CN117349478B - Resource data reconstruction integration system based on digital transformation enterprise - Google Patents
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Abstract
The invention discloses a resource data reconstruction integration system based on a digital transformation enterprise, which relates to the technical field of data integration and solves the problem that the data is easy to lose after the data reconstruction integration is completed.
Description
Technical Field
The invention relates to the technical field of data integration, in particular to a resource data reconstruction integration system based on a digital transformation enterprise.
Background
The digital enterprise of the electric power resource is a digital asset which integrates digital marketing, digital technology, big data and electric power resource data, establishes a software matrix with various forms for the enterprise based on an SAAS cloud engine platform and takes the software matrix as a long-term carrier, the electric power resource is an original energy source which can be converted into electric energy and comprises fossil fuel, water energy, wind energy, nuclear energy and the like, the corresponding electric power resource data are complicated, and the complexity is higher when the actual reconstruction and integration are carried out.
The embodiment of the application with the patent application number of CN114238468A discloses a highway multi-source heterogeneous data reconstruction and integration system, in particular to the technical field of highway network management, which comprises the following steps: a multi-source data acquisition module: the system comprises a highway foundation management information acquisition module and a real-time information acquisition module; a multi-source data transmission module: the system is used for synchronously transmitting the acquired information to a multi-source data management module in different places; a multi-source data management module: the method comprises data resource catalog management, data resource security management and data resource quality management; and the data management center: the data management center comprises a highway data management subsystem, a regional emergency data management subsystem and a data storage module. The highway multisource heterogeneous data reconstruction integration system integrates the highway data management subsystem and the regional emergency data management subsystem, can combine real-time conditions, brings great help to maintenance of the highway network, and has wide application prospects in the technical field of highway network management.
In the process of reconstructing and integrating the resource data, the original power resource digital transformation enterprise generally removes redundant data preferentially according to the overall attribute of the data, and integrates the data, but in the integrating process, whether similarity exists between the data is not analyzed, the integrating mode is irregular, the quality of the integrated data is low, after the data is reconstructed and integrated, the data is easy to lose, and in the later stage, when tracing the source, the lost data cannot be found out rapidly and effectively, and the original data is corrected and filled in time.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a resource data reconstruction and integration system based on a digital transformation enterprise, which solves the problem that the data is easy to lose after the data reconstruction and integration are completed.
In order to achieve the above purpose, the invention is realized by the following technical scheme: a resource data reconstruction integration system based on a digital transformation enterprise comprises an integration center and a tracing center;
The integration center comprises a target attribute confirmation unit, a similar feature confirmation unit and an integration unit;
the target attribute confirmation unit confirms the target data attribute participating in the data reconstruction integration, divides the target data of the same attribute into the same partition, adopts different models to confirm the feature vector of the corresponding data aiming at the data of different attributes, and transmits the determined feature vector into the similar feature confirmation unit;
the similar feature confirming unit confirms different feature vectors corresponding to different target data of the same partition, performs feature vector similarity analysis one by one, and then integrates the target data with the similarity analysis result meeting the integration condition to generate a target data integration packet and transmits the target data integration packet to the tracing center;
The tracing center comprises a relevance analysis unit, a database, a map confirmation unit, a waveform construction unit and a tracing filling unit; the database directly stores the received target data integration packets, a certain time is needed in the storage process, and the association analysis unit carries out association analysis on the target data integration packets in the same partition, confirms that the target data integration packets which are associated with each other exist, confirms association parameters among the corresponding target data integration packets, and records the association parameters;
the map confirmation unit is used for constructing association tree diagrams of a plurality of groups of target data integration packages according to association degree parameters among the plurality of groups of different target data integration packages, wherein the initial target integration packages are placed in an initial stage, subsequent target data integration packages are orderly ordered according to association relations, corresponding association tree diagrams are constructed, and the constructed association tree diagrams are transmitted into the waveform construction unit;
The waveform construction unit confirms the characteristic waveform diagram of the corresponding partition according to the constructed association tree diagram, memorizes the characteristic waveform diagram and transmits the constructed characteristic waveform diagram to the tracing filling unit;
The tracing filling unit is used for receiving and recording the confirmed characteristic waveform patterns, carrying out data analysis after the database stores a plurality of groups of target integration packets of the designated partition, confirming whether the characteristic waveform patterns are consistent or not, and judging whether data correction work is needed or not according to the analysis result;
the specific method is as follows:
After the database stores a plurality of groups of target integration packages of a designated partition, generating a partition analysis instruction, and constructing a comparison waveform diagram belonging to the partition by sequentially passing through a relevance analysis unit, a map confirmation unit and a waveform construction unit according to the partition analysis instruction;
Comparing the comparison waveform diagram with the characteristic waveform diagram, judging whether the corresponding waveform diagrams are consistent, if so, not performing any processing, and if not, determining abnormal points with differences;
Confirming the belonging stage according to the transverse coordinate value of the corresponding abnormal point, analyzing and comparing the association degree parameters of the belonging stage, and rapidly confirming the abnormal target integration packet according to the comparison result;
and tracing and correcting data according to the original data of the target integrated packet.
