A method for implementing on-demand reconstruction of an electromagnetic management and control business software based on a function engine

Abstract

The application discloses a function engine-based electromagnetic management and control business software on-demand reconstruction implementation method, which comprises the following steps: acquiring business software construction demand information; performing software construction processing on the business software construction demand information to obtain first software information; and performing analysis and optimization processing on the first software information to obtain target software information.

Description

Technical Field

[0001] This invention relates to the field of software technology, and in particular to a data processing method and apparatus. Background Technology

[0002] In the field of electromagnetic spectrum control, with the rapid development and widespread application of information technology, the strategic value and fundamental role of the electromagnetic spectrum are becoming increasingly prominent. The development of information technology has also provided new management concepts and technical means for spectrum management. Establishing a sound frequency usage order, scientifically and rationally allocating spectrum resources, and ensuring efficient resource utilization are of great significance to urban development and national defense. Traditional electromagnetic spectrum control systems, which operate in fixed modes and states, face increasing challenges and struggle to cope with the rapidly changing and complex electromagnetic environment. Electromagnetic spectrum control software often adopts a component-based development and integration approach, using component integration platforms for integration and assembly. However, this method suffers from coarse component granularity, preventing arbitrary combinations between components, lacking dynamic functional expansion and process customization capabilities, and requiring new functions to often necessitate the addition of new components or modification of configuration files followed by re-integration, resulting in low conversion efficiency. Furthermore, the system's internal execution is opaque during operation, component states are unknown, and system operation is difficult to monitor and maintain, hindering the development, modification, and maintenance of increasingly complex electromagnetic spectrum control software. Therefore, this paper proposes a method for on-demand refactoring of electromagnetic control business software based on a functional engine. This method standardizes the software refactoring process based on electromagnetic control business, improves software construction efficiency, and solves the problem that the scenarios, functions, processes, and plugins in the operation of electromagnetic control software are relatively fixed, which makes it difficult to flexibly expand functions and dynamically customize processes. This method adapts to the on-demand refactoring of electromagnetic control business under different electromagnetic environments and available resource conditions, and achieves the best utilization of electromagnetic control resources and best practices for business. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide a method for on-demand refactoring of electromagnetic control business software based on a functional engine. This method is beneficial for standardizing the software refactoring process based on electromagnetic control business, improving software construction efficiency, and solving the problem that the scenarios, functions, processes, and plugins in the operation of electromagnetic control software are relatively fixed, which makes it difficult to flexibly expand functions and dynamically customize processes. This method adapts to on-demand refactoring of electromagnetic control business under different electromagnetic environments and available resource conditions, and achieves the best utilization of electromagnetic control resources and best practices for business.

[0004] To address the aforementioned technical problems, a first aspect of the present invention discloses a data processing method, the method comprising:

[0005] Obtain information on business software development requirements;

[0006] The business software construction requirement information is processed by software construction to obtain the first software information;

[0007] The first software information is analyzed and optimized to obtain the target software information.

[0008] A second aspect of the present invention discloses a data processing apparatus, the apparatus comprising:

[0009] The acquisition module is used to acquire information about the business software construction requirements.

[0010] The first processing module is used to perform software construction processing on the business software construction requirement information to obtain first software information;

[0011] The second processing module is used to analyze and optimize the first software information to obtain the target software information.

[0012] A third aspect of the present invention discloses another data processing apparatus, the apparatus comprising:

[0013] Memory containing executable program code;

[0014] A processor coupled to the memory;

[0015] The processor calls the executable program code stored in the memory to execute some or all of the steps in the data processing method disclosed in the first aspect of the present invention.

[0016] The fourth aspect of the present invention discloses a computer-readable storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the data processing method disclosed in the first aspect of the present invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0018] Figure 1 This is a schematic diagram of a data processing system provided in an embodiment of the present invention;

[0019] Figure 2 This is a flowchart illustrating a data processing method disclosed in an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of the structure of a data processing device disclosed in an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of another data processing device disclosed in an embodiment of the present invention;

[0022] Figure 5 This is a schematic diagram of the structure of a target analysis model disclosed in an embodiment of the present invention;

[0023] Figure 6 This is a schematic diagram illustrating the effect of a software implementation process service disclosed in an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0026] 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 the invention. 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.

[0027] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0028] It should be noted that since the method in this application embodiment is executed in a computer device, the processing objects of each computer device exist in the form of data or information, such as time, which is essentially time information. It is understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data that exist so that the computer device can process them. Specific details will not be elaborated here.

[0029] It should be noted that the artificial intelligence-related technologies that may be involved in this application will be briefly described. Artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can react in a way similar to human intelligence. Artificial intelligence is the study of the design principles and implementation methods of various intelligent machines, enabling machines to have the functions of perception, reasoning, and decision-making.

[0030] Artificial intelligence (AI) is a comprehensive discipline encompassing a wide range of fields, including both hardware and software technologies. Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies primarily include computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0031] Computer vision (CV) is the science that studies how to enable machines to "see." More specifically, it refers to machine vision, which uses cameras and computers to replace human eyes in recognizing and measuring targets, and then performs image processing to create images more suitable for human observation or transmission to instruments. As a scientific discipline, computer vision studies related theories and technologies, attempting to build artificial intelligence systems capable of extracting information from images or multidimensional data. Computer vision technologies typically include image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, 3D object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping (SLAM), and common biometric recognition technologies such as facial recognition and fingerprint recognition.

[0032] Monomodal information refers to data of only one type, such as text, images, audio, video, or electromagnetic signals. Multimodal information refers to data that includes at least two types of monomodal information. Furthermore, multimodal information is suitable for complex tasks that require the integration of multiple information sources, such as sentiment analysis, robot interaction, and autonomous driving. By integrating information from multiple modalities, higher performance and accuracy can usually be achieved in these tasks.

