Method and system for data checking and conversion of internet of things platform entity model

Abstract

The application provides an Internet of Things platform entity model data checking and conversion method and system. Based on the entity model data storage state information of all user terminals under the Internet of Things platform, the extracted entity model data is checked, the data attribute information of the entity model data is determined, the data subpart of the entity model data that needs to be converted is further determined, a to-be-converted data subpart set is generated, the data parts with the same conversion processing requirement are integrated, the data conversion is conveniently and uniformly batch processed, the data conversion workload is reduced, all the data subparts after the conversion processing are updated to the corresponding entity model data, the integrity and reliability of the entity model data are ensured, the entity model data is corrected and knowledge graph conversion processing is performed, the matched knowledge graph is generated, the knowledge graph is conveniently and accurately returned to the corresponding user terminal in time, and the conversion reliability of the knowledge graph is improved.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) data processing, and more particularly to a method and system for verifying and converting entity model data on an IoT platform. Background Technology

[0002] The IoT platform can interact with connected user terminals to transform entity model data from these terminals into knowledge graphs. However, the entity model data uploaded by different user terminals varies. During the transformation process, the IoT platform needs to convert the data format and structure. When transforming entity model data from different user terminals individually, the platform requires extensive data conversion work, increasing workload and accuracy. This prevents unified batch conversion of entity model data from different user terminals, hindering rapid conversion and reducing the reliability of entity model data sharing and interaction. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for verifying and converting entity model data on an Internet of Things (IoT) platform. Based on the entity model data storage status information of all user terminals under the IoT platform, the extracted entity model data is verified to determine its data attribute information. This further identifies the data sub-parts of the entity model data that require conversion processing, thereby generating a set of data sub-parts to be converted. Data sub-parts with the same conversion processing requirements are aggregated and integrated for unified batch data conversion, reducing the workload of data conversion. All data sub-parts that have completed conversion processing are then updated to the corresponding entity model data, ensuring the integrity and reliability of the entity model data. Furthermore, the entity model data is corrected and subjected to knowledge graph conversion processing to generate a matching knowledge graph, facilitating the timely and accurate return of the knowledge graph to the corresponding user terminals, thus improving the reliability of knowledge graph conversion.

[0004] This invention is achieved through the following technical solution:

[0005] Methods for verifying and transforming entity model data on IoT platforms include:

[0006] Obtain the entity model data storage status information of each user terminal under the IoT platform; based on the entity model data storage status information, extract the entity model data corresponding to each user terminal; verify and analyze the extracted entity model data to determine the data attribute information existing in the entity model data;

[0007] Based on the data attribute information, the data sub-parts under the entity model data that need to be transformed are determined, thereby generating several sets of data sub-parts to be transformed; after processing the data attributes of each set of data sub-parts to be transformed, all data sub-parts under the set of data sub-parts to be transformed are updated to the corresponding entity model data.

[0008] Perform data detection processing on each entity model data to identify abnormal data blocks in the entity model data, and perform corrective processing on the abnormal data blocks;

[0009] The entity model data that has undergone the correction process is uploaded to the Internet of Things platform, and knowledge graph transformation processing is performed on the corresponding entity model data to generate a knowledge graph that matches the entity model data.

[0010] Optionally, the entity model data storage status information of each user terminal under the IoT platform is obtained, and based on the entity model data storage status information, entity model data corresponding to each user terminal is extracted; the extracted entity model data is verified and analyzed to determine the data attribute information existing in the entity model data, including:

[0011] Obtain the entity model data storage location information of each user terminal under the IoT platform, and based on the entity model data storage location information, perform entity model data lookup processing on the storage range of each user terminal to extract the entity model data corresponding to each user terminal.

[0012] The extracted entity model data is subjected to data format verification and analysis to determine the data format information of each data item under the entity model data, which is then used as the data attribute information.

[0013] Optionally, based on the data attribute information, the data sub-parts of the entity model data that need to be transformed are determined, thereby generating several sets of data sub-parts to be transformed; after performing data attribute processing on each set of data sub-parts to be transformed, all data sub-parts under the set of data sub-parts to be transformed are updated to the corresponding entity model data, including:

[0014] Based on the data format information of all data items under the entity model data, determine whether the data format of each data item belongs to the recognizable data format type of the IoT platform; if not, determine the corresponding data item as a data sub-part that needs to be converted; then integrate all data sub-parts that need to be converted with the same data format into the same set of data sub-parts to be converted.

[0015] For each set of data sub-parts to be converted, a data format conversion process is performed to convert all data sub-parts under the data sub-parts to be converted into the corresponding data format; based on the location information of each data sub-part under the data sub-parts to be converted within the corresponding entity model data, each data sub-part is updated and replaced to the corresponding entity model data.

