CN112327775A - Enterprise re-work and re-production degree monitoring system and method based on artificial intelligence - Google Patents
Enterprise re-work and re-production degree monitoring system and method based on artificial intelligence Download PDFInfo
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Abstract
The invention relates to the technical field of enterprise rework and reproduction degree monitoring, in particular to an enterprise rework and reproduction degree monitoring system and method based on artificial intelligence. The data source of the invention depends on the power consumption information of the enterprise, the enterprise is scientifically clustered through the power consumption information of the enterprise, the integral rework and production recovery situation of the enterprise is effectively and accurately judged, the rework and production recovery information of the enterprise can be timely fed back to a higher-level department, an electric power company can also analyze the rework and production recovery data of the enterprise by analyzing big data, the power consumption situation of the enterprise is monitored, the power consumption guarantee is better provided for the rework and production recovery of the enterprise, the power consumption load of the enterprise is timely known, the hidden danger of power failure is eliminated, the power supply stability is guaranteed, the rework and production recovery energy consumption level of the enterprise can be predicted through the rework and production recovery curve of the enterprise, and the future power consumption situation of the enterprise has better reference value.
Description
Technical Field
The invention relates to the technical field of enterprise rework and reproduction degree monitoring, in particular to an enterprise rework and reproduction degree monitoring system and method based on artificial intelligence.
Background
The re-working and re-production refers to the related matters and series problems of re-working and re-production which are uniformly guided by national and local governments after all or most of enterprises and public institutions are unable to carry out production activities, production and operation activities and shutdown due to special reasons, special periods and extraordinary periods. In order to more timely and accurately know the enterprise rework situation, timely track the enterprise productivity recovery situation, know the enterprise recovery production situation, globally master the production recovery situation of each enterprise in the whole industry, better support government monitoring and supervision functions, support the occurrence of public emergencies such as epidemic prevention and control and the like, a manager needs to analyze and monitor the enterprise production recovery situation. However, at present, an effective method for monitoring the industry reworking and reworking is lacked, the enterprise reworking and reworking degree of the industry cannot be accurately judged, and the overall reworking and reworking degree of the industry cannot be known. The enterprise power consumption is an important index of enterprise rework and reproduction, so that the overall rework and reproduction degree of the industry can be effectively monitored by monitoring the enterprise power consumption. The artificial intelligence technology has great strengthening as the whole due to the improvement of the computing power of hardware, and can play an important role in various fields such as enterprise industry and the like, so that the adoption of the artificial intelligence technology to monitor the repeated work and production of enterprises is worthy of exploration and use, but the content of the technology in the prior art can not be used for reference. Therefore, an enterprise rework and production rate monitoring system and method based on artificial intelligence are provided.
Disclosure of Invention
The invention aims to provide an enterprise rework and reproduction degree monitoring system and method based on artificial intelligence, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: an enterprise reworking and production-resuming degree monitoring system based on artificial intelligence comprises:
the system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring historical rework and rework energy consumption data of a target enterprise and current rework and rework energy consumption data of the target enterprise;
the data analysis unit is used for analyzing the data acquired by the data acquisition unit, fitting the historical rework and rework energy consumption data of the target enterprise to form a fitting curve of the historical rework and rework, and fitting the current rework and rework energy consumption data of the target enterprise to form a fitting curve of the current rework and rework;
the data processing unit is used for sending the parameters of the fitting curve of the current re-work and re-production into the neural network and then outputting the parameters, combining the converted output result with the energy consumption data of the current re-work and re-production of the enterprise to form a re-work and re-production curve, calculating an energy consumption level index, and judging the current re-work and re-production degree of the enterprise according to the energy consumption level index;
and the data display unit displays the processed current energy consumption level of the target enterprise and the current re-work and re-production degree of the target enterprise, which are given by the data processing unit.
Preferably, the data acquisition unit, the data analysis unit, the data processing unit and the data display unit are electrically connected in sequence.
The invention also provides a method of the enterprise reworking and production rate monitoring system based on artificial intelligence, which comprises the following steps:
s1, acquiring historical rework and rework energy consumption data of the target enterprise and current rework and rework energy consumption data of the target enterprise;
s2, fitting a fitting curve of the historical reworking and reproduction: fitting the historical repeated work and repeated production energy consumption data of the target enterprise to form a fitting curve of the historical repeated work and repeated production;
s3, determining a clustering center;
s4, training a neural network: taking parameters of the same class of fitting curves as training input quantity of the neural network, and taking the center of the same class of fitting curve clustering as a training result;
s5, fitting a fitting curve of the current reworking and reproduction: fitting the current reworking and reworking energy consumption data of the target enterprise to form a fitting curve of the current reworking and reworking;
s6, outputting by a neural network;
s7, calculation result: and calculating the energy consumption level index, and judging the current enterprise rework and production rate according to the energy consumption level index.
