Enterprise project management methods, devices, electronic equipment and storage media

Abstract

This invention provides an enterprise project management method, device, electronic device, and storage medium, belonging to the technical field of enterprise information management. In this method, real-time data synchronization across modules is achieved through a unified data bus (UDB), eliminating data silos. Interaction with external systems is achieved through a protocol adapter, greatly improving interface adaptation efficiency. In addition, relevant business services call the target model to make decisions, that is, determine the target solution corresponding to the business event, realizing quantitative support. Furthermore, a microservice architecture is adopted, which has good scalability and real-time performance.

Description

Technical Field

[0001] This invention relates to the technical field of enterprise information management, and in particular to an enterprise project management method, apparatus, electronic device, and storage medium. Background Technology

[0002] Current enterprise project management systems face multiple technical bottlenecks in complex business scenarios, specifically manifested in the following core issues:

[0003] Data silos and failure of dynamic collaboration: Traditional systems adopt modular and independent design, with data from each stage (pre-sales, project initiation, procurement, etc.) stored in separate databases, lacking unified semantic mapping rules;

[0004] Inefficient Multi-System Interface Adaptation: External systems typically employ heterogeneous protocols, which manifest in significant differences in communication protocols. For example, Web Services use XML for data exchange, RESTful APIs tend to use JSON, and some systems may use binary transmission or other formats. These different protocols increase the complexity of inter-system interactions. Furthermore, challenges may arise even between systems with the same business entities. Specifically, when interfacing data from one system with another, it may be found that the field matching rate for the same business entities in both systems is less than 40%. This means that most fields require some form of conversion or mapping. Traditional solutions require writing adaptation logic separately for each pair of systems. This not only consumes significant development resources but also necessitates manual code adjustments if either party upgrades its protocol version, further increasing maintenance costs.

[0005] The decision-making model lacks quantitative support: existing systems still rely on human experience in key aspects such as risk assessment, supplier selection, and resource optimization.

[0006] Insufficient scalability and real-time performance: When traditional software architectures (such as monolithic designs) need to be further expanded to adapt to changes in enterprise scale, costs rise rapidly, and this increase is not linear. This is because traditional monolithic architectures do not support good horizontal scaling (i.e., distributing the load by adding more servers), but instead rely on expensive hardware upgrades. Furthermore, to alleviate pressure in high-concurrency scenarios, asynchronous processing and simplified computational logic may be employed. However, this only provides temporary relief and does not fundamentally solve the performance problem; increased response latency still exists in high-concurrency scenarios.

[0007] In summary, traditional enterprise project management suffers from technical problems such as data silos, inefficient multi-system interface adaptation, lack of quantitative support for decision-making models, and insufficient scalability and real-time performance. Summary of the Invention

[0008] In view of this, the purpose of the present invention is to provide an enterprise project management method, apparatus, electronic device and storage medium to alleviate the technical problems of traditional enterprise project management, such as data silos, low efficiency of multi-system interface adaptation, lack of quantitative support for decision-making models and insufficient scalability and real-time performance.

[0009] In a first aspect, embodiments of the present invention provide an enterprise project management method, applied to an enterprise project management system, the method comprising:

[0010] The first module of the enterprise project management system publishes business events and distributes these business events to the second module via a unified data bus.

[0011] The target service in the second module calls the target model to determine the target solution corresponding to the business event;

[0012] The second module uses a protocol adapter to perform protocol conversion on the target scheme and sends the protocol-converted target scheme to an external system so as to obtain the contract text scheme returned by the external system for the protocol-converted target scheme through the protocol adapter;

[0013] The target service in the second module calls an NLP model to perform a dual-track risk analysis on the contract text scheme to obtain the risk information of the contract text scheme. The dual-track risk analysis includes: a risk analysis method based on a preset rule base and a risk analysis method using a deep learning model.

[0014] The second module triggers a rolling time-domain optimization algorithm based on the risk information to dynamically adjust the target scheme and obtain the adjusted target scheme.

[0015] Furthermore, the business events are distributed to the second module via a unified data bus, including:

[0016] The business events are standardized using a semantic standardization engine to obtain standardized business events.

[0017] The standardized business events are distributed to the second module via the unified data bus.

[0018] Furthermore, the second module employs a protocol adapter to perform protocol conversion on the target scheme, including:

[0019] The target scheme is protocol-transformed using a protocol transformation matrix generated by an adversarial network to obtain the target scheme after the first protocol transformation.

[0020] The first field in the target scheme after the first protocol conversion is mapped and converted according to the protocol template library to obtain the target scheme after the second protocol conversion, wherein the first field is a field that matches the field mapping rules of each pair of systems stored in the protocol template library;

[0021] Fuzzy matching is performed on the second field in the target scheme after the second protocol conversion, and the matched second field is mapped and converted to obtain the target scheme after the third protocol conversion, wherein the second field in the target scheme after the second protocol conversion is a field other than the first field;

[0022] The unmatched second field in the target scheme after the third protocol conversion is identified by type, and the conversion is inferred based on the identified type to obtain the target scheme after the fourth protocol conversion;

[0023] The unconverted fields in the target scheme after the fourth protocol conversion are manually reviewed to obtain the target scheme after protocol conversion.

[0024] Furthermore, the method also includes:

[0025] The protocol conversion quality is evaluated on the target scheme after the protocol conversion to obtain the protocol conversion quality. The protocol conversion quality evaluation includes: syntax layer protocol conversion quality evaluation, semantic layer protocol conversion quality evaluation, and performance index protocol conversion quality.

[0026] Update the protocol template library and / or the adversarial network based on the protocol conversion quality, and / or elastically scale up or down the target resources.

