An expert intelligent recommendation and risk early warning method and system based on project characteristics

Abstract

The present application relates to the technical field of intelligent project management and psychological risk expert resource, and particularly relates to an expert intelligent recommendation and risk early warning method and system based on project characteristics, which comprises the following steps: collecting expert behavior and multi-source risk data through a data access layer; constructing a multi-dimensional portrait based on a graph attention network and establishing a risk tendency evaluation model using a BERT model; calculating dynamic credit points and mapping risk levels using a weighted moving average algorithm; achieving high-precision matching of experts and project characteristics using a hybrid recommendation engine; establishing an active identification mechanism for flying single irregular behavior; and achieving hard constraint control of project flow through a work flow engine pre-check hook. The present application can achieve efficient matching of expert resources and project needs, effectively identify performance decline trends and intercept high-risk behavior, and ensure project safety and compliance.

Description

Technical Field

[0001] This invention relates to the field of intelligent project management and psychological risk expert resource technology, and in particular to a method and system for intelligent expert recommendation and risk warning based on project characteristics. Background Technology

[0002] In service-oriented project management fields such as consulting, training, engineering projects, and scientific research collaborations that rely on expert delivery, accurate matching of expert resources and dynamic risk control are core elements to ensure project quality, delivery safety, and compliance. Existing expert review systems or management platforms, on the one hand, focus primarily on the project review progress or matching degree itself, rather than the professional ethics risks of individual experts, such as experts potentially withdrawing midway through the collaboration process, delaying delivery, or making malicious complaints; on the other hand, based on discrete scores after the completion of a single project, they lack dynamic credit quantification models based on long-term expert behavior data, resulting in the inability to adjust risk levels in real time as expert performance improves or deteriorates.

[0003] More critically, in projects involving high security sensitivity, such as those in government, military, and education, the assessment of individual experts' risk propensities is crucial. However, existing methods relying on inefficient manual background checks struggle to provide real-time coverage of social media extremist rhetoric, mental health status, violent tendencies, and dynamically updated criminal records. This separation of credit and risk assessment systems, coupled with the lack of assessment of risky psychological characteristics, prevents the system from automating interception and early warning at core stages such as recommendation, assignment, and project flow when facing potential risks of terrorism, violence, or other serious violations. Summary of the Invention

[0004] The purpose of this invention is to provide an expert intelligent recommendation and risk warning method and system based on project characteristics, in order to solve the technical problems in the prior art where the credit evaluation and risk warning of expert recommendation systems are disconnected, there is a lack of proactive assessment of psychological risks, and the inability to effectively monitor illegal off-the-books behavior.

[0005] One aspect of the present invention provides an expert-based intelligent recommendation and risk warning method based on project characteristics, comprising: S1. Construct a six-layer technical architecture for the expert management system. The six-layer technical architecture includes at least a data access layer, a data processing layer, a feature engineering layer, an algorithm model layer, an application service layer, and a management and monitoring layer. The data access layer collects static basic data of experts, historical project behavior data, public security and judicial data, social media authorized data, and raw psychological assessment data. The data processing layer normalizes the collected multi-source heterogeneous data, transforming unstructured text into structured data. S2. Construct a multi-dimensional expert profile and project feature vector at the feature engineering layer. The multi-dimensional expert profile includes at least the expert collaboration relationship features generated based on graph attention network, the expert semantic ability labels extracted based on large language model, and the risk tendency features based on multi-source data fusion. The project feature vector is obtained by extracting the industry, technology field, budget size, delivery cycle and security sensitivity level of the project, and the project features are transformed into a high-dimensional space vector using a pre-trained BERT model. S3. Establish an expert credit evaluation model at the algorithm model layer, and dynamically quantify the expert's historical delivery quality, on-time rate, positive review rate and violation records through a weighted moving average algorithm to generate a unified credit score. S4. Run the risk propensity assessment model at the algorithm model layer. The risk propensity assessment model extracts features from expert-authorized social media statements, psychological questionnaire responses, and judicial background data through an algorithm, and outputs five quantitative risk levels. S5. Execute expert intelligent recommendation logic in the application service layer, use a hybrid recommendation engine to calculate the cosine similarity between the expert profile vector and the project feature vector, and introduce a risk penalty factor corresponding to the risk level to correct the similarity score; S6. Establish an active identification mechanism for illegal off-the-books transactions at the management and monitoring layer. Use natural language processing technology to perform semantic analysis on the chat records between experts and customers in the communication module to identify suspected illegal intentions and retrieve the underlying contract database to compare the consistency of the contract subjects. S7. To achieve coupling between the recommendation system and the project management workflow, a pre-inspection hook is deployed in the workflow engine. When switching project phases, the risk assessment interface is automatically called to determine whether the credit score of the currently assigned expert is higher than the preset admission threshold and whether the risk level is lower than the safety red line.