Preferably, the historical rework and rework energy consumption data of the target enterprise in S1 is obtained through the power consumption information collection system and the power marketing system.
Preferably, the S1 includes the following sub-steps:
s11, initializing enterprise user data;
s12, collecting the power utilization information data of the enterprise for the first time;
s13, supplementing and collecting the enterprise electricity utilization information data;
and S14, calculating the previous daily power consumption of the enterprise.
Preferably, the S3 includes the following sub-steps:
s31, setting a plurality of reworking typical curves as a clustering center;
s32, selecting parameters of the typical curve and the fitting curve as dimension values;
s33, performing clustering analysis according to Euclidean libraries among the dimension values;
and S34, updating the clustering center.
Preferably, the S6 includes the following sub-steps:
s61, selecting parameters of a fitting curve of the current re-work and re-production, sending the parameters into a neural network and then outputting the parameters;
and S62, combining the converted output result with the energy consumption data of the current reworking and reworking of the enterprise to form a reworking and reworking curve.
Preferably, the method is used for calculating and monitoring the repeated work and production situations of enterprises of 10kV or above.
The invention also provides a method of the enterprise reworking and production rate monitoring system based on artificial intelligence, which comprises the following steps:
compared with the prior art, the invention has the beneficial effects that: the data source of the invention depends on the power consumption information of the enterprise, the enterprise is scientifically clustered through the power consumption information of the enterprise, the integral multi-project and multi-production condition of the enterprise is effectively and accurately judged, the multi-project and multi-production information of the enterprise can be fed back to the upper-level department in time, the aim of powerfully supporting the upper-level department to conveniently and visually know the multi-project and multi-production condition of the enterprise is achieved, and the upper-level department is powerfully supported to carry out comprehensive decision deployment.
The power company can also analyze the enterprise rework and return production data by analyzing the big data, monitor the power consumption condition of the enterprise, better provide power consumption guarantee for the enterprise rework and return production, know the power consumption load of the enterprise in time, eliminate power failure hidden danger and guarantee the stability of the whole power supply.
The reworking and reworking energy consumption level of the enterprise can be predicted through the reworking and reworking curve of the enterprise, and the enterprise power utilization situation in the future has a good reference value.
Drawings
FIG. 1 is a schematic structural diagram of an enterprise rework and rework degree monitoring system based on artificial intelligence;
FIG. 2 is a flow chart of the method steps of the enterprise rework and rework degree monitoring system based on artificial intelligence according to the present invention;
FIG. 3 is a schematic view of the flow chart of step S1 in the present invention;
FIG. 4 is a schematic view of the flow chart of step S3 in the present invention;
fig. 5 is a schematic view of the flow chart of step S6 in the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1-5, the present invention provides a technical solution: an enterprise reworking and production-resuming degree monitoring system based on artificial intelligence comprises:
the system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring historical rework and rework energy consumption data of a target enterprise and current rework and rework energy consumption data of the target enterprise;
the data analysis unit is used for analyzing the data acquired by the data acquisition unit, fitting the historical rework and rework energy consumption data of the target enterprise to form a fitting curve of the historical rework and rework, and fitting the current rework and rework energy consumption data of the target enterprise to form a fitting curve of the current rework and rework;
the data processing unit is used for sending the parameters of the fitting curve of the current re-work and re-production into the neural network and then outputting the parameters, combining the converted output result with the energy consumption data of the current re-work and re-production of the enterprise to form a re-work and re-production curve, calculating an energy consumption level index, and judging the current re-work and re-production degree of the enterprise according to the energy consumption level index;
and the data display unit displays the processed current energy consumption level of the target enterprise and the current re-work and re-production degree of the target enterprise, which are given by the data processing unit.
Specifically, the data acquisition unit, the data analysis unit, the data processing unit and the data display unit are electrically connected in sequence.
The invention also provides a method of the enterprise reworking and production rate monitoring system based on artificial intelligence, which comprises the following steps:
s1, acquiring historical rework and rework energy consumption data of the target enterprise and current rework and rework energy consumption data of the target enterprise;
s2, fitting a fitting curve of the historical reworking and reproduction: fitting the historical repeated work and repeated production energy consumption data of the target enterprise to form a fitting curve of the historical repeated work and repeated production;
s3, determining a clustering center;
s4, training a neural network: taking parameters of the same class of fitting curves as training input quantity of the neural network, and taking the center of the same class of fitting curve clustering as a training result;
s5, fitting a fitting curve of the current reworking and reproduction: fitting the current reworking and reworking energy consumption data of the target enterprise to form a fitting curve of the current reworking and reworking;
s6, outputting by a neural network;
s7, calculation result: and calculating the energy consumption level index, and judging the current enterprise rework and production rate according to the energy consumption level index.
