CN112580989A - Cloud platform data management system and management method based on industrial big data - Google Patents
Cloud platform data management system and management method based on industrial big data Download PDFInfo
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Abstract
The embodiment of the invention discloses a cloud platform data management system and a management method based on industrial big data for industrial management, wherein the cloud platform data management system based on the industrial big data comprises a data classification module, a data analysis module, a prediction data output module, a purchase management module, an equipment loss prediction module, a loss evaluation module and a remote alarm module; and the data classification module is used for collecting product marketing data and periodically classifying the marketing data. The invention collects the product marketing data, draws graphs in a periodic classification way, inquires the prediction data according to the graph data, confirms and manages the purchasing raw materials for suppliers, can ensure the sufficiency of the raw materials when the prediction data is processed, can conveniently and timely adjust the processing strategy or replace and maintain equipment for processing and production by inquiring the residual loss of the equipment, ensures the safety index during processing and ensures the processing quality of the product.
Description
Technical Field
The embodiment of the invention relates to industrial management, in particular to a cloud platform data management system and a cloud platform data management method based on industrial big data.
Background
The industrial big data is a product of combination of the Internet, the big data and the industrial industry, and simultaneously counteracts the development of industrial upgrading. For the manufacturing industry, the background generated by industry big data is known, the classification and the characteristics of the industry big data are induced, and the practical significance is realized by considering and rebuilding the industry value flow from the perspective of promoting the value creation by data flow.
The existing industrial equipment carries out quantitative processing according to the amount of orders in the product processing process, but when the sale is in a busy season or a slack season, the phenomenon that supply and demand are not met or products are left in the processed products can occur, the prediction and management work of industrial production big data cannot be well realized, and the industrial production is blindness.
Based on the above, the invention designs a cloud platform data management system and a cloud platform data management method based on industrial big data, so as to solve the above problems.
Disclosure of Invention
The embodiment of the invention provides a cloud platform data management system and a cloud platform data management method based on industrial big data, and aims to solve the technical problems in the background technology.
The embodiment of the invention provides a cloud platform data management system based on industrial big data. In one feasible scheme, the system comprises a data classification module, a data analysis module, a prediction data output module, a purchase management module, an equipment loss prediction module, a loss evaluation module and a remote alarm module;
the data classification module is used for collecting product marketing data and periodically classifying the marketing data;
the data analysis module is used for predicting industrial processing data according to the periodic marketing data chart;
the prediction data output module is used for outputting prediction data to the equipment loss prediction module and the purchase management module;
the purchasing management module is used for managing a purchasing list to a purchaser according to the prediction data;
the equipment loss prediction module is used for predicting the equipment loss data according to the industrial processing prediction data;
the loss evaluation module is used for monitoring the residual evaluation loss of the equipment according to the predicted loss;
and the remote alarm module is used for carrying out remote manual end alarm when the residual evaluation loss is lower than a specified value.
The embodiment of the invention provides a cloud platform data management system based on industrial big data. In one possible approach, the data classification module includes a marketing data collection module and a period division module;
the marketing data acquisition module is used for acquiring marketing data information of industrial production products;
and the period division module is used for periodically dividing all marketing data during purchase.
The embodiment of the invention provides a cloud platform data management system based on industrial big data. In one possible scheme, the data analysis module comprises a chart generation module, a data comparison module and a comparison data storage module;
the chart generation module is used for converting the chart data of the periodic classified marketing data;
the comparison data storage module is used for storing processing prediction data information corresponding to all marketing chart data;
and the data comparison module is used for comparing the similarity of the converted graph data with the comparison graph data and searching the processing prediction data of which the similarity with the comparison image reaches the specified similarity.
The embodiment of the invention provides a cloud platform data management system based on industrial big data. In one possible scheme, the purchase management module comprises a purchase list management module, a buyer notification module and a feedback confirmation module;
the purchasing list management module is used for measuring and calculating a purchasing material list according to input processing prediction data;
the buyer notification module is used for notifying the data information of each purchased material to the corresponding buyer;
and the feedback confirmation module is used for collecting feedback confirmation signals of sufficient materials of all the purchasers.
