CN112799359A - Flexible production line material distribution decision method and device based on MES system - Google Patents
Flexible production line material distribution decision method and device based on MES system Download PDFInfo
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- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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Abstract
The invention belongs to the technical field of computers, and particularly relates to a flexible production line material distribution decision method and a device based on an MES system, wherein the method comprises the following steps: step 1: setting a plurality of data acquisition units for respectively acquiring data, wherein one of the data acquisition units is a main node, and the other data acquisition units are auxiliary nodes; each auxiliary node is provided with a data label; the data label is used for marking the production line material distribution station corresponding to the auxiliary node; the data label comprises the following data information: location coordinates and containment capability. The efficiency of material delivery can be higher, in addition, still rectify the optimum solution, guarantee that the result of carrying out the delivery each time can both satisfy the settlement condition by the at utmost, further promote the efficiency of delivery.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a flexible production line material distribution decision method and device based on an MES system.
Background
The MES system is a production informatization management system facing to a workshop execution layer of a manufacturing enterprise. The MES can provide management modules for enterprises, such as manufacturing data management, planning scheduling management, production scheduling management, inventory management, quality management, human resource management, work center/equipment management, tool and tool management, purchasing management, cost management, project bulletin board management, production process control, bottom layer data integration analysis, upper layer data integration decomposition and the like, and create a solid, reliable, comprehensive and feasible manufacturing cooperative management platform for the enterprises.
The MES system applied by the famous enterprises at abroad is a common phenomenon, and a plurality of enterprises at home gradually adopt the technology to enhance the core competitiveness of the enterprises at home. The problem of returning to the information fault between the enterprise planning layer and the process control layer is that the traditional production process adopted by the manufacturing industry in China for many years is characterized by production according to the plan from top to bottom. Briefly, from the planning level to the production control level: the enterprise makes a production plan according to the conditions of orders or markets, the production plan reaches a production site, the production is organized, and products are delivered. The major key of enterprise management informatization construction is also put on a planning layer to perform production planning management and general transaction processing. For example, ERP is located at the upper planning layer of an enterprise and is used for integrating the existing production resources of the enterprise and compiling a production plan. In the lower production control layer, enterprises mainly adopt automatic production equipment, automatic detection instruments, automatic logistics transportation and storage equipment and the like to solve the production bottleneck of specific production (manufacturing process), and realize the automatic control of a production field.
Due to the change of market environment and the continuous update of modern production management concept, whether a manufacturing enterprise can operate well or not is the key point that plan and production are closely matched, and enterprises and workshop managers can master the change of a production field in the shortest time, make accurate judgment and rapid response measures and ensure that a production plan is reasonably and rapidly corrected. Although ERP and field automation systems have been developed to a very mature degree, since the service object of the ERP system is the upper layer of enterprise management, direct and detailed support is not provided for the management flow of the inter-vehicle layer in general. The field automation system, which mainly functions to monitor field devices and process parameters, can provide field monitoring and statistical data to management personnel, but is not a true management system per se. Therefore, a "fault" in management information occurs between the ERP system and the field automation system, and the ERP system and the field automation system often appear to be stranded or have weak functions for the scheduling and management requirements at the user shop level.
Disclosure of Invention
The invention mainly aims to provide a flexible production line material distribution decision method and a device based on an MES (manufacturing execution system). A data acquisition unit is used for data acquisition, a data label is arranged on each data acquisition unit, and a distribution objective function is constructed according to a distribution target and distribution elements influencing the distribution target; meanwhile, determining the constraint of the objective function according to the distribution capacity; and calculating the optimal solution of the distribution elements of the objective function under the constraint, so that the efficiency of material distribution is higher, and in addition, correcting the optimal solution to ensure that the result of distribution every time can meet the set conditions to the maximum extent, thereby further improving the efficiency of distribution.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
the flexible production line material distribution decision method based on the MES system comprises the following steps:
step 1: setting a plurality of data acquisition units for respectively acquiring data, wherein one of the data acquisition units is a main node, and the other data acquisition units are auxiliary nodes; each auxiliary node is provided with a data label; the data label is used for marking the production line material distribution station corresponding to the auxiliary node; the data label comprises the following data information: position coordinates and containment capability;
step 2: constructing a central network by taking the main node as a central node and the auxiliary nodes as branch nodes; the decision server periodically sends a data acquisition command to the main node, and the main node broadcasts the received data acquisition command to other nodes in the central network; the data acquisition command comprises: a delivery target and delivery elements that affect the delivery target;
and step 3: after receiving a data acquisition command, the auxiliary node judges whether the data acquisition command should be responded or not according to a distribution target and distribution elements influencing the distribution target and a data label of the auxiliary node, and if so, the acquired data is sent to a decision server; if not, no response is carried out;
and 4, step 4: after receiving the data collected by each auxiliary node, the decision server constructs a delivery objective function according to a delivery target and delivery elements influencing the delivery target; determining constraints of the objective function according to delivery capacity; calculating an optimal solution for the delivery elements of the objective function under the constraint;
and 5: the decision server substitutes the obtained optimal solution into a correction function for verification to judge whether the optimal solution meets set conditions or not, and if so, deploys delivery logics related to the delivery elements according to the optimal solution of the delivery elements; and if not, retransmitting the data acquisition command to each auxiliary node to acquire the data again.
