Method and system for directly operating process without installing RPA client

Abstract

The invention provides a method and a system for directly running a process without installing an RPA client, and relates to the technical field of robot process automation, and the method comprises the following specific steps: S1, carrying out process analysis and preparation; s2, a process generation engine; s3, environment compatibility judgment; and S4, monitoring and managing process operation. The system comprises a process description language analysis module; a flow generation module; a dependency library management module; a code optimization and localization module; a version control and verification module; a performance monitoring and optimizing module; an exception handling and recovering module; an adaptive environment adaptation module; according to the method, technologies such as dynamic code generation, environment adaptation, performance monitoring and exception handling are combined, so that the automation efficiency and stability of the process are improved; through a recursive descent analysis method and an analysis technology based on a syntax tree, the logic judgment nodes and the dependency relationship between the nodes can be comprehensively evaluated, and a solid technical basis is provided for optimization and automation of the process.

Description

Technical Field

[0001] The present invention relates to the field of robotic process automation technology, and specifically to a method for directly running a process without installing an RPA client, and also to a system for directly running a process without installing an RPA client. Background Art

[0002] RPA is a technology that uses software robots to simulate human operations and automate business processes. It uses a rules engine to drive robots to automatically perform repetitive and standardized tasks, such as data entry, file processing, and system interaction, replacing manual operations, improving efficiency, and reducing error rates.

[0003] The goal of the no-installation RPA client is to automate the target process without installing any client software on the target device. This approach combines multiple technologies, including but not limited to dynamic code generation, remote control, environment adaptation, performance monitoring, and exception handling, to ensure that the process can run seamlessly in different environments.

[0004] The publication number in the prior art is CN115633025B, and its name is Intelligent Integrated Device and Application Method Based on USBServer; it relates to the field of robotics technology, including a computing control device, which controls and manages the programmable USBHUB motherboard, the track robotic arm, and the programmable camera equipment connected thereto through the rail robotic arm control program, KBOT management console, KBOT client (command line and GUI), USB device remote sharing and mounting server, service management Agent, and dynamic password recognition module deployed and running thereon. The present invention supports multi-point continuous click and press (such as entering a password on the U shield), side click and press, and dynamic password recognition backfill of devices such as online banking U shields, and can be directly used as multiple bank-enterprise direct connection front-ends. It can also run different types of RPA process robots, with highly rich and integrated functions, and complete the corresponding functions through remote scheduling or operation of the program.

[0005] The shortcomings of existing technologies include: initial deployment requires complex steps such as traditional downloading, installation, and configuration; if unimplemented functions are encountered during process development, the software needs to be updated and redeployed, and the published processes may need to be re-verified for availability to ensure safe production; generally, software integrates many complex functions, and if one functional module is incompatible, the entire software may become unusable.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide a method and system for directly running a process without installing an RPA client, so as to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for directly running a process without installing an RPA client. The specific steps include:

[0010] S1. Process analysis and preparation: Automatically identify the file information of the target process; at the same time, obtain the environmental information of the target device through dynamic scanning tools, and automatically generate configuration files and necessary environmental parameters;

[0011] S2, process generation engine: parses and processes the target process description file, and constructs an executable program flow chart through dynamic code generation technology;

[0012] S3. Environment compatibility judgment: The generated exe program is copied to the target device, and the system automatically performs an environment compatibility check. Based on the check results, the process parameters are dynamically adjusted or the process is regenerated;

[0013] S4. Process operation monitoring and management: Start the generated exe program to run in real time, monitor the process execution status in real time, analyze the process execution status, generate optimization suggestions, and further improve process efficiency.

[0014] Furthermore, the automated target identification process in S1 obtains the programmed RPA process through an interface and parsing algorithm, and is used to identify multiple common RPA process file formats;

[0015] File format: Identified by file extension and internal structure;

[0016] Document content: extract text, tables and charts, and perform structuring and standardization;

[0017] File encoding method: detect and identify the encoding format used by the file;

[0018] File size and structure: Get the byte size and paragraph structure information of the file for subsequent processing.

[0019] Furthermore, the parsing and processing target process of the description file in S2 uses deep analysis technology to perform detailed analysis of the nodes and operations in the process, including data processing nodes, interface interaction nodes, and logical judgment nodes, extracting key information such as the input and output parameters, execution sequence, and dependencies of each node, and constructing a complete process logic map to provide an accurate data foundation for subsequent file generation.

