Intelligent auxiliary system for bid inviting and purchasing business

Abstract

The invention discloses an intelligent auxiliary system for bid inviting and purchasing business. Comprising the following steps: S1, responding to a bidding document making request of a user, and loading a preset data model associated with a specific purchasing project machine; s2, receiving structured bidding data input by a user through a data field, and receiving an unstructured original file uploaded by the user; s3, based on the structured bidding data and the unstructured original file, generating a unified bidding data packet; s4, performing encryption and fragmentation processing on the unified bidding data packet, and uploading the unified bidding data packet to a bidding server; s5, the received data fragments are recombined, decrypted and verified, and structured bidding data are separated from the bidding data packet passing verification; according to the method, the self-contained data object of deep mapping of the structured metadata and the unstructured load data is used as a core to drive subsequent secure transmission and back-end intelligent processing, so that linkage and collaborative optimization of the whole data processing flow are realized.

Description

Technical Field

[0001] This invention belongs to the field of electronic bidding technology, and in particular relates to an intelligent auxiliary system for bidding and procurement business. Background Technology

[0002] With the deepening development of the digital economy, various industries are placing higher demands on the digital and intelligent transformation of their business processes. In data-intensive business areas such as finance, law, and engineering management, how to efficiently and accurately extract, process, and utilize key information from massive, heterogeneous data sources has become a core issue for improving overall operational efficiency and decision-making quality. In particular, ensuring the consistency, integrity, and reliability of data throughout its entire lifecycle of generation, transmission, and application is a fundamental technical challenge in building robust digital systems and a direction that current technological development continues to focus on.

[0003] The aforementioned challenges are particularly prominent in electronic bidding; existing technologies face limitations in processing bidding data primarily in the following aspects:

[0004] Current technologies generally employ a "submit first, then parse" data processing model. Users freely create and submit unstructured documents using general document tools, and the backend system then relies on technologies such as Optical Character Recognition (OCR) for "remedial" information extraction. This model results in a significant delay in the start of data processing. The system not only incurs high computational costs, but the accuracy of parsing is also significantly affected by document quality and layout complexity, introducing uncertainty into subsequent automated processes.

[0005] At the data transmission level, existing solutions typically employ common file transfer protocols or services. While these solutions can deliver data, they were not originally designed for the specific needs of bidding and tendering processes. Therefore, they lack optimization mechanisms for transmitting large files on unstable networks and cannot provide end-to-end security and traceability guarantees closely linked to business status (such as bid deadlines and document verification status), leaving the reliability and compliance of the transmission process needing further improvement.

[0006] Existing system architectures often treat data acquisition, data transmission, and data application as independent modules. This functional isolation makes it difficult for the system to achieve holistic optimization capabilities. For example, the transmission module cannot perceive the internal structure and importance of the data packets it carries, while the application module cannot utilize contextual information from the data generation phase to improve analysis efficiency. This lack of coordination limits breakthroughs in overall system performance and hinders the realization of synergistic effects between different components. Summary of the Invention

[0007] To address the problems existing in the prior art, the purpose of this invention is to provide an intelligent auxiliary system for bidding and procurement operations. By constructing a self-contained data object on the client side that deeply maps structured metadata and unstructured payload data, and using this as the core to drive subsequent secure transmission and intelligent backend processing, the system achieves linkage and collaborative optimization of the entire data processing process.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an intelligent auxiliary system for bidding and procurement operations, comprising the following steps:

[0009] S1. On the client side, respond to the user's bid preparation request and load the preset data template associated with the specific procurement project. The preset data template contains multiple data fields to guide the user to input structured bidding information.

[0010] S2. Receive structured bidding data input by the user through the data field, and receive unstructured raw files uploaded by the user;

[0011] S3. Based on structured bidding data and unstructured original files, generate a unified bidding data package, in which the structured bidding data and unstructured original files are linked within the bidding data package.

[0012] S4. Encrypt and fragment the unified bid data package, and upload the bid data package to the bidding server;

[0013] S5. On the bidding server side, the received data fragments are reassembled, decrypted and verified, and structured bidding data is separated from the verified bidding data packets so that the structured bidding data can be directly used for subsequent intelligent review or data analysis processes.

