CN112306026A

CN112306026A - Equipment production line scheduling method based on smart park and cloud computing server

Info

Publication number: CN112306026A
Application number: CN202011212718.9A
Authority: CN
Inventors: 陈晓清
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-05-08
Filing date: 2020-05-08
Publication date: 2021-02-02
Also published as: CN111552246B; CN111552246A; CN112306027A

Abstract

The application relates to a smart park-based equipment production line scheduling method and a cloud computing server. The method comprises the steps of firstly obtaining operation parameters of each industrial device, determining a plurality of state types of the corresponding target industrial device and associated information of the target industrial device in each state type, then determining device operation data of each industrial device in the same state type, generating a state characteristic matrix in each state type based on the associated information and the device operation data of each industrial device in the same state type, importing the state characteristic matrix, searching in a matching terminal based on the target state matrix, determining target state data of a target production line based on a searching result, and finally switching the current production line to the target production line when the target state data meet set conditions. Therefore, time cost and labor cost consumed in acquiring the state data of the target production line can be reduced, and timely switching and scheduling of equipment production lines are further ensured.

Description

Equipment production line scheduling method based on smart park and cloud computing server

Technical Field

The application relates to the technical field of smart park production, in particular to a smart park-based equipment production line scheduling method and a cloud computing server.

Background

The rapid development of the smart park provides great convenience for modern industrial production, and can effectively improve the production and manufacturing efficiency of the modern industry. Along with the wide application of intelligent integration technology, present industrial equipment possesses a tractor serves several purposes's function, can make the switching of wisdom garden based on different production lines of a whole set of industrial equipment realization. When switching different production lines, the intelligent park needs to acquire the state data of the target production line after switching in advance to determine whether the prerequisite condition of production line switching is met. However, in the prior art, a large amount of time cost and labor cost are consumed when the state data of the target production line is acquired, so that timely switching and scheduling of equipment production lines are difficult to ensure.

Disclosure of Invention

The application provides an equipment production line scheduling method based on a smart park and a cloud computing server, so as to improve the technical problems in the prior art.

The first technical scheme is used for disclosing an equipment production line scheduling method based on a smart park, which is applied to a cloud computing server communicated with a configuration terminal and a matching terminal, wherein the configuration terminal is also communicated with a plurality of industrial equipment, and the method comprises the following steps:

acquiring the operation parameters of each industrial device through the configuration terminal;

determining a plurality of state categories of corresponding target industrial equipment and associated information of the target industrial equipment in each state category from each group of operation parameters;

determining equipment operation data of each industrial equipment under the same state category according to the operation parameters; generating a state characteristic matrix under each state category based on the associated information and the equipment operation data of each industrial equipment under the same state category, and storing the state characteristic matrix into the matching terminal;

when a production line switching instruction is detected to exist, analyzing the production line switching instruction to obtain a target state matrix corresponding to a target production line;

searching whether a first state characteristic matrix identical to the target state matrix exists in the matching terminal; if yes, determining target state data corresponding to the target production line according to the first state feature matrix; if not, determining a second state feature matrix with the maximum similarity to the target state matrix in the matching terminal; correcting the sample state data corresponding to the second state characteristic matrix according to the similarity between the target state matrix and the second state characteristic matrix to obtain target state data;

and when the target state data meet set conditions, switching the current production line to the target production line.

Preferably, the method further comprises:

and when the target state data does not meet the set conditions, keeping the operation of the current production line.

Preferably, switching the current production line to the target production line includes:

sending target operation parameters corresponding to each industrial device to the configuration terminal;

and enabling the configuration terminal to send each target operation parameter to the corresponding industrial equipment.

Preferably, the causing the configuration terminal to send each target operation parameter to the corresponding industrial device includes:

sending a target instruction to the configuration terminal;

and enabling the configuration terminal to delay and send each target operation parameter to the corresponding industrial equipment according to the delay time length carried in the target instruction.

