CN116362462B - Full-closed-loop production management system based on Internet of things and big data analysis - Google Patents

Full-closed-loop production management system based on Internet of things and big data analysis Download PDF

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CN116362462B
CN116362462B CN202211446020.2A CN202211446020A CN116362462B CN 116362462 B CN116362462 B CN 116362462B CN 202211446020 A CN202211446020 A CN 202211446020A CN 116362462 B CN116362462 B CN 116362462B
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麦枝芳
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Maigao Guangdong Digital Technology Co ltd
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Abstract

The invention provides a full-closed loop production management system based on the Internet of things and big data analysis, which comprises the following components: the data middle station construction module is used for calling the target data component to construct a data middle station based on production management requirements and butting the data middle station with different systems to be managed; the data acquisition module is used for receiving production operation data of different systems to be managed according to the docking result and processing the production operation data to obtain a corresponding target processing result; the data application module is used for carrying out data mining on the target processing result and carrying out production regulation and control on the corresponding system to be managed based on the mining result. The production operation data is received through the data center station, the association that the system and the system are strongly coupled is eliminated, the received production operation data is analyzed, valuable data in the received production operation data are mined, the production processes of different systems to be managed are regulated and controlled through the valuable data, and accurate and effective management of all production links is ensured.

Description

Full-closed-loop production management system based on Internet of things and big data analysis
Technical Field
The invention relates to the technical field of data processing, in particular to a full-closed-loop production management system based on the Internet of things and big data analysis.
Background
At present, the industry is the main body of national economy and is an important foundation and pulse for the development of urban economy and society;
with the increasing scale of manufacturing industry, the aspects of network performance, stability, maintenance cost, service transformation and upgrading and the like in the traditional factory can not keep pace with the development pace of the manufacturing industry, meanwhile, industrial equipment is various, and an industrial management system is inflexible and intercommunicated, so that a lot of data can not be utilized, thereby causing the waste of the data and losing huge commercial value;
therefore, the invention provides a full-closed-loop production management system based on the Internet of things and big data analysis.
Disclosure of Invention
The invention provides a full-closed-loop production management system based on the Internet of things and big data analysis, which is used for receiving production operation data of different systems to be managed through a data center table, so that the correlation of the systems to be strongly coupled is eliminated, the data between the systems are more transparent, the received production operation data are analyzed and processed, valuable data are mined, the production process of the different systems to be managed is regulated and controlled through the valuable data, accurate and effective management of each production link is ensured, and stable operation of each link is ensured.
The invention provides a full-closed loop production management system based on the Internet of things and big data analysis, which comprises the following components:
the data middle station construction module is used for calling the target data component to construct a data middle station based on production management requirements and butting the data middle station with different systems to be managed;
the data acquisition module is used for receiving production operation data of different systems to be managed according to the docking result and processing the production operation data to obtain a corresponding target processing result;
the data application module is used for carrying out data mining on the target processing result and carrying out production regulation and control on the corresponding system to be managed based on the mining result.
Preferably, a full closed loop production management system based on internet of things and big data analysis, the data center station construction module includes:
the structure analysis unit is used for acquiring a structure map of the target mechanism, determining a system set to be managed contained in the target mechanism based on the structure map, and respectively extracting operation attributes of each system to be managed in the system set to be managed;
the information acquisition unit is used for determining the service type of each system to be managed based on the operation attribute and determining the production management requirement of each system to be managed based on the service type;
The data middle stage construction unit is used for matching corresponding target data components from a preset component library based on production management requirements, marking the target data components based on the corresponding relation between the system to be managed and the target data components, and packaging and combining the target data components according to preset data component combination logic based on marking results to obtain the final data middle stage.
Preferably, a full closed loop production management system based on internet of things and big data analysis, the data center station construction module includes:
the middle platform configuration unit is used for acquiring the constructed middle platform of the data, distributing docking ports for the middle platform of the data based on the target number of the systems to be managed, acquiring configuration parameters of the systems to be managed, and adapting the docking ports of the middle platform of the data based on the configuration parameters;
the docking relation determining unit is used for respectively acquiring the first communication addresses of different systems to be managed and the second communication addresses of different docking ports in the data center station based on the adapting result, and adding target identifiers for the first communication addresses and the second communication addresses;
and the system docking unit is used for determining the corresponding relation between the first communication address and the second communication address based on the target identifier and docking the system to be managed with the corresponding docking port in the data terminal based on the corresponding relation.
Preferably, a full closed loop production management system based on internet of things and big data analysis, the data acquisition module includes:
the data pushing type analysis unit is used for acquiring interaction modes between different systems to be managed and the data center station and determining data pushing modes between the different systems to be managed and the data center station based on the interaction modes, wherein the data pushing modes comprise active pushing and inactive pushing;
when the data pushing mode is active pushing, acquiring production operation data of different systems to be managed, carrying out standardization processing on the production operation data based on a target standard format of the data to be received, storing the standardized production operation data in a preset data warehouse, and pushing the stored production operation data to a data center based on a pushing plug-in;
when the data pushing mode is inactive pushing, production operation data of different systems to be managed are obtained, the production operation data are uploaded to the message middleware for caching, and the production operation data are transmitted to the data center station based on the message middleware after caching is finished.
Preferably, a full closed loop production management system based on internet of things and big data analysis, the data acquisition module includes:
The data receiving unit is used for receiving the production operation data generated by different systems to be managed based on the data center, extracting the data characteristics of the different production operation data, and determining the real-time level of the different production operation data based on the data characteristics;
the data classification unit is used for constructing a first hierarchical clustering center and a second hierarchical clustering center based on the real-time level, and clustering the obtained production operation data based on the first hierarchical clustering center and the second hierarchical clustering center to obtain a first data set and a second data set;
the data processing unit is used for determining a first target processing mode and a second target processing mode for the first data set and the second data set based on the real-time performance level, processing the corresponding data sets based on the first target processing mode and the second target processing mode respectively, and storing the processed production operation data.
