A project end-to-end intelligent management and control system based on unified coding

Abstract

The application is suitable for the technical field of project process management and control, and provides a project whole-process intelligent management and control system based on unified coding, which comprises: a unified coding unit, which is used for generating a unique identification code when a project is established; a process driving unit, which is used for automatically generating a notice document, updating a project stage state and an approval state, and tracking an execution progress of a work flow instance in real time; a data processing unit, which is used for collecting multi-source data with the unique identification code as an index, and performing data preprocessing; a service output unit, which is used for providing a subscribable data service based on the unique identification code, implementing data desensitization management and control, and storing data according to a procurement dimension to generate analysis results; a fusion gateway unit, which is used for connecting heterogeneous system data with the unique identification code as an index; a monitoring unit, which is used for monitoring a work flow running state and triggering an alarm when an exception occurs, and monitoring data processing quality and interface service state; and a comprehensive and accurate data basis is provided for procurement projects.

Description

Technical Field

[0001] This invention relates to the field of project process control technology, and in particular to an intelligent project process control system based on unified coding. Background Technology

[0002] With the continuous expansion of enterprise procurement scale and the acceleration of digital transformation, end-to-end project management has become a core requirement for modern enterprise operations. Current procurement management systems involve multiple stages, including bidding, review, contracts, and payments, generating massive amounts of heterogeneous data at each stage, including key information such as supplier qualifications, bid documents, evaluation records, and contract terms. Due to historical development reasons, these systems are typically developed in phases by different vendors, using heterogeneous databases such as MySQL, SQL Server, and Oracle Database, resulting in significant differences in data structures, interface protocols, and business processes between systems.

[0003] In existing technologies, project management operates independently through various subsystems. The procurement system manages the bidding process, the ERP (Enterprise Resource Planning System) handles contract fulfillment, and the CRM (Customer Relationship Management System) maintains supplier information. Each system uses independent coding rules, resulting in fragmented and uncorrelated data. Secondly, document generation requires manual data extraction across systems, manually filling in tables to generate bidding announcements, evaluation reports, and other documents, which is inefficient. Furthermore, auditing requires logging into several systems and manually verifying data, easily leading to high error rates.

[0004] In view of this, a project end-to-end intelligent management and control system based on unified coding is proposed. Summary of the Invention

[0005] This invention provides an intelligent management and control system for the entire project process based on unified coding, which is used to solve problems such as data silos, low file generation efficiency and high data error rate.

[0006] This invention provides a project end-to-end intelligent management and control system based on unified coding, comprising:

[0007] The system comprises a unified coding unit, a process-driven unit, a data processing unit, a service output unit, a converged gateway unit, and a monitoring unit; these units collaboratively execute control operations through the unified coding unit.

[0008] The unified coding unit is used to decompose a project into multiple sub-project domains when the project is initiated; generate a global main identifier segment and a corresponding auxiliary identifier segment for each sub-project domain; and generate an independent globally unique identifier code for each sub-project domain by combining the main identifier segment with each auxiliary identifier segment respectively.

[0009] The process-driven unit is used to automatically generate notification documents, update project stage status and approval status, and track the execution progress of workflow instances in real time.

[0010] The data processing unit is used to collect multi-source data using a unique identifier as an index and to perform data preprocessing.

[0011] The service output unit is used to provide subscribed data services based on a unique identifier, implement data anonymization control, and classify and store data according to procurement dimensions to generate analysis results.

[0012] The convergence gateway unit is used to connect to heterogeneous system data using a unique identifier as an index;

[0013] The monitoring unit is used to monitor the workflow operation status and trigger alarms when abnormalities occur, while also monitoring data processing quality and interface service status.

[0014] Furthermore, the unified coding unit includes:

[0015] The project is decomposed into K sub-project domains, where K is an integer greater than 1;

[0016] A main identifier segment is generated according to preset industry coding rules, and the main identifier segment is applied throughout the entire project lifecycle;

[0017] N auxiliary identifier segments are generated based on the characteristics of the sub-item domain, where N is an integer greater than or equal to K, and each auxiliary identifier segment corresponds to a sub-item domain;

[0018] The main identifier segment and the auxiliary identifier segment are combined to generate a globally unique identifier.

[0019] Furthermore, the process of decomposing the project into K sub-project domains includes:

[0020] When the project phases are a continuous process:

[0021] The total project phases are divided into M consecutive process sub-bands, where M is an integer greater than 1; odd-numbered process sub-bands are assigned a main identifier segment; even-numbered process sub-bands are assigned a secondary identifier segment; where M is determined by dividing the total number of project phases by a preset phase threshold.

