Auxiliary bid evaluation system and method based on intelligent expert management

Abstract

The invention relates to the technical field of auxiliary bid evaluation, in particular to an auxiliary bid evaluation system and method based on intelligent expert management, and the system comprises a project information collection and processing module which is used for collecting bid evaluation time limit characteristics and project data information of a to-be-evaluated project, and carrying out the word segmentation of the project data information, obtaining a project representation phrase set; the bid evaluation time limit matching module is used for traversing in a bid evaluation expert database according to the bid evaluation time limit characteristics, screening out bid evaluation experts matched with the bid evaluation time limit characteristics, and marking the bid evaluation experts as first-order bid evaluation experts; the benefit conflict detection module is used for screening all the first-order bid evaluation experts by utilizing a pre-introduced conflict detection mechanism, rejecting the first-order bid evaluation experts having a benefit relationship with the to-be-evaluated item, and marking the remaining first-order bid evaluation experts as second-order bid evaluation experts; the system can improve the quality and efficiency of the overall bid evaluation work.

Description

Technical Field

[0001] The present invention relates to the technical field of auxiliary bid evaluation, and in particular to an auxiliary bid evaluation system and method based on intelligent expert management. Background Art

[0002] In bidding activities, bid evaluation is a key link to ensure the smooth implementation of the project and the selection of the best supplier. When selecting bid evaluation experts, it is necessary not only to consider their professional background and experience, but also to ensure that there is no conflict of interest between the experts and the bidding units to ensure the fairness of the bid evaluation.

[0003] The existing method of selecting bid evaluation experts mainly relies on the personal connections and subjective judgment of the tendering party or agency, which can only roughly reflect the professional background of the experts and cannot accurately match the technical requirements and professional fields of the project. Moreover, conflict of interest detection mainly relies on the self-declaration of experts and lacks a systematic automatic detection mechanism, which can easily miss potential conflicts of interest. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides an auxiliary bid evaluation system and method based on intelligent expert management to improve the quality and efficiency of the overall bid evaluation work.

[0005] In a first aspect, the present invention provides an auxiliary bid evaluation system and method based on intelligent expert management, the system comprising: The project information collection and processing module is used to collect the evaluation time limit characteristics and project data information of the projects to be evaluated, and to perform word segmentation on the project data information to obtain a set of project representation phrases; The bid evaluation time limit matching module is used to traverse the bid evaluation expert database according to the bid evaluation time limit characteristics, screen out bid evaluation experts matching the bid evaluation time limit characteristics, and mark them as first-order bid evaluation experts; The conflict of interest detection module uses the pre-introduced conflict detection mechanism to screen all first-level bid evaluation experts, eliminate the first-level bid evaluation experts who have a conflict of interest with the bid evaluation project, and mark the remaining first-level bid evaluation experts as second-level bid evaluation experts; The expert information collection and processing module collects the expert information corresponding to the second-order bid evaluation experts, and performs word segmentation processing on the expert information to obtain a set of expert bid evaluation feature phrases; The feature phrase matching calculation module uses the project representation phrase set to perform intersection calculations with the expert bid evaluation feature phrase sets corresponding to each second-order bid evaluation expert in turn, to obtain the project expert feature phrase matching sets corresponding to each second-order bid evaluation expert; The bid evaluation compatibility calculation module performs weight calculation on the project expert feature phrase matching set for each second-order bid evaluation expert to obtain the bid evaluation compatibility between the second-order bid evaluation expert and the project to be evaluated; The bid evaluation expert selection module extracts the second-order bid evaluation experts with the highest bid evaluation fit ranking as the selected bid evaluation experts for the projects to be evaluated based on the preset bid evaluation expert quantity requirement.

[0006] Furthermore, it also includes a word segmentation processing submodule, and the word segmentation processing submodule includes: The format conversion unit is used to convert the collected project information into a text format, remove irrelevant characters and spaces, and delete duplicate information; The text segmentation and part-of-speech tagging unit is used to segment continuous text into individual words, remove words that appear frequently in the document but do not carry actual meaning, tag each word with a part-of-speech tag, and identify different types of words; A related vocabulary extraction unit extracts vocabulary reflecting the nature and category of the project from the project description; The phrase classification unit is used to classify and organize the extracted phrases.

[0007] An auxiliary bid evaluation method based on intelligent expert management, the method comprising: Collecting bid evaluation time limit features and project data information of the bid evaluation projects, and performing word segmentation processing on the project data information to obtain a project characterization phrase set; the project characterization phrase set includes a project type phrase subset, a professional field phrase subset, and a technical requirement phrase subset; the bid evaluation time limit features include a bid evaluation start time node and a bid evaluation end time node; Based on the bid evaluation time limit feature, traverse the bid evaluation expert database, screen out bid evaluation experts matching the bid evaluation time limit feature, and mark them as first-order bid evaluation experts; Using the conflict detection mechanism introduced in advance, all first-level bid evaluation experts are screened, those who have a vested interest in the project to be evaluated are eliminated, and the remaining first-level bid evaluation experts are marked as second-level bid evaluation experts; Collect the expert information corresponding to the second-order bid evaluation experts, and perform word segmentation on the expert information to obtain a set of expert bid evaluation feature phrases; The project representation phrase set is used to perform intersection calculations with the expert bid evaluation feature phrase set corresponding to each second-order bid evaluation expert in turn, so as to obtain the project expert feature phrase matching set corresponding to each second-order bid evaluation expert; For each second-order bid evaluation expert, weight calculation is performed on the project expert characteristic phrase matching set to obtain the bid evaluation fit between the second-order bid evaluation expert and the project to be evaluated; Based on the preset number requirement of bid evaluation experts, the second-level bid evaluation experts with the highest bid evaluation fit ranking are extracted as the selected bid evaluation experts for the projects to be evaluated.

