CN117792926A - Edge gateway modeling method based on TF-IDF algorithm - Google Patents
Edge gateway modeling method based on TF-IDF algorithm Download PDFInfo
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Abstract
The edge gateway modeling method based on the TF-IDF algorithm is characterized by comprising the following steps: collecting edge gateway access side data according to the actual condition of the site, and storing the data into a database; reading information of the model library, and calculating word frequency data TF of each data of the model according to a word frequency algorithm in the TF-IDF algorithm; reading access side database information, and calculating word frequency data TF corresponding to description information of each data of the access device; respectively calculating inverse document frequency IDF of the accessed and transferred items, and generating TF-IDF data of each item; generating access data and vector values of a transfer-out model based on the vocabulary entries according to the vocabulary entry TF-IDF data; and generating a forwarding side database through the created mapping relation. According to the method, the characteristic of the TF-IDF algorithm on text similarity is utilized, the data association relation between the access data and the roll-off model can be rapidly analyzed, the labor investment in the edge gateway modeling process is reduced, and the modeling efficiency of the edge gateway is improved.
Description
Technical Field
The invention belongs to the field of power automation control, and particularly relates to an edge gateway modeling method based on a TF-IDF algorithm.
Background
Along with the rapid development of cloud computing and the internet of things, power systems are also changing. In the field of power automation control, more and more devices need to be connected with the cloud end at the edge through network connection and protocol conversion functions. The gateway machine in the traditional electric power automation field transfers out a communication protocol based on IEC-60870-104, and the modeling of transfer out measurement on data is highly dependent on manual picking. For this case, although the power system proposes an RCD-based roll-out modeling scheme, the RCD scheme only implements an automatic modeling method between 61850 in and 104 roll-out. At present, other scenes are not used except for supporting IEC-61850 standard in an intelligent substation, such as: new energy and distribution network field.
Because the use scene of border gateway is complicated, except that electric power system transformer substation uses the scene, covered photovoltaic station, energy storage station, electric automobile charging station, wind-powered electricity generation field, join in marriage the net district, lead to the equipment that needs to insert various, the communication interface that provides is various, consequently can't carry out the roll-off modeling based on single access type. Meanwhile, due to inconsistent roll-out schemes of various sites, modeling based on a single roll-out model is also not possible. The resulting modeling requires a large number of manual operations to solve the modeling problem in the edge gateway machine configuration process. In order to solve the outbound modeling requirements of different scenes, a modeling method for quickly matching different access models and outbound models is needed.
Disclosure of Invention
In order to solve the problem in the existing edge gateway configuration process, the invention provides an edge gateway modeling method based on a TF-IDF algorithm. The method avoids the uncertainty of the access side equipment and the forwarding point table, and does not depend on specific access and forwarding protocol types. The automatic association of the forwarding data is realized by analyzing the data semantics, so that the efficiency in the configuration process of the edge gateway is improved.
The invention adopts the following technical scheme.
The first aspect of the present invention provides an edge gateway modeling method based on a TF-IDF algorithm, comprising the steps of:
step 1, manufacturing an edge gateway access side database comprising an RTU table, a remote signaling table, a remote measuring table, a remote control table, a remote regulation table, a remote pulse table and a constant value table according to the actual condition of the site, and warehousing the data;
step 2, reading information of a transfer-out model library, obtaining definition association description of each data, constructing a corpus, and calculating word frequency data TF of each data of the transfer-out model according to a word frequency algorithm in a TF-IDF algorithm;
step 3, reading access side database information, referring to a corpus constructed by the transfer model, and calculating word frequency data TF corresponding to description information of each data of the access device according to a word frequency algorithm in the TF-IDF algorithm;
step 4, respectively calculating inverse document frequency IDF of the accessed and transferred items, and generating TF-IDF data of each entry by combining the word frequency data generated in the step 2 and the step 3;
step 5, generating access data according to each term TF-IDF data and a roll-off model based on vector values of the terms, calculating cosine similarity of vectors between any two terms of data one by one, and selecting a pair of data with a value of 1 nearest to the cosine similarity to create a data mapping relation;
and 6, creating a mapping relation through the step 5, and generating a forwarding side database by a configuration tool according to the unique key field of the internally defined data points, wherein the forwarding side database comprises a forwarding side RTU table, a forwarding remote signaling table, a forwarding telemetry table, a forwarding remote control table, a forwarding remote regulation table, a forwarding remote pulse table and a forwarding constant value table, and the forwarding side database comprises defined forwarding data.