[0014] Furthermore, the preset data template in S1 is dynamically determined based on the procurement type of a specific procurement project; and the data field contains mandatory field validation rules and format validation rules corresponding to the procurement type.

[0015] Furthermore, before the bidding data packet is generated, S3 performs digital signature processing, calls the digital certificate bound to the user's identity information, and digitally signs the unified bidding data packet to generate signature information and attach it to the bidding data packet.

[0016] Furthermore, the complete calculation process for the bidding data package is as follows:

[0017] When a user selects a specific project on the client and clicks the "Create Tender Document" button, the client sends a request containing a unique identifier for the project to the bidding server.

[0018] After receiving the request, the server retrieves three basic risk parameters associated with the project's unique identifier from its internal database: project type, project budget amount, and historical failure rate, and returns the project type, project budget amount, and historical failure rate to the client.

[0019] Upon receiving the project type, project budget amount, and historical bid failure rate, immediately calculate the project complexity assessment score;

[0020] After calculating the project complexity assessment score, the client performs logical judgment on the project complexity assessment score according to a preset, segmented threshold mapping rule to determine the template's adaptive level. The rules are as follows:

[0021] If the project complexity assessment score is greater than or equal to 0 and less than 0.4, the template adaptation level is determined to be 1.

[0022] If the project complexity assessment score is greater than or equal to 0.4 and less than 0.8, the template adaptation level is determined to be 2.

[0023] If the project complexity assessment score is greater than or equal to 0.8 and less than or equal to 1, then the template adaptation level is determined to be 3;

[0024] The client loads and renders the corresponding user interface from the local template library based on the determined template adaptive level, according to the following rules:

[0025] Level 1 Basic Template: Includes only the most essential required fields, such as total price and contact person; the validation rules are relatively lenient.

[0026] Level 2 Standard Template: Based on Level 1, it adds fields such as itemized pricing and main technical parameters, and introduces cross-validation logic between fields;

[0027] Level 3 Enhanced Template: Building upon Level 2, this template requires a more detailed index of qualification certificates, a structured description of performance cases, and mandates the uploading of corresponding supporting documents. The verification rules are the most stringent. Subsequently, users fill in structured data in the interface and upload unstructured raw files.

[0028] After the user completes the form and clicks "Generate Bidding Data Package," the process enters the security processing phase:

[0029] The client solidifies all the structured data entered by the user and calculates the hash value of the structured data; the client obtains the security timestamp from the TSA and calculates the timestamp hash value.

[0030] The client integrates structured data hash values, timestamp hash values, and project complexity assessment scores to generate the final data integrity fingerprint;

[0031] The client calls the inserted private key, which is uniquely bound to the user's identity information, to perform a digital signature operation on the generated data integrity fingerprint;

[0032] The output is a unified bid data packet; the internal structure of the bid data packet includes: a digitally signed multi-factor data integrity fingerprint, the user's digital certificate public key, the structured data itself, and all unstructured original files; the bid data packet is then submitted to the subsequent encryption and fragmentation upload module.

[0033] Furthermore, the steps for generating a unified bid data package in S3 are as follows: construct a self-contained data object that uses structured bid data as metadata and unstructured raw files as payload data, and establish a directly addressable mapping relationship between the content of metadata and payload data through a preset index.

[0034] Furthermore, the steps for uploading the bidding data packet in S4 are as follows: During the upload process, the client and server maintain a data fragment confirmation state machine; when a network interruption is detected, the upload will automatically resume after the network is restored, based on the last confirmed data fragment position recorded in the state machine.

[0035] Furthermore, the steps for using the S5 bid data package for subsequent intelligent review or data analysis include: after the bid opening, automatically extracting preset key fields from the structured bid data of multiple users and generating a multi-dimensional, visualized horizontal comparative analysis report.

[0036] Furthermore, the steps for maintaining the data fragmentation confirmation state machine are as follows:

[0037] Before and during the upload process, network performance metrics in at least two dimensions are continuously monitored; instantaneous network quality scores are dynamically calculated; and based on the instantaneous network quality scores, the dynamic fragment size used to segment bid data packets and the number of concurrent upload threads used to upload data fragments are adjusted in real time.

[0038] Furthermore, the steps for calculating the instantaneous network quality score are as follows:

[0039] The network performance metrics are taken as input, and a preset fuzzy logic inference algorithm is applied for fusion calculation to output an instantaneous network quality score.