[0022] Furthermore, the generation of N auxiliary identifier segments based on the sub-item domain characteristics includes:

[0023] When heterogeneous systems are interoperable:

[0024] Obtain the priority parameters of each system data stream; allocate the length of the auxiliary identifier segment according to the priority, allocate long identifier segments to high-priority system data streams, and allocate short identifier segments to low-priority system data streams; the length of the auxiliary identifier segment is positively correlated with the data stream priority.

[0025] Furthermore, the method for automatically generating the notification file includes:

[0026] Based on the project phase, a target notification template is selected from a pre-set template library, and the target notification template contains business semantic placeholders;

[0027] Determine the encoding mapping template corresponding to the target notification template, wherein the encoding mapping template defines encoding index variables that are mapped to business semantic placeholders;

[0028] The parameter value of the encoded index variable is obtained using the unique identifier as an index; wherein, when the parameter value is stored in the local database, it is extracted from the data processing unit; when the parameter value is not stored in the local database, it is extracted from the external system through the convergence gateway unit.

[0029] Replace the business semantic placeholders in the target notification template with the parameter values ​​of the encoded index variables to generate the initial notification file;

[0030] Based on the approval status, add an electronic signature to the initial notification document to generate the official notification document.

[0031] Furthermore, the project phase-based target selection notification template includes:

[0032] When the project is in the project initiation stage, select the tender notice template. The business semantic placeholders include the procurement project name, budget amount, or qualification requirements.

[0033] When the project is in the review stage, select the bid evaluation report template. The business semantic placeholders include the bidding unit, technical score or commercial score.

[0034] When the project is in the contract signing stage, select the bid award notice template. The business semantic placeholders include the winning bidder, contract amount, or performance period.

[0035] Furthermore, the service output unit includes:

[0036] Service publishing module, de-identification control module, topic storage module, and report generation module;

[0037] The service publishing module is used to publish several subscribed data services based on a unique identifier and to count the number of interface calls in real time.

[0038] The desensitization control module is used to implement dynamic desensitization control on the output data according to the unique identifier code permission;

[0039] The subject storage module is used to store data according to procurement type, which includes procurement entity, supplier and expert evaluation;

[0040] The report generation module is used to generate procurement annual reports and risk analysis reports based on the data from the topic storage module.

[0041] Furthermore, the method for implementing dynamic desensitization control of output data based on unique identifier codes includes:

[0042] The security level and user access permission level are determined based on the unique identifier.

[0043] Hierarchical desensitization is performed based on user access permission levels; specifically, sensitive fields are encrypted and key information is masked for unauthorized users, while authorized users are subject to differentiated desensitization according to their permission levels.

[0044] Record audit logs for the anonymization process, including the identifier code, user identity, and operation time.

[0045] Furthermore, the method for interfacing with heterogeneous system data using a unique identifier as an index includes:

[0046] Read the data field definitions and format specifications of heterogeneous systems, and group the field definitions of the same business object into a set of data items;

[0047] Extract cross-system related data using a unique identifier as an index; wherein, obtain the first set of data item values ​​associated with the unique identifier from the first type of system; and obtain the second set of data item values ​​associated with the unique identifier from the second type of system;

[0048] Convert the heterogeneous formats of each data item value set into a unified standard format;

[0049] The converted set of data item values ​​is written to the target location according to preset storage rules; wherein, the extraction of cross-system related data using a unique identifier as an index includes: performing a data extraction process;

[0050] The data extraction process includes: parsing the unique identifier to obtain the system positioning prefix; determining the target system type based on the prefix; and extracting the corresponding data values ​​according to the order of the data item set.

[0051] Repeat the data extraction process until you obtain a set of data item values ​​from all related systems.

[0052] Furthermore, the monitoring unit includes:

[0053] Process monitoring module, data quality module, and service health module;

[0054] The process monitoring module is used to detect the status of workflow instances in real time and trigger alarms when there is a lag.

[0055] The data quality module is used to monitor the data cleaning success rate and mark abnormal data;

[0056] The service health module is used to periodically check the availability of interfaces and generate service health reports.