[0033] Large models refer to artificial neural network models with a very large number of parameters. In the field of artificial intelligence, large models typically refer to models with hundreds of millions to trillions of parameters. These models usually need to be trained on large-scale datasets and require a significant amount of computing resources for optimization and tuning. Large models are commonly used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is a type of AI that can create new content and ideas, including dialogues, stories, images, videos, and music. In this embodiment, the large model can be a language model of the scale of ChatGPT, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Tongwen Qianyi model, MiniMax model, Xinghuo model, Llama model, 360GPT model, Qwen model, Baichuan model, Yunque model, vivoLM model, and Wenxin Yiyan, etc., and this embodiment does not limit the scope of the large model.

[0034] The data processing method, apparatus, computer equipment, and computer-readable storage medium provided in this application embodiment can be embedded in electromagnetic control business software based on a function engine to implement the system on demand. The following are detailed descriptions of each.

[0035] Please see Figure 1 , Figure 1This is a schematic diagram of a data processing system provided in an embodiment of this application. The data processing system may include a computer device 100, which integrates a data processing unit, such as... Figure 1 Computer equipment in the country.

[0036] In this embodiment of the application, the computer device 100 is mainly used to obtain business software construction requirement information;

[0037] The software construction requirements information of the business software is processed to obtain the first software information;

[0038] The target software information is obtained by analyzing and optimizing the first software information.

[0039] It can standardize the software refactoring process based on electromagnetic control business, improve software construction efficiency, and thus solve the problem that the scenarios, functions, processes, and plug-ins in the operation of electromagnetic control software are relatively fixed, which makes it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control business under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for business.

[0040] In this embodiment, the computer device 100 can be a standalone server, a server network, or a server cluster. For example, the computer device 100 described in this embodiment includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0041] It is understood that the computer device 100 used in the embodiments of this application can be a device that includes both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a device may include: cellular or other communication devices having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the computer device 100 may be a desktop terminal or a mobile terminal, and may also be one of a mobile phone, tablet computer, laptop computer, etc.

[0042] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include those that are more specific to this application. Figure 1 The number of computer devices shown is more or less, for example Figure 1Only one computer device is shown in the diagram. It is understood that the data processing system may also include one or more other services, which are not limited here.

[0043] In addition, such as Figure 1 As shown, the data processing system may also include a memory 200 for storing data, such as image data, location information, etc.

[0044] It should be noted that, Figure 1 The schematic diagram of the data processing system shown is merely an example. The data processing system and scenario described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of data processing systems and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0045] The data processing method disclosed in this invention, along with the corresponding functional engine-based on-demand refactoring method for electromagnetic control business software, facilitates the standardization of software refactoring processes for electromagnetic control businesses, improves software development efficiency, and addresses the problem of relatively fixed scenarios, functions, processes, and plugins in electromagnetic control software operation, hindering flexible functional expansion and dynamic process customization. This allows for on-demand refactoring of electromagnetic control businesses under different electromagnetic environments and available resource conditions, achieving optimal utilization of electromagnetic control resources and best business practices. Detailed explanations follow.

[0046] Example 1

[0047] Please see Figure 2 , Figure 2 This is a flowchart illustrating a data processing method disclosed in an embodiment of the present invention. Figure 2 The described data processing method is applied in a management system, such as a local server or cloud server for management, and this embodiment of the invention is not limited thereto. Figure 2 As shown, the data processing method may include the following operations:

[0048] 101. Obtain information on business software construction requirements.

[0049] 102. Process the business software construction requirements information to obtain the first software information.

[0050] 103. Analyze and optimize the first software information to obtain the target software information.

[0051] It should be noted that before obtaining the business software construction requirements, it is necessary to plan one or more business scenarios based on the electromagnetic control business application requirements. This involves designing a business system meta-model, standardizing business components, relationships, and collaboration modes, designing a visual scenario management tool based on the business system meta-model, and using the visual scenario management tool to plan the business scenarios. This embodiment of the invention does not impose limitations on these aspects. Furthermore, the above-mentioned meta-model of the business system, proposed to address the specific requirements and characteristics of electromagnetic control business software, standardizes business components, relationships, and collaboration modes. This model specifies that the electromagnetic control business software operation scenario includes one or more functions. A single function manages multiple processes and the relationships between processes. A single process schedules multiple plugins to collaborate and complete local business functions. When plugins execute collaboratively, they generate various business controls (instructions) and various business data as needed. This embodiment of the invention does not impose limitations on these aspects.

[0052] It should be noted that before obtaining the business software construction requirements information, it is also necessary to plan one or more functions and processes included in a single business scenario, that is, to design a business system metamodel, standardize the business components, relationships and collaboration modes, design a visual process orchestration tool based on the business system metamodel, and use the visual process orchestration tool to orchestrate the functional processes included in the scenario. This embodiment of the invention does not limit this.

[0053] It should be noted that the above-mentioned electromagnetic control business scenario file obtained by using the function engine to plan the business scenario using the visual scenario management tool and the electromagnetic control function process file obtained by using the visual process orchestration tool to orchestrate the functional processes contained in the scenario are used to parse and construct the business functions. This embodiment of the invention is not limited.

[0054] It should be noted that the standardized software dynamic reconfiguration system built based on the data processing method of this application can define the business system metamodel and execution description language, and provide a complete solution in the fields of business system architecture specifications, functional composition and behavior standardization description, and visual orchestration. The business system generates description files through visual orchestration tools, and the functional engine executes the business system according to the description files to form a standardized and customizable business software system. The embodiments of this invention are not limited.