[0040] Furthermore, the steps for a horizontal comparative analysis report are as follows:

[0041] Obtain the association confidence score determined when generating the bid data package and the field compliance verification results performed on the server side; merge the association confidence score and the field compliance verification results to calculate a comprehensive data credibility score for each structured data; in the horizontal comparative analysis report, the comprehensive data credibility score applies differentiated visual styles to different data points.

[0042] The technical effects and advantages of this invention are as follows:

[0043] This invention firstly achieves front-end pre-regulation of key bidding information by providing a structured data template dynamically associated with the procurement project on the client side; by moving the data structuring work forward to the source of data generation, it ensures that the data entering the backend has a high degree of standardization and accuracy.

[0044] This invention deeply integrates high-quality structured data with user-uploaded unstructured raw files to construct a unified, highly self-consistent, self-contained data object. A close relationship is established between structured data and unstructured files through a directly addressable mapping relationship. The self-contained data object is not only a container for data but also a central hub for information linkage.

[0045] This invention enables integrated control throughout the entire process. In the transmission stage, it can identify the structure of data objects and intelligently segment and schedule them based on network status, achieving reliable and efficient delivery. The backend system directly parses metadata, completely bypassing the time-consuming bottleneck of unstructured parsing, and can utilize the mapping relationships contained in the metadata to achieve accurate and rapid tracing of original evidence. Data collection, transmission, and application are no longer isolated, but form an organic and mutually empowering whole through sharing and transmitting self-contained data objects.

[0046] Other features and advantages of the invention will become clear from the following detailed description of exemplary embodiments of the invention with reference to the accompanying drawings. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the steps provided by the present invention;

[0048] Figure 2 This is a schematic diagram of the complete calculation process of the bidding data package provided by the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0050] like Figures 1 to 2 As shown in the figure, an intelligent auxiliary system for bidding and procurement business provided by an embodiment of the present invention includes the following steps:

[0051] S1. On the client side, respond to the user's bid preparation request and load the preset data template associated with the specific procurement project. The preset data template contains multiple data fields to guide the user to input structured bidding information.

[0052] The preset data template in S1 is dynamically determined based on the procurement type of a specific procurement project; and the data field contains mandatory field validation rules and format validation rules corresponding to the procurement type.

[0053] The preset data templates include various standardized templates for goods, services, and engineering projects. When a client initiates a tender document preparation request, the procurement type of the procurement project is queried based on the procurement project ID carried in the request, and a matching standardized template is automatically loaded from the preset data templates. In this embodiment, for goods procurement, the preset data template will require the filling in of structured data fields such as equipment model, technical parameter table, and itemized quotation, and will perform numerical format validation on the price field. This achieves accurate transmission of bidding requirements and refined management of front-end data, ensuring that the core information required for different types of procurement projects can be collected in a standardized and complete manner, further improving the quality of source data and the relevance of subsequent processing.

[0054] S2. Receive structured bidding data input by the user through the data field, and receive unstructured raw files uploaded by the user;

[0055] S3. Based on structured bidding data and unstructured original files, generate a unified bidding data package, in which the structured bidding data and unstructured original files are linked within the bidding data package.

[0056] Before the bidding data packet is generated in S3, it undergoes digital signature processing. The digital certificate bound to the user's identity information is called to digitally sign the unified bidding data packet, so as to generate signature information and attach it to the bidding data packet.

[0057] After the user completes the structured bidding data input in the data field and confirms the generation of the data packet, the private key of the digital certificate in the client environment is invoked. In this embodiment, the private key of the digital certificate is stored in the USB-Key and is used to encrypt the hash value of the entire bidding data packet to generate a digital signature. The digital signature and the user's certificate public key information are encapsulated in the metadata part of the bidding data packet. In the verification step of S5, the server uses the corresponding public key to decrypt the signature and compares it with the recalculated hash value of the bidding data packet to verify the integrity of the bidding documents and the non-repudiation of the sender. This greatly enhances the legal effect and security of the bidding documents, ensures that the bidding documents are not tampered with during transmission and storage, and can be reliably traced back to their source, meeting the strong compliance requirements of bidding business.

