Version consistency evaluation method, device and equipment based on Dianhong operating system

Abstract

The present invention discloses a version consistency evaluation method, device and equipment based on the Dianhong operating system. The method includes: obtaining the original data of at least two device systems, and determining text data and structured data from the original data; wherein the device systems are different versions of the Dianhong operating system; determining text similarity results according to the text data of any two device systems; determining structured similarity results according to the structured data of any two device systems; and determining the final version consistency evaluation results according to the text similarity results and the structured similarity results. The embodiment of the present invention improves the accuracy of the similarity results by splitting the data of different system versions and using different similarity calculation algorithms to determine the corresponding similarity results for different forms of data, thereby improving the accuracy of the evaluation results of the consistency of different versions of the Dianhong operating system.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment, and in particular to a version consistency assessment method, device and equipment based on a Dianhong operating system. Background Art

[0002] With the accelerated advancement of the construction of new power systems, the power industry has an increasingly urgent need for IoT operating systems. The emergence of Dianhong operating system fills the gap in the field of domestic power IoT operating systems. Dianhong operating system can cover large and small, different types and brands of power equipment with one system, realize the unification of power equipment systems, and can be flexibly assembled according to needs.

[0003] However, when the versions of the Dianhong operating system are incompatible or inconsistent, it is easy to cause system failures or problems, which may lead to risks such as system crashes and data loss. Therefore, when performing system unification operations on devices with different versions of the Dianhong operating system, it is necessary to evaluate the version consistency of the system. Summary of the invention

[0004] The present invention provides a version consistency evaluation method, device and equipment based on the telecom operating system to solve the problem of difficulty and inaccuracy in version consistency evaluation of different telecom operating systems.

[0005] According to one aspect of the present invention, a version consistency evaluation method based on a Dianhong operating system is provided, the method comprising:

[0006] Obtaining raw data of at least two device systems, and determining text data and structured data from the raw data; wherein the device systems are different versions of the Dianhong operating system;

[0007] Determine text similarity results based on text data of any two device systems;

[0008] Determining structural similarity results based on structured data of any two device systems;

[0009] A final version consistency evaluation result is determined according to the text similarity result and the structural similarity result.

[0010] According to another aspect of the present invention, a version consistency evaluation device based on a Dianhong operating system is provided, the device comprising:

[0011] A data splitting module, used to obtain raw data of at least two device systems, and determine text data and structured data from the raw data; wherein the device systems are different versions of the Dianhong operating system;

[0012] A text similarity determination module, used to determine text similarity results based on text data of any two device systems;

[0013] A structural similarity determination module, used for determining a structural similarity result according to the structured data of any two device systems;

[0014] The consistency evaluation module is used to determine the final version consistency evaluation result according to the text similarity result and the structural similarity result.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the version consistency assessment method based on the telecom operating system described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the version consistency assessment method based on the Dianhong operating system described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention improves the accuracy of the similarity results by splitting the data of different system versions and using different similarity calculation algorithms to determine the corresponding similarity results for data in different forms, thereby improving the accuracy of the evaluation results of the consistency of different versions of the Dianhong operating system.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1It is a flowchart of a version consistency evaluation method based on the Dianhong operating system provided according to the first embodiment of the present invention;

[0024] Figure 2 is a flowchart of a version consistency evaluation method based on the Dianhong operating system provided according to the second embodiment of the present invention;

[0025] Figure 3 It is a structural diagram of a version consistency evaluation device based on the Dianhong operating system provided according to the third embodiment of the present invention;

[0026] Figure 4 It is a structural diagram of an electronic device that implements the version consistency evaluation method based on the Dianhong operating system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "candidate", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 A flowchart of a version consistency evaluation method based on the Dianhong operating system is provided for the first embodiment of the present invention. This embodiment is applicable to the situation where consistency evaluation is performed on different versions of the Dianhong operating system. The method can be performed by a version consistency evaluation device based on the Dianhong operating system. The version consistency evaluation device based on the Dianhong operating system can be implemented in the form of hardware and / or software. The version consistency evaluation device based on the Dianhong operating system can be configured in a server with computing capabilities. Figure 1As shown, the method includes:

[0031] S110: Acquire original data of at least two device systems, and determine text data and structured data from the original data.

[0032] The equipment is an electric power equipment using the Dianhong operating system, and at least two equipment systems are different versions of the Dianhong operating system. The original data of the equipment system includes initial data that records detailed information about the system version, such as version file data or database records maintained during system operation. Text data and structured data are data in different representation forms in the original data. Text data is data in text form, such as descriptive data, etc.; structured data is data containing clear fields and corresponding values.

