Bidding and tendering data archiving method and system, electronic equipment and storage medium

Abstract

The invention discloses a bidding and tendering data archiving method and system, electronic equipment and a storage medium, and relates to the field of data archiving. In the method, multiple items of target information of bidding and tendering data to be archived are obtained, and the target information comprises project names, bidding units, tendering units, project types, regions and content keywords; according to a preset mapping relation, converting the target information into a target label, and attaching a priority numerical value corresponding to the target label to the target label; and generating a label set according to the multiple target labels, classifying the to-be-archived bidding and tendering data into corresponding categories according to the label set, and archiving the to-be-archived bidding and tendering data to corresponding positions of a distributed database. By implementing the technical scheme provided by the invention, the efficiency and reliability of data management are improved.

Description

Technical Field

[0001] The present application relates to the technical field of document archiving, and in particular to a method, system, electronic device and storage medium for archiving bidding documents. Background Art

[0002] Bidding information management is an important part of modern enterprise information construction. With the increasing number of bidding activities, how to efficiently and accurately manage and archive bidding information has become an urgent problem to be solved. Bidding information covers multiple aspects of information such as project name, bidding unit, bidding unit, project type, region and content keywords. The integrity and accuracy of this information is crucial for subsequent query and management.

[0003] The existing data management methods usually adopt the following methods: First, manual input and classification, that is, the staff manually fills in the relevant information and classifies it according to the content of the bidding materials. Although this method is simple and direct, it is time-consuming and laborious when processing a large amount of data, and it is prone to errors. Secondly, a simple keyword matching algorithm is used to automatically classify the data through a pre-set keyword list. This method can improve work efficiency to a certain extent, but there are still problems such as inaccurate classification and high misjudgment rate. The manual input and classification method is not only inefficient, but also prone to human errors; although the keyword matching algorithm improves the classification speed, the accuracy is not high, and it is easy to miss or over-check.

[0004] Therefore, a more efficient, accurate and flexible bidding document archiving method is needed to solve these problems. Summary of the invention

[0005] The present application provides a method, system, electronic device and storage medium for archiving bidding documents, which effectively improves the efficiency, accuracy and retrievability of document archiving through automation, labeling and distributed storage, while optimizing the classification management of documents and providing strong support for big data analysis and decision support for enterprises.

[0006] In a first aspect of the present application, a method for archiving bidding documents is provided, which is applied to a document archiving platform, and the method comprises: Acquire multiple target information of the bidding materials to be archived, the target information including project name, bidding unit, bidding unit, project type, region and content keywords; According to a preset mapping relationship, the target information is converted into a target tag, and a priority value corresponding to the target tag is attached to the target tag; A tag set is generated according to a plurality of target tags, and the bidding materials to be archived are classified into corresponding categories according to the tag set, and archived to corresponding locations in a distributed database.

[0007] Optionally, the classifying the bidding documents to be archived into corresponding categories according to the tag set includes: Setting a first number of numerical thresholds, and dividing a second number of priority numerical intervals according to the first number of numerical thresholds, where the second number is the first number plus one; Obtaining a priority value set in the tag set, and dividing the priority value set by the first number of value thresholds to determine whether priority values ​​exist in all the second number of priority value intervals; When there is no priority value in the priority value interval, it is prompted that the bidding materials to be archived have missing information; When priority values ​​exist in all of the second number of priority value intervals, the bidding materials to be archived are divided according to the maximum priority value in each priority value interval.

[0008] Optionally, the dividing the bidding materials to be archived according to the maximum priority value in each priority value interval includes: Obtaining a first maximum priority value in a first target priority value interval, and dividing the bidding materials to be archived according to a first target label corresponding to the first maximum priority value, wherein the first target priority value interval is any one of the second number of priority value intervals; When there are multiple identical first maximum priority values ​​in the first target priority value interval, the bidding materials to be archived are divided according to the second maximum priority value in the second target priority value interval, the second target priority value interval is an adjacent interval to the first target priority value interval, and the first target priority value interval is an interval greater than or equal to the target value threshold, the second target priority value interval is an interval less than the target value threshold, and the target value threshold is one of the first number of value thresholds.

[0009] Optionally, generating a tag set according to multiple target tags includes: Deduplication processing is performed on multiple target tags, and cluster analysis is performed on the target tags after deduplication processing according to the semantic similarity and relevance of the tags, and multiple target tags with similarity higher than a first threshold are merged to retain the target tag with the largest priority value.

[0010] Optionally, the corresponding location of the archived data in the distributed database includes: Hash sharding, range sharding or list sharding is used to evenly distribute the bidding materials to be archived to multiple nodes of the database to achieve load balancing, and indexes are established in the database according to the content and title of the bidding materials to be archived.

[0011] Optionally, the obtaining of multiple target information of the bidding documents to be archived includes: Constructing a static vocabulary, wherein the static vocabulary includes basic vocabulary in the bidding field, and crawling bidding vocabulary with a frequency higher than a second threshold from the Internet, and updating the vocabulary to a dynamic word cloud library; Using natural language processing technology, text analysis is performed on the bidding materials to be archived to extract the first keyword; The first keyword is screened in combination with the static vocabulary and the dynamic word cloud library to obtain the second keyword, and the second keyword is used as the content keyword.