[0027] Furthermore, the target service in the second module invokes an NLP model to perform a dual-track risk analysis on the contract text scheme, including:

[0028] The contract text scheme is preprocessed to obtain the contract terms;

[0029] The system detects specific risks corresponding to the contract terms based on a pre-defined rule base.

[0030] A deep learning model is used to predict the risks of the contract terms and obtain potential risks.

[0031] The explicit risks and the potential risks are combined to obtain the risk information of the contract text scheme.

[0032] Furthermore, the second module triggers a rolling time-domain optimization algorithm based on the risk information to dynamically adjust the target solution, including:

[0033] When the risk information is a high-risk clause or meets the dynamic adjustment conditions, the target solution is dynamically adjusted by means of key parameter pruning, constraint relaxation, and parallel distributed solution to obtain the adjusted target solution.

[0034] Furthermore, the dynamic adjustment conditions include: price fluctuations of key resources exceeding a threshold, suppliers being blacklisted, and task progress deviations exceeding a preset threshold.

[0035] Secondly, embodiments of the present invention also provide an enterprise project management device, applied to an enterprise project management system, the device comprising:

[0036] The publishing and distribution unit is used by the first module of the enterprise project management system to publish business events and distribute the business events to the second module through a unified data bus.

[0037] The determining unit is used in the second module to call the target service model to determine the target solution corresponding to the business event;

[0038] The protocol conversion unit is used by the second module to perform protocol conversion on the target scheme using a protocol adapter, and send the target scheme after protocol conversion to an external system, so as to obtain the contract text scheme returned by the external system for the target scheme after protocol conversion through the protocol adapter;

[0039] The dual-track risk analysis unit is used in the second module to call the NLP model to perform dual-track risk analysis on the contract text scheme to obtain the risk information of the contract text scheme. The dual-track risk analysis includes: risk analysis based on a preset rule base and risk analysis using a deep learning model.

[0040] The dynamic adjustment unit is used by the second module to dynamically adjust the target scheme based on the risk information by triggering a rolling time-domain optimization algorithm, so as to obtain the adjusted target scheme.

[0041] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described in any of the first aspects above.

[0042] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing machine-executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method described in any of the first aspects above.

[0043] In this embodiment of the invention, an enterprise project management method is provided, applied to an enterprise project management system. The method includes: a first module of the enterprise project management system publishing business events and distributing the business events to a second module through a unified data bus; a target service in the second module calling a target model to determine the target solution corresponding to the business event; the second module using a protocol adapter to perform protocol conversion on the target solution and sending the protocol-converted target solution to an external system to obtain the contract text solution returned by the external system for the protocol-converted target solution through the protocol adapter; the target service in the second module calling an NLP model to perform dual-track risk analysis on the contract text solution to obtain risk information of the contract text solution; and the second module triggering a rolling time-domain optimization algorithm based on the risk information to dynamically adjust the target solution to obtain the adjusted target solution. As described above, the enterprise project management method of the present invention achieves real-time data synchronization across modules through a unified data bus (UDB), eliminating data silos. It also enables interaction with external systems through a protocol adapter, greatly improving interface adaptation efficiency. Furthermore, relevant business services call the target model for decision-making, i.e., determining the target solution corresponding to the business event, thus achieving quantitative support. In addition, a microservice architecture is adopted, which has good scalability and real-time performance, alleviating the technical problems of traditional enterprise project management, such as data silos, low efficiency of multi-system interface adaptation, lack of quantitative support for decision-making models, and insufficient scalability and real-time performance. Attached Figure Description

[0044] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 A flowchart of an enterprise project management method provided in an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram illustrating the risk consolidation provided in an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of an enterprise project management device provided in an embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0049] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] Traditional enterprise project management suffers from data silos, inefficient multi-system interface adaptation, lack of quantitative support for decision-making models, and insufficient scalability and real-time performance.

[0051] Based on this, the enterprise project management method of the present invention realizes real-time data synchronization across modules through a unified data bus (UDB), eliminating data silos. It realizes interaction with external systems through a protocol adapter, greatly improving interface adaptation efficiency. In addition, relevant business services call the target model to make decisions, that is, determine the target solution corresponding to the business event, realizing quantitative support. Furthermore, a microservice architecture is adopted, which has good scalability and real-time performance.

[0052] To facilitate understanding of this embodiment, a detailed description of an enterprise project management method disclosed in this embodiment of the invention will be provided first.

[0053] Example 1:

[0054] According to an embodiment of the present invention, an embodiment of an enterprise project management method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0055] Figure 1 This is a flowchart of an enterprise project management method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0056] In step S102, the first module of the enterprise project management system publishes business events and distributes them to the second module through a unified data bus.

[0057] In this embodiment of the invention, the enterprise project management system includes: a pre-sales module, a project initiation module, a procurement module, a subcontracting module, an inventory module, a financial module, and multi-system interface collaboration, enabling intelligent collaborative management of the entire lifecycle of enterprise projects. The first module can be a pre-sales module, and the second module can be a procurement module; however, this embodiment of the invention does not impose specific limitations on the first and second modules.

[0058] It should be noted that all modules output standardized JSON data in a unified format, mandatory inclusion of business type tags, and use RabbitMQ / RocketMQ for inter-module communication, eliminating complex stream processing. Each module broadcasts in real time, achieving multi-module synchronization notifications within milliseconds.