[0006] In some embodiments, in S2, the process of generating the expert collaboration relationship features is as follows: all experts in the expert database are defined as graph nodes, the common project collaboration experience between experts is defined as edges, and the weights of the edges are set according to the number of collaborations and the collaboration evaluation; by aggregating the feature information of neighboring nodes, a latent space vector reflecting the expert's cross-domain collaboration ability and industry influence is learned using a graph attention network. In some embodiments, in S3, the process of establishing an expert credit evaluation model is as follows: obtain the performance scores of the expert for the most recent N projects, calculate the current credit score based on the project time proximity and the decreasing weight coefficients of the project weight allocation; use the least squares method to perform linear regression analysis on the trajectory of the expert's credit score change within a preset time period, and calculate the regression coefficient k as a trend term factor; when the regression coefficient k is less than a preset negative threshold and two consecutive credit updates show a downward trend, the system automatically triggers a trend fatigue warning signal.

[0007] In some embodiments, in S4, the process of running the risk propensity assessment model is as follows: the BERT multi-classification algorithm outputs five risk levels, including political sensitivity risk, violent tendency risk, and mental health risk; when the risk level reaches the highest warning threshold, the system locks the expert's status field in the expert database through a trigger, thereby achieving a veto of the expert in all pending projects.

[0008] In some embodiments, the risk propensity assessment model also includes real-time monitoring of the expert's professional conduct. By listening to the expert's task response delay time, document submission frequency, and abnormal login IP during the project cycle, and combining this with long short-term memory networks, the model identifies whether the expert is slacking off or has the risk of lending their account.

[0009] In some embodiments, in S5, the specific process of executing expert intelligent recommendation logic in the application service layer is as follows: a hybrid recommendation engine combining collaborative filtering, knowledge graph and large language model is used to calculate the matching score; the recommendation weight is optimized using reinforcement learning algorithm, and the weight allocation of each feature dimension is adjusted in real time according to the final adoption feedback of the project manager.

[0010] In some embodiments, in S6, the process of establishing an active identification mechanism for non-compliance of orders is as follows: identifying non-compliance through cross-project correlation analysis, that is, by analyzing similar demand projects initiated by different customers in the same time period and comparing the overlap of the experts finally adopted; if it is found that a specific expert appears in the offline collaboration list of related customers without reporting through the platform, then the professional ethics risk level is raised to the highest level.

[0011] In some embodiments, in S7, the coupling process between the recommendation system and the project management workflow is as follows: the project status form is locked through a database cascading binding mechanism. The database cascading binding mechanism establishes a foreign key relationship between the project execution master table and the expert risk status table, and sets a pre-trigger logic on the status update field of the project execution master table. When a status update request is received, the logic retrieves the real-time data of the associated expert in the risk status table, and allows the Update operation to be performed only when the data meets the preset security logic.

[0012] Another aspect of the present invention provides an expert intelligent recommendation and risk warning system based on project characteristics, comprising at least: The data acquisition module is used to perform data acquisition and processing in S1; The feature modeling module is used to perform multi-dimensional expert profiling and project feature vector construction in S2; The evaluation and early warning module is used to perform expert credit evaluation and risk tendency assessment in S3 and S4; The intelligent recommendation module is used to execute the expert intelligent recommendation logic in S5; The violation monitoring module is used to proactively identify illegal activities such as unauthorized orders in S6. The workflow control module is used to couple the recommendation system and project management workflow in S7.

