CN110689282A - Artificial intelligence bid evaluation method and system based on big data analysis - Google Patents
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Abstract
The invention discloses an artificial intelligence evaluation method and system based on big data analysis, wherein the method comprises the following steps: receiving qualification information filled by a supplier; for each supplier to be evaluated, calling historical bidding information, comparing the qualification information of the supplier at this time with the qualification information of the supplier at the last time, identifying difference information, and generating auxiliary preliminary evaluation data; automatically allocating evaluation experts to each evaluation task according to evaluation task allocation rules, sending the evaluation tasks and corresponding auxiliary primary evaluation data to corresponding evaluation expert clients, and entering a primary evaluation stage; receiving the initial evaluation result of the client side of the evaluation expert, distributing the qualified evaluation task of the initial evaluation to the evaluation expert again, and entering a detailed evaluation stage; and (5) carrying out expert scoring. The invention can monitor each process of bid evaluation, effectively ensure the consistency of data in all links of the whole business process, reduce the manual intervention degree of all the business links of bid evaluation and ensure the fair and fair bid evaluation.
Description
Technical Field
The invention belongs to the technical field of big data statistical analysis, and particularly relates to an artificial intelligence bid evaluation method and system based on big data analysis.
Background
At present, related work is carried out on an Electronic Commerce Platform (ECP) by the enterprise bidding service of provinces, an ECP system has a complete bidding purchasing process, but most of information cannot be automatically acquired, manual input of staff, suppliers and experts of bidding agencies is needed, and the working efficiency is greatly influenced.
The supplier needs to make paper bidding documents after buying bidding documents or qualification prequalification documents during bidding each time, and the bidding documents made by the supplier are different in format, version of text editing software and the like and cannot be unified due to strong delicacy.
In the traditional task allocation mode, the expert team leader divides the labor after gathering the bid evaluation data, consumes time and manpower, and cannot ensure the reasonability and fairness of bid evaluation workload allocation.
In the traditional preliminary evaluation mode, an expert compares and verifies the bid documents according to the submitted bid documents of the suppliers, and manually records the verification results, so that the workload is large and errors are easy to occur. In the past, the expert detailed evaluation needs to form an effective bidder list and a disused list after the initial evaluation is finished, the supplier bidding documents are checked again to score the effective bidders in detail, a large amount of repeated checking work exists, a large amount of time is consumed, the manual intervention degree is high, and the scoring standards are inconsistent because everyone has different understandings on certain project information during the expert evaluation.
The traditional bid evaluation process is carried out offline in the whole process, various lists and reports are issued after data collection, preliminary evaluation and detailed evaluation, and the whole process is carried out in a manual gathering, sorting and checking and comparing mode, so that the process control performance is poor, the integrity and the consistency of data collection cannot be ensured, the manual influence is large, a communication channel between a supplier and a bid inviting person is lacked, the information is asymmetric, the bid evaluation service efficiency is greatly reduced, and the time service requirement cannot be met.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an artificial intelligent bid evaluation method and system based on big data analysis, all data are collected, circulated and reviewed on line in the whole process, the consistency of the data in all links of the whole business process is effectively ensured, the manual intervention degree of all business links of bid evaluation is reduced, the data information is tracked in the whole process, and the guarantee is provided for the subsequent bid evaluation work.
In order to achieve the above purpose, one or more embodiments of the present invention adopt the following technical solutions:
an artificial intelligence bid evaluation method based on big data analysis comprises the following steps:
receiving qualification information filled by a supplier;
for each supplier to be evaluated, calling historical bidding information, comparing the qualification information of the supplier at this time with the qualification information of the supplier at the last time, identifying difference information, and generating auxiliary preliminary evaluation data;
automatically allocating evaluation experts to each evaluation task according to evaluation task allocation rules, sending the evaluation tasks and corresponding auxiliary primary evaluation data to corresponding evaluation expert clients, and entering a primary evaluation stage;
receiving the initial evaluation result of the client side of the evaluation expert, distributing the qualified evaluation task of the initial evaluation to the evaluation expert again, and entering a detailed evaluation stage;
and (5) carrying out expert scoring.
Furthermore, a qualification information collecting template is configured in advance and used for collecting qualification information.
Further, after receiving the qualification information filled by the supplier, matching the supplier to be evaluated with a project manager according to a set rule and informing the project manager; and when receiving a review starting instruction sent by the project manager, starting the bid evaluation.