Preferably, in step 1, the edge gateway is a gateway deployed at an access device end, and has protocol conversion, data forwarding, real-time data analysis and application management capabilities.
Preferably, in step 1, the modeled scope covers logical nodes meeting the specifications in DL/T860, and further includes definitions of photovoltaic stations, energy storage stations, charging stations, wind farm stations, and distribution network areas.
Preferably, the photovoltaic stations are classified according to equipment and functions and are divided into photovoltaic station operation monitoring, grid-connected point switches, photovoltaic station step-up transformers, photovoltaic station inverters, photovoltaic station junction boxes, photovoltaic station current collecting circuits and photovoltaic station photovoltaic modules;
the energy storage stations are classified into public information, system information, battery packs, converters, inverters, step-up transformers and current collecting circuits according to equipment and functions;
the charging stations are classified according to equipment and functions and are divided into a wire inlet switch, a branch switch, a circuit and a charging pile;
the wind power station is classified into fans, circuits, junction boxes and grid-connected switches according to equipment and functions;
the distribution network area is classified into public information, measurement information, state information, protection information and transformer information according to equipment and functions.
Preferably, in step 2, the modeling method does not depend on specific access and export protocols, and only correlates with text similarity provided by TF-IDF;
the TF calculation method comprises the following steps:
TF = word definition number of occurrences in roll-out model data
The definition file of the roll-out model is edgejson.csv, the roll-out model definition file contains hierarchical information such as various application scene types, equipment types and the like, names of all nodes and required forwarding data information description, and the first data point 'grid-connected point switch remote/on site' is taken as an example, and the complete description information is 'photovoltaic station-operation monitoring-measuring node-grid-connected point switch remote/on site'.
Preferably, in step 2, the information of the roll-out model library is different according to the different forwarding protocols used, and the supported definition template rules are different;
when modeling the export protocol taking tree structure hierarchy information as a specification, the export protocol comprises corresponding description text for TF-IDF calculation and also covers corresponding hierarchy information;
for modeling a roll-out by using a point table composed of discrete data points, the specification keywords required by the specification are covered;
the specification key is used for analyzing the definition of the data from the message in the communication process.
Preferably, the Inverse Document Frequency (IDF) formula is as follows:
id=log (number of model definitions/(number of model definitions containing the word+1))
The TF-IDF is used as weight data in text similarity judgment, and the formula is as follows:
TF-IDF＝TF*IDF
preferably, in step 4, after TF-IDF data of each term is generated, TF-IDF values may be manually fine-tuned based on past experience, and the range is adjusted based on percentage adjustment to adjust the distinction of a specific word.
Preferably, in step 5, when modeling and matching are performed by using the TF-IDF algorithm, the data node to which the TF-IDF algorithm belongs should be analyzed preferentially according to the type and function of the device;
the created data mapping relation of the access-in and transfer-out model is ordered according to the similarity degree, and the automatic association result is confirmed and adjusted manually, so that the association is ensured to be correct and correct;
the cosine similarity formula is as follows:
wherein, n-dimensional vector [ A ] is constructed for access side word segmentation 1 ,A 2 ,A 3 …A n ]B is the forwarding side to construct n-dimensional vector [ B ] based on word segmentation 1 ,B 2 ,B 3 …B n ]。
Preferably, in step 6, each data table of the access database is provided to the outside as a data reference basis by using ID64 as a data record main key, the forwarding side database is associated to the data source ID of the access side by using sourceID as an external key, each data type needs to be kept consistent, and a single access data can be used as a plurality of forwarding data sources.
A second aspect of the present invention provides an edge gateway modeling system based on TF-IDF algorithm, comprising: the system comprises a data collection module, a corpus construction module, a TF-IDF calculation module, a similarity calculation module and a configuration generation module; an edge gateway modeling method based on TF-IDF algorithm according to any one of claims 1 to 9:
the data collection module is used for acquiring data from the access side of the edge gateway and interacting with the existing model library information;
the corpus construction module is used for providing text data and related information for a modeling process, supporting the operation of a TF-IDF algorithm and helping to establish association and matching between the data;
the TF-IDF calculation module is used for converting text data into numerical data and determining the similarity between the access side data and the transfer-out model data;
the similarity calculation module is used for identifying the similarity between the access side data and the roll-out model data and establishing a data mapping relation;
the configuration generating module is used for generating a database for data forwarding according to the modeling matching result, and storing and forwarding corresponding data.