[0020] Furthermore, the analysis of the data processing node is as follows:

[0021] Input data: Extract the input data characteristics of the data processing node, including data type, data volume, and data structure;

[0022] Output data: Analyze the output data characteristics of data processing nodes, including data format, data volume, and data association relationships;

[0023] Data processing rules: extract the rules and algorithms of data processing nodes, including text recognition rules, table extraction rules, and data conversion rules;

[0024] The analysis of the interface interaction nodes is as follows:

[0025] User interaction type: identifies the user operation type of the interface interaction node;

[0026] Interactive element description: extract interactive elements from interface interactive nodes and record their attribute information;

[0027] User behavior patterns: Analyze user behavior patterns at interface interaction nodes to provide a basis for process interaction simulation;

[0028] Interface element access rights: Check the element access rights in the interface interaction nodes to ensure that the process can correctly access the required interface elements;

[0029] The analysis of the logic judgment node is as follows:

[0030] Logical rules: Extract the conditions and logical rules of logical judgment nodes, including conditional statements, judgment processes, and loop structures;

[0031] Decision-making basis: Analyze the decision-making basis of the logical judgment node, including data input, condition comparison, and rule application;

[0032] Output results: Record the output result type and range of the logic judgment node to ensure that the process can correctly execute the decision logic;

[0033] Logic optimization suggestions: Based on logic rules and execution results, logic optimization suggestions are made to improve process execution efficiency and accuracy;

[0034] The dependency analysis is as follows:

[0035] Execution order: Build an execution sequence diagram between nodes to show the execution order of nodes;

[0036] Dependencies: Identify dependencies between nodes to ensure that processes can be executed in the correct order;

[0037] Process control structure: Analyze the process control structure to provide a basis for process simulation and execution.

[0038] Furthermore, the deep parsing technology uses a recursive descent analysis method or a syntax tree-based parsing technology to traverse each element in the process file, identify and extract node and operation information, convert it into a standardized data structure and store it in an in-memory database for use in subsequent file generation;

[0039] The calculation formula of the recursive descent analysis method is as follows:

[0040] Let x z It is expressed as direct dependency, that is, the number of nodes A directly dependent on node B; x j Expressed as indirect dependency, that is, the number of nodes A indirectly depends on node C, X y Expressed as total dependency, i.e. the total number of nodes that node A depends on;

[0041]

[0042] Where Y represents the recursive relationship dependency; identify the dependencies between nodes, and for complex logical processes, evaluate the impact of the dependencies between nodes on the overall performance of the process.

[0043] Furthermore, the calculation of the inter-node dependency relationship adopts the longest path length evaluation calculation as follows:

[0044] Let x c It is expressed as the longest dependency path, that is, the longest path length formed by the dependency relationship between nodes, x g Expressed as the complexity of the relationship between nodes, that is, the complexity score of the dependency between nodes, such as conditional dependency and data dependency, X d It is expressed as the total number of nodes, i.e. the total number of nodes in the process;

[0045]

[0046] Where f represents the total number of nodes in the process, which is used to identify the complex parts of the execution order in the process. For node groups with multi-layer dependencies, their impact on the overall performance of the process is evaluated.

[0047]

[0048] Among them, x h Expressed as a combination of conditions, that is, the combination of conditions, such as AND, OR, NO, and XOR.

[0049] Furthermore, the engine in S2 selects code snippets corresponding to process nodes from the system's built-in code library or user-defined code templates based on the results of the process analysis;

[0050] The code snippets are optimized and streamlined, redundant parts are removed, and necessary runtime environment encapsulation and startup boot code are added to ensure that the generated independent executable files can run independently on different compatible operating systems; encryption and digital signature technology are used to process independent executable files to prevent independent executable files from being tampered with, and version information and compatibility identification are embedded in independent executable files to facilitate user management and system identification.

[0051] Furthermore, the public and private keys of the encryption method are public key (e,n) and private key (d,n) respectively;

[0052] The encryption process is as follows: C = M c modn;

[0053] The decryption process is as follows: M = C d modn;

[0054] Where M represents the data information of an independent executable file; C represents the encrypted ciphertext; e and d are the exponents of the public key and private key respectively; n is the product of large prime numbers;

[0055] The public and private keys are generated through the following steps:

[0056] Choose two large prime numbers p and q;

[0057] Calculate n = p × q;

[0058] Calculate the Euler function φ(n)=(p-1)(q-1);

[0059] Choose an integer e such that 1<e<φ(n) and gcd(e,φ(n))=1;

[0060] Calculate d so that e×d≡1modφ(n);

[0061] After generating the public key (e,n) and private key (d,n), use the above encryption and decryption formulas to encrypt and decrypt data information;

[0062] The encryption calculation of the digital signature technology is as follows:

[0063] C=E(P,K public ), where C is the ciphertext, P is the plaintext, and K public is the public key, E is the encryption function;

[0064] The decryption calculation is as follows:

[0065] P=D(C,K private ), where K private is the private key and D is the decryption function.