[0033] Specifically, original data related to the version information of the Dianhong operating system are determined from system files corresponding to at least two different device systems, and text data and structured data are selected from the original data according to the content of the original data. For example, the text data is the documents, logs, and comment data of the Dianhong operating system; the structured data is the database records with clear fields and values.

[0034] In a feasible embodiment, before determining the text data and the structured data from the original data, the method further includes:

[0035] Perform data preprocessing operations on the original data; the data preprocessing operations include data verification, data cleaning, format conversion, and deduplication operations.

[0036] After obtaining the original data, data verification is performed, including integrity verification and accuracy verification, to ensure the integrity and accuracy of the data. For example, the data standard format and data standard range are determined based on the data content, and the data integrity verification results and accuracy verification results are determined based on the comparison results of the data format corresponding to each content of the original data and the data standard format, as well as whether the data size is within the data standard range. Preprocessing operations also include data cleaning, that is, removing irrelevant information and processing missing values; format conversion, which is to unify the format of data with the same content to ensure that the data format meets the requirements of the subsequent evaluation module; deduplication operations, which is to unify the data with the same content in the original data to avoid the impact of duplicate data on the evaluation results.

[0037] Preprocessing operations improve the quality and consistency of data, provide a reliable data basis for subsequent evaluations, and thus improve the accuracy of evaluation results.

[0038] S120 . Determine a text similarity result based on text data of any two device systems.

[0039] Since text data and structured data have different data features, corresponding similarity results are determined for text data and structured data according to different data features, thereby improving the accuracy of similarity analysis on original data.

[0040] Specifically, at least two device systems are compared for similarity in pairs, and a corresponding similarity determination algorithm is determined according to the text description features of the text data. For example, feature extraction is performed on the text description content of the text data, and a text similarity result is calculated based on the extracted text.

[0041] In a feasible embodiment, S120 includes:

[0042] A similarity calculation algorithm based on deep learning determines the text similarity results based on the text data of any two device systems.

[0043] Specifically, a pre-trained deep learning model is used to extract features from files and records of different versions of the Dianhong operating system; these features are keywords, phrases, and sentence embeddings in the text content; after extracting the text features, the cosine similarity measurement method is used to calculate the similarity between different system versions as a text similarity result. The higher the similarity value, the closer and more similar the contents between the two system versions are.

[0044] S130: Determine a structural similarity result according to the structured data of any two device systems.

[0045] Specifically, at least two device systems are compared for similarity in pairs, and the corresponding similarity comparison algorithm is determined according to the data features corresponding to the structured data to obtain a structured similarity result. For example, first match according to the field content corresponding to the structured data, compare the values ​​corresponding to the fields with similarity greater than a threshold, process the association between the fields, and identify the differences between the records, such as newly added, deleted, and modified fields, and determine the structured similarity result by combining the similarity comparison results of all fields.

[0046] In a feasible embodiment, S130 includes:

[0047] A graph-theory-based difference analysis algorithm determines the structural similarity results based on the structured data of any two device systems.

[0048] The corresponding graph structure is determined based on the structured data of each device system, and the structural similarity result is determined by comparing the similarity between the graph structures corresponding to the two device systems. The graph theory algorithm is used to analyze the differences between different versions, which are manifested as the addition, deletion, and modification of nodes and edges, as well as the changes in the connection relationship between them.

[0049] Exemplarily, graph theory algorithms (such as shortest path algorithms and graph matching algorithms) are used to analyze the differences between different versions. These differences are manifested as additions, deletions, and modifications of nodes and edges, as well as changes in the connection relationships between them.

[0050] Exemplarily, step 120 and step 130 may be performed in parallel.

[0051] S140: Determine the final version consistency evaluation result according to the text similarity result and the structural similarity result.

[0052] The text similarity results and the structural similarity results are integrated, and the final version consistency evaluation results are determined based on the integrated results, so as to determine whether there is a prospect of integration between different device systems and determine the difference position information, so as to make adjustments based on the difference position information and improve the efficiency and accuracy of the integration of different versions of device systems.

[0053] Exemplarily, after format adjustment and data conversion of the evaluation results consisting of text similarity results and structural similarity results, the processed evaluation results are displayed to the user in a user-friendly manner, including but not limited to web pages, PDF files, and Excel tables to meet the needs and preferences of different users.