[0008] Furthermore, weight calculation is performed on the project expert feature phrase matching set to obtain the bid evaluation fit between the second-order bid evaluation expert and the bid evaluation project, including: Grouping each phrase in the project expert feature phrase matching set according to a project type phrase subset, a professional field phrase subset, and a technical requirement phrase subset; Counting the number of phrases in each phrase subset and marking it as the evaluation match value of the corresponding phrase subset; Presetting the weight coefficients corresponding to each phrase subset; Based on the bid evaluation matching value and weight coefficient corresponding to each phrase subset, a weighted calculation is performed to obtain the bid evaluation fit between the second-order bid evaluation expert and the project to be evaluated.

[0009] Furthermore, factors influencing the setting of the weight coefficient include relevance to the professional field, importance of technical requirements, frequency of phrases, experience and qualifications of experts, previous performance in bid evaluation, research results and awards and honors received.

[0010] Furthermore, the method for constructing the bid evaluation expert database includes: Obtain information on bid evaluation experts through public resources and industry associations; Standardize the collected expert information; Encrypt sensitive information and enter the processed information into the bid evaluation expert database; Set database access permissions so that only authorized personnel can access and modify data in the database; Contact experts regularly to update personal information, synchronize time availability and latest research results.

[0011] Furthermore, the conflict detection mechanism includes: Set up a conflict of interest database to record the potential conflict of interest information of all bid evaluation experts; Compare the information of the first-level bid evaluation experts with the records in the conflict of interest database to check whether the experts have worked in the bidding unit and its affiliated companies; In response to identifying a first-order bid evaluation expert who has a potential conflict of interest with the bid evaluation project, the conflict detection mechanism will mark the identified first-order bid evaluation expert and remove him from the set of first-order bid evaluation experts; and mark the remaining first-order bid evaluation experts as second-order bid evaluation experts; Conduct manual review on the second-level bid evaluation experts to check whether there are misjudgments and omissions in the screening results of the conflict detection mechanism.

[0012] Furthermore, the expert information corresponding to the second-order bid evaluation expert includes professional background, research type, historical bid evaluation participation information, historical published articles and historical awards received.

[0013] Furthermore, the method for obtaining the project expert feature phrase matching set includes: For each second-order bid evaluation expert, the intersection of the corresponding expert bid evaluation feature phrase set and the project representation phrase set is calculated; Record the results of the intersection calculation to form a matching set of project expert feature phrases corresponding to each second-order bid evaluation expert; Performing quality inspection on the formed project expert feature phrase matching set to obtain quality inspection results; The project expert feature phrase matching set whose quality inspection results are higher than the preset quality inspection threshold is applied to the calculation of bid evaluation fit.

[0014] Furthermore, the method for determining the selected bid evaluation experts includes: Preset the number of bid evaluation experts required; All second-level bid evaluation experts are ranked according to their bid evaluation fit; According to the preset number of bid evaluation experts, the top-ranked experts are selected from the sorted second-order bid evaluation experts as the selected bid evaluation experts; in the process of selecting the selected bid evaluation experts, if the top-ranked experts are unable to participate in the bid evaluation for some reason, the alternative experts are selected in descending order according to the ranking order of bid evaluation fit until the preset number of bid evaluation experts is met; Notify the experts selected for bid evaluation and provide them with bid evaluation materials and information; Establish a bid evaluation team based on the list of selected bid evaluation experts.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: the system can collect the information of the project to be evaluated in detail through the project information collection and processing module, and perform word segmentation processing to obtain a set of project characterization phrases; the system can more accurately understand the specific needs and technical requirements of the project; using the feature phrase matching calculation module and the bid evaluation fit calculation module, the system can perform in-depth matching calculations on the professional background of the second-order bid evaluation experts and the project requirements, thereby screening out experts who are highly compatible with the project; through the conflict of interest detection module, using the pre-introduced conflict detection mechanism, all first-order bid evaluation experts are screened, effectively improving the comprehensiveness and accuracy of conflict of interest detection; it helps to reduce the risks brought about by human negligence and subjective judgment, and ensure the fairness of bid evaluation; through automation and intelligent methods, the system can quickly screen out experts who meet the requirements. The bid evaluation experts requested can be selected, avoiding the tedious and time-consuming manual screening in the traditional way; the bid evaluation time limit matching module can quickly screen out available bid evaluation experts according to the bid evaluation time limit characteristics of the project, thereby gaining time for subsequent bid evaluation work; it can intelligently match the most suitable bid evaluation experts according to the specific needs of the project, thereby ensuring that expert resources are reasonably allocated and utilized; it helps to improve the utilization rate of experts and reduce bid evaluation delays or quality problems caused by insufficient or unreasonable allocation of expert resources; by systematically recording and analyzing the data of the bid evaluation process, it can further strengthen the monitoring and evaluation of the bid evaluation quality and improve the fairness and credibility of the bid evaluation; in summary, the auxiliary bid evaluation system based on intelligent expert management not only solves the pain points existing in the current bid evaluation process, but also provides a strong guarantee for the standardization, specialization and efficiency of bidding activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a structural diagram of the auxiliary bid evaluation system based on intelligent expert management; Figure 2 It is a flow chart of the auxiliary bid evaluation method based on intelligent expert management. DETAILED DESCRIPTION

[0017] The present application is described below in conjunction with the drawings in the present application.