[0066] A system that directly runs processes without installing an RPA client, including:

[0067] Process description language parsing module: supports parsing of multiple process description languages, inputs the description file of the target process; outputs the parsed process rules, node information, and data processing rules;

[0068] Process generation module: Automatically generates an executable program adapted to the target device based on the parsed process rules; inputs the parsed process rules, node information, and data processing rules; outputs the executable program, generation log, and version identification;

[0069] Dependency library management module: dynamically loads the dependency libraries installed on the target device and automatically generates the necessary dependency library files based on the environment information; inputs the environment information of the target device; outputs the dependency library files and version verification results;

[0070] Code optimization and localization module: optimizes and localizes the generated code; inputs the generated code file and target language environment information; outputs the optimized code file and localized resource file;

[0071] Version control and verification module: performs version control and verification on the generated exe program to ensure the consistency and integrity of the program; inputs the generated exe program, version identifier, and verification value; outputs verification results and version record files;

[0072] Performance monitoring and optimization module: performs real-time performance monitoring on generated EXE programs, identifies performance bottlenecks, and provides optimization suggestions; inputs EXE program running status and performance monitoring data; outputs performance analysis reports and optimization suggestion files;

[0073] Exception handling and recovery module: Integrates the full-process exception monitoring mechanism, captures and handles exceptions during process operation, and provides automatic recovery solutions; inputs process operation logs and exception information; outputs exception handling reports and recovery solution files;

[0074] Adaptive environment adaptation module: dynamically adjusts process parameters and operating environment configuration according to the environmental information of the target device, and automatically generates adaptation code; inputs the environmental information of the target device and process parameter adjustment requirements; outputs adaptive configuration files and adaptation code files.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] This invention greatly simplifies the deployment process of the RPA process. Users do not need to go through complex installation steps. They only need to download a small, easy-to-transfer independent executable file to start the RPA process. This significantly shortens deployment time, improves work efficiency, and reduces the technical requirements for users, making it easy for non-professional technicians to use RPA technology.

[0077] This effectively improves the cross-device and cross-platform compatibility of RPA processes. Users can freely run RPA processes on different operating systems and hardware devices without worrying about installation and configuration issues, achieving the true meaning of "create once, run anywhere." This also enhances the flexibility of RPA technology in multi-scenario enterprise applications, facilitating rapid deployment and application across office computers, servers, and cloud virtual machines in different departments, promoting the integrated and collaborative development of enterprise digital processes.

[0078] This facilitates the sharing and dissemination of RPA processes. Users can easily share generated independent executable files with other individuals or teams in need, regardless of their RPA software installation status. This promotes extensive communication and collaboration on RPA technology within and outside the enterprise, accelerating the promotion and application of business process automation.

[0079] The present invention realizes efficient and stable automated operation of the target process through a method and system for directly running the process without installing the RPA client; combined with technologies such as dynamic code generation, environment adaptation, performance monitoring and exception handling, it significantly reduces manual intervention and improves the automation efficiency and stability of the process; at the same time, through recursive descent analysis and syntax tree-based parsing technology, the system can comprehensively evaluate logical judgment nodes and the dependencies between nodes, providing a solid technical foundation for process optimization and automation; it has high patent value and broad practical application potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 Schematic diagram of the overall method flow of the present invention;

[0081] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0082] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0083] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0084] Example 1:

[0085] See also Figure 1 The present invention provides a technical solution: a method for directly running a process without installing an RPA client, the specific steps of which include:

[0086] S1. Process analysis and preparation: Automatically identify the file information of the target process; at the same time, obtain the environmental information of the target device through dynamic scanning tools, and automatically generate configuration files and necessary environmental parameters;

[0087] S2, process generation engine: parses and processes the target process description file, and constructs an executable program flow chart through dynamic code generation technology;

[0088] S3. Environment compatibility judgment: The generated exe program is copied to the target device, and the system automatically performs an environment compatibility check. Based on the check results, the process parameters are dynamically adjusted or the process is regenerated;

[0089] S4. Process operation monitoring and management: Start the generated exe program to run in real time, monitor the process execution status in real time, analyze the process execution status, generate optimization suggestions, and further improve process efficiency.