[0012] Optionally, the method further includes: Monitor government websites or legal databases, and when updates to the Tendering and Bidding Law or Government Procurement Regulations are detected, retrieve the latest versions of regulatory documents; Parse the latest captured regulatory documents to identify new, deleted or revised clauses; Update the newly added, deleted or revised clauses to the regulatory knowledge graph in the bidding field, and determine sensitive words and ambiguous expressions based on the regulatory knowledge graph; Conduct a compliance check on the bidding and tendering materials to be archived based on the sensitive words and ambiguous expressions.

[0013] In a second aspect of the present application, a system for archiving bidding documents is provided, including an information module, a label module and an archiving module, wherein: An information module configured to obtain multiple target information of the bidding materials to be archived, wherein the target information includes project name, bidding unit, bidding unit, project type, region and content keywords; A label module, configured to convert the target information into a target label according to a preset mapping relationship, and attach a priority value corresponding to the target label to the target label; The archiving module is configured to generate a tag set according to a plurality of the target tags, classify the bidding materials to be archived into corresponding categories according to the tag set, and archive them to corresponding locations in the distributed database.

[0014] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0015] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0016] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By automatically acquiring multiple target information of the bidding documents to be archived and quickly converting them into target tags based on the preset mapping relationship, this process greatly reduces the time for manual operation and input, thereby improving the overall efficiency of document archiving; 2. Convert target information into target tags with priority values, which helps to quickly locate key information in the subsequent data retrieval process. Tagging makes data query more intuitive and convenient. Users can filter and search based on tags to improve the accuracy and speed of retrieval. 3. The tag set generated based on multiple target tags can accurately classify the bidding materials to be archived into corresponding categories. This classification management not only helps to keep the materials organized and orderly, but also facilitates subsequent information review and use; 4. Storing archived data in the corresponding location of the distributed database can make full use of the scalability and fault tolerance of the distributed database. This storage method not only increases the storage capacity of data, but also improves the access speed and response capability of data through technical means such as parallel processing and load balancing; 5. The large amount of tag information collected during the archiving process provides a rich data source for subsequent big data analysis. Through the analysis and mining of these tag information, the trends, hot spots and potential problems of bidding activities can be revealed, providing strong support for the company's strategic decision-making and market positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the method for archiving bidding documents disclosed in the embodiment of the present application; Figure 2 It is a module schematic diagram of the system for archiving bidding documents disclosed in the embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0018] Explanation of the reference numerals: 201, information module; 202, label module; 203, archiving module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0019] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0020] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0021] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0022] This embodiment discloses a method for archiving bidding documents, which is applied to a document archiving platform. Figure 1 It is a flow chart of the method for filing bidding documents disclosed in the embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps: S110, obtaining multiple target information of the bidding documents to be archived, wherein the target information includes project name, bidding unit, bidding unit, project type, region and content keywords; The project name is the unique identifier of each bidding project and is used to distinguish different projects. The project name is usually concise and clear, and can reflect the core content or purpose of the project. When archiving, the project name is the primary basis for retrieving and finding specific project information. The bidding unit refers to the organization or institution that initiates the bidding activity and proposes specific requirements and standards. In the archived materials, clearly recording the bidding unit helps to understand the background and funding source of the project. The name of the bidding unit should be accurate to facilitate possible subsequent cooperation or legal affairs. The bidding unit refers to the organization or enterprise that responds to the bidding requirements and submits the bidding documents. When archiving, all units participating in the bidding need to be recorded in order to analyze the competition and evaluate the quality of the bid. Basic information such as the name and contact information of the bidding unit should also be included in case of emergency. Project type refers to the nature or category of the bidding project, such as construction engineering, information technology, service procurement, etc. Clarifying the project type helps to classify and archive, and improve the efficiency of data management. The project type may also affect the preservation period and confidentiality requirements of the archived materials. Region refers to the location of the project or the geographical scope of the bidding unit and the bidding unit. Recording regional information when archiving helps to understand the geographical distribution and regional characteristics of the project. Regional information may also affect the project's applicable laws, tax policies, and logistics costs. Content keywords refer to key concepts, technical terms, or business points related to the bidding project. Extracting content keywords helps to quickly understand the core content and characteristics of the project. Content keywords can also be used as search tags to improve the efficiency of finding archived materials.

[0023] Optionally, the obtaining of multiple target information of the bidding documents to be archived includes: Constructing a static vocabulary, wherein the static vocabulary includes basic vocabulary in the bidding field, and crawling bidding vocabulary with a frequency higher than a second threshold from the Internet, and updating the vocabulary to a dynamic word cloud library; Using natural language processing technology, text analysis is performed on the bidding materials to be archived to extract the first keyword; The first keyword is screened in combination with the static vocabulary and the dynamic word cloud library to obtain the second keyword, and the second keyword is used as the content keyword.