Specifically, the historical rework and reproduction energy consumption data of the target enterprise in S1 is acquired through the power consumption information collection system and the power marketing system.
Specifically, the S1 includes the following sub-steps:
s11, initializing enterprise user data;
s12, collecting the power utilization information data of the enterprise for the first time;
s13, supplementing and collecting the enterprise electricity utilization information data;
and S14, calculating the previous daily power consumption of the enterprise.
Specifically, the S3 includes the following sub-steps:
s31, setting a plurality of reworking typical curves as a clustering center;
s32, selecting parameters of the typical curve and the fitting curve as dimension values;
s33, performing clustering analysis according to Euclidean libraries among the dimension values;
and S34, updating the clustering center.
Specifically, the S6 includes the following sub-steps:
s61, selecting parameters of a fitting curve of the current re-work and re-production, sending the parameters into a neural network and then outputting the parameters;
and S62, combining the converted output result with the energy consumption data of the current reworking and reworking of the enterprise to form a reworking and reworking curve.
Specifically, the method is used for calculating and monitoring the repeated work and production conditions of enterprises of 10kV or above.
In summary, compared with the prior art, the data source of the invention depends on the enterprise power consumption information, the enterprise is scientifically clustered through the enterprise power consumption information, the overall rework and production recovery situation of the enterprise is effectively and accurately judged, the rework and production recovery information of the enterprise can be timely fed back to the upper-level department, the upper-level department is powerfully supported to conveniently and intuitively know the rework and production recovery situation of the enterprise, and the upper-level department is powerfully supported to carry out comprehensive decision deployment.
The power company can also analyze the enterprise rework and return production data by analyzing the big data, monitor the power consumption condition of the enterprise, better provide power consumption guarantee for the enterprise rework and return production, know the power consumption load of the enterprise in time, eliminate power failure hidden danger and guarantee the stability of the whole power supply.
Meanwhile, the energy consumption level of the enterprise in the reworking and reworking process can be predicted through the enterprise reworking and reworking process curve, and the method has a good reference value for the future power utilization condition of the enterprise.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. The utility model provides an enterprise's compound industry reproduction degree monitored control system based on artificial intelligence which characterized in that includes:
the system comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring historical rework and rework energy consumption data of a target enterprise and current rework and rework energy consumption data of the target enterprise;
the data analysis unit is used for analyzing the data acquired by the data acquisition unit, fitting the historical rework and rework energy consumption data of the target enterprise to form a fitting curve of the historical rework and rework, and fitting the current rework and rework energy consumption data of the target enterprise to form a fitting curve of the current rework and rework;
the data processing unit is used for sending the parameters of the fitting curve of the current re-work and re-production into the neural network and then outputting the parameters, combining the converted output result with the energy consumption data of the current re-work and re-production of the enterprise to form a re-work and re-production curve, calculating an energy consumption level index, and judging the current re-work and re-production degree of the enterprise according to the energy consumption level index;
and the data display unit displays the processed current energy consumption level of the target enterprise and the current re-work and re-production degree of the target enterprise, which are given by the data processing unit.
2. The system for monitoring the rework and production rate of the enterprise based on the artificial intelligence as claimed in claim 1, wherein: the data acquisition unit, the data analysis unit, the data processing unit and the data display unit are electrically connected in sequence.
3. The method of the enterprise rework and rework degree monitoring system based on artificial intelligence of claim 1, wherein: the method specifically comprises the following steps:
s1, acquiring historical rework and rework energy consumption data of the target enterprise and current rework and rework energy consumption data of the target enterprise;
s2, fitting a fitting curve of the historical reworking and reproduction: fitting the historical repeated work and repeated production energy consumption data of the target enterprise to form a fitting curve of the historical repeated work and repeated production;
s3, determining a clustering center;
s4, training a neural network: taking parameters of the same class of fitting curves as training input quantity of the neural network, and taking the center of the same class of fitting curve clustering as a training result;
s5, fitting a fitting curve of the current reworking and reproduction: fitting the current reworking and reworking energy consumption data of the target enterprise to form a fitting curve of the current reworking and reworking;
s6, outputting by a neural network;
s7, calculation result: and calculating the energy consumption level index, and judging the current enterprise rework and production rate according to the energy consumption level index.