Preferably, a full closed loop production management system based on internet of things and big data analysis, the data processing unit comprises:
the data acquisition subunit is used for acquiring the received production operation data, extracting attribute information of the production operation data and determining time stamps corresponding to different production operation data based on the attribute information;
The data analysis subunit is used for determining whether the production operation data received in the preset time period contains update data or not based on the time stamp, triggering a storage layer in the preset data warehouse to store the update data for the first time when the update data is contained, and simultaneously sending a calculation instruction to a detail layer in the preset data warehouse based on a storage result;
and the normalization subunit is used for controlling the detail layer to perform first statistics on the original data in the storage layer and second statistics on the updated data based on the calculation instructions, determining the latest version of complete data based on the first statistics and the second statistics, and performing second storage on the latest version of complete data.
Preferably, a full closed loop production management system based on internet of things and big data analysis, the regular subunit comprises:
the data processing subunit is used for acquiring the original data and the updated data in the storage layer, determining the checking dimension of the original data and the updated data, performing data traversal on the original data and the updated data based on the checking dimension, and determining the abnormal data field;
the data cleaning subunit is used for determining the field type of the abnormal data field, and cleaning the abnormal data field based on the field type by matching a target data cleaning rule from a preset data cleaning rule base to obtain standard original data and standard updated data;
The data specification subunit is used for acquiring specification standards of the data center station on production operation data of different systems to be managed, and carrying out format conversion on standard original data and standard update data generated by different systems based on the specification standards to obtain final latest version complete data.
Preferably, a full closed loop production management system based on internet of things and big data analysis, the regular subunit comprises:
the information acquisition subunit is used for acquiring the latest version of complete data obtained in the detail layer and acquiring service bodies and service types corresponding to different systems to be managed;
the modeling module is used for respectively determining screening indexes corresponding to the service main body and the service type, and respectively carrying out first modeling and second modeling based on the screening indexes to obtain a first model and a second model;
the data screening subunit is used for respectively carrying out first extraction and second extraction on the acquired latest version of complete data based on the first model and the second model to obtain a first extraction data set and a second extraction data set, respectively carrying out aggregation on the first extraction data set and the second extraction data set based on an aggregation index corresponding to a service main body and a service type, and carrying out third storage on an aggregation layer of the aggregated first extraction data set and second extraction data set in a preset data warehouse.
Preferably, a full closed loop production management system based on internet of things and big data analysis, the data screening subunit comprises:
the data interfacing subunit is used for acquiring the production operation data corresponding to the third storage result, extracting key fields of the production operation data, and determining data dimensions of different production operation data based on the key fields;
the service dimension acquisition subunit is used for acquiring service dimensions corresponding to different systems to be managed, matching the data dimensions with the service dimensions, and determining the data calling relation between different production operation data and different systems to be managed based on the matching result;
and the data storage subunit is used for distributing target data storage libraries for different systems to be managed based on the data calling relation, carrying out fourth storage on corresponding production operation data in the corresponding target data storage libraries, and butting a data interface of the target data storage libraries with a data calling interface of a corresponding system to be managed based on a storage result.
Preferably, a full closed loop production management system based on internet of things and big data analysis, the data application module includes:
the result acquisition unit is used for acquiring the processed production operation data and receiving call requests of different systems to be managed on the production operation data based on the data center;
The data mining unit is used for analyzing the call request, determining a search keyword in the call request, extracting a first feature vector corresponding to the search keyword, extracting a second feature vector of the processed production operation data, matching the first feature vector with the second feature vector to obtain an initial call data set, and extracting attribute values of all the production operation data of the initial call data set;
the optimizing unit is used for analyzing the attribute value of each production operation data based on the preset value evaluation model to obtain an effective value evaluation value corresponding to each production operation data, determining a target call data set based on the effective value evaluation value, and transmitting the target call data set to a corresponding system to be managed through the data call interface for production regulation.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
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 block diagram of a full closed loop production management system based on Internet of things and big data analysis in an embodiment of the invention;
FIG. 2 is a block diagram of a data center module in a full closed loop production management system based on Internet of things and big data analysis in an embodiment of the invention;
fig. 3 is a block diagram of a data acquisition module in a full closed loop production management system based on internet of things and big data analysis in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
the embodiment provides a full-closed loop production management system based on the internet of things and big data analysis, as shown in fig. 1, including:
the data middle station construction module is used for calling the target data component to construct a data middle station based on production management requirements and butting the data middle station with different systems to be managed;
The data acquisition module is used for receiving production operation data of different systems to be managed according to the docking result and processing the production operation data to obtain a corresponding target processing result;
the data application module is used for carrying out data mining on the target processing result and carrying out production regulation and control on the corresponding system to be managed based on the mining result.
In this embodiment, the production management requirements refer to the information construction basis, the data basis, the service characteristics and the like of the organization itself, that is, the related service types, the functions corresponding to the service types and the like.
In this embodiment, the target data component refers to a module capable of interfacing with and processing different traffic type data, and is not unique.
In this embodiment, the data platform refers to a system capable of interfacing with systems of different service types, and capable of uniformly processing production operation data of different systems to form standardized data, mining the most valuable data in the production operation data, and establishing an enterprise data asset library to provide consistent and standard data services.
In this embodiment, the system to be managed refers to an operation system included in the production operation of an enterprise or an organization, and specifically may be a customer relationship management system, an enterprise resource planning system, a production execution system, and the like.