[0016] Optionally, when performing data format conversion processing on each subset of data to be converted, the following steps are also included:

[0017] Step S1: Suppose the i-th identifiable data format of the IoT platform includes J features. To determine whether the data format of the corresponding data item matches the i-th identifiable data format, the data format of the corresponding data item is compared with the features of the i-th identifiable data format. Let F be the comparison result of the j-th feature of the corresponding data item's data format with the i-th identifiable data format. ij And F ij The value of f can only be 0 or 1, and the value of j is any positive integer between 1 and j. When F ij When F = 1, it means that the data format of the corresponding data item matches the j-th feature of the i-th recognizable data format; when F ij When = 0, it indicates that the data format of the corresponding data item does not match the j-th feature of the i-th recognizable data format. Therefore, the generalized similarity between the data format of the corresponding data item and the i-th recognizable data format is:

[0018]

[0019] In the above formula (1), S i The generalized similarity between the data format of the corresponding data item and the i-th recognizable data format;

[0020] Step S2: Assume there are I types of identifiable data formats in the IoT platform. Based on the generalized similarity between the data format of the corresponding data item and each of the I identifiable data formats, determine the identifiable data format that best matches the data format of the corresponding data item among the I identifiable data formats.

[0021] F(I) = max{S1,S2,…S} I} (2)

[0022] In the above formula (2), max{S1,S2,…S I} represents taking S1, S2, ... S I The maximum value in S1, S2, ..., S1 is represented by F(I). I The results corresponding to the maximum values ​​in S1, S2, ... SI This represents the generalized similarity between the data format of the corresponding data item and the 1st, 2nd, ..., Ith identifiable data formats, respectively.

[0023] Based on the above formula (2), S1, S2, ... S I The recognizable data format corresponding to the result of the maximum value in the data is determined as the recognizable data format that best matches the data format of the corresponding data item;

[0024] Step S3: Let x be the type number of the identifiable data format that best matches the data format of the corresponding data item in step S2 in the IoT platform, and let Y be the features of the best-matching identifiable data format. Then, the comparison result between the data format of the corresponding data item and the y-th feature of the x-th identifiable data format is F. xy Where y is a positive integer between 1 and Y, using the following formula (3), based on the comparison result F xy The data format of the corresponding data items is converted.

[0025]

[0026] In the above formula (3), f y ′ f is the value corresponding to the y-th feature in the data format of the corresponding data item after transformation. y f represents the original value of the y-th feature in the data format of the corresponding data item. x ′ y This is the original value of the y-th feature in the x-th recognizable data format.

[0027] Optionally, data detection processing is performed on each entity model data to identify abnormal data blocks in the entity model data, and correction processing is performed on the abnormal data blocks, including:

[0028] For each entity model data, data detection processing is performed to identify all missing data blocks in the entity model data, which are then used as the abnormal data blocks; based on the data editing history of the entity model data, data restoration and correction processing is performed on each missing data block.

[0029] Optionally, the entity model data after the correction process is completed is uploaded to the IoT platform to perform knowledge graph transformation processing on the corresponding entity model data, generating a knowledge graph that matches the entity model data, including:

[0030] After the entity model data that has undergone the correction process is encrypted and compressed, it is added to the knowledge graph conversion task queue of the IoT platform to perform knowledge graph conversion on the corresponding entity model data. Then, the generated knowledge graph that matches the entity model data is backed up and returned to the corresponding user terminal.

[0031] The IoT platform entity model data verification and conversion system includes:

[0032] The entity model data extraction module is used to obtain the entity model data storage status information of all user terminals under the Internet of Things platform, and extract the entity model data corresponding to each user terminal based on the entity model data storage status information.

[0033] The data attribute determination module is used to examine and analyze the extracted entity model data to determine the data attribute information present in the entity model data.

[0034] The data conversion and differentiation module is used to determine the data sub-parts of the entity model data that need to be converted based on the data attribute information, thereby generating a set of several data sub-parts to be converted.

[0035] The data conversion execution and update module is used to process the data attributes of each set of data sub-parts to be converted and then update all data sub-parts under the set of data sub-parts to be converted to the corresponding entity model data.

[0036] An abnormal data detection and correction module is used to perform data detection processing on each entity model data, identify abnormal data blocks in the entity model data, and perform correction processing on the abnormal data blocks.

[0037] The knowledge graph generation and processing module is used to upload the entity model data that has undergone the correction processing to the Internet of Things platform, thereby performing knowledge graph conversion processing on the corresponding entity model data to generate a knowledge graph that matches the entity model data.

[0038] Optionally, the entity model data extraction module is used to obtain the entity model data storage status information of each user terminal under the IoT platform, and extract the entity model data corresponding to each user terminal based on the entity model data storage status information, including:

[0039] Obtain the entity model data storage location information of each user terminal under the IoT platform, and based on the entity model data storage location information, perform entity model data lookup processing on the storage range of each user terminal to extract the entity model data corresponding to each user terminal.

[0040] The data attribute determination module is used to examine and analyze the extracted entity model data to determine the data attribute information present in the entity model data, including:

[0041] The extracted entity model data is subjected to data format verification and analysis to determine the data format information of each data item under the entity model data, which is then used as the data attribute information.

[0042] Optionally, the data conversion and differentiation module is used to determine, based on the data attribute information, the data sub-parts of the entity model data that need to be converted, thereby generating a set of several data sub-parts to be converted, including:

[0043] Based on the data format information of all data items under the entity model data, determine whether the data format of each data item belongs to the recognizable data format type of the IoT platform; if not, determine the corresponding data item as a data sub-part that needs to be converted; then integrate all data sub-parts that need to be converted with the same data format into the same set of data sub-parts to be converted.