[0054] Evaluation method of similarity calculation algorithm based on deep learning For different versions of Dianhong operating system files, the deep learning model can analyze the changes in the file content, such as new features, modified interface descriptions, and performance improvement instructions. These changes will be converted into feature vectors to calculate the similarity between versions. For database text records, the deep learning model can process field values ​​in text data, such as user information, product information, and transaction records. By extracting the features of these fields, the algorithm can compare the similarities between different records, such as identifying duplicate records and detecting abnormal transactions.

[0055] Evaluation method of graph-based difference analysis algorithm For different version files of Dianhong operating system, graph theory algorithm can analyze the changes in functional components and interface call relationships between versions. By constructing a difference graph between versions, the algorithm can clearly show which functions are added, deleted, and modified, and how these changes affect the structure and function of the entire system. For database records, graph theory algorithm can handle the association relationship between records, such as foreign key constraints and transaction dependencies. By constructing a difference graph between records, the algorithm can identify which records are updated, deleted, and added, and how these changes affect the integrity and consistency of the database.

[0056] The embodiment of the present application solves the problem that the existing version consistency evaluation model modeling system cannot fully and accurately reflect the differences between two versions during the evaluation process. The similarity calculation algorithm based on deep learning can learn the intrinsic representation of the data, so as to accurately calculate the similarity between different data points, so that it has a powerful ability to handle complex data (such as images, texts), and the graph theory algorithm is good at analyzing and understanding the structural relationship between data; the difference analysis algorithm based on graph theory is usually represented as a collection of nodes and edges. By calculating the distance, path, and connectivity indicators between nodes, the intrinsic connection and difference between data can be revealed; the similarity calculation algorithm based on deep learning and the difference analysis algorithm based on graph theory have unique advantages in evaluating different versions of files and database records of the Dianhong operating system. They can automatically learn and extract high-level feature representations, handle complex relationships and dependency problems, and display the analysis results in an intuitive way. The application of these algorithms will help improve the accuracy and efficiency of version consistency evaluation.

[0057] The technical solution of the embodiment of the present invention improves the accuracy of the similarity results by splitting the data of different system versions and using different similarity calculation algorithms to determine the corresponding similarity results for data in different forms, thereby improving the accuracy of the evaluation results of the consistency of different versions of the Dianhong operating system.

[0058] Embodiment 2

[0059] Figure 2 This is a flowchart of a version consistency evaluation method based on the Dianhong operating system provided in the second embodiment of the present invention. This embodiment further refines the determination process of the text similarity results and the structural similarity results in the above embodiment. Figure 2 As shown, the method includes:

[0060] S210: Acquire original data of at least two device systems, and determine text data and structured data from the original data.

[0061] S220: Extract features from the text data to obtain text features.

[0062] The text features include content change features and field extraction features of text data of any two device systems.

[0063] A feature extraction model is used to extract features from text data to obtain text features. For example, the feature extraction model is a pre-trained deep learning model or encoder, and the text data is input into the pre-trained deep learning model to obtain the model output as text features.

[0064] The extracted text features include content change features extracted based on change information of the content of the text data, and field sub-extraction features extracted based on the content of the field in the text data.

[0065] S230 . Determine the similarity between the text features of any two device systems using a cosine similarity measurement method as a text similarity result.

[0066] After determining the text features of each device system respectively, the cosine similarity measurement method is used to calculate the cosine similarity of the text systems corresponding to the two device systems to obtain the text similarity result.

[0067] Optionally, the text similarity result is directly obtained according to the pre-trained deep learning model, for example, the text data is input into the pre-trained deep learning model, and the model output is obtained as the text similarity result. The specific output of the model is the text feature or the text similarity result, which is determined according to the model training process and is not limited here. For example, the text similarity result is the similarity value of the text feature.

[0068] S240. Determine a graph structure according to structured data of any two device systems.

[0069] The nodes in the graph structure are functional components, interfaces and database tables determined according to structured data, and the edges represent the relationship between the corresponding data of the nodes.

[0070] Since structured data is a specific field and a corresponding value, a graph structure is used to represent structured data. Specifically, the data type in the structured data is determined, and the nodes in the graph structure are determined according to the different data types. For example, the function component data type, interface data type, and database table data type are determined according to the structured data, and the edges between the nodes are determined according to the relationship between these data in the structured data.

[0071] Exemplarily, a knowledge graph construction method is used to determine a graph structure based on structured data of any two device systems.

[0072] S250, determining the difference between the structured data of any two device systems by calculating the distance, path and connectivity index between the nodes in the graph structure as a structural similarity result.

[0073] In a feasible embodiment, the structural similarity results include at least change information on functional components, interface call relationships, and database table structures between any two device systems. The change information includes at least the addition, deletion, and modification of nodes in the graph structure, the addition, deletion, and modification of edges, and the change information on the connection relationship between nodes and edges.