[0055] It should be noted that after analyzing and optimizing the first software information to obtain the target software information, the functional engine can be used for various processing and querying of process scheduling information, processing and querying of all service information participating in the process, and processing and querying of interactive data information between all plugins or services participating in the process. This embodiment of the invention does not limit these processes. Furthermore, the various processing and querying of process scheduling information by the functional engine provides support for external applications (such as debugging, performance, maintenance, etc.) to query various information related to function scheduling. Furthermore, the processing and querying of all service information participating in the process provides support for the functional engine to calculate and manage the service set formed after process execution, collect and store the service system status in the set, collect and store business status related to the function, collect and store instruction sending and receiving information during function execution, and provide support for external applications (such as debugging, performance, maintenance, etc.) to query various information related to function services. Furthermore, the processing and querying of interactive data information between all plugins or services participating in the process involves using a functional engine to calculate the data interaction blueprint between plugins or services formed after the process is executed, using the functional engine to collect and store actual data relationships and related data information based on the data blueprint, using the functional engine to calculate the data processing performance, and using the functional engine to support various data information related to the query function of external applications (such as debugging, performance, maintenance, etc.). This embodiment of the invention is not limited.

[0056] Furthermore, this application utilizes a functional engine to calculate the data interaction blueprint between plugins or services formed after the process is executed. Based on the process scheduling information executed by the engine, the instruction control information for each service, and the service control collaboration relationship configuration file, the collaborative topology relationship between the scheduled services is calculated to obtain the process service collaborative interaction blueprint. The functional engine collects and stores actual data relationships and related data information based on the data blueprint. Based on the blueprint, the system status of services (online, offline, faulty, etc.) is collected and stored in a targeted manner, recording the system status of the process and providing a data foundation for subsequent system-level anomaly and fault tracing analysis of the process; the business execution status of each service in the process is collected and stored in a targeted manner, recording the business status of the process and plugins, providing a data foundation for subsequent business-level anomaly and fault tracing analysis of the process; all instruction interaction information between services in the process is collected and stored in a targeted manner, recording the control flow within the process, providing a data foundation for subsequent control-level tracing and optimization analysis of the process; all data interactions between services in the process are tracked, collected, recorded, performance calculated, and stored to obtain detailed information on all data interactions between services in the process. A schematic diagram of status information collection and storage is shown below. Figure 6 As shown, this provides a data foundation for subsequent data processing performance analysis of the process.

[0057] It should be noted that the aforementioned functional engine is built using Python, with the configuration file being activiti.cfg.xml. ProcessEngineConfiguration is created programmatically, allowing for the configuration of different bean IDs. Methods such as ProcessEngineConfiguration.createProcessEngineConfigurationFromResourceDefault() and ProcessEngineConfiguration.createProcessEngineConfigurationFromResource(String resource) can be used to create configurations. ProcessEngine is created through ProcessEngineConfiguration, for example, using ProcessEngineConfiguration.createStandaloneInMemProcessEngineConfiguration().buildProcessEngine(). Service interfaces, such as RuntimeService, RepositoryService, and TaskService, are created through ProcessEngine. This embodiment of the invention does not impose limitations on these methods.

[0058] It is evident that implementing the data processing method described in the embodiments of the present invention is beneficial for standardizing the software refactoring process based on electromagnetic control operations, improving software construction efficiency, and thus solving the problem that the scenarios, functions, processes, and plugins in the operation of electromagnetic control software are relatively fixed, making it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0059] In an optional embodiment, the first software information is analyzed and optimized to obtain the target software information, including:

[0060] The first software information is parsed and analyzed to obtain the target analysis result information; the target analysis result information includes verification passed, and / or verification failed.

[0061] Based on the results of the target analysis, the target software information was determined.

[0062] It should be noted that the above-mentioned verification passing indicates that the current software can realize the software functional flow corresponding to the obtained business software construction requirement information; otherwise, it corresponds to verification failing. This embodiment of the invention does not impose any limitations.

[0063] It is evident that implementing the data processing method described in the embodiments of the present invention is beneficial for standardizing the software refactoring process based on electromagnetic control operations, improving software construction efficiency, and thus solving the problem that the scenarios, functions, processes, and plugins in the operation of electromagnetic control software are relatively fixed, making it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0064] In another optional embodiment, the first software information is parsed and analyzed to obtain target analysis result information, including:

[0065] The first software information is extracted to obtain software feature information; the software feature information includes basic feature information and instruction feature information.

[0066] Based on the software feature information, the target analysis results information is determined.

[0067] It should be noted that the above-mentioned determination of target analysis results based on software feature information involves using the functional engine to perform syntax verification and analysis on the input scene file to ensure the legality and validity of the scene file (basic feature information) and to complete the preparation of basic information for creating the scene and function. It also involves performing syntax verification and analysis on the input process scheduling file (instruction feature information) to ensure the legality and validity of the process scheduling file and to complete the preparation of basic information for process scheduling execution. This embodiment of the invention does not limit this.

[0068] It is evident that implementing the data processing method described in the embodiments of the present invention is beneficial for standardizing the software refactoring process based on electromagnetic control operations, improving software construction efficiency, and thus solving the problem that the scenarios, functions, processes, and plugins in the operation of electromagnetic control software are relatively fixed, making it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0069] In another optional embodiment, the target analysis result information is determined based on software feature information, including:

[0070] Perform syntax validation on the basic feature information to obtain the first validation result value;

[0071] When the first verification result value is greater than or equal to the first verification threshold, the integrity of the instruction feature information is verified to obtain the second verification result value.

[0072] When the second verification result value is not less than the second verification threshold, the instruction feature information is subjected to syntax verification to obtain the third verification result value.

[0073] When the third verification result is greater than or equal to the first verification threshold, the verification is determined to be qualified and the target analysis result information is obtained.

[0074] When the third verification result value is less than the first verification threshold, the verification is determined to be unqualified as the target analysis result information;

[0075] When the second verification result value is less than the second verification threshold, the verification is determined to be unqualified as the target analysis result information;

[0076] When the first verification result value is less than the first verification threshold, the verification is determined to be unqualified as the target analysis result information.

