A technical information retrieval consulting method and service platform

Abstract

The present application relates to the technical field of consulting services, in particular to a technical information retrieval consulting method and service platform, comprising: receiving business consulting information, performing integrity check, taking the business consulting information with complete content as the to-be-tested business information; combining with the dynamic risk coefficient, calculating the net income through the net income accounting formula, combining with the business cycle to calculate the unit time income; comparing with the two-level threshold to determine the business value level, based on the business carrying demand in the to-be-tested business information, performing carrying capacity check to obtain the carrying check result, and judging the type of the to-be-tested business information; based on the content in the information library, generating standardized information push to the supplier terminal, and generating an artificial disposal work order for the to-be-tested business information of the complex consulting type and pushing it to the corresponding business specialist. The scheme improves the artificial efficiency through the business income value level and enterprise performance carrying capacity two-dimensional hierarchical coupling discrimination mechanism.

Description

Technical Field

[0001] This invention relates to the field of consulting service technology, and in particular to a technical information retrieval and consulting method and service platform. Background Technology

[0002] Currently, technical consulting services in the supply chain production service sector are becoming increasingly frequent, with suppliers' consulting needs regarding production processes, quality inspection standards, delivery and fulfillment, and cooperation norms becoming more common and diverse. At present, most service platforms in the industry employ a rather crude consultation handling model, generally adopting uniform and fixed standards for business judgment and response, lacking refined and differentiated intelligent judgment mechanisms.

[0003] Existing technological solutions have significant shortcomings in practical applications. Traditional supply chain technology consulting services have long relied on manual screening, judgment, and classification, resulting in extremely low overall automation. The industry lacks a standardized mechanism for intelligent assessment based on both business value and enterprise capacity, making it impossible to automatically stratify and distribute massive amounts of consulting business. This forces all consulting work orders on the platform to be reviewed and processed manually. Furthermore, traditional, fixed, and singular judgment criteria cannot adapt to the dynamic differences in enterprise capacity and business revenue, and cannot independently distinguish between standardized, simple consulting and highly complex customized consulting. This indiscriminate manual processing model results in a large number of simple consulting requests consuming core human resources, leading to low business screening efficiency and high labor costs.

[0004] Meanwhile, the current fully manual handling model is highly susceptible to the influence of staff experience and subjective judgment, leading to inconsistent business classification standards, delayed response times, and misjudgments or omissions in work orders, thus failing to form a standardized and intelligent closed-loop handling process. Simple routine inquiries cannot achieve unmanned rapid responses, while complex and demanding inquiries lack a precise human-targeted fallback mechanism, further exacerbating the current situation of business backlog, chaotic handling, and inconsistent service quality. Overall, the existing consultation processing system severely lacks automation and refined stratification capabilities, resulting in an excessive workload for manual staff and making it difficult to adapt to the large-scale and high-frequency technical consultation service needs of the supply chain. There is an urgent need for an intelligent consultation processing solution that can automatically identify, stratify, and differentiate handling. Summary of the Invention

[0005] This invention achieves refined and intelligent classification of supply chain technology consulting services through a two-dimensional hierarchical coupling judgment mechanism based on business revenue value hierarchy and enterprise performance carrying capacity, thereby solving the problems of heavy workload and low efficiency of manual work.

[0006] The technical solution proposed in this invention is: a method for retrieving and consulting technical information, the method comprising: Receive business inquiry information, verify the completeness of the business inquiry information, and use the complete business inquiry information as the business information to be tested; Based on the business information to be tested, and combined with the dynamic risk coefficient determined according to the supplier credit gradient, the net income is calculated through the net income calculation formula, and the unit time income is calculated in combination with the business cycle. The revenue per unit time is compared with two threshold levels to determine the business value level. Based on the business carrying requirements in the business information to be tested, the carrying capacity is verified by combining relevant information in the preset information database to obtain the carrying verification result. Based on the business value level and business carrying requirements, the type of business information to be tested is determined. For business information under test classified as routine consultation, standardized information is generated based on the content in the information database and pushed to the supplier's terminal. For business information under test classified as complex consultation, manual processing work orders are generated and pushed to the corresponding business specialists.

[0007] Preferably, the specific process for verifying the integrity of the business consultation information is as follows: Pre-build a standardized dataset of required parameters adapted to supply chain consulting scenarios, and standardize the parameter dimensions corresponding to various business consulting. Based on the standardized required parameter dataset, the received business consultation information is compared one by one to complete the full field matching screening, and the business consultation information with complete fields and compliant format is selected and output as the business information to be tested. Identify and intercept business inquiry information with missing fields or abnormal parameters, and terminate the current data flow chain.