[0059] Specifically, after the first module publishes a business event, it distributes the event to the second module via a unified data bus. The second module is specifically the module that subscribes to the aforementioned business event. For example, if the pre-sales module publishes a procurement request event (i.e., a user submits a procurement request in the pre-sales module), the unified data bus (UDB) distributes this procurement request event to the subscribed procurement, inventory, and finance modules. Compared to traditional point-to-point calls (strong coupling (direct interface connection)), this UDB event-driven architecture has looser coupling (each module only needs to interface with the UDB for event subscription). Adding a new module does not require modifying existing interfaces, and UDB event-driven architecture allows for dynamic registration of consumers (i.e., elastic expansion, with consumers capable of independent expansion). Its advantage lies in its short expansion time. In other words, in the UDB event-driven architecture of this invention, adding a new module does not require modifying the pre-sales module code, a single module failure will not cause cascading crashes, and each module can be upgraded and iterated independently. Furthermore, the event stream implemented through UDB has the following characteristics: atomicity: events are either completely transmitted or discarded; sequentiality: ensuring the correct timing of business operations; and persistence: all events are retained in audit logs. Furthermore, in traditional solutions, the data format is customized for each module, new fields require reconstruction, and parsing efficiency depends on type inference. The solution of this invention uses a standardized JSON template for data format, allows dynamic addition of metadata, and accelerates parsing efficiency through a predefined schema.

[0060] Step S104: The target service in the second module calls the target model to determine the target solution corresponding to the business event;

[0061] Specifically, if the procurement module (i.e. the second module, which is used as an example here) receives a procurement demand event (i.e. a business event) from the pre-sales module (i.e. the first module), it calls the supplier service (i.e. the target service), which then calls the supplier graph model (i.e. the target model) to filter the Top-N candidate set, and then calls the cost optimization model (i.e. the target model) to calculate the optimal solution (i.e. the target solution) in the Top-N candidate set.

[0062] This invention does not impose specific limitations on the target model described above, and it can be adjusted according to specific business scenarios. For example, when dealing with uncertainties such as supplier selection, a fuzzy comprehensive evaluation algorithm can be used; when dealing with multi-stage resource allocation problems, a dynamic programming algorithm can be used; when optimizing logistics route planning, a graph theory algorithm can be used; or a combination algorithm can be used. For example, in the business scenario of supplier selection, a combination algorithm of fuzzy evaluation and PageRank can be used (the parameters to be considered include: quality score / historical default rate / price, and the specific output can be a list of top 3 recommended suppliers); in the business scenario of procurement batch planning, a combination algorithm of dynamic programming and linear regression can be used (the parameters to be considered include: inventory cost / transportation rate / demand forecast, and the specific output can be the optimal procurement quantity and time node); in the business scenario of project resource scheduling, a combination algorithm of critical path method (CPM) and genetic algorithm can be used (the parameters to be considered include: task dependency relationship / resource constraint, and the specific output can be a resource Gantt chart and critical path identifier).

[0063] For example, if the input requirement is to purchase 8,000 tons of X70 pipeline steel with a quality standard of API 5L, after calling the supplier graph model (i.e., the target model), 23 qualified suppliers are selected, and the supplier ID, PageRank, and quality score are obtained. Then, the cost optimization model is called to select the first preset number of suppliers with the lowest cost from the 23 qualified suppliers.

[0064] In addition, the above process also supports incremental learning. For example, in the optimization of steel raw material procurement: the initial state is that supplier A's PageRank value is 0.35 and supplier B's PageRank value is 0.28; during the learning process, the incremental data is that supplier A has delayed delivery 3 times in a row. Then, supplier A's PageRank value is updated to 0.7 × 0.35, that is, a penalty coefficient is used to penalize supplier A's PageRank value. Supplier B's PageRank value is updated to 0.1 + 0.28, that is, a competitive reward is used to reward supplier B's PageRank value. The final output result is: after the update, the supplier ranking is B > A, and the order allocation ratio of A is automatically reduced.

[0065] This invention achieves business adaptability and automatically matches the optimal algorithm combination through an innovative architecture of "dynamic combination of algorithms plus real-time incremental learning"; it achieves decision intelligence and continuously evolves the decision model; and it achieves resource economy and maximizes the use of computing resources.

[0066] Step S106: The second module uses a protocol adapter to perform protocol conversion on the target scheme and sends the target scheme after protocol conversion to an external system so as to obtain the contract text scheme returned by the external system for the target scheme after protocol conversion through the protocol adapter.

[0067] The aforementioned external systems can be legal systems, financial systems, auditing systems, bidding systems, etc.

[0068] Step S108: The target service in the second module calls the NLP model to perform a dual-track risk analysis on the contract text scheme to obtain the risk information of the contract text scheme. The dual-track risk analysis includes: risk analysis based on a preset rule base and risk analysis using a deep learning model.

[0069] In step S110, the second module triggers a rolling time-domain optimization algorithm based on risk information to dynamically adjust the target scheme and obtain the adjusted target scheme.

[0070] The process of step S110 is illustrated below with an example:

[0071] In the steel procurement case, the demand input is that 2,000 tons of cold-rolled steel plates need to be purchased, with a deadline of August 30, 2023.

[0072] Supplier Matching: Input 10 qualified suppliers; Output ranking, supplier and overall score: 1, Baosteel, 92.5; 2, Ansteel, 88.3; 3, Shagang, 85.7.

[0073] After compliance verification (risk analysis): It was found that the Baosteel contract contained a clause on "unconditional transfer of raw material price increases" (high risk). The Ansteel solution was automatically downgraded and the final output was: Ansteel, contract_template was STEEL-PURCHASE-2023, and risk_notes were price fluctuation clauses that required manual review.