[0044] The data transformation execution and update module is used to process the data attributes of each set of data to be transformed, and then update all data sub-parts under the set of data to be transformed to the corresponding entity model data, including:

[0045] For each set of data sub-parts to be converted, a data format conversion process is performed to convert all data sub-parts under the data sub-parts to be converted into the corresponding data format; based on the location information of each data sub-part under the data sub-parts to be converted within the corresponding entity model data, each data sub-part is updated and replaced to the corresponding entity model data.

[0046] Optionally, the abnormal data detection and correction module is used to perform data detection processing on each entity model data, identify abnormal data blocks in the entity model data, and perform correction processing on the abnormal data blocks, including:

[0047] For each entity model data, data detection processing is performed to identify all missing data blocks in the entity model data, which are then used as the abnormal data blocks; based on the data editing history of the entity model data, data restoration and correction processing is performed on each missing data block.

[0048] Optionally, the knowledge graph generation and processing module is used to upload the entity model data that has undergone the correction processing to the IoT platform, thereby performing knowledge graph transformation processing on the corresponding entity model data to generate a knowledge graph that matches the entity model data, including:

[0049] After the entity model data that has undergone the correction process is encrypted and compressed, it is added to the knowledge graph conversion task queue of the IoT platform to perform knowledge graph conversion on the corresponding entity model data. Then, the generated knowledge graph that matches the entity model data is backed up and returned to the corresponding user terminal.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The IoT platform entity model data verification and conversion method and system provided in this application are based on the entity model data storage status information of all user terminals under the IoT platform. The extracted entity model data is verified to determine the data attribute information of the entity model data. This further identifies the data sub-parts of the entity model data that require conversion processing, thereby generating a set of data sub-parts to be converted. Data sub-parts with the same conversion processing requirements are aggregated and integrated for unified batch data conversion, reducing the workload of data conversion. All data sub-parts that have completed conversion processing are then updated to the corresponding entity model data to ensure the integrity and reliability of the entity model data. Furthermore, the entity model data is corrected and subjected to knowledge graph conversion processing to generate a matching knowledge graph, facilitating the timely and accurate return of the knowledge graph to the corresponding user terminal and improving the reliability of knowledge graph conversion. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0053] Figure 1 This is a flowchart illustrating the method for verifying and converting entity model data on an IoT platform provided by the present invention.

[0054] Figure 2 This is a schematic diagram of the structure of the IoT platform entity model data verification and conversion system provided by the present invention. Detailed Implementation

[0055] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, it should be noted that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, not the entire structure. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this application.

[0056] The terms “comprising” and “having”, and any variations thereof, used in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0057] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0058] Please see Figure 1 As shown, an embodiment of this application provides a method for verifying and converting entity model data on an IoT platform, including:

[0059] Obtain the entity model data storage status information of each user terminal under the IoT platform; based on the entity model data storage status information, extract the entity model data corresponding to each user terminal; verify and analyze the extracted entity model data to determine the data attribute information of the entity model data.

[0060] Based on the data attribute information, the data sub-parts under the entity model data that need to be transformed are determined, thereby generating several sets of data sub-parts to be transformed; after processing the data attributes of each set of data sub-parts to be transformed, all data sub-parts under the set of data sub-parts to be transformed are updated to the corresponding entity model data.

[0061] Perform data detection processing on each entity model data to identify abnormal data blocks in the entity model data, and perform corrective processing on the abnormal data blocks;

[0062] The entity model data that has undergone the correction process is uploaded to the IoT platform, and knowledge graph transformation is performed on the corresponding entity model data to generate a knowledge graph that matches the entity model data.

[0063] The beneficial effects of the above embodiments are as follows: the IoT platform entity model data verification and conversion method is based on the entity model data storage status information of all user terminals under the IoT platform. The extracted entity model data is verified to determine the data attribute information of the entity model data. This further identifies the data sub-parts of the entity model data that require conversion processing, thereby generating a set of data sub-parts to be converted. Data sub-parts with the same conversion processing requirements are aggregated and integrated, facilitating unified batch data conversion and reducing the workload of data conversion. All data sub-parts that have completed conversion processing are then updated to the corresponding entity model data, ensuring the integrity and reliability of the entity model data. Furthermore, the entity model data is corrected and subjected to knowledge graph conversion processing to generate a matching knowledge graph, facilitating the timely and accurate return of the knowledge graph to the corresponding user terminal and improving the reliability of knowledge graph conversion.

[0064] In another embodiment, the entity model data storage status information of each user terminal under the IoT platform is obtained. Based on the entity model data storage status information, entity model data corresponding to each user terminal is extracted. The extracted entity model data is examined and analyzed to determine the data attribute information existing in the entity model data, including:

[0065] Obtain the entity model data storage location information of each user terminal under the IoT platform, and based on the entity model data storage location information, perform entity model data lookup processing on the storage range of each user terminal to extract the entity model data corresponding to each user terminal.

[0066] The extracted entity model data is analyzed for data format verification to determine the data format information of each data item under the entity model data, which is then used as the data attribute information.

[0067] The beneficial effects of the above embodiments are that the IoT platform connects to multiple user terminals and can process entity model data uploaded by different user terminals. Entity model data uploaded by different user terminals will inevitably have differences in data format and / or data structure. To ensure that the IoT platform can accurately perform knowledge graph conversion processing on the entity model data uploaded by user terminals, it is necessary to accurately extract the entity model data from each user terminal. In practice, the storage location information of the entity model data of each user terminal under the IoT platform is obtained. Then, based on this storage location information, the entity model data is searched within the storage range of each user terminal, thereby accurately locating the entity model data stored within each user terminal. The located entity model data is then extracted, ensuring the comprehensive and complete extraction of the entity model data. Furthermore, the extracted entity model data undergoes data format verification and analysis to determine the data format information of each data item contained within the entity model data, thereby globally determining the data format of the entity model data.