[0018] Embodiment 1: Figure 1 As shown, the auxiliary bid evaluation system based on intelligent expert management of the present invention specifically includes the following modules: The project information collection and processing module is used to collect the evaluation time limit characteristics and project data information of the projects to be evaluated, and to perform word segmentation on the project data information to obtain a set of project representation phrases; The bid evaluation time limit matching module is used to traverse the bid evaluation expert database according to the bid evaluation time limit characteristics, screen out bid evaluation experts matching the bid evaluation time limit characteristics, and mark them as first-order bid evaluation experts; The conflict of interest detection module uses the pre-introduced conflict detection mechanism to screen all first-level bid evaluation experts, eliminate the first-level bid evaluation experts who have a conflict of interest with the bid evaluation project, and mark the remaining first-level bid evaluation experts as second-level bid evaluation experts; The expert information collection and processing module collects the expert information corresponding to the second-order bid evaluation experts, and performs word segmentation processing on the expert information to obtain a set of expert bid evaluation feature phrases; The feature phrase matching calculation module uses the project representation phrase set to perform intersection calculations with the expert bid evaluation feature phrase sets corresponding to each second-order bid evaluation expert in turn, to obtain the project expert feature phrase matching sets corresponding to each second-order bid evaluation expert; The bid evaluation compatibility calculation module performs weight calculation on the project expert feature phrase matching set for each second-order bid evaluation expert to obtain the bid evaluation compatibility between the second-order bid evaluation expert and the project to be evaluated; The bid evaluation expert selection module extracts the second-order bid evaluation experts with the highest bid evaluation fit ranking as the selected bid evaluation experts for the projects to be evaluated based on the preset bid evaluation expert quantity requirement.

[0019] Through the project information collection and processing module, the system can collect detailed information about the project to be evaluated, and perform word segmentation processing to obtain a set of project representation phrases, so that the system can more accurately understand the specific needs and technical requirements of the project; By using the feature phrase matching calculation module and the bid evaluation fit calculation module, the system can conduct in-depth matching calculations between the professional background of the second-order bid evaluation experts and the project requirements, thereby selecting experts who are highly compatible with the project; The system uses the conflict of interest detection module and the pre-introduced conflict detection mechanism to screen all first-level bid evaluation experts, effectively improving the comprehensiveness and accuracy of conflict of interest detection; helping to reduce the risks caused by human negligence and subjective judgment, and ensuring the fairness of bid evaluation; The system uses automation and intelligence to quickly screen out qualified bid evaluation experts, avoiding the tedious and time-consuming manual screening in traditional methods. The bid evaluation time limit matching module can quickly screen out available bid evaluation experts based on the bid evaluation time limit characteristics of the project, thus saving time for subsequent bid evaluation work. The system can intelligently match the most suitable bid evaluation experts according to the specific needs of the project, thereby ensuring that expert resources are reasonably allocated and utilized; it helps to improve the utilization rate of experts and reduce bid evaluation delays or quality problems caused by insufficient or unreasonable allocation of expert resources; The intelligent and automated features of the system make the bid evaluation process more transparent and traceable, helping to reduce the possibility of human intervention and shady operations; by systematically recording and analyzing the data of the bid evaluation process, the monitoring and evaluation of the bid evaluation quality can be further strengthened, and the fairness and credibility of the bid evaluation can be improved; To sum up, this auxiliary bid evaluation system based on intelligent expert management not only solves the pain points in the current bid evaluation process, but also provides a strong guarantee for the standardization, specialization and efficiency of bidding activities; the application of such a system in practice will help promote the healthy development of the industry and improve the quality and efficiency of the overall bid evaluation work.

[0020] The method for constructing a bid evaluation expert database includes: Obtain information on bid evaluation experts through public resources and industry associations; Standardize the collected expert information; Encrypt sensitive information and enter the processed information into the bid evaluation expert database; Set database access permissions so that only authorized personnel can access and modify data in the database; Contact experts regularly to update personal information, synchronize time availability and latest research results.

[0021] By building a multi-dimensional bid evaluation expert database, we can efficiently traverse and accurately match based on the bid evaluation time limit characteristics of the project; this not only greatly shortens the screening time, but also improves the matching accuracy, ensuring that the selected experts not only meet the time requirements, but also have the corresponding professional background and bid evaluation experience.

[0022] Embodiment 2: Figure 2 As shown, the auxiliary bid evaluation method based on intelligent expert management of the present invention specifically includes the following steps: S1. Collect the bid evaluation time limit features and project data information of the bid evaluation project, and perform word segmentation on the project data information to obtain a project characterization phrase set; the project characterization phrase set includes a project type phrase subset, a professional field phrase subset, and a technical requirement phrase subset; the bid evaluation time limit features include a bid evaluation start time node and a bid evaluation end time node; The method for word segmentation processing of project information includes: Convert the collected project information into text format for further processing; Remove irrelevant characters, spaces, and line breaks, and unify the format to ensure the neatness and consistency of the data; Delete duplicate information to avoid unnecessary redundancy in subsequent processing; Split continuous text into individual words; Remove words that appear frequently in documents but do not carry any real meaning; Tag each word with a part of speech tag to identify different types of vocabulary; Identify names of people, places, and institutions in the text. These entities often have important meanings and may play a key role in the matching process. Extracting vocabulary reflecting the nature and category of the project from project descriptions and other relevant documents; Extract vocabulary from professional and technical fields related to the project; Extract specific performance indicators and technical requirements from technical specifications; The extracted phrases are classified and sorted to obtain a set of project representation phrases.

