the equipment and the running power grade of the equipment according to the instruction.
9. And (5) according to the flow sequence of the production line, repeating the steps 6, 7 and 8 on all the equipment on the production line in sequence to finish the scheduling of the working state and the power grade of each production equipment on the production line.
The voting timing chart is shown in fig. 4.
3.4 examples of applications
Suppose that the components of a certain production line of a certain factory are 1 NCE equipment, 2 VPE equipment, 2 STSE equipment, 4 buffer areas and a plurality of production raw materials and final products. The production line is dispatched by using the digital workshop electric energy management research method based on context awareness.
1. And constructing a digital workshop electric energy management architecture based on context awareness.
2. And presetting voting decision configuration. The EMS inputs the actual buffer limit saturation capacity Vm of the plant, multiple power levels of the VPE device, the maximum energy consumption MaxPower of the device. Now assume that the maximum capacity of each buffer is 2 cubic meters, and the limit saturation capacity is 1.7 cubic meters; the VPE equipment has 3 operating power grades, wherein the high power is first-grade 3KW, the medium power is second-grade 1KW, and the low power is third-grade 0.5 KW; the STSE equipment limits the energy consumption to 0.5KW.h at most. I.e. Vm is 1.7m3，VPE∈{3KW，1KW，0.5KW}，MaxPower＝0.5KW.h。
EMS order classification and production
Assuming that a factory receives 5 customer orders, factory personnel base the 5 customer orders onThe order of the order-receiving time is numbered in sequence, i.e. { order1，order2，order3，order4，order5}. Factory personnel input judgment matrix content in the EMS according to the table 4, and the EMS finishes classification work of each order type in the process link.
(1) EMS calculates the comprehensive weight of each order
Factory personnel input judgment matrixes A and B1、B2、B3、B4。
Figure BDA0002425457100000191
Figure BDA0002425457100000192
Figure BDA0002425457100000193
And the EMS obtains characteristic vectors, characteristic values and consistency check indexes of the matrixes through calculation.
TABLE 15 results of matrix calculations
Figure BDA0002425457100000194
Figure BDA0002425457100000201
EMS obtains the comprehensive weight of each order form through calculation
order1Has an overall weight of 0.2368, order2Has an overall weight of 0.2592, order3Has a comprehensive weight of 0.2448, order4Has an overall weight of 0.1568, order5The integrated weight of (a) is 0.085.
(2) EMS carries out order classification according to the comprehensive weight of each order
Table 16 order information table
Figure BDA0002425457100000202
The EMS carries out production sequencing on the 5 orders according to the comprehensive weight:
order2，order3，order1，order4，order5. EMS priority order2. I.e., Order _ Info ═ expedited Order.
4. And factory personnel execute order production and start the production line.
5. And starting the industrial network for detecting and controlling the VPE equipment, the STSE equipment and the nodes on the buffer area to start working.
An example voting decision diagram is shown in fig. 5. Wherein the thick arrows in pink represent the logical voting process.
According to the sequence of the production line processes, the node NF _1 bound by the device VPE _1 is voted first, and elements involved in the voting decision process are shown in table 17.
TABLE 17 voting decision flow elements for NF _1 node
Figure BDA0002425457100000203
Figure BDA0002425457100000211
(1) The EMS is used as a voting initiator to initiate voting on the thrown object NF _1, and the Order _ Info is used as one of the inputs of the voting decision system. After the calculation of (3) in the general flow, the Order _ Info is obtained as an urgent Order;
(2) NH _1 serves as a voting party, and votes for the cast object NF _ 1. NH _1 at time interval Tmid_InfoThe inner completion acquires and sends Mid _ Info to EMS, which is the decider. Let the remaining amount of the intermediate product collected by NH _1 be V1_ now ═ 1m3。；
(3) Then NH _2 is used as a voting party to vote on the thrown object NF _ 1. NH _2 at time interval Tmid_InfoThe inner completion acquires and sends Mid _ Info to EMS, which is the decider. Let the remaining amount of the intermediate product collected by NH _2 be V2_ now ═ 1.2m3。；
(4) NF _1 then acts as a voter voting on itself, which is also the subject of the vote. NF _1 at time interval TequipmentThe inner completion acquires Equipment _ Info and sends Equipment _ Info to the EMS. NF _1 acquisition Equipment _1TypeVPE, NF _1 collects Equipment _1Power＝0.55KW.h。
The above 3 pieces of information are voting information for NF _1 and are sent to the voting decision system in the EMS.