Preferably, the generating a state feature matrix in each state category based on the associated information of each industrial device in the same state category and the device operation data includes:

the method comprises the steps of counting association information and device operation data of each industrial device under each state category, and determining a first time sequence characteristic of first device operation data of each industrial device under a first state category in the plurality of state categories and a second time sequence characteristic of second device operation data of each industrial device under a second state category in the plurality of state categories;

determining a first similarity weight between the first and second timing characteristics based on first association information of each industrial device in the first state category and second association information of each industrial device in the second state category;

judging whether each industrial device has an adjustable state feature type under the first state type or the second state type according to the first similarity weight, mapping the first time sequence feature and the second time sequence feature to the adjustable state feature type to obtain a first mapping feature and a second mapping feature on the premise of determining that the adjustable state feature type exists, and calculating to obtain a third mapping feature under the adjustable state feature type according to the first mapping feature and the second mapping feature;

calculating a first similarity value of the first time sequence feature and the third mapping feature and a second similarity value of the second time sequence feature and the third mapping feature, and calculating a second similarity weight between the first time sequence feature and the second time sequence feature according to the first similarity value and the second similarity value;

calculating a difference between the first similarity weight and the second similarity weight; if the difference is within a set range, weighting the first time sequence characteristics according to the second similarity weight to obtain first target characteristics, weighting the second time sequence characteristics according to the first similarity weight to obtain second target characteristics, and integrating all the determined first target characteristics and all the determined second target characteristics to obtain a state characteristic matrix under each state type; and if the difference value is out of the set range, integrating all the determined first time sequence characteristics and all the determined second time sequence characteristics to obtain a state characteristic matrix under each state type.

Preferably, before analyzing the line switching instruction to obtain a target state matrix corresponding to a target production line, the method further includes:

judging whether the current storage percentage of the cache reaches a set threshold value;

if the current storage percentage reaches the set threshold, deleting the historical state matrix with the longest storage duration in the cache, and continuing to judge whether the current storage percentage reaches the set threshold, if not, entering a step of analyzing the production line switching instruction, and if so, continuing to delete the historical state matrix with the longest storage duration in the cache until the current storage percentage is lower than the set threshold.

Preferably, analyzing the production line switching instruction to obtain a target state matrix corresponding to a target production line includes:

acquiring a plurality of process parameter groups of an analysis process, determining a parameter change track of each process parameter group and generating a parameter change graph according to the parameter change track; the parameter change graph is a block graph, each block subgraph corresponds to one block identifier, and each block identifier corresponds to at least one parameter change track;

reading an instruction stream code of the production line switching instruction, determining a mapping list based on the instruction stream code and the parameter change diagram, and generating a parameter adjustment thread according to the mapping list; wherein, generating a parameter adjustment thread according to the mapping list comprises: converting each process parameter group into a character coding string; respectively generating at least one character characteristic of each character encoding string; acquiring character features which are not repeated in the process parameter set to form a character feature set; mapping each character feature in the character feature set to the parameter variation graph to obtain target coding information corresponding to a parameter adjustment thread, and determining the parameter adjustment thread according to the target coding information;

comparing the parameter change tracks corresponding to the process parameter groups with the parameter change tracks in the parameter adjustment thread one by one to obtain a plurality of comparison results; correcting the parameter variation graph according to a plurality of comparison results to obtain a target parameter variation graph; and obtaining a plurality of target parameter sets based on the target parameter change diagram, and starting the analysis process through the target parameter sets to analyze the production line switching instruction to obtain the target state matrix corresponding to the target production line.

Preferably, storing the state feature matrix in the matching terminal includes:

acquiring a first data format of the state characteristic matrix and a second data format of the matching terminal;

judging whether the first data format is the same as the second data format;

if the state feature matrixes are the same, storing the state feature matrixes into the matching terminal;

if the current state feature matrix is different from the preset state feature matrix, starting a preset format conversion thread to convert the format of the state feature matrix to obtain a current state feature matrix, and storing the current state feature matrix into the matching terminal.

A second technical solution is provided for disclosing a cloud computing server, including: a processor, and

a memory and a network interface connected with the processor; the network interface is connected with a nonvolatile memory in the cloud computing server;

when the processor runs, the processor calls the computer program from the nonvolatile memory through the network interface and runs the computer program through the memory so as to execute the method.

The third technical scheme is used for disclosing a readable storage medium applied to a computer, wherein a computer program is burned on the readable storage medium, and the computer program realizes the method when running in a memory of a cloud computing server.

When the equipment production line scheduling method based on the intelligent park and the cloud computing server are applied, firstly, acquiring the operation parameters of each industrial device, determining a plurality of state categories of the corresponding target industrial device and the associated information of the target industrial device in each state category from each group of operation parameters, secondly, determining the equipment operation data of each industrial equipment in the same state category, generating a state characteristic matrix in each state category based on the associated information of each industrial equipment in the same state category and the equipment operation data, and importing the state characteristic matrix into a matching terminal, and then searching in the matching terminal based on the target state matrix, determining target state data of the target production line based on the searching result, and finally switching the current production line to the target production line when the target state data meets the set conditions.

