CN118014260A - Big data service platform based on artificial intelligence - Google Patents
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Abstract
The invention discloses a big data service platform based on artificial intelligence, which particularly relates to the technical field of big data processing.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to a big data service platform based on artificial intelligence.
Background
The wide spread of social media application data provides a richer and more accurate data source for the production of enterprises, users can search enterprise information meeting the production needs of the users in the Internet, and how to reduce the screening time of the enterprise production information and realize the rapid matching of production supply and demand becomes more important.
The existing big data service platform based on artificial intelligence collects browsing time length, searching times, transaction times, consultation times and consultation time length of different production types in a preset history period of a user, predicts the target production type of the user based on the collected data, screens all enterprise information containing the target production type of the user in the whole network based on a prediction result, sorts the obtained enterprise information from near to far according to the distance between the obtained enterprise information and a user address, synchronizes the sorting result to a recommendation window, and the user can directly browse the enterprise information of the recommendation window to select target enterprises, so that the time for screening a large amount of information is reduced, and the supply and demand matching efficiency is improved.
However, the above system still has some problems: the system predicts the most likely target production types of users, has a narrow application range, integrates browsing duration, searching times, transaction times, consultation times and consultation duration information of different production types in a preset history period to obtain attention evaluation results of the users for the different production types, lists all the likely target production types into a recommendation scheme, improves the prediction accuracy of the target production types, and meanwhile lacks evaluation of enterprise production information, cannot ensure the production quality of the finally recommended enterprises and needs to be optimized.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present invention provides a big data service platform based on artificial intelligence, so as to solve the problems set forth in the above-mentioned background art.
In order to achieve the above purpose, the present invention provides the following technical solutions: an artificial intelligence based big data service platform comprising:
factory production information recording module: acquiring factory production information of an existing manufacturing enterprise based on resource sharing, classifying the factory production information according to enterprise and production types, and then recording the factory production information into a database to serve as a matching resource;
Industrial data acquisition module: the production equipment data, the sensor data and the quality detection data of different factories are acquired and sent to the industrial data processing module;
The industrial data primary processing module: performing primary treatment on the acquired industrial data, and sending the calculated indexes to an intelligent production control capacity index calculation module and an intelligent production control capacity index calculation module;
And the industrial data secondary processing module is used for: performing secondary treatment on the received index to calculate a production automation index and a production quality control stability coefficient, and sending the production automation index and the production quality control stability coefficient to an intelligent production control capacity index calculation module;
An intelligent production control capacity index calculating module: calculating an intelligent production control capability index based on the received data;
And the user data acquisition module is used for: the platform is used for collecting user behavior data and user attribute information of users registered in the platform for different production types;
a user data processing module: processing the collected user behavior data to calculate the degree of interest of the user on different production types;
and the intelligent recommendation module: determining an intelligent recommendation scheme to be put on a platform recommendation interface according to the attention degree of a user to production types of different production types, the attribute information of the user and the intelligent production control capability index of the corresponding production type of an enterprise consistent with the attention production type of the user;
and a real-time updating module: the intelligent recommendation method comprises the steps of updating an intelligent recommendation scheme when a user logs in a platform each time;
Database: the system is used for storing the factory production information and the data of each module in the system, and specifically comprises a factory production information storage unit and a system data storage unit.
Preferably, the industrial data acquisition module comprises a production equipment data acquisition unit, a sensor data acquisition unit, a quality detection data acquisition unit and a data output unit, wherein the production equipment data acquisition unit is used for acquiring the number of automatic equipment used for production, the number of non-automatic equipment, the number of equipment failure and downtime in production, the number of equipment failure and downtime, the successful number of equipment operation parameter regulation and control, the number of equipment operation parameter regulation and control failure and equipment consumption energy parameters; the sensor data acquisition unit is used for acquiring real-time pressure, temperature and flow data and expected pressure, temperature and flow data of the industrial production process; the quality detection data acquisition unit is used for acquiring the product quantity produced by industrial production, the total production duration, the standard-reaching product quantity and the recovery treatment cost of the product which does not reach the standard; the data output unit is used for sending the acquired industrial data information to the industrial data processing module.