[0057] As can be seen from the above technical solutions, the present invention has the following advantages:

[0058] This invention utilizes a unified coding unit to generate unique identifiers for comprehensive management and control throughout the entire process, reducing system silos. A process-driven unit automatically generates notification documents and updates approval status in real time based on the unique identifiers, effectively improving document generation efficiency. A data processing unit uses unique identifiers as indexes to achieve cross-system data collection and cleaning, improving data integration accuracy. A service output unit dynamically controls data anonymization through unique identifier permissions, reducing the risk of data leakage while publishing several subscribing services. A converged gateway unit uses unique identifiers to achieve rapid data synchronization across various heterogeneous systems, reducing interface integration costs. A monitoring unit tracks workflow instance status in real time, improving anomaly response speed. This invention solves technical problems such as data fragmentation, inefficient documentation, and difficult supervision, providing a comprehensive and accurate data foundation for procurement projects. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of an embodiment of an intelligent project management and control system based on unified coding according to the present invention.

[0060] Figure 2 This is a schematic diagram of an embodiment of the unified coding unit in this invention;

[0061] Figure 3 This is a schematic diagram of an embodiment of the process-driven unit in this invention;

[0062] Figure 4 This is a schematic flowchart of an embodiment of the fusion gateway unit in this invention;

[0063] Figure 5 This is a schematic diagram of the structure of each module of the present invention, which includes a service output unit and a monitoring unit. Detailed Implementation

[0064] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “corresponding to,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0065] Example 1

[0066] Please see Figure 1 The intelligent project management system based on unified coding in this invention includes: a unified coding unit 1, a process-driven unit 2, a data processing unit 3, a service output unit 4, a converged gateway unit 5, and a monitoring unit 6. The process-driven unit 2, data processing unit 3, service output unit 4, converged gateway unit 5, and monitoring unit 6 collaboratively execute management operations through the unified coding unit 1. Specifically, the unified coding unit 1 is used to decompose the project into multiple sub-project domains during project initiation; generate a global primary identifier segment and a corresponding secondary identifier segment for each sub-project domain; and generate a unique identifier segment for each sub-project domain by combining the primary identifier segment with each secondary identifier segment. A unique global identifier is used for each workflow instance; the process-driven unit 2 automatically generates notification documents, updates project stage status and approval status, and tracks the execution progress of workflow instances in real time; the data processing unit 3 collects multi-source data using the unique identifier as an index and performs data preprocessing; the service output unit 4 provides subscribed data services based on the unique identifier, implements data anonymization control, and stores data according to procurement dimensions to generate analysis results; the convergence gateway unit 5 connects to heterogeneous system data using the unique identifier as an index; and the monitoring unit 6 monitors the workflow operation status and triggers alarms when anomalies occur, while also monitoring data processing quality and interface service status.

[0067] The workflow of the control system is described in detail below using specific scenarios:

[0068] When a project initiation instruction is triggered, during project initiation, the unified coding unit 1 decomposes the project into multiple sub-project domains; generates a global primary identifier segment and multiple corresponding secondary identifier segments; by combining the primary identifier segment with each secondary identifier segment one by one, a series of globally unique identifier codes corresponding to the sub-project domains are generated; the process-driven unit 2 activates the pre-set bidding template based on the identifier code, automatically generates the bidding announcement notification document, updates the project stage to "under review" in real time, and tracks the bid evaluation progress; the data processing unit 3 collects bidding data from the procurement system, budget information from the ERP system, and supplier historical performance records from the CRM system using the identifier code as an index, performs data cleaning, such as filtering zero-value budgets and unifying the monetary unit to ten thousand yuan, etc.; the integrated gateway order is processed. Unit 5 synchronously connects to the external review expert database system, associating expert qualification data with the current project according to the identification code, and converting the date format to the YYYY-MM-DD standard; Service output unit 4 dynamically desensitizes sensitive fields such as supplier bank accounts based on identification code permissions, such as displaying a mask for primary authorized users and allowing advanced authorized users to view complete information, while storing cleaned data in a three-dimensional classification of purchaser / supplier / expert evaluation, and generating a supplier credit risk analysis report; Monitoring unit 6 monitors the execution status of the evaluation process in real time, and immediately triggers an SMS alarm to the responsible person when a certain link stalls beyond a preset threshold, and simultaneously audits the data cleaning success rate and interface call response time, finally outputting an electronically signed version of the winning bid notice and a procurement efficiency annual report.