[0024] Establish a static vocabulary containing basic vocabulary in the bidding field, which are usually terms that frequently appear in bidding activities and are crucial to understanding the content of the project. The static vocabulary may contain common words such as "tender", "bid", "contract", "project", "engineering", "budget", etc., which constitute the core language of the bidding field. Provide a stable reference framework for subsequent text analysis and keyword extraction. Through Internet crawler technology, crawl bidding vocabulary with a frequency higher than the preset threshold from bidding-related websites, forums, announcements and other sources. The dynamic word cloud library is designed to capture the latest trends and hot words in the bidding field. These words may change over time, reflecting new developments in the industry or special needs of specific projects. Supplement the static vocabulary to make it more comprehensive and up-to-date. Use natural language processing (NLP) techniques such as word segmentation, part-of-speech tagging, named entity recognition, etc. to conduct in-depth analysis of archived bidding materials. Extract words and phrases directly related to bidding activities from the text, which constitute the first keyword set of the text. Preliminary screening of possible content keywords to provide candidates for subsequent screening. Compare the first keyword set with the static vocabulary and dynamic word cloud library, and select the words that appear in these resources at the same time as the second keyword. Through double verification, ensure that the selected keywords not only meet the basic knowledge in the bidding field, but also reflect the current hot spots and trends in the industry. Improve the accuracy and relevance of keywords, and ensure that the content keywords can accurately reflect the core content of the bidding materials. The second keyword selected through the above steps is determined as the content keyword of the bidding materials to be archived. Content keywords are not only used for the classification and retrieval of archived materials, but also can serve as the basis for subsequent data analysis, trend prediction and other work.

[0025] By constructing a static vocabulary containing basic vocabulary in the bidding field, it can ensure that the extracted keywords are closely related to the bidding field and avoid interference from irrelevant vocabulary. The introduction of the dynamic word cloud library can capture new vocabulary with high frequency in the bidding field in real time, making keyword extraction closer to the current actual usage. Using natural language processing technology to perform text analysis on bidding materials can deeply explore the semantic information in the text and extract more representative keywords. Combining the static vocabulary and the dynamic word cloud library to screen the extracted first keyword can further ensure the accuracy and representativeness of the selected keyword, so that it can accurately reflect the core content of the bidding materials. Archiving the extracted second keyword as a content keyword can facilitate subsequent users to retrieve relevant bidding materials through keywords. Since the accuracy and representativeness of keyword extraction have been improved, the user's search results will also be more accurate and comprehensive, which improves the availability of archived materials. Through the automated keyword extraction process, the time for manual participation and judgment can be greatly reduced, and the efficiency of the archiving process can be improved. At the same time, since the accuracy of keyword extraction is guaranteed, archiving errors and repeated archiving caused by manual errors are also reduced.

[0026] S120, converting the target information into a target tag according to a preset mapping relationship, and attaching a priority value corresponding to the target tag to the target tag; A preset mapping relationship refers to a predefined rule or logic used to convert target information (such as project name, tendering unit, bidding unit, etc.) into a target tag. These corresponding relationships may be established based on business logic, data standards, or a specific classification system. The conversion rule specifically describes how to convert each field or element in the target information into a target tag. For example, the project name may be directly used as the tag name, while the tendering unit and bidding unit may be used as the attributes or classification basis of the tag. Priority values ​​are used to indicate the importance of different target tags in the archiving, retrieval, or analysis process. These values ​​may be determined based on historical data and business needs. The acquired tendering and bidding materials to be archived are parsed to extract the target information therein. According to the preset mapping relationship, the parsed target information is converted into a target tag. This process may involve field mapping, attribute addition, or classification determination. After conversion to a target tag, a corresponding priority value needs to be attached to each tag. These values ​​may be stored in the attributes of the tag or associated with the tag through other means (such as index or metadata).

[0027] S130, generating a tag set according to the plurality of target tags, classifying the bidding materials to be archived into corresponding categories according to the tag set, and archiving them to corresponding locations in a distributed database.

[0028] Extract all relevant target tags from the bidding documents to be archived. These tags may include project name, bidding unit, bidding unit, project type, region, content keywords, etc. Since the same bidding document may contain multiple similar tags (such as the abbreviation or full name of the project name), the collected tags need to be deduplicated. At the same time, tags that express the same or similar concepts should be merged to reduce the number of tags and improve the accuracy of classification. After deduplication and merging, the remaining tags are combined into a tag set. This set contains all tags for classification and archiving. According to business needs and data characteristics, formulate a set of reasonable classification rules. These rules may be based on the attributes of tags (such as project type, region), the combination of tags (such as the combination of a specific project name and a bidding unit), or other logic. According to the classification rules, the bidding documents to be archived are assigned to the corresponding categories. This process may involve matching, combining and judging tags. Distributed databases usually contain multiple nodes or partitions for storing and managing large amounts of data. According to the classification results, select a suitable archiving location for each bidding document to be archived. This location may be determined based on the type, size, access frequency or other factors of the data. The classified bidding materials are filed to the corresponding location of the distributed database. This process may involve operations such as data replication, transmission and storage.

[0029] Optionally, generating a tag set according to multiple target tags includes: Deduplication processing is performed on multiple target tags, and cluster analysis is performed on the target tags after deduplication processing according to the semantic similarity and relevance of the tags, and multiple target tags with similarity higher than a first threshold are merged to retain the target tag with the largest priority value.