[0090] In this embodiment, preferably, the automated identification target process in S1 obtains the written RPA process through an interface and a parsing algorithm to identify multiple common RPA process file formats;

[0091] File format: Identified by file extension and internal structure;

[0092] Document content: extract text, tables and charts, and perform structuring and standardization;

[0093] File encoding method: detect and identify the encoding format used by the file;

[0094] File size and structure: Get the byte size and paragraph structure information of the file for subsequent processing;

[0095] It should be noted that the automated target identification process plays a key role in the automation of the entire process in many aspects. It not only improves the efficiency and accuracy of the process, but also enhances the flexibility and adaptability of the system, enabling the process to run stably in different environments, reducing manual intervention, and significantly improving the overall performance and reliability of the automated process.

[0096] In this embodiment, preferably, the parsing of the description file in S2 and the processing target process use deep analysis technology to perform detailed analysis of the nodes and operations in the process, including data processing nodes, interface interaction nodes, and logic judgment nodes, extract key information such as input and output parameters, execution sequence, and dependencies of each node, and construct a complete process logic map to provide an accurate data foundation for subsequent file generation;

[0097] It should be noted that deep parsing technology is used to extract node information and build a process logic map, providing an accurate data foundation for subsequent process generation. It has the advantages of comprehensive node information extraction, efficient process generation, precise dependency processing, cross-platform compatibility, and performance optimization support.

[0098] Cooperating with the environmental compatibility judgment in S3, through automated detection and dynamic adjustment of environmental parameters, it ensures that the process can adapt to different environments. It has the advantages of comprehensive environmental information detection, dynamic parameter adjustment, automatic process regeneration, performance monitoring and optimization, reducing manual intervention and improving system stability.

[0099] Through the collaborative work of S2 and S3, efficient and stable automated operation of the target process can be achieved, which significantly reduces manual intervention, improves the overall performance and reliability of the process, and provides a solid technical foundation for methods and systems that directly run processes without installing an RPA client.

[0100] In this embodiment, preferably, the analysis of the data processing node is as follows:

[0101] Input data: Extract the input data characteristics of the data processing node, including data type, data volume, and data structure;

[0102] Output data: Analyze the output data characteristics of data processing nodes, including data format, data volume, and data association relationships;

[0103] Data processing rules: extract the rules and algorithms of data processing nodes, including text recognition rules, table extraction rules, and data conversion rules;

[0104] The analysis of the interface interaction nodes is as follows:

[0105] User interaction type: identifies the user operation type of the interface interaction node;

[0106] Interactive element description: extract interactive elements from interface interactive nodes and record their attribute information;

[0107] User behavior patterns: Analyze user behavior patterns at interface interaction nodes to provide a basis for process interaction simulation;

[0108] Interface element access rights: Check the element access rights in the interface interaction nodes to ensure that the process can correctly access the required interface elements;

[0109] The analysis of the logic judgment node is as follows:

[0110] Logical rules: Extract the conditions and logical rules of logical judgment nodes, including conditional statements, judgment processes, and loop structures;

[0111] Decision-making basis: Analyze the decision-making basis of the logical judgment node, including data input, condition comparison, and rule application;

[0112] Output results: Record the output result type and range of the logic judgment node to ensure that the process can correctly execute the decision logic;

[0113] Logic optimization suggestions: Based on logic rules and execution results, logic optimization suggestions are made to improve process execution efficiency and accuracy;

[0114] The dependency analysis is as follows:

[0115] Execution order: Build an execution sequence diagram between nodes to show the execution order of nodes;

[0116] Dependencies: Identify dependencies between nodes to ensure that processes can be executed in the correct order;

[0117] Process control structure: Analyze the process control structure to provide a basis for process simulation and execution;

[0118] It should be noted that the advantages of data processing nodes, interface interaction nodes, logic judgment nodes, and dependency analysis fully support the automated operation of processes, ensuring the efficiency, stability, and compatibility of processes in different environments. Analysis and optimization not only improve the automation level of processes, but also provide a solid technical foundation for system expansion and maintenance.