Furthermore, a monitoring device is also arranged on the decision server; the monitoring device monitors the state of the fault and the fault recovery of the decision server in real time; when the decision server fails, each data acquisition unit acquires and stores service logic data in an off-line manner, and transmits the acquired service logic data to other data acquisition units for storage in a broadcast manner; each data acquisition unit is used for acquiring and storing test data when being used as a test workstation; after the server is recovered from the fault, the data acquisition unit serving as the main node uploads the stored service logic data of all the data acquisition units to the decision server; and each data acquisition unit uploads the stored test data to the decision server when being used as a test workstation.
Further, the objective function includes: the system comprises a delivery target, a delivery constant and a delivery variable to be solved, wherein the delivery constant is determined according to the delivery capacity, and the delivery variable is determined according to the delivery element.
Further, the calculating the optimal solution of the delivery element includes: and calculating the optimal solution of the distribution elements by using a linear programming solver.
Further, the step 5: the method for judging whether the optimal solution meets the set conditions by substituting the optimal solution into the correction function by the decision server for verification comprises the following steps: setting a modifier sub-function, expressing the modifier sub-function by P, and performing convolution operation on the optimal solution and the modifier sub-function to obtain a first intermediate result; setting a correction function, wherein the correction function is as follows:
setting the correction threshold as: s; and calculating the first intermediate result, the correction function and the correction threshold value to obtain a correction result:
the correction error is calculated by the following error function:
wherein m represents the number of distribution elements, i represents the ith distribution element variable, and E is the optimal solution; if the error is corrected
And within the set threshold value range, deploying the delivery logic related to the delivery elements according to the optimal solution of the delivery elements.
Further, the monitoring device is configured to obtain the fault occurrence and fault recovery status of the decision server by calling an application programming interface API of an operating system of the decision server.
Further, the monitoring device is further configured to monitor a hardware resource status of the decision server; each data acquisition unit is further used for judging whether the decision server is idle or not according to the monitoring result of the decision server state monitoring unit on the hardware resource state of the decision server when the data acquisition unit is used as a test workstation, and uploading the stored test data to the decision server when the decision server is idle.
Further, the constructing the objective function includes: and constructing an objective function which enables the accommodating quantity of all the auxiliary nodes to be minimum, and taking whether the distribution route is accommodated in the auxiliary nodes as a distribution variable of the objective function.
Further, the constraining includes: and ensuring that the material quantity to be contained in each auxiliary node does not exceed the constraint of the containing capacity of the auxiliary node.
A flexible production line material distribution decision-making device based on an MES system.
The flexible production line material distribution decision method and the device based on the MES system have the following beneficial effects that: the method comprises the steps of utilizing data acquisition units to acquire data, setting a data label on each data acquisition unit, and constructing a distribution objective function according to a distribution target and distribution elements influencing the distribution target; meanwhile, determining the constraint of the objective function according to the distribution capacity; and calculating the optimal solution of the distribution elements of the objective function under the constraint, so that the efficiency of material distribution is higher, and in addition, correcting the optimal solution to ensure that the result of distribution every time can meet the set conditions to the maximum extent, thereby further improving the efficiency of distribution. The method is mainly realized by the following steps: 1. construction of a central network: the method comprises the steps that a main node is used as a central node, an auxiliary node is used as a branch node, and a central network is constructed; the decision server periodically sends a data acquisition command to the main node, and the main node broadcasts the received data acquisition command to other nodes in the central network; the data acquisition command comprises: a delivery target and delivery elements that affect the delivery target; by constructing the central network, on one hand, each auxiliary node can be managed by using the central node, and on the other hand, each auxiliary node can be reached simultaneously when a data acquisition command is issued, so that the operation efficiency is improved; 2. and (3) making a distribution strategy: after receiving data collected from each auxiliary node, a decision server constructs a delivery objective function according to a delivery target and delivery elements influencing the delivery target; determining constraints of the objective function according to delivery capacity; calculating an optimal solution for the delivery elements of the objective function under the constraint; by the method, a distribution strategy can be formulated by combining the data collected by each auxiliary node and distribution conditions, so that the distribution efficiency is improved; 3. a correction decision server of the decision strategy substitutes the obtained optimal solution into a correction function for verification so as to judge whether the optimal solution meets the set condition, and if so, deploying delivery logics related to the delivery elements according to the optimal solution of the delivery elements; if not, retransmitting the data acquisition command to each auxiliary node to acquire data again; the method corrects the decision-making strategy, further ensures the scientificity of the decision-making strategy and improves the efficiency.