[0008] Preferably, the process for determining the dynamic risk coefficient is as follows: Pre-configure supplier risk benchmark coefficients, divide supplier credit gradient ranges into multiple tiers, and bind corresponding differentiated dynamic risk coefficients to each gradient range. Calculate the supplier's real-time credit score based on transaction data, delivery data, and after-sales performance data after each order is fulfilled. Based on the real-time credit score, the corresponding supplier credit gradient range is matched to obtain the supplier's current dynamic risk coefficient.

[0009] Preferably, the calculation process for the net income is as follows: Extract order revenue data, comprehensive cost data, and prepayment data from the business information to be tested, and calculate the business risk loss value by combining dynamic risk coefficients. Based on the net profit calculation formula, the cost deduction, risk loss calculation and deposit hedging calculation are completed in sequence, and the net profit data of the order is output.

[0010] Preferably, the process for determining the business value level is as follows: Collect unit-time revenue data for all compliant historical transactions, perform anomaly cleaning on the original revenue sample, and construct a unit-time revenue statistical data set; Based on the statistical data set of revenue per unit time, hierarchical quantile calculations are performed to iteratively generate two levels of thresholds; Based on real-time revenue data per unit time, numerical comparisons and matching are performed with two levels of thresholds in sequence, and the corresponding business value levels are determined according to the threshold matching results.

[0011] Preferably, the process for obtaining the bearer verification result is as follows: Extract the service carrying requirement feature parameters from the service information to be tested, retrieve the carrying boundary parameter set stored in the preset information database, and compare and match the service carrying requirement feature parameters with the carrying boundary parameter set field by field to obtain the overall matching overlap and over-limit situation. Based on the overall matching overlap and over-limit situation, generate the corresponding load compliance verification result or load over-limit verification result, and output the load verification identifier.

[0012] Preferably, the process of determining the type of the service information to be tested is as follows: Based on the business value level and the verification results, the preset layered coupling discrimination rule matrix is ​​retrieved. According to the combination relationship between the level and the compliance status of the carrier, the standardized classification judgment logic is executed to obtain the business information type identifier to be tested. The business information type identifier to be tested includes the conventional consultation type identifier and the complex consultation type identifier.

[0013] Preferably, different operations are performed according to the type of the service information to be tested, and the specific process is as follows: When identified as a regular consultation type, the system retrieves standardized business data, technical specifications, and process solutions from the preset information database, matches them with the current business information to be tested, generates standardized consultation response information, and pushes the standardized information to the supplier's terminal device. When identified as a complex consultation type, the system collects all parameters of the business information to be tested, the business value level, and the verification results, integrates them to generate a complete business processing work order, and pushes the business processing work order to the corresponding business specialist's backend.

[0014] The present invention also provides a technical information retrieval and consultation service platform, which is used to execute the aforementioned technical information retrieval and consultation method.

[0015] The present invention also provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the aforementioned technical information retrieval and consultation method.

[0016] The beneficial effects of this invention are: 1. This solution constructs a multi-dimensional performance credit scoring system, replacing the traditional fixed risk level assessment model. It can quantify supplier performance risk in real time across the entire chain of transaction, delivery, and after-sales service, dynamically matching accurate risk coefficients. By coupling dynamic risk coefficients to the business net revenue calculation process, it effectively eliminates the revenue calculation deviation caused by fixed risk parameters, and can truly restore the real profitability of a single consulting business under actual performance risk. This achieves refined, dynamic, and accurate business revenue calculation, providing a reliable data foundation for subsequent business value assessment.

[0017] 2. This solution innovatively introduces a capacity load status correction logic, constructing a dynamic threshold stratification mechanism that adaptively iterates based on the enterprise's capacity saturation and historical business profitability, breaking through the technical bottleneck of traditional fixed thresholds being unable to adapt to capacity fluctuations. When capacity is saturated, it rigorously selects high-profit businesses to maximize the profitability of limited capacity; when capacity is idle, it appropriately relaxes the judgment criteria to fully utilize idle capacity resources. This effectively balances enterprise capacity utilization and business profitability, avoiding the problem of overlooking high-quality businesses and allowing inefficient businesses to crowd out capacity, thus achieving intelligent and optimal allocation of capacity resources.