[0023] In this step, by collecting the evaluation time characteristics and project information of the projects to be evaluated and performing preprocessing steps, it is ensured that the data basis for subsequent analysis is neat, consistent and valid; this avoids analysis errors caused by data redundancy and inconsistent formats; segmenting continuous text into separate words and tagging each word with parts of speech helps to understand the text content more accurately and provides a basis for subsequent phrase extraction and classification; at the same time, by removing frequently appearing words that do not carry actual meaning, noise data is reduced and the accuracy of analysis is improved; by identifying entities such as names of people, places, and institutions in the text, and extracting Vocabulary that reflects the nature and category of the project, vocabulary in professional and technical fields, as well as specific performance indicators and technical requirements, can deeply explore the key information in the project materials and provide rich data support for subsequent bid evaluation work; the extracted phrases are classified and organized to form a set of project characterization phrases, which not only helps to clearly present the project characteristics, but also facilitates subsequent matching, analysis and evaluation work; through the preprocessing and phrase extraction in this step, the amount of text that bid evaluators need to read and understand can be greatly reduced, while providing more accurate and targeted project information, which helps to improve the efficiency and accuracy of bid evaluation work.

[0024] S2. Based on the bid evaluation time limit feature, traverse the bid evaluation expert database, select bid evaluation experts matching the bid evaluation time limit feature, and mark them as first-order bid evaluation experts; Design the table structure of the bid evaluation expert database, which includes: Basic information table: used to store the expert's basic information, including name, contact information and unit; Professional background table: used to record the expert's professional field, educational background and work experience; Time availability table: maintain the expert's schedule, including other scheduled bid evaluation tasks, holidays and other unavailable time periods; Historical bid evaluation record table: saves the details of the bid evaluation activities that the experts have participated in in the past, including the name, type and evaluation results of the project; The method for constructing the bid evaluation expert database comprises: Obtain information about bid evaluation experts through public resources and industry associations. Information about bid evaluation experts includes the expert's name, gender, age, professional background, educational background, work experience, contact information, past bid evaluation experience, published articles, and awards received; Standardize the collected expert information and convert the standardized data into a structured format; Encrypt sensitive information and enter the processed information into the bid evaluation expert database to ensure the security of data during storage and transmission; Set access permissions to ensure that only authorized personnel can access and modify data in the database; Conduct qualification review on new experts to confirm that the information they provide is accurate and meets the standards for becoming a bid evaluation expert; Contact experts regularly to update their personal information, availability and latest research results; encourage experts to synchronize their personal calendars with the system so that their availability is automatically updated; Set up a reminder function to notify experts in a timely manner when there is a new bid evaluation invitation and ask them to confirm whether they can participate; when arranging experts, take into account possible emergencies and reserve a certain buffer time for each expert; In this step, by constructing a multi-dimensional bid evaluation expert database, efficient traversal and precise matching can be performed based on the bid evaluation time limit characteristics of the project; not only the screening time is greatly shortened, but also the matching accuracy is improved, ensuring that the selected experts not only meet the time requirements, but also have the corresponding professional background and bid evaluation experience; it effectively avoids the subjectivity and uncertainty of human judgment, and further guarantees the fairness and independence of bid evaluation; the encryption processing of sensitive information and strict access permission settings ensure the security of data in the bid evaluation expert database during storage and transmission; it not only protects the personal privacy of experts, but also prevents illegal access and leakage of data, providing a strong guarantee for the long-term stable operation of the database; the maintenance of the time availability table and the qualification review of new experts ensure the reasonable scheduling and optimal utilization of expert resources; the system can update the expert's schedule and the latest research results in real time, providing more accurate information for the tenderer. , reliable expert resource information; at the same time, by setting reminder functions and reserving buffer time, the system can also effectively respond to possible emergencies and ensure the smooth progress of bid evaluation; the preservation and updating of historical bid evaluation record sheets enable the system to comprehensively record the details of experts' bid evaluation activities and evaluation results; this not only provides rich reference information for the tenderer, but also helps the experts themselves to continuously improve their bid evaluation capabilities and levels; at the same time, by regularly contacting experts to update personal information and research results, the system can also maintain the vitality and timeliness of the expert database, and further improve the quality and efficiency of bid evaluation; this step not only improves the efficiency and accuracy of bid evaluation work by building a complete bid evaluation expert database and introducing advanced screening mechanisms, but also further ensures the fairness and independence of bid evaluation; at the same time, by strengthening data security and privacy protection and optimizing the use of expert resources, it provides more convenient and reliable services for both tenderers and experts.

[0025] S3. Utilize the conflict detection mechanism introduced in advance to screen all first-order bid evaluation experts, remove those who have a vested interest in the project to be evaluated, and mark the remaining first-order bid evaluation experts as second-order bid evaluation experts; The conflict detection mechanism includes: The conflict of interest database is used to record the potential conflict of interest information of all bid evaluation experts, including the companies the experts have worked for, the projects the experts have participated in and their related parties, the cooperation relationship between the experts and the bidders, the tenderers and other related parties, and other interest relationships of the experts; Automatic comparison and identification is used to compare the information of the first-level bid evaluation experts with the records in the conflict of interest database to check whether the experts have worked in the bidding unit and its affiliated companies; check whether the experts have participated in other projects related to the bid to be evaluated; check whether the experts have cooperative relationships with the bidding unit, the tenderer and other relevant parties; check whether the experts have other interest relationships that affect the fairness of the bid evaluation; Marking and screening of potential conflicts of interest. Once first-level bid evaluation experts with potential conflicts of interest with the bid evaluation project are identified, the conflict detection mechanism will mark these experts and remove them from the first-level bid evaluation expert set; the remaining first-level bid evaluation experts, i.e. those who have no conflicts of interest with the bid evaluation project, will be regarded as second-level bid evaluation experts and enter the subsequent bid evaluation process; Manual review and adjustment: In order to ensure the accuracy of the screening results, manual review is carried out; check whether there are misjudgments and omissions in the screening results of the conflict detection mechanism, and make adjustments based on actual conditions; check whether the experts marked as having potential conflicts of interest actually have conflicts of interest; check whether there are potential conflicts of interest that have not been identified by the conflict detection mechanism; adjust and correct the screening results of the conflict detection mechanism based on the results of the manual review.

