CN117148795B - Industrial equipment automatic management system and method based on big data - Google Patents
Industrial equipment automatic management system and method based on big data Download PDFInfo
- Publication number
- CN117148795B CN117148795B CN202311026193.3A CN202311026193A CN117148795B CN 117148795 B CN117148795 B CN 117148795B CN 202311026193 A CN202311026193 A CN 202311026193A CN 117148795 B CN117148795 B CN 117148795B
- Authority
- CN
- China
- Prior art keywords
- equipment
- linkage
- calling
- task
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000007726 management method Methods 0.000 claims abstract description 64
- 238000004519 manufacturing process Methods 0.000 claims abstract description 44
- 230000008569 process Effects 0.000 claims abstract description 29
- 238000004458 analytical method Methods 0.000 claims description 33
- 238000004364 calculation method Methods 0.000 claims description 18
- 230000010354 integration Effects 0.000 claims description 12
- 238000012098 association analyses Methods 0.000 claims description 3
- 238000010219 correlation analysis Methods 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 5
- 230000009471 action Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—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] or computer integrated manufacturing [CIM]
- 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] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an industrial equipment automation management system and method based on big data, and belongs to the technical field of data analysis. Configuring all equipment information required by production, generating an equipment function set according to the function attribute in the equipment information, and generating an equipment call set according to call information in the equipment information; the method comprises the steps of analyzing the association condition of equipment in the production process, generating a task association set, analyzing the linkage condition of the equipment in the calling process, and marking the linkage condition; counting the number of coincidence time lengths corresponding to the marking results, calculating first linkage probability among the devices, and calculating second linkage probability among the devices according to the coincidence time lengths corresponding to the marking results and the number of coincidence time lengths; determining linkage levels among the devices according to the first linkage probability and the second linkage probability, and automatically deciding the calling relation of the devices according to real-time job task arrangement; and further, the efficiency of equipment management is improved, and meanwhile, the efficient completion of the task is ensured.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to an industrial equipment automation management system and method based on big data.
Background
Plant equipment management is roughly divided into 3 stages, wherein the early stage is equipment introduction, the middle stage is equipment production, and the later stage is equipment scrapping, and the early stage and the middle stage are critical to efficient production of a plant; the equipment is imported, equipment which can be efficiently produced is selected according to the process technology, engineering technology, production requirements and equipment standards, and each link from equipment purchase application to equipment ordering cannot be neglected and must be executed according to equipment importing specifications; the equipment production aims at improving the comprehensive efficiency OEE of the equipment, and mainly works around reducing the fault downtime and improving the stability of the equipment;
further, it is known that the industrial equipment management is a very complex and cumbersome matter, and the consideration of multiple dimensions and links improves the accuracy of the equipment management, but is not beneficial to improving the efficiency of the equipment management.
Disclosure of Invention
The invention aims to provide an industrial equipment automation management system and method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
an industrial equipment automation management system based on big data, the system includes: the system comprises a comprehensive management platform module, a relationship analysis module, a data center processing module and an automatic decision module;
the integrated management platform module is used for establishing an industrial equipment integrated management platform and configuring all equipment information required by production; generating a device function set according to the function attribute in the device information, and generating a device call set according to the function attribute in the device information and the call information by taking the job task type as a unified integration scale;
the relation analysis module is used for analyzing the association condition of equipment in the production process according to the equipment function set and generating a task association set; analyzing linkage conditions of equipment in the calling process according to the task association set and the equipment calling set, and marking the linkage conditions;
the data center processing module calculates first linkage probability among the devices according to the marking result and the number of coincidence time lengths corresponding to the marking result; calculating second linkage probability among the devices according to the coincidence duration and the quantity of the coincidence durations corresponding to the marking result;
the automatic decision module determines linkage levels among the devices according to the first linkage probability and the second linkage probability, and calculates linkage level values among the devices; and according to the real-time job task arrangement, the automatic decision is made on the call relation of the equipment.
Further, the integrated management platform module further comprises a data configuration unit and an information overall arrangement unit;
the data configuration unit is used for establishing an industrial equipment integrated management platform, wherein all equipment information required by production is configured in the industrial equipment integrated management platform, the equipment information comprises functional attributes and calling information, the functional attributes are operation task types which can be completed by equipment, the calling information is information used in the production process of the equipment, and the calling information comprises calling times and calling time ranges;
the information overall management unit is used for overall managing all the devices configured in the industrial device overall management platform and carrying out unified numbering, and marking any one device as i; according to the function attribute in the equipment information, carrying out unified numbering on all job task types which can be completed by all the equipment; the equipment i is integrated to correspondingly complete all job task types and generates an equipment function set which is marked as X i ＝{M 1 ，M 2 ，...，M n M is }, where M 1 ，M 2 ，...，M n Respectively representing the 1 st, 2 nd, n job task types of the device i corresponding to the completion;
according to the function attribute and the call information in the equipment information, the call information of the equipment is summarized by taking the type of the operation task as a unified integration scale, and an equipment call set is generated and recorded as Y (M s |X i )＝{T i1 ，T i2 ，...，T im }, wherein T is i1 ，T i2 ，...，T im Respectively representing the calling time range of the device i in the 1 st, 2 nd, M times when the s-th job task type is completed, and M represents the calling times, M s ∈X i 。
Further, the relation analysis module further comprises a correlation analysis unit and a linkage analysis unit;
the association analysis unit analyzes the association condition of equipment in the production process according to the equipment function sets and acquires any two equipment function sets X i And X j Generating a task association set by using the association situation formed between the device i and the device j, and recording the task association set as W ij ＝X i ∩X j ；
The linkage analysis unit analyzes linkage conditions of equipment in the calling process according to the task association set and the equipment calling set, and any job task type M is selected from the task association set s I.e. M s ∈W ij ＝X i ∩X j Device call sets Y (M s |X i ) And Y (M) s |X j ) Task type M of the same time completion job s The calling time range of the time is marked as T ia And T ja And T is ia ∈Y(M s |X i )，T ja ∈Y(M s |X j ) If the time range T is called ia And T ja If there is coincidence, it indicates that the task type M is completed at the a-th time s Devices i and j are called together and the time range T is called ia And T ja Marking otherwise indicating that the job task type M is completed at the a-th time s Devices i and j are not commonly invoked at that time and the time range T is invoked ia And T ja No marking is made.