[0068] In another embodiment, based on the data attribute information, the data sub-parts of the entity model data that need to be transformed are determined, thereby generating several sets of data sub-parts to be transformed; after performing data attribute processing on each set of data sub-parts to be transformed, all data sub-parts under that set are updated to the corresponding entity model data, including:

[0069] Based on the data format information of all data items under the entity model data, determine whether the data format of each data item belongs to the recognizable data format type of the IoT platform; if not, determine the corresponding data item as a data sub-part that needs to be converted; then integrate all data sub-parts that need to be converted with the same data format into the same set of data sub-parts to be converted.

[0070] For each set of data sub-parts to be converted, perform data format conversion processing to convert all data sub-parts under the data sub-parts to be converted into the corresponding data format; based on the location information of each data sub-part under the data sub-parts to be converted within the corresponding entity model data, update and replace each data sub-part with the corresponding entity model data.

[0071] The beneficial effects of the above embodiments are as follows: if the data format information of each data item under the entity model data is inconsistent with the identifiable data format type of the IoT platform itself, the corresponding data item is determined as a data sub-part that needs to be converted; if the two are consistent, the corresponding data item is determined as a data sub-part that does not need to be converted. All data sub-parts that need to be converted and have the same data format are integrated into the same set of data sub-parts to be converted, ensuring that all data sub-parts under the same set of data sub-parts to be converted require the same data format conversion operation during the data format conversion process. This achieves unified batch conversion processing of all data sub-parts under the same set of data sub-parts to be converted, improving the efficiency of data format conversion and reducing the workload of data format conversion. In addition, data format conversion processing is performed on each set of data sub-parts to be converted, converting all data sub-parts under the data sub-parts to be converted into having the corresponding data format. Based on the position information of each data sub-part under the data sub-parts to be converted within the corresponding entity model data, each data sub-part is updated and replaced to the corresponding entity model data, ensuring that the data sub-parts with complete data format conversion can be accurately replaced to their corresponding entity model data, thereby achieving comprehensive and accurate data format updates for entity model data.

[0072] In another embodiment, when performing data format conversion processing on each subset of data to be converted, the method further includes:

[0073] Step S1: Suppose that the i-th identifiable data format of the IoT platform includes J features. To determine whether the data format of the corresponding data item matches the i-th identifiable data format, the data format of the corresponding data item is compared with the features of the i-th identifiable data format. Let F be the comparison result of the j-th feature of the corresponding data item and the i-th identifiable data format. ij And F ij The value of f can only be 0 or 1, and the value of j is any positive integer between 1 and j. When F ij When F = 1, it means that the data format of the corresponding data item matches the j-th feature of the i-th recognizable data format; when F ij When = 0, it indicates that the data format of the corresponding data item does not match the j-th feature of the i-th recognizable data format. Therefore, the generalized similarity between the data format of the corresponding data item and the i-th recognizable data format is:

[0074]

[0075] In the above formula (1), S i The generalized similarity between the data format of the corresponding data item and the i-th recognizable data format;

[0076] Step S2: Assume there are I types of identifiable data formats in the IoT platform. Based on the generalized similarity between the data format of the corresponding data item and each of the I identifiable data formats, determine the identifiable data format that best matches the data format of the corresponding data item among the I identifiable data formats.

[0077] F(I) = max{S1,S2,…S} I} (2)

[0078] In the above formula (2), max{S1,S2,…S I} represents taking S1, S2, ... S I The maximum value in S1, S2, ..., S1 is represented by F(I). I The results corresponding to the maximum values ​​in S1, S2, ... S I This represents the generalized similarity between the data format of the corresponding data item and the 1st, 2nd, ..., Ith identifiable data formats, respectively.

[0079] Based on the above formula (2), S1, S2, ... S I The recognizable data format corresponding to the result of the maximum value in the data is determined as the recognizable data format that best matches the data format of the corresponding data item;

[0080] Step S3: Let x be the type number of the identifiable data format that best matches the data format of the corresponding data item in step S2 on this IoT platform, and let Y be the features of this best-matching identifiable data format. Then, the comparison result between the data format of the corresponding data item and the y-th feature of the x-th identifiable data format is F. xy Where y is a positive integer between 1 and Y, using the following formula (3), based on the comparison result F xy The data format of the corresponding data items is converted.

[0081]

[0082] In the above formula (3), f y ′ f is the value corresponding to the y-th feature in the data format of the corresponding data item after transformation. y f represents the original value of the y-th feature in the data format of the corresponding data item. x ′ y This is the original value of the y-th feature in the x-th recognizable data format.