[0018] 3. This solution constructs a layered, coupled discrimination rule matrix based on both business value hierarchy and enterprise capacity, enabling fully automated intelligent classification and differentiated tiered handling of consulting services. It can accurately distinguish between standard, routine consulting services with full compliance across all dimensions and complex, customized consulting services with issues exceeding capacity, process, delivery, or quality inspection limits. For routine consulting services, it achieves unmanned automated retrieval and response, completely freeing up significant human computing power and reducing manual maintenance workload. For complex consulting services, it automatically collects work orders and pushes them to dedicated human handlers for processing, ensuring accurate handling of complex business needs. This significantly improves the overall efficiency and standardization of consulting services, achieving a fully intelligent closed-loop operation. Attached Figure Description

[0019] Figure 1 A flowchart of a technical information retrieval and consulting method and service platform; Figure 2 A flowchart for calculating the net revenue of a technology information retrieval and consulting method and service platform; Figure 3 A flowchart for evaluating the business value hierarchy of a technology information retrieval and consulting method and service platform; Figure 4 This is a flowchart illustrating the consultation process of a technical information retrieval and consultation method and service platform. Detailed Implementation

[0020] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0021] It is understood that the term "a" should be understood as "at least one" or "one or more," that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0022] like Figure 1 As shown, a method for retrieving and consulting technical information includes: The system receives business inquiries and verifies their completeness, designating complete inquiries as test business information. Based on this information and a dynamic risk coefficient determined according to supplier credit gradients, net revenue is calculated using the net revenue calculation formula, and the revenue per unit time is calculated based on the business cycle. The revenue per unit time is compared with two threshold levels to determine the business value level. Based on the business carrying requirements in the test business information and relevant information in a pre-set information database, carrying capacity is verified to obtain the carrying capacity verification result. The type of test business information is determined based on the business value level and carrying capacity requirements. For test business information classified as routine inquiries, standardized information is generated based on the content in the information database and pushed to the supplier terminal. For test business information classified as complex inquiries, a manual processing work order is generated and pushed to the corresponding business specialist.

[0023] This embodiment relies on a technical information retrieval and consulting service platform. The platform is divided into seven core functional modules based on business flow logic, specifically including a client interaction module, a request parsing and routing module, a supply chain-specific information retrieval module, a manual processing and reminder module, a data storage module, a big data statistical analysis module, and a management backend module. These modules work collaboratively to sequentially complete steps such as receiving consulting requests, parameter parsing and verification, data calculation and judgment, information retrieval and matching, differentiated business processing, and data accumulation and analysis.

[0024] The client-side interaction module, serving as the front-end interaction platform, receives various technical consultation requests submitted by suppliers and acts as the entry point for suppliers to view consultation results and retrieve historical consultation records. The request parsing and routing module is the core processing module of the platform, responsible for parsing parameters, verifying integrity, calculating business parameters, and identifying consultation types in received consultation requests, thus performing initial screening and routing of consultation business. The supply chain-specific information retrieval module internally stores various technical specifications, production process standards, delivery guidelines, and industry information materials for the supply chain, enabling precise information retrieval and content matching based on specific consultation needs. The manual processing reminder module generates standardized handling work orders and pushes them to the corresponding business personnel for complex consultation business that cannot be automatically answered. The data storage module is responsible for uniformly storing consultation data, work order data, search materials, and user business data generated during platform operation. The big data statistical analysis module relies on existing business data to statistically analyze frequently asked questions and business pain points, forming supply chain business optimization analysis data. The management backend module allows platform administrators to view all consultation data, work order processing progress, and supplier consultation hotspots.

[0025] The platform's overall business operation process is as follows: Suppliers submit technical consultation requests through the client interaction module, and the relevant request data is synchronously pushed to the request parsing and distribution module. The request parsing and distribution module performs parameter parsing and integrity verification on the original consultation data, eliminating invalid requests with missing parameters or abnormal formats, and filtering out the business information to be tested that meets the specifications. The request parsing and distribution module then completes revenue calculation, value level determination, and capacity verification based on the business information to be tested, and distinguishes between routine and complex consultations based on multi-dimensional judgment results. For routine consultations, the supply chain-specific information retrieval module is called to match corresponding information, generate standardized responses, and push them to the supplier's terminal. For complex consultations, a business work order is generated through the manual processing reminder module and pushed to the corresponding business specialist for manual contact. All business processing data is stored in the data storage module, and the big data statistical analysis module conducts regular data statistical analysis. The management backend module synchronously displays all platform operation data in real time, realizing closed-loop control of the entire business process. Furthermore, the specific process for verifying the completeness of business consultation information is as follows: A standardized dataset of required parameters adapted to supply chain consulting scenarios is pre-built, and the standardized parameter dimensions corresponding to various business consulting are organized. The received business consulting information is compared one by one with the standardized dataset of required parameters to complete the full field matching screening, and the business consulting information with complete fields and compliant format is selected and output as the business information to be tested. Business consulting information with missing fields or abnormal parameters is identified and intercepted, and the current data flow chain is terminated.