[0030] Among the multiple target tags collected, there may be exactly the same tags or tags that are highly semantically similar. These tags need to be deduplicated to avoid redundancy in subsequent steps. Identify and remove duplicate tags by comparing the string content or semantic features of the tags. Ensure that each tag is unique in the tag set. Cluster analysis aims to group similar tags into one category. Here, similarity can be determined based on factors such as the semantic content, context, and frequency of use of the tags. Use natural language processing techniques or machine learning algorithms to calculate the similarity between the deduplicated tags. This may involve steps such as text vectorization, distance calculation, or probability models. Based on the calculated similarity, apply clustering algorithms (such as K-means, hierarchical clustering, DBSCAN, etc.) to divide the tags into different categories. These categories represent similar sets of tags. Set a first threshold to determine which tags should be merged. This threshold can be determined based on business needs or data characteristics. In the clustering results, find the tag pairs or tag groups whose similarity is higher than the first threshold. These tags are highly semantically similar and can be regarded as different expressions of the same concept. For the filtered similar tags, select the tag with the largest priority value as the representative, and merge other tags into this representative tag. During the merging process, the priority value of the representative tag can be retained or adjusted as needed. Suppose there is a bidding document to be archived. This document involves a complex engineering project and contains multiple tags at different levels. These tags may include project name (highest priority), project type (second highest priority), project phase (medium priority), technical features (lower priority), etc. Each tag has a corresponding priority value, which is used to indicate the importance of the tag in the archiving and retrieval process. The following are some of the target tags extracted from this bidding document and their corresponding priority values: Project name: XX Expressway Construction Project (priority value: 10); Project type: Transportation infrastructure (priority value: 8); Project phase: Design phase (priority value: 6); Technical features: Use of environmentally friendly materials (priority value: 4); Project name (duplicate): XX Expressway Construction Project (priority value: 10); Project type (similar): Highway construction (priority value: 7); Project phase (similar): preliminary design (priority value: 5); Geographical location: XX city, XX province (priority value: 3).

[0031] First, the target labels are deduplicated. In this example, it is found that the label "Project Name" has two identical values ​​("XX Highway Construction Project"), so they are considered duplicate labels and only one of them is retained (if the priority values ​​are different, the one with the largest priority value is retained). Cluster analysis is performed based on the semantic similarity and relevance of the labels. In this example, it is found that "Transportation Infrastructure" and "Highway Construction" in "Project Type" are very close in semantics, and they can both be used to describe the nature of the project. Similarly, "Design Phase" and "Preliminary Design" in "Project Phase" are also semantically similar, but it should be noted that although they are similar, they belong to the same level and have different priorities, so in actual operations, it may be necessary to decide whether to merge based on business needs. In this example, a first threshold (such as 0.8) is set to determine which labels should be merged. Assuming that after the semantic similarity calculation, the similarity between "transportation infrastructure" and "highway construction" is higher than 0.8, and the priority value of "transportation infrastructure" (8) is higher than the priority value of "highway construction" (7), then "highway construction" will be merged into "transportation infrastructure" and "transportation infrastructure" will be retained as the final label with a priority value of 8 (or adjusted according to business needs). For "design phase" and "preliminary design", although they are semantically similar, since they belong to the same level and have different priorities (6 and 5), it may be necessary to decide whether to merge them according to business needs. If the business believes that these two phases can be merged into a broader "design phase" and wants to retain the higher priority value (6), then "preliminary design" can be merged into "design phase". However, if the business believes that the two phases are clearly different and wants to retain them separately, then we will not merge them. It should be noted that the label "geographic location" is different from other labels in terms of hierarchy. It describes the specific location information of the project. Therefore, it may not be necessary to merge it with other labels during cluster analysis and merging similar labels. Instead, it can be retained as an independent label and used in the archiving and retrieval process. After the above processing, the final tag set may include: "XX Expressway Construction Project" (project name), "Transportation Infrastructure" (merged project type), "Design Phase" (may merge the preliminary design phase of the project) and "XX Province XX City" (geographic location). Such a tag set is not only more streamlined and efficient, but also can better reflect the theme and content of the bidding materials, while retaining the priority numerical information of different levels of tags.

[0032] By deduplicating a plurality of the target tags, redundant tags can be eliminated, and confusion and errors can be avoided in the subsequent archiving and retrieval process. This helps to improve the accuracy and consistency of the tag set. Using cluster analysis technology, tags can be grouped according to the semantic similarity and relevance of the tags. This grouping method helps to discover the intrinsic connection between tags, making the tag set more orderly and structured. By merging multiple target tags with a similarity higher than the first threshold, the number of tags can be significantly reduced, thereby optimizing the utilization of storage space. This helps to reduce storage costs and improve the overall performance of the system. At the same time, a streamlined tag set can speed up retrieval. When a user needs to find specific bidding materials, the system can locate relevant tags more quickly, thereby improving retrieval efficiency. In the process of merging tags, retaining the target tag with the largest priority value can ensure that important tag information is not lost. This helps to maintain the authority and accuracy of the tags during the archiving and retrieval process. By generating a streamlined and efficient tag set, the archiving and retrieval process can be made more convenient. Users can more easily understand and use these tags, thereby finding the required bidding materials more quickly. It can be flexibly applied to different business scenarios. In the field of bidding, different tag sets can be generated according to the characteristics, type, stage, etc. of the project to meet different archiving and retrieval needs. As the business develops and changes, it may be necessary to add new tags or modify existing tags. Through the embodiments of the present application, new tags can be easily added to the tag set, or existing tags can be updated and adjusted. This helps to maintain the scalability and flexibility of the system to adapt to changing business needs.

[0033] Optionally, the classifying the bidding documents to be archived into corresponding categories according to the tag set includes: Setting a first number of numerical thresholds, and dividing a second number of priority numerical intervals according to the first number of numerical thresholds, where the second number is the first number plus one; Obtaining a priority value set in the tag set, and dividing the priority value set by the first number of value thresholds to determine whether priority values ​​exist in all the second number of priority value intervals; When there is no priority value in the priority value interval, it is prompted that the bidding materials to be archived have missing information; When priority values ​​exist in all of the second number of priority value intervals, the bidding materials to be archived are divided according to the maximum priority value in each priority value interval.