[0119] Data processing nodes

[0120] Multiple data format support: Ability to process multiple file formats to ensure process compatibility;

[0121] Automated data processing: Through dynamic code generation technology, data extraction, conversion and structuring are achieved, reducing dependence on specific tools;

[0122] Interface interaction nodes

[0123] Diverse interaction support: supports multiple user operation types such as clicking buttons, entering text, and drop-down selection to adapt to different interaction scenarios;

[0124] Accurate element access: Through interactive element description and access permission detection, it ensures that the process can correctly access the required interface elements, avoiding operation failures caused by element unrecognition;

[0125] User behavior simulation: Analyze user behavior patterns to provide a basis for process interaction simulation and support unmanned interaction scenarios;

[0126] Logical judgment node

[0127] Complex logic processing: Through deep analysis technology, clearly identify the conditional statements, judgment processes and loop structures in the process and handle complex logic;

[0128] Clear decision-making basis: Analyze the decision basis, including data input, condition comparison and rule application, to ensure the accuracy and consistency of decision-making logic;

[0129] Optimization suggestion generation: Provide optimization suggestions based on logical rules and execution results to improve process efficiency and accuracy and reduce potential errors;

[0130] Dependency Analysis

[0131] Accurate execution order: Through recursive descent analysis or syntax tree-based parsing technology, an execution sequence diagram is constructed to ensure that the process is executed in the correct order;

[0132] Efficient dependency management: Identify direct and indirect dependencies between nodes, optimize process execution paths, and improve efficiency;

[0133] Process control structure support: Analyze process control structures, provide a basis for process simulation and execution, and optimize the execution efficiency of complex processes.

[0134] In this embodiment, preferably, the deep parsing technology uses a recursive descent analysis method or a syntax tree-based parsing technology to traverse each element in the process file, identify and extract node and operation information, convert it into a standardized data structure and store it in an in-memory database for subsequent file generation;

[0135] The calculation formula of the recursive descent analysis method is as follows:

[0136] Let x zIt is expressed as direct dependency, that is, the number of nodes A directly dependent on node B; x j Expressed as indirect dependency, that is, the number of nodes A indirectly depends on node C, X y Expressed as total dependency, i.e. the total number of nodes that node A depends on;

[0137]

[0138] Where Y represents the recursive dependency. This approach identifies the dependencies between nodes and, for complex logical processes, evaluates the impact of dependencies between nodes on the overall performance of the process.

[0139] It should be noted that the recursive descent analysis method is adopted as the core method of deep parsing technology. By recursively traversing each node and operation information in the process file, it identifies and extracts the direct dependencies, indirect dependencies, and total dependencies of nodes and operations, and constructs a complete process dependency graph.

[0140] Comprehensive identification of dependencies: Through recursive descent analysis, we can comprehensively identify direct dependencies, indirect dependencies, and recursive dependencies in the process, and build a complete process dependency graph; ensuring that the dependencies of every node and operation in the process are accurately identified without omission;

[0141] Recursive relationship dependency evaluation: This method calculates recursive relationship dependencies by measuring the depth and width of the recursive tree, quantifying the impact of recursive relationships on process performance. This provides a basis for process optimization and identifies performance bottlenecks that may be caused by complex recursive relationships.

[0142] Support process optimization: Through recursive relationship dependency assessment, it can identify highly complex recursive areas in the process and provide optimization suggestions (such as reducing recursion depth, optimizing loop structure, etc.); improve process execution efficiency and reduce performance issues;

[0143] Improve the visualization of process dependency graphs: After building a process dependency graph, you can use graphical tools to visualize it, providing intuitive feedback on the dependencies in the process; this helps developers and maintainers quickly understand the process structure and dependencies.

[0144] In this embodiment, preferably, the calculation of the inter-node dependency relationship adopts the longest path length evaluation calculation as follows:

[0145] Let x c It is expressed as the longest dependency path, that is, the longest path length formed by the dependency relationship between nodes, x g Expressed as the complexity of the relationship between nodes, that is, the complexity score of the dependency between nodes, such as conditional dependency and data dependency, X d It is expressed as the total number of nodes, i.e. the total number of nodes in the process;

[0146]

[0147] Where f represents the total number of nodes in the process, which is used to identify the complex parts of the execution order in the process. For node groups with multi-layer dependencies, their impact on the overall performance of the process is evaluated.