[0069] Example 2

[0070] Please see Figure 2 The implementation principle of the unified coding unit in this invention, which generates a unique identifier code during project initiation, includes the following steps:

[0071] S21. Decompose the project into K sub-project domains, where K is an integer greater than 1;

[0072] Based on the project work breakdown structure, the project is divided into K atomic-level sub-project domains, such as the tender preparation domain (technical specification preparation), the supplier management domain (qualification review), and the contract execution domain (performance monitoring); here, K = ceil (project complexity coefficient * number of critical path nodes), where the complexity coefficient is obtained by training with historical project data.

[0073] When the project phases are continuous processes: the total project phases are divided into M continuous process sub-bands, where M is an integer greater than 1; the odd-numbered process sub-bands are set to apply the main identifier segment; the even-numbered process sub-bands are set to apply the auxiliary identifier segment; where M is determined by dividing the total number of project phases by a preset phase threshold.

[0074] Specifically, the total project phases (e.g., 12 phases) are divided into M consecutive process sub-bands, where M = total number of phases / phase threshold. The phase threshold is dynamically set based on the project type; for example, the threshold is set to 3 for engineering projects and 4 for goods projects. Odd-numbered sub-bands (1, 3, 5…) use the primary identifier segment, while even-numbered sub-bands (2, 4, 6…) use the secondary identifier segment. This step resolves the issue of chaotic status tracking in multi-phase projects, and the alternating application of primary and secondary identifier segments achieves process load balancing.

[0075] S22. Generate a main identifier segment according to the preset industry coding rules. The main identifier segment is applied throughout the entire project lifecycle.

[0076] The preset industry coding rules here generate basic code segments by calling the national industry coding library (GB / T 4754-2017), where the first two bits are the regional code, the third and fourth bits are the industry code, and the fifth and sixth bits are the project type. In addition, a timestamp fingerprint is superimposed: the last 6 bits of the Unix timestamp at the time of project initiation are taken and a 3-bit check code is generated through hash compression.

[0077] S23. Generate N auxiliary identifier segments based on the characteristics of the sub-item domain, where N is an integer greater than or equal to K, and each auxiliary identifier segment corresponds to a sub-item domain;

[0078] The system analyzes the business characteristics of each sub-project domain. For example, the characteristics of the bidding preparation domain include technical parameter complexity, number of suppliers, and budget size. A unique feature fingerprint is calculated for this sub-project domain using a feature weight model. Next, a three-segment auxiliary identifier is generated based on the feature fingerprint: a domain identifier code (2 bits): a fixed code assigned according to the sub-project domain type; a feature fingerprint code (variable length): the feature fingerprint is Base64 encoded to generate a 6-24 bit variable string; and a version control code (2 bits): a rolling version number is used to resolve data conflicts.

[0079] When there is heterogeneous system interaction: obtain the data flow priority parameters of each system; allocate the length of the auxiliary identifier segment according to the priority, allocate long identifier segments to high-priority system data flows and short identifier segments to low-priority system data flows; the length of the auxiliary identifier segment is positively correlated with the data flow priority.

[0080] Specifically, ERP financial data streams are assigned long fingerprint codes (24 digits), while OA notification data streams are assigned short fingerprint codes (6 digits), retaining only key features. The length adjustment formula is: Feature fingerprint code length = base length 6 + 18 × (priority weight / 10), where the weight is calculated by comprehensively considering data sensitivity, update frequency, and business value.

[0081] S24. Combine the primary identifier segment and the secondary identifier segment to generate a globally unique identifier.

[0082] Finally, fractal coding technology is used to combine the main and auxiliary identifier segments. The main identifier segment is fixed at the beginning (16 bits), and the auxiliary identifier segments are arranged in descending order of priority (high → low). The segments are separated by "-" to enhance readability.

[0083] The above steps effectively improve the efficiency of project phase traceability and increase the speed of cross-system data association.

[0084] Example 3

[0085] Please see Figure 3 The implementation principle of the automatic generation of notification files by the process-driven unit in this invention is as follows:

[0086] S31. Select a target notification template from the pre-built template library based on the project phase. The target notification template contains business semantic placeholders.

[0087] The target notification template here is a predefined standardized document framework, stored in XML structure, containing business semantic placeholders, which are natural language variables marked with square brackets, such as [procurement project name].

[0088] Specifically, when the project is in the project initiation stage, select the tender announcement template, and the business semantic placeholders include the procurement project name, budget amount, or qualification requirements; when the project is in the review stage, select the bid evaluation report template, and the business semantic placeholders include the bidding unit, technical score, or commercial score; when the project is in the contract signing stage, select the award notice template, and the business semantic placeholders include the winning bidder, contract amount, or performance period.