In this way, if the target state matrix corresponding to the target production line matches the preset state feature matrix, the state data of the target production line can be determined directly from the preset state feature matrix, and if the target state matrix corresponding to the target production line does not match the preset state feature matrix, the state data of the target production line can be determined based on the similarity between the target state matrix and the state feature matrix. Therefore, time cost and labor cost consumed in acquiring the state data of the target production line can be reduced, and timely switching and scheduling of equipment production lines are further ensured.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

FIG. 1 is a schematic diagram of an intelligent campus-based equipment line scheduling system according to an exemplary embodiment of the present application.

FIG. 2 is a flow chart illustrating a smart campus-based equipment line scheduling method according to an exemplary embodiment of the present application.

FIG. 3 is a block diagram illustrating one embodiment of an intelligent campus-based equipment line scheduler in accordance with the present application, according to an illustrative embodiment.

Fig. 4 is a hardware structure diagram of a cloud computing server in which the apparatus of the present application is located.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

In order to reduce the time cost and the labor cost consumed in acquiring the state data of the target production line and further ensure the timely switching and scheduling of the equipment production lines, the invention discloses an equipment production line scheduling method based on a smart park and a cloud computing server, which can integrate the operation parameters of each industrial equipment in the scene of a plurality of production lines in advance and further form state characteristic matrixes corresponding to different production lines. In this way, when switching between different production lines, if the target state matrix corresponding to the target production line coincides with the preset state feature matrix, the state data of the target production line can be directly determined according to the preset state feature matrix, and if the target state matrix corresponding to the target production line does not coincide with the preset state feature matrix, the state data of the target production line can be determined based on the similarity between the target state matrix and the state feature matrix. Therefore, time cost and labor cost consumed in acquiring the state data of the target production line can be reduced, and timely switching and scheduling of the equipment production line are further ensured.

Referring to fig. 1, a communication connection diagram of an equipment line dispatching system 100 based on a smart park according to the present invention is shown, where the equipment line dispatching system 100 includes a cloud computing server 110, a configuration terminal 120, a matching terminal 130, and a plurality of industrial equipments 140. The cloud computing server 110 is in communication connection with the configuration terminal 120 and the matching terminal 130, and the configuration terminal 120 is in communication connection with the plurality of industrial devices 140.

In the present embodiment, the configuration terminal 120 and the matching terminal 130 may be electronic devices having data transmission and processing capabilities, and are not limited herein. The industrial device 140 may be applied to a plurality of manufacturing fields, such as new energy automobile manufacturing, intelligent medical device manufacturing, 5G device manufacturing, live e-commerce device manufacturing, unmanned aerial vehicle manufacturing, and the like. When the industrial equipment 140 is applied to different manufacturing fields, the model and type of the industrial equipment 140 are different, which is not listed here.

On the basis of the above, please refer to fig. 2, which is a flowchart of the intelligent park-based equipment production line scheduling method according to the present invention, the method may be applied to the cloud computing server 110 in fig. 1, and the method specifically includes the following steps.

And step S210, acquiring the operation parameters of each industrial device through the configuration terminal.

In the invention, the operation parameters are acquired by the configuration terminal from each industrial device in real time. The operating parameters of the industrial equipment include, but are not limited to, voltage data, current data, active power, operating duration, and mechanical loss values.

Step S220, determining a plurality of state categories of the corresponding target industrial device and associated information of the target industrial device in each state category from each group of operating parameters.

In step S220, the association information is used to characterize a first industrial device and a second industrial device that have a connection relationship with the target industrial device, where the first industrial device is an upstream industrial device of the target industrial device, and the second industrial device is a downstream industrial device of the target industrial device.

Further, the upstream industrial device characterizes that the first industrial device is located at an upstream node of the production line of the target industrial device, and the downstream industrial device characterizes that the second industrial device is located at a downstream node of the production line of the target industrial device.

Step S230, determining equipment operation data of each industrial equipment under the same state category according to the operation parameters; and generating a state characteristic matrix under each state type based on the associated information of each industrial device under the same state type and the device operation data, and storing the state characteristic matrix into the matching terminal.

In the present invention, for example, 10 industrial devices exist, and each industrial device has 3 status categories: and the A state type, the B state type and the C state type can respectively count the device operation data of 10 industrial devices under the A state type, the B state type and the C state type. The device operating data are used for representing operating parameters of the industrial device under different state categories.

