Bid evaluation expert extraction system

Abstract

The invention provides a bid evaluation expert extraction system. The bid evaluation expert extraction system comprises a black box module and an expert extraction module. The black box module ensures data security and operation compliance through data isolation and authority control. The expert extraction module realizes automatic expert extraction, notification and feedback management, and covers the functions of parameter configuration, random extraction, scheme supplementation, timed tasks and automatic settlement. The system reduces manual intervention, optimizes operation timeliness, ensures fairness, transparency and high efficiency of the bid evaluation process through task automation and intelligent scheme control, and improves the overall operation efficiency and data security.

Description

Technical Field

[0001] The present invention relates to a bid evaluation expert extraction system, in particular to a bid evaluation expert extraction system based on RPA (robotic process automation) and black box design. Background Art

[0002] In today's information society, the bid evaluation expert extraction system plays a key role in various bidding and procurement activities. With the rapid development of science and technology and the widespread popularity of the Internet, the traditional bid evaluation expert extraction method has gradually exposed many shortcomings. The traditional bid evaluation expert extraction method mainly relies on manual operation, which has a series of problems, such as cumbersome operation, long time consumption and easy human error. In addition, the manual extraction method also has risks such as data leakage and irregular operation. These problems seriously affect the fairness, transparency and efficiency of the bid evaluation process, and it is difficult to meet the needs of modern management and efficient operation. In view of this, the bid evaluation expert extraction system has gradually become a research hotspot in the field of bidding and procurement, in order to improve the fairness, transparency and efficiency of the bid evaluation process through technical means.

[0003] At present, in the fields of government procurement, engineering construction, and scientific and technological project evaluation, contractors or suppliers are usually selected through open bidding or invitation bidding. In order to ensure the fairness, impartiality, and transparency of the bidding process, relevant fields often randomly select a group of experts with relevant professional knowledge and experience from the bid evaluation expert pool to form a bid evaluation committee. These experts are responsible for conducting a comprehensive review of the bid documents to ensure the objectivity and scientificity of the bid evaluation results. However, the traditional method of selecting bid evaluation experts mainly relies on manual operation, that is, the bidding agency or the tendering party manually selects or randomly selects a certain number of experts from the expert pool within a specific time period. This manual operation method has many disadvantages.

[0004] First, manual operation is prone to errors. In actual operation, manual selection of experts may result in repeated selection, omission or wrong selection, which not only affects the fairness of the bid evaluation, but may also have an adverse impact on the final bid evaluation results. For example, during manual operation, due to personnel fatigue, distraction or unskilled operation, it is easy to repeatedly select the same expert or omit the expert who should be selected, which in turn affects the professionalism of the expert group and the fairness of the evaluation results.

[0005] Secondly, manual operation is inefficient. The traditional manual extraction method consumes a lot of time and manpower, especially when the expert database is large and a large number of experts need to be extracted. The problem of operational efficiency is particularly obvious. Specifically, manual operation not only requires manual screening and recording of the expert database, but also requires contacting the extracted experts one by one to confirm their participation intention and time arrangement. This series of operation steps is time-consuming and laborious, which seriously affects the overall progress of the bidding review work.

[0006] In addition, low data security is also a major drawback of traditional manual operation. During manual operation, operators directly contact and operate expert information, which increases the risk of data leakage. Due to the lack of effective monitoring and auditing mechanisms, improper operations or even information leakage may occur. For example, when operators extract experts, they may leak expert information due to subjective or objective factors. This situation not only violates the principle of data confidentiality, but also may infringe on the personal privacy of experts, thereby affecting the fairness and authority of the review work.

[0007] Furthermore, the randomness and fairness of the traditional manual extraction method are difficult to guarantee. Due to the lack of a scientific random extraction mechanism, the manual extraction method is susceptible to human intervention, and it is difficult to fully guarantee the randomness and fairness of the extraction process. For example, during the manual operation process, the operator may give priority to specific experts or exclude certain experts based on their own judgment or external pressure. This will directly affect the randomness and fairness of expert extraction, and thus affect the objectivity and scientificity of the bid evaluation results.

[0008] In order to overcome the limitations of traditional manual extraction methods, automation and intelligent technologies have gradually been introduced into the design of bid evaluation expert extraction systems. The application of these technologies not only significantly improves the efficiency of the extraction process, but also greatly enhances the randomness and fairness of the extraction process. Automated extraction technology uses computer programs and algorithms to automatically complete the expert extraction process. Through preset rules and algorithms, the randomness and fairness of each extraction can be ensured. In addition, automated extraction technology can also significantly shorten the extraction time and improve work efficiency. Through the design of the automated system, the links of manual operation are reduced, the errors and deviations caused by human factors are reduced, and the reliability of the system and the credibility of the extraction results are improved.

[0009] The rule-based extraction mechanism is an important application of automation technology. This mechanism automatically selects qualified experts to participate in the bid evaluation according to pre-set rules. These rules usually include the expert's professional field, years of experience, and the number of times he has participated in the bid evaluation in the past. Through this mechanism, the professionalism and rationality of the extracted experts can be guaranteed. For example, in the bid evaluation of a specific engineering project, the preset rules can be used to ensure that the extracted experts have professional knowledge and experience in the relevant field, thereby improving the professionalism and scientificity of the bid evaluation results.

[0010] With the continuous development of artificial intelligence technology, intelligent technology has gradually been applied to expert extraction systems. By analyzing historical bid evaluation data, intelligent technology can optimize the algorithms and strategies of expert extraction, thereby further improving the fairness and rationality of the extraction process. For example, machine learning algorithms can analyze previous expert extraction and bid evaluation data to identify potential rules and patterns, thereby optimizing the expert extraction process, reducing human intervention and operational bias, and improving the fairness and scientificity of the extraction results.