[0034] A first number (set to N) of numerical thresholds are set. These thresholds are used to divide the priority values ​​into different intervals. According to the set N numerical thresholds, N+1 (i.e., the second number) priority numerical intervals can be divided. These intervals divide the priority numerical range into several parts, each of which represents a different priority level. From the tag set of the tendering and bidding materials to be archived, the priority values ​​of all tags are extracted to form a priority numerical set. This priority numerical set is divided using the set N numerical thresholds to check whether there is at least one priority value in each of the N+1 priority numerical intervals. This step is to ensure that each priority level has a corresponding tag to avoid missing information. If no priority value is found in a priority numerical interval, that is, the interval is empty, the system will prompt that there is missing information in the tendering and bidding materials to be archived. This may be because some important information is not correctly marked or omitted. If there is at least one priority value in all N+1 priority numerical intervals, the tendering and bidding materials to be archived will be divided according to the maximum priority value in each interval. This means that for each interval, the label with the highest priority (i.e., the label with the largest priority value) will be selected as the representative of the interval, and then the bidding documents with this label will be divided into the corresponding categories. Suppose we have three value thresholds (N=3): 5, 10, and 15. Then, four priority value intervals (N+1=4) can be divided: [0, 5), [5, 10), [10, 15) and [15, +∞). If the priority values ​​extracted from a label set of a bidding document are 3, 7, 9, 12, and 18, then these values ​​will fall into the above four intervals respectively, indicating that each interval has corresponding bidding documents. If no priority value is found in an interval (for example, [5, 10)), the system will prompt that the information is missing and may need to be further checked and supplemented. In the case where there is at least one priority value in all intervals, the archived bidding documents will be divided according to the maximum priority value in each interval (for example, the maximum value in the interval [5, 10) is 9).

[0035] By setting numerical thresholds and dividing priority numerical intervals, it is possible to ensure that the bidding documents are assigned to categories that match their priorities. This classification method is more detailed and accurate than simple binary or multi-classification, and can better reflect the importance and urgency of bidding documents. Before classifying the documents, the information integrity of the bidding documents to be archived is verified by checking whether there is a priority value in each priority numerical interval. If there is no priority value in a certain interval, the system will prompt that the information is missing, which helps to timely discover and supplement the missing important information, thereby improving the quality and availability of archived documents. It is allowed to set different numbers of numerical thresholds according to actual needs, thereby dividing different numbers of priority numerical intervals. This flexibility can adapt to different business scenarios and changes in demand, and improves its applicability and scalability. By adopting a unified classification standard and priority numerical interval division method, it is helpful to improve the standardization and standardization level of archive management. This helps to reduce classification errors and confusion caused by human factors and improve the consistency and comparability of archived documents.

[0036] Optionally, the dividing the bidding materials to be archived according to the maximum priority value in each priority value interval includes: Obtaining a first maximum priority value in a first target priority value interval, and dividing the bidding materials to be archived according to a first target label corresponding to the first maximum priority value, wherein the first target priority value interval is any one of the second number of priority value intervals; When there are multiple identical first maximum priority values ​​in the first target priority value interval, the bidding materials to be archived are divided according to the second maximum priority value in the second target priority value interval, the second target priority value interval is an adjacent interval to the first target priority value interval, and the first target priority value interval is an interval greater than or equal to the target value threshold, the second target priority value interval is an interval less than the target value threshold, and the target value threshold is one of the first number of value thresholds.

[0037] Select one of the second number (N+1) priority value intervals divided previously as the first target priority value interval. This interval can be any one, but usually starts from the interval with the highest priority. Determine the first maximum priority value: In the first target priority value interval, find the maximum value among all priority values, that is, the first maximum priority value. The label corresponding to this value will be used as the main basis for classifying the bidding documents in this interval. If there are multiple identical maximum priority values ​​in the first target priority value interval, that is, multiple labels have the same highest priority, then this situation needs to be further processed. In this case, select the second target priority value interval as the basis for auxiliary classification. This interval is an adjacent interval of the first target priority value interval, and its priority is lower than the first target priority value interval. Specifically, if the first target priority value interval is an interval greater than or equal to a certain target value threshold (that is, a high priority interval), then the second target priority value interval is an adjacent interval less than the target value threshold (that is, a low priority interval). In the second target priority value interval, find the maximum value among all priority values, that is, the second maximum priority value. The label corresponding to this value will be used as the basis for auxiliary classification to resolve conflicts of identical maximum priority values ​​in the first target priority value interval. After determining the first maximum priority value (and possible second maximum priority value), the tender documents to be archived are divided according to the labels corresponding to these values. Specifically, the tender documents with labels corresponding to the first maximum priority value are divided into corresponding categories. If there are multiple identical maximum priority values ​​in the first target priority value interval, the labels corresponding to the second maximum priority value are further considered to divide these tender documents more finely. Assume that there are three value thresholds (N=3): 5, 10 and 15, and four priority value intervals are divided: [0, 5), [5, 10), [10, 15) and [15, +∞). Now, there is a tender document to be archived, and the priority values ​​of its labels are as follows: Label A: 7 (belongs to the interval [5, 10)); Label B: 12 ​​(belongs to the interval [10, 15)); Label C: 7 (also belongs to the interval [5, 10)); Label D: 4 (belongs to the next level label of label A); Label E: 3 (belongs to the next level label of label C). If the priority values ​​of label A and label C are the same, they are divided according to the maximum priority value in the interval [0, 5), that is, they are divided according to label D.