In this embodiment, interfacing the data center with different systems to be managed refers to using the data center as an intermediate component, so that the systems are not directly related to the data center, but are directly related to the data center, and the data between the systems is more transparent from eliminating the association of the systems with strong coupling.
In this embodiment, the production operation data refers to operation data generated in the operation process of different systems, and specifically may be an execution condition of a service, a distribution condition of current resources of an enterprise, and the like.
In this embodiment, processing production operation data refers to extracting, cleaning, classifying, storing, and the like, of production operation data of different systems by a data center.
In this embodiment, the target processing result refers to a final processing result obtained after processing the production operation data of different systems by the data center.
In this embodiment, data mining is performed on the target processing result, which refers to analyzing extracted, cleaned, classified, and stored data, and extracting data that helps production operations of different systems, so as to facilitate optimization of a winning field for different systems according to the mined data, and the like.
In this embodiment, performing production control on the corresponding system to be managed based on the mining result refers to adjusting production operation modes or processes of different systems according to the mined data, so as to integrate the different systems and ensure the operation effect of the systems.
The beneficial effects of the technical scheme are as follows: the production operation data of different systems to be managed are received through the data center, so that the correlation of strong coupling between the systems is eliminated, the data between the systems are transparent, the received production operation data are analyzed and processed, valuable data are mined, the production processes of the different systems to be managed are regulated and controlled through the valuable data, accurate and effective management of all production links is ensured, and stable operation of all links is ensured.
Example 2:
on the basis of embodiment 1, this embodiment provides a full closed loop production management system based on the internet of things and big data analysis, as shown in fig. 2, the data center station building module includes:
the structure analysis unit is used for acquiring a structure map of the target mechanism, determining a system set to be managed contained in the target mechanism based on the structure map, and respectively extracting operation attributes of each system to be managed in the system set to be managed;
The information acquisition unit is used for determining the service type of each system to be managed based on the operation attribute and determining the production management requirement of each system to be managed based on the service type;
the data middle stage construction unit is used for matching corresponding target data components from a preset component library based on production management requirements, marking the target data components based on the corresponding relation between the system to be managed and the target data components, and packaging and combining the target data components according to preset data component combination logic based on marking results to obtain the final data middle stage.
In this embodiment, the target organization refers to an enterprise or organization that needs production management.
In this embodiment, the structural map refers to a form of a chart representing departments included in the target organization, running systems corresponding to the departments, and the like.
In this embodiment, the set of systems to be managed refers to all systems in the target organization that need to be managed, including customer relationship management systems, enterprise resource planning systems, and production execution systems.
In this embodiment, the operation attribute refers to an operation characteristic of each system to be managed in a production operation process, and may specifically be a business property, an operation mode method, and the like.
In this embodiment, the preset component library is preset in advance for storing different data components.
In this embodiment, the purpose of marking the target data component based on the correspondence between the system to be managed and the target data component is to facilitate, when different target data components are combined, corresponding combination of the target data components according to the type of the system to be managed corresponding to the target data component, so as to ensure that the constructed data center station can be effectively docked with different systems to be managed.
In this embodiment, the preset data assembly combination logic is set in advance, and is used to characterize a combination mode when different target data assemblies are combined, and so on.
In this embodiment, the packaging and combining of the target data components refers to packaging the independent target data components, that is, applying the target data components, so as to facilitate docking with the corresponding systems to be managed.
The beneficial effects of the technical scheme are as follows: the system to be managed contained in the target mechanism is determined, the system to be managed is analyzed, production management requirements of different systems to be managed are accurately and effectively acquired, corresponding target data components are conveniently matched from a preset data component library according to the production management requirements of different systems to be managed, finally, the matched target data components are packaged and combined, accuracy and reliability of data center table construction are guaranteed, butt joint with different systems to be managed is facilitated, and effective production management of different systems to be managed is achieved.
Example 3:
on the basis of embodiment 1, this embodiment provides a full closed loop production management system based on thing networking and big data analysis, the platform construction module in data includes:
the middle platform configuration unit is used for acquiring the constructed middle platform of the data, distributing docking ports for the middle platform of the data based on the target number of the systems to be managed, acquiring configuration parameters of the systems to be managed, and adapting the docking ports of the middle platform of the data based on the configuration parameters;
the docking relation determining unit is used for respectively acquiring the first communication addresses of different systems to be managed and the second communication addresses of different docking ports in the data center station based on the adapting result, and adding target identifiers for the first communication addresses and the second communication addresses;
and the system docking unit is used for determining the corresponding relation between the first communication address and the second communication address based on the target identifier and docking the system to be managed with the corresponding docking port in the data terminal based on the corresponding relation.
In this embodiment, the target number refers to the number of systems to be managed.
In this embodiment, the configuration parameters refer to the types of production operation data generated by different systems to be managed, the requirements in transmitting the production operation data to the data center, and the like.
In this embodiment, adapting the docking port of the data center station based on the configuration parameters refers to configuring parameters of the port of the data center station according to transmission requirements of different systems to be managed on production operation data, so as to ensure that the data center station can effectively dock with different systems to be managed.
In this embodiment, the first communication address refers to a network address corresponding to a different system to be managed.
In this embodiment, the second communication address refers to a network address corresponding to a different port in the data center station.
In this embodiment, the target identifier is a tag label for marking different communication addresses, and by using the identifier, a matching relationship between the communication addresses of the system to be managed and the docking port can be quickly and accurately determined.
The beneficial effects of the technical scheme are as follows: the data terminals are allocated with the docking ports according to the number of the systems to be managed, the docking ports are adapted according to the configuration parameters of different systems to be managed, and then the corresponding relation between the docking ports and the corresponding systems to be managed is determined according to the adapting result, so that the different systems to be managed and the data center station are accurately and effectively docked, and a favorable guarantee is provided for realizing management of production management.