[0011] Although the application of automation and intelligent technology in expert extraction systems has achieved remarkable results, there are still some challenges and problems in practical applications. First of all, data isolation and security is an important issue. In the expert extraction process, how to ensure the security and confidentiality of the extracted data and prevent data leakage or improper use are factors that need to be considered in system design.

[0012] Secondly, the black-box design of the system is also an important challenge. In order to avoid human intervention and improve the reliability of the system, a black-box design is usually adopted to encapsulate the specific extraction process in an invisible module that cannot be directly accessed or modified by external users. Although this design improves the security and stability of the system, it also reduces operational flexibility and transparency. For example, the black-box design may limit users from adjusting extraction rules and strategies according to actual needs, thereby affecting the applicability and flexibility of the system.

[0013] In addition, process optimization and task scheduling are also important difficulties in the design of expert extraction systems. In practical applications, the expert extraction process often needs to consider multiple factors, such as the type and scale of the project, the time and location of the bid evaluation, and the expert's availability. Therefore, how to optimize the extraction process to ensure that the expert extraction and notification work is completed in the shortest time is a difficulty in system design. For example, the system needs to design a reasonable task scheduling mechanism to automatically trigger corresponding operations according to the status of the project and plan to improve work efficiency and response speed.

[0014] Finally, regulatory compliance and compliance are also key considerations in the design of expert extraction systems. During the expert extraction process, it is necessary to strictly comply with the requirements of relevant laws and regulations to ensure the compliance of the extraction process. For example, the system needs to meet regulatory requirements in algorithm and process design to ensure the fairness and impartiality of the extraction process. In addition, effective supervision and auditing are required in actual operations to ensure that all operations are carried out within the framework of legality and compliance. Summary of the invention

[0015] In order to solve the above problems in the prior art, the present invention proposes a bid evaluation expert extraction system, which includes:

[0016] The black box module is used to execute the expert extraction process. During the extraction process, the data does not interact directly with the main database and controls the data access rights;

[0017] The expert extraction module, based on RPA technology, realizes automatic extraction and notification of experts. The expert extraction module includes:

[0018] Parameter configuration function component, used to set the expert notification ratio and extraction times;

[0019] An automated extraction component to generate a random extraction sequence of experts that meets regulatory requirements;

[0020] Added a plan control component to preset notification time, reply time and report time;

[0021] Scheduled task module, used to trigger expert extraction and notification tasks based on project status;

[0022] The automatic settlement function component is used to perform system settlement after each round of notification and adjust the solution status and expert attendance.

[0023] The black box module includes:

[0024] Data isolation component, which performs data operations by setting up data users isolated from the main database and implements data isolation through database virtualization or partitioning technology;

[0025] The data user control component manages the permissions of isolated users based on fine-grained access control policies, adopts the principle of least privilege, and has multi-level permission control, real-time monitoring and auditing functions to prevent unauthorized access and operations.

[0026] The parameter configuration functional component includes:

[0027] Parameter extraction module, used to automatically identify and extract expert notification ratio, extraction times and notification time parameters;

[0028] Parameter configuration module, used to configure and adjust the extracted parameters;

[0029] Parameter storage module, used to store configured parameter sets;

[0030] The parameter application module is used to call and apply the configured parameters during the extraction and notification process.

[0031] The automatic extraction component comprises:

[0032] Data analysis module, used to obtain qualified expert information from the expert database and conduct preliminary screening according to project requirements;

[0033] The rule verification module is used to further screen and sort the initially screened expert list according to regulations and preset rules;

[0034] The randomization algorithm module is used to generate a random extraction sequence that meets the requirements and optimize the sequence according to multiple constraints;

[0035] The result verification module is used to verify whether the generated expert extraction sequence meets the preset conditions and regulatory requirements.

[0036] The supplementary scheme control component includes:

[0037] Time parameter setting module, used to set notification time, reply time and reporting time;

[0038] Time range restriction module, used to restrict notification and feedback operations to the set working hours;

[0039] Task scheduling module, used to schedule expert extraction and notification tasks and ensure the timeliness of operations;

[0040] The feedback monitoring module is used to monitor the expert's response in real time, send reminder notifications and update the expert's status.

[0041] The timing task module includes:

[0042] Task schedule generator, used to generate task schedule and set the time and frequency of task execution;

[0043] Conditional triggers are used to determine whether the task execution conditions are met based on the project and solution status, and trigger the task when the conditions are met;

[0044] Time scheduler, used to schedule task execution according to the task schedule and perform priority management;

[0045] The task executor is used to execute tasks at set time points and record the execution results and status.

[0046] The automatic settlement function component includes:

[0047] The attendance status determination module is used to determine the attendance status according to the expert's response after the expert notification ends. If there is no response, it is set as "not present";

[0048] The program status adjustment module is used to automatically adjust the current program status according to the attendance of experts and the number of shortfalls;

[0049] A rule matching module is used to automatically adjust the status of experts not notified in this round according to preset notification rules;

[0050] The notification sending module is used to generate notification content according to the current solution status and send notifications to relevant personnel.