Further, the data center processing module further comprises a first linkage probability analysis unit and a second linkage probability analysis unit;
the first linkage probability analysis unit identifies a calling time range T according to the marking result ia And T ja The coincidence time between them is denoted as t a ：[T ia ，T ja ]The method comprises the steps of carrying out a first treatment on the surface of the Counting the number of coincidence time lengths corresponding to all marking results, namely A, calculating first linkage probability between the equipment i and j, wherein the specific calculation formula is as follows:
P 1 (i、j|M s )＝A/(m i +m j )
wherein P is 1 (i, j) represents the type M of task at completion s First linkage probability, m, between time equipment i and j i Representing a device call set Y (M s |X i ) Number of middle call time ranges, m j Representing a device call set Y (M s |X j ) The number of calling time ranges;
the second linkage probability analysis unit calculates the second linkage probability between the devices i and j according to the coincidence time length and the number of the coincidence time lengths corresponding to the marking result, and the specific calculation formula is as follows:
P 2 (i、j|M s )＝Σ a＝1 A {1-|t a /[T ia ]-t a /[T ja ]|}
wherein P is 2 (i, j) represents the type M of task at completion s Second probability of linkage between devices i and j, [ T ] ia ]And [ T ] ja ]Respectively represent the calling time ranges T ia And T ja Is a time range duration of (a).
Further, the automatic decision module further comprises a linkage grade analysis unit and an automatic output unit;
the linkage level analysis unit determines the linkage level between the equipment i and j according to the first linkage probability and the second linkage probability, calculates the linkage level value between the equipment i and j, and the specific calculation formula is as follows:
P(i，j)＝Σ M∈W P 1 (i、j|M s )×P 2 (i、j|M s )，M＝M s ，W＝W ij
wherein P (i, j) represents a linkage level value between devices i and j;
the automatic output unit is used for completing the job task type M according to real-time job task arrangement s When the operation task type M is called out reversely according to the equipment function set s And preferentially selecting the equipment with the largest linkage level value to participate in completing the job task type M s 。
An industrial equipment automation management method based on big data comprises the following steps:
step S100: establishing an industrial equipment comprehensive management platform and configuring all equipment information required by production; generating a device function set according to the function attribute in the device information, and generating a device call set according to the function attribute in the device information and the call information by taking the job task type as a unified integration scale;
step S200: according to the equipment function set, analyzing the association condition of equipment in the production process, and generating a task association set; analyzing linkage conditions of equipment in the calling process according to the task association set and the equipment calling set, and marking the linkage conditions;
step S300: counting the number of coincidence time lengths corresponding to the marking result according to the marking result, and calculating first linkage probability among the devices; calculating second linkage probability among the devices according to the coincidence duration and the quantity of the coincidence durations corresponding to the marking result;
step S400: determining linkage levels among the devices according to the first linkage probability and the second linkage probability, and calculating linkage level values among the devices; and according to the real-time job task arrangement, the automatic decision is made on the call relation of the equipment.
Further, the specific implementation process of the step S100 includes:
step S101: establishing an industrial equipment integrated management platform, wherein all equipment information required by production is configured in the industrial equipment integrated management platform, the equipment information comprises functional attributes and calling information, the functional attributes are job task types which can be completed by equipment, the calling information is information used in the production process of the equipment, and the calling information comprises calling times and calling time ranges;
step S102: all the devices configured in the industrial device integrated management platform are integrated and numbered uniformly, and any one device is marked as i; according to the function attribute in the equipment information, carrying out unified numbering on all job task types which can be completed by all the equipment; the equipment i is integrated to correspondingly complete all job task types and generates an equipment function set which is marked as X i ＝{M 1 ，M 2 ，...，M n M is }, where M 1 ，M 2 ，...，M n Respectively representing the 1 st, 2 nd, n job task types of the device i corresponding to the completion;
according to the function attribute and the call information in the equipment information, the call information of the equipment is summarized by taking the type of the operation task as a unified integration scale, and an equipment call set is generated and recorded as Y (M s |X i )＝{T i1 ，T i2 ，...，T im }, wherein T is i1 ，T i2 ，...，T im Respectively representing the calling time range of the device i at 1,2,..m times when the s-th job task type is completed, and mRepresents the number of calls, M s ∈X i 。
Further, the specific implementation process of the step S200 includes:
step S201: according to the equipment function set, analyzing the association condition of equipment in the production process to obtain any two equipment function sets X i And X j Generating a task association set by using the association situation formed between the device i and the device j, and recording the task association set as W ij ＝X i ∩X j ；
Step S202: according to the task association set and the equipment call set, analyzing linkage condition of equipment in the call process, and taking any one job task type M in the task association set s I.e. M s ∈W ij ＝X i ∩X j Device call sets Y (M s |X i ) And Y (M) s |X j ) Task type M of the same time completion job s The calling time range of the time is marked as T ia And T ja And T is ia ∈Y(M s |X i )，T ja ∈Y(M s |X j ) If the time range T is called ia And T ja If there is coincidence, it indicates that the task type M is completed at the a-th time s Devices i and j are called together and the time range T is called ia And T ja Marking otherwise indicating that the job task type M is completed at the a-th time s Devices i and j are not commonly invoked at that time and the time range T is invoked ia And T ja No marking is made.