[0077] It should be noted that the first and second verification thresholds mentioned above are set by the user, or can be default values ​​given by the system, or can be obtained by statistical analysis of historical verification thresholds using large datasets; this embodiment of the invention does not impose any limitations on these values. Furthermore, the first verification threshold is a positive number not greater than 1 and not less than 0.6. Furthermore, the second verification threshold is a positive integer not less than 3; this embodiment of the invention does not impose any limitations on this value.

[0078] Furthermore, this application uses two different verification thresholds to analyze and judge whether two different verifications are qualified or not. This is to take into account the differences in the output data of the two different types of verification methods, so as to analyze and judge the quality of software data in a targeted and efficient manner, thereby improving the efficiency of software construction. This embodiment of the invention is not limited.

[0079] It should be noted that the above-mentioned syntax verification can be achieved by calling a pre-trained RNN model through the functional engine, or by using a fine-tuned large model; this embodiment of the invention does not impose any limitations. The above-mentioned completeness verification can be achieved by statistical analysis of the data packets in the instruction feature information. This can be achieved by the functional engine calling a data quality management tool, or by calling the deepchecks tool in the Python library; this embodiment of the invention does not impose any limitations. Furthermore, the above-mentioned syntax verification characterizes the verification and parsing of software feature information, mainly checking whether the composition and relationship of the scenario (i.e., business working mode) and the description syntax of the functional (process) composition and relationship within each scenario meet the requirements and whether the semantics are complete; whether the relevant triggering instructions, initial instructions, and conversion rules are defined and meet the description syntax; and the legality and validity of the process scheduling file, such as parsing and performing syntax and semantic verification on the process triggering instructions, process execution initial instructions, instruction conversion rules, instruction decomposition rules, normal step instruction calculation rules, reset instruction calculation rules, process intermediate result caching rules, and process final result return rules required for process scheduling. This embodiment of the invention does not impose any limitations.

[0080] It is evident that implementing the data processing method described in the embodiments of the present invention is beneficial for standardizing the software refactoring process based on electromagnetic control operations, improving software construction efficiency, and thus solving the problem that the scenarios, functions, processes, and plugins in the operation of electromagnetic control software are relatively fixed, making it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0081] In yet another alternative embodiment, such as Figure 5 As shown, information extraction of the first software information is implemented based on a target parsing model. This target parsing model includes a first convolution module, a second convolution module, a third convolution module, a fourth convolution module, a first normalization module, a second normalization module, a third normalization module, a fourth normalization module, a first activation module, a second activation module, a third activation module, a first sampling module, a second sampling module, a third sampling module, a first fusion module, a second fusion module, a third fusion module, and a first pooling module.

[0082] The second pooling module and the first network module; wherein...

[0083] The input of the first convolutional module is configured to receive the model input of the target analytical model. The output of the first convolutional module is connected to the input of the first normalization module. The output of the first normalization module is connected to the inputs of the second activation module and the first fusion module. The output of the second activation module is connected to the input of the fourth convolutional module. The output of the fourth convolutional module is connected to the inputs of the first fusion module and the third normalization module. The output of the third normalization module is connected to the input of the third activation module. The output of the third activation module is connected to the input of the third sampling module. The output of the third sampling module is connected to the input of the second fusion module. The output of the first fusion module is connected to the input of the second convolutional module. The output of the second convolutional module is connected to the input of the first sampling module. The output of the first sampling module is connected to the first activation module. The input terminals of the modules are as follows: the output terminal of the first activation module is connected to the input terminal of the second sampling module and the input terminal of the first network module; the output terminal of the first network module is connected to the input terminal of the first pooling module and the input terminal of the third fusion module; the output terminal of the first pooling module is connected to the input terminal of the third convolution module; the output terminal of the third convolution module is connected to the input terminal of the second normalization module; the output terminal of the second normalization module is connected to the input terminal of the third fusion module; the output terminal of the third fusion module is configured to output the first model output of the target analytical model; the input terminal of the second sampling module is connected to the input terminal of the second fusion module; the output terminal of the second fusion module is connected to the input terminal of the second pooling module; the output terminal of the second pooling module is connected to the input terminal of the fourth normalization module; the output terminal of the fourth normalization module is configured to output the second model output of the target analytical model.

[0084] It should be noted that the above-mentioned target parsing model first performs preliminary feature extraction on the first software information through a convolution module and a normalization module, and then divides it into two branches. Considering the certain cross-correlation relationship between instruction features and basic features, after convolution processing on the basic feature branch, some feature information is fused into the instruction feature branch again. Then, convolution and upsampling processing are performed on the fused features, and the upsampled features are fused into the basic features again, thereby achieving multi-dimensional and different-depth feature fusion of basic features, which is more conducive to the accurate extraction of basic features. At the same time, considering the information diversity of instruction features, the completeness of the number of features and the rationality of the syntax must be considered during subsequent verification. Therefore, after feature extraction using a feedforward neural network, it is divided into two branches to form quantitative and syntactic features, which are then concatenated in the third fusion module. These two feature information are concatenated rather than multiplied or added element-wise to avoid feature information confusion and difficulty in recognition, thereby improving the extraction efficiency and accuracy of instruction feature information. This embodiment of the invention is not limited.

[0085] It should be noted that the first model outputs representation instruction feature information, the second model outputs representation basic feature information, and the above models input representation first software information. This embodiment of the invention does not limit the scope of the invention.

[0086] It should be noted that the kernel sizes of the first, second, third, and fourth convolutional modules are 1×1, 3×3, 1×1, and 1×1, respectively, and the number of channels is 1, so as to achieve multi-dimensional deep extraction of different features in the first software information. This embodiment of the invention does not limit this.