Example 4:
on the basis of embodiment 1, this embodiment provides a full closed loop production management system based on thing networking and big data analysis, the data acquisition module includes:
the data pushing type analysis unit is used for acquiring interaction modes between different systems to be managed and the data center station and determining data pushing modes between the different systems to be managed and the data center station based on the interaction modes, wherein the data pushing modes comprise active pushing and inactive pushing;
when the data pushing mode is active pushing, acquiring production operation data of different systems to be managed, carrying out standardization processing on the production operation data based on a target standard format of the data to be received, storing the standardized production operation data in a preset data warehouse, and pushing the stored production operation data to a data center based on a pushing plug-in;
when the data pushing mode is inactive pushing, production operation data of different systems to be managed are obtained, the production operation data are uploaded to the message middleware for caching, and the production operation data are transmitted to the data center station based on the message middleware after caching is finished.
In this embodiment, the interaction mode refers to a manner of data interaction between different systems to be managed and the data center.
In this embodiment, the data pushing mode includes active pushing and inactive pushing, where active pushing refers to that a system stores data into a warehouse that can be accessed by a certain data center system according to a specification of the data center, and then pulls the data to the data center through a flime;
the inactive pushing refers to that the system uploads the produced data to Kafka for data caching [ kafak does not process the data and only does data transmission and caching ], and then a data middle platform consumes the data through kafak.
In this embodiment, the target specification format refers to a standard required by the data center station for the format of the data to be received, so as to facilitate unifying the formats of the production operation data generated by different systems to be managed.
In this embodiment, the preset data warehouse is set in advance, and is used for performing operations such as cleaning, classifying, and storing on production operation data generated by different systems to be managed.
In this embodiment, the push plug-in refers to a flime.
In this embodiment, the message middleware refers to a module for buffering data between different systems to be managed and the data center, and may be Kafka.
The beneficial effects of the technical scheme are as follows: the data pushing modes between different systems to be managed and the data center are determined, so that the produced production operation data can be conveniently transmitted to the data center according to the data pushing modes of the different systems to be managed, the data center can conveniently process the production operation data, accurate and effective production regulation and control of all systems in production operation are realized, and the effect of production management is guaranteed.
Example 5:
on the basis of embodiment 1, this embodiment provides a full closed loop production management system based on the internet of things and big data analysis, as shown in fig. 3, the data acquisition module includes:
the data receiving unit is used for receiving the production operation data generated by different systems to be managed based on the data center, extracting the data characteristics of the different production operation data, and determining the real-time level of the different production operation data based on the data characteristics;
the data classification unit is used for constructing a first hierarchical clustering center and a second hierarchical clustering center based on the real-time level, and clustering the obtained production operation data based on the first hierarchical clustering center and the second hierarchical clustering center to obtain a first data set and a second data set;
The data processing unit is used for determining a first target processing mode and a second target processing mode for the first data set and the second data set based on the real-time performance level, processing the corresponding data sets based on the first target processing mode and the second target processing mode respectively, and storing the processed production operation data.
In this embodiment, the data features refer to the value feature of the production operation data, the corresponding data type, and the like.
In this embodiment, the real-time level is used to characterize the real-time level of the different production operation data that needs to be processed, and specifically includes data that needs to be processed in real-time and non-real-time processing data.
In this embodiment, the first hierarchical clustering center refers to a data center of production operation data that needs to be processed in real time, so as to facilitate classifying the production operation data that needs to be processed in real time.
In this embodiment, the second hierarchical clustering center refers to a data center that does not require production run data for real-time processing, thereby facilitating categorizing the production run data that does not require real-time processing.
In this embodiment, the first data set refers to a production run data set that requires real-time processing.
In this embodiment, the second data set refers to a production run data set that does not require real-time processing.
In this embodiment, the first target processing mode refers to a processing mode of production operation data that needs to be processed in real time, and specifically may be stream processing, where the stream processing is to process the real-time data in real time and store the real-time data in a memory.
In this embodiment, the second target processing mode refers to a processing mode of production operation data that does not need to be processed in real time, and specifically may be batch processing/micro batch processing, where the batch processing/micro batch processing is to save acquired data first, and then perform unified processing after reaching a preset trigger condition, where the preset trigger condition may be that a data center station sends a data processing instruction.
The beneficial effects of the technical scheme are as follows: the real-time grade of the production operation data of different systems to be managed received in the data center is determined, so that the data to be processed in real time and the data to be processed in non-real time are classified, different processing modes are set for the production operation data to be processed in real time and the production operation data to be processed in non-real time, the production operation data with different real-time grades are accurately and efficiently processed according to the requirements, the data center can efficiently manage the different systems to be managed conveniently, and the management effect of the different systems to be managed is guaranteed.
Example 6:
on the basis of embodiment 5, this embodiment provides a full closed loop production management system based on internet of things and big data analysis, the data processing unit includes:
the data acquisition subunit is used for acquiring the received production operation data, extracting attribute information of the production operation data and determining time stamps corresponding to different production operation data based on the attribute information;
the data analysis subunit is used for determining whether the production operation data received in the preset time period contains update data or not based on the time stamp, triggering a storage layer in the preset data warehouse to store the update data for the first time when the update data is contained, and simultaneously sending a calculation instruction to a detail layer in the preset data warehouse based on a storage result;
and the normalization subunit is used for controlling the detail layer to perform first statistics on the original data in the storage layer and second statistics on the updated data based on the calculation instructions, determining the latest version of complete data based on the first statistics and the second statistics, and performing second storage on the latest version of complete data.
In this embodiment, the attribute information refers to reception information of different production operation data by the station in the data, and specifically includes the amount of received data, the time information of reception, and the like.