[0026] In this step, through the detailed records of the conflict of interest database and the automatic comparison and identification functions, individuals among the first-level bid evaluation experts who have potential conflicts of interest with the project to be evaluated can be accurately screened out; the marking and screening process of potential conflicts of interest ensures that experts with direct or indirect interests in the project are eliminated in a timely manner, thereby avoiding the impact of conflicts of interest on the fairness of the bid evaluation results; the conflict detection mechanism has a high degree of automation and can quickly complete the screening of a large number of first-level bid evaluation experts, significantly improving the efficiency of the bid evaluation process; although the manual review and adjustment link adds additional steps, the double verification of the screening results ensures the accuracy and reliability of the final list of second-level bid evaluation experts, avoiding repeated work and time caused by misjudgment or omissions. waste; through a strict conflict detection mechanism, it is ensured that there is no conflict of interest between the selected second-level evaluation experts and the projects to be evaluated, so that they can focus more on the technical requirements and professional fields of the project itself and conduct more objective and professional evaluation work; it helps to improve the professional level of the entire evaluation process and ensure that the final selected suppliers or service providers can truly meet the needs of the project; the introduction and application of the conflict detection mechanism will help to build a more fair, just and transparent bidding environment and enhance the trust and satisfaction of the participants; this step introduces a conflict detection mechanism to conduct strict screening and selection of the first-level evaluation experts, which not only ensures the fairness and efficiency of the evaluation, but also improves the professionalism of the evaluation and the healthy development level of the entire bidding activity.

[0027] S4, collecting the expert information corresponding to the second-order bid evaluation experts, and performing word segmentation processing on the expert information to obtain a set of expert bid evaluation feature phrases; The expert information corresponding to the second-level bid evaluation experts includes: Professional background: including the expert’s education, professional qualifications and work experience; Research direction: including the experts’ main research areas and research results; Previous bid evaluation experience: including the expert’s historical bid evaluation records, bid evaluation experience and evaluation feedback; Published articles: including academic papers, conference reports and technical articles by experts; Awards received: including academic awards, industry honors and other achievements from experts; The method for performing word segmentation processing on expert information includes: Use the selected word segmentation tool to perform basic word segmentation on the text in the expert information; cut a continuous text into individual words; Remove words that appear frequently in most documents but do not carry any real meaning. These words usually do not help to understand the core content of the text. Perform part-of-speech tagging on each word after segmentation to identify different types of words such as nouns, verbs, adjectives, etc. Identify proper nouns such as names of people, places, and institutions in the text; Using parsers or sequence models to identify collocations and phrases in text that better express an expert’s domain and technical expertise than individual words; Identify and extract technical terms and industry-specific vocabulary related to the project based on domain-specific glossaries or dictionaries; Merge the processed words and phrases to form a comprehensive expert bid evaluation feature phrase set; Remove duplicates and sort or classify phrases according to importance and relevance to facilitate subsequent matching calculations; The processed expert bid evaluation feature phrase set is stored in the database for quick retrieval and call.

[0028] In this step, by collecting comprehensive information of the second-order bid evaluation experts, we can fully and deeply understand the experts' abilities and expertise; the integration of information provides a solid foundation for subsequent word segmentation and feature extraction; the selected word segmentation tool is used to segment the expert information, and by removing high-frequency meaningless words, part-of-speech tagging, identifying proper nouns, identifying fixed collocations and phrases, and extracting technical terms and industry-specific vocabulary, we can accurately extract feature phrases related to expert bid evaluation; it can more accurately reflect the experts' professional fields and technical expertise, and provide strong support for subsequent matching calculations; by removing duplicates, sorting or classifying the expert bid evaluation feature phrase set, we can further improve the matching accuracy. The efficiency and accuracy of the matching calculation can be improved; this enables experts who match the project requirements to be found more quickly in the subsequent bid evaluation process, thereby improving the fairness and professionalism of the bid evaluation; the processed expert bid evaluation feature phrase set is stored in the database, which can be easily retrieved and called; it not only improves work efficiency, but also makes the use of expert information more flexible and convenient; this step systematically collects and processes the information of the second-order bid evaluation experts, and accurately extracts the feature phrases related to the bid evaluation, which provides strong support and guarantee for the subsequent bid evaluation work; at the same time, by improving the matching efficiency and accuracy and facilitating rapid retrieval and calling and other beneficial effects, the quality and efficiency of the bid evaluation work are further improved.

[0029] S5, using the project representation phrase set and the expert bid evaluation feature phrase set corresponding to each second-order bid evaluation expert to perform intersection calculation in turn, to obtain the project expert feature phrase matching set corresponding to each second-order bid evaluation expert; For each second-level bid evaluation expert, prepare the corresponding expert bid evaluation feature phrase set, and ensure that these phrases have been cleaned and standardized for accurate matching calculations; similarly, the project characterization phrase set also needs to be kept clean and standardized to ensure the accuracy of the calculation results; The method for obtaining the project expert feature phrase matching set includes: For each second-order bid evaluation expert, the intersection of the corresponding expert bid evaluation feature phrase set and the project representation phrase set is calculated; the common phrases in the two sets are found, and the common phrases reflect the common points and fit between the expert and the project; The results of the intersection calculation are recorded to form a matching set of project expert feature phrases corresponding to each second-order bid evaluation expert; the matching set of project expert feature phrases contains the common feature phrases between experts and projects; when recording, the data needs to be kept clear and readable for subsequent analysis and evaluation; Perform quality checks on the formed project expert feature phrase matching set to ensure the accuracy and completeness of the matching results; including checking whether the matching set contains all the phrases that should be matched, and whether the phrases that should not be matched are excluded; The quality-checked project expert feature phrase matching set is stored in the database for subsequent analysis and evaluation.