[0087] It should be noted that the first activation module, the second activation module, the third activation module, and the fourth activation module described above are constructed based on the ReLU activation function, and this embodiment of the invention does not limit them.

[0088] It should be noted that the first network module described above can be constructed based on a feedforward neural network, and this embodiment of the invention does not impose any limitations.

[0089] It should be noted that the first sampling module, the second sampling module, and the third sampling module described above are constructed based on upsampling operations, and this embodiment of the invention does not limit them.

[0090] It should be noted that the first fusion module and the second fusion module mentioned above are both constructed based on element-wise addition operations, and the embodiments of the present invention are not limited thereto.

[0091] It should be noted that the third fusion module described above is constructed based on splicing operations, and this embodiment of the invention does not impose any limitations on it.

[0092] It should be noted that the first normalization module, the second normalization module, the third normalization module, and the fourth normalization module mentioned above are constructed based on the batch normalization layer, and this embodiment of the invention does not limit them.

[0093] It is evident that implementing the data processing method described in the embodiments of the present invention is beneficial for standardizing the software refactoring process based on electromagnetic control operations, improving software construction efficiency, and thus solving the problem that the scenarios, functions, processes, and plugins in the operation of electromagnetic control software are relatively fixed, making it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0094] In an optional embodiment, target software information is determined based on the target analysis results, including:

[0095] When the target analysis result is verified as qualified, the first software information is determined as the target software information;

[0096] When the target analysis result is found to be unqualified, the execution of software construction processing on the business software construction requirement information is triggered to obtain the first software information.

[0097] It should be noted that the above method determines whether to convert the generated software information by checking whether the verification result is qualified, thereby ensuring the quality of software generation while meeting the requirements of business software construction. This embodiment of the invention does not impose any limitations on this method.

[0098] In this optional embodiment, as an optional implementation, determining the first software information as the target software information includes:

[0099] The execution time of the first software information is calculated to obtain the process execution time information; the process execution time information includes the overall process execution time and the step execution time information; the step execution time information includes the execution time of MM process steps;

[0100] Determine whether the execution time of all process steps in the step execution time information is less than or equal to the first time threshold, and obtain the first time judgment result;

[0101] When the first time judgment result is yes, determine whether the overall execution time of the process is less than or equal to the second time threshold, and obtain the second time judgment result;

[0102] If the second time-based judgment result is yes, the first software information is identified as the target software information.

[0103] When the second time judgment result is negative, the execution of software construction processing on the business software construction requirement information is triggered to obtain the first software information;

[0104] When the initial judgment result is negative, the software construction process is triggered to process the business software construction requirement information and obtain the first software information.

[0105] It should be noted that the above-mentioned execution time calculation processing of the first software information can be performed through a function engine call. <chrono>The program's execution time can be calculated using a library, the time() function provided in the time.h header file, or the Stopwatch class in the Guava library. This embodiment of the invention does not limit the implementation.

[0106] It should be noted that the above-mentioned calculation and analysis of the overall execution time and the execution time of each step of the process to evaluate the software process scheduling performance, thereby determining whether to continue to optimize it, further improve the quality of software generation, and ensure the efficiency of producing software in one go, is not limited in the embodiments of the present invention.

[0107] It should be noted that the aforementioned first and second time thresholds can be set by the user, or be default values ​​given by the system, or obtained through big data statistics based on historical time thresholds; this embodiment of the invention does not impose any limitations. Furthermore, the aforementioned first time threshold is a time value not greater than 1 minute. Furthermore, the second time threshold is not greater than MM times the first time threshold, i.e., the number of MM is not less than 6; this embodiment of the invention does not impose any limitations. Furthermore, this application both evaluates the execution time of a single-step functional process to ensure that each functional time step is completed efficiently, and controls the overall process time to ensure that the total time does not exceed the upper limit of the sum of the time thresholds of each individual functional step, thereby controlling the efficiency of the overall execution time and ensuring the overall performance efficiency of the software; this embodiment of the invention does not impose any limitations. Furthermore, the aforementioned functional processes include querying, debugging, maintenance, instruction decomposition, reset, caching, instruction conversion, etc.; this embodiment of the invention does not impose any limitations.

[0108] It is evident that implementing the data processing method described in the embodiments of the present invention is beneficial for standardizing the software refactoring process based on electromagnetic control operations, improving software construction efficiency, and thus solving the problem that the scenarios, functions, processes, and plugins in the operation of electromagnetic control software are relatively fixed, making it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0109] In another optional embodiment, the business software construction requirement information is processed to obtain first software information, including:

[0110] The software information is generated from the business software construction requirements to obtain basic software information;

[0111] Detect whether a user modification instruction was received within the time interval and obtain the detection result;

[0112] When the detection result is yes, the basic software information is updated using the user modification instruction information, and the detection is triggered to check whether the user modification instruction information was received within the time interval, and the detection result is obtained.

[0113] If the test result is negative, the basic software information will be identified as the first software information.