[0026] like Figure 2As shown, the platform, in accordance with the acceptance standards for supply chain technology consulting, pre-standardizes the general filling standards for consulting business and builds a standardized dataset of required parameters adapted to supply chain scenarios. This dataset lists four dimensions—production customization, quality inspection and delivery, cooperation and coordination, and risk management—as universally required fields for all technical consulting. Every valid business consulting request must simultaneously include all four dimensions of parameters, forming a complete order business consulting information. Only consulting information with complete information will undergo type judgment and potentially reach the manual response stage, thereby improving the effectiveness of manual processing and reducing the number of invalid requests reaching manual processing. The dataset clearly defines the specific fields, standard data formats, and legal value ranges corresponding to the four parameter categories, serving as a unified verification basis for all consulting requests and ensuring the completeness and consistency of consulting data dimensions before subsequent in-depth analysis. Customers can refer to the detailed examples in the filling section when filling out the form.

[0027] Among them, the required parameters for production customization include product specifications, customization process requirements, production batch, material standards, and processing precision requirements; the required parameters for quality inspection and delivery include delivery cycle, delivery location, quality inspection item standards, acceptance threshold, and batch inspection requirements; the required parameters for cooperation and coordination include cooperation mode, settlement method, price range, and service cycle; and the required parameters for risk management include prepayment ratio, performance guarantee requirements, after-sales accountability standards, and abnormal handling requirements.

[0028] After a supplier submits business inquiry information through the client interaction module, the request parsing and routing module immediately retrieves a pre-defined standardized dataset of required parameters and performs a full-field matching and screening process on the business inquiry information. Simultaneously, it iterates through all required fields across four dimensions of the business inquiry information: production customization, quality inspection and delivery, cooperation coordination, and risk control. Each field is comprehensively compared and verified for completeness, format compliance, and parameter value validity to identify any information defects in each dimension.

[0029] During the verification process, the request parsing and routing module identifies anomalies in any dimension, such as blank fields, missing key fields, incorrect parameter formats, values ​​exceeding limits, or invalid fill content. This indicates that the current consultation information does not meet the full-dimensional access criteria. In this case, a corresponding prompt is given, requesting modification. If the prompt is ignored and the consultation continues, the request parsing and routing module will lock and terminate the current data flow, and will no longer perform net benefit calculations or other processing on this data. All consultation requests determined to have incomplete information dimensions are directly processed using standardized information push procedures for regular consultation types.

[0030] Only when all required fields across the four dimensions of production customization, quality inspection and delivery, cooperation and coordination, and risk management are complete, the parameter formats are standardized, and the values ​​are all within the legal range, will the request parsing and routing module archive the business consultation information in compliance and mark it as business information to be tested. The compliantly generated business information to be tested fully integrates the valid parameter content of the four dimensions and the supplier's specific consultation requirements description. Only this type of complete data across all dimensions can enter the subsequent multi-dimensional accounting, hierarchical judgment, and refined business type identification process, ensuring the accuracy and effectiveness of complex consultation identification results.

[0031] Furthermore, the process for determining the dynamic risk coefficient is as follows: The supplier risk benchmark coefficient is pre-configured, and multiple supplier credit gradient ranges are divided. Each gradient range is bound to a corresponding differentiated dynamic risk coefficient. Based on the transaction data, delivery data, and after-sales performance data after each order is fulfilled, the supplier's real-time credit score is calculated. Based on the real-time credit score, the corresponding supplier credit gradient range is matched to obtain the supplier's current dynamic risk coefficient.

[0032] The platform pre-configures the supplier risk parameter system, including three basic configurations: benchmark coefficient setting, credit gradient range division, and dynamic risk coefficient binding. First, based on the overall supply chain operational risk control standards, a supplier risk benchmark coefficient is set. This benchmark coefficient serves as a basic risk reference value under normal supply chain performance conditions. Second, according to the supply chain's long-term cooperation credit evaluation standards and considering differences in supplier performance capabilities, supplier credit ratings are divided into five standardized gradient ranges, from highest to lowest: excellent credit, good credit, compliant credit, general risk, and high risk. Each gradient range has a different credit score threshold range. Finally, a unique and differentiated dynamic risk coefficient is independently configured for each credit gradient range; the higher the credit gradient level and the lower the performance risk, the smaller the corresponding dynamic risk coefficient value.