In the invention, feature extraction can be carried out on each group of equipment operation data under each state category so as to obtain operation feature vectors, and then the operation feature vectors are integrated according to the associated information of the industrial equipment corresponding to each group of operation feature vectors under each state category so as to obtain the state feature matrix.

In the present invention, determining the device operation data of each industrial device in the same state category according to the operation parameters may specifically include: and listing the parameter state categories of the operation parameters, and then counting the equipment operation data of each industrial equipment under the same state category according to the listed parameter state categories.

Step S240, when the production line switching instruction is detected to exist, the production line switching instruction is analyzed to obtain a target state matrix corresponding to a target production line.

Step S250, searching whether a first state characteristic matrix which is the same as the target state matrix exists in the matching terminal; if yes, determining target state data corresponding to the target production line according to the first state feature matrix; if not, determining a second state feature matrix with the maximum similarity to the target state matrix in the matching terminal; and correcting the sample state data corresponding to the second state characteristic matrix according to the similarity between the target state matrix and the second state characteristic matrix to obtain target state data.

And step S260, when the target state data meet set conditions, switching the current production line to the target production line.

It can be understood that, through the above steps S210 to S260, firstly, the operation parameters of each industrial device are obtained, and a plurality of state types of the corresponding target industrial device and the association information of the target industrial device in each state type are determined from each group of operation parameters, secondly, the device operation data of each industrial device in the same state type is determined, and a state feature matrix in each state type is generated based on the association information of each industrial device in the same state type and the device operation data, and is imported into the matching terminal, then, the matching terminal is searched based on the target state matrix, the target state data of the target production line is determined based on the search result, and finally, when the target state data meets the set condition, the current production line is switched to the target production line.

Optionally, when the target state data does not meet the set condition, the operation of the current production line is maintained. So, can avoid switching the production accident that the production line brought at will, ensure the normal production in wisdom garden.

The inventor finds that, in applying the above method to an actual intelligent campus, since there may be partial overlap in the device operation data in each state class, the accuracy of the state feature matrix in each state class may be reduced. In order to improve the above problem, in step S230, a state feature matrix in each state category is generated based on the association information of each industrial device in the same state category and the device operation data, and the following steps may be specifically included.

Step S231, counting the association information and the device operation data of each industrial device in each state category, and determining a first timing characteristic of the first device operation data of each industrial device in a first state category of the plurality of state categories and a second timing characteristic of the second device operation data of each industrial device in a second state category of the plurality of state categories.

In the present invention, the first state class and the second state class are different state classes.

Step S232, determining a first similarity weight between the first timing characteristic and the second timing characteristic based on the first association information of each industrial device in the first state category and the second association information of each industrial device in the second state category.

Step S233, determining whether each industrial device has an adjustable status feature type in the first status type or the second status type according to the first similarity weight, mapping the first timing feature and the second timing feature to the adjustable status feature type to obtain a first mapping feature and a second mapping feature on the premise that the adjustable status feature type is determined to exist, and calculating a third mapping feature in the adjustable status feature type according to the first mapping feature and the second mapping feature.

Step S234, calculating a first similarity value between the first time sequence feature and the third mapping feature and a second similarity value between the second time sequence feature and the third mapping feature, and calculating a second similarity weight between the first time sequence feature and the second time sequence feature according to the first similarity value and the second similarity value.

Step S235, calculating a difference between the first similarity weight and the second similarity weight; if the difference is within a set range, weighting the first time sequence characteristics according to the second similarity weight to obtain first target characteristics, weighting the second time sequence characteristics according to the first similarity weight to obtain second target characteristics, and integrating all the determined first target characteristics and all the determined second target characteristics to obtain a state characteristic matrix under each state type; and if the difference value is out of the set range, integrating all the determined first time sequence characteristics and all the determined second time sequence characteristics to obtain a state characteristic matrix under each state type.

It can be understood that, with the above, whether the device operation data in each state category overlap or not can be determined based on the similarity weight and the mapping process, so that the device operation data can be corrected when the device operation data overlap, and thus the accuracy of the state feature matrix in each state category can be ensured.

In specific implementation, the inventor has further found that, when a production line switching instruction is analyzed, an analysis process is often slow, and the reason for this is that a large number of historical state matrices are stored in a cache corresponding to the analysis process, which may cause that when the current production line switching instruction is analyzed, the cache cannot store a target state matrix, and a part of vectors of the target state matrix has to be stored in another storage space. In order to improve the above problem, in step S240, before the line switching instruction is analyzed to obtain the target state matrix corresponding to the target production line, the method may further include the following steps.