[0114] It should be noted that the above-mentioned generation of software information for business software construction requirements can be based on the functional engine's analysis of the parsed business software construction requirements, and this embodiment of the invention is not limited to this. Furthermore, the above-mentioned information generation can be the generation of information on business components, relationships, and system patterns, or it can be the generation of information on functional processes, and this embodiment of the invention is not limited to this. Furthermore, the above-mentioned generation of information on business components, relationships, and system patterns is achieved by the functional engine calling an interface description language to express the composition and behavior of domain business in a multi-layered and multi-dimensional manner, including element composition (scenes, processes, plugins, data, instructions), construction methods, construction parameters, relationships (relationships between scenes, relationships between processes, relationships between internal steps of a process, relationships between processes and plugins), and collaboration (control collaboration and instruction collaboration between plugins), etc., to reuse the functional engine across different business systems, thereby quickly realizing the functional reconstruction of business software, and this embodiment of the invention is not limited to this. Furthermore, the aforementioned generation of functional process information involves the functional engine calling a visual process orchestration tool to visually orchestrate the composition of business scenarios and processes, forming an executable business description file. The visual process orchestration tool creates business work scenarios and orchestrates processes according to business requirements. The orchestration work includes the orchestration of process components, process steps and relationships, process step parameters, and return value construction rules. Ultimately, a complete application is built and an execution script is output. The scenario file is a collection of functional structures and parameters. When a scenario's functional structure and parameters change and it is deemed suitable for a specific purpose, the functional engine can save its functional structure and parameters as a functional template. By configuring these templates, a functional template library for each type of workstation is formed. In future tasks, workstations can use these typical scenario templates to quickly launch suitable business scenarios. This embodiment of the invention does not impose limitations. Furthermore, compared to traditional coding methods for building applications, this application's method of generating software information for business software construction requirements fully considers the necessity of using tools. The tools are responsible for visual and graphical display, as well as automatically generating standardized script files, reducing the probability of errors in manual script file configuration and improving work efficiency.

[0115] It should be noted that the above-mentioned process of updating basic software information by using user-modified instruction information is a manual modification of the script file to further optimize the automatically generated software configuration. This ensures the efficiency of software configuration while reducing the occurrence of script format errors and unparsable issues, thus guaranteeing the efficiency and reliability of software generation. This embodiment of the invention does not limit the scope of the invention.

[0116] It should be noted that the aforementioned time interval represents the time between the execution of the previous action instruction and the time interval threshold. Furthermore, the previous action instruction can be a software information generation instruction or a software information update instruction; this embodiment of the invention does not impose any limitations. Furthermore, the aforementioned time interval threshold can be one time interval unit or two time interval units. Furthermore, the aforementioned time interval unit can be 3 minutes or 5 minutes, to ensure that the user has sufficient time to modify the software while ensuring efficient software information generation, thus balancing convenient operation with processing efficiency; this embodiment of the invention does not impose any limitations.

[0117] It is evident that implementing the data processing method described in the embodiments of the present invention is beneficial for standardizing the software refactoring process based on electromagnetic control operations, improving software construction efficiency, and thus solving the problem that the scenarios, functions, processes, and plugins in the operation of electromagnetic control software are relatively fixed, making it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0118] Example 2

[0119] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a data processing device disclosed in an embodiment of the present invention. Figure 3 The described apparatus can be applied in management systems, such as local servers or cloud servers for management, and the embodiments of the present invention are not limited thereto. Figure 3 As shown, the device may include:

[0120] Module 201 is used to obtain business software construction requirement information;

[0121] The first processing module 202 is used to process the business software construction requirement information to obtain the first software information.

[0122] The second processing module 203 is used to analyze and optimize the first software information to obtain the target software information.

[0123] It is evident that implementation Figure 3 The described data processing device facilitates the standardization of software refactoring processes based on electromagnetic control operations, improves software construction efficiency, and solves the problem that electromagnetic control software is relatively fixed in terms of scenarios, functions, processes, and plugins, which makes it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0124] In another alternative embodiment, such as Figure 3 As shown, the first software information is analyzed and optimized to obtain the target software information, including:

[0125] The first software information is parsed and analyzed to obtain the target analysis result information; the target analysis result information includes verification passed, and / or verification failed.

[0126] Based on the results of the target analysis, the target software information was determined.

[0127] It is evident that implementation Figure 3 The described data processing device facilitates the standardization of software refactoring processes based on electromagnetic control operations, improves software construction efficiency, and solves the problem that electromagnetic control software is relatively fixed in terms of scenarios, functions, processes, and plugins, which makes it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0128] In yet another alternative embodiment, such as Figure 3 As shown, the first software information is parsed and analyzed to obtain the target analysis result information, including:

[0129] The first software information is extracted to obtain software feature information; the software feature information includes basic feature information and instruction feature information.

[0130] Based on the software feature information, the target analysis results information is determined.

[0131] It is evident that implementation Figure 3 The described data processing device facilitates the standardization of software refactoring processes based on electromagnetic control operations, improves software construction efficiency, and solves the problem that electromagnetic control software is relatively fixed in terms of scenarios, functions, processes, and plugins, which makes it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0132] In yet another alternative embodiment, such as Figure 3 As shown, based on software feature information, the target analysis results information is determined, including:

[0133] Perform syntax validation on the basic feature information to obtain the first validation result value;

[0134] When the first verification result value is greater than or equal to the first verification threshold, the integrity of the instruction feature information is verified to obtain the second verification result value.

[0135] When the second verification result value is not less than the second verification threshold, the instruction feature information is subjected to syntax verification to obtain the third verification result value.

[0136] When the third verification result value is greater than or equal to the first verification threshold, the verification is determined to be qualified and the target analysis result information is obtained.

[0137] When the third verification result value is less than the first verification threshold, the verification is determined to be unqualified as the target analysis result information;

[0138] When the second verification result value is less than the second verification threshold, the verification is determined to be unqualified as the target analysis result information;

[0139] When the first verification result value is less than the first verification threshold, the verification is determined to be unqualified as the target analysis result information.