Furthermore, in the detailed evaluation stage, part of indexes in the evaluation task are objective scoring items and are automatically scored by the system, and the other part of indexes are subjective scoring items and are scored by an evaluation expert.
Further, the scores of the objective scoring items and the scores of the subjective scoring items are summarized to obtain the total score of each supplier of the bid to be scored, and the total score meeting a specific score system is obtained according to a quantitative scoring rule; the quantitative scoring rule is established according to the specific score system.
One or more embodiments provide an artificial intelligence bid evaluation system based on big data analysis, which comprises a server and a database server, wherein the server implements the following steps:
receiving qualification information filled by a supplier;
for each supplier to be evaluated, calling historical bidding information from a database server, comparing the qualification information of the supplier at this time with the qualification information of the supplier at the last time, identifying difference information, and generating auxiliary initial evaluation data;
automatically allocating evaluation experts to each evaluation task according to evaluation task allocation rules, sending the evaluation tasks and corresponding auxiliary primary evaluation data to corresponding evaluation expert clients, and entering a primary evaluation stage;
receiving the initial evaluation result of the client side of the evaluation expert, distributing the qualified evaluation task of the initial evaluation to the evaluation expert again, and entering a detailed evaluation stage;
and (5) carrying out expert scoring.
Further, the database server comprises a qualification information database, a bidding document database, a backup database and a location server for positioning and storing information.
One or more embodiments provide that the database server is connected to the server through a database connection pool, which is used to manage the allocation and release of connections between databases and servers in the database server.
One or more embodiments provide an artificial intelligence bid evaluation system based on big data analysis, comprising: a supplier bidding subsystem and an artificial intelligence evaluation subsystem,
the supplier bidding subsystem is used for receiving qualification information filled by a supplier and sending the qualification information to the artificial intelligence bid evaluation subsystem;
the artificial intelligence bid evaluation subsystem realizes the bid evaluation method.
One or more of the technical schemes have the following beneficial effects:
1. the system adopts an intelligent data collection means, flexibly configures a qualification information collection template, can directly call supplier qualification information, supplier performance information and a supplier registration list for preliminary evaluation during evaluation, effectively ensures the consistency of data in all links of the whole business process, avoids the risk of data errors, reduces the manual intervention degree of all business links of the evaluation, reduces the influence of human factors on the evaluation result, realizes the tracking of data information in the whole process, provides guarantee for the subsequent evaluation work, and greatly ensures the fairness and strict confidentiality of the evaluation work.
2. By adopting an intelligent task decomposition means, suppliers and projects are averagely divided into evaluation group experts, the same supplier business preliminary evaluation belongs to the same expert, the different supplier technology preliminary evaluation belongs to different experts, the task allocation of each expert is reasonable and fair, the phenomena of uneven allocation, intentional allocation and the like caused by artificial allocation are avoided, and the artificial deviation is effectively avoided.
3. By adopting intelligent data comparison, historical preliminary evaluation records are automatically collected in the preliminary evaluation early stage and are compared with qualification information filled by the bidding supplier, deviation information is automatically identified, important attention information and deviation information verification basis are provided for the preliminary evaluation of experts, a large amount of manpower is saved, offline comparison verification differences are avoided, and manual verification omission is effectively avoided. In the detailed evaluation stage, the items to be scored are divided into objective scoring items and subjective scoring items, scores are automatically calculated for an objective scoring item system, the situation that manual repeated checking work consumes a large amount of manpower and time is avoided, meanwhile, the influence of manual intervention on a scoring result is effectively reduced, and the risk of manual intervention is avoided.
4. Data collection, initial evaluation data comparison and automatic detailed evaluation scoring in the intelligent management bid evaluation service protect the vital interests of the bidders and the tenderers and provide guarantee for creating a reasonable, legal, fair and fair bidding environment.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
Fig. 1 is a system architecture diagram according to a first embodiment of the disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example one
The embodiment discloses an artificial intelligence system of evaluating mark based on big data analysis, includes: supplier bidding subsystem, artificial intelligence evaluation subsystem. Wherein the content of the first and second substances,
a supplier bidding subsystem, comprising:
the registration module is used for filling registration information by a supplier;
the qualification information input module is used for receiving qualification information input by a supplier based on the qualification information collecting template and sending the qualification information to the evaluation system; the entry module standardizes the supplier qualification information filling template and is beneficial to data collection and use. The qualification information comprises basic enterprise information, quality system certification, existing performance, production licenses, manufacturing equipment, testing equipment, manufacturing processes, technical strength, production environment, product performance and the like;
and the item reply module is used for receiving and displaying the item notification sent by the bid evaluation system, receiving the reply information of the supplier and sending the reply information to the bid evaluation system.