Advantageous effects
The invention provides a resource data reconstruction integration system based on a digital transformation enterprise. Compared with the prior art, the method has the following beneficial effects:
According to the method, data with different attributes are classified in advance in the data reconstruction and integration process, after classification is completed, feature vectors of the data with different attributes are confirmed according to different models, then different feature vectors are combined and analyzed, similarity is confirmed, attribute data with high similarity are integrated, a target data integration packet is confirmed, and a subsequent target integration packet is confirmed in sequence;
after the data reconstruction and integration are completed, the association degree analysis is adopted, the subsequent waveform mode is confirmed, abnormal data can be confirmed quickly, the position of the abnormal data is confirmed, tracing is performed quickly, the stage with the abnormal data can be determined quickly by adopting the layer-by-layer progressive mode, tracing range can be shortened, tracing speed is increased, meanwhile, correction can be performed on the data quickly, correction efficiency is improved, and correction time is shortened.
Drawings
Fig. 1 is a schematic diagram of a principle frame of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the application provides a resource data reconstruction integration system based on a digital transformation enterprise, which comprises an integration center and a tracing center;
the integration center comprises a target attribute confirmation unit, a similar feature confirmation unit and an integration unit, wherein the target attribute confirmation unit is electrically connected with an input node of the similar feature confirmation unit, the similar feature confirmation unit is electrically connected with an input node of the integration unit, the tracing center comprises a relevance analysis unit, a database, a map confirmation unit, a waveform construction unit and a tracing filling unit, the relevance analysis unit is electrically connected with the database and the input node of the map confirmation unit, the map confirmation unit is electrically connected with the input node of the waveform construction unit, and the waveform construction unit is electrically connected with the input node of the tracing filling unit;
the target attribute confirmation unit confirms the target data attribute participating in data reconstruction and integration, divides the target data of the same attribute into the same partition, adopts different models to confirm the feature vector of the corresponding data aiming at the data of different attributes, and transmits the determined feature vector into the similar feature confirmation unit, wherein the specific mode for confirming the feature vector of the corresponding data is as follows:
Determining a conversion model to be extracted of the target data according to specific attributes of the target data, wherein the specific attributes comprise: bag of words data, numerical data, image data or time series data;
And outputting the feature vector of the target data according to the determined conversion model to be extracted, and transmitting the output feature vector into a similar feature confirmation unit, wherein the conversion model to be extracted is a preset model, is constructed in advance for the target data with different attributes, and is constructed by an operator by self.
The method further comprises the output mode of the feature vector:
For the bag-of-word data, the conversion model to be extracted is a bag-of-word model: each word is weighted by taking into account the importance of the word in the overall corpus.
For numerical data, the conversion model to be extracted is an original numerical feature model: for numeric data, the original numeric value can be directly used as one dimension of the vector for representation; normalization: for numerical data with different numerical ranges, normalization processing can be performed to scale it to a uniform range.