[0051] Beneficial effects:

[0052] The bid evaluation expert extraction system of the present invention ensures the security and independence of data operations through the combination of black box modules and RPA technology, while realizing the automation of the expert extraction process, effectively reducing human intervention. The black box module prevents unauthorized access and data leakage through data isolation and fine-grained permission control, ensuring the compliance of operations. The expert extraction module realizes the automatic extraction, notification and feedback management of experts, and the parameter configuration and automatic extraction components improve the flexibility and fairness of the system. The scheduled task module optimizes the timeliness of operations and enhances the automation level of the system by automatically scheduling expert extraction and notification tasks. The automatic settlement function component ensures the efficient execution of the notification and extraction process, automatically adjusts the scheme status, reduces human errors, and improves the overall operating efficiency and fairness of the system. The overall system improves the efficiency and accuracy of the bid evaluation process through automated and intelligent management processes, ensuring the transparency and fairness of the bid evaluation work. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present application, but do not constitute an improper limitation of the present invention. In the drawings:

[0054] Figure 1 The overall architecture diagram of the bid evaluation expert extraction system of the present invention is shown. DETAILED DESCRIPTION

[0055] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the illustrative embodiments and descriptions are only used to explain the present invention but are not intended to limit the present invention.

[0056] Example 1: Overall architecture of the bid evaluation expert extraction system based on RPA and black box design

[0057] like Figure 1As shown, this embodiment provides a bid evaluation expert extraction system based on RPA (Robotic Process Automation) and black box design. The system includes a black box module 101 and an expert extraction module 102, wherein the black box module 101 is used to execute the expert extraction process and standardize the extraction operation from the business operation level; the expert extraction module 102 is based on RPA technology to realize automatic extraction and notification operations of experts.

[0058] The black box module 101 further includes a data isolation component 101a and a data user control component 101b. The data isolation component 101a performs data operations by setting up data users isolated from the main database to ensure that the extracted process data does not directly interact with the original database, thereby ensuring the security and independence of the data. The data user control component 101b is used to control the access rights of the extracted process data to prevent unauthorized users from accessing or operating the data.

[0059] The expert extraction module 102 includes a parameter configuration functional component 102a, an automated extraction component 102b, a supplementary solution control component 102c, a timed task module 102d and an automatic settlement functional component 102e.

[0060] The parameter configuration function component 102a is used to extract parameters such as the expert notification ratio and the number of extractions in the original process, and establish a unified parameter configuration function to reduce repeated operations and improve system efficiency.

[0061] The automated extraction component 102b automatically generates a random extraction sequence of experts that complies with regulatory requirements through automatic system identification and logic analysis.

[0062] The supplementary scheme control component 102c pre-sets parameters such as notification time, response time and reporting time in the expert extraction scheme preparation stage, and restricts operations within the prescribed working hours to ensure the timeliness and humanity of the notification.

[0063] The scheduled task module 102d sets scheduled tasks for expert extraction and notification, triggers corresponding operations based on the project and solution status, and ensures that the expert extraction and notification operations are automatically executed at the set time and under the set conditions.

[0064] The automatic settlement function component 102e is used to automatically execute the system settlement function after each round of expert notification, and adjust the solution status and expert attendance. Specifically, the automatic settlement function includes changing the status of unresponsive experts to "not present", adjusting the status of experts not notified in this round according to the notification rules, and adjusting the solution status according to different situations, such as "pending confirmation", "pending selection" or "pending issuance".

[0065] The working principle of this system is as follows: When it is necessary to extract bid evaluation experts, the system first isolates the extracted data through the data isolation component 101a in the black box module 101 to ensure data security. Then, the expert extraction module 102 automatically performs expert extraction and notification operations based on RPA technology. The system automatically generates the expert extraction sequence according to the set parameters and rules, and completes the notification and reply operations within the specified time range through the supplementary solution control component 102c. Through the scheduled task module 102d, the system can automatically trigger the expert extraction and notification tasks at a predetermined time point or under specific conditions. Finally, the automatic settlement function component 102e automatically settles according to the expert's response and the solution status to ensure the fairness and transparency of the entire bid evaluation process.

[0066] The bid evaluation expert extraction system based on RPA and black box design of the present invention effectively improves the efficiency and security of the bid evaluation process by introducing automation technology and data isolation mechanism, and ensures the randomness, fairness and compliance of the extraction process.

[0067] Embodiment 2: Black box module 101

[0068] The data isolation component 101a and the data user control component 101b are core components of the black box module 101 in the present invention. Their design and implementation are of great significance for ensuring the security, data independence and operational compliance of the bid evaluation expert extraction system.

[0069] The data isolation component 101a performs data operations by setting up data users isolated from the main database. The specific implementation is as follows:

[0070] One or more data users isolated from the main database (hereinafter referred to as "isolated users") are set up in the system. These isolated users are specially configured to handle data operations in the process of bid evaluation expert extraction. Isolated users are completely independent of other users in the main database and cannot directly access sensitive information in the main database or modify core data. The operating permissions of isolated users are strictly limited to the data processing scope of the extraction process, such as reading extraction results, generating extraction logs, storing temporary data, etc.

[0071] In system design, the data involved in the extraction process is physically separated from other data in the main database through database virtualization or partitioning technology. This separation can be achieved through the following technical means:

[0072] Database virtualization: Using database virtualization technology, the data in the extraction process is stored in an independent virtual database. The virtual database is separated from the main database through independent access control policies and network isolation measures to ensure data independence and security.

[0073] Partition isolation: In the same database instance, the extracted data is physically isolated from other data through data partitioning technology. Each partition has an independent access control list (ACL) to ensure that only specific isolated users can access the partition where the extracted data is located.

[0074] Under the control of the data isolation component 101a, when the isolated user performs data operations, the system first checks the access rights of the data through the data isolation mechanism to ensure that the operation is legal and compliant. After the extraction operation is completed, all temporary data and operation logs generated by the isolated user are stored in the isolated partition or virtual database, and a regular cleanup mechanism is used to prevent data leakage or accumulation.