[0148]

[0149] Among them, x h Expressed as a combination of conditions, that is, the combination of conditions, such as AND, OR, NO, and XOR;

[0150] It should be noted that recursive descent analysis and maximum path length evaluation can comprehensively identify and calculate direct, indirect, and recursive dependencies between nodes, and assess the impact of these dependencies on the overall performance of the process. Furthermore, conditional combination evaluation can provide a deep understanding of the complexity of conditional dependencies within the process, providing comprehensive technical support for process optimization and automation.

[0151] It not only helps the process identify potential problems and optimization points, but also provides a solid technical basis for process automation and optimization; through dynamic code generation, performance monitoring and exception handling mechanisms, it ensures that the process can adapt to various environments, reduces manual intervention, and improves system reliability and scalability.

[0152] In this embodiment, preferably, the engine in S2 filters out code snippets corresponding to process nodes from a system built-in code library or a user-defined code template based on the result of process analysis;

[0153] The code snippets are optimized and streamlined, redundant parts are removed, and necessary runtime environment encapsulation and boot code are added to ensure that the generated independent executable files can run independently on different compatible operating systems. Encryption and digital signature technologies are used to process independent executable files to prevent tampering with them. Version information and compatibility identifiers are embedded in independent executable files to facilitate user management and system identification.

[0154] It should be noted that by screening, optimizing and encapsulating the code snippets of the process analysis results, and combining them with encryption, digital signatures and anti-tampering technologies, it can be ensured that the generated independent executable files can run independently on different compatible operating systems and have a high degree of security and credibility; it can not only improve the efficiency and stability of process automation, but also enhance users' trust and management capabilities in executable files, and has important application value and market potential.

[0155] In this embodiment, preferably, the public and private keys of the encryption method are public key (e, n) and private key (d, n) respectively;

[0156] The encryption process is as follows: C = M c modn;

[0157] The decryption process is as follows: M = C d modn;

[0158] Where M represents the data information of an independent executable file; C represents the encrypted ciphertext; e and d are the exponents of the public key and private key respectively; n is the product of large prime numbers;

[0159] The public and private keys are generated through the following steps:

[0160] Choose two large prime numbers p and q;

[0161] Calculate n = p × q;

[0162] Calculate the Euler function φ(n)=(p-1)(q-1);

[0163] Choose an integer e such that 1<e<φ(n) and gcd(e,φ(n))=1;

[0164] Calculate d so that e×d≡1modφ(n);

[0165] After generating the public key (e,n) and private key (d,n), use the above encryption and decryption formulas to encrypt and decrypt data information;

[0166] The encryption calculation of the digital signature technology is as follows:

[0167] C=E(P,K public ), where C is the ciphertext, P is the plaintext, and K public is the public key, E is the encryption function;

[0168] The decryption calculation is as follows:

[0169] P=D(C,K private ), where K private is the private key, D is the decryption function;

[0170] It should be noted that by adopting RSA encryption and digital signature technology, the data information of independent executable files can be effectively protected, tampering can be prevented, and the integrity and security of the files can be ensured; it can not only enhance users' trust in the files, but also simplify the file management and verification process; in actual applications, it has high security and efficiency and can meet the needs of complex process automation.

[0171] A system that directly runs processes without installing an RPA client, including:

[0172] Process description language parsing module: supports parsing of multiple process description languages, inputs the description file of the target process; outputs the parsed process rules, node information, and data processing rules;

[0173] Process generation module: Automatically generates an executable program adapted to the target device based on the parsed process rules; inputs the parsed process rules, node information, and data processing rules; outputs the executable program, generation log, and version identification;

[0174] Dependency library management module: dynamically loads the dependency libraries installed on the target device and automatically generates the necessary dependency library files based on the environment information; inputs the environment information of the target device; outputs the dependency library files and version verification results;

[0175] Code optimization and localization module: optimizes and localizes the generated code; inputs the generated code file and target language environment information; outputs the optimized code file and localized resource file;

[0176] Version control and verification module: performs version control and verification on the generated exe program to ensure the consistency and integrity of the program; inputs the generated exe program, version identifier, and verification value; outputs verification results and version record files;

[0177] Performance monitoring and optimization module: performs real-time performance monitoring on generated EXE programs, identifies performance bottlenecks, and provides optimization suggestions; inputs EXE program running status and performance monitoring data; outputs performance analysis reports and optimization suggestion files;

[0178] Exception handling and recovery module: Integrates the full-process exception monitoring mechanism, captures and handles exceptions during process operation, and provides automatic recovery solutions; inputs process operation logs and exception information; outputs exception handling reports and recovery solution files;

[0179] Adaptive environment adaptation module: dynamically adjusts process parameters and operating environment configuration according to the environmental information of the target device, and automatically generates adaptation code; inputs the environmental information of the target device and process parameter adjustment requirements; outputs adaptive configuration files and adaptation code files.