Preferably, the industrial data processing module comprises a data receiving unit, a production equipment data processing unit, a sensor data processing unit, a quality detection data processing unit and a data output unit, wherein the data receiving unit is used for receiving the data sent by the industrial data acquisition module; the production equipment data processing unit calculates the automation equipment utilization rate F szi of the ith production based on the automation equipment number m zi and the non-automation equipment number m fzi used in the ith production of the factory, and a specific calculation formula is as follows: Calculating the equipment operation stability coefficient W ysi of the ith production based on the equipment failure shutdown times n gi and the total equipment failure shutdown time T bi in the ith production, wherein the specific calculation formula is as follows: /(I) T ai is the total production duration of the ith production, the total equipment failure shutdown duration and the equipment start-up duration are added numerically, the intelligent equipment regulation success rate C tki of the ith production is calculated based on the equipment operation parameter regulation success times m cti and the equipment operation parameter regulation failure times m csi in the ith production, and the specific calculation formula is as follows: /(I)The energy consumption overrun coefficient b cxi of the ith production is calculated based on the numerical relation between the equipment consumption energy parameter b ai and the expected consumption energy parameter bei in the ith production, and the specific calculation formula is as follows: /(I)The sensor data processing unit calculates a pressure comprehensive overrun coefficient X pi of the ith production based on the real-time pressure data m pai, the overrun pressure data m pbi of the ith production and the deviation between the jth overrun pressure p cij and the expected pressure p eij, wherein the specific calculation formula is as follows: /(I)Epsilon peij is the preset deviation of the jth overrun pressure corresponding to the expected pressure in the ith production, and the temperature comprehensive overrun coefficient X ti of the ith production is calculated based on the real-time temperature data amount m tai, the overrun temperature data amount m tbi and the deviation between the jth overrun temperature t cij and the expected temperature t eij of the ith production, wherein the specific calculation formula is as follows: /(I)Epsilon teij is the preset deviation of the jth overrun temperature corresponding to the expected temperature in the ith production, and the flow comprehensive overrun coefficient X Gi of the ith production is calculated based on the real-time flow data m Gai, the overrun flow data m Gbi and the deviation between the jth overrun flow G cij and the expected flow G eij of the ith production, wherein the specific calculation formula is as follows: /(I)Epsilon Geij is the preset deviation of the jth overrun pressure corresponding to the expected pressure in the ith production; the quality detection data processing unit calculates the production efficiency X Ci of the ith production based on the product quantity m cai of the ith production and the total production duration T ai, and the specific calculation formula is as follows: /(I)Calculating the product standard-reaching rate B mci of the ith production based on the product quantity m cai of the ith production and the product quantity m cbi reaching the standard, wherein the specific calculation formula is as follows: The product defect coefficient X cqi of the ith production is calculated based on the expected recovery total cost w hi and the production total cost w ci of the ith production, and the specific calculation formula is as follows: /(I) The data output unit is used for sending the calculated indexes except the energy consumption overrun coefficient to the industrial data secondary processing module and sending the calculated energy consumption overrun coefficient to the intelligent production control capacity index calculation module.
Preferably, the industrial data secondary processing module comprises a data receiving unit, a production automation index calculating unit, a production condition control stability coefficient calculating unit, a production quality control capacity coefficient calculating unit and a data output unit, wherein the data receiving unit is used for receiving the index output by the industrial data primary processing module; the production automation index calculation unit calculates the production automation index Z sci of the ith production based on the utilization rate F szi of the ith production automation equipment and the intelligent equipment regulation success rate C tki, and the specific calculation formula is as follows: e is a natural constant; the production condition control stability coefficient calculating unit calculates a production condition control stability coefficient W csi based on an ith production pressure comprehensive overrun coefficient X pi, a temperature comprehensive overrun coefficient X ti and a flow comprehensive overrun coefficient X Gi, and the specific calculation formula is as follows: The production quality control capability coefficient calculation unit calculates a production quality control stability coefficient W czi of the ith production based on an ith production equipment operation stability coefficient W ysi, a production condition control stability coefficient W csi, a production efficiency X Ci, a product standard reaching rate B mci and a product defect coefficient X cqi, wherein the specific calculation formula is as follows: The data output unit is used for sending the calculated production automation index and the production quality control stability coefficient to the intelligent production control capacity index calculation module.
Preferably, the intelligent production control capability index calculation module comprises a data receiving unit, an intelligent production control capability index calculation unit and a data output unit, wherein the data receiving unit is used for receiving the energy consumption overrun coefficient, the production automation index and the production quality control stability coefficient; the specific calculation formula of the intelligent production control capability index C zi for calculating the ith production based on the ith production energy consumption overrun coefficient b cxi, the production automation index Z sci and the production quality control stability coefficient W czi by the intelligent production control capability index calculation unit is as follows: and the data output unit sends the calculated intelligent production control capacity index of the factory produced at the ith time to the intelligent recommendation module.
Preferably, the user data acquisition module comprises a user behavior data acquisition unit, a user attribute information acquisition unit and a data output unit, wherein the user behavior data acquisition unit is used for acquiring browsing time length, searching times, transaction times, consultation times and consultation time length of a user for different production types in a preset history period Tc; the user attribute information acquisition unit is used for acquiring the communication address of a user; the data output unit is used for outputting the collected user behavior data to the user data processing module and outputting the collected user attribute information to the intelligent recommendation module.