[0074] In this embodiment of the invention, an enterprise project management method is provided, applied to an enterprise project management system. The method includes: a first module of the enterprise project management system publishing business events and distributing the business events to a second module through a unified data bus; a target service in the second module calling a target model to determine the target solution corresponding to the business event; the second module using a protocol adapter to perform protocol conversion on the target solution and sending the protocol-converted target solution to an external system to obtain the contract text solution returned by the external system for the protocol-converted target solution through the protocol adapter; the target service in the second module calling an NLP model to perform dual-track risk analysis on the contract text solution to obtain risk information of the contract text solution; and the second module triggering a rolling time-domain optimization algorithm based on the risk information to dynamically adjust the target solution to obtain the adjusted target solution. As described above, the enterprise project management method of the present invention achieves real-time data synchronization across modules through a unified data bus (UDB), eliminating data silos. It also enables interaction with external systems through a protocol adapter, greatly improving interface adaptation efficiency. Furthermore, relevant business services call the target model for decision-making, i.e., determining the target solution corresponding to the business event, thus achieving quantitative support. In addition, a microservice architecture is adopted, which has good scalability and real-time performance, alleviating the technical problems of traditional enterprise project management, such as data silos, low efficiency of multi-system interface adaptation, lack of quantitative support for decision-making models, and insufficient scalability and real-time performance.

[0075] The above provides a brief overview of the enterprise project management method of the present invention. The specific details involved are described in detail below.

[0076] In an optional embodiment of the present invention, the business events are distributed to the second module via a unified data bus, specifically including the following steps:

[0077] (1) Use a semantic standardization engine to standardize business events to obtain standardized business events;

[0078] (2) Distribute standardized business events to the second module through a unified data bus.

[0079] Specifically, after the pre-sales module submits requirements in JSON format, the events are serialized and stored in a Kafka partition, where consumer groups process them in parallel (millisecond-level response). In implementation, a semantic standardization engine is first used to standardize business events, resulting in standardized business events that ensure consistent semantics in the data flowing through the bus. After conversion at the production end, the consumer end can directly use the standard data. In other words, this invention achieves real-time data synchronization across modules based on a unified data bus (UDB). The semantic standardization engine eliminates data silos, meaning that requirement / resource data flows in real-time through the UDB, and semantic standardization ensures consistency.

[0080] Semantic Standardization Engine: Defines a Global Metadata Model (GMM) to map data from all modules into a fixed format.

[0081] Unified Data Bus (UDB): Based on Apache Kafka, it builds a real-time data streaming pipeline that supports the transmission of multi-module data categorized by business topic.

[0082] In an optional embodiment of the present invention, the second module uses a protocol adapter to perform protocol conversion on the target scheme, specifically including the following steps:

[0083] (1) The target scheme is converted using the protocol conversion matrix generated by the adversarial network to obtain the target scheme after the first protocol conversion;

[0084] Specifically, the protocol transformation matrix is ​​used to handle differences in protocol structure. It is generated through an adversarial network (i.e., a generative adversarial network) and is triggered only when a new protocol type is accessed for the first time, thereby generating the protocol transformation matrix. It mainly addresses macroscopic protocols. In the architecture of the adversarial network, the generator structure can be: Protocol A structure → Encoder → Latent space → Decoder → Protocol B structure.

[0085] During training, the adversarial network takes 100,000 protocol pairs (WSDL / OpenAPI / binary, etc.) as input and uses the loss function L = αLrecon + βLadv + γLstruct, where the structural loss Lstruct ensures that key business elements are not lost.

[0086] The protocol transformation matrix generated by GAN can be as follows: Protocol A field 1 → Protocol B field 1, Protocol A field 2 → Protocol B field 2+3, Protocol A field 3 → Protocol B field 3. It supports parallel processing of protocol transformation tasks such as HTTP / API / WebService.

[0087] (2) Map the first field of the target scheme after the first protocol conversion according to the protocol template library to obtain the target scheme after the second protocol conversion, wherein the first field is the field that matches the field mapping rules of each pair of systems stored in the protocol template library;

[0088] Specifically, this invention employs a three-level conversion of the protocol. The first level is template mapping, which involves initializing the protocol template library: extracting high-frequency field mappings from historical data and storing them in the protocol template library. In other words, the preset protocol templates in the protocol template library are mapping rules for each pair of system-defined fields. Then, based on the protocol template library, the first field in the target scheme after the first protocol conversion is mapped and converted to obtain the target scheme after the second protocol conversion.

[0089] (3) Perform fuzzy matching on the second field in the target scheme after the second protocol conversion, and perform mapping conversion on the matched second field to obtain the target scheme after the third protocol conversion, wherein the second field in the target scheme after the second protocol conversion is a field other than the first field;

[0090] Specifically, the second level of the three-level transformation is intelligent mapping, which includes fuzzy matching. This involves using the Levenshtein distance algorithm to calculate field name similarity. If the field name similarity reaches a preset threshold of 80%, automatic mapping is achieved. Fuzzy matching can handle common field differences (case sensitivity / separators / abbreviations) and can cover 60%-70% of common field mismatches.

[0091] (4) Type identification is performed on the unmatched second field in the target scheme after the third protocol conversion, and the conversion is inferred based on the identified type to obtain the target scheme after the fourth protocol conversion;

[0092] Specifically, intelligent mapping also includes type-driven mapping. For example, identifying the types of ¥ and $ reveals them to be currency types, allowing them to be mapped to price fields. Similarly, identifying the yyy-mm-dd format reveals it to be a date type, allowing it to be standardized to ISO8601 format. This conversion can quickly perform pattern recognition based on regular expressions, supports inference for 12 basic data types, and can handle approximately 15%-20% of unconventional fields.

[0093] (5) Manually review the unconverted fields in the target scheme after the fourth protocol conversion to obtain the target scheme after protocol conversion.

[0094] Specifically, the third level of the three-level conversion is manual review (a fallback mechanism), which is triggered when the first two levels of processing fail.