Compared with the prior art, the invention has the beneficial effects that at least: in the configuration process of the edge gateway, the definition of the roll-out model is analyzed through a TF-IDF algorithm, so that automatic association of access and roll-out data is realized, different field requirements are met, a personalized solution is provided for engineering application, and the customization requirements of different field roll-out models can be met; the quality and efficiency of the implementation process of the shutdown of the edge network are improved, and the labor cost investment of engineering implementation is reduced.
Drawings
FIG. 1 is a flow chart of an edge gateway modeling method based on the TF-IDF algorithm;
fig. 2 is a schematic diagram of a roll-out template definition file of an edge gateway modeling method based on TF-IDF algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are merely some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present invention.
The technical scheme of the invention is further described in detail below with reference to the accompanying drawings:
the edge gateway modeling method based on TF-IDF fully considers the requirements of various application fields of the edge gateway, and the forwarding modeling method based on protocol independence can be well adapted to roll-out modeling of different equipment types.
As shown in fig. 1, the flow of an edge gateway automatic modeling method based on TF-IDF algorithm includes the following steps:
step 1, collecting edge gateway access side data, including RTU (real time unit) table, remote signaling table, telemetry table, remote control table, remote regulation table, remote pulse table and constant value table, according to the actual condition of the site, and storing the data into a database;
preferably, in step 1, the edge gateway is a gateway deployed at an access device end, and has protocol conversion, data forwarding, real-time data analysis and application management capabilities.
Preferably, in step 1, the modeled scope covers logical nodes meeting the specifications in DL/T860, and further includes definitions of photovoltaic stations, energy storage stations, charging stations, wind farm stations, and distribution network areas.
Preferably, the photovoltaic stations are classified according to equipment and functions and are divided into photovoltaic station operation monitoring, grid-connected point switches, photovoltaic station step-up transformers, photovoltaic station inverters, photovoltaic station junction boxes, photovoltaic station current collecting circuits and photovoltaic station photovoltaic modules;
the energy storage stations are classified into public information, system information, battery packs, converters, inverters, step-up transformers and current collecting circuits according to equipment and functions;
the charging stations are classified according to equipment and functions and are divided into a wire inlet switch, a branch switch, a circuit and a charging pile;
the wind power station is classified into fans, circuits, junction boxes and grid-connected switches according to equipment and functions;
the distribution network area is classified into public information, measurement information, state information, protection information and transformer information according to equipment and functions.
Step 2, reading information of a transfer-out model library, obtaining definition association description of each data, constructing a corpus, and calculating word frequency data TF of each data of the transfer-out model according to a word frequency algorithm in a TF-IDF algorithm;
preferably, in step 2, the modeling method does not depend on specific access and export protocols, and only correlates with text similarity provided by TF-IDF;
the TF calculation method comprises the following steps:
TF = word definition number of occurrences in roll-out model data
The definition file of the roll-out model is edgejson.csv, and the text content is shown in fig. 2. The roll-out model definition file contains hierarchical information such as various application scene types, equipment types and the like, names of all nodes and required forwarding data information description. Taking the first data point "grid-connected point switch remote/local" as an example, the complete description information is "photovoltaic station-operation monitoring-measuring node-grid-connected point switch remote/local".
Preferably, in step 2, the information of the roll-out model library is different according to the different forwarding protocols used, and the supported definition template rules are different;
when modeling the export protocol taking tree structure hierarchy information as a specification, the export protocol comprises corresponding description text for TF-IDF calculation and also covers corresponding hierarchy information;
for modeling a roll-out by using a point table composed of discrete data points, covering the specification keywords required by the specification;
the specification key is used for analyzing the definition of the data from the message in the communication process.
Step 3, reading access side database information, referring to a corpus constructed by the transfer model, and calculating word frequency data TF corresponding to description information of each data of the access device according to a word frequency algorithm in the TF-IDF algorithm;
step 4, respectively calculating inverse document frequency IDF of the accessed and transferred items, and generating TF-IDF data of each entry by combining the word frequency data generated in the step 2 and the step 3;
the Inverse Document Frequency (IDF) formula is as follows:
idf=log (number of model definitions/(number of model definitions containing the word+1))
The TF-IDF is used as weight data in text similarity judgment, and the formula is as follows:
TF-IDF＝TF*IDF
preferably, in step 4, after TF-IDF data of each term is generated, TF-IDF values may be manually fine-tuned based on past experience, and the range is adjusted based on percentage adjustment to adjust the distinction of a specific word.