Further, the implementation process of the step S300 includes:
step S301: based on the marking result, the calling time range T is identified ia And T ja The coincidence time between them is denoted as t a ：[T ia ，T ja ]The method comprises the steps of carrying out a first treatment on the surface of the Counting the number of coincidence time lengths corresponding to all marking results, namely A, calculating first linkage probability between the equipment i and j, wherein the specific calculation formula is as follows:
P 1 (i、j|M s )＝A/(m i +m j )
wherein,P 1 (i, j) represents the type M of task at completion s First linkage probability, m, between time equipment i and j i Representing a device call set Y (M s |X i ) Number of middle call time ranges, m j Representing a device call set Y (M s |X j ) The number of calling time ranges;
step S302: according to the coincidence duration and the quantity of the coincidence durations corresponding to the marking result, calculating the second linkage probability between the equipment i and j, wherein the specific calculation formula is as follows:
P 2 (i、j|M s )＝Σ a＝1 A {1-|t a /[T ia ]-t a /[T ja ]|}
wherein P is 2 (i, j) represents the type M of task at completion s Second probability of linkage between devices i and j, [ T ] ia ]And [ T ] ja ]Respectively represent the calling time ranges T ia And T ja Is a time range duration of (a).
Further, the specific implementation process of the step S400 includes:
step S401: according to the first linkage probability and the second linkage probability, determining a linkage level between the equipment i and the equipment j, and calculating a linkage level value between the equipment i and the equipment j, wherein a specific calculation formula is as follows:
P(i，j)＝Σ M∈W P 1 (i、j|M s )×P 2 (i、j|M s )，M＝M s ，W＝W ij
wherein P (i, j) represents a linkage level value between devices i and j;
step S402: according to real-time job task arrangement, when the job task type M needs to be completed s When the operation task type M is called out reversely according to the equipment function set s And preferentially selecting the equipment with the largest linkage level value to participate in completing the job task type M s 。
According to the above method, the conventional equipment management method selects equipment capable of efficient production according to the process technology, engineering technology, production requirements and equipment standards, and also needs to be reducedThe equipment management method has the advantages that the fault downtime is short, the equipment stability is improved to further develop work, and then the traditional equipment management method is complex and tedious, and the equipment management precision is improved, but the equipment management efficiency is not improved; however, the essence of the device management is to better realize the production requirement, namely, what device is required to be used at what time, and what production task is completed; furthermore, the invention is based on the core problem of equipment management, the operation task type is used as a guide, the equipment function set and the equipment calling set are comprehensively prepared, and further, the calling times and the time ranges of equipment are considered in the process of completing the operation task type each time when different operation task types are completed, so that the association condition of the equipment in the production process and the linkage condition of the equipment in the calling process are analyzed, the association condition of the equipment in the production process is not represented, the linkage is deeper, the association condition formed between the equipment is further generated into the task association set, the linkage condition of the equipment in the calling process is further analyzed, namely, the first linkage probability and the second linkage probability which are based on the calling time ranges and the calling times are deeply analyzed, the first linkage probability is a macroscopic linkage probability based on the quantity of the coincidence time length, the second linkage probability is a microscopic probability based on the difference of the coincidence time length, if the linkage between the two equipment is strong, the coincidence time length is large, and the occupation ratio of coincidence in the respective calling time is large, and the coincidence time length t|t| is larger a /[T ia ]-t a /[T ja ]The smaller the I, the opposite P 2 (i、j|M s ) The greater the value; and determining the linkage level between the devices through the first linkage probability and the second linkage probability.
