CN114238383A - Big data extraction method and device for supply chain monitoring - Google Patents
Big data extraction method and device for supply chain monitoring Download PDFInfo
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- CN114238383A CN114238383A CN202111569266.4A CN202111569266A CN114238383A CN 114238383 A CN114238383 A CN 114238383A CN 202111569266 A CN202111569266 A CN 202111569266A CN 114238383 A CN114238383 A CN 114238383A
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
Abstract
The invention discloses a big data extraction method and device for supply chain monitoring. Wherein, the method comprises the following steps: obtaining supply chain information, wherein the supply chain information comprises: cargo information, equipment information, transportation information; calculating the complexity o of the supply chain information according to the supply chain information; judging a threshold value according to the complexity o, or analyzing a complexity-big data matrix to obtain a data acquisition parameter; acquiring a data summarizing result and a judgment result according to the data acquisition parameters, wherein the judgment result comprises: abnormal information and non-abnormal information. The invention solves the technical problems that in the prior art, a supply chain monitoring mode only performs data acquisition and comparison algorithm operation from a single supply chain data acquisition node, so that the comparison of multiple supply chain nodes is reduced, the supply chain nodes are not uniformly updated and monitored and controlled in real time, and meanwhile, when a data source of big data is extracted, the big data cannot be intelligently acquired according to the running condition and the complexity of the supply chain, the burden of computing resources is increased, and the efficiency and the accuracy of supply chain monitoring are reduced.
Description
Technical Field
The invention relates to the field of data monitoring, in particular to a big data extraction method and device for supply chain monitoring.
Background
Along with the continuous development of intelligent science and technology, people use intelligent equipment more and more among life, work, the study, use intelligent science and technology means, improved the quality of people's life, increased the efficiency of people's study and work.
At present, in the aspect of monitoring a supply chain of a provider, a conventional monitoring method is to screen and record data uploaded by nodes in the supply chain according to a supply chain system platform, compare and process the recorded data according to a certain preset rule, obtain an abnormal condition of the supply chain nodes, and process the abnormal condition. However, in the supply chain monitoring mode in the prior art, data acquisition and comparison algorithm operation is performed only from a single supply chain data acquisition node, so that comparison of multiple supply chain nodes is reduced, and unified updating and monitoring control of the supply chain nodes in real time are not performed, and meanwhile, when a data source of big data is extracted, intelligent acquisition cannot be performed according to the running condition and complexity of the supply chain, so that the burden of computing resources is increased, and the efficiency and accuracy of supply chain monitoring are reduced.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a big data extraction method and a big data extraction device for supply chain monitoring, which at least solve the technical problems that in the prior art, a supply chain monitoring mode only performs data acquisition and comparison algorithm operation from a single supply chain data acquisition node, so that the comparison of multiple supply chain nodes is reduced, the supply chain nodes do not have real-time unified updating and monitoring control, and meanwhile, when a data source of big data is extracted, the big data cannot be intelligently acquired according to the running condition and the complexity of the supply chain, the burden of computing resources is increased, and the efficiency and the accuracy of supply chain monitoring are reduced.
According to an aspect of the embodiments of the present invention, there is provided a big data extraction method for supply chain monitoring, including: obtaining supply chain information, wherein the supply chain information comprises: cargo information, equipment information, transportation information; calculating the complexity o of the supply chain information according to the supply chain information; judging a threshold value according to the complexity o, or analyzing a complexity-big data matrix to obtain a data acquisition parameter; acquiring a data summarizing result and a judgment result according to the data acquisition parameters, wherein the judgment result comprises: abnormal information and non-abnormal information.
Optionally, the calculating the complexity o of the supply chain information according to the supply chain information includes: and calculating a comprehensive complexity factor o 'through the weight value K of the goods information, the equipment information and the transportation information, and generating final supply chain information complexity through f (o') ═ o.
Optionally, the f (o') ═ o includes: the complexity o is calculated by k2 ∑ M (N), where M is a parameter quantization value of the supply chain equipment and N is a representation of the number of equipment.