In this embodiment, the timestamp is used to characterize the specific receiving time of the production running data of the different systems to be managed by the data center, so as to facilitate judging whether the storage layer in the preset data warehouse has newly added production running data according to the time information.
In this embodiment, the preset time period is set in advance.
In this embodiment, the update data refers to whether the production run data stored in the preset data warehouse generates new production run data on an original basis within a preset period of time.
In this embodiment, the first storage means to control a storage layer in a preset data warehouse to store newly added field operation data, where the storage layer is a data storage module in the preset data warehouse, the preset data warehouse includes a storage layer, a detail layer, an aggregation layer and an application layer, and the production operation data is processed by the storage layer, and after sequentially undergoing processing of the detail layer and the aggregation layer, the data is finally docked at the application layer, so that different systems to be managed can call the production operation data, and the storage of the production operation data by each layer is sequentially reduced.
In this embodiment, the calculation instruction refers to controlling the detail layer to count the complete data of the current latest version.
In this embodiment, the original data refers to the original production run data in the storage layer.
In this embodiment, the first statistics refers to statistics of the data amount and the data content of the original production operation data in the storage layer.
In this embodiment, the second statistics refer to statistics on the data amount and data content of the update data generated in the storage.
In this embodiment, the most complete data refers to the sum of the original data and the updated data.
In this embodiment, the second storage refers to storing the most complete data in the detail layer.
The beneficial effects of the technical scheme are as follows: by determining the time stamps of different production operation data, whether the storage layer in the preset data warehouse is updated with the updated data or not is accurately judged, and when the storage layer is generated with the updated data, the latest version of complete data is counted and stored through the detail layer in the preset data warehouse, so that the production operation data generated by different systems to be managed can be accurately and effectively acquired by the data center, the latest version of complete data can be accurately and reliably analyzed, and reliable and beneficial data support can be provided for production regulation and control.
Example 7:
on the basis of embodiment 6, this embodiment provides a full closed loop production management system based on internet of things and big data analysis, the regular subunit includes:
the data processing subunit is used for acquiring the original data and the updated data in the storage layer, determining the checking dimension of the original data and the updated data, performing data traversal on the original data and the updated data based on the checking dimension, and determining the abnormal data field;
the data cleaning subunit is used for determining the field type of the abnormal data field, and cleaning the abnormal data field based on the field type by matching a target data cleaning rule from a preset data cleaning rule base to obtain standard original data and standard updated data;
the data specification subunit is used for acquiring specification standards of the data center station on production operation data of different systems to be managed, and carrying out format conversion on standard original data and standard update data generated by different systems based on the specification standards to obtain final latest version complete data.
In this embodiment, the inspection dimension refers to the angle at which the original data and the updated data are inspected for eligibility.
In this embodiment, the abnormal data field refers to a field missing, a data field non-uniform, a format error, a key information loss, and the like existing in the original data and the updated data.
In this embodiment, the preset data cleansing rule base is set in advance, and is used for storing data cleansing rules corresponding to different field types.
In this embodiment, the target data cleansing rule refers to a data cleansing rule adapted to cleansing data of a current abnormal data field.
In this embodiment, the standard original data and the standard update data refer to data obtained by removing abnormal data fields in the original data and the update data.
In this embodiment, the specification standard refers to a standard or a method of unifying data formats of production operation data of different systems to be managed by a data center.
The beneficial effects of the technical scheme are as follows: by determining the checking dimension of the original data and the updated data, the abnormal data fields in the original data and the updated data are accurately and effectively determined, the abnormal data fields are analyzed, the corresponding data cleaning rules are matched from the preset data cleaning rule base, the original data and the updated data are strictly and effectively cleaned, the data formats of the production operation data of different systems to be managed are unified after cleaning, and therefore the accuracy and the reliability of the production operation data are guaranteed, meanwhile, the accurate and effective processing of the production operation data by a data center is facilitated, and the processing efficiency of the production operation data is improved.
Example 8:
on the basis of embodiment 6, this embodiment provides a full closed loop production management system based on internet of things and big data analysis, the regular subunit includes:
the information acquisition subunit is used for acquiring the latest version of complete data obtained in the detail layer and acquiring service bodies and service types corresponding to different systems to be managed;
the modeling module is used for respectively determining screening indexes corresponding to the service main body and the service type, and respectively carrying out first modeling and second modeling based on the screening indexes to obtain a first model and a second model;
the data screening subunit is used for respectively carrying out first extraction and second extraction on the acquired latest version of complete data based on the first model and the second model to obtain a first extraction data set and a second extraction data set, respectively carrying out aggregation on the first extraction data set and the second extraction data set based on an aggregation index corresponding to a service main body and a service type, and carrying out third storage on an aggregation layer of the aggregated first extraction data set and second extraction data set in a preset data warehouse.
In this embodiment, the service body refers to a production theme corresponding to different systems to be managed.
In this embodiment, the screening index refers to a data screening standard corresponding to a service body and a service type, and aims to screen production operation data related to the service body and the service type corresponding to the system to be managed from a preset data warehouse.
In this embodiment, the first modeling and the second modeling respectively construct a corresponding data screening model according to the screening indexes corresponding to the service main body and the service type.
In this embodiment, the first model and the second model refer to data screening models that are constructed according to screening indexes and correspond to service subjects and service types.
In this embodiment, the first extraction refers to extracting data related to the business entity from production operation data in the detail layer through the first model.
In this embodiment, the first extraction refers to extracting data related to the service type from the production operation data in the detail layer through the first model.
In this embodiment, the first extraction data set and the second extraction data set refer to data related to the business entity and data related to the business type, which are obtained only from extraction of production operation data in the detail layer through the first model and the second model.
In this embodiment, the aggregation indicator refers to a criterion for aggregating the first and second extraction data sets, and specifically may be aggregating daily active users and aggregating operation bit dimension data.