The artificial intelligence evaluation subsystem comprises:
the project manager client is used for configuring and maintaining the bid discarding rule and synchronizing the bid discarding rule to the bid evaluation system; and starting the bid evaluation process.
And (3) configuration and maintenance of the rule of the waste mark:
the bid discarding rule subdivides the requirements of the initial evaluation items of the bid document, and gives corresponding reasons, problem types and the like when any item does not meet the requirements. Aiming at the bid evaluation task which does not meet the initial evaluation requirement, the file containing the content such as the conclusion, the reason and the like can be automatically generated, and the workload is saved.
The rule template of the logoff rule in this embodiment includes: reasons for repudiation, reasons for not entering detailed comments, bid disuse templates, bid document requirements, bid document responses, business/technical classes, and overrules options.
Table 1 examples of rules templates for revocation
The evaluation expert client is used for receiving and displaying the initial evaluation task and the corresponding auxiliary initial evaluation data and receiving the evaluation result of the evaluation expert; and receiving the detailed evaluation task and receiving the scores of the evaluation experts. And comparing and displaying the qualification information of this time and the qualification information of the last time in the auxiliary initial evaluation data, and displaying the difference of the inconsistency of the two information.
And the tender company client is used for inquiring or downloading the supplier qualification summary table.
A server, the server comprising: the system comprises a data management module, a qualification information collection template configuration module, a quantitative scoring rule configuration module, a bid evaluation module and a matter notification module.
And the data management module is used for maintaining data such as project managers, review expert information, supplier registration information, qualification information collection templates, review task allocation rules, quantitative scoring rules and the like. The system comprises the following modules:
the qualification information collection template configuration module is used for configuring a qualification information collection template;
the quantitative scoring rule configuration module is used for configuring specific scoring rules;
and the bid evaluation module executes the following steps:
after the project is started, selecting a qualification information collecting template at a project layer, a mark layer and a cladding layer, and automatically generating a qualification information filling form by the system according to the template and sending the qualification information filling form to a supplier client;
receiving and summarizing qualification information filled by the suppliers, and distributing the qualification information to one or more project manager clients according to a set rule;
receiving a waste mark rule template confirmed by a project manager client and a review starting instruction related to a certain project group;
calling historical bidding information of related suppliers in a database, comparing the qualification information of the supplier at the current time with the qualification information of the supplier at the last time, identifying difference information and generating auxiliary preliminary evaluation data;
automatically allocating evaluation experts to each evaluation task according to evaluation task allocation rules, sending the evaluation tasks and corresponding auxiliary primary evaluation data to corresponding evaluation expert clients, and entering a primary evaluation stage;
receiving a primary evaluation result of an evaluation expert client, judging whether the evaluation result is qualified or not according to the evaluation result and a waste bid rule template, and entering a detailed evaluation stage for a primary evaluation qualified bid evaluation task after verification; for the bid evaluation task with unqualified initial evaluation, the system automatically triggers logic value taking, generates 'bid document provision' and 'bid document response' description, fuses project information (sub-table name, package number, reject template, reject reason and other information), and generates report materials and a waste bid list;
in the detailed evaluation stage, part of indexes in the evaluation task are objective scoring items and are automatically scored by a system, and the other part of indexes are subjective scoring items and are scored by an evaluation expert; and distributing the bidding evaluation task qualified by the initial evaluation to the evaluation experts again for the evaluation experts to score the subjective scoring items.
Furthermore, in the detailed evaluation stage, subjective evaluation items are subjectively evaluated by experts on the relevant evaluation elements, such as excellent, good and general. In the detailed evaluation stage, the scoring items are viewed by the customers, the data sources comprise reading records, letters, performance evaluation, bad behaviors, qualification prequalification grading levels and the like, before starting, related data are imported into the system and automatically calculated by a computer according to the detailed evaluation rules after quantification, the quantified data are rechecked by experts, and after the data needing to be corrected are provided by the experts, the scores are recalculated by the computer.