[0083] The beneficial effects of the above embodiments are that, due to the large number of data items under the entity model data and the variety of data formats, it is more difficult to determine whether the data format of each data item belongs to the identifiable data format type of the IoT platform. This is because each data format has many characteristics, including but not limited to a series of features such as square brackets, keywords, values, and connectors (corresponding to JSON format). Moreover, different data formats also have similarities. If a data item that is very different from the target data format type is forcibly converted into the target data format, the modification and computational resource consumption are huge and unnecessary. To conserve computing resources and facilitate quick and accurate determination of whether the entity model data item belongs to the identifiable data format type of the IoT platform, and to convert non-compliant parts with minimal resources, thus avoiding excessive computing resource consumption due to data format type issues; a generalized similarity calculation is performed between the target data item and all identifiable data format types of the IoT platform to obtain the most similar identifiable data format type of the target data item, and conversion and repair are performed according to the format characteristics of the most similar identifiable data format type, thereby avoiding excessive computing resource consumption due to data format type issues and improving the efficiency of data format conversion processing.

[0084] In another embodiment, data detection processing is performed on each entity model data to identify abnormal data blocks in the entity model data, and correction processing is performed on the abnormal data blocks, including:

[0085] For each entity model data, perform data detection processing to identify all missing data blocks in the entity model data, which are then used as the abnormal data blocks; based on the data editing history of the entity model data, perform data restoration and correction processing on each missing data block.

[0086] The beneficial effects of the above embodiments are that data detection processing is performed on each entity model data to identify all missing data blocks in the entity model data, thereby accurately locating the data loss caused by the data format conversion process of the entity model data. Then, based on the data editing history of the entity model data, data restoration and correction processing is performed on each missing data block to achieve complete supplementation of the entity model data.

[0087] In another embodiment, the entity model data that has undergone the correction process is uploaded to the IoT platform to perform knowledge graph transformation on the corresponding entity model data, generating a knowledge graph that matches the entity model data, including:

[0088] After the entity model data that has undergone the correction process is encrypted and compressed, it is added to the knowledge graph conversion task queue of the IoT platform. This is used to perform knowledge graph conversion on the corresponding entity model data. The generated knowledge graph that matches the entity model data is then backed up and returned to the corresponding user terminal.

[0089] The beneficial effects of the above embodiments are that the entity model data after the correction processing is encrypted and compressed, and then added to the knowledge graph conversion task queue of the IoT platform. This allows the IoT platform to perform indicator graph conversion processing on the entity model data in the corresponding knowledge graph conversion task, improving the efficiency and accuracy of specialized knowledge graph generation. The generated knowledge graph matching the entity model data is then backed up and returned to the corresponding user terminal, ensuring that the user terminal can receive the corresponding knowledge graph completely and promptly.

[0090] Please see Figure 2 As shown, an embodiment of this application provides an IoT platform entity model data verification and conversion system, which includes:

[0091] The entity model data extraction module is used to obtain the entity model data storage status information of all user terminals under the IoT platform, and extract the entity model data corresponding to each user terminal based on the entity model data storage status information.

[0092] The data attribute determination module is used to examine and analyze the extracted entity model data to determine the data attribute information that exists in the entity model data.

[0093] The data transformation and differentiation module is used to determine the data sub-parts of the entity model data that need to be transformed based on the data attribute information, thereby generating a set of several data sub-parts to be transformed.

[0094] The data transformation execution and update module is used to process the data attributes of each data sub-set to be transformed and then update all data sub-parts under that data sub-set to the corresponding entity model data.

[0095] The abnormal data detection and correction module is used to perform data detection processing on each entity model data, identify the abnormal data blocks in the entity model data, and perform correction processing on the abnormal data blocks.

[0096] The knowledge graph generation and processing module is used to upload the entity model data that has undergone the correction process to the IoT platform, thereby performing knowledge graph transformation processing on the corresponding entity model data and generating a knowledge graph that matches the entity model data.

[0097] The beneficial effects of the above embodiments are that the IoT platform entity model data verification and conversion system, based on the entity model data storage status information of all user terminals under the IoT platform, extracts and verifies the entity model data to determine the data attribute information of the entity model data. This further identifies the data sub-parts of the entity model data that require conversion processing, thereby generating a set of data sub-parts to be converted. Data sub-parts with the same conversion processing requirements are aggregated and integrated, facilitating unified batch data conversion and reducing the workload of data conversion. All data sub-parts that have completed conversion processing are then updated to the corresponding entity model data, ensuring the integrity and reliability of the entity model data. Furthermore, the entity model data is corrected and subjected to knowledge graph conversion processing to generate a matching knowledge graph, facilitating the timely and accurate return of the knowledge graph to the corresponding user terminal and improving the reliability of knowledge graph conversion.

[0098] In another embodiment, the entity model data extraction module is used to obtain the entity model data storage status information of each user terminal under the IoT platform, and based on the entity model data storage status information, extract the entity model data corresponding to each user terminal, including:

[0099] Obtain the entity model data storage location information of each user terminal under the IoT platform, and based on the entity model data storage location information, perform entity model data lookup processing on the storage range of each user terminal to extract the entity model data corresponding to each user terminal.

[0100] This data attribute determination module is used to examine and analyze the extracted entity model data to determine the data attribute information present in the entity model data, including:

[0101] The extracted entity model data is analyzed for data format verification to determine the data format information of each data item under the entity model data, which is then used as the data attribute information.