In this embodiment, the third storage means that the production operation data after being extracted and aggregated is stored in an aggregation layer in a preset warehouse.
The beneficial effects of the technical scheme are as follows: corresponding data modeling is carried out according to the service main body and the service type by determining the service main body and the service type of different systems to be managed, the production operation data stored in the detail layer is extracted through the built model, the data related to the service main body of the different systems to be managed and the data related to the service type are extracted from a plurality of production operation data, the extracted data are aggregated and stored according to aggregation indexes, the production operation data generated by the different systems to be managed are further filtered through a data center, accurate and efficient processing of the production operation data is facilitated, and efficient and accurate management of all links in the production operation is achieved.
Example 9:
on the basis of embodiment 8, this embodiment provides a full closed loop production management system based on internet of things and big data analysis, the data screening subunit includes:
the data interfacing subunit is used for acquiring the production operation data corresponding to the third storage result, extracting key fields of the production operation data, and determining data dimensions of different production operation data based on the key fields;
the service dimension acquisition subunit is used for acquiring service dimensions corresponding to different systems to be managed, matching the data dimensions with the service dimensions, and determining the data calling relation between different production operation data and different systems to be managed based on the matching result;
and the data storage subunit is used for distributing target data storage libraries for different systems to be managed based on the data calling relation, carrying out fourth storage on corresponding production operation data in the corresponding target data storage libraries, and butting a data interface of the target data storage libraries with a data calling interface of a corresponding system to be managed based on a storage result.
In this embodiment, the key field refers to a piece of data that can characterize the content of production run data and the value characteristics.
In this embodiment, the data dimension refers to different service types in the production operation process corresponding to different production operation data.
In this embodiment, the service dimension refers to the service type involved in the different systems to be managed.
In this embodiment, the target data repository is set in advance, and is set in an application layer in the preset data repository, so as to store production running data corresponding to different systems to be managed into the corresponding target databases, thereby facilitating the call of the different systems to be managed to the data.
In this embodiment, the fourth storage means that production operation data corresponding to different systems to be managed are stored in corresponding target data storage libraries.
The beneficial effects of the technical scheme are as follows: the data dimension corresponding to different production operation data and the service dimension corresponding to different systems to be managed in the data center are determined, so that efficient determination of the production operation data corresponding to different systems to be managed is realized, the production operation data corresponding to different systems to be managed are respectively stored in the corresponding target data storage libraries, the target storage databases are in butt joint with the corresponding systems to be managed through the storage results, the different systems to be managed are convenient to call the production operation data, regulation and control of all links in production operation are facilitated, and strict and effective management of all links in the production operation process is guaranteed.
Example 10:
on the basis of embodiment 1, this embodiment provides a full closed loop production management system based on internet of things and big data analysis, the data application module includes:
the result acquisition unit is used for acquiring the processed production operation data and receiving call requests of different systems to be managed on the production operation data based on the data center;
the data mining unit is used for analyzing the call request, determining a search keyword in the call request, extracting a first feature vector corresponding to the search keyword, extracting a second feature vector of the processed production operation data, matching the first feature vector with the second feature vector to obtain an initial call data set, and extracting attribute values of all the production operation data of the initial call data set;
the optimizing unit is used for analyzing the attribute value of each production operation data based on the preset value evaluation model to obtain an effective value evaluation value corresponding to each production operation data, determining a target call data set based on the effective value evaluation value, and transmitting the target call data set to a corresponding system to be managed through the data call interface for production regulation.
In this embodiment, the call request includes the number of calls of different systems to be managed to production run data in the data center station, the type of the data to be called, and the like.
In this embodiment, the search keyword refers to a field in the call request that characterizes the kind and number of calls to production run data.
In this embodiment, the first feature vector refers to specific content and a value condition corresponding to the search keyword.
In this embodiment, the second feature vector refers to the specific content of the production operation data and the corresponding value condition.
In this embodiment, the initial call data set refers to production operation data which can be called and is matched from the data center according to the search keyword, wherein the production operation data includes useless or low-value production operation data.
In this embodiment, the attribute value refers to the value and the type of each production run data of the initial call data set.
In this embodiment, the preset value evaluation model is set in advance, and is used to evaluate the useful degree of the different production operation data to the system to be managed or the degree of correlation with the working performance of the system to be managed.
In this embodiment, the effective value evaluation value is a value representing the degree of the available value of different production operation data, and the larger the value is, the higher the available value is.
In this embodiment, the target call data set refers to a data set obtained by picking valuable production run data in the initial call data set.
In this embodiment, transmitting the target call data set to the corresponding system to be managed through the data call interface for production regulation, including:
the method comprises the steps of obtaining the data quantity of initial production operation data received by a storage layer in a preset data warehouse, calculating the data quantity of valuable production operation data reserved in an application layer in the preset data warehouse based on the data quantity of the initial production operation data, and calculating the production regulation and control efficiency of a system to be managed based on the data quantity of the valuable production operation data, wherein the specific steps comprise:
the data amount of valuable production run data retained in the application layer in the preset data warehouse is calculated according to the following formula:
wherein m represents an application layer in a preset data warehouseThe amount of valuable production run data retained; m represents the data quantity of initial production operation data received by a storage layer in a preset data warehouse; i represents the current times of screening the initial production operation data received by a storage layer in a preset data warehouse, and the value range is [1, n ] ]The method comprises the steps of carrying out a first treatment on the surface of the n represents the total times of screening the initial production operation data received by a storage layer in a preset data warehouse; s is S i Representing the data amount of the ith rejection of the production run data, and S i Is an integer; k (k) i The false judgment rate when the production operation data is screened for the ith time is represented, and the value range is 0.01,0.03; omega i A data amount representing production operation data before the i-th screening is performed; []Represents a rounding function for k i *ω i Rounding the result of (2);
calculating the production regulation efficiency of the system to be managed according to the following formula:
wherein η represents the production regulation efficiency of the system to be managed; μ represents an error factor, and the value range is (0.02, 0.04); t represents an expected time length value required for completing regulation and control of a system to be managed; gamma represents the calling proportion of the system to be managed to valuable production operation data, and the value range is (0, 1); m represents the data amount of valuable production operation data reserved in an application layer in a preset data warehouse; v represents the calling rate of the system to be managed on valuable production operation data reserved in an application layer in a preset data warehouse;
comparing the calculated production regulation efficiency with a preset threshold value;
If the calculated production regulation efficiency is greater than or equal to a preset threshold value, judging that the production regulation of the system to be managed based on the data center is qualified;
otherwise, judging that the production regulation and control of the data center station to the system to be managed is unqualified, and improving the calling rate of the system to be managed to valuable production operation data in the data center station until the calculated production regulation and control efficiency is greater than or equal to a preset threshold value.