The objective scoring item is that the joint operation inspection department further refines and perfects the detailed evaluation template of the distribution network materials, compiles the grading evaluation standard of each evaluation element, combs up key elements, and forms a standard quantitative evaluation detailed rule standard to be applied to system evaluation.
Receiving the scores of the subjective scoring items sent by the evaluation expert client and summarizing the scores of the objective scoring items;
in order to make the score standard of the system consistent with the Electronic Commerce Platform (ECP), the embodiment further calculates the final score according to the quantitative scoring rule, and forms the final reading record.
The quantitative scoring rule adopts a rule of entering 1 and removing the tail, for example, the total score of the ECP financial status is only 14/16/20, and if the total score obtained by the system is 14, the final score is 14; if the total score obtained by the system is more than 14 and less than 18, the final score is 16; if the total score obtained by the system is more than 18 points, the final score is 20 points.
The evaluation task allocation rule is allocated by system intelligence according to the specialty, region, specialty, number of suppliers and the number of experts, and each expert group leader is responsible for checking and manually changing the expert tasks. By utilizing a big data technology, the field where the expert excels and the place where the work unit is located are intelligently collected, the system intelligently analyzes the specialty, the specialty and the like of the expert, avoids the region where the work unit where the expert is located is the same as the place where the supplier is located, and intelligently distributes tasks according to the number of the suppliers and the number of the experts. The project manager is divided into technical and business groups.
And the event notification module is used for initiating event notification of the supplier by the bidding agency and receiving clarification of the supplier. The tender selects suppliers by bid or package, issues notice of the matter, and receives the reply of the suppliers according to the question.
In this embodiment, the database system includes a qualification information database, a bid document database, and a backup database, where the qualification information database stores various qualification information (including but not limited to performance information, bad behaviors, and bid winning situations of historical suppliers) and associated information with bid documents.
Since the tender document belongs to sensitive information, the storage and transmission of data have high security requirements.
In the embodiment, the bidding document data is transmitted from the supplier bidding subsystem to the database server of the artificial intelligent bid evaluation subsystem in a breakpoint continuous transmission mode. And setting the length of each time the client reads the content. The client reads the file content each time according to the set length, establishes http connection, transmits the read file content and the parameter data to the background database system, receives background transfer parameters, continues to read the content behind the initial coordinate if the writing is successful until the end, and retransmits the content until the success or the three times of failure prompt if the writing is failed.
The transmission process comprises the following steps:
segmenting the bidding document according to the set length of each read content;
uploading all the file fragments to a server in sequence, and uploading parameters such as the file fragments and initial coordinates, end coordinates, total file length, names and the like of the fragments simultaneously when each file fragment is uploaded;
the server receives each file fragment and corresponding parameters, and if the initial coordinate is 0, ". temp" file of the current file name is generated; and writing the received file fragments into the temp file in sequence according to the initial coordinates of the file fragments until the ending coordinates are equal to the total length of the file, and modifying the name of the temp file into the name of the bid file after writing the file data.
The qualification information data and other related data are stored in a database server by a distributed data storage mode, and the database server comprises a plurality of independent computer devices. If the data stored in one of the devices is incomplete, the data is invalidated and cannot be extracted by the bid evaluation server, the data has extremely high availability and confidentiality, and when the data in all the devices is complete, the bid evaluation server extracts the data.
The distributed storage system adopts an expandable system structure, utilizes a plurality of storage servers to share the storage load, and utilizes the position server to position the storage information, thereby not only improving the reliability, the availability and the access efficiency of the system, but also being easy to expand.
As shown in fig. 1, the overall technical architecture of the system is based on the JavaEE architecture, and includes an internal network subsystem and an external network subsystem, both of which adopt a B/S layered architecture.
The outer network subsystem and the inner network subsystem both comprise:
the display layer is used for displaying the business data, displaying the business data to a supplier or a bidding agency for checking, and returning a related business data processing result to the supplier;
the presentation layer is used for collecting data input by the presentation layer and controlling the circulation of the supplier bid micro application and the intelligent auxiliary bid evaluation system page;
the application layer is used for processing the business logic of the supplier bid micro-application and the intelligent auxiliary bid evaluation system;
the database layer is used for interacting with the DB connection pool and storing data; a DB connection pool responsible for allocating, managing and releasing database connections, allowing applications to reuse one existing database connection instead of re-establishing one; the database connection with the idle time exceeding the maximum idle time is released, so that the omission of the database connection caused by the fact that the database connection is not released is avoided, and the performance of database operation is improved;
in this embodiment, the database server includes a qualification information database, a bid document database, and a backup database, where the qualification information database stores various qualification information and associated information with the bid document.