Step 5, generating access data according to each term TF-IDF data and a roll-off model based on vector values of the terms, calculating cosine similarity of vectors between any two terms of data one by one, and selecting a pair of data with a value of 1 nearest to the cosine similarity to create a data mapping relation;
preferably, in step 5, when modeling and matching are performed by using a TF-IDF algorithm, analysis is performed preferentially according to the type and function of the device, and the data node to which the data node belongs is analyzed;
the created data mapping relation of the access-in and transfer-out model is ordered according to the similarity degree, and the automatic association result is confirmed and adjusted manually, so that the association is ensured to be correct and correct;
the cosine similarity formula is as follows:
wherein A is an access side for constructing an n-dimensional vector [ A ] based on word segmentation 1 ,A 2 ,A 3 …A n ]B is the forwarding side to construct n-dimensional vector [ B ] based on word segmentation 1 ,B 2 ,B 3 …B n ]。
And 6, creating a mapping relation through the step 5, and generating a forwarding side database by a configuration tool according to the unique key field of the internally defined data points, wherein the forwarding side database comprises a forwarding side RTU table, a forwarding remote signaling table, a forwarding telemetry table, a forwarding remote control table, a forwarding remote regulation table, a forwarding remote pulse table and a forwarding constant value table, and the forwarding side database comprises defined forwarding data.
Preferably, in step 6, each data table of the access database is provided to the outside as a data reference basis by using ID64 as a data record main key, the forwarding side database is associated to the data source ID of the access side by using sourceID as an external key, each data type needs to be kept consistent, and a single access data can be used as a plurality of forwarding data sources.
An edge gateway modeling system based on TF-IDF algorithm, comprising: the system comprises a data collection module, a corpus construction module, a TF-IDF calculation module, a similarity calculation module and a configuration generation module; an edge gateway modeling method based on TF-IDF algorithm according to any one of claims 1 to 9:
a data collection module; the method is used for acquiring data from the access side of the edge gateway and interacting with the existing model library information;
the corpus construction module is used for providing text data and related information for a modeling process, supporting the operation of a TF-IDF algorithm and helping to establish association and matching between the data;
the TF-IDF calculation module is used for converting text data into numerical data and determining the similarity between the access side data and the transfer-out model data;
the similarity calculation module is used for identifying the similarity between the access side data and the roll-out model data and establishing a data mapping relation;
the configuration generating module is used for generating a database for data forwarding according to the modeling matching result, and storing and forwarding corresponding data.
The beneficial effects of the invention at least comprise: in the configuration process of the edge gateway, the definition of the roll-out model is analyzed through a TF-IDF algorithm, so that automatic association of access and roll-out data is realized, different field requirements are met, a personalized solution is provided for engineering application, and the customization requirements of different field roll-out models can be met; the quality and efficiency of the implementation process of the shutdown of the edge network are improved, and the labor cost investment of engineering implementation is reduced.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. The edge gateway modeling method based on the TF-IDF algorithm is characterized by comprising the following steps:
step 1, manufacturing an edge gateway access side database comprising an RTU table, a remote signaling table, a remote measuring table, a remote control table, a remote regulation table, a remote pulse table and a constant value table according to the actual condition of the site, and warehousing the data;
step 2, reading information of a transfer-out model library, obtaining definition association description of each data, constructing a corpus, and calculating word frequency data TF of each data of the transfer-out model according to a word frequency algorithm in a TF-IDF algorithm;
step 3, reading access side database information, referring to a corpus constructed by the transfer model, and calculating word frequency data TF corresponding to description information of each data of the access device according to a word frequency algorithm in the TF-IDF algorithm;
step 4, respectively calculating inverse document frequency IDF of the accessed and transferred items, and generating TF-IDF data of each entry by combining the word frequency data generated in the step 2 and the step 3;
step 5, generating access data according to each term TF-IDF data and a roll-off model based on vector values of the terms, calculating cosine similarity of vectors between any two terms of data one by one, and selecting a pair of data with a value of 1 nearest to the cosine similarity to create a data mapping relation;
and 6, creating a mapping relation through the step 5, and generating a forwarding side database by a configuration tool according to the unique key field of the internally defined data points, wherein the forwarding side database comprises a forwarding side RTU table, a forwarding remote signaling table, a forwarding telemetry table, a forwarding remote control table, a forwarding remote regulation table, a forwarding remote pulse table and a forwarding constant value table, and the forwarding side database comprises defined forwarding data.
2. The edge gateway modeling method based on TF-IDF algorithm according to claim 1, wherein:
in step 1, the edge gateway is a gateway deployed at the access device end, and has protocol conversion, data forwarding, real-time data analysis and application management capabilities.
