Method for constructing digital employee

Abstract

The invention discloses a method for constructing a digital employee, which comprises the following steps of: collecting and analyzing enterprise business processes and data, designing a working process and a task distribution strategy of the digital employee, training the digital employee by utilizing a machine learning algorithm to enable the digital employee to have the capability of processing the business processes, and simultaneously constructing the digital employee according to the characteristics of the business processes and the data. According to the method, the working process of the digital employees is designed, a task allocation strategy is formulated, efficient execution and cooperative processing of tasks are achieved, and the stability and reliability of the digital employees in the actual operation process are ensured through real-time monitoring, performance evaluation and fault diagnosis of the digital employees. According to the method, digital employees capable of simulating work of human employees can be constructed according to specific business requirements of enterprises, automatic processing of business processes of the enterprises is achieved, and compared with a traditional IT system, the method has higher intelligence and automation, can better adapt to complex and changeable business scenes, and is high in practicability. And the operation efficiency and competitiveness of enterprises are improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method for building a digital employee. Background Art

[0002] With the rapid development of the Internet, big data and artificial intelligence technologies, enterprises have a growing demand for information processing and business process automation. However, traditional IT systems often require manual intervention and are difficult to adapt to complex and changing business scenarios. Therefore, it is particularly important to build a digital employee that can simulate the work of human employees. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method for constructing digital employees so as to realize the automated processing of enterprise business processes and improve the operational efficiency of the enterprise.

[0004] In order to solve the above technical problems, the technical solution of the present invention is:

[0005] A method for building a digital workforce, comprising the following steps:

[0006] (1) Collect and analyze enterprise business processes and data, including detailed information on each business link, data flow methods, and key business rules;

[0007] (2) Design the workflow and task allocation strategy for digital employees. Based on the business process and data characteristics, design the workflow for digital employees, including the specific execution steps, decision-making logic, and exception handling mechanism of the task; formulate a task allocation strategy to reasonably allocate tasks to digital employees with different permissions and expertise based on the nature and complexity of the tasks;

[0008] (3) Use machine learning algorithms to train digital employees to enable them to handle business processes;

[0009] (4) Deploy digital employees to the enterprise information system. Deploy the trained digital employees to the enterprise information system to realize the automation of business processes.

[0010] Furthermore, in step 1, relevant data of each business link of the enterprise is collected in detail, including order data, customer data, product data, and financial data; at the same time, key business rules of each business link need to be collected, including order review standards and customer credit assessment standards.

[0011] Furthermore, the collected data in step 1 will be integrated to form a complete business process data set; through in-depth analysis of the data set, the business characteristics and process bottlenecks of the enterprise can be understood, providing a basis for designing the workflow and task allocation strategy of digital employees.

[0012] Furthermore, step 3 specifically includes:

[0013] Training data preparation: Collect historical business data, professional knowledge base and best practice cases as training data, clean and pre-process the data to ensure data quality and availability;

[0014] Training algorithm selection: Select appropriate machine learning algorithms, including deep learning and reinforcement learning algorithms, based on business needs and data characteristics;

[0015] Training process: Use training data to train digital employees, adjust algorithm parameters, optimize model performance, and continuously improve the accuracy and efficiency of digital employees in processing tasks through iterative training.

[0016] Furthermore, step 4 specifically includes:

[0017] System integration: Integrate the trained digital workers with the enterprise's information system to ensure that the digital workers can work with the existing IT system;

[0018] Deployment and implementation: Deploy digital employees to the enterprise information system to automate business processes, ensure that digital employees can perform tasks efficiently and accurately, and improve the efficiency and accuracy of overall business process execution;

[0019] Monitoring and optimization: Monitor the operating status of digital employees, collect operating data, and optimize and adjust the digital employees' workflow and task allocation strategies based on data feedback.

[0020] The advantages of the present invention are:

[0021] The method of the present invention can construct digital employees that can simulate the work of human employees according to the specific business needs of the enterprise, and realize the automated processing of the enterprise's business processes. Compared with traditional IT systems, the present invention has a higher degree of intelligence and automation, can better adapt to complex and changing business scenarios, and improve the operational efficiency and competitiveness of the enterprise. The digital employees constructed by the present invention can be applied to various industries and fields, such as finance, e-commerce, logistics, medical care, etc., and have broad market prospects and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a schematic diagram of the patent process of this invention. DETAILED DESCRIPTION

[0023] The specific embodiments of the present invention are further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0024] As shown in the figure:

[0025] The present invention proposes a method for constructing a digital employee, comprising the following steps:

[0026] 1. Collect and analyze the enterprise's business processes and data: This step involves collecting data related to the enterprise's business processes, including detailed information on each business link, data flow methods, key business rules, etc. By deeply analyzing this data, we can understand the business characteristics and process bottlenecks of the enterprise, and provide a basis for designing the workflow and task allocation strategy of digital employees.