[0075] The data user control component 101b is used to control the access rights of the extracted process data to prevent unauthorized users from accessing or operating the data. The specific implementation is as follows:

[0076] The data user control component 101b manages the permissions of each isolated user through a fine-grained access control policy. The access permissions are based on the principle of least privilege, that is, each isolated user can only access and operate the data within his or her scope of responsibility. For example, the user responsible for generating the extraction sequence can only read part of the information in the expert database, but cannot access the complete expert profile or historical bid evaluation data.

[0077] To ensure data security, the system has set up a multi-level permission control mechanism for data access. First, users need to pass the identity authentication and authorization process to obtain basic permissions to access isolated data. Secondly, for sensitive operations (such as modifying extraction results or exporting extraction logs), the system requires secondary verification, usually using multi-factor authentication (MFA) or approval processes to ensure the legitimacy of the operation.

[0078] The data user control component 101b also includes real-time monitoring and auditing functions. Each data access and operation request will be recorded and monitored in real time by the system to prevent unauthorized access or potential data tampering. The audit log generated by the system includes information such as access time, operation user, operation type, and data change content, and is stored through encryption technology to ensure the integrity and non-tamperability of the log.

[0079] The data isolation component 101a and the data user control component 101b jointly ensure the security, independence and compliance of data operations in the bid evaluation expert extraction system, thereby preventing data leakage, unauthorized access and operational errors. This implementation method has a high degree of security and operational transparency, meets the high standards of modern information security management, and ensures the fairness and credibility of the bid evaluation expert extraction process.

[0080] Embodiment 3: Parameter configuration functional component 102a

[0081] The parameter configuration function component 102a is used to extract parameters such as the expert notification ratio and the number of extractions in the original process, and establish a unified parameter configuration function to reduce repeated operations and improve system efficiency. The design and implementation of this component aims to uniformly configure and manage various parameters involved in the extraction and notification process in a systematic and modular way, thereby optimizing the overall process and reducing manual intervention.

[0082] Specifically, the parameter configuration functional component 102a includes a parameter extraction module, a parameter configuration module, a parameter storage module and a parameter application module.

[0083] 1. Parameter extraction module:

[0084] The parameter extraction module is responsible for automatically identifying and extracting key parameters from the original expert extraction process. These parameters usually include expert notification ratio, extraction times, notification time, response time, reporting time, etc. By analyzing historical data and configuration files, the parameter extraction module can automatically generate the parameter set required for the current task.

[0085] During implementation, the parameter extraction module can be operated through the following steps:

[0086] Historical data analysis: Analyze past expert extraction and notification operations to identify commonly used parameter values ​​and their changing patterns.

[0087] Parameter scanning: Scan and extract extraction and notification related parameters from existing process configuration files.

[0088] Automatic extraction: Use specific character matching rules to extract required parameters from complex process documents or configuration files.

[0089] For example, assuming that the original process file contains information such as expert notification ratio, extraction times and notification time window, the parameter extraction module can automatically identify and extract these parameter values ​​through specific character matching rules, and use them for subsequent configuration and operation.

[0090] 2. Parameter configuration module:

[0091] The parameter configuration module provides a unified interface for configuring and adjusting the various parameter values ​​obtained from the parameter extraction module. Operators can use this module to modify the extracted parameters or set new parameter values. This module allows users to flexibly configure according to specific project requirements to ensure that the extraction and notification process meets the actual requirements of the project.

[0092] In specific implementation, the parameter configuration module can adopt a graphical user interface (GUI) or a web-based configuration interface, and users can adjust parameters through controls such as drop-down menus, check boxes, and input boxes. For example, the operator can adjust the notification ratio to 40%, set the number of extractions to 3 times, and set the notification time window to "09:00-11:00" through the interface. The module will verify in real time whether the input parameter values ​​are legal, such as the notification ratio must be between 0-100%, the number of extractions must be a positive integer, and the notification time window must be a valid time range.

[0093] 3. Parameter storage module:

[0094] The parameter storage module is used to store the configured parameter set in the system's configuration database so that subsequent extraction and notification operations can be directly called. In order to improve the stability and reliability of the system, the parameter storage module usually adopts redundant storage and encrypted storage technology to ensure the security and availability of parameter data.

[0095] During implementation, the parameter storage module can be operated in the following ways:

[0096] Redundant storage: Store configuration parameters in multiple databases or tables simultaneously to prevent parameter loss due to a single point of failure.

[0097] Encrypted storage: Use encryption algorithms to encrypt and store sensitive parameters to prevent unauthorized access.

[0098] For example, the system can store configuration parameters such as expert notification ratio, extraction times, and notification time window in separate tables of the database, and set appropriate permissions and security measures for these parameters to ensure that only authorized users can access or modify these parameters.

[0099] 4. Parameter application module:

[0100] The parameter application module is responsible for calling and applying the configured and stored parameter values ​​in the actual expert extraction and notification process. This module automatically loads the relevant parameters each time the extraction and notification task is started, and performs corresponding operations based on these parameter values.

[0101] The implementation of this module can be done by following the steps below:

[0102] Parameter loading: Load the parameter set required for the current task from the parameter storage module.

[0103] Application logic: According to the loaded parameter values, the corresponding extraction and notification operations are performed. For example, according to the configured notification ratio, the system will automatically calculate the number of experts to be notified; according to the set extraction times, the system will execute the extraction operation repeatedly until the specified number is reached.

[0104] Assuming the configuration parameters are a notification ratio of 40% and a number of extractions of 3 times, the parameter application module will calculate the number of experts to be notified based on these parameters and perform the corresponding extraction operation. Specifically, the system first determines the total number of experts in the expert database, then calculates the number of experts to be notified, and finally, according to the setting of the number of extractions, executes the extraction operation cyclically until all extraction tasks are completed.