[0030] In this step, through intersection calculation, the commonalities and compatibility between the project and the expert can be accurately found, avoiding the errors caused by relying on subjective judgment or rough matching in the past; cleaning and standardization processing ensure the accuracy and consistency of the phrases, further improving the accuracy of matching; the project expert feature phrase matching set provides an objective and quantitative basis for the selection of evaluation experts, which helps the tenderer or agency to more scientifically and reasonably screen out evaluation experts that meet project needs; by comparing the matching sets of different experts, the degree of compatibility between the expert and the project can be more intuitively evaluated, so as to select the most suitable evaluation expert; the automated processing of intersection calculation and data storage greatly shortens the screening time of evaluation experts and improves the efficiency of the evaluation process. ; Clear and readable data records make subsequent analysis and evaluation more convenient and efficient; through objective matching calculations, the impact of human intervention and subjective judgment is reduced, and the fairness and transparency of the bid evaluation process is enhanced; the quality inspection of the matching set ensures the accuracy and completeness of the matching results, and further guarantees the fairness of the bid evaluation; the project expert feature phrase matching set stored in the database provides valuable reference data for the subsequent bid evaluation of similar projects; the S5 step brings significant beneficial effects to the auxiliary bid evaluation process through precise intersection calculation, cleaning and standardization processing, objective selection basis, efficient automatic processing and strict quality inspection, and improves the accuracy, efficiency, fairness and knowledge reusability of the bid evaluation.

[0031] S6. For each second-order bid evaluation expert, weight calculation is performed on the project expert characteristic phrase matching set to obtain the bid evaluation fit between the second-order bid evaluation expert and the project to be evaluated; The method for obtaining the bid evaluation conformity includes: Grouping each phrase in the project expert feature phrase matching set according to a project type phrase subset, a professional field phrase subset, and a technical requirement phrase subset; Counting the number of phrases in each phrase subset and marking it as the evaluation match value of the corresponding phrase subset; Assigning weight coefficients to different phrase subsets in the project characterization phrase set and corresponding feature phrases in the expert evaluation feature phrase set; Calculate the weighted cumulative value of the intersection phrases in the matching set of project expert characteristic phrases corresponding to each second-order bid evaluation expert; after calculating the intersection of the project representation phrase set and the expert bid evaluation characteristic phrase set, accumulate each phrase in the intersection according to its assigned weight, and use it to quantify the matching degree between the expert and the project; Since the sizes of expert evaluation feature phrase sets of different second-order evaluation experts are different, the number of intersection phrases will also be different, and the accumulated weight values ​​need to be normalized; the evaluation fit values ​​of different experts are unified into a comparable range for subsequent ranking and selection; According to the normalized weight accumulation value, the bid evaluation fit between each second-order bid evaluation expert and the bid evaluation project is calculated, which reflects the matching degree between the expert and the project. The higher the value, the higher the fit between the expert and the project, and the more suitable the expert is to be the bid evaluation expert for the project. The factors affecting the setting of the weight coefficient include: Professional field relevance: the degree of relevance of the professional field represented by each phrase to the project to be evaluated; if a phrase is very specific and directly related to the specific needs of the project, it should be given a higher weight; Importance of technical requirements: The degree to which the technical requirements represented by each phrase are critical to the successful implementation of the project; some technical requirements may be critical to the quality and safety of the project and should be given a higher weight; Frequency of a phrase: how often a phrase appears in the set of item representation phrases and how common it is in the entire database; phrases that appear frequently and are highly relevant should be given higher weights, while phrases that appear but are not unique can be given lower weights; The expert’s experience and qualifications: The expert’s level of experience and qualifications in their field of expertise; experienced experts may contribute more in certain key areas, so phrases related to these areas may be given higher weight; Past bid evaluation performance: the types and scale of projects the expert has evaluated in the past and their performance; if the expert has a good record of evaluating similar projects in the past, then phrases related to those projects can be given a higher weight; Research results: The number and quality of articles published by experts in their professional fields, as well as the influence of their research results; experts who have published a large number of articles in high-impact journals or have significant research results should be given higher weights in their relevant fields; this reflects the authority and depth of the expert in the field; Awards and honors received: relevant awards and honors received by the expert in his or her career; for experts who have received industry-recognized awards or honors, phrases in their relevant fields should be given higher weights; this indicates the expert’s outstanding contributions and professional abilities in the field.

[0032] In this step, by assigning weights to the phrases in the project characterization phrase set and the expert evaluation feature phrase set, and calculating the weight accumulation value of the intersection phrase, the fit between each second-order evaluation expert and the project to be evaluated can be accurately quantified; the evaluation process is made more objective and scientific, and the influence of subjective judgment is reduced; by normalizing the weight accumulation value, the evaluation fit values ​​of different experts are unified into a comparable range, which is convenient for subsequent ranking and selection; the efficiency of the evaluation work is greatly improved, making the evaluation process faster and more efficient; the influencing factors of weight allocation include multiple aspects, which are factors jointly determine the fit between experts and projects; this comprehensive consideration method ensures the fairness and rationality of the bid evaluation process; by calculating the bid evaluation fit, it can be intuitively seen which experts are more matched with the project to be evaluated, so as to more accurately select the bid evaluation experts who are most suitable for the project; this not only improves the quality of the bid evaluation results, but also provides a strong guarantee for the successful implementation of the project; the bid evaluation fit calculation method and weight allocation mechanism established in this step can be dynamically adjusted and optimized according to actual conditions; it helps to continuously improve the quality and efficiency of bid evaluation work and promote the continuous improvement and development of bid evaluation work.