[0058] In this embodiment, the key parameters are defined as follows:

[0059] The parameter symbol for the project type is Used to characterize the business classification and project type of a project. It is an enumerated value, determined by the bidding server according to the classification standards preset in the bidding documents; the acquisition method is that when the client sends a bid preparation request to the server, the server queries the project database of the bidding documents and returns the type code corresponding to the project ID; in this embodiment, the preset type is 1 for goods, 2 for services, and 3 for engineering.

[0060] The parameter symbol for the project budget amount is This is used to represent the funding scale of the project; it is a floating-point value, with the unit being ten thousand yuan; its acquisition method is the same as that of the procurement project type, and it is directly read and returned by the server from the project database.

[0061] The symbol for the historical failure rate is: , is used to characterize the failure probability of similar projects in historical bidding activities; it is a floating-point value in the range [0,1]; it is obtained through server-side statistical analysis, which regularly mines historical bidding data, groups it according to the type of procurement project, calculates the proportion of failed projects under each type due to insufficient effective users or invalid bids to the total number of projects, and stores the statistical results in the database for client access.

[0062] The parameter symbols for the project complexity assessment score are: It is a dimensionless score that is standardized within the range of [0,1] and used to comprehensively and quantitatively assess the inherent complexity and risk level of a specific procurement project.

[0063] The parameter symbol for the template adaptive level is: , is a discrete integer level used to determine the level of detail of the data template that the client should load and the strictness of the validation rules; it is determined by the project complexity assessment score through a preset threshold mapping rule; in this embodiment, level 1 corresponds to the basic template, level 2 corresponds to the standard template, and level 3 corresponds to the enhanced template.

[0064] The parameter symbol for the structured data hash value is , is a fixed-length hexadecimal string used to uniquely identify all the structured data entered by the user; when the user confirms the submission of data, the client concatenates all structured named fields and their corresponding values ​​in a preset lexicographical order to form a long string, and then calls a cryptographic hash function to calculate the result of the string;

[0065] The parameter symbol of the timestamp hash value is The timestamp is a fixed-length hexadecimal string used to uniquely identify the time point when the data packet was generated. The client requests a secure timestamp from a trusted timestamp synchronization center. The timestamp contains time information accurate to the second and is signed by TSA. The client then performs a cryptographic hash calculation on the original message containing the timestamp.

[0066] The parameter symbols for data integrity fingerprints are: , is a fixed-length hexadecimal string, which is the core object generated by cryptographically fusing the three key elements of project complexity, data content and time point, and is used for subsequent digital signatures;

[0067] Obtain the basic risk parameters associated with the project, including project type, project budget amount, and historical bid failure rate; perform numerical normalization on the basic risk parameters; and perform linear weighted summation on the normalized basic risk parameters to obtain the project complexity assessment score.

[0068] The calculated project complexity assessment score is compared with a preset multi-level numerical threshold; the project complexity assessment score is then mapped to a unique template adaptive level.

[0069] Calculate the structured data hash value of the structured bidding data; obtain the secure timestamp and calculate the timestamp hash value;

[0070] The structured data hash value, timestamp hash value, and project complexity assessment score are concatenated and concatenated; and a cryptographic hash function is applied to generate a data integrity fingerprint.

[0071] The complete calculation process for the bid data package in this embodiment is as follows:

[0072] When a user selects a specific project on the client and clicks the "Create Tender Document" button, the client sends a request containing a unique identifier for the project to the bidding server.

[0073] After receiving the request, the server retrieves three basic risk parameters associated with the project's unique identifier from its internal database: project type. Project budget amount and historical bid failure rate and project type Project budget amount and historical bid failure rate Return to the client;

[0074] Received project type Project budget amount and historical bid failure rate Then, immediately implement the project complexity assessment score. Calculation;

[0075] The project complexity assessment score was calculated. Then, the client evaluates the project complexity score according to a preset, segmented threshold mapping rule. Perform logical judgments to determine the template's adaptive level. The rules are as follows:

[0076] If the project complexity assessment score is If the value is greater than or equal to 0 and less than 0.4, then the template adaptive level is set. It was determined to be 1;

[0077] If the project complexity assessment score is If the value is greater than or equal to 0.4 and less than 0.8, then the template adaptive level is set. It was determined to be 2;

[0078] If the project complexity assessment score is If the value is greater than or equal to 0.8 and less than or equal to 1, then the template adaptive level is set. It was determined to be 3;

[0079] The client adapts to the specified template level. The corresponding user interface is loaded and rendered from the local template library, according to the following rules:

[0080] Level 1 Basic Template: Includes only the most essential required fields, such as total price and contact person; the validation rules are relatively lenient.