[0038] When there are multiple identical maximum priority values ​​in the same priority value interval, the second maximum priority value of the adjacent interval is introduced as the auxiliary classification basis to effectively resolve the classification conflict. This strategy not only improves the flexibility of classification, but also ensures accurate archiving even in complex situations. Through this refined classification method, the required bidding materials can be found and retrieved more quickly, improving the efficiency of archiving management. At the same time, since the classification is clearer, it also reduces the duplication of work and waste of resources caused by misclassification. It allows different numbers of numerical thresholds to be set according to actual needs, thereby dividing different numbers of priority value intervals. This flexibility enables archiving management to adapt to different business scenarios and demand changes, improving its applicability and scalability. By adopting a unified classification standard and priority value interval division method, it helps to improve the standardization of archiving management. This helps to reduce classification errors and confusion caused by human factors and improve the consistency and comparability of archived materials.

[0039] Optionally, the corresponding location of the archived data in the distributed database includes: Hash sharding, range sharding or list sharding is used to evenly distribute the bidding materials to be archived to multiple nodes of the database to achieve load balancing, and indexes are established in the database according to the content and title of the bidding materials to be archived.

[0040] Hash sharding is to perform hash operations on the key information (such as ID, title, etc.) of the tendering and bidding materials to be archived, and then distribute the materials to different database nodes according to the hash value. This method can ensure that the same key information is always distributed to the same node, thereby achieving data locality. Hash sharding can evenly distribute data, reduce the load difference between nodes, and quickly locate the corresponding node for known key information. Range sharding is to distribute the tendering and bidding materials to be archived to different database nodes according to the value of a certain field (such as timestamp, ID range, etc.) of the tendering and bidding materials to be archived. Each node is responsible for a specific range of values. Range sharding facilitates querying of continuous ranges of data because query requests can easily locate the corresponding node. List sharding is to predefine a node list, and then distribute it to the corresponding node according to the value of a certain field (such as category, keyword, etc.) of the tendering and bidding materials to be archived. This method is similar to hash sharding, but the basis for allocation is a predefined list. List sharding can flexibly adjust the node allocation strategy according to actual needs, and is suitable for data distribution needs in some specific scenarios. In a distributed database, in order to speed up data retrieval and query, it is usually necessary to establish an index based on the content and title of the tendering and bidding materials to be archived. An index is similar to a directory of data, which can help the database quickly locate the required data. Improve query efficiency: Indexes can greatly speed up data retrieval, especially for large-scale data sets. Through indexes, more complex query operations can be supported, such as fuzzy queries and range queries. According to the characteristics and actual needs of the tendering and bidding materials to be archived, select a suitable sharding strategy. According to the selected sharding strategy, the tendering and bidding materials to be archived are evenly distributed to multiple nodes of the database. In the database, indexes are established based on the content and title of the tendering and bidding materials to be archived for subsequent rapid retrieval and query. In a distributed database, it is necessary to ensure data consistency, especially when sharding data, attention should be paid to avoiding data duplication or omission. When selecting a sharding strategy, full consideration should be given to the problem of load balancing to ensure that the load of each node is relatively balanced and avoid data skew. When establishing an index, optimization should be performed according to actual query needs to ensure the effectiveness and efficiency of the index.

[0041] Through technologies such as hash sharding, range sharding or list sharding, the bidding and tendering materials to be archived can be evenly distributed to multiple nodes of the database. This distribution method avoids overloading of a single node, realizes load balancing, and improves the overall performance and stability of the database. At the same time, since the data is stored in a decentralized manner, the utilization rate of storage space is also improved, and the storage cost is reduced. Establishing an index in the database is an important means to improve data retrieval efficiency. Establishing an index based on the content and title of the bidding and tendering materials to be archived allows users to quickly locate the required information during retrieval, reducing retrieval time and cost. This indexing mechanism is particularly important for processing large-scale bidding and tendering databases, and can significantly improve the user experience. Using a distributed database to store bidding and tendering materials can easily add, delete and modify data according to business needs. At the same time, since the distributed database has good scalability, it can easily expand storage nodes with the development of business and the growth of data volume to meet the growing data storage needs. Distributed databases usually have data backup and recovery mechanisms to ensure data security and reliability. Even if a node fails, data can be restored through other nodes to avoid data loss and damage. This mechanism is particularly important for the storage and management of important data such as bidding and tendering materials.

[0042] Optionally, the method further includes: Monitor government websites or legal databases, and when updates to the Tendering and Bidding Law or Government Procurement Regulations are detected, retrieve the latest versions of regulatory documents; Parse the latest captured regulatory documents to identify new, deleted or revised clauses; Update the newly added, deleted or revised clauses to the regulatory knowledge graph in the bidding field, and determine sensitive words and ambiguous expressions based on the regulatory knowledge graph; Conduct a compliance check on the bidding and tendering materials to be archived based on the sensitive words and ambiguous expressions.

[0043] Obtain the latest regulatory documents such as the Tendering and Bidding Law or Government Procurement Regulations in a timely manner.