[0074] Exemplarily, step 220 and step 240 may be performed in parallel.

[0075] S260: Determine the final version consistency evaluation result based on the text similarity result and the structural similarity result.

[0076] In a feasible embodiment, S260 includes:

[0077] Determine a difference graph and a similarity distribution graph according to the text similarity results and the structural similarity results;

[0078] Determine the difference type, difference location and difference impact range based on the difference map and similarity distribution map;

[0079] The final version consistency assessment result is determined based on the difference type, difference location and difference impact scope.

[0080] The final version consistency assessment results include a detailed difference list, similarity score, and visualization and explanation. The detailed difference list is specifically: a detailed difference list is generated based on the structural similarity results, listing the changes in functional components, interface call relationships, database table structures, and records between different versions. These changes include the addition, deletion, and modification of nodes, the addition, deletion, and modification of edges, and the changes in the connection relationship between them. The similarity score is specifically: the similarity scores between different versions and records determined based on the text similarity results are presented in an intuitive way. Through the form of bar charts and line charts, the similarity score can help users quickly understand the degree of consistency between versions. The higher the score, the closer the content between the two versions; the lower the score, the greater the difference between them. Visualization and explanation are specifically: using visualization technology to present the differences in a graphical way; this includes the difference map between versions and the similarity score distribution map. At the same time, detailed explanatory information is provided to help users understand the specific content and impact of the differences, including the difference type, location, and scope of impact.

[0081] The embodiment of the present invention can select a suitable evaluation algorithm according to the data type to flexibly adjust the evaluation algorithm according to different data types and evaluation objectives, so as to obtain more accurate evaluation results. In addition, the embodiment of the present invention adopts an efficient data processing algorithm, which can quickly complete data cleaning, feature extraction, similarity calculation and difference analysis tasks, generate evaluation reports in a shorter time, and improve work efficiency. The embodiment of the present invention also uses visualization technology to present the differences in a graphical manner, while providing detailed explanatory information; this helps users to more intuitively understand the specific content and impact of the differences, so as to make more informed decisions. The output evaluation results provide a variety of display methods to meet the needs and preferences of different users. This makes the system more user-friendly and can better serve users.

[0082] The embodiment of the present invention achieves accurate determination of the similarity of different data types by accurately determining the text similarity results and the structured similarity results, thereby ensuring the comprehensiveness of the consistency evaluation of the Dianhong operating system version. Both textual and structured data can be effectively processed and analyzed.

[0083] Embodiment 3

[0084] Figure 3 This is a schematic diagram of the structure of a version consistency evaluation device based on a Dianhong operating system provided in the third embodiment of the present invention. Figure 3 As shown, the device comprises:

[0085] The data splitting module 310 is used to obtain raw data of at least two device systems and determine text data and structured data from the raw data; wherein the device systems are different versions of the Dianhong operating system;

[0086] A text similarity determination module 320, used to determine a text similarity result based on text data of any two device systems;

[0087] A structural similarity determination module 330, used to determine a structural similarity result according to the structured data of any two device systems;

[0088] The consistency evaluation module 340 is used to determine the final version consistency evaluation result according to the text similarity result and the structural similarity result.

[0089] The technical solution of the embodiment of the present invention improves the accuracy of the similarity results by splitting the data of different system versions and using different similarity calculation algorithms to determine the corresponding similarity results for data in different forms, thereby improving the accuracy of the evaluation results of the consistency of different versions of the Dianhong operating system.

[0090] Optionally, a text similarity determination module is specifically used to:

[0091] Extracting features from the text data to obtain text features; wherein the text features include content change features and field extraction features of the text data of any two device systems;

[0092] The cosine similarity measurement method is used to determine the similarity between the text features of any two device systems as the text similarity result.

[0093] Optionally, a structured similarity determination module is specifically used to:

[0094] Determine a graph structure based on structured data of any two device systems; wherein nodes in the graph structure are functional components, interfaces and database tables determined based on the structured data, and edges represent the relationship between the data corresponding to the nodes;

[0095] The difference between the structured data of any two device systems is determined by calculating the distance, path and connectivity index between the nodes in the graph structure as the structural similarity result.

[0096] Optionally, the structural similarity results include at least change information on functional components, interface call relationships, and database table structures between any two device systems, and the change information includes at least change information on additions, deletions, and modifications of nodes in the graph structure, additions, deletions, and modifications of edges, and change information on connection relationships between nodes and edges.