Optionally, after obtaining the data summarization result and the judgment result according to the data obtaining parameter, the method further includes: and monitoring operation is carried out according to the judgment result, the abnormal information is reported to a cloud server and then sent to the user side, and the abnormal information is stored in the cloud server to be used as historical data for follow-up supply chain monitoring.
According to another aspect of the embodiments of the present invention, there is also provided a big data extraction apparatus for supply chain monitoring, including: an obtaining module, configured to obtain supply chain information, where the supply chain information includes: cargo information, equipment information, transportation information; the calculating module is used for calculating the complexity o of the supply chain information according to the supply chain information; the judging module is used for judging a threshold value according to the complexity o or analyzing a complexity-big data matrix to obtain a data acquisition parameter; the early warning module is used for acquiring a data summarizing result and a judgment result according to the data acquisition parameters, wherein the judgment result comprises: abnormal information and non-abnormal information.
Optionally, the calculation module includes: and the calculating unit is used for calculating the comprehensive complexity factor o 'through the weight value K of the goods information, the equipment information and the transportation information, and generating the final supply chain information complexity through f (o') ═ o.
Optionally, the f (o') ═ o includes: the complexity o is calculated by k2 ∑ M (N), where M is a parameter quantization value of the supply chain equipment and N is a representation of the number of equipment.
Optionally, the apparatus further comprises: and the reporting module is used for monitoring operation according to the judgment result, reporting the abnormal information to the cloud server, further sending the abnormal information to the user side, and storing the abnormal information in the cloud server to be used as historical data for monitoring a subsequent supply chain.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium including a stored program, wherein the program controls a device in which the non-volatile storage medium is located to execute a big data extraction method for supply chain monitoring when running.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions when executed perform a big data fetch method for supply chain monitoring.
Compared with the prior art, the invention has the beneficial effects that: in the embodiment of the present invention, acquiring supply chain information is adopted, wherein the supply chain information includes: cargo information, equipment information, transportation information; calculating the complexity o of the supply chain information according to the supply chain information; judging a threshold value according to the complexity o, or analyzing a complexity-big data matrix to obtain a data acquisition parameter; acquiring a data summarizing result and a judgment result according to the data acquisition parameters, wherein the judgment result comprises: the method solves the technical problems that in the prior art, a supply chain monitoring mode only performs data acquisition and comparison algorithm operation from a single supply chain data acquisition node, so that the comparison of multiple supply chain nodes is reduced, the supply chain nodes are not uniformly updated and monitored and controlled in real time, and meanwhile, when a data source of big data is extracted, the big data cannot be intelligently acquired according to the running condition and the complexity of the supply chain, the burden of computing resources is increased, and the efficiency and the accuracy of supply chain monitoring are reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow diagram of a big data extraction method for supply chain monitoring, according to an embodiment of the present invention;
fig. 2 is a block diagram of a big data extraction apparatus for supply chain monitoring according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided a method embodiment of a big data extraction method for supply chain monitoring, it is noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Example one
Fig. 1 is a flowchart of a big data extraction method for supply chain monitoring according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, obtaining supply chain information, wherein the supply chain information includes: cargo information, equipment information, transportation information.
Step S104, calculating the complexity o of the supply chain information according to the supply chain information.
And step S106, judging a threshold value according to the complexity o, or analyzing a complexity-big data matrix to obtain a data acquisition parameter.
Step S108, acquiring a data summarizing result and a judgment result according to the data acquisition parameters, wherein the judgment result comprises: abnormal information and non-abnormal information.
Optionally, the calculating the complexity o of the supply chain information according to the supply chain information includes: and calculating a comprehensive complexity factor o 'through the weight value K of the goods information, the equipment information and the transportation information, and generating final supply chain information complexity through f (o') ═ o.
Optionally, the f (o') ═ o includes: the complexity o is calculated by k2 ∑ M (N), where M is a parameter quantization value of the supply chain equipment and N is a representation of the number of equipment.