Compared with the prior art, the invention has the following beneficial effects: in the industrial equipment automation management system and method based on big data, all equipment information required by production is configured, an equipment function set is generated according to the function attribute in the equipment information, and an equipment call set is generated according to call information in the equipment information; the method comprises the steps of analyzing the association condition of equipment in the production process, generating a task association set, analyzing the linkage condition of the equipment in the calling process, and marking the linkage condition; counting the number of coincidence time lengths corresponding to the marking results, calculating first linkage probability among the devices, and calculating second linkage probability among the devices according to the coincidence time lengths corresponding to the marking results and the number of coincidence time lengths; determining linkage levels among the devices according to the first linkage probability and the second linkage probability, and automatically deciding the calling relation of the devices according to real-time job task arrangement; and further, the efficiency of equipment management is improved, and meanwhile, the efficient completion of the task is ensured.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of an industrial equipment automation management system based on big data according to the present invention;
FIG. 2 is a schematic diagram of steps of an industrial equipment automation management method based on big data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions:
referring to fig. 1, in a first embodiment: there is provided an industrial equipment automation management system based on big data, the system comprising: the system comprises a comprehensive management platform module, a relationship analysis module, a data center processing module and an automatic decision module;
the integrated management platform module is used for establishing an industrial equipment integrated management platform and configuring all equipment information required by production; generating a device function set according to the function attribute in the device information, and generating a device call set according to the function attribute in the device information and the call information by taking the job task type as a unified integration scale;
the comprehensive management platform module further comprises a data configuration unit and an information overall planning unit;
the data configuration unit is used for establishing an industrial equipment integrated management platform, wherein all equipment information required by production is configured in the industrial equipment integrated management platform, the equipment information comprises functional attributes and calling information, the functional attributes are job task types which can be completed by equipment, the calling information is information used in the production process of the equipment, and the calling information comprises calling times and a calling time range;
the information overall management unit is used for overall managing all the equipment configured in the industrial equipment overall management platform and carrying out unified numbering, and marking any one equipment as i; according to the function attribute in the equipment information, carrying out unified numbering on all job task types which can be completed by all the equipment; the equipment i is integrated to correspondingly complete all job task types and generates an equipment function set which is marked as X i ＝{M 1 ，M 2 ，...，M n M is }, where M 1 ，M 2 ，...，M n Respectively representing the 1 st, 2 nd, n job task types of the device i corresponding to the completion;
according to the function attribute and the call information in the equipment information, the call information of the equipment is summarized by taking the type of the operation task as a unified integration scale, and an equipment call set is generated and recorded as Y (M s |X i )＝{T i1 ，T i2 ，...，T im And } wherein,
T i1 ，T i2 ，...，T im respectively representing the calling time range of the device i in the 1 st, 2 nd, M times when the s-th job task type is completed, and M represents the calling times, M s ∈X i ；
The relation analysis module is used for analyzing the association condition of equipment in the production process according to the equipment function set and generating a task association set; analyzing linkage conditions of equipment in the calling process according to the task association set and the equipment calling set, and marking the linkage conditions;
the relation analysis module further comprises a correlation analysis unit and a linkage analysis unit;
the association analysis unit is used for analyzing the association condition of equipment in the production process according to the equipment function sets and acquiring any two equipment function sets X i And X j Generating a task association set by using the association situation formed between the device i and the device j, and recording the task association set as W ij ＝X i ∩X j ；
The linkage analysis unit analyzes linkage conditions of equipment in the calling process according to the task association set and the equipment calling set, and any job task type M is selected in the task association set s I.e. M s ∈W ij ＝X i ∩X j Device call sets Y (M s |X i ) And Y (M) s |X j ) Task type M of the same time completion job s The calling time range of the time is marked as T ia And T ja And T is ia ∈Y(M s |X i )，T ja ∈Y(M s |X j ) If the time range T is called ia And T ja If there is coincidence, it indicates that the task type M is completed at the a-th time s Devices i and j are called together and the time range T is called ia And T ja Marking otherwise indicating that the job task type M is completed at the a-th time s Devices i and j are not commonly invoked at that time and the time range T is invoked ia And T ja Marking is not carried out;
the data center processing module calculates first linkage probability among the devices according to the marking result and the number of coincidence time lengths corresponding to the marking result; calculating second linkage probability among the devices according to the coincidence duration and the quantity of the coincidence durations corresponding to the marking result;
the data center processing module further comprises a first linkage probability analysis unit and a second linkage probability analysis unit;
first linkage probability analysis unitAccording to the marking result, identifying the calling time range T ia And T ja The coincidence time between them is denoted as t a ：[T ia ，T ja ]The method comprises the steps of carrying out a first treatment on the surface of the Counting the number of coincidence time lengths corresponding to all marking results, namely A, calculating first linkage probability between the equipment i and j, wherein the specific calculation formula is as follows:
P 1 (i、j|M s )＝A/(m i +m j )
wherein P is 1 (i, j) represents the type M of task at completion s First linkage probability, m, between time equipment i and j i Representing a device call set Y (M s |X i ) Number of middle call time ranges, m j Representing a device call set Y (M s |X j ) The number of calling time ranges;