[0102] The beneficial effects of the above embodiments are that the IoT platform connects to multiple user terminals and can process entity model data uploaded by different user terminals. Entity model data uploaded by different user terminals will inevitably have differences in data format and / or data structure. To ensure that the IoT platform can accurately perform knowledge graph conversion processing on the entity model data uploaded by user terminals, it is necessary to accurately extract the entity model data from each user terminal. In practice, the storage location information of the entity model data of each user terminal under the IoT platform is obtained. Then, based on this storage location information, the entity model data is searched within the storage range of each user terminal, thereby accurately locating the entity model data stored within each user terminal. The located entity model data is then extracted, ensuring the comprehensive and complete extraction of the entity model data. Furthermore, the extracted entity model data undergoes data format verification and analysis to determine the data format information of each data item contained within the entity model data, thereby globally determining the data format of the entity model data.

[0103] In another embodiment, the data transformation and differentiation module is used to determine, based on the data attribute information, the data sub-parts of the entity model data that require transformation processing, thereby generating a set of several data sub-parts to be transformed and processed, including:

[0104] Based on the data format information of all data items under the entity model data, determine whether the data format of each data item belongs to the recognizable data format type of the IoT platform; if not, determine the corresponding data item as a data sub-part that needs to be converted; then integrate all data sub-parts that need to be converted with the same data format into the same set of data sub-parts to be converted.

[0105] This data transformation execution and update module is used to process the data attributes of each data sub-set to be transformed, and then update all data sub-parts under that data sub-set to the corresponding entity model data, including:

[0106] For each set of data sub-parts to be converted, perform data format conversion processing to convert all data sub-parts under the data sub-parts to be converted into the corresponding data format; based on the location information of each data sub-part under the data sub-parts to be converted within the corresponding entity model data, update and replace each data sub-part with the corresponding entity model data.

[0107] The beneficial effects of the above embodiments are as follows: if the data format information of each data item under the entity model data is inconsistent with the identifiable data format type of the IoT platform itself, the corresponding data item is determined as a data sub-part that needs to be converted; if the two are consistent, the corresponding data item is determined as a data sub-part that does not need to be converted. All data sub-parts that need to be converted and have the same data format are integrated into the same set of data sub-parts to be converted, ensuring that all data sub-parts under the same set of data sub-parts to be converted require the same data format conversion operation during the data format conversion process. This achieves unified batch conversion processing of all data sub-parts under the same set of data sub-parts to be converted, improving the efficiency of data format conversion and reducing the workload of data format conversion. In addition, data format conversion processing is performed on each set of data sub-parts to be converted, converting all data sub-parts under the data sub-parts to be converted into having the corresponding data format. Based on the position information of each data sub-part under the data sub-parts to be converted within the corresponding entity model data, each data sub-part is updated and replaced to the corresponding entity model data, ensuring that the data sub-parts with complete data format conversion can be accurately replaced to their corresponding entity model data, thereby achieving comprehensive and accurate data format updates for entity model data.

[0108] In another embodiment, the abnormal data detection and correction module is used to perform data detection processing on each entity model data, identify abnormal data blocks in the entity model data, and perform correction processing on the abnormal data blocks, including:

[0109] For each entity model data, perform data detection processing to identify all missing data blocks in the entity model data, which are then used as the abnormal data blocks; based on the data editing history of the entity model data, perform data restoration and correction processing on each missing data block.

[0110] The beneficial effects of the above embodiments are that data detection processing is performed on each entity model data to identify all missing data blocks in the entity model data, thereby accurately locating the data loss caused by the data format conversion process of the entity model data. Then, based on the data editing history of the entity model data, data restoration and correction processing is performed on each missing data block to achieve complete supplementation of the entity model data.

[0111] In another embodiment, the knowledge graph generation and processing module is used to upload the entity model data that has undergone the correction processing to the IoT platform, thereby performing knowledge graph transformation processing on the corresponding entity model data to generate a knowledge graph that matches the entity model data, including:

[0112] After the entity model data that has undergone the correction process is encrypted and compressed, it is added to the knowledge graph conversion task queue of the IoT platform. This is used to perform knowledge graph conversion on the corresponding entity model data. The generated knowledge graph that matches the entity model data is then backed up and returned to the corresponding user terminal.

[0113] The beneficial effects of the above embodiments are that the entity model data after the correction processing is encrypted and compressed, and then added to the knowledge graph conversion task queue of the IoT platform. This allows the IoT platform to perform indicator graph conversion processing on the entity model data in the corresponding knowledge graph conversion task, improving the efficiency and accuracy of specialized knowledge graph generation. The generated knowledge graph matching the entity model data is then backed up and returned to the corresponding user terminal, ensuring that the user terminal can receive the corresponding knowledge graph completely and promptly.

[0114] In summary, the IoT platform entity model data verification and conversion method and system are based on the entity model data storage status information of all user terminals under the IoT platform. The extracted entity model data is verified to determine its data attribute information, thereby further identifying the data sub-parts of the entity model data that require conversion processing. This generates a set of data sub-parts to be converted, and data parts with the same conversion processing requirements are aggregated and integrated for unified batch data conversion, reducing the workload. All converted data sub-parts are then updated to the corresponding entity model data to ensure the integrity and reliability of the entity model data. Furthermore, the entity model data is corrected and subjected to knowledge graph conversion processing to generate a matching knowledge graph, facilitating timely and accurate return of the knowledge graph to the corresponding user terminals and improving the reliability of knowledge graph conversion.

[0115] The above is only one specific embodiment of the present invention, and any improvements made based on the concept of the present invention shall be considered within the scope of protection of the present invention.