[0033] The data storage module continuously collects performance ledger data for each completed order from various suppliers, covering three dimensions: transaction performance data, delivery execution data, and after-sales performance data. Transaction performance data includes order compliance, settlement performance rate, and contract terms fulfillment. Delivery execution data includes batch delivery on-time rate, delivery completeness, and production delivery compliance. After-sales performance data includes after-sales issue occurrence rate, issue rectification completion rate, after-sales response timeliness, and quality re-inspection pass rate. The big data statistical analysis module, based on the aforementioned existing performance ledger data, uses a weighted comprehensive scoring algorithm to quantify and calculate the supplier's real-time credit score, enabling the updating and iteration of the credit score.

[0034] The big data statistical analysis module calculates the supplier's real-time credit score using a weighted comprehensive scoring algorithm. The specific calculation formula is as follows: ; In the formula: This is the supplier's real-time credit score, with a score range of [0, 100]. A score greater than 100 is taken as 100, and a score less than 0 is taken as 0. , , These are the weighting coefficients for the transaction dimension, delivery dimension, and after-sales dimension, respectively. The sum of the three weighting coefficients is 1. In this embodiment... , , Adjustable; , , The penalty weights are for overdue payments, bulk rejections, and malicious order cancellations, with each category having a weight of 1.

[0035] A score is assigned to the transaction fulfillment dimension, with a fixed range of [0, 100]. This score is calculated based on the percentage of compliant transactions fulfilled by the supplier's historical completed orders. The formula for calculating the transaction compliance fulfillment rate is as follows: ; In the formula: To improve the compliance rate of transactions; This represents the total number of valid completed orders from suppliers within a preset statistical period. This is the number of orders that the supplier fully complies with within the preset statistical period.

[0036] Based on the transaction compliance rate, the final score for the transaction compliance dimension is calculated using the following formula: ; In the specific calculation process, the system filters all valid completed orders from suppliers using a preset statistical period, removing invalid orders that were canceled by users, terminated by the platform, or not yet effective. This score reflects the supplier's compliance level in transaction signing, contract performance, and settlement performance; the higher the compliance rate, the better. The higher the score.

[0037] For the delivery performance dimension scoring, the score range is fixed at [0, 100]. It is calculated by weighting and integrating two core indicators, on-time delivery rate and delivery compliance rate, with each indicator having a fixed weight of 0.5. The specific calculation formula is as follows: ; In the formula: To calculate the on-time delivery rate for customers, the statistical period is calculated as the number of order batches that were received on time as agreed, divided by the total number of order batches that arrived. To measure the customer's acceptance compliance rate, the statistical period is calculated as the number of compliant acceptance batches that have not been delayed or refused to cooperate with quality inspection, divided by the total number of delivered order batches.

[0038] In the specific accounting process, the system collects data on all formal delivery batches from suppliers within the statistical period, excluding sample deliveries, replenishment deliveries, and temporary allocation batches. The on-time delivery rate is among the metrics used. The delivery compliance rate is the ratio of the number of batches delivered on time to the total number of batches delivered. The percentage of qualified delivery batches whose specifications, quantity, material, and precision all meet the technical requirements of the order.

[0039] The after-sales performance evaluation score is fixed in the range of [0, 100] and is calculated by combining the after-sales rectification pass rate and the after-sales performance stability coefficient. The specific calculation formula is as follows: ; In the formula: To improve the after-sales rectification pass rate; This is the after-sales performance stability coefficient, with a value range of [0.8, 1.0].

[0040] This is a penalty item for overdue payments. Failure by the purchaser to make payment within the contractually agreed payment period will trigger a penalty, with a base penalty of 3 points for each instance of overdue payment. Multiple overdue payments for the same order will result in cumulative penalty points; multiple consecutive overdue payments will incur an additional 5-point penalty. ; In the formula: Total number of overdue payment orders; This is a continuous arrears indicator (1 if there are continuous arrears, 0 otherwise).

[0041] This is a penalty for unjustified batch rejections. If the buyer rejects an entire batch of goods without a quality basis, 10 points will be deducted for each batch rejection; if two or more batch rejections occur within a cycle, an additional 8 points will be deducted. ; In the formula: This refers to the number of times a batch of packages are refused without a reason. This is a flag indicating multiple rejections (1 for ≥2 rejections, 0 otherwise).

[0042] Points will be deducted for malicious order cancellation. Any act of malicious breach of contract by the buyer, such as unilaterally terminating an order, misappropriating prepayments, or refusing to settle payment after delivery, will result in a fixed deduction of 20 points per instance, with no upper limit on the number of deductions. ; In the formula: This refers to the number of orders maliciously canceled or unilaterally breached within the specified period.