[0089] Specifically, when a project phase change event is triggered, the system queries the process state machine and matches the phase and template mapping rules: the bidding announcement template (including 12 placeholders such as [budget amount]) is activated in the project initiation phase; the bid evaluation report template (including 8 placeholders such as [technical score]) is called in the review phase; and the award notice template (including 6 placeholders such as [performance period]) is activated in the contract signing phase.

[0090] S32. Determine the encoding mapping template corresponding to the target notification template. The encoding mapping template defines encoding index variables that are mapped to business semantic placeholders.

[0091] The encoding mapping template is a machine-parseable JSON configuration file that defines the mapping relationship between business semantic placeholders and encoding index variables. For example, in the tender notice template, [Procurement Project Name] is mapped to the variable projectName, and [Budget Amount] is mapped to budgetAmount. The system uses regular expressions to scan the target notification template to extract all placeholders, automatically generates an initial mapping template, and then optimizes it through a rule engine: synonym merging (e.g., mapping "amount" and "fee" to a unified amount); type validation (binding amount-type placeholders to numeric variables); and cross-template inheritance (inheriting supplier variables from the project initiation stage during the review stage).

[0092] S33. Obtain the parameter value of the encoded index variable using the unique identifier as the index; wherein, when the parameter value is stored in the local database, it is extracted from the data processing unit; when the parameter value is not stored in the local database, it is extracted from the external system through the convergence gateway unit;

[0093] Construct a parameter acquisition decision tree using the unique identifier as the root key:

[0094] 1. Local-first query: Concatenate the coded index variable with the identifier code to form the query key, and extract data from the subject database of the data processing unit;

[0095] 2. External cross-system retrieval: When there is no local cache, a targeted request is initiated to the external system through the converged gateway unit:

[0096] ERP system: SQL query: SELECT project_name FROM orders WHERE project_id='GZ2023-P001';

[0097] CRM system: RESTful API call / supplier?projectCode=GZ2023-P001;

[0098] 3. Real-time calculation and filling: For derived variables (such as technical score = weight × sum of each item), the built-in formula engine is called to calculate.

[0099] S34. Replace the business semantic placeholders in the target notification template with the parameter values ​​of the encoded index variables to generate the initial notification file;

[0100] This process uses DOM (Document Object Model Tree) manipulation techniques for replacement. First, the notification template is parsed into a DOM tree, and all placeholder nodes are located. Then, placeholders are replaced with parameter values ​​according to mapping rules, with text nodes being directly replaced and table cells being dynamically filled. Next, adaptive formatting is performed, such as right-aligning monetary values ​​and adding thousands separators, and converting dates to YYYY (year, month, day). Finally, an initial notification file is generated (PDF / Word format optional).

[0101] S35. Add an electronic signature to the initial notification document based on the approval status to generate the official notification document.

[0102] When the approval status is "Approved": retrieve the digital certificate from the CA certification center, add a visual signature image and digital fingerprint to the specified location in the file, and store the signature hash value on the blockchain; when the approval status is "Rejected": add a "Draft - Not Effective" watermark and lock the file editing permissions; when the approval status is "Under Approval": add an "Approval Process Number" header and retain the version traceability chain.

[0103] The above-mentioned automatic generation method for notification documents effectively improves generation efficiency and the accuracy of document data.

[0104] Example 4

[0105] Please see Figure 4 The implementation principle of the converged gateway unit in this invention, which uses a unique identifier as an index to connect to heterogeneous system data, is as follows:

[0106] S41. Read the data field definitions and format specifications of heterogeneous systems, and group the field definitions of the same business object into a set of data items;

[0107] Here, "same business object" refers to an entity unit with independent business semantics, such as "purchase order" or "supplier qualification," whose complete description requires collaboration of fields from multiple systems. A data item set aggregates scattered fields describing the same object into logical units. For example, a "purchase order" object might contain order number / amount in ERP, supplier ID / contact information in CRM, and shipping address / waybill number in a logistics system. The system uses a metadata scanning engine to read the table structure definitions and field format specifications of each system, such as MySQL's DESC table and SOAP interface WSDL descriptions, and clusters data items based on business tags such as "order identifier," "funds," and "logistics." This process uses a semantic similarity algorithm (cosine similarity > 0.85 for merging) to ensure the homogeneity of field business.