[0013] In some embodiments, the workflow coupling process executed by the workflow control module is as follows: when the project switches from the expert matching stage to the task execution stage, the risk assessment interface is automatically called through the pre-inspection hook; if the real-time risk data of the assigned expert fails the pre-inspection, the status update interface is blocked through the workflow control module, and the expert replacement process is forcibly triggered.

[0014] Compared with the prior art, the present invention has the following advantages: 1. This invention solves the technical problem of isolated competence evaluation and risk management in the prior art by constructing a unified credit and risk mapping model that quantitatively integrates experts’ historical delivery behavior, professional ethics performance and psychological risk tendency. 2. This invention introduces a risk propensity assessment model, which extends risk prevention and control to the field of individual psychological safety by mining social media, judicial data and psychological tests. This pre-identification, in high-security sensitive projects such as government and military industries, through a veto system and risk penalty factors, prevents high-risk individuals from entering core collaborative links, significantly improving the overall safety of the project. 3. In response to the widespread illegal practice of unauthorized contracts in the industry, this invention utilizes the BERT model and semantic analysis technology to construct an active monitoring mechanism. Through real-time identification of the intent of communication content and cross-validation with the contract database, it achieves automated detection and blocking of unauthorized contract signing activities that bypass the platform. 4. This invention uses a workflow engine pre-check hook to couple the recommendation results with the project execution process. This makes the risk status directly determine the flow permission of the business process. If the expert's credit score is not up to standard or an early warning is triggered, the project status field will be locked through the underlying logic, forcing the project manager to replace the expert with a qualified one. 5. The six-layer technical architecture adopted in this invention uses graph attention networks to capture complex collaborative relationships among experts and combines reinforcement learning to dynamically optimize recommendation weights, so that the accuracy of the recommendation results continuously evolves as the amount of data increases. 6. This invention is deployed through a microservice architecture and interacts with standard API interfaces, enabling the early warning logic to be flexibly embedded into different types of project management systems, thus possessing technical scalability and universality. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a six-layer technical architecture diagram of the expert management system of the present invention; Figure 2 This is a flowchart of the expert intelligent recommendation and risk warning method based on project characteristics according to the present invention; Figure 3 This is a diagram showing the composition of the expert intelligent recommendation and risk warning system based on project characteristics according to the present invention. Detailed Implementation

[0017] The following will be based on embodiments of the present invention. Figures 1-3 The technical solutions in the embodiments of the present invention will be clearly and completely described together. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] To better understand this invention, some definitions are provided below: 1. Dangerous tendencies: refers to the psychological or behavioral characteristics of an expert that may pose a threat to the safety of project personnel, property, or social stability, including but not limited to terrorism-related, violent, extreme remarks, serious criminal records, and history of mental illness; 2. Database cascading binding: This refers to the synchronous updating of state fields in two different database tables through database triggers or application-layer transactions, so that when one field changes, the other field automatically changes accordingly; 3. Q-learning algorithm: a model-free, offline policy reinforcement learning algorithm; 4. Pre-inspection hook: A pre-inspection script that is automatically triggered before the actual operation to intercept non-compliant operations, which is equivalent to a security gate in the process.

[0019] Example This embodiment provides an expert-based intelligent recommendation and risk warning system based on project characteristics, referencing... Figure 1 The system adopts a six-layer technical architecture. The bottom layer is the data access layer, which connects in real time with the databases of the National Enterprise Credit Information Publicity System, China Judgments Online, and third-party psychological testing institutions through a pre-defined API interface protocol, simultaneously capturing experts' criminal records, information on defaulters, and raw psychological testing scale data. The data processing layer uses ETL tools to clean the incoming unstructured data, transforming social media comments and historical evaluation texts into structured feature vectors. The feature engineering layer is responsible for building expert profile modules and project feature vectors. The intelligent recommendation layer, or algorithm model layer, integrates credit evaluation models and risk tendency assessment models. The application service layer provides users with expert retrieval, task assignment, and risk display interfaces. The top-level management and monitoring layer is responsible for monitoring abnormal behaviors throughout the entire business lifecycle, especially the real-time interception of unauthorized transactions.