Preferably, the user data processing module includes a data receiving unit, a production type attention calculating unit and a data output unit, where the data receiving unit is configured to receive the acquired browsing duration T di, the search number n ai, the transaction number n bi, the consultation number n ci and the consultation duration T zi of the user for the ith production type in the preset history period T c; the production type attention calculating unit calculates the production type attention beta ci of the user for the ith production type based on the received data, and a specific calculation formula is as follows: n L is the number of production types focused by the user in a preset history period, k 1、k2、k3、k4 is a weight coefficient, and k 3>k4>k2>k1 is more than 0; the data output unit is used for sending the calculated production type attention degree of the user for the ith production type to the intelligent recommendation module.
Preferably, the intelligent recommendation module comprises an information receiving unit, a production information calling unit, a production information correcting unit, a condition matching unit and an intelligent recommendation scheme determining unit, wherein the information receiving unit is used for receiving the attention degree of a user on different production types and the communication address of the user; the production information calling unit is used for calling all production records of the corresponding production types of enterprises, which are consistent with the production type concerned by the user, from the database; the resource screening unit calculates an average value C ze of the intelligent production control capacity indexes of enterprises with multiple production records, and the specific calculation formula is as follows: n x is a specific number of multiple production records, n x >0, and an accidental correction coefficient ζ 0 is set for the intelligent production control ability index of an enterprise with only a single production record and multiplied by the accidental correction coefficient ζ 0 to obtain a corrected intelligent production control ability index C za, wherein a specific calculation formula is as follows: c za＝Cz1*ζ0, representing the finally obtained processed intelligent production control capacity index by using C zt; the condition matching unit calculates an enterprise recommendation index Q ai based on an intelligent production control capability index C zti of an ith enterprise consistent with the type of production focused by a user and a distance L ai between an enterprise production address and a user communication address, and the specific calculation formula is as follows: /(I) Y 1、y2 is the scaling factor, y 1>y2 >0; the intelligent recommendation scheme determining unit ranks production types from high to low according to the calculated production type attention degree, ranks enterprises consistent with the user attention production types from high to low according to enterprise recommendation indexes, and ranks the enterprises meeting the existing conditions according to the production type attention degree and the enterprise recommendation indexes to generate an intelligent recommendation scheme.
The invention has the technical effects and advantages that:
1. The invention sets an industrial data acquisition module, an industrial data primary processing module, an industrial data secondary processing module and an intelligent production control capacity index calculation module, and performs primary processing on production equipment data, sensor data and quality detection data of different factories to obtain automatic equipment utilization rate, equipment operation stability coefficient, equipment intelligent regulation success rate, energy consumption overrun coefficient, pressure comprehensive overrun coefficient, temperature comprehensive overrun coefficient, flow comprehensive overrun coefficient, production efficiency, product standard reaching rate and product defect coefficient, performs secondary processing on indexes obtained by primary processing to calculate a production automation index and a production quality control stability coefficient, calculates an intelligent production control capacity index based on the energy consumption overrun coefficient, the production automation index and the production quality control stability coefficient, and the connection of the modules realizes the evaluation of the intelligent production control capacity of enterprises and provides data support for the intelligent recommendation of follow-up enterprises.
2. According to the invention, the user data processing module is arranged to process and calculate the attention degree of the user to the production types of different production types, the accuracy of user demand information prediction is improved, the intelligent recommendation module is arranged to determine an intelligent recommendation scheme according to the attention degree of the user to the production types of different production types, the user attribute information and the intelligent production control capability index of the enterprise corresponding to the production type consistent with the attention production type of the user, and the intelligent recommendation scheme of the obtained enterprise is put on a platform recommendation interface, and the accuracy of demand prediction and the intelligent production control capability of the enterprise are obviously improved.
Drawings
Fig. 1 is a block diagram of a module structure according to the present invention.
Fig. 2 is a flow chart of the operation of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment as shown in fig. 1 provides an artificial intelligence-based big data service platform, which comprises a factory production information recording module, an industrial data acquisition module, an industrial data primary processing module, an industrial data secondary processing module, an intelligent production control capability index calculation module, a user data acquisition module, a user data processing module, an intelligent recommendation module, a real-time updating module and a database, wherein the factory production information recording module, the industrial data acquisition module, the industrial data primary processing module, the industrial data secondary processing module and the intelligent production control capability index calculation module are sequentially connected, the industrial data primary processing module is connected with the intelligent production control capability index calculation module, the user data acquisition module, the user data processing module, the intelligent recommendation module and the real-time updating module are sequentially connected, the user data acquisition module is connected with the intelligent recommendation module, and all modules in the system are connected with the database.