[0108] S42. Extract cross-system associated data using a unique identifier as an index; wherein, obtain a set of first data item values ​​associated with the unique identifier from the first type of system; and obtain a set of second data item values ​​associated with the unique identifier from the second type of system;

[0109] The first type of system is the core business system, such as ERP. It locates the system through the identifier prefix. For example, in GZ2023-P001, P points to the procurement system. Executing SQL: SELECT order_number, amount FROM orders WHERE project_id='GZ2023-P001', the output of the first data item value set is: {PO202308001, 12,307,800 yuan}.

[0110] The second type of system is an auxiliary system, such as CRM. Its identifier code suffix determines the subsystem. For example, -S points to the supplier subsystem. RESTful call: GET / supplier?projectCode=GZ2023-P001, outputs the set of the second data item values: {S10012, 138****5678}.

[0111] S43. Convert the heterogeneous formats of each data item value set into a unified standard format;

[0112] Here, a rule engine and machine learning work together to convert the heterogeneous formats of data item value sets from various systems into a unified standard format: First, the semantic type of the data (such as date, amount, text) is identified. For date data, regular expressions are used to match the source format (such as MM / DD / YYYY or DD-Mon-YY) and convert it to the YYYY-MM-DD international standard. For amount data, currency symbols and thousands separators are removed (such as ¥1,234.56→1234.56), and then the unit is converted according to the requirements of the target system (dividing by 10000 when converting yuan to ten thousand yuan). For text data, business terms (such as "urgent" and "high priority") are identified through an NLP model and mapped to a preset enumeration value (priority=1). After key fields of unstructured data are identified by OCR, the integrity is checked by a verification model to ensure that the converted data item value set conforms to the ISO 8000 data quality standard.

[0113] S44. Write the converted set of data item values ​​to the target location according to the preset storage rules; wherein, extracting cross-system related data using a unique identifier as an index includes: performing a data extraction process;

[0114] The data extraction process includes: parsing the unique identifier to obtain the system location prefix; determining the target system type based on the prefix; and extracting the corresponding data values ​​according to the order of the data item set.

[0115] Repeat the data extraction process until you obtain the set of data item values ​​from all related systems.

[0116] The converted set of data item values ​​is written to the target location according to the preset storage rules. The storage path is dynamically generated based on the unique identifier, and the rule is / topic library / {identifier prefix} / {business object type} / {year and month}. For example, the purchase order data with identifier GZ2023-P001 is written to / purchase topic / P / purchase order / 202308. At the same time, the data is automatically tiered according to the data popularity, that is, high-frequency access data is stored in SSD, and low-frequency data is archived to object storage.

[0117] The data extraction process is driven by the identification code: First, the identification code is parsed to obtain the system location prefix, such as P in GZ2023-P001 representing the procurement system. The target system type is routed according to the prefix (procurement system → ERP database). Then, the values ​​are extracted according to the order defined by the data item set, first the order number and then the amount. After the extraction of a single system is completed, the extraction cycle of the next system is immediately triggered, such as switching from ERP to CRM, until all related systems are covered.

[0118] Example 5

[0119] Please see Figure 5 The service output unit 4 in this invention includes the following:

[0120] The system comprises a service publishing module 401, a data masking control module 402, a theme storage module 403, and a report generation module 404. The service publishing module 401 publishes several subscribed data services based on unique identifiers and tracks the number of interface calls in real time. The data masking control module 402 dynamically controls the masking of output data according to the unique identifier's permissions. The theme storage module 403 stores data categorized by procurement type, including purchasers, suppliers, and expert bidders. The report generation module 404 generates annual procurement reports and risk analysis reports based on the data from the theme storage module.

[0121] In this embodiment, the method for dynamically desensitizing output data based on unique identifier codes includes the following steps:

[0122] 1. Decipher the project security level and user access permission level based on the unique identifier;

[0123] 2. Perform hierarchical desensitization based on user access permission levels; specifically, perform sensitive field encryption and key information masking for unauthorized users, and implement differentiated desensitization for authorized users according to their permission levels;

[0124] 3. Record audit logs for the de-identification operation, including the identification code, user identity, and operation time.