[0044] Monitor relevant government websites or legal databases, such as the Chinese government website, the website of the National Development and Reform Commission, and professional legal databases in a regular or real-time manner. Use web crawlers, API interfaces and other technical means to achieve automatic capture and update reminders of regulatory documents. Identify new, deleted or revised clauses in regulatory documents. Parse the latest captured regulatory documents, which usually includes steps such as text preprocessing, semantic analysis, and keyword extraction. Use natural language processing (NLP) and machine learning technologies to deeply analyze regulatory documents to identify changes. Update the parsed new, deleted or revised clauses to the regulatory knowledge graph in the bidding field. The regulatory knowledge graph is a structured knowledge base used to store regulatory knowledge in the bidding field. By updating the knowledge graph, you can ensure that the information in it is up to date. Use graph databases, knowledge graph construction tools and other technical means to achieve storage and update of regulatory knowledge. According to the updated regulatory knowledge graph, determine the sensitive words and fuzzy expressions that may appear in the bidding materials. By analyzing the keywords, concepts and their relationships in the regulatory knowledge graph, extract sensitive words and fuzzy expressions that are closely related to bidding activities. Use semantic analysis, text mining and other technical means to realize automatic identification of sensitive words and fuzzy expressions. Perform compliance checks on the tender documents to be archived based on sensitive words and fuzzy expressions. Match the tender documents to be archived with sensitive words and fuzzy expressions to check whether there is any illegal content. Use text matching algorithms, rule engines and other technical means to realize automation and intelligence of compliance checks.

[0045] By monitoring government websites or legal databases, updates to the Tendering and Bidding Law or Government Procurement Regulations can be detected in real time or regularly. This ensures that enterprises can obtain the latest regulatory information as soon as possible and avoid compliance risks caused by regulatory lags. Parsing the latest captured regulatory documents can accurately identify new, deleted or revised clauses. This parsing process not only improves the accuracy of regulatory understanding, but also improves processing efficiency through automation and reduces the need for manual intervention. Updating new, deleted or revised clauses to the regulatory knowledge graph in the bidding field can continuously enrich and improve the regulatory knowledge system in this field. The construction of this knowledge graph helps enterprises better understand and apply regulations and improve the level of compliance management. According to the sensitive words and fuzzy expressions determined by the regulatory knowledge graph, more accurate and comprehensive compliance checks can be performed on archived bidding materials. This inspection method can timely discover potential compliance issues and reduce the legal risks of enterprises. Through continuous regulatory updates and compliance checks, the standardization and standardization of bidding activities can be promoted. This helps to create a fair, just and transparent bidding environment and improve the efficiency and quality of resource allocation. A sound regulatory management and compliance inspection mechanism can enhance the image and reputation of a company and strengthen customers’ trust in the company. This helps the company stand out in the fierce market competition and increase its market share and competitiveness.

[0046] This embodiment also discloses a system for archiving bidding documents. Figure 2 Schematic diagram of the module of the system for archiving bidding documents disclosed in the embodiment of the present application. Figure 2 As shown, the system includes an information module 201, a label module 202 and an archiving module 203, wherein: Information module 201, configured to obtain multiple target information of the bidding materials to be archived, the target information including project name, bidding unit, bidding unit, project type, region and content keywords; The label module 202 is configured to convert the target information into a target label according to a preset mapping relationship, and attach a priority value corresponding to the target label to the target label; The archiving module 203 is configured to generate a tag set according to a plurality of target tags, classify the bidding materials to be archived into corresponding categories according to the tag set, and archive them to corresponding locations in the distributed database.

[0047] Optionally, the archiving module 203 is configured to: Setting a first number of numerical thresholds, and dividing a second number of priority numerical intervals according to the first number of numerical thresholds, where the second number is the first number plus one; Obtaining a priority value set in the tag set, and dividing the priority value set by the first number of value thresholds to determine whether priority values ​​exist in all the second number of priority value intervals; When there is no priority value in the priority value interval, it is prompted that the bidding materials to be archived have missing information; When priority values ​​exist in all of the second number of priority value intervals, the bidding materials to be archived are divided according to the maximum priority value in each priority value interval.

[0048] Optionally, the archiving module 203 is configured to: Obtaining a first maximum priority value in a first target priority value interval, and dividing the bidding materials to be archived according to a first target label corresponding to the first maximum priority value, wherein the first target priority value interval is any one of the second number of priority value intervals; When there are multiple identical first maximum priority values ​​in the first target priority value interval, the bidding materials to be archived are divided according to the second maximum priority value in the second target priority value interval, the second target priority value interval is an adjacent interval to the first target priority value interval, and the first target priority value interval is an interval greater than or equal to the target value threshold, the second target priority value interval is an interval less than the target value threshold, and the target value threshold is one of the first number of value thresholds.

[0049] Optionally, the archiving module 203 is configured to: Deduplication processing is performed on multiple target tags, and cluster analysis is performed on the target tags after deduplication processing according to the semantic similarity and relevance of the tags, and multiple target tags with similarity higher than a first threshold are merged to retain the target tag with the largest priority value.

[0050] Optionally, the archiving module 203 is configured to: Hash sharding, range sharding or list sharding is used to evenly distribute the bidding materials to be archived to multiple nodes of the database to achieve load balancing, and indexes are established in the database according to the content and title of the bidding materials to be archived.

[0051] Optionally, the information module 201 is configured to: Constructing a static vocabulary, wherein the static vocabulary includes basic vocabulary in the bidding field, and crawling bidding vocabulary with a frequency higher than a second threshold from the Internet, and updating the vocabulary to a dynamic word cloud library; Using natural language processing technology, text analysis is performed on the bidding materials to be archived to extract the first keyword; The first keyword is screened in combination with the static vocabulary and the dynamic word cloud library to obtain the second keyword, and the second keyword is used as the content keyword.