The factory production information recording module acquires factory production information of an existing manufacturing enterprise based on resource sharing, classifies the factory production information according to enterprise and production types, and records the factory production information into a database to serve as matched resources.
The industrial data acquisition module is used for acquiring production equipment data, sensor data and quality detection data of different factories and sending the data to the industrial data processing module.
Further, the industrial data acquisition module comprises a production equipment data acquisition unit, a sensor data acquisition unit, a quality detection data acquisition unit and a data output unit, wherein the production equipment data acquisition unit is used for acquiring the number of automatic equipment used for production, the number of non-automatic equipment, the number of equipment failure and downtime in production, the number of equipment failure and downtime, the number of successful equipment operation parameter regulation and control times, the number of equipment operation parameter regulation and control failure times and the equipment energy consumption parameters; the sensor data acquisition unit is used for acquiring real-time pressure, temperature and flow data and expected pressure, temperature and flow data of the industrial production process; the quality detection data acquisition unit is used for acquiring the product quantity produced by industrial production, the total production duration, the standard-reaching product quantity and the recovery treatment cost of the product which does not reach the standard; the data output unit is used for sending the acquired industrial data information to the industrial data processing module.
The industrial data primary processing module performs primary processing on the acquired industrial data, and sends the calculated indexes to the intelligent production control capacity index calculation module and the intelligent production control capacity index calculation module.
Further, the industrial data processing module comprises a data receiving unit, a production equipment data processing unit, a sensor data processing unit, a quality detection data processing unit and a data output unit, wherein the data receiving unit is used for receiving the data sent by the industrial data acquisition module; the production equipment data processing unit calculates the automation equipment utilization rate F szi of the ith production based on the automation equipment number m zi and the non-automation equipment number m fzi used in the ith production of the factory, and a specific calculation formula is as follows: Calculating the equipment operation stability coefficient W ysi of the ith production based on the equipment failure shutdown times n gi and the total equipment failure shutdown time T bi in the ith production, wherein the specific calculation formula is as follows: /(I) T ai is the total production duration of the ith production, the total equipment failure shutdown duration and the equipment start-up duration are added numerically, the intelligent equipment regulation success rate C tki of the ith production is calculated based on the equipment operation parameter regulation success times m cti and the equipment operation parameter regulation failure times m csi in the ith production, and the specific calculation formula is as follows: /(I)The energy consumption overrun coefficient b cxi of the ith production is calculated based on the numerical relation between the equipment consumption energy parameter b ai and the expected consumption energy parameter bei in the ith production, and the specific calculation formula is as follows: /(I)The sensor data processing unit calculates a pressure comprehensive overrun coefficient X pi of the ith production based on the real-time pressure data m pai, the overrun pressure data m pbi of the ith production and the deviation between the jth overrun pressure p cij and the expected pressure p eij, wherein the specific calculation formula is as follows: /(I)Epsilon peij is the preset deviation of the jth overrun pressure corresponding to the expected pressure in the ith production, and the temperature comprehensive overrun coefficient X ti of the ith production is calculated based on the real-time temperature data amount m tai, the overrun temperature data amount m tbi and the deviation between the jth overrun temperature t cij and the expected temperature t eij of the ith production, wherein the specific calculation formula is as follows: /(I)Epsilon teij is the preset deviation of the jth overrun temperature corresponding to the expected temperature in the ith production, and the flow comprehensive overrun coefficient X Gi of the ith production is calculated based on the real-time flow data m Gai, the overrun flow data m Gbi and the deviation between the jth overrun flow G cij and the expected flow G eij of the ith production, wherein the specific calculation formula is as follows: /(I)Epsilon Geij is the preset deviation of the jth overrun pressure corresponding to the expected pressure in the ith production; the quality detection data processing unit calculates the production efficiency X Ci of the ith production based on the product quantity m cai of the ith production and the total production duration T ai, and the specific calculation formula is as follows: /(I)Calculating the product standard-reaching rate B mci of the ith production based on the product quantity m cai of the ith production and the product quantity m cbi reaching the standard, wherein the specific calculation formula is as follows: The product defect coefficient X cqi of the ith production is calculated based on the expected recovery total cost w hi and the production total cost w ci of the ith production, and the specific calculation formula is as follows: /(I) The data output unit is used for sending the calculated indexes except the energy consumption overrun coefficient to the industrial data secondary processing module and sending the calculated energy consumption overrun coefficient to the intelligent production control capacity index calculation module.
And the industrial data secondary processing module carries out secondary processing on the received indexes to calculate a production automation index and a production quality control stability coefficient and sends the production automation index and the production quality control stability coefficient to the intelligent production control capacity index calculating module.