[0180] It should be noted that: All calculation formulas in this application document use regression analysis including but not limited to machine learning algorithms to deeply analyze the relevant parameters collected and identify their natural trends and relationships. Use professional software, such as Python's Scikit-learn library or R language, to automatically generate mathematical models that match the data. Then, objectively evaluate the performance of the model through methods such as cross-validation, and combine continuous feedback and optimization to ensure that the created formula truly reflects the inherent laws of the data, thereby ensuring its effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula are dimensionally non-dimensionalized within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless technical means include but are not limited to Min-Max Normalization and Z-Score standardization;

[0181] The technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0182] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0183] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0184] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for directly running a process without installing an RPA client, characterized in that: The specific steps include: S1. Process analysis and preparation: Automatically identify the file information of the target process; at the same time, obtain the environmental information of the target device through dynamic scanning tools, and automatically generate configuration files and necessary environmental parameters; S2, process generation engine: parses and processes the target process description file, and constructs an executable program flow chart through dynamic code generation technology; S3. Environment compatibility judgment: The generated exe program is copied to the target device, and the system automatically performs an environment compatibility check. Based on the check results, the process parameters are dynamically adjusted or the process is regenerated; S4. Process operation monitoring and management: Start the generated exe program to run in real time, monitor the process execution status in real time, analyze the process execution status, generate optimization suggestions, and further improve process efficiency.

2. The method for directly running a process without installing an RPA client according to claim 1, characterized in that: The automated identification target process in S1 obtains the programmed RPA process through an interface and parsing algorithm to identify various common RPA process file formats; File format: Identified by file extension and internal structure; Document content: extract text, tables and charts, and perform structuring and standardization; File encoding method: detect and identify the encoding format used by the file; File size and structure: Get the byte size and paragraph structure information of the file for subsequent processing.

3. The method for directly running a process without installing an RPA client according to claim 1, characterized in that: The parsing and processing target process of the description file in S2 uses deep analysis technology to perform detailed analysis of the nodes and operations in the process, including data processing nodes, interface interaction nodes, and logical judgment nodes, extracting key information such as input and output parameters, execution sequence, and dependencies of each node, and constructing a complete process logic map to provide an accurate data foundation for subsequent file generation.

4. The method for directly running a process without installing an RPA client according to claim 3, characterized in that: The analysis of the data processing node is as follows: Input data: Extract the input data characteristics of the data processing node, including data type, data volume, and data structure; Output data: Analyze the output data characteristics of data processing nodes, including data format, data volume, and data association relationships; Data processing rules: extract the rules and algorithms of data processing nodes, including text recognition rules, table extraction rules, and data conversion rules; The analysis of the interface interaction nodes is as follows: User interaction type: identifies the user operation type of the interface interaction node; Interactive element description: extract interactive elements from interface interactive nodes and record their attribute information; User behavior patterns: Analyze user behavior patterns at interface interaction nodes to provide a basis for process interaction simulation; Interface element access rights: Check the element access rights in the interface interaction nodes to ensure that the process can correctly access the required interface elements; The analysis of the logic judgment node is as follows: Logical rules: Extract the conditions and logical rules of logical judgment nodes, including conditional statements, judgment processes, and loop structures; Decision-making basis: Analyze the decision-making basis of the logical judgment node, including data input, condition comparison, and rule application; Output results: Record the output result type and range of the logic judgment node to ensure that the process can correctly execute the decision logic; Logic optimization suggestions: Based on logic rules and execution results, logic optimization suggestions are made to improve process execution efficiency and accuracy; The dependency analysis is as follows: Execution order: Build an execution sequence diagram between nodes to show the execution order of nodes; Dependencies: Identify dependencies between nodes to ensure that processes can be executed in the correct order; Process control structure: Analyze the process control structure to provide a basis for process simulation and execution.