[0140] It is evident that implementation Figure 3 The described data processing device facilitates the standardization of software refactoring processes based on electromagnetic control operations, improves software construction efficiency, and solves the problem that electromagnetic control software is relatively fixed in terms of scenarios, functions, processes, and plugins, which makes it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0141] In yet another alternative embodiment, such as Figure 3 As shown, information extraction of the first software information is implemented based on a target parsing model. This target parsing model includes a first convolutional module, a second convolutional module, a third convolutional module, a fourth convolutional module, a first normalization module, a second normalization module, a third normalization module, a fourth normalization module, a first activation module, a second activation module, a third activation module, a first sampling module, a second sampling module, a third sampling module, a first fusion module, a second fusion module, a third fusion module, a first pooling module, a second pooling module, and a first network module; wherein,

[0142] The input of the first convolutional module is configured to receive the model input of the target analytical model. The output of the first convolutional module is connected to the input of the first normalization module. The output of the first normalization module is connected to the inputs of the second activation module and the first fusion module. The output of the second activation module is connected to the input of the fourth convolutional module. The output of the fourth convolutional module is connected to the inputs of the first fusion module and the third normalization module. The output of the third normalization module is connected to the input of the third activation module. The output of the third activation module is connected to the input of the third sampling module. The output of the third sampling module is connected to the input of the second fusion module. The output of the first fusion module is connected to the input of the second convolutional module. The output of the second convolutional module is connected to the input of the first sampling module. The output of the first sampling module is connected to the first activation module. The input terminals of the modules are as follows: the output terminal of the first activation module is connected to the input terminal of the second sampling module and the input terminal of the first network module; the output terminal of the first network module is connected to the input terminal of the first pooling module and the input terminal of the third fusion module; the output terminal of the first pooling module is connected to the input terminal of the third convolution module; the output terminal of the third convolution module is connected to the input terminal of the second normalization module; the output terminal of the second normalization module is connected to the input terminal of the third fusion module; the output terminal of the third fusion module is configured to output the first model output of the target analytical model; the input terminal of the second sampling module is connected to the input terminal of the second fusion module; the output terminal of the second fusion module is connected to the input terminal of the second pooling module; the output terminal of the second pooling module is connected to the input terminal of the fourth normalization module; the output terminal of the fourth normalization module is configured to output the second model output of the target analytical model.

[0143] It is evident that implementation Figure 3 The described data processing device facilitates the standardization of software refactoring processes based on electromagnetic control operations, improves software construction efficiency, and solves the problem that electromagnetic control software is relatively fixed in terms of scenarios, functions, processes, and plugins, which makes it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0144] In yet another alternative embodiment, such as Figure 3 As shown, based on the target analysis results, the target software information is determined, including:

[0145] When the target analysis result is verified as qualified, the first software information is determined as the target software information;

[0146] When the target analysis result is found to be unqualified, the execution of software construction processing on the business software construction requirement information is triggered to obtain the first software information.

[0147] It is evident that implementation Figure 3 The described data processing device facilitates the standardization of software refactoring processes based on electromagnetic control operations, improves software construction efficiency, and solves the problem that electromagnetic control software is relatively fixed in terms of scenarios, functions, processes, and plugins, which makes it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0148] In yet another alternative embodiment, such as Figure 3 As shown, the business software construction requirement information is processed to obtain the first software information, including:

[0149] The software information is generated from the business software construction requirements to obtain basic software information;

[0150] Detect whether a user modification instruction was received within the time interval and obtain the detection result;

[0151] When the detection result is yes, the basic software information is updated using the user modification instruction information, and the detection is triggered to check whether the user modification instruction information was received within the time interval, and the detection result is obtained.

[0152] If the test result is negative, the basic software information will be identified as the first software information.

[0153] It is evident that implementation Figure 3 The described data processing device facilitates the standardization of software refactoring processes based on electromagnetic control operations, improves software construction efficiency, and solves the problem that electromagnetic control software is relatively fixed in terms of scenarios, functions, processes, and plugins, which makes it difficult to flexibly expand functions and dynamically customize processes. It can adapt to the on-demand refactoring of electromagnetic control operations under different electromagnetic environments and available resource conditions, and achieve the best utilization of electromagnetic control resources and best practices for operations.

[0154] Example 3

[0155] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of another data processing device disclosed in an embodiment of the present invention. Wherein, Figure 4 The described apparatus can be applied in management systems, such as local servers or cloud servers for management, and the embodiments of the present invention are not limited thereto. Figure 4 As shown, the device may include:

[0156] Memory 301 storing executable program code;

[0157] Processor 302 coupled to memory 301;

[0158] The processor 302 calls the executable program code stored in the memory 301 to execute the steps in the data processing method described in Embodiment 1.

[0159] Example 4

[0160] This invention discloses a computer-readable storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to perform the steps of the data processing method described in Embodiment 1.

[0161] Example 5

[0162] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the data processing method described in Embodiment 1.

[0163] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0164] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0165] Finally, it should be noted that the method for on-demand refactoring of electromagnetic control business software based on a functional engine disclosed in the embodiments of the present invention is only a preferred embodiment of the present invention and is only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.< / chrono>

Claims

1. A method for on-demand refactoring of electromagnetic control business software based on a functional engine, characterized in that, The method includes: Obtain information on business software development requirements; The business software construction requirement information is processed by software construction to obtain the first software information; The first software information is analyzed and optimized to obtain the target software information; The step of analyzing and optimizing the first software information to obtain the target software information includes: The first software information is parsed and analyzed to obtain target analysis result information; the target analysis result information includes verification qualified, and / or verification unqualified. Based on the target analysis results, the target software information is determined; The step of determining the target software information based on the target analysis results includes: When the target analysis result information is qualified, the first software information is determined as the target software information; When the target analysis result information is that the verification is unqualified, the software construction process of the business software construction requirement information is triggered to obtain the first software information; The first software information is identified as the target software information, including: The execution time of the first software information is calculated to obtain the process execution time information; the process execution time information includes the overall execution time of the process and the execution time information of each step; the step execution time information includes the execution time of MM process steps. Determine whether the execution time of all process steps in the step execution time information is less than or equal to the first time threshold, and obtain the first time judgment result; When the first time judgment result is yes, determine whether the overall execution time of the process is less than or equal to the second time threshold, and obtain the second time judgment result; When the second time judgment result is yes, the first software information is identified as the target software information; When the second judgment result is negative, the execution of software construction processing on the business software construction requirement information is triggered to obtain the first software information; When the initial judgment result is negative, the software construction process is triggered to process the business software construction requirement information and obtain the first software information.