[0020] Specifically, the construction process of the expert profile module is as follows: The system abstracts each expert in the expert database as a node in a graph data structure, and uses a graph attention network (GAT) to establish an expert collaboration network. When two experts have jointly participated in the same project, a connection edge is generated between the nodes. The weight of the edge is determined by the duration of collaboration between the two parties and the final customer rating of the project. The multi-head attention mechanism of GAT learns the influence representation vector of the expert in a specific industry by aggregating the feature information of neighboring nodes. At the same time, the system calls a pre-trained large language model to perform semantic extraction on the expert's historical delivery documents and customer-written evaluation content, generating ability labels that reflect the expert's expertise in specific sub-fields, communication style, and professional depth. These labels are then fused with the representation vector generated by GAT to form a high-dimensional expert fusion representation.

[0021] refer to Figure 2 It provides an expert-driven intelligent recommendation and risk warning method based on project characteristics. The specific steps are as follows: In S1, the system initiates a data collection process, acquiring multi-dimensional data from experts through the data access layer. This data includes, but is not limited to: basic identity information, professional qualification certificates, a list of historical projects participated in, customer evaluation scores, and behavioral logs within the platform. Specifically, within the scope of legal authorization, the system accesses the publicly available criminal record query interface of public security authorities via API, focusing on checking for records of terrorist activities, violent crimes, and general criminal offenses. Simultaneously, the system collects the experts' historical statements on authorized social media platforms and analyzes the text content using a natural language processing engine.

[0022] In S2, the feature engineering layer extracts multi-dimensional features of the target project. After the project manager inputs project description text into the client, the system uses the BERT model to extract its semantic features, identifying the project's industry category, technical field, budget range, and security sensitivity level. The security sensitivity level is divided into three levels: normal, sensitive, and highly sensitive, which directly determines the stringency of subsequent risk filtering. For example, for highly sensitive projects in the military or government sectors, the system automatically increases the weight of the hazard propensity assessment model.

[0023] In S3, the algorithm model layer constructs and runs a unified credit rating model. This model uses a weighted moving average algorithm to dynamically score experts' historical behavior. The initial credit score for each expert is set at 80 points, and the update cycle is after each project acceptance. The weighted moving average formula is defined as the current credit score equal to the weighted sum of the current project performance score and the historical credit score, where the weight coefficient w for the current project is set to 0.6 to reflect the dominant role of recent performance in credit rating. Reward and penalty rules are strictly enforced: 2 points are added for completing a project without complaints, 1 point is added for early delivery, 5 points are deducted for project delays, 10 points are deducted after a verified customer complaint, and 15 points are deducted for withdrawing from a project without consent.

[0024] In S4, the algorithm model layer initiates a parallel risk propensity assessment model. Based on the data collected in S1, this model categorizes experts' risk levels into no risk, low risk, medium risk, high risk, and severe risk. The judgment logic is as follows: if a record of terrorism is found or the individual is blacklisted in the industry, they are directly judged as severely risky; a record of violent crime is judged as high risk; and violent inflammatory remarks on social media or a violent tendency score exceeding 75 points in psychological tests are judged as medium risk. These levels are not only displayed as labels but also factors that participate in subsequent intelligent recommendations and process blocking.

[0025] In S5, the system first calculates the cosine similarity between the project feature vector and the expert fusion representation vector to obtain a preliminary matching score. Subsequently, the system introduces a credit score factor and a risk filtering coefficient to correct the score. If an expert's risk level is medium risk, the matching score is automatically multiplied by a weighting factor of 0.5; if the level is high risk or severe risk, the expert is directly removed from the candidate pool. Finally, the system sorts the experts in descending order of their comprehensive scores and pushes a Top-K expert list to the client, displaying the risk level in each list item using a color-coded indicator.