[0095] This invention achieves adaptive protocol conversion through "configuration templates and intelligent fallback," improving development efficiency. The implementation is divided into three levels: the first level uses pre-set templates to handle 80% of common conversions; the second level uses fuzzy field matching and type-driven processing to handle common differences; and the third level uses manual review to handle exceptions. In practice, the adversarial network is only triggered when a new protocol type is first accessed; all data conversions must undergo these three levels; protocols with existing conversion matrices can bypass GAN processing. The goal of the three-level conversion process is to solve field-level mapping, employing rule engines, fuzzy matching, and type-driven techniques. This process must be executed during each data conversion, ultimately outputting a field mapping table.

[0096] It should be noted that manual review results will automatically generate new protocol templates. For example, if similar fields appear more than 3 times, they will be automatically added to the protocol template library.

[0097] In an optional embodiment of the present invention, the method further includes the following steps:

[0098] (1) Evaluate the protocol conversion quality of the target scheme after protocol conversion to obtain the protocol conversion quality. The protocol conversion quality evaluation includes: syntax layer protocol conversion quality evaluation, semantic layer protocol conversion quality evaluation and performance index protocol conversion quality.

[0099] (2) Update the protocol template library and / or adversarial network according to the protocol conversion quality, and / or elastically expand or shrink the target resources.

[0100] Specifically, the system achieves self-evolution based on protocol conversion quality metrics. This is primarily built from three dimensions: First, the syntax layer, quantifying basic conversion quality using field mapping completeness (number of missing fields / total number of fields) and data type matching rate (number of type errors / total converted fields). For example, missing required fields trigger an emergency alarm. Second, the semantic layer, validating the logical rationality of the converted data through business rules. For instance, a sudden drop in purchase amount to zero is clearly unreasonable, quantified by the number of rule violations / total validation counts. Finally, performance metrics, such as abnormal latency and throughput, may indicate a decrease in protocol adapter efficiency.

[0101] Specifically, a multi-level threshold design is used: hard errors (such as missing fields) directly trigger real-time updates to the protocol template library; soft deviations (such as numerical fluctuations) accumulate to a threshold, triggering batch training (i.e., adversarial network training); if performance degrades, resource expansion is initiated. In implementation, when a hard error of an abnormal level occurs, such as missing key fields or severe type mismatch, the process can be immediately halted; real-time rule updates can be triggered; and an alarm notification to operations and maintenance can be sent. When a soft deviation of an abnormal level occurs, such as numerical fluctuations exceeding ±15% but not exceeding business red lines, anomaly logs can be recorded; after 10 such occurrences, model retraining is triggered; and a diagnostic report is generated. When performance deteriorates due to an abnormal level, such as latency >200ms for 5 minutes or a 20% drop in TPS, container instances can be automatically expanded (supporting horizontal scaling, microservice splitting, and containerized deployment, with automatic resource allocation adjustment based on load); a backup protocol path can be switched; and performance optimization tasks can be triggered.

[0102] In implementation, the comprehensive evaluation formula for protocol conversion quality can be: ,in, Indicates the quality of protocol conversion. Indicates the quality of syntax layer transformation. Indicates the quality of semantic layer conversion. Indicates the quality of performance indicators. , , All ∈ [0,1] ,default value , , .

[0103] Formula for evaluating the quality of syntax-level transformations: ,in, Indicates the quality of syntax layer transformation. This represents the value of the i-th field in the source protocol. This indicates the value of the field corresponding to the target protocol. Indicates the field tolerance threshold. Indicates field weight;

[0104] The formula for evaluating the quality of semantic layer translation: ,in, Indicates the quality of semantic layer conversion. This represents the accuracy of the j-th business rule. This represents the recall rate of the j-th business rule;

[0105] Performance indicator quality evaluation formula: ,in, Indicates the quality of performance indicators. Indicates actual delay / required delay. Indicates actual throughput / required throughput. This represents the delay sensitivity coefficient;

[0106] When the value is less than 0.8, the protocol template library is updated. The template library update strategy is as follows: ,in, This represents the weight of the k-th rule in the protocol template library. Indicates the learning rate. Indicates the quality of protocol conversion. Indicates the quality acceptance threshold;

[0107] When the value is less than 0.7, the adversarial network is retrained, and the GAN network update strategy is as follows: ,in, Represents the original loss function. This represents the quality adjustment coefficient. This represents the generator output. This represents an ideal transformed sample;

[0108] When the value is less than 0.6, the target resource is flexibly scaled up or down. Resource scaling decision: ,in, This represents the single-node processing capacity coefficient, when... When the value is greater than 0, expansion is triggered. When <0, shrinkage is triggered.

[0109] In an optional embodiment of the present invention, the target service in the second module invokes an NLP model to perform a dual-track risk analysis on the contract text scheme, specifically including the following steps:

[0110] (1) Perform text preprocessing on the contract text to obtain the contract terms;

[0111] (2) Detect explicit risks corresponding to contract terms based on a pre-set rule base;

[0112] (3) Use deep learning models to predict risks in contract terms and obtain potential risks;

[0113] (4) Combine the explicit risks and potential risks to obtain the risk information of the contract text scheme.

[0114] Specifically, during text preprocessing, the contract text is segmented according to the legal clause structure, identifying the contract subjects and key entities, and removing non-normative content. The dual-track analysis includes: a rule engine: detecting explicit risks corresponding to contract clauses based on a pre-set rule base (keyword regular expression matching, pre-set risk rule base (e.g., payment exceeding 60 days, unequal contractual liability, intellectual property ownership, etc.)); and a deep learning model: identifying potential risks in contract clauses through fine-tuning BERT (e.g., input: contract clause (e.g., "delayed delivery due to force majeure is not considered a breach of contract") output: corresponding risk label and confidence level). The dual-track results are then weighted and fused based on confidence level, outputting a classification report with risk levels (i.e., risk information). The fine-tuned BERT is trained using 10,000 historical contract annotations (normal / high-risk clauses), with a network structure of a 4-layer distilled BERT (model size <50MB). After deployment, the CPU inference speed is <0.5 seconds / case.