[0043] In the specific accounting process, the system collects all formal after-sales account data within the statistical period, removing abnormal records such as natural wear and tear outside the warranty period and user-caused damage that are not the supplier's responsibility. After-sales rectification pass rate. The percentage of after-sales orders where suppliers complete rectification on time and according to standards out of the total number of valid after-sales orders; after-sales performance stability coefficient. The stability coefficient is determined based on the frequency of after-sales anomalies. The more times there are repeated after-sales anomalies or batch quality recurrences within a period, the lower the value of the stability coefficient.

[0044] Once each order fulfillment loop is completed, the system automatically collects three types of fulfillment data for that order, updates the corresponding dimension scores, and recalculates the supplier's real-time credit score to replace the historical score.

[0045] In this embodiment, the score range [0, 100] is evenly divided into five credit gradients. Each gradient range has a pre-set dynamic risk coefficient. The higher the gradient level, the better the performance credit, the lower the risk coefficient value, and the smaller the risk conversion loss. The specific gradient ranges and corresponding dynamic risk coefficients are configured as follows: Excellent credit range: score 90-100, matched with a dynamic risk coefficient of 1.05; Good credit range: score 80-89, matched with a dynamic risk coefficient of 1.10; Compliant credit range: score 70-79, matched with a dynamic risk coefficient of 1.15; Average risk range: score 60-69, matched with a dynamic risk coefficient of 1.20; High risk range: score 0-59, matched with a dynamic risk coefficient of 1.25.

[0046] Furthermore, the calculation process for net income is as follows: Extract order revenue data, comprehensive cost data, and prepayment deposit data from the business information to be tested, and calculate the business risk loss value by combining dynamic risk coefficients; based on the net income calculation formula, complete the cost deduction, risk loss conversion, and deposit hedging calculation in sequence, and output the net income data of the order.

[0047] This embodiment adopts a risk deposit coupled net income accounting logic, which comprehensively considers three influencing factors: inherent business costs, supplier credit performance risk losses, and prepayment deposit hedging compensation. It achieves standardized and differentiated quantitative accounting of business income corresponding to a single supply chain consultation, avoiding the calculation deviation problems of traditional fixed income accounting that do not distinguish supplier credit risk or deduct risk losses.

[0048] The specific formula for calculating net income from business operations is as follows: ; In the formula: This refers to the standardized net income corresponding to this consulting engagement; Total order revenue refers to the contractually approved revenue amount corresponding to this transaction. It is the sum of production costs and operation and maintenance costs, covering all comprehensive investments in product production consumables, equipment wear and tear, labor costs, and subsequent operation and maintenance support. It is obtained by adding up all comprehensive investments. The dynamic risk coefficient of the supplier is matched and locked for this business, which is obtained by matching five credit tiers based on the supplier's real-time credit score. To standardize and fix the baseline loss value for risk, and to set the basic risk loss value for routine supply chain business for the platform; This prepayment amount is used to offset some business losses and adjust the final revenue results.

[0049] This accounting formula uses a dynamic risk coefficient to adjust the base return for credit purposes, achieving a differentiated accounting effect that reduces risk losses for high-quality credit suppliers and increases risk losses for high-risk suppliers. It also incorporates a prepayment hedging compensation mechanism to obtain the most accurate possible net return value.

[0050] The specific accounting process is executed step by step according to the hierarchical operation logic: First, the request parsing and distribution module deducts the comprehensive operating costs from the total order revenue to obtain the basic business revenue value; second, the supplier dynamic risk coefficient K is introduced to complete the credit risk correction of the basic revenue, realizing the differentiated correction of revenue for suppliers with different credit ratings; third, the platform's preset standardized fixed risk benchmark loss RISK is deducted to quantify the revenue loss caused by the inherent performance risk of the business; finally, the prepaid deposit ADV is superimposed to complete the hedging compensation correction, eliminating interference variables such as fixed costs, credit risk, basic loss, and deposit deduction, to obtain objective and accurate standardized business net revenue data PRO.

[0051] Based on the standardized net revenue, the request parsing and distribution module further performs time-series conversion processing to calculate the unit-time revenue that can be used for horizontal comparison, serving as a quantitative basis for determining the business value level. The formula for calculating unit-time revenue is as follows: ; In the formula: This refers to the revenue per unit of time for this transaction. The full lifecycle duration of the order recorded in the business information to be tested refers to the total performance cycle duration of the business corresponding to this consultation, from the start of production to the completion of delivery and acceptance.

[0052] The revenue per unit time can eliminate the comparison bias caused by the difference in the fulfillment cycle of different orders, and uniformly convert the revenue of a single business into the output value per unit cycle, so as to objectively reflect the true profitability and output value of the business corresponding to this consultation.