4. Decision making system for entering voting
Inputting:
{ Order _ Info ═ expedited Order ", Equipment _1Type＝VPE，Equipment_1Power＝0.55KW.h，V1_now＝1.3m3，V2_now＝0.7m3}
The voting decision system carries out voting calculation, and can know according to the variable factors and the working state mapping table of the production equipment in the table 11:
Figure BDA0002425457100000212
as can be seen from table 14, the device control correspondence table:
Figure BDA0002425457100000213
Figure BDA0002425457100000221
and (3) outputting: the VPE operates at a primary power of 3 KW.
5, the EMS issues a scheduling instruction to the NF _1 according to the result of the previous step, the NF _1 controls the working state of the VPE _1 to be 'running' and the running power grade of the equipment to be '3 KW' according to the instruction; or NF _1 controls the working state of VPE _1 to be 'running' and the running power level of equipment to be '1 KW' according to the instruction "
6. And (5) repeating the steps (3), (4) and (5), and sequentially carrying out circulating voting decision on the NF nodes bound by the production equipment according to the flow sequence of the production line.
Finally, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (5)

1. The digital workshop electric energy management research method based on context awareness is characterized by comprising the following steps: the method comprises the following steps:
s1: establishing a digital workshop electric energy management architecture based on context awareness;
s2: classifying the order;
s3: monitoring electric energy;
s4: voting decision;
s5: production scheduling;
the S2 includes the steps of:
s21: building a hierarchical structure
The uppermost layer is a target layer, and aims to select one order from a batch of customer orders to produce; the middle layer is a criterion layer and comprises various factors influencing the action of selecting an order, the delivery time of a product, the quantity of the product required by the order, the delay punishment to be suffered by the delay delivery of the product and the relationship between a client and a manufacturing enterprise; the lowest layer is a scheme layer, namely, the existing alternative customer orders are indicated;
the factory personnel numbers the received n customer orders according to the order of order receiving time, namely { order1，order2，order3，…ordern}；
S22: constructing a contrast matrix
In order to determine the influence weight of each factor of the criterion layer on the order selection behavior of the target layer and the influence weight of each customer order of the scheme layer on the product delivery, the product quantity, the delay punishment and the customer importance of the criterion layer, a ratio scale of 1-9 is introduced;
judgment matrix A of the criterion layer to the target layer:
Figure FDA0003591674070000011
the matrix represents the relative importance comparison of factors such as the total target of order selection, product delivery, product quantity, delay punishment and customer importance;
the judgment matrixes of the scheme layer alignment rule layer are 4 in total1、B2、B3、B4
Figure FDA0003591674070000021
Figure FDA0003591674070000022
The matrixes respectively represent relative importance comparison of n customer orders for 'product delivery date', 'product quantity', 'delay penalty', 'customer importance';
s23: calculating each matrix eigenvector, eigenvalue and consistency check index
Calculating a feature vector according to a root finding method;
calculation of B1、B2、B3、B4And determining a consistency requirement;
according to the calculation, the criterion layer ' product delivery date ', ' product quantity ', ' delay penalty ', ' customer importance ' is selected for the target layer ' order "Is W ═ W (W)1，...w4)T；
Scheme layer { order1，order2，order3，…ordernThe weight of the influence on the product delivery period is W1＝(α1，…αn)T；
Scheme layer { order1，order2，order3，…ordernThe weight of influence on the product quantity is W2＝(β1，…βn)T；
Scheme layer { order1，order2，order3，…ordernThe weight of the influence on the 'deferral penalty' is W3＝(γ1，…γn)T；
Scheme layer { order1，order2，order3，…ordernThe weight of the influence on the client importance is W4＝(δ1，…δn)T；
S24: hierarchical overall ordering and decision
Calculating the weight vector of the lowest scheme layer to the uppermost target layer, wherein the weight vector is the weight of each customer order, and order selection is made according to the result; the integrated weight calculation formula is as follows:
ordernthe comprehensive weight of (a) is: w'n＝αn*w1+βn*w2+γn*w3+δn*w4；
S25: w 'of the order'iAre sorted, w'iThe larger orders are sorted in the front, and orders with the same comprehensive weight value are sorted according to the time sequence of receiving the orders;
and sorting the comprehensive weight of the n customer orders, and classifying the sorted n customer orders.