Drawings
FIG. 1 is a schematic flow chart of a method for a MES system-based flexible production line material distribution decision method according to an embodiment of the present invention;
fig. 2 is a schematic monitoring flow diagram of a monitoring device of the MES system-based flexible production line material delivery decision method and device according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a decision-making server of the MES-based flexible production line material distribution decision-making method and apparatus according to an embodiment of the present invention;
fig. 4 is a graph diagram illustrating changes in delivery efficiency over time of the MES system-based flexible production line material delivery decision method and apparatus according to an embodiment of the present invention, and a comparison experiment effect diagram of the prior art.
Detailed Description
The technical solution of the present invention is further described in detail below with reference to the following detailed description and the accompanying drawings:
example 1
As shown in fig. 1, a flexible production line material distribution decision method based on MES system, the method executes the following steps:
step 1: setting a plurality of data acquisition units for respectively acquiring data, wherein one of the data acquisition units is a main node, and the other data acquisition units are auxiliary nodes; each auxiliary node is provided with a data label; the data label is used for marking the production line material distribution station corresponding to the auxiliary node; the data label comprises the following data information: position coordinates and containment capability;
step 2: constructing a central network by taking the main node as a central node and the auxiliary nodes as branch nodes; the decision server periodically sends a data acquisition command to the main node, and the main node broadcasts the received data acquisition command to other nodes in the central network; the data acquisition command comprises: a delivery target and delivery elements that affect the delivery target;
and step 3: after receiving a data acquisition command, the auxiliary node judges whether the data acquisition command should be responded or not according to a distribution target and distribution elements influencing the distribution target and a data label of the auxiliary node, and if so, the acquired data is sent to a decision server; if not, no response is carried out;
and 4, step 4: after receiving the data collected by each auxiliary node, the decision server constructs a delivery objective function according to a delivery target and delivery elements influencing the delivery target; determining constraints of the objective function according to delivery capacity; calculating an optimal solution for the delivery elements of the objective function under the constraint;
and 5: the decision server substitutes the obtained optimal solution into a correction function for verification to judge whether the optimal solution meets set conditions or not, and if so, deploys delivery logics related to the delivery elements according to the optimal solution of the delivery elements; and if not, retransmitting the data acquisition command to each auxiliary node to acquire the data again.
As shown in fig. 3, a in fig. 31、a2…anRepresenting each secondary node, and the numbers on each connecting line represent the amount of material being distributed between the two nodes. The production material distribution station corresponding to each auxiliary node has corresponding holding capacity, and if the holding capacity exceeds the amount of the distributed materials, the auxiliary node can distribute the corresponding production material distribution station; if the containing capacity is lower than the amount of the delivered materials, the production material delivery station corresponding to the auxiliary node cannot deliver the materials, and delivery congestion exists; if the holding capacity is equal to the amount of material being dispensed, this indicates that the generating material dispensing station can be operated with maximum efficiency.
When the decision server of the invention is in operation and the constraint of the objective function is determined, the holding capacity of each generation material distribution station is ensured to be equal to the amount of the distributed materials, so that the operation efficiency is highest.