Further, the industrial data secondary processing module comprises a data receiving unit, a production automation index calculating unit, a production condition control stability coefficient calculating unit, a production quality control capacity coefficient calculating unit and a data output unit, wherein the data receiving unit is used for receiving the index output by the industrial data primary processing module; the production automation index calculation unit calculates the production automation index Z sci of the ith production based on the utilization rate F szi of the ith production automation equipment and the intelligent equipment regulation success rate C tki, and the specific calculation formula is as follows: e is a natural constant; the production condition control stability coefficient calculating unit calculates a production condition control stability coefficient W csi based on an ith production pressure comprehensive overrun coefficient X pi, a temperature comprehensive overrun coefficient X ti and a flow comprehensive overrun coefficient X Gi, and the specific calculation formula is as follows: The production quality control capability coefficient calculation unit calculates a production quality control stability coefficient W czi of the ith production based on an ith production equipment operation stability coefficient W ysi, a production condition control stability coefficient W csi, a production efficiency X Ci, a product standard reaching rate B mci and a product defect coefficient X cqi, wherein the specific calculation formula is as follows: The data output unit is used for sending the calculated production automation index and the production quality control stability coefficient to the intelligent production control capacity index calculation module.
The intelligent production control capability index calculation module calculates an intelligent production control capability index based on the received data.
Further, the intelligent production control capability index calculation module comprises a data receiving unit, an intelligent production control capability index calculation unit and a data output unit, wherein the data receiving unit is used for receiving the energy consumption overrun coefficient, the production automation index and the production quality control stability coefficient; the specific calculation formula of the intelligent production control capability index C zi for calculating the ith production based on the ith production energy consumption overrun coefficient b cxi, the production automation index Z sci and the production quality control stability coefficient W czi by the intelligent production control capability index calculation unit is as follows: and the data output unit sends the calculated intelligent production control capacity index of the factory produced at the ith time to the intelligent recommendation module.
The user data acquisition module is used for acquiring user behavior data and user attribute information of users registered in the platform for different production types.
Further, the user data acquisition module comprises a user behavior data acquisition unit, a user attribute information acquisition unit and a data output unit, wherein the user behavior data acquisition unit is used for acquiring browsing time length, searching times, transaction times, consultation times and consultation time length of users for different production types in a preset history period Tc; the user attribute information acquisition unit is used for acquiring the communication address of a user; the data output unit is used for outputting the collected user behavior data to the user data processing module and outputting the collected user attribute information to the intelligent recommendation module.
And the user data processing module processes the collected user behavior data and calculates the production type attention degree of the user for different production types.
Further, the user data processing module includes a data receiving unit, a production type attention calculating unit and a data output unit, where the data receiving unit is configured to receive the acquired browsing duration T di, the search frequency n ai, the transaction frequency n bi, the consultation frequency n ci, and the consultation duration T zi of the user for the ith production type in the preset history period T c; the production type attention calculating unit calculates the production type attention beta ci of the user for the ith production type based on the received data, and a specific calculation formula is as follows: n L is the number of production types focused by the user in a preset history period, k 1、k2、k3、k4 is a weight coefficient, and k 3>k4>k2>k1 is more than 0; the data output unit is used for sending the calculated production type attention degree of the user for the ith production type to the intelligent recommendation module.
The intelligent recommendation module determines that an intelligent recommendation scheme is put on a platform recommendation interface according to the intelligent production control capability index of the user on the production type attention degree of different production types, the user attribute information and the enterprise corresponding to the production type consistent with the user attention production type.
Further, the intelligent recommendation module comprises an information receiving unit, a production information calling unit, a production information correcting unit, a condition matching unit and an intelligent recommendation scheme determining unit, wherein the information receiving unit is used for receiving the attention degree of a user on different production types and the communication address of the user; the production information calling unit is used for calling all production records of the corresponding production types of enterprises, which are consistent with the production type concerned by the user, from the database; the resource screening unit calculates an average value C ze of the intelligent production control capacity indexes of enterprises with multiple production records, and the specific calculation formula is as follows: n x is a specific number of multiple production records, n x >0, and an accidental correction coefficient ζ 0 is set for the intelligent production control ability index of an enterprise with only a single production record and multiplied by the accidental correction coefficient ζ 0 to obtain a corrected intelligent production control ability index C za, wherein a specific calculation formula is as follows: c za＝Cz1*ζ0, representing the finally obtained processed intelligent production control capacity index by using C zt; the condition matching unit calculates an enterprise recommendation index Q ai based on an intelligent production control capability index C zti of an ith enterprise consistent with the type of production focused by a user and a distance L ai between an enterprise production address and a user communication address, and the specific calculation formula is as follows: /(I) Y 1、y2 is the scaling factor, y 1>y2 >0; the intelligent recommendation scheme determining unit ranks production types from high to low according to the calculated production type attention degree, ranks enterprises consistent with the user attention production types from high to low according to enterprise recommendation indexes, and ranks the enterprises meeting the existing conditions according to the production type attention degree and the enterprise recommendation indexes to generate an intelligent recommendation scheme.