Optionally, after obtaining the data summarization result and the judgment result according to the data obtaining parameter, the method further includes: and monitoring operation is carried out according to the judgment result, the abnormal information is reported to a cloud server and then sent to the user side, and the abnormal information is stored in the cloud server to be used as historical data for follow-up supply chain monitoring.
In a specific implementation, whether an internet of things device of supply chain goods and equipment related to a supply chain is normally operated is firstly determined, then supply chain information existing in the internet of things is activated and collected through an activation command, the information is analyzed and calculated, and supply chain information complexity o is calculated by using the information quantity and the information quantity type of each piece of sub information in the supply chain information, wherein the formula is f (o ') -o, a function can be o' U, U is a supply chain information total throughput value, and complexity o is calculated through o ═ k2 ∑ M (N), for example, M is a parameter quantization value of supply chain equipment, and N is a representation of the number of equipment. Further, the calculation of the comprehensive complexity factor o' can be performed through the weight value K of the cargo information, the equipment information and the transportation information, in order to further acquire additional big data platform information to increase the accuracy of supply chain monitoring, which big data need to be selected according to the information complexity o, if the complexity o is greater than the threshold a, diversified big data information such as temperature, humidity, personnel and historical data is acquired, and if the complexity o is less than the threshold a, historical data is acquired to perform operation of big data-internet of things combination judgment on how to perform supply chain monitoring. After the information of the big data platform is collected, the internet of things and all data of the big data platform need to be transmitted through a low-delay 5G independent network, and all data are gathered, so that whether the data exceed a threshold standard or not is determined, abnormal information is judged, and if the conditions of supply chain abnormity, such as cargo damage, temperature exceeding the standard, transportation blockage and the like, engineering personnel need to be informed to overhaul in time, and a supply system is controlled to reduce loss expansion.
It should be noted that, a threshold value is determined according to the complexity o, or a complexity-big data matrix analysis is performed to obtain data acquisition parameters, where the complexity-big data matrix analysis may be to configure a two-dimensional matrix corresponding to big data of N × N and complexity, and to analyze and match the big data obtained in the foregoing embodiment according to the complexity value, where the big data corresponding to the certain complexity is the data. When the big data matrix is constructed, a procat database can be used for constructing plug-ins, and MySQL is used for constructing an index data table of the big data matrix.
Through the embodiment, the technical problems that in the prior art, a supply chain monitoring mode only performs data acquisition and comparison algorithm operation from a single supply chain data acquisition node, so that comparison of multiple supply chain nodes is reduced, real-time unified updating and monitoring control of the supply chain nodes are not available, and meanwhile, when a data source of big data is extracted, intelligent acquisition cannot be performed according to the operation condition and complexity of the supply chain, the burden of computing resources is increased, and therefore the efficiency and accuracy of supply chain monitoring are reduced are solved.
Example two
Fig. 2 is a block diagram of a big data extraction apparatus for supply chain monitoring according to an embodiment of the present invention, as shown in fig. 2, the apparatus includes:
an obtaining module 20, configured to obtain supply chain information, where the supply chain information includes: cargo information, equipment information, transportation information.
A calculating module 22, configured to calculate the complexity o of the supply chain information according to the supply chain information.
And the judging module 24 is configured to perform threshold judgment according to the complexity o, or perform complexity-big data matrix analysis to obtain a data acquisition parameter.
The early warning module 26 is configured to obtain a data summarization result and a judgment result according to the data acquisition parameter, where the judgment result includes: abnormal information and non-abnormal information.
Optionally, the calculation module includes: and the calculating unit is used for calculating the comprehensive complexity factor o 'through the weight value K of the goods information, the equipment information and the transportation information, and generating the final supply chain information complexity through f (o') ═ o.
Optionally, the f (o') ═ o includes: the complexity o is calculated by k2 ∑ M (N), where M is a parameter quantization value of the supply chain equipment and N is a representation of the number of equipment.
Optionally, the apparatus further comprises: and the reporting module is used for monitoring operation according to the judgment result, reporting the abnormal information to the cloud server, further sending the abnormal information to the user side, and storing the abnormal information in the cloud server to be used as historical data for monitoring a subsequent supply chain.
In a specific implementation, whether an internet of things device of supply chain goods and equipment related to a supply chain is normally operated is firstly determined, then supply chain information existing in the internet of things is activated and collected through an activation command, the information is analyzed and calculated, and supply chain information complexity o is calculated by using the information quantity and the information quantity type of each piece of sub information in the supply chain information, wherein the formula is f (o ') -o, a function can be o' U, U is a supply chain information total throughput value, and complexity o is calculated through o ═ k2 ∑ M (N), for example, M is a parameter quantization value of supply chain equipment, and N is a representation of the number of equipment. Further, the calculation of the comprehensive complexity factor o' can be performed through the weight value K of the cargo information, the equipment information and the transportation information, in order to further acquire additional big data platform information to increase the accuracy of supply chain monitoring, which big data need to be selected according to the information complexity o, if the complexity o is greater than the threshold a, diversified big data information such as temperature, humidity, personnel and historical data is acquired, and if the complexity o is less than the threshold a, historical data is acquired to perform operation of big data-internet of things combination judgment on how to perform supply chain monitoring. After the information of the big data platform is collected, the internet of things and all data of the big data platform need to be transmitted through a low-delay 5G independent network, and all data are gathered, so that whether the data exceed a threshold standard or not is determined, abnormal information is judged, and if the conditions of supply chain abnormity, such as cargo damage, temperature exceeding the standard, transportation blockage and the like, engineering personnel need to be informed to overhaul in time, and a supply system is controlled to reduce loss expansion.
It should be noted that, a threshold value is determined according to the complexity o, or a complexity-big data matrix analysis is performed to obtain data acquisition parameters, where the complexity-big data matrix analysis may be to configure a two-dimensional matrix corresponding to big data of N × N and complexity, and to analyze and match the big data obtained in the foregoing embodiment according to the complexity value, where the big data corresponding to the certain complexity is the data. When the big data matrix is constructed, a procat database can be used for constructing plug-ins, and MySQL is used for constructing an index data table of the big data matrix.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium including a stored program, wherein the program controls a device in which the non-volatile storage medium is located to execute a big data extraction method for supply chain monitoring when running.
Specifically, the big data extraction method for supply chain monitoring includes: obtaining supply chain information, wherein the supply chain information comprises: cargo information, equipment information, transportation information; calculating the complexity o of the supply chain information according to the supply chain information; judging a threshold value according to the complexity o, or analyzing a complexity-big data matrix to obtain a data acquisition parameter; acquiring a data summarizing result and a judgment result according to the data acquisition parameters, wherein the judgment result comprises: abnormal information and non-abnormal information. Optionally, the calculating the complexity o of the supply chain information according to the supply chain information includes: and calculating a comprehensive complexity factor o 'through the weight value K of the goods information, the equipment information and the transportation information, and generating final supply chain information complexity through f (o') ═ o. Optionally, the f (o') ═ o includes: the complexity o is calculated by k2 ∑ M (N), where M is a parameter quantization value of the supply chain equipment and N is a representation of the number of equipment. Optionally, after obtaining the data summarization result and the judgment result according to the data obtaining parameter, the method further includes: and monitoring operation is carried out according to the judgment result, the abnormal information is reported to a cloud server and then sent to the user side, and the abnormal information is stored in the cloud server to be used as historical data for follow-up supply chain monitoring.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions when executed perform a big data fetch method for supply chain monitoring.
Specifically, the big data extraction method for supply chain monitoring includes: obtaining supply chain information, wherein the supply chain information comprises: cargo information, equipment information, transportation information; calculating the complexity o of the supply chain information according to the supply chain information; judging a threshold value according to the complexity o, or analyzing a complexity-big data matrix to obtain a data acquisition parameter; acquiring a data summarizing result and a judgment result according to the data acquisition parameters, wherein the judgment result comprises: abnormal information and non-abnormal information. Optionally, the calculating the complexity o of the supply chain information according to the supply chain information includes: and calculating a comprehensive complexity factor o 'through the weight value K of the goods information, the equipment information and the transportation information, and generating final supply chain information complexity through f (o') ═ o. Optionally, the f (o') ═ o includes: the complexity o is calculated by k2 ∑ M (N), where M is a parameter quantization value of the supply chain equipment and N is a representation of the number of equipment. Optionally, after obtaining the data summarization result and the judgment result according to the data obtaining parameter, the method further includes: and monitoring operation is carried out according to the judgment result, the abnormal information is reported to a cloud server and then sent to the user side, and the abnormal information is stored in the cloud server to be used as historical data for follow-up supply chain monitoring.
Through the embodiment, the technical problems that in the prior art, a supply chain monitoring mode only performs data acquisition and comparison algorithm operation from a single supply chain data acquisition node, so that comparison of multiple supply chain nodes is reduced, real-time unified updating and monitoring control of the supply chain nodes are not available, and meanwhile, when a data source of big data is extracted, intelligent acquisition cannot be performed according to the operation condition and complexity of the supply chain, the burden of computing resources is increased, and therefore the efficiency and accuracy of supply chain monitoring are reduced are solved.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A big data extraction method for supply chain monitoring, comprising:
obtaining supply chain information, wherein the supply chain information comprises: cargo information, equipment information, transportation information;
calculating the complexity o of the supply chain information according to the supply chain information;
judging a threshold value according to the complexity o, or analyzing a complexity-big data matrix to obtain a data acquisition parameter;
acquiring a data summarizing result and a judgment result according to the data acquisition parameters, wherein the judgment result comprises: abnormal information and non-abnormal information.
2. The method of claim 1, wherein calculating the complexity o of supply chain information from the supply chain information comprises:
and calculating a comprehensive complexity factor o 'through the weight value K of the goods information, the equipment information and the transportation information, and generating final supply chain information complexity through f (o') ═ o.
3. The method of claim 1, wherein f (o') ═ o comprises:
the complexity o is calculated by k2 ∑ M (N), where M is a parameter quantization value of the supply chain equipment and N is a representation of the number of equipment.
4. The method of claim 1, wherein after the obtaining data summary results and decision results according to the data acquisition parameters, the method further comprises:
and monitoring operation is carried out according to the judgment result, the abnormal information is reported to a cloud server and then sent to the user side, and the abnormal information is stored in the cloud server to be used as historical data for follow-up supply chain monitoring.
5. A big data extraction apparatus for supply chain monitoring, comprising:
an obtaining module, configured to obtain supply chain information, where the supply chain information includes: cargo information, equipment information, transportation information;
the calculating module is used for calculating the complexity o of the supply chain information according to the supply chain information;
the judging module is used for judging a threshold value according to the complexity o or analyzing a complexity-big data matrix to obtain a data acquisition parameter;
the early warning module is used for acquiring a data summarizing result and a judgment result according to the data acquisition parameters, wherein the judgment result comprises: abnormal information and non-abnormal information.
6. The apparatus of claim 5, wherein the computing module comprises:
and the calculating unit is used for calculating the comprehensive complexity factor o 'through the weight value K of the goods information, the equipment information and the transportation information, and generating the final supply chain information complexity through f (o') ═ o.
7. The apparatus of claim 5, wherein f (o') ═ o comprises:
the complexity o is calculated by k2 ∑ M (N), where M is a parameter quantization value of the supply chain equipment and N is a representation of the number of equipment.
8. The apparatus of claim 5, further comprising:
and the reporting module is used for monitoring operation according to the judgment result, reporting the abnormal information to the cloud server, further sending the abnormal information to the user side, and storing the abnormal information in the cloud server to be used as historical data for monitoring a subsequent supply chain.
9. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the non-volatile storage medium is located to perform the method of any one of claims 1 to 4.
10. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions when executed perform the method of any one of claims 1 to 4.
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