the second linkage probability analysis unit calculates the second linkage probability between the devices i and j according to the coincidence time length and the number of the coincidence time lengths corresponding to the marking result, and the specific calculation formula is as follows:
P 2 (i、j|M s )＝Σ a＝1 A {1-|t a /[T ia ]-t a /[T ja ]|}
wherein P is 2 (i, j) represents the type M of task at completion s Second probability of linkage between devices i and j, [ T ] ia ]And [ T ] ja ]Respectively represent the calling time ranges T ia And T ja Is a time range duration of (a);
the automatic decision module is used for determining linkage levels among the devices according to the first linkage probability and the second linkage probability and calculating linkage level values among the devices; according to real-time job task arrangement, carrying out automatic decision on the calling relation of the equipment;
the automatic decision module further comprises a linkage grade analysis unit and an automatic output unit;
the linkage level analysis unit determines the linkage level between the equipment i and j according to the first linkage probability and the second linkage probability, calculates the linkage level value between the equipment i and j, and the specific calculation formula is as follows:
P(i，j)＝Σ M∈W P 1 (i、j|M s )×P 2 (i、j|M s )，M＝M s ，W＝W ij
wherein P (i, j) represents a linkage level value between devices i and j;
the automatic output unit is used for completing the job task type M according to real-time job task arrangement s When the operation task type M is called out reversely according to the equipment function set s And preferentially selecting the equipment with the largest linkage level value to participate in completing the job task type M s 。
Referring to fig. 2, in the second embodiment: an industrial equipment automation management method based on big data is provided, and the method comprises the following steps:
establishing an industrial equipment comprehensive management platform and configuring all equipment information required by production; generating a device function set according to the function attribute in the device information, and generating a device call set according to the function attribute in the device information and the call information by taking the job task type as a unified integration scale;
establishing an industrial equipment integrated management platform, wherein all equipment information required by production is configured in the industrial equipment integrated management platform, the equipment information comprises functional attributes and calling information, the functional attributes are job task types which can be completed by equipment, the calling information is information used in the production process of the equipment, and the calling information comprises calling times and calling time ranges;
all the devices configured in the industrial device integrated management platform are integrated and numbered uniformly, and any one device is marked as i; according to the function attribute in the equipment information, carrying out unified numbering on all job task types which can be completed by all the equipment; the equipment i is integrated to correspondingly complete all job task types and generates an equipment function set which is marked as X i ＝{M 1 ，M 2 ，...，M n M is }, where M 1 ，M 2 ，...，M n Respectively representing the 1 st, 2 nd, n job task types of the device i corresponding to the completion;
according to the equipment informationFunction attribute and call information in the message are integrated into a unified integration scale by taking the type of the job task, call information of the device is summarized, and a device call set is generated and recorded as Y (M s |X i )＝{T i1 ，T i2 ，...，T im And } wherein,
T i1 ，T i2 ，...，T im respectively representing the calling time range of the device i in the 1 st, 2 nd, M times when the s-th job task type is completed, and M represents the calling times, M s ∈X i ；
According to the equipment function set, analyzing the association condition of equipment in the production process, and generating a task association set; analyzing linkage conditions of equipment in the calling process according to the task association set and the equipment calling set, and marking the linkage conditions;
according to the equipment function set, analyzing the association condition of equipment in the production process to obtain any two equipment function sets X i And X j Generating a task association set by using the association situation formed between the device i and the device j, and recording the task association set as W ij ＝X i ∩X j ；
According to the task association set and the equipment call set, analyzing linkage condition of equipment in the call process, and taking any one job task type M in the task association set s I.e. M s ∈W ij ＝X i ∩X j Device call sets Y (M s |X i ) And Y (M) s |X j ) Task type M of the same time completion job s The calling time range of the time is marked as T ia And T ja And T is ia ∈
Y(M s |X i )，T ja ∈Y(M s |X j ) If the time range T is called ia And T ja If there is coincidence, it indicates that the task type M is completed at the a-th time s Devices i and j are called together and the time range T is called ia And T ja Marking otherwise indicating that the job task type M is completed at the a-th time s Devices i and j are not commonly invoked at the time and the time range is invokedT ia And T ja Marking is not carried out;
counting the number of coincidence time lengths corresponding to the marking result according to the marking result, and calculating first linkage probability among the devices; calculating second linkage probability among the devices according to the coincidence duration and the quantity of the coincidence durations corresponding to the marking result;
based on the marking result, the calling time range T is identified ia And T ja The coincidence time between them is denoted as t a ：[T ia ，T ja ]The method comprises the steps of carrying out a first treatment on the surface of the Counting the number of coincidence time lengths corresponding to all marking results, namely A, calculating first linkage probability between the equipment i and j, wherein the specific calculation formula is as follows:
P 1 (i、j|M s )＝A/(m i +m j )
wherein P is 1 (i, j) represents the type M of task at completion s First linkage probability, m, between time equipment i and j i Representing a device call set Y (M s |X i ) Number of middle call time ranges, m j Representing a device call set Y (M s |X j ) The number of calling time ranges;
according to the coincidence duration and the quantity of the coincidence durations corresponding to the marking result, calculating the second linkage probability between the equipment i and j, wherein the specific calculation formula is as follows:
P 2 (i、j|M s )＝Σ a＝1 A {1-|t a /[T ia ]-t a /[T ja ]|}
wherein P is 2 (i, j) represents the type M of task at completion s Second probability of linkage between devices i and j, [ T ] ia ]And [ T ] ja ]Respectively represent the calling time ranges T ia And T ja Is a time range duration of (a);
determining linkage levels among the devices according to the first linkage probability and the second linkage probability, and calculating linkage level values among the devices; according to real-time job task arrangement, carrying out automatic decision on the calling relation of the equipment;