[0027] -Detailed collection: In this step, the relevant data of each business link of the enterprise needs to be collected in detail, including but not limited to order data, customer data, product data, financial data, etc. At the same time, it is also necessary to collect the key business rules of each business link, such as order review standards, customer credit assessment standards, etc.

[0028] -Data integration: Integrate the collected data to form a complete business process data set. Through in-depth analysis of the data set, we can understand the business characteristics and process bottlenecks of the enterprise, and provide a basis for designing the workflow and task allocation strategy of digital employees.

[0029] 2. Design the workflow and task allocation strategy for digital employees: Design the workflow for digital employees based on business processes and data characteristics, including specific execution steps, decision logic, and exception handling mechanisms. At the same time, formulate a task allocation strategy to reasonably allocate tasks to digital employees with different permissions and expertise based on the nature and complexity of the tasks.

[0030] -Workflow design: Design the workflow of digital employees based on the characteristics of the business process, including the specific execution steps of the task, decision-making logic and exception handling mechanism. Ensure that the workflow of digital employees can cover all business links of the enterprise and can handle various business scenarios.

[0031] -Task allocation strategy: Develop a task allocation strategy based on the nature and complexity of the task. Rationally allocate tasks to digital employees with corresponding authority and expertise to ensure that tasks can be executed efficiently and accurately.

[0032] 3. Use machine learning algorithms to train digital employees: Use machine learning algorithms to train digital employees so that they have the ability to handle business processes. Training data includes historical business data, professional knowledge bases, best practice cases, etc. Through training, digital employees can learn and understand business rules and improve the accuracy and efficiency of processing tasks.

[0033] - Training data preparation: Collect historical business data, professional knowledge base and best practice cases as training data. Clean and pre-process the data to ensure data quality and availability.

[0034] - Training algorithm selection: According to business needs and data characteristics, select appropriate machine learning algorithms, such as deep learning, reinforcement learning, etc. Use algorithms to train digital employees so that they have the ability to handle business processes.

[0035] - Training process: Use training data to train digital employees, adjust algorithm parameters, and optimize model performance. Through iterative training, the accuracy and efficiency of digital employees in processing tasks are continuously improved.

[0036] 4. Deploy digital employees to enterprise information systems: Deploy trained digital employees to enterprise information systems to automate business processes. Digital employees can be integrated with existing IT systems and work together to improve the efficiency and accuracy of overall business process execution.

[0037] -System integration: Integrate the trained digital employees with the enterprise's information system to ensure that the digital employees can work together with the existing IT system.

[0038] -Deployment and implementation: Deploy digital employees to the enterprise information system to automate business processes. Ensure that digital employees can perform tasks efficiently and accurately, and improve the efficiency and accuracy of overall business process execution.

[0039] -Monitoring and optimization: Monitor the operation status of digital employees and collect operation data. Based on data feedback, optimize and adjust the workflow and task allocation strategy of digital employees to improve the accuracy and efficiency of their task processing.

[0040] This technical solution can effectively build digital employees, realize the automation of enterprise business processes, and improve enterprise operational efficiency. Through the training of machine learning algorithms, digital employees can learn and understand business rules and improve the accuracy and efficiency of processing tasks. At the same time, by designing reasonable workflows and task allocation strategies, it ensures that digital employees can perform tasks efficiently and accurately, improving the execution efficiency and accuracy of the overall business process.

[0041] Case 1:

[0042] A certain enterprise needs to process a large number of customer orders and now uses the present invention to build digital employees to realize the automation of order processing.

[0043] Step 1: Collect and analyze the company's business processes and data: Collect the company's business processes and data related to order processing, including order creation, order review, order delivery, order after-sales and other links.

[0044] Step 2: Design the workflow and task allocation strategy for digital employees: Design the workflow for digital employees based on business processes and data characteristics. For example, in the order review process, digital employees need to determine whether the order has passed the review based on information such as order amount and customer credit. At the same time, formulate a task allocation strategy, such as assigning order review tasks to digital employees with corresponding permissions.

[0045] Step 3: Use machine learning algorithms to train digital employees: Use machine learning algorithms to train digital employees so that they have the ability to handle business processes such as order review. The training data includes historical order data, customer credit data, etc.

[0046] Step 4: Deploy digital employees to the enterprise information system: Deploy the trained digital employees to the enterprise information system to automate order processing.