[0115] For example, taking a procurement contract returned by the legal system as an example, text preprocessing extracts the following: segmented extraction of clauses (such as "payment method" and "liability for breach of contract"), removal of irrelevant symbols, and retention of the legal entity (Party A / Party B). Dual-track analysis reveals that "payment cycle: final payment 90 days after order confirmation" triggers the risk of "excessively long payment cycle," leading to uncontrolled price fluctuations. These risks are consolidated as follows: Figure 2 The results are shown.

[0116] In an optional embodiment of the present invention, the second module dynamically adjusts the target scheme based on a rolling time-domain optimization algorithm triggered by risk information, specifically including the following steps:

[0117] When the risk information is a high-risk clause or meets the dynamic adjustment conditions, the target solution is dynamically adjusted by means of key parameter pruning, constraint relaxation, and parallel distributed solution to obtain the adjusted target solution.

[0118] Specifically, dynamic adjustments based on Rolling Time-Domain Optimization (RHO) trigger resource reallocation, generating the adjusted target scheme.

[0119] The dynamic adjustment scheme based on Rolling Time-Domain Optimization (RHO) includes: key parameter pruning (adjusting resources involved in changes, such as calculating only steel-related tasks if steel prices rise), constraint relaxation (allowing temporary breakthroughs of existing limits, such as adjusting the budget ceiling), and parallel distributed solution (independent calculation by resource type, with results merged).

[0120] Emergency response plan includes: setting trigger conditions; meeting even one condition will initiate subsequent emergency procedures (i.e., dynamic adjustment). Trigger conditions (dynamically adjustable conditions) could include: key resource price fluctuations exceeding a threshold (e.g., a 15% increase in steel prices), suppliers being blacklisted, task progress deviations exceeding 10%, etc. Automatic approval rules can be implemented based on business needs, such as: cost increase <5% and no delays to deadlines → immediate effect. Manual processing can also be added simultaneously, such as: cost increase >5% → email notification to project manager for confirmation.

[0121] In an optional embodiment of the present invention, the dynamic adjustment conditions include: price fluctuations of key resources exceeding a threshold, suppliers being blacklisted, and task progress deviations exceeding a preset threshold.

[0122] The solution of this invention adopts an "event-driven + rule engine" approach to replace complex algorithm coupling, enabling self-learning capabilities. This involves automated rule generation, coupled with a closed-loop human feedback mechanism to achieve self-learning within the architecture. By avoiding complex AI training at low cost, it meets the actual needs of enterprises. This invention also provides a visual decision-making platform, embedding risk warning and solution simulation functions, and offering multi-dimensional views such as risk heatmaps, resource Gantt charts, and supplier topology diagrams.

[0123] This invention overcomes the challenges of data fragmentation, delayed response, and reliance on manual labor in traditional systems by leveraging three pillars: multi-model fusion, intelligent interfaces, and data collaboration. It provides enterprises with a project management hub that is adaptive and optimized throughout the entire lifecycle, thereby promoting operational efficiency improvements.

[0124] Example 2:

[0125] This invention also provides an enterprise project management device, which is mainly used to execute the enterprise project management method provided in Embodiment 1 of this invention. The following is a detailed description of the enterprise project management device provided in this invention.

[0126] Figure 3 This is a schematic diagram of an enterprise project management device according to an embodiment of the present invention, such as... Figure 3As shown, the device mainly includes: a publishing and distribution unit 10, a determination unit 20, a protocol conversion unit 30, a dual-track risk analysis unit 40, and a dynamic adjustment unit 50, wherein:

[0127] The publishing and distribution unit is used by the first module of the enterprise project management system to publish business events and distribute them to the second module through a unified data bus.

[0128] The determination unit is used in the second module to determine the target solution corresponding to the business event by calling the target model of the target service.

[0129] The protocol conversion unit is used by the second module to convert the target scheme using a protocol adapter and send the converted target scheme to an external system so as to obtain the contract text scheme returned by the external system for the converted target scheme through the protocol adapter.

[0130] The dual-track risk analysis unit is used in the second module to call the NLP model to perform dual-track risk analysis on the contract text scheme and obtain the risk information of the contract text scheme. The dual-track risk analysis includes: risk analysis based on a preset rule base and risk analysis using a deep learning model.

[0131] The dynamic adjustment unit is used by the second module to dynamically adjust the target scheme based on the risk information-triggered rolling time-domain optimization algorithm, thereby obtaining the adjusted target scheme.

[0132] In this embodiment of the invention, an enterprise project management device is provided, applied to an enterprise project management system. The device includes: a first module of the enterprise project management system publishing business events and distributing the business events to a second module via a unified data bus; a target service in the second module calling a target model to determine the target solution corresponding to the business event; the second module using a protocol adapter to perform protocol conversion on the target solution and sending the protocol-converted target solution to an external system to obtain the contract text solution returned by the external system for the protocol-converted target solution through the protocol adapter; the target service in the second module calling an NLP model to perform dual-track risk analysis on the contract text solution to obtain risk information of the contract text solution; and the second module triggering a rolling time-domain optimization algorithm based on the risk information to dynamically adjust the target solution to obtain the adjusted target solution. As described above, the enterprise project management device of the present invention achieves real-time data synchronization across modules through a unified data bus (UDB), eliminating data silos. It also enables interaction with external systems through a protocol adapter, greatly improving interface adaptation efficiency. Furthermore, relevant business services call the target model for decision-making, i.e., determining the target solution corresponding to the business event, thus achieving quantitative support. In addition, it adopts a microservice architecture, which has good scalability and real-time performance, alleviating the technical problems of traditional enterprise project management, such as data silos, low efficiency of multi-system interface adaptation, lack of quantitative support for decision-making models, and insufficient scalability and real-time performance.