[0081] Level 2 Standard Template: Based on Level 1, it adds fields such as itemized pricing and main technical parameters, and introduces cross-validation logic between fields;

[0082] Level 3 Enhanced Template: Building upon Level 2, this template requires a more detailed index of qualification certificates, a structured description of performance cases, and mandates the uploading of corresponding supporting documents. The verification rules are the most stringent. Subsequently, users fill in structured data in the interface and upload unstructured raw files.

[0083] After the user completes the form and clicks "Generate Bidding Data Package," the process enters the security processing phase:

[0084] The client program solidifies all structured data entered by the user and calculates the hash value of the structured data. The client obtains the security timestamp from the TSA and calculates the timestamp hash value. ;

[0085] Client-side fusion of structured data hash values timestamp hash value and project complexity assessment score Generate the final data integrity fingerprint. ;

[0086] The client invokes the inserted private key, uniquely bound to the user's identity information, to verify the generated data integrity fingerprint. Perform a digital signature operation;

[0087] The output is a unified bid data packet; the internal structure of the bid data packet includes: a digitally signed, multi-factor data integrity fingerprint. The data includes the user's digital certificate public key, the structured data itself, and all unstructured raw files; the bid data package is then submitted to the subsequent encryption and fragmentation upload module.

[0088] The steps for generating a unified bid data package in S3 are as follows: Construct a self-contained data object that uses structured bid data as metadata and unstructured raw files as payload data, and establish a directly addressable mapping relationship between the content of metadata and payload data through a preset index.

[0089] Structured data is encoded into metadata files in XML or JSON format, and all unstructured raw files are used as payload data in a binary stream. Unstructured raw files include PDFs and images. The metadata file contains not only information such as quotations and qualifications, but also an index table. In this embodiment, an index record in the index table is set as follows: {"fieldName":"Business License","file":"scan.pdf","page":3,"coordinates":[x1,y1,x2,y2]}, clearly indicating that the original evidence corresponding to the structured entry "Business License" is located in a specific coordinate area on page 3 of the scan.pdf file. The collection containing the manifest file and all payload files is packaged into a self-contained data object in a format such as ZIP. This is not simply packaging two types of data, but creating a completely new, highly self-consistent data structure. This allows the backend system to get rid of blindly searching and parsing the full text content and transform into efficient and accurate address-based object retrieval based on metadata.

[0090] S4. Encrypt and fragment the unified bid data package, and upload the bid data package to the bidding server;

[0091] The steps for uploading the bidding data packet in S4 are as follows: During the upload process, the client and the server maintain a data fragment confirmation state machine; when a network interruption is detected, the upload will automatically resume after the network is restored, based on the last confirmed data fragment position recorded in the state machine.

[0092] The data packet is divided into N fixed-size fragments; each time the client successfully uploads a fragment... The server then returned a confirmation message. The client maintains state variables to record the maximum consecutive data received. Number; if uploading fragments If not received within the specified time If the network connection fails, it is determined to be a transmission interruption or failure; after the network is restored, the method will directly start from the fragment. Instead of starting from scratch, the upload process is restarted; the instruction signal (retransmission instruction) is the result of a joint decision based on the output data packet status (incomplete upload) and the network status; the generated unified data packet is used as the control object, enabling refined and intelligent control of the transmission process; this ensures that even in unstable network conditions, large-volume tender documents can be submitted reliably and efficiently, demonstrating the robustness of the method.

[0093] S5. On the bidding server side, the received data fragments are reassembled, decrypted and verified, and structured bidding data is separated from the verified bidding data packets so that the structured bidding data can be directly used for subsequent intelligent review or data analysis processes.