Step S310, determine whether the current storage percentage of the cache reaches a set threshold.

Step S320, if the current storage percentage reaches the set threshold, deleting the historical state matrix with the longest storage duration in the cache, and continuing to determine whether the current storage percentage reaches the set threshold, if not, entering a step of analyzing the production line switching instruction, and if so, continuing to delete the historical state matrix with the longest storage duration in the cache until the current storage percentage is lower than the set threshold.

It can be understood that, through the above, the cache can be released, so that it is ensured that the target state matrix is completely stored in the cache, and thus, the efficiency of the analysis process can be ensured.

In practical applications, in order to ensure the accuracy and comprehensiveness of the target state matrix obtained by the analysis, it is necessary to perform parameter adjustment on an analysis process, and for this reason, in step S240, the analysis of the line switching instruction obtains the target state matrix corresponding to the target production line, which may specifically include the contents described in the following steps.

Step S241, acquiring a plurality of process parameter groups of the analysis process, determining a parameter change track of each process parameter group, and generating a parameter change graph according to the parameter change track; the parameter change graph is a block graph, each block subgraph corresponds to one block identifier, and each block identifier corresponds to at least one parameter change track.

Step S242, reading the instruction stream code of the production line switching instruction, determining a mapping list based on the instruction stream code and the parameter change diagram, and generating a parameter adjustment thread according to the mapping list; wherein, generating a parameter adjustment thread according to the mapping list comprises: converting each process parameter group into a character coding string; respectively generating at least one character characteristic of each character encoding string; acquiring character features which are not repeated in the process parameter set to form a character feature set; mapping each character feature in the character feature set to the parameter variation graph to obtain target coding information corresponding to a parameter adjustment thread, and determining the parameter adjustment thread according to the target coding information.

Step S243, comparing the parameter change trajectory corresponding to each process parameter group with each parameter change trajectory in the parameter adjustment thread one by one to obtain a plurality of comparison results; correcting the parameter variation graph according to a plurality of comparison results to obtain a target parameter variation graph; and obtaining a plurality of target parameter sets based on the target parameter change diagram, and starting the analysis process through the target parameter sets to analyze the production line switching instruction to obtain the target state matrix corresponding to the target production line.

When the content described in the above steps is applied, the parameter adjustment can be performed on the analysis process based on the process parameter group of the analysis process, so that the production line switching instruction can be analyzed through the analysis process for completing the parameter adjustment, and the accuracy and the comprehensiveness of the target state matrix obtained through analysis are ensured.

In a specific embodiment, in order to improve the operation efficiency of the cloud computing server, the switching the current production line to the target production line described in step S260 specifically includes the following: and sending the target operation parameter corresponding to each industrial device to the configuration terminal, so that the configuration terminal sends each target operation parameter to the corresponding industrial device.

In the present invention, the target operation parameters can be obtained according to the target state data corresponding to the target production line, and will not be further described herein. It can be understood that, through the above content, the target operation parameter can be issued through the configuration terminal, so that the cloud computing server can be prevented from allocating excessive time slice resources to issue the target operation parameter, and the operation efficiency of the cloud computing server is improved.

Further, in order to improve flexibility of switching (scheduling) of the production line, on the basis, the causing the configuration terminal to send each target operation parameter to the corresponding industrial device may further include: and sending a target instruction to the configuration terminal, so that the configuration terminal sends each target operation parameter to the corresponding industrial equipment in a delayed manner according to the delay time length carried in the target instruction. Thus, by setting the transmission delay, the flexibility of production line switching (scheduling) can be improved.

In a specific implementation, in order to ensure the integrity of the state feature matrix in the process of storing the state feature matrix in the matching terminal, the storing the state feature matrix in the matching terminal described in step S230 may further include: acquiring a first data format of the state characteristic matrix and a second data format of the matching terminal; judging whether the first data format is the same as the second data format; if the state feature matrixes are the same, storing the state feature matrixes into the matching terminal; if the current state feature matrix is different from the preset state feature matrix, starting a preset format conversion thread to convert the format of the state feature matrix to obtain a current state feature matrix, and storing the current state feature matrix into the matching terminal.