[0133] Optionally, the publishing and distribution unit is also used to: standardize business events using a semantic standardization engine to obtain standardized business events; and distribute the standardized business events to the second module through a unified data bus.

[0134] Optionally, the protocol conversion unit is further configured to: perform protocol conversion on the target scheme using a protocol conversion matrix generated by the adversarial network to obtain a first protocol-converted target scheme; perform mapping conversion on the first field of the first protocol-converted target scheme according to the protocol template library to obtain a second protocol-converted target scheme, wherein the first field is a field that matches the field mapping rules of each pair of systems stored in the protocol template library; perform fuzzy matching on the second field of the second protocol-converted target scheme and perform mapping conversion on the matched second field to obtain a third protocol-converted target scheme, wherein the second field of the second protocol-converted target scheme is a field other than the first field; perform type identification on the unmatched second field in the third protocol-converted target scheme and perform inference conversion based on the identified type to obtain a fourth protocol-converted target scheme; and perform manual review on the unconverted fields in the fourth protocol-converted target scheme to obtain a protocol-converted target scheme.

[0135] Optionally, the device is also used to: evaluate the protocol conversion quality of the target scheme after protocol conversion to obtain the protocol conversion quality, wherein the protocol conversion quality evaluation includes: syntax layer protocol conversion quality evaluation, semantic layer protocol conversion quality evaluation and performance index protocol conversion quality; update the protocol template library and / or adversarial network according to the protocol conversion quality, and / or elastically expand or shrink the target resources.

[0136] Optionally, the dual-track risk analysis unit is also used for: preprocessing the contract text to obtain contract terms; detecting explicit risks corresponding to the contract terms based on a preset rule base; using a deep learning model to predict risks of the contract terms to obtain potential risks; and merging explicit and potential risks to obtain risk information of the contract text.

[0137] Optionally, the dynamic adjustment unit is also used to: when the risk information is a high-risk clause or the dynamic adjustment conditions are met, dynamically adjust the target solution by means of key parameter trimming, constraint relaxation, and parallel distributed solution to obtain the adjusted target solution.

[0138] Optionally, the dynamic adjustment conditions include: price fluctuations of key resources exceeding a threshold, suppliers being blacklisted, and task progress deviations exceeding a preset threshold.

[0139] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0140] like Figure 4 As shown in the embodiment of this application, an electronic device 600 includes a processor 601, a memory 602, and a bus. The memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 via the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the enterprise project management method described above.

[0141] Specifically, the memory 602 and processor 601 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 601 runs the computer program stored in the memory 602, it can execute the above-mentioned enterprise project management method.

[0142] The processor 601 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 601 or by instructions in software form. The processor 601 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 602, and processor 601 reads the information from memory 602 and, in conjunction with its hardware, completes the steps of the above method.

[0143] Corresponding to the above-described enterprise project management method, this application embodiment also provides a computer-readable storage medium storing machine-executable instructions. When the machine-executable instructions are invoked and executed by a processor, the machine-executable instructions cause the processor to perform the steps of the above-described enterprise project management method.

[0144] The enterprise project management device provided in this application embodiment can be specific hardware on a device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in this application embodiment are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0145] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0146] For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0147] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0149] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the enterprise project management method described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0150] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0151] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for enterprise project management, characterized in that, Applied to an enterprise project management system, the method includes: The first module of the enterprise project management system publishes business events and distributes these business events to the second module via a unified data bus. The target service in the second module calls the target model to determine the target solution corresponding to the business event; The second module uses a protocol adapter to perform protocol conversion on the target scheme and sends the protocol-converted target scheme to an external system so as to obtain the contract text scheme returned by the external system for the protocol-converted target scheme through the protocol adapter; The target service in the second module calls an NLP model to perform a dual-track risk analysis on the contract text scheme to obtain the risk information of the contract text scheme. The dual-track risk analysis includes: a risk analysis method based on a preset rule base and a risk analysis method using a deep learning model. The second module triggers a rolling time-domain optimization algorithm based on the risk information to dynamically adjust the target solution, thereby obtaining the adjusted target solution; The distribution of the business events to the second module via a unified data bus includes: The business events are standardized using a semantic standardization engine to obtain standardized business events. The standardized business events are distributed to the second module via the unified data bus. The second module employs a protocol adapter to perform protocol conversion on the target scheme, including: The target scheme is protocol-transformed using a protocol transformation matrix generated by an adversarial network to obtain the target scheme after the first protocol transformation. The first field in the target scheme after the first protocol conversion is mapped and converted according to the protocol template library to obtain the target scheme after the second protocol conversion, wherein the first field is a field that matches the field mapping rules of each pair of systems stored in the protocol template library; Fuzzy matching is performed on the second field in the target scheme after the second protocol conversion, and the matched second field is mapped and converted to obtain the target scheme after the third protocol conversion, wherein the second field in the target scheme after the second protocol conversion is a field other than the first field; The unmatched second field in the target scheme after the third protocol conversion is identified by type, and the conversion is inferred based on the identified type to obtain the target scheme after the fourth protocol conversion; The unconverted fields in the target scheme after the fourth protocol conversion are manually reviewed to obtain the target scheme after protocol conversion; The method further includes: The protocol conversion quality is evaluated on the target scheme after the protocol conversion to obtain the protocol conversion quality. The protocol conversion quality evaluation includes: syntax layer protocol conversion quality evaluation, semantic layer protocol conversion quality evaluation, and performance index protocol conversion quality. Update the protocol template library and / or the adversarial network according to the protocol conversion quality, and / or elastically scale up or down the target resources; The comprehensive evaluation formula for protocol conversion quality is as follows: ,in, Indicates the quality of protocol conversion. Indicates the quality of syntax layer transformation. Indicates the quality of semantic layer conversion. Indicates the quality of performance indicators. , , All ∈ [0,1] ,default value , , ; Formula for evaluating the quality of syntax-level transformations: ,in, Indicates the quality of syntax layer transformation. This represents the value of the i-th field in the source protocol. This indicates the value of the field corresponding to the target protocol. Indicates the field tolerance threshold. Indicates field weight; The formula for evaluating the quality of semantic layer translation: ,in, Indicates the quality of semantic layer conversion. This represents the accuracy of the j-th business rule. This represents the recall rate of the j-th business rule; Performance indicator quality evaluation formula: ,in, Indicates the quality of performance indicators. Indicates actual delay / required delay. Indicates actual throughput / required throughput. This represents the delay sensitivity coefficient.