[0094] The steps for using the S5 bid data package for subsequent intelligent review or data analysis include: after the bid opening, automatically extracting preset key fields from the structured bid data of multiple users and generating a multi-dimensional, visualized horizontal comparison analysis report;

[0095] Once the bid opening instruction is triggered, a pre-set data processing script is automatically executed. The script concurrently queries the structured databases of all valid users, extracts the values ​​of key fields such as total price, core technical parameters, and delivery date, aggregates these values ​​into a data matrix, and calls the visualization engine to generate comparative bar charts, radar charts, or difference tables. If a user's field is missing, the report will clearly mark the item as missing data and provide a link to directly jump to the corresponding page in its unstructured original file, initiating a downgrade to manual verification mode. It can automatically drive complex comparative analysis work that previously required experts to complete for several hours, realizing intelligent linkage from data to decision-making.

[0096] In this embodiment, the key parameters are defined as follows:

[0097] The parameter symbol for network round-trip time is: This is used to characterize the time elapsed from when the client sends a probe packet to the server and receives a response;

[0098] The parameter symbol for network packet loss rate is: , is a floating-point value in the range [0,1], used to represent the proportion of data packets lost during network transmission;

[0099] The parameter symbol for the available bandwidth estimate is: This characterizes the bandwidth available for data transmission on the network link between the current client and the server, measured in Mbps.

[0100] The parameter symbols for the instantaneous network quality score are: , is a dimensionless score that is standardized within the [0,1] interval and used to comprehensively quantify the quality of the current network transmission channel; the score is the core driving factor for realizing adaptive transmission control;

[0101] The symbol for the dynamic slice size parameter is: , is an integer representing the size of each fragment during packet segmentation, in KB; it is dynamically determined by the instantaneous network quality score through a preset mapping function, and its value is within a preset minimum fragment size. and maximum slice size Changes between;

[0102] The parameter symbol for the number of concurrent upload threads is: , is an integer representing the number of concurrent threads used by the client to upload different data shards simultaneously; it is dynamically determined by the instantaneous network quality score, and its value is within the preset minimum number of threads. and maximum number of threads Changes between;

[0103] The parameter symbol for the association confidence score is: It is a floating-point value in the range [0,1].

[0104] The parameter symbol for the field compliance validation result is: , is a Boolean value (or mapped to 1 and 0) used to indicate whether the content of a structured data field has passed the preset business rule validation;

[0105] The parameter symbol for the overall data credibility score is: It is a floating-point value in the range [0,1], used to comprehensively quantify the overall reliability of structured data points;

[0106] The steps for maintaining the data fragmentation confirmation state machine are as follows:

[0107] Before and during the upload process, continuously monitor network performance metrics in at least two dimensions; dynamically calculate instantaneous network quality scores; and adjust the dynamic fragment size used to segment bid data packets and the number of concurrent upload threads used to upload data fragments in real time based on the instantaneous network quality scores.

[0108] The steps for calculating the instantaneous network quality score are as follows:

[0109] The network performance metrics are taken as input, and a preset fuzzy logic inference algorithm is applied for fusion calculation to output an instantaneous network quality score.

[0110] The steps for a horizontal comparative analysis report are as follows:

[0111] Obtain the association confidence score determined when generating the bid data package and the field compliance verification results performed on the server side; merge the association confidence score and the field compliance verification results to calculate a comprehensive data credibility score for each structured data; in the horizontal comparative analysis report, the comprehensive data credibility score applies differentiated visual styles to different data points.

[0112] Specific steps for uploading the bid data package:

[0113] After the client generates a unified bidding data package, it prepares to start uploading;

[0114] During transmission, a separate background network monitoring thread is started; the network monitoring thread continuously performs the following operations at fixed time intervals:

[0115] Measure and update network round-trip time Moving average; measure and update network packet loss rate ; Measure and update the available bandwidth estimate ;

[0116] Whenever the network monitoring thread updates any basic network parameter, the main transmission control flow immediately invokes the fusion algorithm to recalculate the instantaneous network quality score. ;

[0117] Calculate the new instantaneous network quality score Then, the transmission parameters are adjusted in real time based on two independent mapping functions:

[0118] Call the function, with the instantaneous network quality score as input. The output is the dynamic slice size. The basic calculation logic is: instantaneous network quality score. The higher the value, the larger the output dynamic fragment size. ;

[0119] Call another function, taking the instantaneous network quality score as input. The output is the number of concurrent upload threads. The basic calculation logic is: instantaneous network quality score. The higher the number of concurrent upload threads, the higher the output. ;

[0120] The main upload process is based on the dynamically adjusted fragment size from the previous step. The remaining unuploaded data is split and processed according to the adjusted number of concurrent upload threads. Start or destroy the upload thread to send data fragments to the server concurrently;

[0121] Maintain the data fragment acknowledgment state machine. The data fragment acknowledgment state machine is a bitmap that records whether each fragment has been acknowledged and received by the server.