In the above content, the data format of the current state feature matrix is consistent with the data format of the matching terminal, so that the consistency between the state feature matrix and the data format of the matching terminal can be ensured through the content described in the above steps, thereby ensuring the integrity of the state feature matrix in the process of storing the state feature matrix in the matching terminal and avoiding the data loss of the state feature matrix in the process of storing the state feature matrix due to the mismatching of the data formats.

In an alternative embodiment, in order to accurately obtain the target state data, the step S250 of modifying the sample state data corresponding to the second state feature matrix according to the similarity between the target state matrix and the second state feature matrix to obtain the target state data may specifically include the following steps.

Step S251, counting first matrix description information corresponding to a target state matrix and second matrix description information corresponding to a second state feature matrix, where the first matrix description information and the second matrix description information respectively include a plurality of information packets with different information tag values.

Step S252, calculating a first data capacity value of the target state matrix in any packet of the first matrix description information, and determining a packet having a minimum tag value in the second matrix description information as a first target packet; importing the first data capacity value into the first destination packet to obtain a second data capacity value in the first destination packet; establishing a target list between the target state matrix and the second state feature matrix according to the first data capacity value and the second data capacity value; the target list is a set of differences of state data of the target state matrix and the second state feature matrix under different similarities.

Step S253, obtaining a third data capacity value in the first destination packet by using the second data capacity value as a reference value, importing the third data capacity value into the packet where the first data capacity value is located through the destination list, obtaining a second destination packet corresponding to the third data capacity value in the packet where the first data capacity value is located, and determining that the second destination packet is correction information of the sample state data corresponding to the second state feature matrix.

Step S254, determining a plurality of information fields in the second target information packet corresponding to the sample status data, calculating a priority of each information field, sequentially modifying the status data sets corresponding to each information field in the sample status data according to the descending order of the priority of each information field, and integrating the modified status data sets to obtain the target status data.

By executing the contents described in steps S251 to S254, the first matrix description information of the target state matrix and the second matrix description information of the second state feature matrix can be analyzed, so as to determine the correction information corresponding to the sample state data. Therefore, the sample state data can be completely and accurately corrected based on the correction information, and the target state data can be accurately obtained.

In another alternative embodiment, whether the target state data satisfies the set condition may be specifically realized by the following steps.

(1) And extracting a production thread switching record, determining a state data change track from the production thread record, and acquiring track nodes obtained by dividing the state data change track.

(2) For a current track node in the track nodes, determining an update weight of the current track node in a preset interval based on a first frequency updated by the current track node in the preset interval and a second frequency updated by each track node in the preset interval.

(3) And determining an updated frequency change curve of the current track node between two adjacent preset intervals according to the updated weight of the current track node in the two adjacent preset intervals.

(4) And determining whether the current track node is an abnormal track node or not based on the updating frequency variation curve.

(5) When the current track node is an abnormal track node, mapping the target state data to the state data change track to obtain a first target node; calculating a first node degree of the first target node in the state data change track; and if the first node degree is greater than a first set node degree, determining that the target state data meets set conditions, otherwise, determining that the target state data does not meet equipment conditions.

(6) When the current track node is a normal track node, mapping the target state data to the state data change track to obtain a second target node; calculating a second node degree of the second target node in the state data change track; and if the second node degree is less than or equal to a second set node degree, determining that the target state data meets a set condition, otherwise, determining that the target state data does not meet the equipment condition.

It can be understood that, through the above, the track nodes in the state data change track can be analyzed, so that when the current track node is judged to be the abnormal track node or the normal track node, different node degrees corresponding to the target state data are calculated, and whether the target state data meets the equipment condition is judged based on the node degrees. Therefore, whether the target state data meet equipment conditions or not can be accurately and reliably judged, and faults caused by random switching of production lines are avoided.

On the basis of the above, please refer to fig. 3, and also provide an equipment line dispatching device 300 based on the intelligent park, and refer to the following for the description of the equipment line dispatching device 300.