5. The method for directly running a process without installing an RPA client according to claim 4, characterized in that: The deep parsing technology uses recursive descent analysis or syntax tree-based parsing technology to traverse each element in the process file, identify and extract node and operation information, convert it into a standardized data structure and store it in an in-memory database for subsequent file generation; The calculation formula of the recursive descent analysis method is as follows: Let x z It is expressed as direct dependency, that is, the number of nodes A directly dependent on node B; x j Expressed as indirect dependency, that is, the number of nodes A indirectly depends on node C, X y Expressed as total dependency, i.e. the total number of nodes that node A depends on; Where Y represents the recursive relationship dependency; identify the dependencies between nodes, and for complex logical processes, evaluate the impact of the dependencies between nodes on the overall performance of the process.

6. The method of directly running a process without installing an RPA client according to claim 5, characterized in that: The calculation of the inter-node dependency relationship adopts the longest path length evaluation calculation as follows: Let x c It is expressed as the longest dependency path, that is, the longest path length formed by the dependency relationship between nodes, x g Expressed as the complexity of the relationship between nodes, that is, the complexity score of the dependency between nodes, such as conditional dependency and data dependency, X d It is expressed as the total number of nodes, i.e. the total number of nodes in the process; Where f represents the total number of nodes in the process, which is used to identify the complex parts of the execution order in the process. For node groups with multi-layer dependencies, their impact on the overall performance of the process is evaluated. Among them, x h Expressed as a combination of conditions, that is, the combination of conditions, such as AND, OR, NO, and XOR.

7. The method for directly running a process without installing an RPA client according to claim 1, characterized in that: The engine in S2 filters out code snippets corresponding to process nodes from the system's built-in code library or user-defined code templates based on the results of process analysis; The code snippets are optimized and streamlined, redundant parts are removed, and necessary runtime environment encapsulation and startup boot code are added to ensure that the generated independent executable files can run independently on different compatible operating systems; encryption and digital signature technology are used to process independent executable files to prevent independent executable files from being tampered with, and version information and compatibility identification are embedded in independent executable files to facilitate user management and system identification.

8. The method for directly running a process without installing an RPA client according to claim 7, characterized in that: The public and private keys of the encryption method are public key (e,n) and private key (d,n) respectively; The encryption process is as follows: C = M c modn; The decryption process is as follows: M = C d modn; Where M represents the data information of an independent executable file; C represents the encrypted ciphertext; e and d are the exponents of the public key and private key respectively; n is the product of large prime numbers; The public and private keys are generated through the following steps: Choose two large prime numbers p and q; Calculate n = p × q; Calculate the Euler function φ(n)=(p-1)(q-1); Choose an integer e such that 1<e<φ(n) and gcd(e,φ(n))=1; Calculate d so that e×d≡1modφ(n); After generating the public key (e,n) and private key (d,n), use the above encryption and decryption formulas to encrypt and decrypt data information; The encryption calculation of the digital signature technology is as follows: C=E(P,K public ), where C is the ciphertext, P is the plaintext, and K public is the public key, E is the encryption function; The decryption calculation is as follows: P=D(C,K private ), where K private is the private key and D is the decryption function.

9. A system for directly running processes without installing an RPA client, characterized by: include: Process description language parsing module: supports parsing of multiple process description languages ​​and inputs description files of target processes; Output parsed process rules, node information, and data processing rules; Process generation module: Automatically generates an executable program adapted to the target device based on the parsed process rules; inputs the parsed process rules, node information, and data processing rules; outputs the executable program, generation log, and version identification; Dependency library management module: dynamically loads the dependency libraries installed on the target device and automatically generates the necessary dependency library files based on the environment information; inputs the environment information of the target device; Output dependent library files and version verification results; Code optimization and localization module: optimizes and localizes the generated code; inputs the generated code file and target language environment information; Output optimized code files and localized resource files; Version control and verification module: performs version control and verification on the generated exe program to ensure the consistency and integrity of the program; input the generated exe program, version identifier, and verification value; Output verification results and version record files; Performance monitoring and optimization module: performs real-time performance monitoring on generated exe programs, identifies performance bottlenecks, and provides optimization suggestions; Input the running status and performance monitoring data of the exe program; Output performance analysis reports and optimization suggestion files; Exception handling and recovery module: Integrates the full-process exception monitoring mechanism, captures and handles exceptions during process operation, and provides automatic recovery solutions; inputs process operation logs and exception information; outputs exception handling reports and recovery solution files; Adaptive environment adaptation module: dynamically adjusts process parameters and operating environment configuration according to the environmental information of the target device, and automatically generates adaptation code; inputs the environmental information of the target device and process parameter adjustment requirements; outputs adaptive configuration files and adaptation code files.

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