[0033] S7. Based on the preset number requirement of bid evaluation experts, the second-level bid evaluation experts with the highest bid evaluation fit ranking are selected as the bid evaluation experts for the bid evaluation project; The method for determining the selected bid evaluation experts includes: According to the scale, complexity and budget of the project, a reasonable number of evaluation experts should be set. The number of experts should be sufficient to ensure the comprehensiveness and professionalism of the evaluation work and avoid unnecessary waste of resources. According to the evaluation fit of each second-level evaluation expert, all experts are ranked from large to small according to the fit value; the larger the value, the higher the fit between the expert and the project to be evaluated; According to the preset number of bid evaluation experts, the top-ranked experts are selected from the sorted second-level bid evaluation experts as the selected bid evaluation experts; these experts will form the final bid evaluation team and be responsible for reviewing the bid documents of the bid evaluation projects; In the process of selecting the selected bid evaluation experts, if the top-ranked experts are unable to participate in the bid evaluation for some reason, the alternative experts will be selected in descending order according to the ranking order of bid evaluation fit until the preset number of bid evaluation experts is met; Once the list of selected bid evaluation experts is determined, these experts should be notified in a timely manner to inform them that they have been selected as members of the bid evaluation team and to arrange the specific time and place for the bid evaluation. At the same time, the experts should also be provided with necessary bid evaluation materials and information to ensure that they can fully understand the situation of the project to be evaluated; Establish a bid evaluation team based on the list of selected bid evaluation experts; the bid evaluation team should have comprehensive professional knowledge and rich bid evaluation experience to ensure the fairness, accuracy and efficiency of the bid evaluation work; at the same time, it is also necessary to clarify the responsibilities and division of labor of the bid evaluation team to ensure the smooth progress of the bid evaluation work.

[0034] In this step, by sorting the second-order bid evaluation experts based on the bid evaluation fit and selecting the top-ranked experts to form the bid evaluation team, it can be ensured that the bid evaluation work is highly professional and comprehensive; the experts have a high degree of fit with the projects to be evaluated, and can more accurately understand the project needs and technical requirements, so as to make more fair and reasonable bid evaluation decisions; pre-setting a reasonable number of bid evaluation experts and selecting experts based on the fit ranking can avoid unnecessary waste of resources and improve bid evaluation efficiency; this targeted selection method reduces the redundancy and delays that may occur in the bid evaluation process, so that the bid evaluation work can be carried out more efficiently; through intelligent expert management and conflict detection mechanisms, it can ensure that there is no conflict of interest between the selected bid evaluation experts and the bidding units, thereby enhancing the fairness of the bid evaluation; it helps to maintain the fairness and transparency of bidding activities and protect the interests of all parties; in the extraction of selected bid evaluation experts In the process of bid evaluation, if the top-ranked experts are unable to participate in the bid evaluation, this step provides an alternative plan, that is, to select experts in descending order according to the degree of fit. This flexible resource allocation method ensures the continuity and stability of the bid evaluation work and avoids interruptions or delays in the bid evaluation due to the absence of experts. Once the list of selected bid evaluation experts is determined, this step also emphasizes the importance of timely notifying experts, arranging the time and place of bid evaluation, and providing necessary bid evaluation materials and information. It helps to ensure the smooth progress of bid evaluation and improve the quality and efficiency of bid evaluation. This step ensures the professionalism and comprehensiveness of bid evaluation through intelligent expert management and scientific bid evaluation expert selection methods, improves bid evaluation efficiency, enhances bid evaluation fairness, optimizes bid evaluation resource allocation, and promotes the smooth progress of bid evaluation. Together, it promotes the healthy development of bidding activities and provides a strong guarantee for the smooth implementation of projects and the selection of suppliers.

[0035] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An auxiliary bid evaluation system based on intelligent expert management, characterized in that: The system comprises: The project information collection and processing module is used to collect the evaluation time limit characteristics and project data information of the projects to be evaluated, and to perform word segmentation on the project data information to obtain a set of project representation phrases; The bid evaluation time limit matching module is used to traverse the bid evaluation expert database according to the bid evaluation time limit characteristics, screen out bid evaluation experts matching the bid evaluation time limit characteristics, and mark them as first-order bid evaluation experts; The conflict of interest detection module uses the pre-introduced conflict detection mechanism to screen all first-level bid evaluation experts, eliminate the first-level bid evaluation experts who have a conflict of interest with the bid evaluation project, and mark the remaining first-level bid evaluation experts as second-level bid evaluation experts; The expert information collection and processing module collects the expert information corresponding to the second-order bid evaluation experts, and performs word segmentation processing on the expert information to obtain a set of expert bid evaluation feature phrases; The feature phrase matching calculation module uses the project representation phrase set to perform intersection calculations with the expert bid evaluation feature phrase sets corresponding to each second-order bid evaluation expert in turn, to obtain the project expert feature phrase matching sets corresponding to each second-order bid evaluation expert; The bid evaluation compatibility calculation module performs weight calculation on the project expert feature phrase matching set for each second-order bid evaluation expert to obtain the bid evaluation compatibility between the second-order bid evaluation expert and the project to be evaluated; The bid evaluation expert selection module extracts the second-order bid evaluation experts with the highest bid evaluation fit ranking as the selected bid evaluation experts for the projects to be evaluated based on the preset bid evaluation expert quantity requirement.

2. The auxiliary bid evaluation system based on intelligent expert management as claimed in claim 1 is characterized in that: It also includes a word segmentation processing submodule, which includes: The format conversion unit is used to convert the collected project information into a text format, remove irrelevant characters and spaces, and delete duplicate information; The text segmentation and part-of-speech tagging unit is used to segment continuous text into individual words, remove words that appear frequently in the document but do not carry actual meaning, tag each word with a part-of-speech tag, and identify different types of words; A related vocabulary extraction unit extracts vocabulary reflecting the nature and category of the project from the project description; The phrase classification unit is used to classify and organize the extracted phrases.