Claims

1. A method for verifying and converting entity model data on an Internet of Things (IoT) platform, characterized in that, include: Obtain the entity model data storage status information of each user terminal under the IoT platform; based on the entity model data storage status information, extract the entity model data corresponding to each user terminal; verify and analyze the extracted entity model data to determine the data attribute information existing in the entity model data; Based on the data attribute information, determine the data sub-parts of the entity model data that need to be transformed, thereby generating a set of several data sub-parts to be transformed. After processing the data attributes of each set of data sub-parts to be converted, all data sub-parts under the set of data sub-parts to be converted are updated to the corresponding entity model data. This includes: determining whether the data format of each data item belongs to the recognizable data format type of the IoT platform based on the data format information of each data item under the entity model data; if not, identifying the corresponding data item as a data sub-part that needs to be converted; then integrating all data sub-parts that need to be converted with the same data format into the same set of data sub-parts to be converted; performing data format conversion processing on each set of data sub-parts to be converted, converting all data sub-parts under the set of data sub-parts to be converted into having the corresponding data format; and updating and replacing each data sub-part with the corresponding entity model data based on the position information of each data sub-part within the corresponding entity model data. Perform data detection processing on each entity model data to identify abnormal data blocks in the entity model data, and perform corrective processing on the abnormal data blocks; The entity model data that has undergone the correction process is uploaded to the Internet of Things platform, thereby performing knowledge graph conversion processing on the corresponding entity model data to generate a knowledge graph that matches the entity model data; This includes performing data format conversion processing on each subset of data to be converted, as well as: Step S1: Suppose the i-th identifiable data format of the IoT platform includes J features. To determine whether the data format of the corresponding data item matches the i-th identifiable data format, the data format of the corresponding data item is compared with the features of the i-th identifiable data format. Let the comparison result of the j-th feature of the corresponding data item's data format with the i-th identifiable data format be... ,and The value of can only be 0 or 1, and the value of j is any positive integer between 1 and J. When =1, it means that the data format of the corresponding data item matches the j-th feature of the i-th recognizable data format; when When = 0, it indicates that the data format of the corresponding data item does not match the j-th feature of the i-th recognizable data format. Therefore, the generalized similarity between the data format of the corresponding data item and the i-th recognizable data format is: （1） In the above formula (1), The generalized similarity between the data format of the corresponding data item and the i-th recognizable data format; Step S2: Assume there are I types of identifiable data formats in the IoT platform. Based on the generalized similarity between the data format of the corresponding data item and each of the I identifiable data formats, determine the identifiable data format that best matches the data format of the corresponding data item among the I identifiable data formats. （2） In the above formula (2), Indicates taking The maximum value in, Indicates taking The result corresponding to the maximum value in, This represents the generalized similarity between the data format of the corresponding data item and the 1st, 2nd, ..., Ith identifiable data formats, respectively. Based on the above formula (2), The recognizable data format corresponding to the result of the maximum value in the data is determined as the recognizable data format that best matches the data format of the corresponding data item; Step S3: Let x be the type number of the identifiable data format that best matches the data format of the corresponding data item in step S2 in the IoT platform, and let Y be the features of the best-matching identifiable data format. Then, the comparison result between the data format of the corresponding data item and the y-th feature of the x-th identifiable data format is: Where y is a positive integer between 1 and Y, using the following formula (3), based on the comparison results The data format of the corresponding data items is converted. （3） In the above formula (3), This represents the value of the y-th feature in the data format of the corresponding data item after conversion. This represents the original value of the y-th feature in the data format of the corresponding data item. This is the original value of the y-th feature in the x-th recognizable data format.

2. The method for verifying and converting entity model data on an IoT platform as described in claim 1, characterized in that: Obtain the entity model data storage status information of each user terminal under the IoT platform, and extract the entity model data corresponding to each user terminal based on the entity model data storage status information; The extracted entity model data is examined and analyzed to determine the data attribute information present in the entity model data, including: Obtain the entity model data storage location information of each user terminal under the IoT platform, and based on the entity model data storage location information, perform entity model data lookup processing on the storage range of each user terminal to extract the entity model data corresponding to each user terminal. The extracted entity model data is subjected to data format verification and analysis to determine the data format information of each data item under the entity model data, which is then used as the data attribute information.

3. The method for verifying and converting entity model data on an IoT platform as described in claim 1, characterized in that: For each entity model data, data inspection processing is performed to identify abnormal data blocks, and the abnormal data blocks are corrected, including: For each entity model data, data detection processing is performed to identify all missing data blocks in the entity model data, which are then used as the abnormal data blocks; based on the data editing history of the entity model data, data restoration and correction processing is performed on each missing data block.

4. The method for verifying and converting entity model data on an IoT platform as described in claim 1, characterized in that: The entity model data that has undergone the correction process is uploaded to the IoT platform, whereby knowledge graph transformation is performed on the corresponding entity model data to generate a knowledge graph that matches the entity model data, including: After the entity model data that has undergone the correction process is encrypted and compressed, it is added to the knowledge graph conversion task queue of the IoT platform to perform knowledge graph conversion on the corresponding entity model data. Then, the generated knowledge graph that matches the entity model data is backed up and returned to the corresponding user terminal.