[0105] Through the above design and implementation of parameter configuration function component 102a, the present invention can effectively reduce repeated operations in the expert extraction and notification process, ensuring efficient operation of the system. Through the unified parameter configuration function, not only the flexibility and accuracy of the operation are improved, but also the foundation for the automation and intelligent operation of the system is laid.

[0106] Embodiment 4: Automated extraction component 102b

[0107] The automated extraction component 102b automatically generates a random extraction order of experts that meets regulatory requirements through automatic system identification and logical analysis. The design of this component is intended to reduce human intervention, improve the efficiency and fairness of the extraction process, and ensure compliance with relevant regulations and policy requirements. The specific implementation of the automated extraction component 102b is described in detail below so that technicians in the relevant technical field can implement it.

[0108] The core function of the automated extraction component 102b is to automatically generate a random and fair expert extraction order based on the rules and algorithms preset in the system. This component implements the automation and compliance of expert extraction through steps such as data analysis, rule verification, randomization algorithm, and result verification. The specific implementation process is as follows:

[0109] 1. Data analysis module:

[0110] The data analysis module first obtains all qualified expert information from the expert database. This information usually includes the expert's professional field, years of experience, geographical location, availability, and historical review records. The system will initially screen this data and exclude experts who do not meet the current review project requirements, such as experts whose professional fields do not match or who have participated in similar project reviews in the past.

[0111] The working steps of the data analysis module include:

[0112] Expert information acquisition: Extract all potentially eligible expert records from the expert database.

[0113] Preliminary screening: Screening is conducted based on project requirements and the experts’ professional background, experience and other information, and experts who do not meet the requirements are excluded.

[0114] 2. Rule verification module:

[0115] The rule verification module is responsible for ensuring that the automatic extraction process complies with regulatory requirements and preset rules. This module uses a series of logical rules to further screen and sort the preliminary selected expert list to ensure that all qualified experts have an equal chance of being selected, while meeting the regulatory requirements for randomness, fairness and non-repetitiveness.

[0116] The implementation steps of the rule verification module include:

[0117] Regulatory matching: Check whether the current extraction process complies with national or industry regulations. For example, experts who have participated in the review of certain specific projects should not be repeatedly extracted.

[0118] Logical verification: According to preset rules, such as regional balance, expert category ratio, etc., the preliminary screening results are adjusted and optimized to ensure that the final list meets the requirements of the rules.

[0119] 3. Randomization algorithm module:

[0120] The randomization algorithm module is the core function of the automated extraction component 102b, and is used to generate a random extraction order of experts that meets the requirements. In order to ensure the randomness and fairness of the extraction process, this module uses a high-quality random number generation algorithm, such as the Mersenne Twister algorithm or other random number generators that meet industry standards. The randomization process also needs to consider a variety of constraints, such as the professional matching degree of the experts, historical participation, etc., to ensure the rationality and effectiveness of the random extraction.

[0121] The randomization algorithm works as follows:

[0122] Random number generation: Use a high-quality random number generator to generate a random sequence to shuffle the list of screened experts.

[0123] Sequence application: Apply the generated random sequence to the list of experts and arrange the experts in the order of the random sequence to generate the final random selection order.

[0124] Multi-constraint processing: When generating random sequences, multiple constraints on experts are considered, such as each expert can only be selected once in a certain period of time, certain experts should be prioritized or excluded, etc.

[0125] To implement this step, the system first creates a random sequence with the same length as the expert list through a random number generator, and then rearranges the expert list according to this sequence. Assuming there is a list of experts, numbered from 1 to N, the system will generate a corresponding random number for each expert, and then reorder the expert list according to the size of these random numbers, thereby obtaining a random and non-repetitive extraction order.

[0126] 4. Result verification module:

[0127] The result verification module is used to verify whether the automatically generated random selection order of experts meets all preset conditions and regulatory requirements. The module will perform a series of checks and verifications on the generated order to ensure that there are no violations of the rules. For example, it verifies whether experts are repeatedly selected, whether all categories of experts are evenly distributed, and whether regional or other preferences are followed.

[0128] The working steps of the result verification module include:

[0129] Repeatability check: Ensure that no experts are drawn repeatedly in the generated random sequence.

[0130] Compliance verification: Check whether the extraction results comply with relevant laws and regulations, such as geographical distribution, fairness, etc.

[0131] Final confirmation: After all checks, confirm that the generated expert extraction sequence meets the requirements and apply it to the actual extraction and notification process.

[0132] Through the collaborative work of the above modules, the automated extraction component 102b can efficiently and fairly complete the random extraction task of experts, while strictly complying with relevant laws and policies to ensure the fairness and transparency of the review process. The design of this component not only reduces human intervention and improves the automation of the system, but also improves the fairness and rationality of the extraction results by optimizing algorithms and logical rules.

[0133] Embodiment 5: Supplemental solution control component 102c

[0134] In the expert extraction scheme preparation phase, the supplementary scheme control component 102c pre-sets parameters such as notification time, response time, and reporting time, and restricts operations within the specified working hours to ensure the timeliness and humanity of notifications. The design of this component aims to optimize the notification and feedback links in the expert extraction process through systematic time management and task scheduling, and improve the overall operation efficiency and user experience. The specific implementation of the supplementary scheme control component 102c is described in detail below.