3. An auxiliary bid evaluation method based on intelligent expert management, characterized in that: The method is applied to the auxiliary bid evaluation system based on intelligent expert management as claimed in claim 1, and the method comprises: Collecting bid evaluation time limit features and project data information of the bid evaluation projects, and performing word segmentation processing on the project data information to obtain a project characterization phrase set; the project characterization phrase set includes a project type phrase subset, a professional field phrase subset, and a technical requirement phrase subset; the bid evaluation time limit features include a bid evaluation start time node and a bid evaluation end time node; Based on the bid evaluation time limit feature, traverse the bid evaluation expert database, screen out bid evaluation experts matching the bid evaluation time limit feature, and mark them as first-order bid evaluation experts; Using the conflict detection mechanism introduced in advance, all first-level bid evaluation experts are screened, those who have a vested interest in the project to be evaluated are eliminated, and the remaining first-level bid evaluation experts are marked as second-level bid evaluation experts; Collect the expert information corresponding to the second-order bid evaluation experts, and perform word segmentation on the expert information to obtain a set of expert bid evaluation feature phrases; The project representation phrase set is used to perform intersection calculations with the expert bid evaluation feature phrase set corresponding to each second-order bid evaluation expert in turn, so as to obtain the project expert feature phrase matching set corresponding to each second-order bid evaluation expert; For each second-order bid evaluation expert, weight calculation is performed on the project expert characteristic phrase matching set to obtain the bid evaluation fit between the second-order bid evaluation expert and the project to be evaluated; Based on the preset number requirement of bid evaluation experts, the second-level bid evaluation experts with the highest bid evaluation fit ranking are extracted as the selected bid evaluation experts for the projects to be evaluated.

4. The auxiliary bid evaluation method based on intelligent expert management as claimed in claim 3 is characterized in that: The weight calculation is performed on the project expert feature phrase matching set to obtain the evaluation fit between the second-order evaluation expert and the project to be evaluated, including: Grouping each phrase in the project expert feature phrase matching set according to a project type phrase subset, a professional field phrase subset, and a technical requirement phrase subset; Counting the number of phrases in each phrase subset and marking it as the evaluation match value of the corresponding phrase subset; Presetting the weight coefficients corresponding to each phrase subset; Based on the bid evaluation matching value and weight coefficient corresponding to each phrase subset, a weighted calculation is performed to obtain the bid evaluation fit between the second-order bid evaluation expert and the project to be evaluated.

5. The auxiliary bid evaluation method based on intelligent expert management as claimed in claim 4 is characterized in that: The factors affecting the setting of the weight coefficient include relevance to the professional field, importance of technical requirements, frequency of phrases, experience and qualifications of experts, historical bid evaluation performance, research results and awards and honors received.

6. The auxiliary bid evaluation method based on intelligent expert management as claimed in claim 3 is characterized in that: The method for constructing a bid evaluation expert database includes: Obtain information on bid evaluation experts through public resources and industry associations; Standardize the collected expert information; Encrypt sensitive information and enter the processed information into the bid evaluation expert database; Set database access permissions so that only authorized personnel can access and modify data in the database; Contact experts regularly to update personal information, synchronize time availability and latest research results.

7. The auxiliary bid evaluation method based on intelligent expert management as claimed in claim 3 is characterized in that: The conflict detection mechanism includes: Set up a conflict of interest database to record the potential conflict of interest information of all bid evaluation experts; Compare the information of the first-level bid evaluation experts with the records in the conflict of interest database to check whether the experts have worked in the bidding unit and its affiliated companies; In response to identifying a first-order bid evaluation expert who has a potential conflict of interest with the bid evaluation project, the conflict detection mechanism will mark the identified first-order bid evaluation expert and remove him from the set of first-order bid evaluation experts; and mark the remaining first-order bid evaluation experts as second-order bid evaluation experts; Conduct manual review on the second-level bid evaluation experts to check whether there are misjudgments and omissions in the screening results of the conflict detection mechanism.

8. The auxiliary bid evaluation method based on intelligent expert management as claimed in claim 3 is characterized in that: The expert information corresponding to the second-level bid evaluation experts includes professional background, research type, historical bid evaluation participation information, historical published articles and historical awards received.

9. The auxiliary bid evaluation method based on intelligent expert management as claimed in claim 3, characterized in that: The method for obtaining the project expert feature phrase matching set includes: For each second-order bid evaluation expert, the intersection of the corresponding expert bid evaluation feature phrase set and the project representation phrase set is calculated; Record the results of the intersection calculation to form a matching set of project expert feature phrases corresponding to each second-order bid evaluation expert; Performing quality inspection on the formed project expert feature phrase matching set to obtain quality inspection results; The project expert feature phrase matching set whose quality inspection results are higher than the preset quality inspection threshold is applied to the calculation of bid evaluation fit.

10. The auxiliary bid evaluation method based on intelligent expert management as claimed in claim 3, characterized in that: The method for determining the selected bid evaluation experts includes: Preset the number of bid evaluation experts required; All second-level bid evaluation experts are ranked according to their bid evaluation fit; According to the preset number of bid evaluation experts, the top-ranked experts are selected from the sorted second-order bid evaluation experts as the selected bid evaluation experts; in the process of selecting the selected bid evaluation experts, if the top-ranked experts are unable to participate in the bid evaluation for some reason, the alternative experts are selected in descending order according to the ranking order of bid evaluation fit until the preset number of bid evaluation experts is met; Notify the experts selected for bid evaluation and provide them with bid evaluation materials and information; Establish a bid evaluation team based on the list of selected bid evaluation experts.

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