[0026] In S6, the application service layer implements an automated closed loop for recommendation and assignment. After the project manager selects an expert from the recommendation list, the system automatically generates a structured task card in the project management platform. The task card includes a task ID, assigned expert, deliverable list, acceptance criteria, and deadline. The system uses database triggers to cascade task cards with sections of the project plan. When an expert submits deliverables on the client and the project manager clicks "accept," the task card's status field is updated to "completed," and the database trigger automatically updates the status of the corresponding project section to "transferable."

[0027] In S7, the system scans communication records between experts and clients within the platform in real time, using a classification model to identify any intention to bypass third-party companies and directly sign contracts. The keyword recognition engine captures frequently used terms like "private transfers" and "avoiding company involvement" for unauthorized transactions. If unauthorized transactions are detected, the system immediately triggers an emergency response procedure, resetting the expert's credit score to zero and blocking all ongoing projects involving that expert from being involved in by the workflow control module.

[0028] In managing credit scores and risk warnings, the system establishes a rigorous tiered mapping mechanism. Experts with a credit score of 90 or higher (Level A) are marked as low-risk, receiving priority recommendations on the homepage and higher commission rates. Experts with a credit score between 60 and 74 (Level C) are marked as medium-high risk; the system automatically limits their order-taking frequency and adds a prominent risk warning to the recommendation interface. If a credit score is below 60, the expert is defined as a Level D risk expert, and the system automatically suspends their order-taking privileges.

[0029] In addition, the system maintains a credit score time series of length 10 and uses a linear regression algorithm to calculate the trend of credit score changes. If the slope of the trend term is updated to a negative value twice consecutively, even if the current score is still within the acceptable range, the system will push a trend warning to the administrator, indicating that the expert's performance is continuously declining.

[0030] The system employs an event-triggered mechanism to dynamically update risk propensity. Whenever new psychological assessment results are input or social media posts are captured, the risk propensity assessment model immediately recalculates the level. If an expert's risk level rises from low to medium risk, the system automatically deducts 15 points from their credit score and triggers an alert. If it rises to high risk, the system will ban the account. This linkage between credit score and risk level ensures the authority of the risk assessment.

[0031] It's worth noting that when processing phase transition requests, the workflow engine will forcibly execute pre-check hooks. The specific operating principle is as follows: When the project manager initiates a phase switch from review preparation to on-site review, the pre-screening hook instantly queries the real-time risk data of all assigned experts in the current project. If any expert's credit score is found to be below 60, or their risk level is in a high-risk or severe-risk state, the pre-screening hook will return an interrupt signal to the workflow engine. Upon receiving this signal, the workflow engine will block the status update interface and pop up a blocking window on the client, informing the project manager that a high-risk expert exists and the process can only continue after the expert is replaced.

[0032] In the sub-process of replacing experts, the system automatically calls the recommendation engine in S5, but adds a parameter to the filtering conditions to exclude currently high-risk experts, providing project managers with alternative expert options. Once the new expert is assigned, the pre-check hook performs another verification to confirm that all participants meet the safety thresholds before the workflow is unlocked. This pre-check hook-based design upgrades risk warnings from simple text prompts to physical-level process control.

[0033] Meanwhile, to proactively identify unauthorized transactions, the system monitoring module continuously compares data in the contract database. If it finds that a client has signed a service contract with an entity affiliated with an expert, and the content of the contract highly overlaps with projects currently running on the platform, and the expert's third-party company is missing from the contract, the system will determine it as unauthorized transaction behavior using a correlation analysis algorithm. At the semantic analysis level, the system uses the BERT classification model to binary classify chat logs, setting the confidence threshold for distinguishing between normal communication and suspected unauthorized transaction intent at 0.85. Once this threshold is exceeded, the system will automatically retrieve the expert's cross-project behavior records for the past 30 days to analyze whether they have frequent private communications with multiple clients. After verification, the system will implement the most severe punishment: the credit score will be directly reduced to 0, and the expert's identity information will be synchronized to the industry integrity alliance's shared blacklist, achieving cross-platform collaborative supervision.