For image data, the conversion model to be extracted is an image feature extraction model: for image data, various feature extraction algorithms, such as SI FT, HOG, CNN, etc., may be used to extract a feature vector representation of the image.
For time series data, the conversion model to be extracted is a statistical feature model: for time series data, various statistical features, such as mean, variance, maximum, minimum, etc., may be selected as dimensions of the vector according to the time interval.
The similar feature confirming unit confirms different feature vectors corresponding to different target data of the same partition, performs feature vector similarity analysis one by one, and then integrates the target data with the similarity analysis result meeting the integration condition to generate a target data integration packet, and transmits the target data integration packet to the tracing center, wherein the specific mode for integrating is as follows:
Randomly selecting feature vectors of two groups of target data, constructing a group of two-dimensional coordinate system, and combining one end of the feature vector with the origin of the two-dimensional coordinate system, so that two groups of feature vectors form a group of included angles, wherein the included angle parameter is X i, and i represents the included angle formed by different feature vectors;
F (X i)＝COSXi) is adopted to obtain a verification parameter F (X i) of a corresponding included angle, and F (X i) epsilon [ -1,1];
and comparing the verification parameter F (X i) with a preset value Y1, wherein the specific value of Y1 is determined by an operator according to experience, and the value of Y1 is generally 0.5, when F (X i) is not less than Y1, integrating the target data corresponding to the two sets of feature vectors, and confirming a set of target data integration package, otherwise, not integrating.
Specifically, the angle X i between two feature vectors in two-dimensional coordinates is only smaller than or equal to 180 °, so that the corresponding verification parameter F (X i) only belongs to [ -1,1] and does not exceed the range value, and the closer F (X i) is to 1, the higher the similarity between the two feature vectors is, the more consistent the angle is, so that the similarity between the two target data is higher, and the integration can be performed, and the corresponding target data integration packet is confirmed;
And when the target data are integrated, only two groups of target data with highest similarity are considered for integration, after the integration, the rest data are integrated in a pairwise analysis way, and the subsequent target integration packages are sequentially confirmed.
The database in the tracing center directly stores the received target data integration packets, a certain time is needed in the storage process, the association analysis unit carries out association analysis on the target data integration packets in the same partition, confirms that the target data integration packets which are associated with each other exist, confirms association parameters between the corresponding target data integration packets, and records the association parameters, wherein the specific mode for confirming the association parameters of the corresponding target data integration packets is as follows:
Carrying out association analysis on a plurality of groups of target data integration packets, randomly selecting a group of target data integration packets, confirming the data source inside the target data integration packets, confirming the last group of target data integration packets according to the data source, and confirming association parameters with the last group of target data integration packets, wherein the association parameters = source data capacity ≡integral capacity of the target data integration packets;
And sequentially confirming the association degree parameters among a plurality of groups of different target data integration packets, and transmitting the confirmed association degree parameters to the map confirming unit.
The map confirmation unit constructs association tree diagrams of a plurality of groups of target data integration packages according to association degree parameters among the plurality of groups of different target data integration packages, wherein an initial target integration package is placed in an initial stage, subsequent target data integration packages are sequentially ordered according to association relations, corresponding association tree diagrams are constructed, and the constructed association tree diagrams are transmitted into the waveform construction unit;
The waveform construction unit confirms the characteristic waveform diagram of the corresponding partition according to the constructed association tree diagram, memorizes the characteristic waveform diagram, transmits the constructed characteristic waveform diagram into the tracing filling unit until all data integration packets in the corresponding partition are stored, and then carries out comparison and analysis, if no error exists, the deletion is carried out, if the error exists, the data tracing is carried out according to the characteristic waveform diagram, and the problem of corresponding data missing or abnormality in the database is timely solved, wherein the specific mode of confirming the characteristic waveform diagram of the corresponding partition is as follows:
according to the constructed association tree diagram, taking an initial target integration packet as an initial point, confirming the number of target integration packets appearing in a subsequent first stage, marking the initial point as G1, confirming the average value of a plurality of association parameters appearing in the first stage, marking the average value as J1, sequentially confirming the number Gt of target integration packets appearing in the subsequent stage, and simultaneously confirming the average value Jt appearing in the subsequent stage, wherein t=1, 2, … … and n, and particularly, in the tree diagram, a plurality of groups of different association objects and generated association parameters in different stages exist according to the trend from top to bottom, wherein the number of association objects can be used as transverse coordinates, the average value of the association parameters can be used as vertical coordinates, and corresponding waveform point coordinates can be determined, so that corresponding waveform curves can be determined;
Determining corresponding point positions in a two-dimensional coordinate system according to the confirmed point position coordinates, connecting the confirmed point positions, confirming a corresponding characteristic waveform diagram, marking the characteristic waveform diagram of the subarea, and transmitting the marked characteristic waveform diagram to a tracing filling unit;
The tracing filling unit receives and records the confirmed characteristic waveform diagrams, after the database stores a plurality of groups of target integration packets of the designated partition, performs data analysis, confirms whether the characteristic waveform diagrams are consistent, and judges whether data correction work is needed according to analysis results, wherein the specific mode for performing the data analysis is as follows:
After the database stores a plurality of groups of target integration packages of a designated partition, generating a partition analysis instruction, and constructing a comparison waveform diagram belonging to the partition by sequentially passing through a relevance analysis unit, a map confirmation unit and a waveform construction unit according to the partition analysis instruction;
Comparing the comparison waveform diagram with the characteristic waveform diagram, judging whether the corresponding waveform diagrams are consistent, if so, not performing any processing, and if not, determining abnormal points with differences;
Confirming the belonging stage according to the transverse coordinate value of the corresponding abnormal point, analyzing and comparing the association degree parameters of the belonging stage, and rapidly confirming the abnormal target integration packet according to the comparison result;
and tracing the source according to the original data of the target integration packet, correcting the data, and filling the data.
Specifically, after reconstruction and integration are performed on target data, the target data can be stored, a certain period of time is needed in the storage process, partial data can be lost due to the existence of external interference factors in the storage process, the degree of difficulty is relatively high when the lost partial data is traced to the source subsequently due to the fact that the target data is stored as a whole, the stage with data abnormality can be rapidly determined by adopting the layer-by-layer progressive mode, tracing range can be shortened, tracing speed is accelerated, meanwhile, correction can be rapidly performed on the data, correction efficiency is improved, and correction time is shortened.
Some of the data in the above formulas are numerical calculated by removing their dimensionality, and the contents not described in detail in the present specification are all well known in the prior art.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (4)
1. The resource data reconstruction integration system based on the digital transformation enterprise is characterized by comprising an integration center and a tracing center;
The integration center comprises a target attribute confirmation unit, a similar feature confirmation unit and an integration unit;
the target attribute confirmation unit confirms the target data attribute participating in the data reconstruction integration, divides the target data of the same attribute into the same partition, adopts different models to confirm the feature vector of the corresponding data aiming at the data of different attributes, and transmits the determined feature vector into the similar feature confirmation unit;
the similar feature confirming unit confirms different feature vectors corresponding to different target data of the same partition, performs feature vector similarity analysis one by one, and then integrates the target data with the similarity analysis result meeting the integration condition to generate a target data integration packet and transmits the target data integration packet to the tracing center;
The tracing center comprises a relevance analysis unit, a database, a map confirmation unit, a waveform construction unit and a tracing filling unit; the database directly stores the received target data integration packets, a certain time is needed in the storage process, and the association analysis unit carries out association analysis on the target data integration packets in the same partition, confirms that the target data integration packets which are associated with each other exist, confirms association parameters among the corresponding target data integration packets, and records the association parameters;
the map confirmation unit is used for constructing association tree diagrams of a plurality of groups of target data integration packages according to association degree parameters among the plurality of groups of different target data integration packages, wherein the initial target integration packages are placed in an initial stage, subsequent target data integration packages are orderly ordered according to association relations, corresponding association tree diagrams are constructed, and the constructed association tree diagrams are transmitted into the waveform construction unit;