In this embodiment, it is specifically required to explain the following data to facilitate understanding of the intelligent recommendation scheme combination principle: assuming that the three production types A1, A2 and A3 are concerned, the corresponding production type attention degree beta c1>βc3>βc2 is used for paying attention to, for A1, 3 existing 3 recommended enterprises are respectively B1, B2 and B3, the corresponding enterprise recommended index Q a1>Qa3>Qa2 is B4 and B5 for A2 existing 2 recommended enterprises, the corresponding enterprise recommended index Q a4>Qa5 is respectively B6, B7, B8 and B9 for A3 existing 4 recommended enterprises, the corresponding enterprise recommended index Q a6>Qa7>Qa8>Qa9 is used for carrying out circulation according to the production type attention degree, the recommended enterprises are sequentially carried out according to the enterprise recommended indexes, the production type of the 1 st position in the recommended order is A1 and the recommended enterprise is B1, the production type of the 2 nd position in the recommended order is A3, the recommended enterprise is B6, the production type of the 3 rd position in the recommended order is A2 and the recommended enterprise is B4, the recommended type of the 4 th position is A1, the recommended enterprise is B5, the production type of the 5 th position is A3, the enterprise is B7, the order is the recommended type of the 6 th position is the enterprise is B7, the type of the A2 and the position of the 2 th position is the recommended by the 4, at this moment, the order is no more than the recommended by the type of the A8, the type of the 2 is the recommended type of the 2, and the order is the type 9 is the recommended according to the theoretical attention degree has been determined, but the existing attention degree is not shown.
The real-time updating module is used for updating the intelligent recommendation scheme when the user logs in the platform each time.
The database is used for storing factory production information and data of each module in the system, and specifically comprises a factory production information storage unit and a system data storage unit.
In this embodiment, it is specifically required to explain that the preset value and the coefficient are all selected based on actual needs, and the specific value is not limited here.
The embodiment as shown in fig. 2 provides an operation flow of a big data service platform based on artificial intelligence, which comprises the following steps:
S1: acquiring factory production information of an existing manufacturing enterprise based on resource sharing, classifying the factory production information according to enterprise and production types, and then recording the factory production information into a database to serve as a matching resource;
S2: acquiring production equipment data, sensor data and quality detection data of different factories;
S3: performing primary treatment on the acquired data to obtain an automatic equipment utilization rate, an equipment operation stability coefficient, an equipment intelligent regulation success rate, an energy consumption overrun coefficient, a pressure comprehensive overrun coefficient, a temperature comprehensive overrun coefficient, a flow comprehensive overrun coefficient, a production efficiency, a product standard reaching rate and a product defect coefficient;
S4: performing secondary treatment on the index obtained by the primary treatment to calculate a production automation index and a production quality control stability coefficient;
S5: calculating an intelligent production control capability index based on the energy consumption overrun coefficient, the production automation index and the production quality control stability coefficient;
s6: collecting user behavior data and user attribute information of users registered in the platform for different production types;
s7: processing the collected user behavior data to calculate the degree of interest of the user on different production types;
S8: determining an intelligent recommendation scheme to be put on a platform recommendation interface according to the attention degree of a user to production types of different production types, the attribute information of the user and the intelligent production control capability index of the corresponding production type of an enterprise consistent with the attention production type of the user;
s9: the intelligent recommendation scheme is updated each time the user logs into the platform.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. The big data service platform based on artificial intelligence is characterized in that: comprising the following steps:
factory production information recording module: acquiring factory production information of an existing manufacturing enterprise based on resource sharing, classifying the factory production information according to enterprise and production types, and then recording the factory production information into a database to serve as a matching resource;
Industrial data acquisition module: the production equipment data, the sensor data and the quality detection data of different factories are acquired and sent to the industrial data processing module;
The industrial data primary processing module: performing primary treatment on the acquired industrial data, and sending the calculated indexes to an intelligent production control capacity index calculation module and an intelligent production control capacity index calculation module;
And the industrial data secondary processing module is used for: performing secondary treatment on the received index to calculate a production automation index and a production quality control stability coefficient, and sending the production automation index and the production quality control stability coefficient to an intelligent production control capacity index calculation module;
An intelligent production control capacity index calculating module: calculating an intelligent production control capability index based on the received data;
And the user data acquisition module is used for: the platform is used for collecting user behavior data and user attribute information of users registered in the platform for different production types;
a user data processing module: processing the collected user behavior data to calculate the degree of interest of the user on different production types;
and the intelligent recommendation module: determining an intelligent recommendation scheme to be put on a platform recommendation interface according to the attention degree of a user to production types of different production types, the attribute information of the user and the intelligent production control capability index of the corresponding production type of an enterprise consistent with the attention production type of the user;
And a real-time updating module: for updating the intelligent recommendation scheme each time the user logs into the platform.