[0097] Optionally, a text similarity determination module is specifically used to:

[0098] A similarity calculation algorithm based on deep learning determines the text similarity results based on the text data of any two device systems.

[0099] Optionally, the device further includes a data preprocessing module, which is used to determine the text data and structured data from the original data, and is specifically used to:

[0100] Perform data preprocessing operations on the original data; the data preprocessing operations include data verification, data cleaning, format conversion, and deduplication operations.

[0101] Optional, consistency assessment module, specifically for:

[0102] Determine a difference graph and a similarity distribution graph according to the text similarity results and the structural similarity results;

[0103] Determine the difference type, difference location and difference impact range based on the difference map and similarity distribution map;

[0104] The final version consistency assessment result is determined based on the difference type, difference location and difference impact scope.

[0105] The version consistency assessment device based on the Dianhong operating system provided in the embodiment of the present invention can execute the version consistency assessment method based on the Dianhong operating system provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0106] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations and do not violate public order and good morals.

[0107] Embodiment 4

[0108] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0109] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0110] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0111] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0112] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a version consistency evaluation method based on the Dianhong operating system.

[0113] In some embodiments, the method for determining the parameters for extracting liquid from an oil field during a high water cut period can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps in the method for determining the parameters for extracting liquid from an oil field during a high water cut period described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the method for determining the parameters for extracting liquid from an oil field during a high water cut period by any other appropriate means (e.g., by means of firmware).

[0114] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific reference products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0116] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0117] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0118] The systems and techniques described herein may be implemented in a computing system that includes a backend component (e.g., as a data server), or a computing system that includes a switch component (e.g., an application server), or a computing system that includes a frontend component (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, switch components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0119] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0120] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0121] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A version consistency evaluation method based on the Dianhong operating system, characterized in that: The method includes: Obtaining raw data of at least two device systems, and determining text data and structured data from the raw data; wherein the device systems are different versions of the Dianhong operating system; Determine text similarity results based on text data of any two device systems; Determining structural similarity results based on structured data of any two device systems; A final version consistency evaluation result is determined according to the text similarity result and the structural similarity result.

2. The method according to claim 1, characterized in that Determine text similarity results based on text data from any two device systems, including: Performing feature extraction on the text data to obtain text features; wherein the text features include content change features and field extraction features of the text data of any two device systems; The similarity between the text features of the arbitrary two device systems is determined using a cosine similarity measurement method as a text similarity result.

3. The method according to claim 1, characterized in that: Determine structural similarity results based on the structured data of any two device systems, including: Determine a graph structure according to the structured data of the arbitrary two device systems; wherein the nodes in the graph structure are functional components, interfaces and database tables determined according to the structured data, and the edges represent the relationship between the data corresponding to the nodes; The difference between the structured data of the arbitrary two device systems is determined by calculating the distance, path and connectivity index between the nodes in the graph structure as a structural similarity result.

4. The method according to claim 3, characterized in that The structural similarity results include at least change information on functional components, interface call relationships, and database table structures between any two device systems, and the change information includes at least additions, deletions, and modifications of nodes, additions, deletions, and modifications of edges in the graph structure, as well as change information on connection relationships between nodes and edges.

5. The method according to claim 1 or 2, characterized in that: Determine text similarity results based on text data from any two device systems, including: A similarity calculation algorithm based on deep learning determines the text similarity results based on the text data of any two device systems.

6. The method according to claim 1, characterized in that Before determining the text data and the structured data from the original data, the method further includes: Performing data preprocessing operations on the original data; wherein the data preprocessing operations include data verification, data cleaning, format conversion and deduplication operations.

7. The method according to claim 1, characterized in that Determining a final version consistency evaluation result according to the text similarity result and the structural similarity result, including: Determining a difference graph and a similarity distribution graph according to the text similarity result and the structural similarity result; Determine the difference type, difference location and difference impact range according to the difference map and the similarity distribution map; The final version consistency evaluation result is determined based on the difference type, difference location and difference impact scope.

8. A version consistency evaluation device based on the Dianhong operating system, characterized in that: The device includes: A data splitting module, used to obtain raw data of at least two device systems, and determine text data and structured data from the raw data; wherein the device systems are different versions of the Dianhong operating system; A text similarity determination module, used to determine text similarity results based on text data of any two device systems; A structural similarity determination module, used for determining a structural similarity result according to the structured data of any two device systems; The consistency evaluation module is used to determine the final version consistency evaluation result according to the text similarity result and the structural similarity result.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the version consistency assessment method based on the telecom operating system as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the version consistency assessment method based on the Dianhong operating system as described in any one of claims 1-7 when executed.

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