By adopting the technical scheme, the data acquisition units are used for acquiring data, then the data labels are arranged on each data acquisition unit, and then the distribution objective function is constructed according to the distribution targets and the distribution factors influencing the distribution targets; meanwhile, determining the constraint of the objective function according to the distribution capacity; and calculating the optimal solution of the distribution elements of the objective function under the constraint, so that the efficiency of material distribution is higher, and in addition, correcting the optimal solution to ensure that the result of distribution every time can meet the set conditions to the maximum extent, thereby further improving the efficiency of distribution. The method is mainly realized by the following steps: 1. construction of a central network: the method comprises the steps that a main node is used as a central node, an auxiliary node is used as a branch node, and a central network is constructed; the decision server periodically sends a data acquisition command to the main node, and the main node broadcasts the received data acquisition command to other nodes in the central network; the data acquisition command comprises: a delivery target and delivery elements that affect the delivery target; by constructing the central network, on one hand, each auxiliary node can be managed by using the central node, and on the other hand, each auxiliary node can be reached simultaneously when a data acquisition command is issued, so that the operation efficiency is improved; 2. and (3) making a distribution strategy: after receiving data collected from each auxiliary node, a decision server constructs a delivery objective function according to a delivery target and delivery elements influencing the delivery target; determining constraints of the objective function according to delivery capacity; calculating an optimal solution for the delivery elements of the objective function under the constraint; by the method, a distribution strategy can be formulated by combining the data collected by each auxiliary node and distribution conditions, so that the distribution efficiency is improved; 3. and (3) correcting the decision strategy: the decision server substitutes the obtained optimal solution into a correction function for verification to judge whether the optimal solution meets set conditions or not, and if so, deploys delivery logics related to the delivery elements according to the optimal solution of the delivery elements; if not, retransmitting the data acquisition command to each auxiliary node to acquire data again; the method corrects the decision-making strategy, further ensures the scientificity of the decision-making strategy and improves the efficiency.
Example 2
On the basis of the previous embodiment, a monitoring device is further arranged on the decision server; the monitoring device monitors the state of the fault and the fault recovery of the decision server in real time; when the decision server fails, each data acquisition unit acquires and stores service logic data in an off-line manner, and transmits the acquired service logic data to other data acquisition units for storage in a broadcast manner; each data acquisition unit is used for acquiring and storing test data when being used as a test workstation; after the server is recovered from the fault, the data acquisition unit serving as the main node uploads the stored service logic data of all the data acquisition units to the decision server; and each data acquisition unit uploads the stored test data to the decision server when being used as a test workstation.
Particularly, the MES system applied by the abroad well-known enterprises is a common phenomenon, and many domestic enterprises gradually adopt the technology to enhance the core competitiveness of the enterprises. The problem of returning to the information fault between the enterprise planning layer and the process control layer is that the traditional production process adopted by the manufacturing industry in China for many years is characterized by production according to the plan from top to bottom.
The MES location is an execution layer between the planning layer and the field automation system, and is mainly responsible for the production management and scheduling execution of the workshop. A well-designed MES system can integrate management functions such as production scheduling, product tracking, quality control, equipment failure analysis, network report and the like on a unified platform, and can simultaneously provide workshop management information services for production departments, quality inspection departments, process departments, logistics departments and the like by using a unified database and connecting through a network.
Example 3
On the basis of the above embodiment, the objective function includes: the system comprises a delivery target, a delivery constant and a delivery variable to be solved, wherein the delivery constant is determined according to the delivery capacity, and the delivery variable is determined according to the delivery element.
Specifically, the MES can optimally manage the whole production process from order placement to product completion through information transfer. When real-time events occur at the plant, the MES can react to them, report them on time, and direct and process them with the current accurate data. The rapid response to the state change enables the MES to reduce the activities without added values in enterprises and effectively guide the production operation process of factories, thereby improving the timely delivery capacity of the factories, improving the circulation performance of materials and increasing the production return rate. The MES also provides mission critical information about product behavior within the enterprise and throughout the product supply chain via two-way direct communication.
Example 4
On the basis of the above embodiment, the calculating an optimal solution for the delivery elements includes: and calculating the optimal solution of the distribution elements by using a linear programming solver.
Example 5
On the basis of the above embodiment, the step 5: the method for judging whether the optimal solution meets the set conditions by substituting the optimal solution into the correction function by the decision server for verification comprises the following steps: setting a modifier sub-function, expressing the modifier sub-function by P, and performing convolution operation on the optimal solution and the modifier sub-function to obtain a first intermediate result; setting a correction function, wherein the correction function is as follows:
setting the correction threshold as: s; and calculating the first intermediate result, the correction function and the correction threshold value to obtain a correction result:
the correction error is calculated by the following error function:
wherein m represents the number of distribution elements, i represents the ith distribution element variable, and E is the optimal solution; if the error is corrected
And within the set threshold value range, deploying the delivery logic related to the delivery elements according to the optimal solution of the delivery elements.