The isolation device is used for mapping an internal network and an external network, the supplier bidding micro application WEB server is an external network server, the intelligent auxiliary bid evaluation system server and the database server are internal network servers, all operations of the database are monitored and recorded, and the safety and the data traceability are improved.
Cluster management is adopted, distributed storage software adopts a cluster management mode, single-point faults of the system cannot occur from the framework, faults of one node or one hard disk are automatically isolated from the cluster, and the use of the whole system service is not influenced. The method specifically comprises the following steps: in the MDC clustering mode, 3-5 MDC modules are deployed in the system, a main and standby working mode is adopted among the MDC modules, and when the MDC fails, the standby MDC is upgraded to the MDC;
a main-standby mode, wherein the system is provided with 2 fusion storage manager modules;
OSD: and in the main/standby mode, the MDC monitors the state of the OSD in real time, and when the main OSD where the Partition is positioned fails, the storage service can be automatically switched to the standby OSD in real time, so that the continuity of the service is ensured.
And multiple data copies, wherein the fusion storage adopts a data multiple copy mechanism to ensure the reliability of data, namely, the same data can be copied and stored for 2-3 copies. And for each volume in the system, the default is to fragment according to 1MB, and the fragmented data is stored on the cluster nodes according to the DHT algorithm.
Data consistency, when an application successfully writes a copy of data to a storage system, several copies of the data of the storage system must be consistent, and when the application reads again, whichever copy was read, is the previously written data.
One or more embodiments have the following technical effects:
1. the system adopts an intelligent data collection means, flexibly configures a qualification information collection template, can directly call supplier qualification information, supplier performance information and a supplier registration list for preliminary evaluation during evaluation, effectively ensures the consistency of data in all links of the whole business process, avoids the risk of data errors, reduces the manual intervention degree of all business links of the evaluation, reduces the influence of human factors on the evaluation result, realizes the tracking of data information in the whole process, provides guarantee for the subsequent evaluation work, and greatly ensures the fairness and strict confidentiality of the evaluation work.
2. By adopting an intelligent task decomposition means, suppliers and projects are averagely divided into evaluation group experts, the same supplier business preliminary evaluation belongs to the same expert, the different supplier technology preliminary evaluation belongs to different experts, the task allocation of each expert is reasonable and fair, the phenomena of uneven allocation, intentional allocation and the like caused by artificial allocation are avoided, and the artificial deviation is effectively avoided.
3. By adopting intelligent data comparison, historical preliminary evaluation records are automatically collected in the preliminary evaluation early stage and are compared with qualification information filled by the bidding supplier, deviation information is automatically identified, important attention information and deviation information verification basis are provided for the preliminary evaluation of experts, a large amount of manpower is saved, offline comparison verification differences are avoided, and manual verification omission is effectively avoided. In the detailed evaluation stage, the items to be scored are divided into objective scoring items and subjective scoring items, scores are automatically calculated for an objective scoring item system, the situation that manual repeated checking work consumes a large amount of manpower and time is avoided, meanwhile, the influence of manual intervention on a scoring result is effectively reduced, and the risk of manual intervention is avoided.
4. Data collection, initial evaluation data comparison and automatic detailed evaluation scoring in the intelligent management bid evaluation service protect the vital interests of the bidders and the tenderers and provide guarantee for creating a reasonable, legal, fair and fair bidding environment.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. An artificial intelligence bid evaluation method based on big data analysis is characterized by comprising the following steps:
receiving qualification information filled by a supplier;
for each supplier to be evaluated, calling historical bidding information, comparing the qualification information of the supplier at this time with the qualification information of the supplier at the last time, identifying difference information, and generating auxiliary preliminary evaluation data;
automatically allocating evaluation experts to each evaluation task according to evaluation task allocation rules, sending the evaluation tasks and corresponding auxiliary primary evaluation data to corresponding evaluation expert clients, and entering a primary evaluation stage;
receiving the initial evaluation result of the client side of the evaluation expert, distributing the qualified evaluation task of the initial evaluation to the evaluation expert again, and entering a detailed evaluation stage;
and (5) carrying out expert scoring.