4. The method for the enterprise rework and replication degree monitoring system based on artificial intelligence of claim 3, wherein: and acquiring the historical repeated work and repeated production energy consumption data of the target enterprise in the S1 through the electricity utilization information acquisition system and the electricity marketing system.
5. The method for the enterprise rework and replication degree monitoring system based on artificial intelligence of claim 3, wherein: the S1 includes the following substeps:
s11, initializing enterprise user data;
s12, collecting the power utilization information data of the enterprise for the first time;
s13, supplementing and collecting the enterprise electricity utilization information data;
and S14, calculating the previous daily power consumption of the enterprise.
6. The method for the enterprise rework and replication degree monitoring system based on artificial intelligence of claim 3, wherein: the S3 includes the following substeps:
s31, setting a plurality of reworking typical curves as a clustering center;
s32, selecting parameters of the typical curve and the fitting curve as dimension values;
s33, performing clustering analysis according to Euclidean libraries among the dimension values;
and S34, updating the clustering center.
7. The method for the enterprise rework and replication degree monitoring system based on artificial intelligence of claim 3, wherein: the S6 includes the following substeps:
s61, selecting parameters of a fitting curve of the current re-work and re-production, sending the parameters into a neural network and then outputting the parameters;
and S62, combining the converted output result with the energy consumption data of the current reworking and reworking of the enterprise to form a reworking and reworking curve.
8. The method for the enterprise rework and replication degree monitoring system based on artificial intelligence of claim 3, wherein: the method is used for calculating and monitoring the repeated work and production situations of enterprises of 10kV and above.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113935568A (en) * | 2021-08-30 | 2022-01-14 | 国网江苏省电力有限公司物资分公司 | Auxiliary decision-making method for making purchasing strategy in productivity recovery stage |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111539604A (en) * | 2020-04-13 | 2020-08-14 | 国家电网有限公司 | Enterprise rework and production recovery index measuring and monitoring method based on electric power data |
| CN111582568A (en) * | 2020-04-28 | 2020-08-25 | 国网湖南省电力有限公司 | Electric power data-based enterprise rework prediction method during spring festival |
| CN111680764A (en) * | 2020-08-13 | 2020-09-18 | 国网浙江省电力有限公司 | Industry reworking and production-resuming degree monitoring method |
| CN111680939A (en) * | 2020-08-13 | 2020-09-18 | 国网浙江省电力有限公司 | Enterprise re-work and re-production degree monitoring method based on artificial intelligence |
| CN111680938A (en) * | 2020-08-13 | 2020-09-18 | 国网浙江省电力有限公司 | Power flow type big data based rework and production monitoring method and system and readable medium |
| CN111680937A (en) * | 2020-08-13 | 2020-09-18 | 国网浙江省电力有限公司营销服务中心 | Small and micro enterprise rework rate evaluation method based on power data grading and empowerment |
| CN111680852A (en) * | 2020-08-13 | 2020-09-18 | 国网浙江省电力有限公司 | Method and system for monitoring overall energy consumption of area |
-
2020
- 2020-11-10 CN CN202011248470.1A patent/CN112327775A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111539604A (en) * | 2020-04-13 | 2020-08-14 | 国家电网有限公司 | Enterprise rework and production recovery index measuring and monitoring method based on electric power data |
| CN111582568A (en) * | 2020-04-28 | 2020-08-25 | 国网湖南省电力有限公司 | Electric power data-based enterprise rework prediction method during spring festival |
| CN111680764A (en) * | 2020-08-13 | 2020-09-18 | 国网浙江省电力有限公司 | Industry reworking and production-resuming degree monitoring method |
| CN111680939A (en) * | 2020-08-13 | 2020-09-18 | 国网浙江省电力有限公司 | Enterprise re-work and re-production degree monitoring method based on artificial intelligence |
| CN111680938A (en) * | 2020-08-13 | 2020-09-18 | 国网浙江省电力有限公司 | Power flow type big data based rework and production monitoring method and system and readable medium |
| CN111680937A (en) * | 2020-08-13 | 2020-09-18 | 国网浙江省电力有限公司营销服务中心 | Small and micro enterprise rework rate evaluation method based on power data grading and empowerment |
| CN111680852A (en) * | 2020-08-13 | 2020-09-18 | 国网浙江省电力有限公司 | Method and system for monitoring overall energy consumption of area |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113935568A (en) * | 2021-08-30 | 2022-01-14 | 国网江苏省电力有限公司物资分公司 | Auxiliary decision-making method for making purchasing strategy in productivity recovery stage |
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