according to the first linkage probability and the second linkage probability, determining a linkage level between the equipment i and the equipment j, and calculating a linkage level value between the equipment i and the equipment j, wherein a specific calculation formula is as follows:
P(i，j)＝Σ M∈W P 1 (i、j|M s )×P 2 (i、j|M s )，M＝M s ，W＝W ij
wherein P (i, j) represents a linkage level value between devices i and j;
according to real-time job task arrangement, when the job task type M needs to be completed s When the operation task type M is called out reversely according to the equipment function set s And preferentially selecting the equipment with the largest linkage level value to participate in completing the job task type M s ；
For example, according to real-time job task scheduling, when job task type M is required to be completed s When the operation task type M is called out reversely according to the equipment function set s Including device i, device j and device r, and by calculating P (i, j) =0.6, P (i, w) =0.5, and further selecting devices i and j to participate in completing job task type M s More spectrally, excluding device r.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, 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.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention and is not intended to limit the present invention, but although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for some of the technical features thereof. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. An industrial equipment automation management method based on big data is characterized by comprising the following steps:
step S100: establishing an industrial equipment comprehensive management platform and configuring all equipment information required by production; generating a device function set according to the function attribute in the device information, and generating a device call set according to the function attribute in the device information and the call information by taking the job task type as a unified integration scale;
step S200: according to the equipment function set, analyzing the association condition of equipment in the production process, and generating a task association set; analyzing linkage conditions of equipment in the calling process according to the task association set and the equipment calling set, and marking the linkage conditions;
step S300: counting the number of coincidence time lengths corresponding to the marking result according to the marking result, and calculating first linkage probability among the devices; calculating second linkage probability among the devices according to the coincidence duration and the quantity of the coincidence durations corresponding to the marking result;
step S400: determining linkage levels among the devices according to the first linkage probability and the second linkage probability, and calculating linkage level values among the devices; according to real-time job task arrangement, carrying out automatic decision on the calling relation of the equipment;
the specific implementation process of the step S100 includes:
step S101: establishing an industrial equipment integrated management platform, wherein all equipment information required by production is configured in the industrial equipment integrated management platform, the equipment information comprises functional attributes and calling information, the functional attributes are job task types which can be completed by equipment, the calling information is information used in the production process of the equipment, and the calling information comprises calling times and calling time ranges;
step S102: all the devices configured in the industrial device integrated management platform are integrated and numbered uniformly, and any one device is marked as i; according to the function attribute in the equipment information, carrying out unified numbering on all job task types which can be completed by all the equipment; the equipment i is integrated to correspondingly complete all job task types and generates an equipment function set which is marked as X i ＝{M 1 ，M 2 ，...，M n M is }, where M 1 ，M 2 ，...，M n Respectively representing the 1 st, 2 nd, n job task types of the device i corresponding to the completion;
according to the function attribute and the call information in the equipment information, the call information of the equipment is summarized by taking the type of the operation task as a unified integration scale, and an equipment call set is generated and recorded as Y (M s |X i )＝{T i1 ，T i2 ，...，T im }, wherein T is i1 ，T i2 ，...，T im Respectively representing the calling time range of the device i in the 1 st, 2 nd, M times when the s-th job task type is completed, and M represents the calling times, M s ∈X i ；
The specific implementation process of the step S200 includes:
step S201: according to the equipment function set, analyzing the association condition of equipment in the production process to obtain any two equipment function sets X i And X j Generating a task association set by using the association situation formed between the device i and the device j, and recording the task association set as W ij ＝X i ∩X j ；
Step S202: according to the task association set and the equipment call set, analyzing linkage condition of equipment in the call process, and taking any one job task type M in the task association set s I.e. M s ∈W ij ＝X i ∩X j Device call sets Y (M s |X i ) And Y (M) s |X j ) Task type M of the same time completion job s The calling time range of the time is marked as T ia And T ja And T is ia ∈Y(M s |X i )，T ja ∈Y(M s |X j ) If the time range T is called ia And T ja If there is coincidence, it indicates that the task type M is completed at the a-th time s Devices i and j are called together and the time range T is called ia And T ja Marking otherwise indicating that the job task type M is completed at the a-th time s Devices i and j are not commonly invoked at that time and the time range T is invoked ia And T ja Marking is not carried out;
the specific implementation process of the step S300 includes:
step S301: based on the marking result, the calling time range T is identified ia And T ja The coincidence time between them is denoted as t a ：[T ia ，T ja ]The method comprises the steps of carrying out a first treatment on the surface of the Counting the number of coincidence time lengths corresponding to all marking results, namely A, calculating first linkage probability between the equipment i and j, wherein the specific calculation formula is as follows:
P 1 (i、j|M s )＝A/(m i +m j )
wherein P is 1 (i, j) represents the type M of task at completion s First linkage probability, m, between time equipment i and j i Representing a device call set Y (M s |X i ) Number of middle call time ranges, m j Representing a device call set Y (M s |X j ) The number of calling time ranges;
step S302: according to the coincidence duration and the quantity of the coincidence durations corresponding to the marking result, calculating the second linkage probability between the equipment i and j, wherein the specific calculation formula is as follows:
P 2 (i、j|M s )＝Σ a＝1 A {1-|t a /[T ia ]-t a /[T ja ]|}
wherein P is 2 (i, j) represents the type M of task at completion s Second probability of linkage between devices i and j, [ T ] ia ]And [ T ] ja ]Respectively represent the calling time ranges T ia And T ja Is a time range duration of (a).