[0034] In a real-world application scenario, such as a digital transformation project undertaken by a consulting firm, after the project manager creates the project and extracts its features on the client-side, the system recommends three experts. Expert A has a credit score of 92, posing no risk, and is marked as a priority recommendation. Expert B has a credit score of 78 and is marked as a normal recommendation. Expert C, while possessing relevant expertise, is assessed as having medium risk due to recent extreme statements on social media. When the system displays Expert C, it automatically places him at the bottom with a red warning message. After the project manager assigns Expert A through the system, the task card is automatically attached to the project plan. During execution, if Expert A causes significant project delays due to personal reasons, the system automatically deducts his credit score during the acceptance phase. If his credit score falls below 60, the pre-inspection hook will detect this credit risk when the project enters the next stage of on-site investigation, thereby halting project progress and forcing the consulting firm to intervene or replace the expert, thus mitigating the risk of project failure.

[0035] In some possible implementations, this embodiment also continuously optimizes the allocation of recommendation weights through reinforcement learning algorithms. The system defines the project manager's adoption of recommended experts as a positive reward and ignores or rejects them as negative feedback. Based on these feedback signals, the Q-learning algorithm adjusts the values ​​of the feature weights such as α, β, and γ in the matching degree calculation formula online. As the number of projects increases, the system gradually learns the differences in the preferences for expert qualities among projects of different industries and sizes. For example, in engineering projects, the system automatically increases the weight of on-time delivery rate; while in consulting projects, it increases the weight of customer ratings and semantic ability tags.

[0036] In some possible implementations, the system in this embodiment also has a strong compliance reporting function. Once the risk propensity assessment model identifies clues that an expert is involved in terrorist activities or serious violent crimes, the system monitoring layer will automatically extract relevant evidence chains, including identity information, behavioral trajectory and abnormal speech records, generate an encrypted risk report, and report it to the public security department through a dedicated regulatory interface.

[0037] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0038] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. An expert-based intelligent recommendation and risk warning method based on project characteristics, characterized in that, include: S1. Construct a six-layer technical architecture for the expert management system. The six-layer technical architecture includes at least a data access layer, a data processing layer, a feature engineering layer, an algorithm model layer, an application service layer, and a management and monitoring layer. The data access layer collects static basic data of experts, historical project behavior data, public security and judicial data, social media authorized data, and raw psychological assessment data. The data processing layer normalizes the collected multi-source heterogeneous data, transforming unstructured text into structured data. S2. Construct a multi-dimensional expert profile and project feature vector in the feature engineering layer. The multi-dimensional expert profile includes at least the expert collaboration relationship features generated based on graph attention network, the expert semantic ability labels extracted based on large language model, and the risk tendency features based on multi-source data fusion. The project feature vector is obtained by extracting the project's industry, technology field, budget size, delivery cycle, and security sensitivity level, and then using a pre-trained BERT model to transform the project features into a high-dimensional space vector. S3. Establish an expert credit evaluation model at the algorithm model layer, and dynamically quantify the expert's historical delivery quality, on-time rate, positive review rate and violation records through a weighted moving average algorithm to generate a unified credit score. S4. Run the risk propensity assessment model at the algorithm model layer. The risk propensity assessment model extracts features from expert-authorized social media statements, psychological questionnaire responses, and judicial background data through an algorithm, and outputs five quantitative risk levels. S5. Execute expert intelligent recommendation logic in the application service layer, use a hybrid recommendation engine to calculate the cosine similarity between the expert profile vector and the project feature vector, and introduce a risk penalty factor corresponding to the risk level to correct the similarity score; S6. Establish an active identification mechanism for illegal off-the-books transactions at the management and monitoring layer. Use natural language processing technology to perform semantic analysis on the chat records between experts and customers in the communication module to identify suspected illegal intentions and retrieve the underlying contract database to compare the consistency of the contract subjects. S7. To achieve coupling between the recommendation system and the project management workflow, a pre-inspection hook is deployed in the workflow engine. When switching project phases, the risk assessment interface is automatically called to determine whether the credit score of the currently assigned expert is higher than the preset admission threshold and whether the risk level is lower than the safety red line.