[0053] Furthermore, the process for determining the business value level is as follows: Collect unit-time revenue data from all historical compliant transactions, perform anomaly cleaning on the original revenue sample, and construct a unit-time revenue statistical data set; perform hierarchical quantile calculations on the unit-time revenue statistical data set to iteratively generate two-level thresholds; based on real-time unit-time revenue data, compare and match the data with the two-level thresholds in turn, and divide the corresponding business value level according to the threshold matching results.

[0054] like Figure 3 As shown, the big data statistical analysis module periodically collects the unit time revenue corresponding to historical compliant orders retained by the data storage module. The sample data first undergoes standardized data cleaning. During cleaning, invalid samples such as extreme deviation data, platform-subsidized order data, and single-event abnormal loss orders are uniformly removed, retaining only valid sample data under normal fulfillment and regular revenue conditions to construct a statistical dataset of revenue per unit time. Supply chain companies face upper limits on production capacity, human resources, and equipment load, making it impossible to indiscriminately accept all consulting orders. Given limited capacity resources, the platform needs to dynamically select high-quality order resources: when capacity is saturated, high-profit business is prioritized to maximize the value of output per unit capacity; when capacity is not saturated, a balance between revenue level and capacity utilization is considered to fill empty capacity, avoid resource idleness, and achieve a two-way balance between optimal revenue and optimal capacity utilization.

[0055] Based on the statistical data set of revenue per unit time after cleaning completion, the big data statistical analysis module adopts a capacity-coupled quantile hierarchical statistical algorithm to adaptively iterate the two-level thresholds required for business value determination, namely the high-value determination threshold. Value judgment threshold The adaptive calculation formula for the two-level threshold of coupled production capacity status is as follows: ; ; In the formula: Threshold for determining the high-value level of a business; The threshold for determining the value level in the business; Q() is the quantile value function; This is a statistical data set of revenue per unit time after standardized cleaning. This represents the real-time capacity load factor, with a value range of [0,1]. The capacity load factor is... Calculated based on the ratio of a company's current occupied capacity to its total capacity, the higher the capacity ratio, the better. The larger the value, the stricter the threshold screening criteria. When = 1, the enterprise's capacity is fully saturated. The threshold quantile is taken as the upper limit, selecting only high-yield, high-quality orders and prioritizing maximizing capacity profitability. When the threshold is less than 1, the enterprise has spare capacity. The threshold quantile is adjusted downwards accordingly, and the business revenue access standards are appropriately relaxed. Under the premise of ensuring basic revenue, the enterprise can attract medium-profit businesses, fully utilize spare capacity, and avoid idle and wasted capacity resources. After each round of data collection, the system automatically updates the two-level thresholds based on the real-time capacity load coefficient, realizing the dynamic adaptive adjustment of the judgment criteria according to both capacity status and profitability level.

[0056] The request is to parse the two-level adaptive thresholds of the traffic splitting module, which are based on the completion of iterative updates and the coupled real-time capacity status, and to calculate the unit-time revenue of the current test service in real time. ,and , The business value is assessed through a step-by-step comparison, categorized into three levels based on the numerical range, to form a standardized level determination result that fits the company's current production capacity. The specific rules for determining the business value level are as follows: when ≥ At this time, it is identified as a high-value business level. The revenue per unit time of this level of business is significantly higher than the normal level. When the capacity is saturated, it can be given priority to accept orders to maximize the profitability of the capacity. When the capacity is not saturated, it can be prioritized to lock in orders and consolidate the overall revenue foundation.

[0057] when ≤ < At this stage, the business is classified as a medium-value business. Businesses at this level generate revenue within a reasonable range and can be taken on normally when capacity is not saturated, to fill spare capacity and improve overall capacity utilization. When capacity is saturated, the best businesses can be selected selectively, with priority given to ensuring the implementation of high-return businesses.

[0058] when < At this stage, the business is classified as a low-value business. Businesses at this level have low revenue potential and are directly filtered out when capacity is saturated to avoid consuming limited capacity resources and lowering overall profitability. They can only be taken on to a certain extent when there is a significant capacity gap, thus ensuring full utilization of capacity resources.

[0059] Furthermore, the process of obtaining the bearer verification result is as follows: Extract the service carrying requirement feature parameters from the service information to be tested, retrieve the carrying boundary parameter set stored in the preset information database, compare and match the service carrying requirement feature parameters with the carrying boundary parameter set field by field to obtain the overall matching overlap and over-limit situation; based on the overall matching overlap and over-limit situation, generate the corresponding carrying compliance verification result or carrying over-limit verification result, and output the carrying verification identifier.