2. The big data service platform based on artificial intelligence according to claim 1, wherein: the industrial data acquisition module comprises a production equipment data acquisition unit, a sensor data acquisition unit, a quality detection data acquisition unit and a data output unit, wherein the production equipment data acquisition unit is used for acquiring the number of automatic equipment used for production, the number of non-automatic equipment, the number of equipment failure and shutdown times in production, the equipment failure and shutdown time, the successful times of equipment operation parameter regulation and control, the failed times of equipment operation parameter regulation and control and energy consumption parameters; the sensor data acquisition unit is used for acquiring real-time pressure, temperature and flow data and expected pressure, temperature and flow data of the industrial production process; the quality detection data acquisition unit is used for acquiring the product quantity produced by industrial production, the total production duration, the standard-reaching product quantity and the recovery treatment cost of the product which does not reach the standard; the data output unit is used for sending the acquired industrial data information to the industrial data processing module.
3. The big data service platform based on artificial intelligence according to claim 1, wherein: the industrial data processing module comprises a data receiving unit, a production equipment data processing unit, a sensor data processing unit, a quality detection data processing unit and a data output unit, wherein the data receiving unit is used for receiving the data sent by the industrial data acquisition module; the production equipment data processing unit calculates the automation equipment utilization rate F szi of the ith production based on the automation equipment number m zi and the non-automation equipment number m fzi used in the ith production of the factory, and a specific calculation formula is as follows: Calculating the equipment operation stability coefficient W ysi of the ith production based on the equipment failure shutdown times n gi and the total equipment failure shutdown time T bi in the ith production, wherein the specific calculation formula is as follows: /(I) T ai is the total production duration of the ith production, the total equipment failure shutdown duration and the equipment start-up duration are added numerically, the intelligent equipment regulation success rate C tki of the ith production is calculated based on the equipment operation parameter regulation success times m cti and the equipment operation parameter regulation failure times m csi in the ith production, and the specific calculation formula is as follows: /(I)The energy consumption overrun coefficient b cxi of the ith production is calculated based on the numerical relation between the equipment consumption energy parameter b ai and the expected consumption energy parameter bei in the ith production, and the specific calculation formula is as follows: The sensor data processing unit calculates a pressure comprehensive overrun coefficient X pi of the ith production based on the real-time pressure data m pai, the overrun pressure data m pbi of the ith production and the deviation between the jth overrun pressure p cij and the expected pressure p eij, wherein the specific calculation formula is as follows: /(I) Epsilon peij is the preset deviation of the jth overrun pressure corresponding to the expected pressure in the ith production, and the temperature comprehensive overrun coefficient X ti of the ith production is calculated based on the real-time temperature data amount m tai, the overrun temperature data amount m tbi and the deviation between the jth overrun temperature t cij and the expected temperature t eij of the ith production, wherein the specific calculation formula is as follows: /(I)Epsilon teij is the preset deviation of the jth overrun temperature corresponding to the expected temperature in the ith production, and the flow comprehensive overrun coefficient X Gi of the ith production is calculated based on the real-time flow data m Gai, the overrun flow data m Gbi and the deviation between the jth overrun flow G cij and the expected flow G eij of the ith production, wherein the specific calculation formula is as follows: /(I)Epsilon Geij is the preset deviation of the jth overrun pressure corresponding to the expected pressure in the ith production; the quality detection data processing unit calculates the production efficiency X Ci of the ith production based on the product quantity m cai of the ith production and the total production duration T ai, and the specific calculation formula is as follows: /(I)Calculating the product standard-reaching rate B mci of the ith production based on the product quantity m cai of the ith production and the product quantity m cbi reaching the standard, wherein the specific calculation formula is as follows: /(I)The product defect coefficient X cqi of the ith production is calculated based on the expected recovery total cost w hi and the production total cost w ci of the ith production, and the specific calculation formula is as follows: /(I)The data output unit is used for sending the calculated indexes except the energy consumption overrun coefficient to the industrial data secondary processing module and sending the calculated energy consumption overrun coefficient to the intelligent production control capacity index calculation module.