[0122] If the network monitoring thread detects a network connection failure during the upload process, in this embodiment, if there is no response after multiple consecutive probes, all upload activities will be paused.

[0123] After the network connection is restored, the client requests the successfully received fragment bitmap from the server to synchronize the state; based on the data fragment, the first unacknowledged position recorded in the state machine is confirmed, and the dynamic upload process is resumed from that fragment.

[0124] The final output is that the entire bidding data package is uploaded to the server completely and reliably;

[0125] Authorized review experts click the "Generate Comparative Analysis Report" button on the web front-end interface; the front-end sends a request containing a list of all valid user IDs to the back-end server;

[0126] After receiving the request, the server concurrently retrieves the following two types of core data from the database for each user:

[0127] The content of structured data fields stored in metadata, and their corresponding association confidence scores. ; Field compliance verification results associated with each structured field ;

[0128] For each user and each extracted key field, the server performs a comprehensive data credibility score. Calculation;

[0129] The server collects the raw values ​​of all key fields for each user, along with a calculated overall data credibility score. Together with the index information (filename, page number, coordinates) used for evidence chain penetration, it is aggregated into a structured JSON object and returned to the front end as an API response;

[0130] After receiving the JSON data, the front end calls the visualization chart library for rendering; in this embodiment, when generating a bar chart comparing prices, the height of each bar is determined by the price amount.

[0131] The color or transparency of each column is determined by its corresponding overall data credibility score. The color gradient mapping function determines the color confidence level; in this embodiment, scores close to 1 are displayed as dark green (high confidence), and scores lower are displayed as yellow or red (low confidence).

[0132] A click event is bound to each data point in the chart (in this embodiment, data points are represented as bars); when a user clicks a data point, the front end obtains the index information bound to the data point;

[0133] The front end triggers a modal window. In the modal window, the corresponding unstructured raw file is loaded using libraries such as PDF.js, and the page is automatically scrolled to the specified page number. Then, a highlighted rectangle is drawn in the specified coordinate area.

[0134] The final output is an interactive, in-depth information visualization-based comparative analysis report presented in a browser. Review experts can visually perceive the data quality and trace the original evidence of any data with a single click.

[0135] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent auxiliary system for bidding and procurement operations, characterized in that, Includes the following steps: S1. On the client side, respond to the user's bid preparation request and load the preset data template associated with the specific procurement project. The preset data template contains multiple data fields to guide the user to input structured bidding information. S2. Receive structured bidding data input by the user through the data field, and receive unstructured raw files uploaded by the user; S3. Based on structured bidding data and unstructured original files, generate a unified bidding data package, in which the structured bidding data and unstructured original files are linked within the bidding data package. S4. Encrypt and fragment the unified bid data package, and upload the bid data package to the bidding server; S5. On the bidding server side, the received data fragments are reassembled, decrypted and verified, and structured bidding data is separated from the verified bidding data packets so that the structured bidding data can be directly used for subsequent intelligent review or data analysis processes.

2. The intelligent auxiliary system for bidding and procurement business according to claim 1, characterized in that: The preset data template in S1 is dynamically determined based on the procurement type of a specific procurement project; and the data field contains mandatory field validation rules and format validation rules corresponding to the procurement type.

3. The intelligent auxiliary system for bidding and procurement business according to claim 2, characterized in that: Before the bidding data packet is generated in S3, it undergoes digital signature processing. The digital certificate bound to the user's identity information is called to digitally sign the unified bidding data packet, so as to generate signature information and attach it to the bidding data packet.