2. The automated industrial equipment management method based on big data according to claim 1, wherein the specific implementation process of step S400 includes:
step S401: according to the first linkage probability and the second linkage probability, determining a linkage level between the equipment i and the equipment j, and calculating a linkage level value between the equipment i and the equipment j, wherein a specific calculation formula is as follows:
P(i，j)＝Σ M∈W P 1 (i、j|M s )×P 2 (i、j|M s )，M＝M s ，W＝W ij
wherein P (i, j) represents a linkage level value between devices i and j;
step S402: according to real-time job task arrangement, when the job task type M needs to be completed s When the operation task type M is called out reversely according to the equipment function set s And preferentially selecting the equipment with the largest linkage level value to participate in completing the job task type M s 。
3. An industrial equipment automation management system based on big data, the system comprising: the system comprises a comprehensive management platform module, a relationship analysis module, a data center processing module and an automatic decision module;
the integrated management platform module is used for establishing an industrial equipment integrated management platform and configuring all equipment information required by production; generating a device function set according to the function attribute in the device information, and generating a device call set according to the function attribute in the device information and the call information by taking the job task type as a unified integration scale;
the relation analysis module is used for analyzing the association condition of equipment in the production process according to the equipment function set and generating a task association set; analyzing linkage conditions of equipment in the calling process according to the task association set and the equipment calling set, and marking the linkage conditions;
the data center processing module calculates first linkage probability among the devices according to the marking result and the number of coincidence time lengths corresponding to the marking result; calculating second linkage probability among the devices according to the coincidence duration and the quantity of the coincidence durations corresponding to the marking result;
the automatic decision module determines linkage levels among the devices according to the first linkage probability and the second linkage probability, and calculates linkage level values among the devices; according to real-time job task arrangement, carrying out automatic decision on the calling relation of the equipment;
the integrated management platform module also comprises a data configuration unit and an information overall planning unit;
the data configuration unit is used for establishing an industrial equipment integrated management platform, wherein all equipment information required by production is configured in the industrial equipment integrated management platform, the equipment information comprises functional attributes and calling information, the functional attributes are operation task types which can be completed by equipment, the calling information is information used in the production process of the equipment, and the calling information comprises calling times and calling time ranges;
the information overall management unit is used for overall managing all the devices configured in the industrial device overall management platform and carrying out unified numbering, and marking any one device as i; according to the function attribute in the equipment information, carrying out unified numbering on all job task types which can be completed by all the equipment; the equipment i is integrated to correspondingly complete all job task types and generates an equipment function set which is marked as X i ＝{M 1 ，M 2 ，...，M n M is }, where M 1 ，M 2 ，...，M n Respectively representing the 1 st, 2 nd, n job task types of the device i corresponding to the completion;
according to the function attribute and the call information in the equipment information, the call information of the equipment is summarized by taking the type of the operation task as a unified integration scale, and an equipment call set is generated and recorded as Y (M s |X i )＝{T i1 ，T i2 ，...，T im }, wherein T is i1 ，T i2 ，...，T im Respectively representing the calling time range of the device i in the 1 st, 2 nd, M times when the s-th job task type is completed, and M represents the calling times, M s ∈X i ；
The relation analysis module further comprises a correlation analysis unit and a linkage analysis unit;
the association analysis unit analyzes the association condition of equipment in the production process according to the equipment function sets and acquires any two equipment function sets X i And X j Generating a task association set by using the association situation formed between the device i and the device j, and recording the task association set as W ij ＝X i ∩X j ；
The linkage analysis unit analyzes linkage conditions of equipment in the calling process according to the task association set and the equipment calling set, and any job task type M is selected from the task association set s I.e. M s ∈W ij ＝X i ∩X j Device call sets Y (M s |X i ) And Y (M) s |X j ) Task type M of the same time completion job s The calling time range of the time is marked as T ia And T ja And T is ia ∈Y(M s |X i )，T ja ∈Y(M s |X j ) If the time range T is called ia And T ja If there is coincidence, it indicates that the task type M is completed at the a-th time s Devices i and j are called together and the time range T is called ia And T ja Marking otherwise indicating that the job task type M is completed at the a-th time s Devices i and j are not commonly invoked at that time and the time range T is invoked ia And T ja Marking is not carried out;
the data center processing module further comprises a first linkage probability analysis unit and a second linkage probability analysis unit;
the first linkage probability analysis unit identifies a calling time range T according to the marking result ia And T ja The coincidence time between them is denoted as t a ：[T ia ，T ja ]The method comprises the steps of carrying out a first treatment on the surface of the Counting the number of coincidence time lengths corresponding to all marking results, namely A, calculating first linkage probability between the equipment i and j, wherein the specific calculation formula is as follows:
P 1 (i、j|M s )＝A/(m i +m j )
wherein P is 1 (i, j) represents the type M of task at completion s First linkage probability, m, between time equipment i and j i Representing a device call set Y (M s |X i ) Number of middle call time ranges, m j Representing a device call set Y (M s |X j ) The number of calling time ranges;
the second linkage probability analysis unit calculates the second linkage probability between the devices i and j according to the coincidence time length and the number of the coincidence time lengths corresponding to the marking result, and the specific calculation formula is as follows:
P 2 (i、j|M s )＝Σ a＝1 A {1-|t a /[T ia ]-t a /[T ja ]|}
wherein P is 2 (i, j) represents the type M of task at completion s Second probability of linkage between devices i and j, [ T ] ia ]And [ T ] ja ]Respectively represent the calling time ranges T ia And T ja Is a time range duration of (a).