A1. The utility model provides an equipment production line scheduling device based on wisdom garden is applied to the cloud computing server with configuration terminal and matching terminal communication, configuration terminal still communicates with a plurality of industrial equipment, the device includes:

a parameter obtaining module 310, configured to obtain an operating parameter of each industrial device through the configuration terminal;

a category determining module 320, configured to determine, from each set of operating parameters, a plurality of status categories of corresponding target industrial devices and associated information of the target industrial devices in each status category;

the characteristic generating module 330 is configured to determine, according to the operation parameter, device operation data of each industrial device in the same state category; generating a state characteristic matrix under each state category based on the associated information and the equipment operation data of each industrial equipment under the same state category, and storing the state characteristic matrix into the matching terminal;

the instruction analyzing module 340 is configured to analyze the production line switching instruction to obtain a target state matrix corresponding to a target production line when the production line switching instruction is detected to exist;

a data determining module 350, configured to search whether a first state feature matrix identical to the target state matrix exists in the matching terminal; if yes, determining target state data corresponding to the target production line according to the first state feature matrix; if not, determining a second state feature matrix with the maximum similarity to the target state matrix in the matching terminal; correcting the sample state data corresponding to the second state characteristic matrix according to the similarity between the target state matrix and the second state characteristic matrix to obtain target state data;

and the production line scheduling module 360 is configured to switch the current production line to the target production line when the target state data meets a set condition.

A2. The apparatus of a1, the line scheduling module 360, further configured to:

A3. The apparatus of a1, the line scheduling module 360, further configured to:

A4. The apparatus of a3, the line scheduling module 360, further configured to:

sending a target instruction to the configuration terminal;

A5. The apparatus of any one of a1-a4, the feature generation module 330, to be used in particular to:

A6. The apparatus of a1, the apparatus further comprising a cache release module 370 configured to:

before the production line switching instruction is analyzed to obtain a target state matrix corresponding to a target production line, judging whether the current storage percentage of the cache reaches a set threshold value;

A7. In the apparatus of a6, the instruction parsing module 340 is specifically configured to:

A8. The apparatus of a1, the feature generation module 330, further configured to:

judging whether the first data format is the same as the second data format;

A9. The apparatus of a1, the data determination module 350 is specifically configured to:

counting first matrix description information corresponding to a target state matrix and second matrix description information corresponding to a second state characteristic matrix, wherein the first matrix description information and the second matrix description information respectively comprise a plurality of information packets with different information tag values;

calculating a first data capacity value of the target state matrix in any information packet of the first matrix description information, and determining an information packet with a minimum information tag value in the second matrix description information as a first target information packet; importing the first data capacity value into the first destination packet to obtain a second data capacity value in the first destination packet; establishing a target list between the target state matrix and the second state feature matrix according to the first data capacity value and the second data capacity value; the target list is a set of differences of state data of the target state matrix and the second state characteristic matrix under different similarities;

acquiring a third data capacity value in the first target information packet by taking the second data capacity value as a reference value, importing the third data capacity value into the information packet where the first data capacity value is located through the target list, obtaining a second target information packet corresponding to the third data capacity value in the information packet where the first data capacity value is located, and determining that the second target information packet is correction information of sample state data corresponding to the second state feature matrix;

determining a plurality of information fields in the second target information packet, which have a corresponding relationship with the sample state data, calculating the priority of each information field, sequentially correcting the state data sets corresponding to the information fields in the sample state data according to the descending order of the priority of each information field, and integrating the corrected state data sets to obtain target state data;

A10. the apparatus according to a1, the production line scheduling module 360 is specifically configured to:

extracting a production thread switching record, determining a state data change track from the production thread record, and acquiring track nodes obtained by dividing the state data change track;

for a current track node in the track nodes, determining an update weight of the current track node in a preset interval based on a first frequency updated by the current track node in the preset interval and a second frequency updated by each track node in the preset interval;

determining an updated frequency change curve of the current track node between two adjacent preset intervals according to the updated weight of the current track node in the two adjacent preset intervals;

determining whether the current track node is an abnormal track node or not based on the updating frequency variation curve;

when the current track node is an abnormal track node, mapping the target state data to the state data change track to obtain a first target node; calculating a first node degree of the first target node in the state data change track; if the first node degree is larger than a first set node degree, determining that the target state data meets a set condition, otherwise, determining that the target state data does not meet equipment conditions;

when the current track node is a normal track node, mapping the target state data to the state data change track to obtain a second target node; calculating a second node degree of the second target node in the state data change track; and if the second node degree is less than or equal to a second set node degree, determining that the target state data meets a set condition, otherwise, determining that the target state data does not meet the equipment condition.

For the description of the functional modules, reference is made to the description of the corresponding method steps, which are not further described here.

On the basis of the above, an equipment production line dispatching system based on the intelligent park is also provided, and the functionality of the system is described as follows.