4. The intelligent auxiliary system for bidding and procurement business according to claim 3, characterized in that: The complete calculation process for the bidding data package is as follows: When a user selects a specific project on the client and clicks the "Create Tender Document" button, the client sends a request containing a unique identifier for the project to the bidding server. After receiving the request, the server retrieves three basic risk parameters associated with the project's unique identifier from its internal database: project type, project budget amount, and historical failure rate, and returns the project type, project budget amount, and historical failure rate to the client. Upon receiving the project type, project budget amount, and historical bid failure rate, immediately calculate the project complexity assessment score; After calculating the project complexity assessment score, the client performs logical judgment on the project complexity assessment score according to a preset, segmented threshold mapping rule to determine the template's adaptive level. The rules are as follows: If the project complexity assessment score is greater than or equal to 0 and less than 0.4, the template adaptation level is determined to be 1. If the project complexity assessment score is greater than or equal to 0.4 and less than 0.8, the template adaptation level is determined to be 2. If the project complexity assessment score is greater than or equal to 0.8 and less than or equal to 1, then the template adaptation level is determined to be 3; The client loads and renders the corresponding user interface from the local template library based on the determined template adaptive level, according to the following rules: Level 1 Basic Template: Includes only the most essential required fields, such as total price and contact person; the validation rules are relatively lenient. Level 2 Standard Template: Based on Level 1, it adds fields such as itemized pricing and main technical parameters, and introduces cross-validation logic between fields; Level 3 Enhanced Template: Building upon Level 2, this template requires a more detailed index of qualification certificates, a structured description of performance cases, and mandates the uploading of corresponding supporting documents. The verification rules are the most stringent. Subsequently, the user fills in structured data and uploads unstructured raw files in the interface; After the user completes the form and clicks "Generate Bidding Data Package," the process enters the security processing phase: The client solidifies all the structured data entered by the user and calculates the hash value of the structured data; The client obtains the security timestamp from the TSA and calculates the timestamp hash value; The client integrates structured data hash values, timestamp hash values, and project complexity assessment scores to generate the final data integrity fingerprint; The client calls the inserted private key, which is uniquely bound to the user's identity information, to perform a digital signature operation on the generated data integrity fingerprint; The output is a unified bid data packet; the internal structure of the bid data packet includes: a digitally signed multi-factor data integrity fingerprint, the user's digital certificate public key, the structured data itself, and all unstructured original files; the bid data packet is then submitted to the subsequent encryption and fragmentation upload module.

5. The intelligent auxiliary system for bidding and procurement business according to claim 4, characterized in that: The steps for generating a unified bid data package in S3 are as follows: Construct a self-contained data object that uses structured bid data as metadata and unstructured raw files as payload data, and establish a directly addressable mapping relationship between the content of metadata and payload data through a preset index.

6. The intelligent auxiliary system for bidding and procurement business according to claim 5, characterized in that: The steps for uploading the bidding data packet in S4 are as follows: During the upload process, the client and the server maintain a data fragment confirmation state machine; when a network interruption is detected, the upload will automatically resume after the network is restored, based on the last confirmed data fragment position recorded in the state machine.

7. The intelligent auxiliary system for bidding and procurement business according to claim 6, characterized in that: The steps for using the S5 bid data package for subsequent intelligent review or data analysis include: after the bid opening, automatically extracting preset key fields from the structured bid data of multiple users and generating a multi-dimensional, visualized horizontal comparison analysis report.

8. The intelligent auxiliary system for bidding and procurement business according to claim 7, characterized in that: The steps for maintaining the data fragmentation confirmation state machine are as follows: Before and during the upload process, network performance metrics in at least two dimensions are continuously monitored; instantaneous network quality scores are dynamically calculated; and based on the instantaneous network quality scores, the dynamic fragment size used to segment bid data packets and the number of concurrent upload threads used to upload data fragments are adjusted in real time.

9. The intelligent auxiliary system for bidding and procurement business according to claim 8, characterized in that: The steps for calculating the instantaneous network quality score are as follows: The network performance metrics are taken as input, and a preset fuzzy logic inference algorithm is applied for fusion calculation to output an instantaneous network quality score.

10. The intelligent auxiliary system for bidding and procurement business according to claim 9, characterized in that: The steps for a horizontal comparative analysis report are as follows: Obtain the association confidence score determined when generating the bid data package and the field compliance verification results performed on the server side; integrate the association confidence score and the field compliance verification results to calculate a comprehensive data credibility score for each structured data; In the horizontal comparative analysis report, the overall data credibility score is used to apply differentiated visual styles to different data points.

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