5. An IoT platform entity model data verification and conversion system, characterized in that, include: The entity model data extraction module is used to obtain the entity model data storage status information of all user terminals under the Internet of Things platform, and extract the entity model data corresponding to each user terminal based on the entity model data storage status information. The data attribute determination module is used to examine and analyze the extracted entity model data to determine the data attribute information present in the entity model data. The data conversion and differentiation module is used to determine the data sub-parts under the entity model data that need to be converted based on the data attribute information, thereby generating a set of several data sub-parts to be converted. This includes: determining whether the data format of each data item belongs to the recognizable data format type of the IoT platform based on the data format information of all data items under the entity model data; if not, determining the corresponding data item as a data sub-part that needs to be converted; and then integrating all data sub-parts that need to be converted with the same data format into the same set of data sub-parts to be converted. The data conversion execution and update module is used to process the data attributes of each set of data sub-parts to be converted and then update all data sub-parts under the set of data sub-parts to be converted to the corresponding entity model data. This includes: performing data format conversion processing on each set of data sub-parts to be converted, converting all data sub-parts under the set of data sub-parts to be converted to have the corresponding data format; and updating and replacing each data sub-part with the corresponding entity model data based on the position information of each data sub-part within the corresponding entity model data. An abnormal data detection and correction module is used to perform data detection processing on each entity model data, identify abnormal data blocks in the entity model data, and perform correction processing on the abnormal data blocks. The knowledge graph generation and processing module is used to upload the entity model data that has completed the correction processing to the Internet of Things platform, thereby performing knowledge graph conversion processing on the corresponding entity model data to generate a knowledge graph that matches the entity model data. This includes performing data format conversion processing on each subset of data to be converted, as well as: Step S1: Suppose the i-th identifiable data format of the IoT platform includes J features. To determine whether the data format of the corresponding data item matches the i-th identifiable data format, the data format of the corresponding data item is compared with the features of the i-th identifiable data format. Let the comparison result of the j-th feature of the corresponding data item's data format with the i-th identifiable data format be... ,and The value of can only be 0 or 1, and the value of j is any positive integer between 1 and J. When =1, it means that the data format of the corresponding data item matches the j-th feature of the i-th recognizable data format; when When = 0, it indicates that the data format of the corresponding data item does not match the j-th feature of the i-th recognizable data format. Therefore, the generalized similarity between the data format of the corresponding data item and the i-th recognizable data format is: （1） In the above formula (1), The generalized similarity between the data format of the corresponding data item and the i-th recognizable data format; Step S2: Assume there are I types of identifiable data formats in the IoT platform. Based on the generalized similarity between the data format of the corresponding data item and each of the I identifiable data formats, determine the identifiable data format that best matches the data format of the corresponding data item among the I identifiable data formats. （2） In the above formula (2), Indicates taking The maximum value in, Indicates taking The result corresponding to the maximum value in, This represents the generalized similarity between the data format of the corresponding data item and the 1st, 2nd, ..., Ith identifiable data formats, respectively. Based on the above formula (2), The recognizable data format corresponding to the result of the maximum value in the data is determined as the recognizable data format that best matches the data format of the corresponding data item; Step S3: Let x be the type number of the identifiable data format that best matches the data format of the corresponding data item in step S2 in the IoT platform, and let Y be the features of the best-matching identifiable data format. Then, the comparison result between the data format of the corresponding data item and the y-th feature of the x-th identifiable data format is: Where y is a positive integer between 1 and Y, using the following formula (3), based on the comparison results The data format of the corresponding data items is converted. （3） In the above formula (3), This represents the value of the y-th feature in the data format of the corresponding data item after conversion. This represents the original value of the y-th feature in the data format of the corresponding data item. This is the original value of the y-th feature in the x-th recognizable data format.

6. The IoT platform entity model data verification and conversion system as described in claim 5, characterized in that: The entity model data extraction module is used to obtain the entity model data storage status information of each user terminal under the IoT platform, and extract the entity model data corresponding to each user terminal based on the entity model data storage status information, including: Obtain the entity model data storage location information of each user terminal under the IoT platform, and based on the entity model data storage location information, perform entity model data lookup processing on the storage range of each user terminal to extract the entity model data corresponding to each user terminal. The data attribute determination module is used to examine and analyze the extracted entity model data to determine the data attribute information present in the entity model data, including: The extracted entity model data is subjected to data format verification and analysis to determine the data format information of each data item under the entity model data, which is then used as the data attribute information.

7. The IoT platform entity model data verification and conversion system as described in claim 5, characterized in that: The abnormal data detection and correction module is used to perform data detection processing on each entity model data, identify abnormal data blocks in the entity model data, and perform correction processing on the abnormal data blocks, including: For each entity model data, data detection processing is performed to identify all missing data blocks, which are then designated as anomalous data blocks. Based on the data editing history of the entity model data, data restoration and correction processing is performed on each missing data block. The knowledge graph generation and processing module is used to upload the entity model data that has undergone the correction processing to the IoT platform, thereby performing knowledge graph transformation processing on the corresponding entity model data to generate a knowledge graph that matches the entity model data, including: After the entity model data that has undergone the correction process is encrypted and compressed, it is added to the knowledge graph conversion task queue of the IoT platform to perform knowledge graph conversion on the corresponding entity model data. Then, the generated knowledge graph that matches the entity model data is backed up and returned to the corresponding user terminal.

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