2. The artificial intelligence bid evaluation method based on big data analysis as claimed in claim 1, wherein a qualification information collecting template is configured in advance for collecting qualification information.
3. The artificial intelligence bid evaluation method based on big data analysis according to claim 1, characterized in that after receiving qualification information filled by suppliers, matching the bid evaluation suppliers to a project manager according to a set rule and informing the project manager; and when receiving a review starting instruction sent by the project manager, starting the bid evaluation.
4. The artificial intelligence bid evaluation method based on big data analysis as claimed in claim 1, wherein in the preliminary evaluation stage, a preliminary evaluation result of an expert client for evaluation is received, whether the bid evaluation is qualified or not is judged according to the evaluation result and a rule of discarding bid, and after verification, a bid evaluation task qualified in the preliminary evaluation enters a detailed evaluation stage; for the bidding evaluation task with unqualified initial evaluation, generating a reporting material and a waste bidding document according to a waste bidding rule; wherein, the rule of the bid abandonment comprises a corresponding reason and a related bid document regulation when any one of the preliminary evaluation items does not meet the requirement.
5. The artificial intelligence bid evaluation method based on big data analysis of claim 1, wherein in the detailed evaluation stage, part of the indicators in the review task are objective scoring items and automatically scored by the system, and the other part of the indicators are subjective scoring items and scored by the review experts.
6. The artificial intelligence bid evaluation method based on big data analysis of claim 5, characterized in that, the scores of the objective scoring items and the subjective scoring items are collected to obtain the total score of each bid evaluation supplier, and the total score meeting a specific score system is obtained according to the quantitative scoring rule; the quantitative scoring rule is established according to the specific score system.
7. The artificial intelligence bid evaluation system based on big data analysis is characterized by comprising a bid evaluation server and a database server, wherein the bid evaluation server realizes the following steps:
receiving qualification information filled by a supplier;
for each supplier to be evaluated, calling historical bidding information from a database server, comparing the qualification information of the supplier at this time with the qualification information of the supplier at the last time, identifying difference information, and generating auxiliary initial evaluation data;
automatically allocating evaluation experts to each evaluation task according to evaluation task allocation rules, sending the evaluation tasks and corresponding auxiliary primary evaluation data to corresponding evaluation expert clients, and entering a primary evaluation stage;
receiving the initial evaluation result of the client side of the evaluation expert, distributing the qualified evaluation task of the initial evaluation to the evaluation expert again, and entering a detailed evaluation stage;
and (5) carrying out expert scoring.
8. The artificial intelligence bid evaluation system based on big data analysis of claim 6, wherein the database server is connected with the bid evaluation server through a database connection pool, and the database connection pool is used for managing distribution and release of connection between each database in the database server and the bid evaluation server.
9. The artificial intelligence bid evaluation system based on big data analysis of claim 6, wherein the database server comprises a qualification information database, a bid document database, a backup database, and a location server for locating stored information.
10. An artificial intelligence bid evaluation system based on big data analysis is characterized by comprising: a supplier bidding subsystem and an artificial intelligence evaluation subsystem,
the supplier bidding subsystem is used for receiving qualification information filled by a supplier and sending the qualification information to the artificial intelligence bid evaluation subsystem;
the artificial intelligence bid evaluation subsystem realizes the bid evaluation method according to any one of claims 1 to 6.
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| CN111291122A (en) * | 2020-02-04 | 2020-06-16 | 重庆大学 | Competitive bidding method and device based on block chain |
| CN112017059A (en) * | 2020-07-14 | 2020-12-01 | 北京淇瑀信息科技有限公司 | Hierarchical optimization risk control method and device and electronic equipment |
| CN112581209A (en) * | 2020-11-17 | 2021-03-30 | 上海同在互联网科技有限公司 | Highly configurable bid evaluation terminal and bid evaluation method |
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| CN113591142A (en) * | 2021-06-18 | 2021-11-02 | 国网宁夏电力有限公司物资公司 | Bidding evaluation method and device for bidding and storage medium |
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| CN117314599A (en) * | 2023-09-13 | 2023-12-29 | 国网物资有限公司 | Bid data processing method and system |
| CN117314599B (en) * | 2023-09-13 | 2024-03-08 | 国网物资有限公司 | Bid data processing method and system |
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