[0047] Case 2:

[0048] A certain enterprise needs to handle a large number of customer consultations and now uses the present invention to build a digital employee to automate customer consultations.

[0049] Step 1: Collect and analyze enterprise business processes and data: Collect business processes and data related to enterprise customer consultations, including consultation access, consultation classification, consultation response and other links.

[0050] Step 2: Design the workflow and task allocation strategy for digital employees: Design the workflow for digital employees based on business processes and data characteristics. For example, in the consultation classification phase, digital employees need to classify customer consultations into pre-sales, after-sales, technical support, etc. At the same time, formulate a task allocation strategy, such as assigning consultation response tasks to digital employees with corresponding professional knowledge.

[0051] Step 3: Use machine learning algorithms to train digital employees so that they can handle customer inquiries. Training data includes historical consultation data, professional knowledge data, etc.

[0052] Step 4: Deploy digital employees to the enterprise information system to automate the processing of customer inquiries.

[0053] Case 3:

[0054] A certain enterprise needs to handle a large amount of data analysis and report generation work, and now uses the present invention to build a digital employee to realize the automation of data analysis.

[0055] Step 1: Collect and analyze enterprise business processes and data, including data collection, data processing, data analysis, report generation, etc.

[0056] Step 2: Design the workflow and task allocation strategy for digital employees based on business processes and data characteristics. For example, in the data analysis phase, digital employees need to select appropriate data analysis methods based on business needs, such as regression analysis, cluster analysis, etc.

[0057] Step 3: Use machine learning algorithms to train digital employees so that they can handle business processes such as data analysis. Training data includes historical data analysis cases, report templates, etc.

[0058] Step 4: Deploy digital employees to enterprise information systems to automate data analysis.

[0059] The present invention collects and analyzes enterprise business processes and data, designs the workflow and task allocation strategy of digital employees, and uses machine learning algorithms to train digital employees so that they have the ability to handle business processes. At the same time, according to the characteristics of business processes and data, the workflow of digital employees is designed, and task allocation strategies are formulated to achieve efficient execution and collaborative processing of tasks. Through real-time monitoring, performance evaluation and fault diagnosis of digital employees, the stability and reliability of digital employees in the actual operation process are ensured.

[0060] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions and variations of these embodiments are made without departing from the principles and spirit of the present invention, and still fall within the scope of protection of the present invention.

Claims

1. A method for building a digital employee, characterized in that: The following steps are involved: (1) Collect and analyze enterprise business processes and data, including detailed information on each business link, data flow methods, and key business rules; (2) Design the workflow and task allocation strategy for digital employees. Based on the business process and data characteristics, design the workflow for digital employees, including the specific execution steps, decision-making logic, and exception handling mechanism of the task; formulate a task allocation strategy to reasonably allocate tasks to digital employees with different permissions and expertise based on the nature and complexity of the tasks; (3) Use machine learning algorithms to train digital employees to enable them to handle business processes; (4) Deploy digital employees to the enterprise information system. Deploy the trained digital employees to the enterprise information system to realize the automation of business processes.

2. The method for building a digital employee according to claim 1, characterized in that: In step 1, relevant data of each business link of the enterprise is collected in detail, including order data, customer data, product data, and financial data; at the same time, key business rules of each business link need to be collected, including order review standards and customer credit assessment standards.

3. The method for building a digital employee according to claim 1, characterized in that: In step 1, the collected data will be integrated to form a complete business process data set; through in-depth analysis of the data set, the business characteristics and process bottlenecks of the enterprise can be understood, providing a basis for designing the workflow and task allocation strategy of digital employees.

4. The method for building a digital employee according to claim 1, characterized in that: Step 3 specifically includes: Training data preparation: Collect historical business data, professional knowledge base and best practice cases as training data, clean and pre-process the data to ensure data quality and availability; Training algorithm selection: Select appropriate machine learning algorithms, including deep learning and reinforcement learning algorithms, based on business needs and data characteristics; Training process: Use training data to train digital employees, adjust algorithm parameters, optimize model performance, and continuously improve the accuracy and efficiency of digital employees in processing tasks through iterative training.

5. The method for building a digital employee according to claim 1, characterized in that: Step 4 specifically includes: System integration: Integrate the trained digital workers with the enterprise's information system to ensure that the digital workers can work with the existing IT system; Deployment and implementation: Deploy digital employees to the enterprise information system to automate business processes, ensure that digital employees can perform tasks efficiently and accurately, and improve the efficiency and accuracy of overall business process execution; Monitoring and optimization: Monitor the operating status of digital employees, collect operating data, and optimize and adjust the digital employees' workflow and task allocation strategies based on data feedback.

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