[0052] Optionally, the system further comprises a compliance module, wherein the compliance module is configured to: Monitor government websites or legal databases, and when updates to the Tendering and Bidding Law or Government Procurement Regulations are detected, retrieve the latest versions of regulatory documents; Parse the latest captured regulatory documents to identify new, deleted or revised clauses; Update the newly added, deleted or revised clauses to the regulatory knowledge graph in the bidding field, and determine sensitive words and ambiguous expressions based on the regulatory knowledge graph; Conduct a compliance check on the bidding and tendering materials to be archived based on the sensitive words and ambiguous expressions.

[0053] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0054] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0055] The communication bus 302 is used to realize the connection and communication between these components.

[0056] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0057] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0058] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0059] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a method for archiving bidding documents.

[0060] exist Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call the application program of the method for archiving bidding documents stored in the memory 305. When executed by one or more processors 301, the electronic device executes one or more methods in the above-mentioned embodiments.

[0061] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0062] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0063] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0064] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0065] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0066] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory 305 and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0067] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for archiving bidding documents, characterized in that: Applied to a data archiving platform, the method comprises: Acquire multiple target information of the bidding materials to be archived, the target information including project name, bidding unit, bidding unit, project type, region and content keywords; According to a preset mapping relationship, the target information is converted into a target tag, and a priority value corresponding to the target tag is attached to the target tag; A tag set is generated according to a plurality of target tags, and the bidding materials to be archived are classified into corresponding categories according to the tag set, and archived to corresponding locations in a distributed database.

2. The method for archiving bidding documents according to claim 1, characterized in that: The step of classifying the bidding documents to be archived into corresponding categories according to the tag set includes: Setting a first number of numerical thresholds, and dividing a second number of priority numerical intervals according to the first number of numerical thresholds, where the second number is the first number plus one; Obtaining a priority value set in the tag set, and dividing the priority value set by the first number of value thresholds to determine whether priority values ​​exist in all the second number of priority value intervals; When there is no priority value in the priority value interval, it is prompted that the bidding materials to be archived have missing information; When priority values ​​exist in all of the second number of priority value intervals, the bidding materials to be archived are divided according to the maximum priority value in each priority value interval.

3. The method for archiving bidding documents according to claim 2, characterized in that: The dividing of the bidding documents to be filed according to the maximum priority value in each priority value interval includes: Obtaining a first maximum priority value in a first target priority value interval, and dividing the bidding materials to be archived according to a first target label corresponding to the first maximum priority value, wherein the first target priority value interval is any one of the second number of priority value intervals; When there are multiple identical first maximum priority values ​​in the first target priority value interval, the bidding materials to be archived are divided according to the second maximum priority value in the second target priority value interval, the second target priority value interval is an adjacent interval to the first target priority value interval, and the first target priority value interval is an interval greater than or equal to the target value threshold, the second target priority value interval is an interval less than the target value threshold, and the target value threshold is one of the first number of value thresholds.

4. The method for archiving bidding documents according to claim 1, characterized in that: Generating a tag set according to a plurality of target tags comprises: Deduplication processing is performed on multiple target tags, and cluster analysis is performed on the target tags after deduplication processing according to the semantic similarity and relevance of the tags, and multiple target tags with similarity higher than a first threshold are merged to retain the target tag with the largest priority value.

5. The method for archiving bidding documents according to claim 1, characterized in that: The corresponding locations of the archived distributed database include: Hash sharding, range sharding or list sharding is used to evenly distribute the bidding materials to be archived to multiple nodes of the database to achieve load balancing, and indexes are established in the database according to the content and title of the bidding materials to be archived.

6. The method for archiving bidding documents according to claim 1, characterized in that: The multiple target information of obtaining the bidding documents to be archived includes: Constructing a static vocabulary, wherein the static vocabulary includes basic vocabulary in the bidding field, and crawling bidding vocabulary with a frequency higher than a second threshold from the Internet, and updating the vocabulary to a dynamic word cloud library; Using natural language processing technology, text analysis is performed on the bidding materials to be archived to extract the first keyword; The first keyword is screened in combination with the static vocabulary and the dynamic word cloud library to obtain the second keyword, and the second keyword is used as the content keyword.

7. The method for archiving bidding documents according to claim 1, characterized in that: The method further comprises: Monitor government websites or legal databases, and when updates to the Tendering and Bidding Law or Government Procurement Regulations are detected, retrieve the latest versions of regulatory documents; Parse the latest captured regulatory documents to identify new, deleted or revised clauses; Update the newly added, deleted or revised clauses to the regulatory knowledge graph in the bidding field, and determine sensitive words and ambiguous expressions based on the regulatory knowledge graph; Conduct a compliance check on the bidding and tendering materials to be archived based on the sensitive words and ambiguous expressions.

8. A system for archiving bidding documents, characterized in that: It includes information module, label module and archive module, among which: An information module configured to obtain multiple target information of the bidding materials to be archived, wherein the target information includes project name, bidding unit, bidding unit, project type, region and content keywords; A label module, configured to convert the target information into a target label according to a preset mapping relationship, and attach a priority value corresponding to the target label to the target label; The archiving module is configured to generate a tag set according to a plurality of the target tags, classify the bidding materials to be archived into corresponding categories according to the tag set, and archive them to corresponding locations in the distributed database.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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