2. The digital workshop electric energy management research method based on context awareness is characterized in that: the digital workshop electric energy management architecture based on context awareness comprises the following steps:
the digital workshop connects workshop personnel, workshop field equipment information and production information by using a network information technology to implement a production task; in the process of executing the production task, factory personnel pay attention to the state of the production task, including whether the production task is finished or not, whether the yield reaches the standard or not, the running state of production line equipment and the electric energy consumption of a production line;
three production facilities are defined: the method comprises the following steps that a non-closeable device NCE, a variable power device VPE and a short-term closeable device STSE are built, based on the three production devices, a digital workshop electric energy management framework based on context awareness is built, and the framework content comprises an energy management system EMS, an industrial network and a workshop production line consisting of production line elements;
the production process information, the customer order information and the equipment information are used as the input of a network voting system, the working state of equipment and the running power level of the equipment on a production line are flexibly scheduled through a voting mechanism, a production line model with energy priority is automatically switched according to a network voting result, and production is arranged with low energy consumption.
3. The digital workshop electric energy management research method based on context awareness is characterized in that: the S3 specifically includes:
the electric energy information of the equipment in the workshop production layer is uploaded to the EMS through the industrial network, so that the EMS can master the electric energy consumption condition of the equipment in real time; the equipment power consumption information is used as one of the inputs of the voting decision system to change the running state of the production equipment of the workshop production layer.
4. The digital workshop electric energy management research method based on context awareness is characterized in that: the S4 specifically includes:
the voting decision system is used for receiving the voting information and controlling the working state and the power level of equipment in a production workshop after voting calculation;
(1) the input of the voting system comprises order information, production process information and equipment information;
order information Order _ Info:
after the EMS classifies all orders, Order information is obtained, namely Order _ Info belongs to { urgent Order, conventional Order and schedulable Order }; the three orders are produced with different consumed electric quantity, and the order demand is used as one of the indexes of the dispatching production line;
production process information Mid _ info:
the production process information is the current margin Vm _ now of the intermediate product in the buffer area of the workshop production line, and the Vm _ now is not more than the saturated limited capacity Vm; a buffer area sensing node NH detects the current allowance of a buffer area intermediate product through a sensing technology, takes a sensing result as voting information, and votes for the power level of production line equipment and the running state of the equipment;
device information Equipment _ Info:
the method comprises the steps that the NF acquires information of production Equipment by using a perception technology, namely Equipment _ Info, wherein the information comprises Equipment type EquipmentTypeAnd Equipment power consumption EquipmentPowerWherein EquipmentTypeIndicating that the type of production Equipment belongs to one of { NCE, VPE, STSE }, EquipmentPowerIndicating the overall energy consumption of the STSE equipment during a certain period of operation; the composite node NF takes the sensing result as voting information to vote the working state of the production line equipment and the running power of the equipment;
(2) output of the voting system:
the voting decision system divides the contents of the voting information of 'Order _ Infc', 'Mid _ Info' and 'Equipment _ Infc' into limiting factors of Order _ Info and Equipment _ qType and variable factors of Vm _ now, V (m +1) _ now and Equipment _ qPower, wherein the limiting factors influence the running power of the production Equipment and determine the number of variable factors, and the variable factors influence the working state of the production Equipment;
constructing a mapping relation between variable factors and the working state of production equipment:
the variable factors generate logic voting according to actually acquired data values, namely, putting 'on', 'off' and 'uncertain' on production equipment; stipulate that if the logical ticket is "on", then the mathematical expression is "1"; if the logic ticket is 'off', the mathematical expression is '0'; if the logic ticket is 'uncertain', the mathematical expression is '0.5', namely a 'variable factor and production equipment working state mapping table' is constructed;
calculate the logical voting result for "different types of production equipment":
setting the number of variable factors according to the limiting factor Equipment _ qType, designing weight values for the variable factors, and calculating the logical voting result of the different types of production Equipment by integrating the mapping relation and the weight of the variable factors and the working state of the production Equipment;
generating a 'corresponding table of working states and operating power grades of production equipment':
the voting decision system integrates the limiting factor Order _ Info, the mapping relation between the variable factors and the working states of the production equipment and the logical voting results of the production equipment of different types to generate a corresponding table of the working states and the operating power levels of the production equipment, wherein the working states comprise an operating state, a shutdown state and an operating power which are divided into a high level, a medium level and a low level.
5. The digital workshop electric energy management research method based on context awareness is characterized in that: the S5 specifically includes:
the EMS obtains the operation power level voting result and the working state voting result of the workshop production line equipment through the voting decision system, and the production scheduling module issues a corresponding control instruction according to the voting result to complete the change of the working state of the workshop production line equipment so as to schedule the production task.
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