The embodiment of the invention provides a cloud platform data management system based on industrial big data. In one possible solution, the purchase list management module includes a prediction data extraction module, a demand material calculation module, an inventory amount query module, an inventory data storage module, and a purchase amount calculation module;
the predicted data extraction module is used for extracting the machining predicted quantity information input by the predicted data output module;
the required material calculating module is used for calculating and calculating the required machining material quantity according to the machining predicted quantity information;
the stock data storage module is used for storing stock data of all materials;
the inventory quantity query module is used for calling and querying the residual inventory quantity corresponding to the predicted data material for query;
and the purchase quantity calculation module is used for measuring and calculating the data of the required purchase quantity according to the quantity of the required materials and the residual inventory quantity.
The embodiment of the invention provides a cloud platform data management system based on industrial big data. In one possible scheme, the loss evaluation module comprises a loss amount calculation module, an evaluation loss storage module and a data monitoring module;
the estimated loss storage module is used for storing loss estimation data of production and processing of each device;
the loss amount calculation module is used for calculating residual loss data of the equipment according to the prediction loss calculated by the equipment loss prediction module and the loss evaluation of the equipment;
and the data monitoring module is used for monitoring a control signal to the remote alarm module when the residual loss data is lower than a specified value.
The embodiment of the invention also provides a cloud platform data management method based on the industrial big data. In one possible embodiment, the method comprises the following steps:
s1, collecting marketing big data of industrial products and carrying out periodic division;
s2, carrying out graph analysis on the periodic marketing data, and searching and comparing predicted data of which the graph big data reach specified similarity;
s3, measuring and calculating a raw material list according to the prediction data, sending a notice to a buyer and receiving a material sufficiency feedback signal of the buyer;
s4, predicting the loss data of the processing equipment according to the predicted data, and measuring and calculating the residual loss of the equipment according to the estimated loss;
and S5, when the residual loss of the equipment is lower than the specified value, controlling to alarm the manual end.
Based on the scheme, the invention has the advantages that the purchase raw materials are confirmed and managed by suppliers by acquiring product marketing data, drawing charts in a classified mode periodically and inquiring the prediction data according to the chart data, so that the sufficiency of the raw materials during processing of the prediction data can be ensured, the residual loss of equipment can be inquired, the processing strategy can be conveniently adjusted in time or the equipment can be conveniently replaced and maintained for processing production, the safety index during processing is ensured, and the processing quality of the product is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is an overall system architecture diagram of the present invention;
FIG. 2 is a diagram of the system of FIG. 1 of the present invention;
FIG. 3 is a system diagram of a purchase list management module according to the present invention;
FIG. 4 is a flow chart of the management method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "axial," "radial," "circumferential," and the like are used in the indicated orientations and positional relationships based on the drawings for convenience in describing and simplifying the description, but do not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention.
In the present invention, unless otherwise specifically stated or limited, the terms "mounted," "connected," "fixed," and the like are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally formed; the connection can be mechanical connection, electrical connection or communication connection; either directly or indirectly through intervening media, either internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 1-3 are a cloud platform data management system based on industrial big data provided by the invention; the system comprises a data classification module, a data analysis module, a prediction data output module, a purchase management module, an equipment loss prediction module, a loss evaluation module and a remote alarm module;
the data classification module is used for collecting product marketing data and periodically classifying the marketing data;
the data analysis module is used for predicting industrial processing data according to the periodic marketing data chart;
the prediction data output module is used for outputting prediction data to the equipment loss prediction module and the purchase management module;
the purchasing management module is used for managing a purchasing list to a purchaser according to the prediction data;
the equipment loss prediction module is used for predicting the equipment loss data according to the industrial processing prediction data;
the loss evaluation module is used for monitoring the residual evaluation loss of the equipment according to the predicted loss;
and the remote alarm module is used for carrying out remote manual end alarm when the residual evaluation loss is lower than a specified value.
Through the content, in the process of carrying out cloud platform management on industrial data by using the cloud platform data management system based on industrial big data, the period marketing data of industrial production products, such as weekly statistic or monthly statistic data, is collected through the data classification module, the marketing data of each period is drawn into a chart through the data analysis module, the chart is compared and judged with the big data of the comparison chart, the prediction data of future processing products corresponding to the comparison chart and reaching the specified similarity are searched, and the prediction data are respectively output to the equipment loss prediction module and the purchase management module through the prediction data output module; in the purchase management module, the amount information of each processing material is determined by extracting the processing amount prediction information in the prediction data, and the amount information is informed to a purchaser to confirm whether the supply of goods is sufficient, so that the high efficiency of processing work is ensured, in order to ensure the normal use of processing equipment, the loss information of the equipment is further predicted by the equipment loss prediction module for the prediction data, then the remaining usable equipment information is inquired by the loss total evaluation value information of the loss when each equipment is used in the loss evaluation module, and when the remaining loss is lower than a specified value, the signal is informed to a background manager by the remote alarm module, so that the equipment can be ensured to finish normal production and processing.