[0135] The core function of the supplementary scheme control component 102c is to control each link of the expert notification and feedback process through the system preset time parameters and rules. The design of this component includes a time parameter setting module, a time range limit module, a task scheduling module and a feedback monitoring module. The functions and implementation methods of each module are detailed as follows:

[0136] 1. Time parameter setting module:

[0137] The time parameter setting module is used to pre-set key time parameters such as notification time, response time and reporting time during the expert extraction plan preparation stage. These time parameters can be adjusted according to the specific needs of the project and the actual situation of the experts to ensure the timeliness and effectiveness of the notification.

[0138] In the specific implementation process, the time parameter setting module allows the operator to input or select the required time parameters through the user interface. These parameters include but are not limited to:

[0139] Notification time: the time point or time period when the expert receives the extraction notification.

[0140] Response time: The time window within which the expert needs to respond whether he or she accepts the extraction task.

[0141] Reporting time: The time when the selected experts need to report to the designated location or online platform within a specific time.

[0142] The module provides default time parameter settings, such as "notification time is 9:00-11:00 am on weekdays", "response time is within 24 hours", and "check-in time is 2 hours before the project starts". Operators can also customize these time parameters according to actual needs.

[0143] 2. Time range limit module:

[0144] The time range limit module is used to ensure that all notification and feedback operations are carried out within the specified working hours. This module limits the system's notification and feedback functions through logical judgment of time parameters, and only executes them during working hours to ensure that experts receive notifications within a reasonable time frame and have sufficient time to respond.

[0145] The implementation steps of the time range limit module include:

[0146] Working time setting: pre-define the working time range of the system, such as "8:30-17:30 on weekdays".

[0147] Time judgment logic: Before each notification and feedback operation, the system first checks whether the current time is within the specified working time range. If not, the operation is postponed to the next working time period.

[0148] For example, at 7:00 a.m. on a weekday, when the system attempts to send a notification, the time range restriction module will determine that the current time is not within the set working hours, and will therefore delay the notification task until after 8:30 a.m. on the same day.

[0149] 3.Task scheduling module:

[0150] The task scheduling module is responsible for automatically scheduling expert extraction and notification tasks according to the set time parameters and working time range. This module uses a preset scheduling algorithm to ensure that all notification and feedback operations can be carried out at the most appropriate time, avoiding interruptions during non-working hours and ensuring the timeliness of operations.

[0151] The implementation steps of the task scheduling module are as follows:

[0152] Scheduling queue establishment: Establish a task scheduling queue based on the set time parameters and working time range, and add all notification and feedback tasks to be executed to the queue.

[0153] Prioritization: Sort the tasks in the scheduling queue according to their importance and urgency. Usually, tasks with a deadline approaching have a higher priority.

[0154] Automatic execution: When the time range conditions are met, the tasks in the queue are executed in order of priority.

[0155] In this way, the system can automatically check and execute all pending notifications and feedback tasks at the beginning of each working day, ensuring that all actions are completed within a reasonable time.

[0156] 4. Feedback monitoring module:

[0157] The feedback monitoring module is used to monitor the expert's response in real time and automatically take corresponding measures according to the preset time parameters. For example, if the expert does not respond within the specified response time, the system will automatically send a reminder notification or set the expert's status to "not participating".

[0158] The working steps of the feedback monitoring module include:

[0159] Real-time monitoring: After the notification is issued, the system monitors the experts' responses in real time and records the response time and content of each expert.

[0160] Automatic reminder: If you do not receive a reply from the expert within the set response time, the system will automatically send a reminder notification to prompt the expert to reply as soon as possible.

[0161] Status update: After the response time is over, the system will automatically update the expert's participation status based on the responses received. For example, the status of an expert who has not responded will be set to "not participating" to facilitate subsequent supplementary operations or notification of other experts.

[0162] Through the collaborative work of the above modules, the supplementary solution control component 102c can effectively manage and control the time parameters in the expert extraction process, ensuring that all operations are performed within the specified working time range, thereby improving the timeliness and humanity of notifications. At the same time, this component optimizes the overall process of expert extraction and notification through automated task scheduling and feedback monitoring, ensuring efficient operation of the system and user experience.

[0163] Embodiment 6: Scheduled task module 102d

[0164] The scheduled task module 102d is used to set the scheduled tasks of expert extraction and notification, trigger the corresponding operations based on the project and solution status, and ensure that the expert extraction and notification operations are automatically executed at the set time and under the set conditions. The design of this module aims to optimize the timeliness and accuracy of the expert extraction and notification process through automated scheduling and execution strategies, reduce manual intervention and improve the automation level of the system. The specific implementation of the scheduled task module 102d is described in detail below.

[0165] The core function of the scheduled task module 102d is to automatically schedule and execute expert extraction and notification tasks according to preset time and conditions. The design of this module includes a task plan generator, a condition trigger, a time scheduler, and a task executor. The functions and implementation methods of each module are described in detail as follows:

[0166] 1. Mission plan generator:

[0167] The task plan generator is responsible for generating the time schedule for expert extraction and notification tasks. According to the specific requirements and solution status of the project, the task plan generator sets the execution time and frequency of the task and generates the corresponding task plan.

[0168] In the specific implementation process, the task plan generator can be operated through the following steps:

[0169] Task type identification: Based on the current project and solution status, identify the type of task that needs to be performed (such as expert extraction or notification tasks).

[0170] Time parameter setting: Set the execution time and frequency of each task. For example, set the expert extraction task to be executed at 9:00 and 15:00 every day, and the notification task to be executed immediately after the expert extraction is completed.

[0171] Schedule generation: Generate a task schedule and record all planned tasks and their execution time and frequency in the schedule for use by subsequent modules.