3. The edge gateway modeling method based on TF-IDF algorithm according to claim 1, wherein:
in step 1, the modeled scope encompasses logical nodes that meet the specifications in DL/T860, and also includes definitions of photovoltaic stations, energy storage stations, charging stations, wind farm stations, and distribution network areas.
4. A method for modeling an edge gateway based on TF-IDF algorithm according to claim 3, wherein:
the photovoltaic station is classified according to equipment and functions and is divided into a photovoltaic station operation monitor, a grid-connected point switch, a photovoltaic station step-up transformer, a photovoltaic station inverter, a photovoltaic station combiner box, a photovoltaic station current collecting circuit and a photovoltaic station photovoltaic module;
the energy storage stations are classified into public information, system information, battery packs, converters, inverters, step-up transformers and current collecting circuits according to equipment and functions;
the charging stations are classified according to equipment and functions and are divided into a wire inlet switch, a branch switch, a circuit and a charging pile;
the wind power station is classified into fans, circuits, junction boxes and grid-connected switches according to equipment and functions;
the distribution network area is classified into public information, measurement information, state information, protection information and transformer information according to equipment and functions.
5. The edge gateway modeling method based on TF-IDF algorithm according to claim 1, wherein:
in the step 2, the modeling method does not depend on specific access and transfer-out protocols, and is only related through text similarity provided by TF-IDF;
the TF calculation method comprises the following steps:
TF = word definition number of occurrences in roll-out model data
The definition file of the roll-out model is edgejson.csv, and the roll-out model definition file contains hierarchical information such as various application scene types, equipment types and the like, names of all nodes and required forwarding data information description.
6. The edge gateway modeling method based on TF-IDF algorithm according to claim 1, wherein:
in the step 2, the information of the transfer-out model library is different according to the different forwarding protocols used, and the supported definition template rules are different;
when modeling the export protocol taking tree structure hierarchy information as a specification, the export protocol comprises corresponding description text for TF-IDF calculation and also covers corresponding hierarchy information;
for modeling a roll-out by using a point table composed of discrete data points, covering the specification keywords required by the specification;
the specification key is used for analyzing the definition of the data from the message in the communication process.
7. The edge gateway modeling method based on TF-IDF algorithm according to claim 1, wherein:
in step 4, the inverse document frequency formula is as follows:
idf=log (number of model definitions/(number of model definitions containing the word+1))
The TF-IDF is used as weight data in text similarity judgment, and the formula is as follows:
TF-IDF＝TF*IDF
after generating the TF-IDF data of each term, the TF-IDF values can be manually fine-tuned based on past experience, and the range is adjusted based on percentage adjustment to adjust the distinction of specific terms.
8. The edge gateway modeling method based on TF-IDF algorithm according to claim 1, wherein:
in step 5, when modeling and matching are carried out by utilizing a TF-IDF algorithm, the data nodes to which the data nodes belong are analyzed preferentially according to the equipment type and the function;
the created data mapping relation of the access-in and transfer-out model is ordered according to the similarity degree, and the automatic association result is confirmed and adjusted manually, so that the association is ensured to be correct and correct;
the cosine similarity formula is as follows:
wherein, n-dimensional vector [ A ] is constructed for access side word segmentation 1 ,A 2 ,A 3 …A n ]B is the forwarding side to construct n-dimensional vector [ B ] based on word segmentation 1 ,B 2 ,B 3 …B n ]。
9. The edge gateway modeling method based on TF-IDF algorithm according to claim 1, wherein:
in step 6, each data table of the access database is used as a data record main key through an ID64, and is provided for the outside to be used as a data reference basis, the forwarding side database is related to the data source ID of the access side through a sourceID as an external key, each data type is required to be kept consistent, and a single access data can be used as a plurality of forwarding data sources.
10. An edge gateway modeling system based on TF-IDF algorithm, comprising: the system comprises a data collection module, a corpus construction module, a TF-IDF calculation module, a similarity calculation module and a configuration generation module; an edge gateway modeling method based on TF-IDF algorithm according to any of claims 1 to 9, characterized by:
a data collection module; the method is used for acquiring data from the access side of the edge gateway and interacting with the existing model library information;
the corpus construction module is used for providing text data and related information for a modeling process, supporting the operation of a TF-IDF algorithm and helping to establish association and matching between the data;
the TF-IDF calculation module is used for converting text data into numerical data and determining the similarity between the access side data and the transfer-out model data;
the similarity calculation module is used for identifying the similarity between the access side data and the roll-out model data and establishing a data mapping relation;
the configuration generating module is used for generating a database for data forwarding according to the modeling matching result, and storing and forwarding corresponding data.
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