The preset threshold is set in advance, is used for providing a judging basis for judging whether the regulation and control efficiency of the system to be managed is qualified, and can be adjusted.
The beneficial effects of the technical scheme are as follows: the method comprises the steps of receiving call requests of different systems to be managed, analyzing the call requests, accurately and effectively confirming an initial call data set, analyzing effective value evaluation values of production operation data in the initial call data set through a preset value evaluation model, accurately and effectively confirming a final target call data set, conveniently accurately regulating and controlling the systems to be managed through the target call data set, guaranteeing the regulation accuracy, and accurately and effectively regulating and controlling the production operation state of the systems to be managed through calculation of the production regulation efficiency of the systems to be managed, and guaranteeing the steady operation of the systems to be managed.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (9)

1. The utility model provides a full closed loop production management system based on thing networking and big data analysis which characterized in that includes:
the data middle station construction module is used for calling the target data component to construct a data middle station based on production management requirements and butting the data middle station with different systems to be managed;
the data acquisition module is used for receiving production operation data of different systems to be managed according to the docking result and processing the production operation data to obtain a corresponding target processing result;
the data application module is used for carrying out data mining on the target processing result and carrying out production regulation and control on the corresponding system to be managed based on the mining result;
the data application module comprises:
the result acquisition unit is used for acquiring the processed production operation data and receiving call requests of different systems to be managed on the production operation data based on the data center;
The data mining unit is used for analyzing the call request, determining a search keyword in the call request, extracting a first feature vector corresponding to the search keyword, extracting a second feature vector of the processed production operation data, matching the first feature vector with the second feature vector to obtain an initial call data set, and extracting attribute values of all the production operation data of the initial call data set;
the optimizing unit is used for analyzing the attribute value of each production operation data based on the preset value evaluation model to obtain an effective value evaluation value corresponding to each production operation data, determining a target call data set based on the effective value evaluation value, and transmitting the target call data set to a corresponding system to be managed through a data call interface for production regulation;
the method for transmitting the target call data set to the corresponding system to be managed through the data call interface for production regulation and control comprises the following steps:
the method comprises the steps of obtaining the data quantity of initial production operation data received by a storage layer in a preset data warehouse, calculating the data quantity of valuable production operation data reserved in an application layer in the preset data warehouse based on the data quantity of the initial production operation data, and calculating the production regulation and control efficiency of a system to be managed based on the data quantity of the valuable production operation data, wherein the specific steps comprise:
The data amount of valuable production run data retained in the application layer in the preset data warehouse is calculated according to the following formula:
wherein m represents the data amount of valuable production operation data reserved in an application layer in a preset data warehouse; m represents a storage layer in a preset data warehouseThe received data amount of the initial production operation data; i represents the current times of screening the initial production operation data received by a storage layer in a preset data warehouse, and the value range is [1, n ]]The method comprises the steps of carrying out a first treatment on the surface of the n represents the total times of screening the initial production operation data received by a storage layer in a preset data warehouse; s is S i Representing the data amount of the ith rejection of the production run data, and S i Is an integer; k (k) i The false judgment rate when the production operation data is screened for the ith time is represented, and the value range is 0.01,0.03; omega i A data amount representing production operation data before the i-th screening is performed; []Represents a rounding function for k i *ω i Rounding the result of (2);
calculating the production regulation efficiency of the system to be managed according to the following formula:
wherein η represents the production regulation efficiency of the system to be managed; μ represents an error factor, and the value range is (0.02, 0.04); t represents an expected time length value required for completing regulation and control of a system to be managed; gamma represents the calling proportion of the system to be managed to valuable production operation data, and the value range is (0, 1); m represents the data amount of valuable production operation data reserved in an application layer in a preset data warehouse; v represents the calling rate of the system to be managed on valuable production operation data reserved in an application layer in a preset data warehouse;
Comparing the calculated production regulation efficiency with a preset threshold value;
if the calculated production regulation efficiency is greater than or equal to a preset threshold value, judging that the production regulation of the system to be managed based on the data center is qualified;
otherwise, judging that the production regulation and control of the data center station to the system to be managed is unqualified, and improving the calling rate of the system to be managed to valuable production operation data in the data center station until the calculated production regulation and control efficiency is greater than or equal to a preset threshold value.
2. The full closed loop production management system based on internet of things and big data analysis of claim 1, wherein the data center building module comprises:
the structure analysis unit is used for acquiring a structure map of the target mechanism, determining a system set to be managed contained in the target mechanism based on the structure map, and respectively extracting operation attributes of each system to be managed in the system set to be managed;
the information acquisition unit is used for determining the service type of each system to be managed based on the operation attribute and determining the production management requirement of each system to be managed based on the service type;
the data middle stage construction unit is used for matching corresponding target data components from a preset component library based on production management requirements, marking the target data components based on the corresponding relation between the system to be managed and the target data components, and packaging and combining the target data components according to preset data component combination logic based on marking results to obtain the final data middle stage.