Example 6
On the basis of the above embodiment, the monitoring device is configured to learn the state of the decision server that has failed and the failure recovery state by calling an application programming interface API of an operating system of the decision server.
Specifically, an API (Application Programming Interface) is a predefined Interface (e.g. function, HTTP Interface) or a convention for linking different components of a software system. To provide a set of routines that applications and developers can access based on certain software or hardware without accessing source code or understanding the details of the internal workings.
Example 7
On the basis of the previous embodiment, the monitoring device is further configured to monitor a hardware resource status of the decision server; each data acquisition unit is further used for judging whether the decision server is idle or not according to the monitoring result of the decision server state monitoring unit on the hardware resource state of the decision server when the data acquisition unit is used as a test workstation, and uploading the stored test data to the decision server when the decision server is idle.
Specifically, data acquisition, also known as data acquisition, utilizes a device to acquire data from outside the system and input the data to an interface inside the system. Data acquisition techniques are widely used in various fields. Such as a camera and a microphone, are data acquisition tools.
The collected data are various physical quantities such as temperature, water level, wind speed, pressure, etc. which have been converted into electrical signals, and may be analog quantities or digital quantities. The acquisition is generally a sampling mode, that is, the same point data is repeatedly acquired at certain time intervals (called sampling period). The acquired data are mostly instantaneous values, but also characteristic values within a certain period of time. Accurate data measurements are the basis for data acquisition. The data measurement method includes contact and non-contact, and the detection elements are various. No matter which method and element, the data correctness is ensured on the premise of not influencing the state of the object to be measured and the measurement environment. The data collection is very broad, and comprises the collection of planar continuous physical quantities. In computer-aided drawing, mapping, designing, the process of digitizing a graphic or image may also be referred to as data acquisition, where geometric (or physical, e.g., grayscale) data is acquired.
Example 8
On the basis of the above embodiment, the constructing the objective function includes: and constructing an objective function which enables the accommodating quantity of all the auxiliary nodes to be minimum, and taking whether the distribution route is accommodated in the auxiliary nodes as a distribution variable of the objective function.
Specifically, the method takes a main node as a central node and an auxiliary node as a branch node to construct a central network; the decision server periodically sends a data acquisition command to the main node, and the main node broadcasts the received data acquisition command to other nodes in the central network; the data acquisition command comprises: a delivery target and delivery elements that affect the delivery target; through constructing the central network, on the one hand, the central node can be used for managing each auxiliary node, and on the other hand, when a data acquisition command is issued, the data acquisition command can reach each auxiliary node simultaneously, so that the operation efficiency is improved.
Example 9
On the basis of the above embodiment, the constraint includes: and ensuring that the material quantity to be contained in each auxiliary node does not exceed the constraint of the containing capacity of the auxiliary node.
Example 10
A flexible production line material distribution decision-making device based on an MES system.
In the prior art, production material delivery is often scheduled manually. This results in lower efficiency.
The method comprises the steps of utilizing data acquisition units to acquire data, setting a data label on each data acquisition unit, and constructing a distribution objective function according to a distribution target and distribution elements influencing the distribution target; meanwhile, determining the constraint of the objective function according to the distribution capacity; and calculating the optimal solution of the distribution elements of the objective function under the constraint, so that the efficiency of material distribution is higher, and in addition, correcting the optimal solution to ensure that the result of distribution every time can meet the set conditions to the maximum extent, thereby further improving the efficiency of distribution.
Specifically, after receiving data collected from each auxiliary node, a decision server of the invention constructs a delivery objective function according to a delivery objective and delivery elements affecting the delivery objective; determining constraints of the objective function according to delivery capacity; calculating an optimal solution for the delivery elements of the objective function under the constraint; by the method, a distribution strategy can be formulated by combining the data collected by each auxiliary node and distribution conditions, so that the distribution efficiency is improved; the decision server substitutes the obtained optimal solution into a correction function for verification to judge whether the optimal solution meets set conditions or not, and if so, deploys delivery logics related to the delivery elements according to the optimal solution of the delivery elements; if not, retransmitting the data acquisition command to each auxiliary node to acquire data again; the method corrects the decision-making strategy, further ensures the scientificity of the decision-making strategy and improves the efficiency.