2. The method according to claim 1, characterized in that, The target service in the second module invokes an NLP model to perform a dual-track risk analysis on the contract text scheme, including: The contract text scheme is preprocessed to obtain the contract terms; The system detects specific risks corresponding to the contract terms based on a pre-defined rule base. A deep learning model is used to predict the risks of the contract terms and obtain potential risks. The explicit risks and the potential risks are combined to obtain the risk information of the contract text scheme.

3. The method according to claim 1, characterized in that, The second module dynamically adjusts the target solution based on the risk information by triggering a rolling time-domain optimization algorithm, including: When the risk information is a high-risk clause or meets the dynamic adjustment conditions, the target solution is dynamically adjusted by means of key parameter pruning, constraint relaxation, and parallel distributed solution to obtain the adjusted target solution.

4. The method according to claim 3, characterized in that, The dynamic adjustment conditions include: price fluctuations of key resources exceeding a threshold, suppliers being blacklisted, and task progress deviations exceeding a preset threshold.

5. An enterprise project management device, characterized in that, The device, used in an enterprise project management system, includes: The publishing and distribution unit is used by the first module of the enterprise project management system to publish business events and distribute the business events to the second module through a unified data bus. The determining unit is used in the second module to call the target service model to determine the target solution corresponding to the business event; The protocol conversion unit is used by the second module to perform protocol conversion on the target scheme using a protocol adapter, and send the target scheme after protocol conversion to an external system, so as to obtain the contract text scheme returned by the external system for the target scheme after protocol conversion through the protocol adapter; The dual-track risk analysis unit is used in the second module to call the NLP model to perform dual-track risk analysis on the contract text scheme to obtain the risk information of the contract text scheme. The dual-track risk analysis includes: risk analysis based on a preset rule base and risk analysis using a deep learning model. The dynamic adjustment unit is used by the second module to dynamically adjust the target scheme based on the risk information by triggering a rolling time-domain optimization algorithm, so as to obtain the adjusted target scheme. The publishing and distribution unit is further configured to: standardize the business event using a semantic standardization engine to obtain a standardized business event; and distribute the standardized business event to the second module through the unified data bus. The protocol conversion unit is further configured to: perform protocol conversion on the target scheme using a protocol conversion matrix generated by an adversarial network to obtain a first protocol-converted target scheme; perform mapping conversion on a first field in the first protocol-converted target scheme according to a protocol template library to obtain a second protocol-converted target scheme, wherein the first field is a field that matches the field mapping rules of each pair of systems stored in the protocol template library; perform fuzzy matching on a second field in the second protocol-converted target scheme and perform mapping conversion on the matched second field to obtain a third protocol-converted target scheme, wherein the second field in the second protocol-converted target scheme is a field other than the first field; perform type identification on the unmatched second field in the third protocol-converted target scheme and perform inference conversion based on the identified type to obtain a fourth protocol-converted target scheme; and perform manual review on the unconverted fields in the fourth protocol-converted target scheme to obtain the protocol-converted target scheme. The device is further configured to: evaluate the protocol conversion quality of the target scheme after protocol conversion to obtain the protocol conversion quality, wherein the protocol conversion quality evaluation includes: syntax layer protocol conversion quality evaluation, semantic layer protocol conversion quality evaluation and performance index protocol conversion quality; update the protocol template library and / or the adversarial network according to the protocol conversion quality, and / or elastically expand or shrink the target resources; The comprehensive evaluation formula for protocol conversion quality is as follows: ,in, Indicates the quality of protocol conversion. Indicates the quality of syntax layer transformation. Indicates the quality of semantic layer conversion. Indicates the quality of performance indicators. , , All ∈ [0,1] ,default value , , ; Formula for evaluating the quality of syntax-level transformations: ,in, Indicates the quality of syntax layer transformation. This represents the value of the i-th field in the source protocol. This indicates the value of the field corresponding to the target protocol. Indicates the field tolerance threshold. Indicates field weight; The formula for evaluating the quality of semantic layer translation: ,in, Indicates the quality of semantic layer conversion. This represents the accuracy of the j-th business rule. This represents the recall rate of the j-th business rule; Performance indicator quality evaluation formula: ,in, Indicates the quality of performance indicators. Indicates actual delay / required delay. Indicates actual throughput / required throughput. This represents the delay sensitivity coefficient.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores machine-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 4.

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