[0125] The service output unit 4 is described below in the context of a specific scenario:

[0126] When an external user initiates a data service request, the service publishing module 401 locates the subscribed service resource based on the unique identifier and simultaneously counts the frequency of interface calls in real time. The data masking control module 402 initiates a dynamic data masking process: first, it parses the identifier to obtain the project security level and user permission level; for unauthorized users, it automatically triggers AES-256 encryption of sensitive fields and masking of key information; for primary authorized users, it retains the numerical values ​​but masks sensitive attributes; and for advanced authorized users, it outputs complete data. The entire data masking operation generates an audit log, such as {Identifier GZ2023-P001, User: U789, Operation: Mask, Time: 2023-08-20 14:30}. The subject storage module 403 routes data according to procurement type: purchaser data is stored in the purchaser subject database (including unit attributes / historical procurement records), supplier data is written to the supplier subject database (including credit rating / performance capability), and expert evaluation data is collected in the evaluation subject database (including qualifications / historical scores). The report generation module 404 is based on thematic library data linkage analysis: the procurement annual report automatically aggregates core indicators such as annual procurement amount / completion rate, while the risk analysis report outputs a supply chain risk heat map by linking supplier credit and expert rating data. Finally, the anonymized report is pushed to subscribers through the service publishing module 401.

[0127] In addition, the monitoring unit 6 of the present invention includes the following:

[0128] The system includes a process monitoring module 601, a data quality module 602, and a service health module 603. The process monitoring module 601 is used to detect the status of workflow instances in real time and trigger alarms when there is a bottleneck. The data quality module 602 is used to monitor the success rate of data cleaning and mark abnormal data. The service health module 603 is used to periodically check the availability of interfaces and generate service health reports.

[0129] The monitoring unit 6 is described below in the context of a specific scenario:

[0130] During the bidding review phase, the process monitoring module 601 scans the workflow instance status in real time. If the evaluation task has been stalled for 32 minutes, it automatically triggers a three-level alarm: Level 1: A text message is sent to the responsible person, containing the identifier code GZ2023-P001 and the code of the stalled node; Level 2: A detailed stack analysis is sent via email; Level 3: The alarm flashes red on the command screen. Simultaneously, the process reverts to the previous approval node and releases the occupied expert resources. The data quality module 602 monitors that the supplier data cleaning success rate has plummeted below the 99% threshold. It immediately identifies the source of the anomaly as a missing qualification certificate field in the CRM system, automatically marks the problematic data, and initiates a compensation process: calling the Rongtong gateway unit 5 to collect supplementary data in real time. After cleaning, the success rate recovers to 99.7%, and the anomaly event is recorded as {Identifier code: GZ2023-P001, Anomaly type: E207, Time: 2023-08-20 14:35}. The service health module 603 polls at least 77 data service interfaces every 5 minutes. If it detects that the response delay of the tender document download interface is greater than 5 seconds (normal threshold ≤ 1 second), it immediately isolates the faulty node and switches to the backup cluster, and generates a health report to display key indicators.

[0131] The above embodiments, through the coordinated operation of various unit modules, can reduce system silos, improve file generation efficiency, and reduce data error rates, providing a comprehensive and accurate data foundation for procurement projects.

[0132] It is understood that those skilled in the art can combine various implementation methods in the above embodiments under the guidance of the above examples to obtain technical solutions with multiple implementation methods.