[0172] 2. Conditional triggers:

[0173] Conditional triggers are used to determine whether to execute specific tasks in the task plan based on the status of projects and plans. This module monitors the project progress and plan status in the system to determine whether the conditions for executing the task are met.

[0174] The steps to implement a conditional trigger include:

[0175] Status monitoring: Continuously monitor the status of projects and plans, such as project progress, expert responses, task completion, etc.

[0176] Condition judgment: Based on the preset conditions (such as the solution status is "pending extraction" or "pending notification"), determine whether the corresponding task needs to be triggered.

[0177] Trigger operation: If the conditions are met, the time scheduler is triggered to execute the corresponding task; if the conditions are not met, monitoring continues.

[0178] For example, if the project status is updated to "pending extraction", the conditional trigger will detect this status change and trigger the next expert extraction task.

[0179] 3. Time Scheduler:

[0180] The time scheduler is used to arrange the specific execution time of the task according to the set time of the task schedule under the premise of meeting the conditions. This module ensures that all tasks are executed within the predetermined time period to achieve the best operation efficiency and effect.

[0181] The working steps of the time scheduler are as follows:

[0182] Time calibration: Calibrate the current time according to the system clock and task schedule, and determine the time for the next task to be executed.

[0183] Task scheduling: When the scheduled execution time arrives, the task is added to the queue for execution and prepared for execution.

[0184] Priority management: In the task queue, tasks are sorted according to their urgency and importance, and high-priority tasks are executed first.

[0185] Through the management of the time scheduler, the system can execute tasks at the appropriate time and avoid executing important tasks during non-working hours or inappropriate time periods.

[0186] 4. Task Executor:

[0187] The task executor is responsible for actually executing the task at the time scheduled by the time scheduler. The task executor will call the corresponding functional module to perform specific operations, such as expert extraction or sending notifications, according to the type and requirements of the task.

[0188] The implementation steps of the task executor include:

[0189] Task loading: Load the task to be executed from the queue to be executed, including the task type, target, and parameters.

[0190] Execution logic: Execute corresponding operations according to the task type. For example, for an expert extraction task, call the extraction algorithm module to extract experts; for a notification task, call the notification module to send a notification.

[0191] Result recording: After the task is completed, the execution results and status are recorded for subsequent analysis and scheduling.

[0192] During the task execution process, the task executor will monitor the execution progress and results in real time to ensure that the task is completed as planned, and take corrective measures when necessary, such as automatic retry or alarm when the task fails.

[0193] Through the collaborative work of the above modules, the timed task module 102d can effectively manage and execute the expert extraction and notification tasks, ensuring that all operations are automatically executed at the set time and under the set conditions. This automated task management and execution mechanism not only improves the efficiency and accuracy of the system, but also reduces human intervention, ensuring the fairness and timeliness of the bid evaluation process. The design of this module optimizes the overall process of expert extraction and notification by combining time management and conditional triggering strategies, and improves the system's automation level and user experience.

[0194] Embodiment 7: Automatic settlement function component 102e

[0195] The automatic settlement function component 102e is used to automatically execute the system settlement function after each round of expert notification, and adjust the solution status and expert attendance. The design of this component aims to transform the steps that originally required manual judgment into tasks automatically executed by the system through an automated process, thereby improving operational efficiency, reducing human errors, and ensuring the fairness and consistency of the settlement process. The specific implementation of the automatic settlement function component 102e is described in detail below.

[0196] The core functions of the automatic settlement function component 102e include automatically determining the attendance status of experts, adjusting the solution status, and automatically performing subsequent operations according to business rules. The design of this component includes an attendance status determination module, a solution status adjustment module, a rule matching module, and a notification sending module. The functions and implementation methods of each module are detailed as follows:

[0197] 1. Attendance status determination module:

[0198] The attendance status determination module is used to automatically determine the attendance status of each expert after the expert notification ends. Specifically, the module checks whether the expert has responded to the notification within the specified response time. If the expert does not respond within the specified time, the system will automatically set the expert's attendance status to "not present".

[0199] The implementation process includes the following steps:

[0200] Response time monitoring: The system monitors each expert's response time and compares it with the set response deadline.

[0201] Status update logic: For all experts who do not respond within the specified time, their attendance status will be automatically updated to "not present".

[0202] 2. Solution status adjustment module:

[0203] The solution status adjustment module is responsible for automatically adjusting the status of the current solution according to the attendance of experts and notification rules. This module determines the next operation status of the solution through logical judgment and conditional checking.

[0204] The specific logic of the solution status adjustment is as follows:

[0205] Calculation of the difference: The system first calculates the difference between the number of experts who have confirmed their attendance and the number of experts required in the current plan. If the difference is 0, the plan status changes to "pending confirmation".

[0206] When the difference number is not 0:

[0207] If the number of people with a shortfall is greater than 0, the number of people who have not been notified is 0, and the number of people who can be drawn is 0, the plan status changes to "pending confirmation", and the SMS platform is called to send a notification to the relevant personnel, indicating that the draw does not meet the requirements.

[0208] If the number of vacancies is greater than 0, the number of people who have not been notified is 0, there are still experts to be drawn, and the number of draws is less than the maximum number of draws, the plan status will be changed to "pending for selection".

[0209] If the number of people with a shortfall is greater than 0, the number of people who have not been notified is 0, there are still experts to be drawn, and the number of draws is equal to the maximum number of draws, the plan status is changed to "pending confirmation" and the SMS platform is called to send a notification.

[0210] If the number of people with a difference is greater than 0 and the number of people who have not been notified is greater than 0, the plan status will be changed to "pending for issuance".