The waveform construction unit confirms the characteristic waveform diagram of the corresponding partition according to the constructed association tree diagram, memorizes the characteristic waveform diagram and transmits the constructed characteristic waveform diagram to the tracing filling unit;
the waveform construction unit confirms the specific mode of the characteristic waveform diagram of the corresponding partition as follows:
According to the constructed association tree diagram, taking an initial target integration packet as an initial point, confirming the number of target integration packets appearing in a subsequent first stage, marking the number as G1, confirming the average value of a plurality of association parameters appearing in the first stage, marking the average value as J1, sequentially confirming the number Gt of target integration packets appearing in the subsequent stage, and simultaneously confirming the average value Jt appearing in the subsequent stage, wherein t=1, 2, … … and n;
Determining corresponding point positions in a two-dimensional coordinate system according to the confirmed point position coordinates, connecting the confirmed point positions, confirming a corresponding characteristic waveform diagram, marking the characteristic waveform diagram of the subarea, and transmitting the marked characteristic waveform diagram to a tracing filling unit;
The tracing filling unit is used for receiving and recording the confirmed characteristic waveform patterns, carrying out data analysis after the database stores a plurality of groups of target integration packets of the designated partition, confirming whether the characteristic waveform patterns are consistent or not, and judging whether data correction work is needed or not according to the analysis result;
The specific mode of the tracing filling unit for data analysis is as follows:
After the database stores a plurality of groups of target integration packages of a designated partition, generating a partition analysis instruction, and constructing a comparison waveform diagram belonging to the partition by sequentially passing through a relevance analysis unit, a map confirmation unit and a waveform construction unit according to the partition analysis instruction;
Comparing the comparison waveform diagram with the characteristic waveform diagram, judging whether the corresponding waveform diagrams are consistent, if so, not performing any processing, and if not, determining abnormal points with differences;
Confirming the belonging stage according to the transverse coordinate value of the corresponding abnormal point, analyzing and comparing the association degree parameters of the belonging stage, and rapidly confirming the abnormal target integration packet according to the comparison result;
and tracing and correcting data according to the original data of the target integrated packet.
2. The system for reconstructing and integrating resource data based on digitized transformation enterprises according to claim 1, wherein the target attribute confirmation unit confirms the specific manner of the corresponding data feature vector is as follows:
Determining a conversion model to be extracted of the target data according to specific attributes of the target data, wherein the specific attributes comprise: bag of words data, numerical data, image data or time series data;
And outputting the feature vector of the target data according to the determined conversion model to be extracted, and transmitting the output feature vector into a similar feature confirmation unit, wherein the conversion model to be extracted is a preset model, and is constructed in advance for the target data with different attributes.
3. The system for reconstructing and integrating resource data based on digital transformation enterprises according to claim 1, wherein the similar feature confirmation unit generates the target data integration packet in the following specific manner:
Randomly selecting feature vectors of two groups of target data, constructing a group of two-dimensional coordinate system, and combining one end of the feature vector with the origin of the two-dimensional coordinate system, so that two groups of feature vectors form a group of included angles, wherein the included angle parameter is X i, and i represents the included angle formed by different feature vectors;
F (X i)＝COSXi) is adopted to obtain a verification parameter F (X i) of a corresponding included angle, and F (X i) epsilon [ -1,1];
And comparing the verification parameter F (X i) with a preset value Y1, and integrating the target data corresponding to the two sets of feature vectors when F (X i) is not less than Y1 to confirm a set of target data integration package, otherwise, not integrating.
4. The system for reconstructing and integrating resource data based on digitized transformation enterprises according to claim 1, wherein the association degree analysis unit confirms the association degree parameter of the corresponding target data integration packet in the following specific manner:
Carrying out association analysis on a plurality of groups of target data integration packets, randomly selecting a group of target data integration packets, confirming the data source inside the target data integration packets, confirming the last group of target data integration packets according to the data source, and confirming association parameters with the last group of target data integration packets, wherein the association parameters = source data capacity ≡integral capacity of the target data integration packets;
And sequentially confirming the association degree parameters among a plurality of groups of different target data integration packets, and transmitting the confirmed association degree parameters to the map confirming unit.
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