[0133] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A project whole-process intelligent management and control system based on unified coding, characterized in that, The application relates to a unified coding unit, a process driving unit, a data processing unit, a service output unit, a fusion gateway unit and a monitoring unit; the process driving unit, the data processing unit, the service output unit, the fusion gateway unit and the monitoring unit cooperatively execute control operations through the unified coding unit; the unified coding unit is used for decomposing a project into multiple sub-project domains when the project is established; a global main identification segment is generated, and a corresponding auxiliary identification segment is generated for each sub-project domain; a global unique identification code is generated for each sub-project domain by combining the main identification segment with each auxiliary identification segment; the unified coding unit comprises the following steps: a project is decomposed into K sub-project domains, K is an integer greater than 1; the project is decomposed into K sub-project domains, which comprises the following steps: when the project stage is a continuous process, the project total stage is divided into M continuous process sub-bands, M is an integer greater than 1; odd process sub-bands are provided with the main identification segment; even process sub-bands are provided with the auxiliary identification segment; wherein M is determined by dividing the total number of project stages by a preset stage threshold; a main identification segment is generated according to a preset industry coding rule, and the main identification segment is applied to the whole life cycle of the project; N auxiliary identification segments are generated according to the characteristics of the sub-project domains, N is an integer greater than or equal to K, each auxiliary identification segment corresponds to a sub-project domain; when there is a heterogeneous system docking, the length of the auxiliary identification segment is positively correlated with the data flow priority; the main identification segment and the auxiliary identification segment are combined to generate a global unique identification code; the process driving unit is used for automatically generating a notice file, updating the project stage state and the approval state, and tracking the execution progress of a work flow instance in real time; the data processing unit is used for collecting multi-source data with the unique identification code as an index and performing data preprocessing; the service output unit is used for providing a subscribable data service based on the unique identification code, implementing data desensitization control, and storing data according to procurement dimensions to generate analysis results; the fusion gateway unit is used for docking heterogeneous system data with the unique identification code as an index; the monitoring unit is used for monitoring the work flow running state and triggering an alarm when an exception occurs, and monitoring the data processing quality and the interface service state; the method for automatically generating a notice file comprises the following steps: a target notice template is selected from a preset template library based on a project stage, the target notice template contains a business semantic placeholder; a coding mapping template corresponding to the target notice template is determined, the coding mapping template defines a coding index variable mapped with the business semantic placeholder; the parameter value of the coding index variable is obtained with the unique identification code as an index; when the parameter value is stored in a local database, the parameter value is extracted from the data processing unit; when the parameter value is not stored in the local database, the parameter value is extracted from an external system through the fusion gateway unit. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 2.The project whole-process intelligent management and control system based on uniform coding according to claim 1, characterized in that, ​ ​ ​ ​ Replace the parameter values of the coding index variable with the business semantic placeholders in the target notification letter template to generate an initial notification letter file; Add an electronic seal to the initial notification letter file based on the approval status to generate an official notification letter file. 3.The project whole-process intelligent management and control system based on uniform coding according to claim 2, characterized in that, The target notification letter template is selected based on the project phase, including: When the project phase is the project establishment phase, a bidding announcement template is selected, and the business semantic placeholders include the procurement project name, budget amount, or qualification requirements; When the project phase is the review phase, a bid evaluation report template is selected, and the business semantic placeholders include the bidding unit, technical score, or business score; When the project phase is the signing phase, a bid-winning notification letter template is selected, and the business semantic placeholders include the bid-winning unit, contract amount, or performance period. 4.The project whole-process intelligent management and control system based on uniform coding according to claim 1, characterized in that, The service output unit includes: a service publishing module, a desensitization control module, a theme storage module, and a report generation module; The service publishing module is configured to publish a plurality of subscribable data services based on unique identification codes and to count the number of interface calls in real time; The desensitization control module is configured to perform dynamic desensitization control on output data according to unique identification code permissions; The theme storage module is configured to store data by procurement type, including the purchaser, the supplier, and the expert bid evaluation; The report generation module is configured to generate procurement annual reports and risk analysis reports based on the data in the theme storage module. 5.The project whole-process intelligent management and control system based on uniform coding according to claim 4, characterized in that, The method for performing dynamic desensitization control on output data according to unique identification code permissions includes: Analyzing the project classification and user access permission level according to the unique identification code; Performing hierarchical desensitization operations based on the user access permission level; wherein, for non-authorized users, sensitive field encryption and key information masking processing are performed, and for authorized users, differential desensitization is performed according to permission levels; Recording audit logs of the desensitization operations, including the identification code, user identity, and operation time. 6.The project whole-process intelligent management and control system based on uniform coding according to claim 1, wherein, The method for indexing heterogeneous system data with unique identification codes includes: Reading the data field definitions and format specifications of the heterogeneous systems, and taking the field definitions of the same business object as a data item set; Extracting cross-system associated data with unique identification codes as indexes; wherein, a first data item value set associated with the unique identification code is obtained from a first type of system, and a second data item value set associated with the unique identification code is obtained from a second type of system; Converting the heterogeneous formats of the data item value sets into a unified standard format; Writing the converted data item value sets to the target location according to the preset storage rules; wherein, the method for extracting cross-system associated data with unique identification codes as indexes includes a data extraction process; The data extraction process includes: analyzing the unique identification code to obtain a system positioning prefix; determining the target system type according to the prefix; and extracting the corresponding data values in the order of the data item set; The data extraction process is repeated until all the associated system data item value sets are obtained. 7.The project whole-process intelligent management and control system based on unified coding according to claim 1, wherein, The monitoring unit includes: a process monitoring module, a data quality module, and a service health module; The process monitoring module is configured to detect the workflow instance state in real time and trigger an alarm when it is stuck; The data quality module is configured to monitor the data cleaning success rate and mark abnormal data; The service health module is configured to periodically check the interface available state and generate a service health report.

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