2. The method for on-demand refactoring of electromagnetic control business software based on a functional engine as described in claim 1, characterized in that, The step of parsing and analyzing the first software information to obtain target analysis result information includes: The first software information is extracted to obtain software feature information; the software feature information includes basic feature information and instruction feature information. Based on the software feature information, the target analysis result information is determined.

3. The method for on-demand refactoring of electromagnetic control business software based on a functional engine according to claim 2, characterized in that, The determination of target analysis result information based on the software feature information includes: The basic feature information is subjected to syntax verification to obtain a first verification result value; When the first verification result value is greater than or equal to the first verification threshold, the completeness of the instruction feature information is verified to obtain the second verification result value. When the second verification result value is not less than the second verification threshold, the instruction feature information is subjected to syntax verification to obtain the third verification result value. When the third verification result value is greater than or equal to the first verification threshold, the verification is determined to be qualified as the target analysis result information. When the third verification result value is less than the first verification threshold, the verification is determined to be unqualified as the target analysis result information; When the second verification result value is less than the second verification threshold, the verification is determined to be unqualified as the target analysis result information; When the first verification result value is less than the first verification threshold, the verification is determined to be unqualified as the target analysis result information.

4. The method for on-demand refactoring of electromagnetic control business software based on a functional engine as described in claim 2, characterized in that, Information extraction from the first software information is achieved based on a target parsing model, which includes a first convolutional module, a second convolutional module, a third convolutional module, a fourth convolutional module, a first normalization module, a second normalization module, a third normalization module, a fourth normalization module, a first activation module, a second activation module, a third activation module, a first sampling module, a second sampling module, a third sampling module, a first fusion module, a second fusion module, a third fusion module, a first pooling module, a second pooling module, and a first network module; wherein, The input of the first convolutional module is configured to receive the model input of the target parsing model. The output of the first convolutional module is connected to the input of the first normalization module. The output of the first normalization module is connected to the input of the second activation module and the input of the first fusion module. The output of the second activation module is connected to the input of the fourth convolutional module. The output of the fourth convolutional module is connected to the input of the first fusion module and the input of the third normalization module. The output of the third normalization module is connected to the input of the third activation module. The output of the third activation module is connected to the input of the third sampling module. The output of the third sampling module is connected to the input of the second fusion module. The output of the first fusion module is connected to the input of the second convolutional module. The output of the second convolutional module is connected to the input of the first sampling module. The output of the first sampling module is connected to the first... The first activation module has an input terminal; its output terminal is connected to the input terminal of the second sampling module and the input terminal of the first network module; the output terminal of the first network module is connected to the input terminal of the first pooling module and the input terminal of the third fusion module; the output terminal of the first pooling module is connected to the input terminal of the third convolution module; the output terminal of the third convolution module is connected to the input terminal of the second normalization module; the output terminal of the second normalization module is connected to the input terminal of the third fusion module; the output terminal of the third fusion module is configured to output the first model output of the target analytical model; the input terminal of the second sampling module is connected to the input terminal of the second fusion module; the output terminal of the second fusion module is connected to the input terminal of the second pooling module; the output terminal of the second pooling module is connected to the input terminal of the fourth normalization module; the output terminal of the fourth normalization module is configured to output the second model output of the target analytical model.

5. The method for on-demand refactoring of electromagnetic control business software based on a functional engine according to claim 1, characterized in that, The process of processing the business software construction requirements information to obtain first software information includes: The software information is generated from the business software construction requirement information to obtain basic software information; Detect whether a user modification instruction was received within the time interval and obtain the detection result; When the detection result is yes, the basic software information is updated using the user modification instruction information, and the detection is triggered to determine whether the user modification instruction information was received within the time interval, and the detection result is obtained. When the detection result is negative, the basic software information is determined as the first software information.

6. A device for on-demand reconfiguration of electromagnetic control business software based on a functional engine, characterized in that, The device includes: The acquisition module is used to acquire information about the business software construction requirements. The first processing module is used to perform software construction processing on the business software construction requirement information to obtain first software information; The second processing module is used to analyze and optimize the first software information to obtain the target software information; The step of analyzing and optimizing the first software information to obtain the target software information includes: The first software information is parsed and analyzed to obtain target analysis result information; the target analysis result information includes verification qualified, and / or verification unqualified. Based on the target analysis results, the target software information is determined; The step of determining the target software information based on the target analysis results includes: When the target analysis result information is qualified, the first software information is determined as the target software information; When the target analysis result information is that the verification is unqualified, the software construction process of the business software construction requirement information is triggered to obtain the first software information; The first software information is identified as the target software information, including: The execution time of the first software information is calculated to obtain the process execution time information; the process execution time information includes the overall execution time of the process and the execution time information of each step; the step execution time information includes the execution time of MM process steps. Determine whether the execution time of all process steps in the step execution time information is less than or equal to the first time threshold, and obtain the first time judgment result; When the first time judgment result is yes, determine whether the overall execution time of the process is less than or equal to the second time threshold, and obtain the second time judgment result; If the second time-based judgment result is yes, the first software information is identified as the target software information. When the second judgment result is negative, the execution of software construction processing on the business software construction requirement information is triggered to obtain the first software information; When the initial judgment result is negative, the software construction process is triggered to process the business software construction requirement information and obtain the first software information.

7. A device for on-demand reconfiguration of electromagnetic control business software based on a functional engine, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the on-demand refactoring method for electromagnetic control business software based on the functional engine as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked, are used to execute the on-demand refactoring implementation method for electromagnetic control business software based on a functional engine as described in any one of claims 1-5.

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