B1. An equipment production line scheduling system based on a smart park comprises a cloud computing server, a configuration terminal, a matching terminal and a plurality of industrial equipment; the configuration terminal and the matching terminal are both communicated with the cloud computing server, and the configuration terminal is also communicated with a plurality of industrial devices;

a configuration terminal for:

collecting operating parameters from each industrial device;

a cloud computing server to:

acquiring the operation parameters of each industrial device through the configuration terminal; determining a plurality of state categories of corresponding target industrial equipment and associated information of the target industrial equipment in each state category from each group of operation parameters; determining equipment operation data of each industrial equipment under the same state category according to the operation parameters; generating a state characteristic matrix under each state category based on the associated information and the equipment operation data of each industrial equipment under the same state category, and storing the state characteristic matrix into the matching terminal;

a matching terminal for:

storing the state feature matrix;

a cloud computing server to:

B2. The system of B1, the cloud computing server, further configured to:

B3. The system of B1, the cloud computing server, further to:

B4. The system of B3, the cloud computing server, further to:

sending a target instruction to the configuration terminal;

B5. The system of any one of B1-B4, the cloud computing server, further to:

B6. The system of B1, the cloud computing server, further configured to:

B7. The system of B6, the cloud computing server, further to:

B8. The system of B1, the cloud computing server, further to:

judging whether the first data format is the same as the second data format;

B9. The system of B1, the cloud computing server, further to:

B10. the system of B1, the cloud computing server, further to:

For the description of the above system, refer to the description of the method shown in fig. 2, and will not be further described here.

On the basis, the present disclosure further discloses a hardware structure diagram of a cloud computing server, as shown in fig. 4, the cloud computing server 110 may include: a processor 111, and a memory 112 and a network interface 113 connected to the processor 111; the network interface 113 is connected to a nonvolatile memory 114 in the cloud computing server 110.

Further, the processor 111 retrieves a computer program from the non-volatile memory 114 via the network interface 113 when running, and runs the computer program via the memory 112 to perform the above method.

On the basis, a readable storage medium applied to a computer is further disclosed, and a computer program is burned in the readable storage medium, and when the computer program runs in the memory 112 of the cloud computing server 110, the method is implemented.

In summary, when the intelligent park-based equipment production line scheduling method and the cloud computing server according to the embodiment of the present application are applied, firstly, acquiring the operation parameters of each industrial device, determining a plurality of state categories of the corresponding target industrial device and the associated information of the target industrial device in each state category from each group of operation parameters, secondly, determining the equipment operation data of each industrial equipment in the same state category, generating a state characteristic matrix in each state category based on the associated information of each industrial equipment in the same state category and the equipment operation data, and importing the state characteristic matrix into a matching terminal, and then searching in the matching terminal based on the target state matrix, determining target state data of the target production line based on the searching result, and finally switching the current production line to the target production line when the target state data meets the set conditions.

The various technical features in the above embodiments can be arbitrarily combined, so long as there is no conflict or contradiction between the combinations of the features, but the combination is limited by the space and is not described one by one, and therefore, any combination of the various technical features in the above embodiments also belongs to the scope disclosed in the present specification.

Claims

1. A device production line scheduling method based on a smart park is applied to a cloud computing server which is communicated with a configuration terminal and a matching terminal, the configuration terminal is also communicated with a plurality of industrial devices, and the method comprises the following steps:

wherein: the operation parameters are acquired by the configuration terminal from each industrial device in real time, and comprise voltage data, current data, active power, operation duration and a mechanical loss value;

2. The method of claim 1, further comprising:

3. The method of claim 1, switching a current production line to the target production line, comprising:

4. The method of claim 3, causing the configuration terminal to send each target operational parameter to a corresponding industrial device, comprising:

sending a target instruction to the configuration terminal;

5. The method of any one of claims 1-4, generating a state feature matrix in each state category based on the association information and device operational data for each industrial device in the same state category, comprising:

6. The method of claim 1, before parsing the line switch command to obtain a target state matrix corresponding to a target production line, the method further comprising:

7. The method of claim 6, wherein analyzing the line switching command to obtain a target state matrix corresponding to a target production line comprises:

8. The method of claim 1, storing the state feature matrix in the matching terminal, comprising:

judging whether the first data format is the same as the second data format;

9. A cloud computing server, comprising:

a processor, and

a memory and a network interface connected with the processor;

the network interface is connected with a nonvolatile memory in the cloud computing server;

the processor, when running, retrieves a computer program from the non-volatile memory via the network interface and runs the computer program via the memory to perform the method of any of claims 1-8.