4. The big data service platform based on artificial intelligence according to claim 1, wherein: the industrial data secondary processing module comprises a data receiving unit, a production automation index calculating unit, a production condition control stability coefficient calculating unit, a production quality control capacity coefficient calculating unit and a data output unit, wherein the data receiving unit is used for receiving the index output by the industrial data primary processing module; the production automation index calculation unit calculates the production automation index Z sci of the ith production based on the utilization rate F szi of the ith production automation equipment and the intelligent equipment regulation success rate C tki, and the specific calculation formula is as follows: e is a natural constant; the production condition control stability coefficient calculating unit calculates a production condition control stability coefficient W csi based on an ith production pressure comprehensive overrun coefficient X pi, a temperature comprehensive overrun coefficient X ti and a flow comprehensive overrun coefficient X Gi, and the specific calculation formula is as follows: /(I) The production quality control capability coefficient calculation unit calculates a production quality control stability coefficient W czi of the ith production based on an ith production equipment operation stability coefficient W ysi, a production condition control stability coefficient W csi, a production efficiency X Ci, a product standard reaching rate B mci and a product defect coefficient X cqi, wherein the specific calculation formula is as follows: /(I)The data output unit is used for sending the calculated production automation index and the production quality control stability coefficient to the intelligent production control capacity index calculation module.
5. The big data service platform based on artificial intelligence according to claim 1, wherein: the intelligent production control capacity index calculation module comprises a data receiving unit, an intelligent production control capacity index calculation unit and a data output unit, wherein the data receiving unit is used for receiving an energy consumption overrun coefficient, a production automation index and a production quality control stability coefficient; the specific calculation formula of the intelligent production control capability index C zi for calculating the ith production based on the ith production energy consumption overrun coefficient b cxi, the production automation index Z sci and the production quality control stability coefficient W czi by the intelligent production control capability index calculation unit is as follows: and the data output unit sends the calculated intelligent production control capacity index of the factory produced at the ith time to the intelligent recommendation module.
6. The big data service platform based on artificial intelligence according to claim 1, wherein: the user data acquisition module comprises a user behavior data acquisition unit, a user attribute information acquisition unit and a data output unit, wherein the user behavior data acquisition unit is used for acquiring browsing duration, searching times, transaction times, consultation times and consultation duration of users for different production types in a preset history period Tc; the user attribute information acquisition unit is used for acquiring the communication address of a user; the data output unit is used for outputting the collected user behavior data to the user data processing module and outputting the collected user attribute information to the intelligent recommendation module.
7. The big data service platform based on artificial intelligence according to claim 1, wherein: the user data processing module comprises a data receiving unit, a production type attention calculating unit and a data output unit, wherein the data receiving unit is used for receiving the acquired browsing duration T di, searching times n ai, transaction times n bi, consultation times n ci and consultation duration T zi of the user for the ith production type in a preset historical period T c; the production type attention calculating unit calculates the production type attention beta ci of the user for the ith production type based on the received data, and a specific calculation formula is as follows: n L is the number of production types focused by the user in a preset history period, k 1、k2、k3、k4 is a weight coefficient, and k 3>k4>k2>k1 is more than 0; the data output unit is used for sending the calculated production type attention degree of the user for the ith production type to the intelligent recommendation module.
8. The big data service platform based on artificial intelligence according to claim 1, wherein: the intelligent recommendation module comprises an information receiving unit, a production information calling unit, a production information correcting unit, a condition matching unit and an intelligent recommendation scheme determining unit, wherein the information receiving unit is used for receiving the attention degree of a user on different production types and the communication address of the user; the production information calling unit is used for calling all production records of the corresponding production types of enterprises, which are consistent with the production type concerned by the user, from the database; the resource screening unit calculates an average value C ze of the intelligent production control capacity indexes of enterprises with multiple production records, and the specific calculation formula is as follows: n x is a specific number of multiple production records, n x >0, and an accidental correction coefficient ζ 0 is set for the intelligent production control ability index of an enterprise with only a single production record and multiplied by the accidental correction coefficient ζ 0 to obtain a corrected intelligent production control ability index C za, wherein a specific calculation formula is as follows: c za＝Cz1*ζ0, representing the finally obtained processed intelligent production control capacity index by using C zt; the condition matching unit calculates an enterprise recommendation index Q ai based on an intelligent production control capability index C zti of an ith enterprise consistent with the type of production focused by a user and a distance L ai between an enterprise production address and a user communication address, and the specific calculation formula is as follows: /(I) Y 1、y2 is the scaling factor, y 1>y2 >0; the intelligent recommendation scheme determining unit ranks production types from high to low according to the calculated production type attention degree, ranks enterprises consistent with the user attention production types from high to low according to enterprise recommendation indexes, and ranks the enterprises meeting the existing conditions according to the production type attention degree and the enterprise recommendation indexes to generate an intelligent recommendation scheme.
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