2. The method according to claim 1, characterized in that, In S2, the process of generating the expert collaboration relationship features is as follows: all experts in the expert database are defined as graph nodes, the common project collaboration experience between experts is defined as edges, and the weights of the edges are set according to the number of collaborations and the collaboration evaluation; by aggregating the feature information of neighboring nodes, a latent space vector reflecting the cross-domain collaboration ability and industry influence of experts is learned using a graph attention network.

3. The method according to claim 2, characterized in that, In S3, the process of establishing the expert credit evaluation model is as follows: obtain the performance scores of the expert for the most recent N projects, calculate the current credit score based on the time distance of the projects and the decreasing weight coefficients of the project weight allocation; use the least squares method to perform linear regression analysis on the trajectory of the expert's credit score change within a preset time period, and calculate the regression coefficient k as a trend term factor. When the regression coefficient k is less than the preset negative threshold and two consecutive credit updates show a downward trend, the system automatically triggers a trend fatigue warning signal.

4. The method according to claim 1, characterized in that, In S4, the process of running the risk propensity assessment model is as follows: the BERT multi-classification algorithm outputs five risk levels, including political sensitivity risk, violent tendency risk, and mental health risk; when the risk level reaches the highest warning threshold, the system locks the expert's status field in the expert database through a trigger, thereby achieving a veto of the expert in all pending projects.

5. The method according to claim 4, characterized in that, The risk propensity assessment model also includes real-time monitoring of experts' professional conduct. By listening to the expert's task response delay time, document submission frequency, and abnormal login IP during the project cycle, and combining the long short-term memory network, it identifies whether the expert is slacking off or has the risk of lending their account.

6. The method according to claim 1, characterized in that, In S5, the specific process of executing expert intelligent recommendation logic in the application service layer is as follows: a hybrid recommendation engine combining collaborative filtering, knowledge graph and large language model is used to calculate the matching score; the recommendation weight is optimized using reinforcement learning algorithm, and the weight allocation of each feature dimension is adjusted in real time according to the final adoption feedback of the project manager.

7. The method according to claim 1, characterized in that, In S6, the process of establishing a proactive identification mechanism for non-compliance of orders is as follows: identify non-compliance through cross-project correlation analysis, that is, by analyzing similar demand projects initiated by different customers in the same time period and comparing the overlap of the experts finally adopted; if it is found that a specific expert appears in the offline collaboration list of related customers without reporting through the platform, then the professional ethics risk level is raised to the highest level.

8. The method according to claim 1, characterized in that, In S7, the coupling process between the recommendation system and the project management workflow is as follows: the project status form is locked through a database cascading binding mechanism. The database cascading binding mechanism establishes a foreign key relationship between the project execution master table and the expert risk status table, and sets a pre-trigger logic on the status update field of the project execution master table. When a status update request is received, this logic retrieves the real-time data of the associated expert in the risk status table, and allows the Update operation to be performed only when the data meets the preset security logic.

9. An expert-based intelligent recommendation and risk warning system based on project characteristics, characterized in that, At least including: The data acquisition module is used to perform data acquisition and processing in S1; The feature modeling module is used to perform multi-dimensional expert profiling and project feature vector construction in S2; The evaluation and early warning module is used to perform expert credit evaluation and risk tendency assessment in S3 and S4; The intelligent recommendation module is used to execute the expert intelligent recommendation logic in S5; The violation monitoring module is used to proactively identify illegal activities such as unauthorized orders in S6; The workflow control module is used to couple the recommendation system and project management workflow in S7.

10. The system according to claim 9, characterized in that, The workflow coupling process of the workflow control module is as follows: when the project switches from the expert matching stage to the task execution stage, the risk assessment interface is automatically called through the pre-inspection hook; if the real-time risk data of the assigned expert fails the pre-inspection, the status update interface is blocked through the workflow control module, and the expert replacement process is forcibly triggered.

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