Optionally, the data classification module includes a marketing data acquisition module and a period division module;
the marketing data acquisition module is used for acquiring marketing data information of industrial production products;
and the period division module is used for periodically dividing all marketing data during purchase. It should be noted that, in this embodiment, after the marketing data of the industrial product is collected, the periodic classification processing is performed according to the purchase information, so that the chart production work can be performed on the periodic marketing data according to the period conveniently.
In addition, the data analysis module comprises a chart generation module, a data comparison module and a comparison data storage module;
the chart generation module is used for converting the chart data of the periodic classified marketing data;
the comparison data storage module is used for storing processing prediction data information corresponding to all marketing chart data;
the data comparison module is used for comparing the similarity of the converted graph data with the comparison graph data and searching the processing prediction data of which the similarity with the comparison image reaches the specified similarity; after the periodic marketing data is generated into a chart, similarity query is carried out on the big data of the comparison chart corresponding to the big data of the forecast, and forecast data information corresponding to the specified comparison chart is queried, so that further management work can be carried out on the industrial data according to the forecast data.
More specifically, the purchasing management module comprises a purchasing list management module, a purchasing merchant notification module and a feedback confirmation module;
the purchasing list management module is used for measuring and calculating a purchasing material list according to input processing prediction data;
the buyer notification module is used for notifying the data information of each purchased material to the corresponding buyer;
the feedback confirmation module is used for collecting feedback confirmation signals of sufficient materials of all purchasers; in the process of purchasing management, a purchasing list of purchased materials is measured and calculated through the forecast data and is sent to each supplier, whether the goods source of each supplier and buyer is sufficient or not is confirmed, and the problems that the supplies are insufficient and the goods are inconvenient to transfer when more orders are placed in the later period are solved.
Further, the purchase inventory management module comprises a prediction data extraction module, a demand material calculation module, a stock quantity inquiry module, an inventory data storage module and a purchase quantity calculation module;
the predicted data extraction module is used for extracting the machining predicted quantity information input by the predicted data output module;
the required material calculating module is used for calculating and calculating the required machining material quantity according to the machining predicted quantity information;
the stock data storage module is used for storing stock data of all materials;
the inventory quantity query module is used for calling and querying the residual inventory quantity corresponding to the predicted data material for query;
the purchasing quantity calculating module is used for measuring and calculating the required purchasing quantity data according to the required material quantity and the remaining inventory quantity; in the process of managing the purchase list, the required raw material quantity information is measured and calculated through the processing quantity prediction information in the prediction data, and the inventory quantity information of the system is called, so that the required supply and purchase information can be further confirmed.
Preferably, the loss evaluation module comprises a loss amount calculation module, an evaluation loss storage module and a data monitoring module;
the estimated loss storage module is used for storing loss estimation data of production and processing of each device;
the loss amount calculation module is used for calculating residual loss data of the equipment according to the prediction loss calculated by the equipment loss prediction module and the loss evaluation of the equipment;
the data monitoring module is used for monitoring a control signal to the remote alarm module when the residual loss data is lower than a specified value; in the process of estimating the loss, the production loss of the processing equipment is estimated according to experts, system factory estimation and the like, the loss estimation result is stored, the residual available loss information is measured and calculated according to the predicted equipment loss information, and then the alarm control is carried out when the residual loss is lower than the specified value, so that the danger brought to the processing production when the equipment exceeds the loss upper limit and works can be prevented.
Fig. 4 is a cloud platform data management method based on industrial big data, which includes the following steps:
s1, collecting marketing big data of industrial products and carrying out periodic division;
s2, carrying out graph analysis on the periodic marketing data, and searching and comparing predicted data of which the graph big data reach specified similarity;
s3, measuring and calculating a raw material list according to the prediction data, sending a notice to a buyer and receiving a material sufficiency feedback signal of the buyer;
s4, predicting the loss data of the processing equipment according to the predicted data, and measuring and calculating the residual loss of the equipment according to the estimated loss;
and S5, when the residual loss of the equipment is lower than the specified value, controlling to alarm the manual end.
By using the cloud platform data management method based on the industrial big data, the chart data can be conveniently determined according to the acquired marketing data, the industrial production prediction data can be analyzed according to the chart data, the supply and allocation of raw materials are realized, the prediction of the processing loss of production equipment is realized, the residual loss of the processing equipment is further measured, and the management work of the upper loss limit of the processing equipment is realized.