4. A big data based industrial equipment automation management system according to claim 3, wherein: the automatic decision module further comprises a linkage grade analysis unit and an automatic output unit;
the linkage level analysis unit determines the linkage level between the equipment i and j according to the first linkage probability and the second linkage probability, calculates the linkage level value between the equipment i and j, and the specific calculation formula is as follows:
P(i，j)＝Σ M∈W P 1 (i、j|M s )×P 2 (i、j|M s )，M＝M s ，W＝W ij
wherein P (i, j) represents a linkage level value between devices i and j;
the automatic output unit is used for completing the job task type M according to real-time job task arrangement s When the operation task type M is called out reversely according to the equipment function set s And preferably selects the setting with the largest linkage level valueTask type M of standby participation completion job s 。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311026193.3A CN117148795B (en) | 2023-08-15 | 2023-08-15 | Industrial equipment automatic management system and method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311026193.3A CN117148795B (en) | 2023-08-15 | 2023-08-15 | Industrial equipment automatic management system and method based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117148795A CN117148795A (en) | 2023-12-01 |
| CN117148795B true CN117148795B (en) | 2024-02-27 |
Family
ID=88901835
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311026193.3A Active CN117148795B (en) | 2023-08-15 | 2023-08-15 | Industrial equipment automatic management system and method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117148795B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112330128A (en) * | 2020-10-29 | 2021-02-05 | 广东电网有限责任公司 | Production operation management method, device, equipment and storage medium |
| CN112580993A (en) * | 2020-12-23 | 2021-03-30 | 云南电网有限责任公司昆明供电局 | Power grid equipment fault probability analysis method |
| CN112800102A (en) * | 2019-11-13 | 2021-05-14 | 中国移动通信有限公司研究院 | Alarm correlation calculation method and device and calculation equipment |
| CN115345485A (en) * | 2022-08-17 | 2022-11-15 | 珠海爱浦京软件股份有限公司 | Intelligent factory equipment data analysis management system and method based on big data |
| CN115657611A (en) * | 2022-09-23 | 2023-01-31 | 北京京东乾石科技有限公司 | Task allocation and processing method and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012004933A1 (en) * | 2010-07-09 | 2012-01-12 | パナソニック株式会社 | Object mapping device, method of mapping object, program and recording medium |
-
2023
- 2023-08-15 CN CN202311026193.3A patent/CN117148795B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112800102A (en) * | 2019-11-13 | 2021-05-14 | 中国移动通信有限公司研究院 | Alarm correlation calculation method and device and calculation equipment |
| CN112330128A (en) * | 2020-10-29 | 2021-02-05 | 广东电网有限责任公司 | Production operation management method, device, equipment and storage medium |
| CN112580993A (en) * | 2020-12-23 | 2021-03-30 | 云南电网有限责任公司昆明供电局 | Power grid equipment fault probability analysis method |
| CN115345485A (en) * | 2022-08-17 | 2022-11-15 | 珠海爱浦京软件股份有限公司 | Intelligent factory equipment data analysis management system and method based on big data |
| CN115657611A (en) * | 2022-09-23 | 2023-01-31 | 北京京东乾石科技有限公司 | Task allocation and processing method and device |
Non-Patent Citations (1)
| Title |
|---|
| 基于大数据分析的安全生产信息共享平台建设及管理;杨景峰;刘战武;;陕西煤炭;20200514(03);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117148795A (en) | 2023-12-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US7006878B2 (en) | Computer-implemented method for analyzing a problem statement based on an integration of Six Sigma, Lean Manufacturing, and Kaizen analysis techniques | |
| JP5002963B2 (en) | Factor estimation device, factor estimation program, recording medium storing factor estimation program, and factor estimation method | |
| CN111813624A (en) | Robot execution time length estimation method based on time length analysis and related equipment thereof | |
| CN115170057B (en) | OA approval control system and method based on machine learning | |
| JP2002076087A (en) | Managing system for sampling inspection | |
| CN112306004B (en) | Semiconductor process recipe management method and system | |
| CN112036806A (en) | Project full life cycle approximate calculation management method, device, equipment and storage medium | |
| CN117148795B (en) | Industrial equipment automatic management system and method based on big data | |
| CN111178017A (en) | Method and device for generating flow chart, storage medium and electronic equipment | |
| CN113570214A (en) | Nuclear power project risk network diagram analysis technology implementation method | |
| CN112860672A (en) | Method and device for determining label weight | |
| JP2020004161A (en) | Examination support apparatus, examination support method, and service providing method | |
| CN113887964B (en) | Production line simulation system based on man-machine method | |
| CN113992806A (en) | Intelligent voice RPA robot outbound method and device | |
| CN1329950C (en) | System and method for process contamination prevention for semiconductor manufacturing | |
| CN113935295A (en) | Valuation table summarizing method, valuation table summarizing device, valuation table summarizing equipment and valuation table summarizing medium | |
| CN117579812B (en) | Digital television platform intelligent operation and maintenance system and method based on big data | |
| CN107862501A (en) | A kind of solve PCB based on ERP and be merged method, terminal and the readable storage medium storing program for executing that plate scraps | |
| CN115277487B (en) | Communication processing system of electronic terminal | |
| CN114418456B (en) | Machine learning progress control method based on working conditions and related device | |
| Liyanage et al. | Design and development of a rapid data collection methodology | |
| JP2008152350A (en) | Working time estimation device, working time estimation method, program and machine-readable recording medium | |
| CN117745101A (en) | Computing method, computing system and terminal for industry recommendation technology service | |
| CN117217561A (en) | Data quality management system | |
| Mansor et al. | Dynamic Evaluation of Single Minutes of Exchange Die (SMED) Using Network Data Envelopment Analysis (DEA) |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |