CN114398436A - Factory information management method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a factory information management method, a device, equipment and a storage medium, wherein a plurality of groups of factory data information with uniform formats corresponding to target factories in a plurality of data sources are acquired, and the plurality of groups of factory data information are integrated to generate a first factory data set; establishing a plant identification model, inputting the first plant data set into the plant identification model, and judging whether the type of the target plant is a plant; and when the type of the target plant is judged to be a plant, performing data processing on the first plant data set according to a preset preprocessing rule to generate a second plant data set. Compared with the prior art, the accuracy and the efficiency of acquiring the factory data information are improved by carrying out information identification and information processing on a plurality of groups of factory data information corresponding to the target factory in the acquired data sources.
Description
Technical Field
The present invention relates to the field of big data technologies, and in particular, to a method, an apparatus, a device, and a storage medium for managing factory information.
Background
In the prior art, in order to search information of a factory meeting specific conditions, a main method is to use a searching and retrieving tool provided by a b2b platform such as access 1688, smart network, love purchase and the like to search; for users who are unfamiliar with the operation, it takes a lot of time to find the information of stores meeting specific conditions; in the prior art, factory information is scattered in the whole network, cross-platform information cannot be retrieved uniformly, so that the searched factory data information may not be comprehensive, and based on numerous current enterprise types, most platforms for inquiring enterprise information cannot provide clear identification to determine whether an inquired enterprise is a factory, so that whether acquired data is really factory data cannot be distinguished, accuracy of data acquired by a user is easily reduced, and therefore, a more efficient management scheme is urgently needed to realize uniform management of the cross-platform factory data information.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: provided are a plant information management method, device, equipment and storage medium, which can improve the accuracy and efficiency of obtaining plant data information by performing information identification and information processing on a plurality of sets of plant data information corresponding to a target plant in a plurality of obtained data sources.
In order to solve the technical problem, the invention provides a method and a device for generating a data model of a power grid data relay station, wherein the method comprises the following steps:
acquiring multiple groups of factory data information with uniform formats corresponding to target factories in multiple data sources, integrating the multiple groups of factory data information, and generating a first factory data set;
establishing a plant identification model, inputting the first plant data set into the plant identification model, and judging whether the type of the target plant is a plant;
and when the type of the target plant is judged to be a plant, performing data processing on the first plant data set according to a preset preprocessing rule to generate a second plant data set.
Further, the acquiring multiple sets of factory data information with uniform formats corresponding to the target factories in the multiple data sources specifically includes:
acquiring multiple groups of factory data information corresponding to target factories in multiple data sources, and judging whether the formats of the multiple groups of factory data information meet corresponding preset formats or not;
and when the plurality of groups of factory data information are judged to meet the corresponding preset format, storing the plurality of groups of factory data information.
Further, according to a preset preprocessing rule, the data processing is performed on the first factory data set, specifically:
dividing the first plant data set into a plant data information set with numerical values and a plant data information set without numerical values;
acquiring the priorities of a plurality of data sources corresponding to each group of factory data information with numerical values in a factory data information set with numerical values, sequencing the priorities of the plurality of data sources, and acquiring factory data information with numerical values corresponding to the highest priority in each group of factory data information with numerical values;
and carrying out deduplication processing on each group of non-numerical factory data information in the non-numerical factory data information set.
Further, the establishing of the plant identification model specifically includes:
and pre-training the neural network model according to a preset factory identification rule, and establishing a factory identification model.
Further, the present invention provides a plant information management apparatus, including: the system comprises an acquisition module, a model identification module and a data processing module;
the acquisition module is used for acquiring a plurality of groups of factory data information with uniform formats corresponding to target factories in a plurality of data sources, integrating the plurality of groups of factory data information and generating a first factory data set;
the model identification module is used for establishing a plant identification model, inputting the first plant data set into the plant identification model and judging whether the type of the target plant is a plant;
and the data processing module is used for processing the data of the first factory data set according to a preset preprocessing rule to generate a second factory data set when the type of the target factory is judged to be the factory.
Further, the obtaining module is configured to obtain multiple sets of factory data information with uniform formats corresponding to target factories in multiple data sources, and specifically includes:
acquiring multiple groups of factory data information corresponding to target factories in multiple data sources, and judging whether the formats of the multiple groups of factory data information meet corresponding preset formats or not;
and when the plurality of groups of factory data information are judged to meet the corresponding preset format, storing the plurality of groups of factory data information.
Further, the data processing module is configured to perform data processing on the first factory data set according to a preset preprocessing rule, and specifically includes:
dividing the first plant data set into a plant data information set with numerical values and a plant data information set without numerical values;
acquiring the priorities of a plurality of data sources corresponding to each group of factory data information with numerical values in a factory data information set with numerical values, sequencing the priorities of the plurality of data sources, and acquiring factory data information with numerical values corresponding to the highest priority in each group of factory data information with numerical values;
and carrying out deduplication processing on each group of non-numerical factory data information in the non-numerical factory data information set.
Further, the model identification module is used for establishing a plant identification model, and specifically comprises:
and pre-training the neural network model according to a preset factory identification rule, and establishing a factory identification model.
Further, the present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the plant information management method according to any one of the above.
Further, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the plant information management method according to any one of the above items.
Compared with the prior art, the factory information management method, the device, the equipment and the storage medium provided by the embodiment of the invention have the following beneficial effects:
integrating multiple groups of factory data information with uniform formats corresponding to target factories in multiple data sources to generate a first factory data set, so as to realize integration of cross-platform factory data information; meanwhile, a factory identification model is established, the first factory data set is input into the factory identification model, and whether the type of the target factory is a factory or not is judged, so that the acquired information is further verified, and the accuracy of information acquisition is improved; and when the type of the target plant is judged to be a plant, performing data processing on the first plant data set according to a preset preprocessing rule to generate a second plant data set. Compared with the prior art, the method and the device have the advantages that the information identification and information processing are carried out on the multiple groups of factory data information corresponding to the target factory in the multiple acquired data sources, so that the accuracy and the efficiency of acquiring the factory data information are improved.
Drawings
FIG. 1 is a flow chart illustrating a method for managing plant information according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an embodiment of a plant information management apparatus provided 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 present application, 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.
Example 1
Referring to fig. 1, fig. 1 is a schematic flowchart of an embodiment of a plant information management method provided by the present invention, and as shown in fig. 1, the method includes steps 101 to 104, which are specifically as follows:
step 101: and acquiring multiple groups of factory data information with uniform formats corresponding to target factories in multiple data sources, integrating the multiple groups of factory data information, and generating a first factory data set.
In the embodiment, a plurality of groups of factory data information corresponding to target factories in websites of a plurality of data sources are crawled through a web crawler technology; specifically, the plurality of data sources include, but are not limited to 1688, smart networks, love purchases, and chinese supplier websites; the multiple sets of factory data information include, but are not limited to, factory name, management system authentication, production system authentication, factory picture, operation mode, industrial and commercial goods, factory floor area, monthly output value, production flow line number, and number of staff in factory, and each set of factory data information includes the same kind of factory data information in the collected multiple data sources.
In this embodiment, whether the formats of the multiple sets of factory data information satisfy the corresponding preset formats is judged; specifically, by presetting the corresponding formats of different pieces of plant data information, different sets of plant data information may have different types of fields, so after multiple sets of plant data information corresponding to a target plant in multiple data sources are obtained, the field type corresponding to each set of plant data information is obtained, and the field type corresponding to each set of plant data information is compared with the preset format. As an example in this embodiment, for a factory building area, a preset format of the factory building area is a digital format, and the preset format can be set to a number smaller than a specified value, a factory building area data set in multiple sets of factory data information is compared with the preset format, and whether fields in the factory building area data set are all in the digital format is determined; similarly, the data set of the number of plant employees is set as described above.
In this embodiment, when it is determined that the plurality of sets of factory data information satisfy the corresponding preset formats, the plurality of sets of factory data information are stored; when the plurality of groups of factory data information are judged not to meet the corresponding preset format, the data information is considered to be wrong in acquisition, and the data information is not stored; therefore, the uniform format of each group of collected factory data information can be ensured, and meanwhile, the accuracy of factory data information collection can be improved.
In this embodiment, the stored multiple sets of factory data information are stored in the B2B table of the local system, and the information of different target factories is stored in the enterprise table of the local system based on the obtained different target factories.
Step 102: and establishing a plant identification model, inputting the first plant data set into the plant identification model, and judging whether the type of the target plant is a plant.
In this embodiment, the preset factory identification rule is to obtain a B2B table and an enterprise table in the local system; specifically, firstly, traversing the B2B table in the B2B table, and judging that a { B2 by entity Type } in the B2B table contains a "factory" character, or judging that any main body containing "production" exists in { B2 by entity Type list, B2 by entity Type, B2 by supplied business model, B2 by business model }, or judging that any value in { OEM, ODM, OBM, CMT, processing } is contained in the enterprise tag { B2 by business model }, and the supplier tag { B2 by supplied business model }; secondly, in the B2B table, it is determined that the primary industry to which the target plant belongs is "manufacturing industry", and the number of plant employees { B2bProductionStaffNumber } is not empty, or the monthly output value { B2 bpmantlyproductionamount } is not empty, or the plant area { B2bFactoryArea } is not empty, or the plant area { B2 bproductroomaea } is not empty, or the monthly output { B2bOutput } is not empty; thirdly, in the B2B table, it is determined that the primary industry to which the target factory belongs is "manufacturing industry", and { B2bBusinessModel } contains "business recruitment agent", and the enterprise profile contains "production" or "manufacturing"; fourthly, in the enterprise table, the last word in the enterprise name of the target factory is judged as factory. Based on the preset factory identification rules, the target factory can be judged to be the type of the factory only by meeting any one of the preset factory identification rules.
In this embodiment, the preset factory identification rule may be adjusted according to the user's needs.
In this embodiment, the first plant data set obtained in step 101 is divided according to a preset ratio to establish a plant identification data set; as an example in the present embodiment, the first plant data set is expressed as 8: 1: 1, dividing the data into three data sets, namely a training set, a testing set and a verification set, and generating a factory identification data set.
In this embodiment, the neural network model is designed according to the preset factory identification rule, trained according to the factory identification data set, adjusted, and compared with the recognition effect of the neural network model through the verification set, so as to determine the final neural network model as the factory identification model.
In this embodiment, the first plant data set is input into the plant identification model, so that the plant identification model performs identification and determination on the first plant data set to determine whether the type of the target plant is a plant.
Step 103: and when the type of the target plant is judged to be a plant, performing data processing on the first plant data set according to a preset preprocessing rule to generate a second plant data set.
In this embodiment, when the type of the target plant is determined to be a plant, data processing is performed on the first plant data set according to a preset preprocessing rule, specifically, the preset preprocessing rule is to divide the first plant data set into a plant data information set with a numerical value and a plant data information set without a numerical value, where the divided plant data information set with a numerical value includes, but is not limited to, plant area data information of each group, monthly production value data information of each group, production line data information of each group, and staff number data information of each group; the divided factory data information sets without numerical values include, but are not limited to, each group of operation mode data information, each group of management system authentication data information, each group of production system authentication data information, each group of factory picture data information and each group of industrial and commercial commodity data information.
In this embodiment, for a factory data information set with numerical values, by obtaining the priorities of a plurality of data sources corresponding to each group of factory data information with numerical values in the factory data information set with numerical values and sorting the priorities of the plurality of data sources, the factory data information with numerical values corresponding to the highest priority in each group of factory data information with numerical values is obtained, and the factory data information with numerical values corresponding to the highest priority in each group of factory data information with numerical values is collected to generate a high-priority factory data information set. In this embodiment, the priorities of the plurality of data sources may be designed according to user requirements.
In this embodiment, for a factory data information set without numerical values, since countless factory data information worth of is not changed, deduplication processing is performed on each group of factory data information without numerical values in the factory data information set with countless values, and duplicate factory data information in each group of countless factory data information worth of is deleted, so that the problem of data redundancy caused by data duplication is solved, and meanwhile, the pressure of data storage is reduced.
In this embodiment, the first plant data set obtained in step 101, the high-priority plant data information set obtained in step 103, and the deduplicated plant data information set without numerical values are also integrated and displayed through a big data visualization technology, and based on product applications such as keyword retrieval, conventional dimension screening, processing dimension information viewing, advanced screening, and the like provided by a display interface, a user can retrieve and view plant data of a relevant target plant in a cross-platform real-time manner, so that data integration and centralized display of multi-data source information are realized.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an embodiment of a plant information management apparatus provided in the present invention, as shown in fig. 2, the apparatus includes an obtaining module 201, a model identifying module 202, and a data processing module 203, which are as follows:
the obtaining module 201 is configured to obtain multiple sets of factory data information with uniform formats corresponding to target factories in multiple data sources, integrate the multiple sets of factory data information, and generate a first factory data set.
In the embodiment, a plurality of groups of factory data information corresponding to target factories in websites of a plurality of data sources are crawled through a web crawler technology; specifically, the plurality of data sources include, but are not limited to 1688, smart networks, love purchases, and chinese supplier websites; the multiple sets of factory data information include, but are not limited to, factory name, management system authentication, production system authentication, factory picture, operation mode, industrial and commercial goods, factory floor area, monthly output value, production flow line number, and number of staff in factory, and each set of factory data information includes the same kind of factory data information in the collected multiple data sources.
In this embodiment, whether the formats of the multiple sets of factory data information satisfy the corresponding preset formats is judged; specifically, by presetting the corresponding formats of different pieces of plant data information, different sets of plant data information may have different types of fields, so after multiple sets of plant data information corresponding to a target plant in multiple data sources are obtained, the field type corresponding to each set of plant data information is obtained, and the field type corresponding to each set of plant data information is compared with the preset format. As an example in this embodiment, for the factory floor area, the preset format is a number format, and the preset format can be set to a number smaller than a specified value, compare the factory floor area data set in the multiple sets of factory data information with the preset format, and determine whether all fields in the factory floor area data set are in the number format. Similarly, the data set of the number of plant employees is set as described above.
In this embodiment, when it is determined that the plurality of sets of factory data information satisfy the corresponding preset formats, the plurality of sets of factory data information are stored; when the plurality of groups of factory data information are judged not to meet the corresponding preset format, the data information is considered to be wrong in acquisition, and the data information is not stored; therefore, the uniform format of each group of collected factory data information can be ensured, and meanwhile, the accuracy of factory data information collection can be improved.
In this embodiment, the stored multiple sets of factory data information are stored in the B2B table of the local system, and the information of different target factories is stored in the enterprise table of the local system based on the obtained different target factories.
The model identification module 202 is configured to establish a plant identification model, input the first plant data set into the plant identification model, and determine whether the type of the target plant is a plant.
In this embodiment, the preset factory identification rule is to obtain a B2B table and an enterprise table in the local system; specifically, firstly, traversing the B2B table in the B2B table, and judging that a { B2 by entity Type } in the B2B table contains a "factory" character, or judging that any main body containing "production" exists in { B2 by entity Type list, B2 by entity Type, B2 by supplied business model, B2 by business model }, or judging that any value in { OEM, ODM, OBM, CMT, processing } is contained in the enterprise tag { B2 by business model }, and the supplier tag { B2 by supplied business model }; secondly, in the B2B table, it is determined that the primary industry to which the target plant belongs is "manufacturing industry", and the number of plant employees { B2bProductionStaffNumber } is not empty, or the monthly output value { B2 bpmantlyproductionamount } is not empty, or the plant area { B2bFactoryArea } is not empty, or the plant area { B2 bproductroomaea } is not empty, or the monthly output { B2bOutput } is not empty; thirdly, in the B2B table, it is determined that the primary industry to which the target factory belongs is "manufacturing industry", and { B2bBusinessModel } contains "business recruitment agent", and the enterprise profile contains "production" or "manufacturing"; fourthly, in the enterprise table, the last word in the enterprise name of the target factory is judged as factory. Based on the preset factory identification rules, the target factory can be judged to be the type of the factory only by meeting any one of the preset factory identification rules.
In this embodiment, the preset factory identification rule may be adjusted according to the user's needs.
In this embodiment, the first plant data set obtained in the obtaining module 201 is divided according to a preset proportion, and a plant identification data set is established; as an example in the present embodiment, the first plant data set is expressed as 8: 1: 1, dividing the data into three data sets, namely a training set, a testing set and a verification set, and generating a factory identification data set.
In this embodiment, the neural network model is designed according to the preset factory identification rule, trained according to the factory identification data set, adjusted, and compared with the recognition effect of the neural network model through the verification set, so as to determine the final neural network model as the factory identification model.
In this embodiment, the first plant data set is input into the plant identification model, so that the plant identification model performs identification and determination on the first plant data set to determine whether the type of the target plant is a plant.
The data processing module 203 is configured to, when it is determined that the type of the target plant is a plant, perform data processing on the first plant data set according to a preset preprocessing rule, and generate a second plant data set.
In this embodiment, when the type of the target plant is determined to be a plant, data processing is performed on the first plant data set according to a preset preprocessing rule, specifically, the preset preprocessing rule is to divide the first plant data set into a plant data information set with a numerical value and a plant data information set without a numerical value, where the divided plant data information set with a numerical value includes, but is not limited to, plant area data information of each group, monthly production value data information of each group, production line data information of each group, and staff number data information of each group; the divided factory data information sets without numerical values include, but are not limited to, each group of operation mode data information, each group of management system authentication data information, each group of production system authentication data information, each group of factory picture data information and each group of industrial and commercial commodity data information.
In this embodiment, for a factory data information set with numerical values, by obtaining the priorities of a plurality of data sources corresponding to each group of factory data information with numerical values in the factory data information set with numerical values and sorting the priorities of the plurality of data sources, the factory data information with numerical values corresponding to the highest priority in each group of factory data information with numerical values is obtained, and the factory data information with numerical values corresponding to the highest priority in each group of factory data information with numerical values is collected to generate a high-priority factory data information set. In this embodiment, the priorities of the plurality of data sources may be designed according to user requirements.
In this embodiment, for a factory data information set without numerical values, since countless factory data information worth of is not changed, deduplication processing is performed on each group of factory data information without numerical values in the factory data information set with countless values, and duplicate factory data information in each group of countless factory data information worth of is deleted, so that the problem of data redundancy caused by data duplication is solved, and meanwhile, the pressure of data storage is reduced.
In this embodiment, the first factory data set acquired in the acquisition module 201, the high-priority factory data information set acquired in the data processing module 203, and the deduplicated factory data information set are also integrated and displayed by a big data visualization technology, and based on product applications such as keyword retrieval, conventional dimension screening, processing dimension information viewing, advanced screening, and the like provided by a display interface, a user can retrieve and view factory data of a related target factory in a cross-platform real-time manner, so that data integration and centralized display of multi-data source information are realized.
In this embodiment, an apparatus for plant information management is further provided, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the apparatus implements the plant information management method.
In an embodiment of the present invention, a computer-readable storage medium is further provided, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the above plant information management method.
Illustratively, the computer program may be partitioned into one or more modules that are stored in the memory and executed by the processor to implement the invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program in the plant information management device.
The factory information management equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The equipment for store factory information management may include, but is not limited to, a processor, memory, and a display. It will be understood by those skilled in the art that the above components are merely examples of a plant information management apparatus and do not constitute a limitation on the plant information management apparatus, and may include more or less components than the described components, or some components in combination, or different components, for example, the plant information management apparatus may further include an input-output device, a network access device, a bus, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that is the control center of the plant information management device and that connects the various parts of the overall plant information management device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the plant information management apparatus by executing or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, a text conversion function, etc.), and the like; the storage data area may store data (such as audio data, text message data, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein the device-integrated module for plant information management may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
One of ordinary skill in the art can understand and implement it without inventive effort.
To sum up, the invention discloses a factory information management method, a device, equipment and a storage medium, and discloses a factory information management method, a device, equipment and a storage medium, which integrate a plurality of groups of factory data information by acquiring a plurality of groups of factory data information with uniform formats corresponding to target factories in a plurality of data sources to generate a first factory data set; establishing a plant identification model, inputting the first plant data set into the plant identification model, and judging whether the type of the target plant is a plant; and when the type of the target plant is judged to be a plant, performing data processing on the first plant data set according to a preset preprocessing rule to generate a second plant data set. Compared with the prior art, the accuracy and the efficiency of acquiring the factory data information are improved by carrying out information identification and information processing on a plurality of groups of factory data information corresponding to the target factory in the acquired data sources.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and substitutions can be made without departing from the technical principle of the present invention, and these modifications and substitutions should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method for managing plant information, comprising:
acquiring multiple groups of factory data information with uniform formats corresponding to target factories in multiple data sources, integrating the multiple groups of factory data information, and generating a first factory data set;
establishing a plant identification model, inputting the first plant data set into the plant identification model, and judging whether the type of the target plant is a plant;
and when the type of the target plant is judged to be a plant, performing data processing on the first plant data set according to a preset preprocessing rule to generate a second plant data set.
2. The method for managing plant information according to claim 1, wherein the acquiring of multiple sets of plant data information with uniform formats corresponding to target plants in multiple data sources specifically comprises:
acquiring multiple groups of factory data information corresponding to target factories in multiple data sources, and judging whether the formats of the multiple groups of factory data information meet corresponding preset formats or not;
and when the plurality of groups of factory data information are judged to meet the corresponding preset format, storing the plurality of groups of factory data information.
3. The plant information management method according to claim 1, wherein the data processing is performed on the first plant data set according to a preset preprocessing rule, specifically:
dividing the first plant data set into a plant data information set with numerical values and a plant data information set without numerical values;
acquiring the priorities of a plurality of data sources corresponding to each group of factory data information with numerical values in a factory data information set with numerical values, sequencing the priorities of the plurality of data sources, and acquiring factory data information with numerical values corresponding to the highest priority in each group of factory data information with numerical values;
and carrying out deduplication processing on each group of non-numerical factory data information in the non-numerical factory data information set.
4. The plant information management method according to claim 1, wherein the establishing of the plant identification model specifically includes:
and pre-training the neural network model according to a preset factory identification rule, and establishing a factory identification model.
5. A plant information management apparatus, comprising: the system comprises an acquisition module, a model identification module and a data processing module;
the acquisition module is used for acquiring a plurality of groups of factory data information with uniform formats corresponding to target factories in a plurality of data sources, integrating the plurality of groups of factory data information and generating a first factory data set;
the model identification module is used for establishing a plant identification model, inputting the first plant data set into the plant identification model and judging whether the type of the target plant is a plant;
and the data processing module is used for processing the data of the first factory data set according to a preset preprocessing rule to generate a second factory data set when the type of the target factory is judged to be the factory.
6. The plant information management apparatus of claim 5, wherein the obtaining module is configured to obtain a plurality of sets of plant data information with a uniform format corresponding to a target plant in a plurality of data sources, and specifically:
acquiring multiple groups of factory data information corresponding to target factories in multiple data sources, and judging whether the formats of the multiple groups of factory data information meet corresponding preset formats or not;
and when the plurality of groups of factory data information are judged to meet the corresponding preset format, storing the plurality of groups of factory data information.
7. The plant information management device according to claim 5, wherein the data processing module is configured to perform data processing on the first plant data set according to a preset preprocessing rule, specifically:
dividing the first plant data set into a plant data information set with numerical values and a plant data information set without numerical values;
acquiring the priorities of a plurality of data sources corresponding to each group of factory data information with numerical values in a factory data information set with numerical values, sequencing the priorities of the plurality of data sources, and acquiring factory data information with numerical values corresponding to the highest priority in each group of factory data information with numerical values;
and carrying out deduplication processing on each group of non-numerical factory data information in the non-numerical factory data information set.
8. The plant information management apparatus of claim 5, wherein the model identification module is configured to establish a plant identification model, specifically:
and pre-training the neural network model according to a preset factory identification rule, and establishing a factory identification model.
9. A terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the plant information management method according to any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the plant information management method according to any one of claims 1 to 4.
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| CN202111683351.3A CN114398436A (en) | 2021-12-31 | 2021-12-31 | Factory information management method, device, equipment and storage medium |
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| CN202111683351.3A CN114398436A (en) | 2021-12-31 | 2021-12-31 | Factory information management method, device, equipment and storage medium |
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