In the present invention, unless otherwise explicitly specified or limited, the first feature "on" or "under" the second feature may be directly contacting the first feature and the second feature or indirectly contacting the first feature and the second feature through an intermediate.
Also, a first feature "on," "above," and "over" a second feature may mean that the first feature is directly above or obliquely above the second feature, or that only the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lower level than the second feature.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example" or "some examples," or the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A cloud platform data management system based on industrial big data is characterized by comprising a data classification module, a data analysis module, a prediction data output module, a purchase management module, an equipment loss prediction module, a loss evaluation module and a remote alarm module;
the data classification module is used for collecting product marketing data and periodically classifying the marketing data;
the data analysis module is used for predicting industrial processing data according to the periodic marketing data chart;
the prediction data output module is used for outputting prediction data to the equipment loss prediction module and the purchase management module;
the purchasing management module is used for managing a purchasing list to a purchaser according to the prediction data;
the equipment loss prediction module is used for predicting the equipment loss data according to the industrial processing prediction data;
the loss evaluation module is used for monitoring the residual evaluation loss of the equipment according to the predicted loss;
and the remote alarm module is used for carrying out remote manual end alarm when the residual evaluation loss is lower than a specified value.
2. The industrial big data-based cloud platform data management system according to claim 1, wherein the data classification module comprises a marketing data acquisition module and a period division module;
the marketing data acquisition module is used for acquiring marketing data information of industrial production products;
and the period division module is used for periodically dividing all marketing data during purchase.
3. The cloud platform data management system based on industrial big data is characterized in that the data analysis module comprises a chart generation module, a data comparison module and a comparison data storage module;
the chart generation module is used for converting the chart data of the periodic classified marketing data;
the comparison data storage module is used for storing processing prediction data information corresponding to all marketing chart data;
and the data comparison module is used for comparing the similarity of the converted graph data with the comparison graph data and searching the processing prediction data of which the similarity with the comparison image reaches the specified similarity.
4. The cloud platform data management system based on industrial big data as claimed in claim 1, wherein said purchase management module includes a purchase list management module, a buyer notification module and a feedback confirmation module;
the purchasing list management module is used for measuring and calculating a purchasing material list according to input processing prediction data;
the buyer notification module is used for notifying the data information of each purchased material to the corresponding buyer;
and the feedback confirmation module is used for collecting feedback confirmation signals of sufficient materials of all the purchasers.
5. The cloud platform data management system based on industrial big data as claimed in claim 4, wherein said purchase inventory management module includes a prediction data extraction module, a demand material calculation module, a stock quantity query module, an inventory data storage module and a purchase quantity calculation module;
the predicted data extraction module is used for extracting the machining predicted quantity information input by the predicted data output module;
the required material calculating module is used for calculating and calculating the required machining material quantity according to the machining predicted quantity information;
the stock data storage module is used for storing stock data of all materials;
the inventory quantity query module is used for calling and querying the residual inventory quantity corresponding to the predicted data material for query;
and the purchase quantity calculation module is used for measuring and calculating the data of the required purchase quantity according to the quantity of the required materials and the residual inventory quantity.
6. The cloud platform data management system based on industrial big data is characterized in that the loss evaluation module comprises a loss amount calculation module, an evaluation loss storage module and a data monitoring module;
the estimated loss storage module is used for storing loss estimation data of production and processing of each device;
the loss amount calculation module is used for calculating residual loss data of the equipment according to the prediction loss calculated by the equipment loss prediction module and the loss evaluation of the equipment;
and the data monitoring module is used for monitoring a control signal to the remote alarm module when the residual loss data is lower than a specified value.
7. A cloud platform data management method based on industrial big data is characterized by comprising the following steps:
s1, collecting marketing big data of industrial products and carrying out periodic division;
s2, carrying out graph analysis on the periodic marketing data, and searching and comparing predicted data of which the graph big data reach specified similarity;
s3, measuring and calculating a raw material list according to the prediction data, sending a notice to a buyer and receiving a material sufficiency feedback signal of the buyer;
s4, predicting the loss data of the processing equipment according to the predicted data, and measuring and calculating the residual loss of the equipment according to the estimated loss;
and S5, when the residual loss of the equipment is lower than the specified value, controlling to alarm the manual end.
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| Publication number | Priority date | Publication date | Assignee | Title |
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