[0211] 3. Rule matching module:

[0212] The rule matching module is used to automatically adjust the status of experts not notified in this round according to the preset notification rules. This module will analyze the responses of experts not notified in this round and make corresponding adjustments according to the preset rules.

[0213] The implementation process includes the following steps:

[0214] Analysis of responses not related to this round: Check the responses of all experts not notified in this round to determine whether they meet the requirements of the current program.

[0215] Status adjustment logic: Based on the analysis results, update the status of experts not notified in this round, such as adjusting the status of non-responding experts to "not present" or decide whether to continue notifying based on the response.

[0216] 4. Notification sending module:

[0217] The notification sending module is responsible for sending necessary notifications to relevant personnel according to different scheme statuses during the automatic settlement process. This module uses preset notification templates to ensure that relevant personnel are informed of the latest status of the scheme and the actions that need to be taken in a timely manner through SMS platforms or other communication means.

[0218] The implementation process includes the following steps:

[0219] Notification content generation: Generate corresponding notification content based on the current solution status and business rules. For example, when the number of people with a shortfall is greater than 0 and the conditions are met, generate a text message that says "the number of people who are not satisfied with the requirements"

[0220] Notification sending and execution: Call the SMS platform or other communication tools to send the generated notification content to relevant personnel, including review experts, selection and compilation personnel, and technical support department administrators.

[0221] The logic of notification sending ensures that at each key node, all relevant personnel can receive information in a timely manner and take necessary actions or adjustments.

[0222] Through the collaborative work of the above modules, the automatic settlement function component 102e can automatically execute the system settlement function and adjust the solution status and expert attendance after each round of expert notification. This automatic processing mechanism greatly reduces the need for manual intervention, ensures the efficiency and accuracy of the settlement process, and enhances the system's intelligence level and user experience through preset rules and automatic notifications.

[0223] The above content is a specific implementation of the bid evaluation expert extraction system based on RPA and black box design. The system architecture and working principle of the present invention are described in detail in conjunction with the accompanying drawings to facilitate a better understanding and implementation of the present invention.

[0224] The above description is only a preferred embodiment of the present invention, so all equivalent changes or modifications made according to the structure, characteristics and principles described in the scope of the patent application of the present invention are included in the scope of the patent application of the present invention.

Claims

1. A bid evaluation expert extraction system, characterized by: The system comprises: A black box module (101) is used to execute the expert extraction process, during which the data does not interact directly with the main database and controls data access rights; The expert extraction module (102) implements automatic extraction and notification of experts based on RPA technology. The expert extraction module (102) includes: Parameter configuration function component (102a), used to set up expert notification ratio and extraction times; An automated extraction component (102b) for generating a random extraction sequence of experts that meets regulatory requirements; Supplementary scheme control component (102c), used to preset notification time, reply time and report time; A timed task module (102d), used to trigger expert extraction and notification tasks based on project status; The automatic settlement function component (102e) is used to perform system settlement after each round of notification, and adjust the solution status and expert attendance.

2. A bid evaluation expert extraction system as claimed in claim 1, characterized in that: The black box module (101) comprises: A data isolation component (101a) performs data operations by setting up data users isolated from the main database and implements data isolation through database virtualization or partitioning technology; The data user control component (101b) manages the permissions of isolated users based on fine-grained access control policies, adopts the principle of least privilege, and has multi-level permission control, real-time monitoring and auditing functions to prevent unauthorized access and operations.

3. A bid evaluation expert extraction system as claimed in claim 1, characterized in that: The parameter configuration function component (102a) comprises: Parameter extraction module, used to automatically identify and extract expert notification ratio, extraction times and notification time parameters; Parameter configuration module, used to configure and adjust the extracted parameters; Parameter storage module, used to store configured parameter sets; The parameter application module is used to call and apply the configured parameters during the extraction and notification process.

4. A bid evaluation expert extraction system as claimed in claim 1, characterized in that: The automatic extraction component (102b) comprises: Data analysis module, used to obtain qualified expert information from the expert database and conduct preliminary screening according to project requirements; The rule verification module is used to further screen and sort the initially screened expert list according to regulations and preset rules; The randomization algorithm module is used to generate a random extraction sequence that meets the requirements and optimize the sequence according to multiple constraints; The result verification module is used to verify whether the generated expert extraction sequence meets the preset conditions and regulatory requirements.

5. The bid evaluation expert extraction system according to claim 1, characterized in that: The supplementation scheme control component (102c) comprises: Time parameter setting module, used to set notification time, reply time and reporting time; Time range restriction module, used to restrict notification and feedback operations to the set working hours; Task scheduling module, used to schedule expert extraction and notification tasks and ensure the timeliness of operations; The feedback monitoring module is used to monitor the expert's response in real time, send reminder notifications and update the expert's status.

6. A bid evaluation expert extraction system as claimed in claim 1, characterized in that: The timing task module (102d) comprises: Task schedule generator, used to generate task schedule and set the time and frequency of task execution; Conditional triggers are used to determine whether the task execution conditions are met based on the project and solution status, and trigger the task when the conditions are met; Time scheduler, used to schedule task execution according to the task schedule and perform priority management; The task executor is used to execute tasks at set time points and record the execution results and status.

7. The bid evaluation expert extraction system according to claim 1, characterized in that: The automatic settlement function component (102e) includes: The attendance status determination module is used to determine the attendance status according to the expert's response after the expert notification ends. If there is no response, it is set as "not present"; The program status adjustment module is used to automatically adjust the current program status according to the attendance of experts and the number of shortfalls; A rule matching module is used to automatically adjust the status of experts not notified in this round according to the preset notification rules; The notification sending module is used to generate notification content according to the current solution status and send notifications to relevant personnel.

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