3. The full closed loop production management system based on internet of things and big data analysis of claim 1, wherein the data center building module comprises:
the middle platform configuration unit is used for acquiring the constructed middle platform of the data, distributing docking ports for the middle platform of the data based on the target number of the systems to be managed, acquiring configuration parameters of the systems to be managed, and adapting the docking ports of the middle platform of the data based on the configuration parameters;
the docking relation determining unit is used for respectively acquiring the first communication addresses of different systems to be managed and the second communication addresses of different docking ports in the data center station based on the adapting result, and adding target identifiers for the first communication addresses and the second communication addresses;
and the system docking unit is used for determining the corresponding relation between the first communication address and the second communication address based on the target identifier and docking the system to be managed with the corresponding docking port in the data terminal based on the corresponding relation.
4. The full closed loop production management system based on internet of things and big data analysis of claim 1, wherein the data acquisition module comprises:
the data pushing type analysis unit is used for acquiring interaction modes between different systems to be managed and the data center station and determining data pushing modes between the different systems to be managed and the data center station based on the interaction modes, wherein the data pushing modes comprise active pushing and inactive pushing;
When the data pushing mode is active pushing, acquiring production operation data of different systems to be managed, carrying out standardization processing on the production operation data based on a target standard format of the data to be received, storing the standardized production operation data in a preset data warehouse, and pushing the stored production operation data to a data center based on a pushing plug-in;
when the data pushing mode is inactive pushing, production operation data of different systems to be managed are obtained, the production operation data are uploaded to the message middleware for caching, and the production operation data are transmitted to the data center station based on the message middleware after caching is finished.
5. The full closed loop production management system based on internet of things and big data analysis of claim 1, wherein the data acquisition module comprises:
the data receiving unit is used for receiving the production operation data generated by different systems to be managed based on the data center, extracting the data characteristics of the different production operation data, and determining the real-time level of the different production operation data based on the data characteristics;
the data classification unit is used for constructing a first hierarchical clustering center and a second hierarchical clustering center based on the real-time level, and clustering the obtained production operation data based on the first hierarchical clustering center and the second hierarchical clustering center to obtain a first data set and a second data set;
The data processing unit is used for determining a first target processing mode and a second target processing mode for the first data set and the second data set based on the real-time performance level, processing the corresponding data sets based on the first target processing mode and the second target processing mode respectively, and storing the processed production operation data.
6. The full closed loop production management system based on internet of things and big data analysis of claim 5, wherein the data processing unit comprises:
the data acquisition subunit is used for acquiring the received production operation data, extracting attribute information of the production operation data and determining time stamps corresponding to different production operation data based on the attribute information;
the data analysis subunit is used for determining whether the production operation data received in the preset time period contains update data or not based on the time stamp, triggering a storage layer in the preset data warehouse to store the update data for the first time when the update data is contained, and simultaneously sending a calculation instruction to a detail layer in the preset data warehouse based on a storage result;
and the normalization subunit is used for controlling the detail layer to perform first statistics on the original data in the storage layer and second statistics on the updated data based on the calculation instructions, determining the latest version of complete data based on the first statistics and the second statistics, and performing second storage on the latest version of complete data.
7. The full closed loop production management system based on internet of things and big data analysis of claim 6, wherein the regular sub-unit comprises:
the data processing subunit is used for acquiring the original data and the updated data in the storage layer, determining the checking dimension of the original data and the updated data, performing data traversal on the original data and the updated data based on the checking dimension, and determining the abnormal data field;
the data cleaning subunit is used for determining the field type of the abnormal data field, and cleaning the abnormal data field based on the field type by matching a target data cleaning rule from a preset data cleaning rule base to obtain standard original data and standard updated data;
the data specification subunit is used for acquiring specification standards of the data center station on production operation data of different systems to be managed, and carrying out format conversion on standard original data and standard update data generated by different systems based on the specification standards to obtain final latest version complete data.
8. The full closed loop production management system based on internet of things and big data analysis of claim 6, wherein the regular sub-unit comprises:
The information acquisition subunit is used for acquiring the latest version of complete data obtained in the detail layer and acquiring service bodies and service types corresponding to different systems to be managed;
the modeling module is used for respectively determining screening indexes corresponding to the service main body and the service type, and respectively carrying out first modeling and second modeling based on the screening indexes to obtain a first model and a second model;
the data screening subunit is used for respectively carrying out first extraction and second extraction on the acquired latest version of complete data based on the first model and the second model to obtain a first extraction data set and a second extraction data set, respectively carrying out aggregation on the first extraction data set and the second extraction data set based on an aggregation index corresponding to a service main body and a service type, and carrying out third storage on an aggregation layer of the aggregated first extraction data set and second extraction data set in a preset data warehouse.
9. The full closed loop production management system based on internet of things and big data analysis of claim 8, wherein the data screening subunit comprises:
the data interfacing subunit is used for acquiring the production operation data corresponding to the third storage result, extracting key fields of the production operation data, and determining data dimensions of different production operation data based on the key fields;
The service dimension acquisition subunit is used for acquiring service dimensions corresponding to different systems to be managed, matching the data dimensions with the service dimensions, and determining the data calling relation between different production operation data and different systems to be managed based on the matching result;
and the data storage subunit is used for distributing target data storage libraries for different systems to be managed based on the data calling relation, carrying out fourth storage on corresponding production operation data in the corresponding target data storage libraries, and butting a data interface of the target data storage libraries with a data calling interface of a corresponding system to be managed based on a storage result.
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