The above description is only an embodiment of the present invention, but not intended to limit the scope of the present invention, and any structural changes made according to the present invention should be considered as being limited within the scope of the present invention without departing from the spirit of the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative modules, method steps, and modules described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the software modules, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (10)
1. The flexible production line material distribution decision method based on the MES system is characterized by comprising the following steps:
step 1: setting a plurality of data acquisition units for respectively acquiring data, wherein one of the data acquisition units is a main node, and the other data acquisition units are auxiliary nodes; each auxiliary node is provided with a data label; the data label is used for marking the production line material distribution station corresponding to the auxiliary node; the data label comprises the following data information: position coordinates and containment capability;
step 2: constructing a central network by taking the main node as a central node and the auxiliary nodes as branch nodes; the decision server periodically sends a data acquisition command to the main node, and the main node broadcasts the received data acquisition command to other nodes in the central network; the data acquisition command comprises: a delivery target and delivery elements that affect the delivery target;
and step 3: after receiving a data acquisition command, the auxiliary node judges whether the data acquisition command should be responded or not according to a distribution target and distribution elements influencing the distribution target and a data label of the auxiliary node, and if so, the acquired data is sent to a decision server; if not, no response is carried out;
and 4, step 4: after receiving the data collected by each auxiliary node, the decision server constructs a delivery objective function according to a delivery target and delivery elements influencing the delivery target; determining constraints of the objective function according to delivery capacity; calculating an optimal solution for the delivery elements of the objective function under the constraint;
and 5: the decision server substitutes the obtained optimal solution into a correction function for verification to judge whether the optimal solution meets set conditions or not, and if so, deploys delivery logics related to the delivery elements according to the optimal solution of the delivery elements; and if not, retransmitting the data acquisition command to each auxiliary node to acquire the data again.
2. The method of claim 1, wherein a monitoring device is further provided on the decision server; the monitoring device monitors the state of the fault and the fault recovery of the decision server in real time; when the decision server fails, each data acquisition unit acquires and stores service logic data in an off-line manner, and transmits the acquired service logic data to other data acquisition units for storage in a broadcast manner; each data acquisition unit is used for acquiring and storing test data when being used as a test workstation; after the server is recovered from the fault, the data acquisition unit serving as the main node uploads the stored service logic data of all the data acquisition units to the decision server; and each data acquisition unit uploads the stored test data to the decision server when being used as a test workstation.
3. The method of claim 2, wherein the objective function comprises: the system comprises a delivery target, a delivery constant and a delivery variable to be solved, wherein the delivery constant is determined according to the delivery capacity, and the delivery variable is determined according to the delivery element.
4. The method of claim 3, wherein the calculating the optimal solution for the delivery element comprises: and calculating the optimal solution of the distribution elements by using a linear programming solver.
5. The method of claim 4, wherein the step 5: the method for judging whether the optimal solution meets the set conditions by substituting the optimal solution into the correction function by the decision server for verification comprises the following steps: setting a modifier sub-function, expressing the modifier sub-function by P, and performing convolution operation on the optimal solution and the modifier sub-function to obtain a first intermediate result; setting a correction function, wherein the correction function is as follows:
setting the correction threshold as: s; and calculating the first intermediate result, the correction function and the correction threshold value to obtain a correction result:
the correction error is calculated by the following error function:
wherein m represents the number of distribution elements, i represents the ith distribution element variable, and E is the optimal solution; if the error is corrected
And within the set threshold value range, deploying the delivery logic related to the delivery elements according to the optimal solution of the delivery elements.
6. The method of claim 5, wherein the monitoring means is configured to learn about the status of the decision server failure and failure recovery by calling an Application Programming Interface (API) of an operating system of the decision server.
7. The method of claim 6, wherein the monitoring device is further configured to monitor a hardware resource status of the decision server; each data acquisition unit is further used for judging whether the decision server is idle or not according to the monitoring result of the decision server state monitoring unit on the hardware resource state of the decision server when the data acquisition unit is used as a test workstation, and uploading the stored test data to the decision server when the decision server is idle.
8. The method of claim 7, wherein said constructing the objective function comprises: and constructing an objective function which enables the accommodating quantity of all the auxiliary nodes to be minimum, and taking whether the distribution route is accommodated in the auxiliary nodes as a distribution variable of the objective function.
9. The method of claim 8, wherein the constraining comprises: and ensuring that the material quantity to be contained in each auxiliary node does not exceed the constraint of the containing capacity of the auxiliary node.
10. A MES system based flexible production line material delivery decision making device for implementing the method of any one of claims 1 to 9.
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