[0060] like Figure 4 As shown, the request parsing and routing module extracts full-dimensional business capacity requirement feature parameters corresponding to this consulting service from the business information to be tested, serving as the demand-side basis for verifying the enterprise's performance capacity. The core requirement feature parameters extracted include the scale of business capacity requirements. Customized process requirements level Order delivery time limit requirements Quality inspection and acceptance standard level .

[0061] Simultaneously, the request parsing and distribution module retrieves the pre-stored, fixed enterprise capacity boundary parameter set within the supply chain-specific information retrieval module. This parameter set, pre-entered by the platform based on the enterprise's equipment capacity, technological level, delivery capability, and quality inspection capability, constitutes the supply-side boundary basis for capacity verification. Specifically, it includes: the enterprise's maximum capacity limit. The highest process level that the enterprise can adapt to Enterprise's shortest delivery time threshold Enterprise's highest quality inspection adaptation standard .

[0062] After completing the bidirectional extraction of requirement parameters and boundary parameters, the request parsing and distribution module performs a dimension-by-dimensional parameter comparison and verification, and statistically analyzes the compliance and excess dimensions.

[0063] The system uses a single-item direct threshold comparison method to perform compliance judgments dimension by dimension. The specific single-dimensional judgment rule is: capacity demand. ≤Capacity Limit Process requirement level ≤ Process compatibility level Delivery time limit ≥Delivery timeliness threshold Quality inspection requirement level ≤Quality Inspection Adaptation Standard If the conditions for the corresponding dimension are met, the performance requirements for that dimension are deemed compliant and the enterprise can handle them normally; otherwise, the dimensions are deemed to be out of bounds and the enterprise's existing capabilities cannot meet the business requirements for that dimension.

[0064] Based on the individual comparison and verification results of each dimension, the compliance and over-limit status of all verification dimensions are statistically analyzed, and two types of load verification labels are generated in a standardized manner. The specific judgment rules are as follows: when all four dimensions of capacity, process, delivery and quality inspection are compliant and there are no over-limit items, a load compliance label is output; when any one or more dimension parameters exceed the limit and cannot meet the load requirements of the corresponding dimension, a load over-limit label is output.

[0065] Furthermore, the process for determining the type of business information to be tested is as follows: Based on the business value level and the verification results, the preset layered coupling discrimination rule matrix is ​​retrieved. According to the combination relationship between the level and the compliance status of the carrier, the standardized classification judgment logic is executed to obtain the business information type identifier to be tested. The business information type identifier to be tested includes the conventional consultation type identifier and the complex consultation type identifier.

[0066] The platform pre-builds a standardized layered coupling discrimination rule matrix, which uses three levels of business value and two types of bearer verification identifiers as its two core discrimination dimensions. The request parsing and routing module, based on the obtained business value level (high value, medium value, low value) and bearer verification identifiers (bearer compliance identifier, bearer over-limit identifier), retrieves the platform's pre-built layered coupling discrimination rule matrix for linked matching and judgment. The specific process is as follows: Scenario 1: The business is of high value, and the verification result indicates compliance. This business offers significant advantages in terms of revenue per unit time and excellent profitability. Furthermore, the company's existing capacity, processes, delivery, and quality inspection capabilities are fully adapted to the business requirements. There is no risk of exceeding performance limits. The business handling standards and procedures are standardized, and the information database can fully respond. Therefore, this is classified as a routine inquiry.

[0067] Scenario 2: The business is at a mid-value level, and the compliance verification result indicates compliance. The revenue per unit time for this business is within the reasonable and normal range of the industry, the profitability is stable and controllable, and the company's performance capability can fully cover the business needs. There are no performance risks, and it belongs to the standard business that the platform routinely undertakes. The information database can fully respond, and it is judged to be a regular inquiry type.

[0068] Scenario 3: The business is of low value, and the verification result shows compliance. The revenue level of this business is low, but the company's capacity, process, delivery, and quality inspection are all within normal limits. There are no difficulties in fulfilling the contract or risks of exceeding limits. The only issue is the limited value of the business output, which requires customized manual handling. Therefore, it is classified as a complex consulting type.

[0069] Condition 4: Regardless of whether the business value level is high, medium, or low, if the capacity verification result is a capacity over-limit flag, this business transaction indicates that at least one fulfillment requirement exceeds the company's existing capacity boundaries, or the process standards are too high, the capacity requirements are exceeded, or the delivery / quality inspection requirements are incompatible. The standard fulfillment process cannot meet the business needs, resulting in fulfillment uncertainty and difficulty in handling the situation. Therefore, an automatic information matching system on the platform cannot provide a response, and this is classified as a complex inquiry.

[0070] Based on the four standardized coupling judgment scenarios mentioned above, the system provides corresponding business type identifiers, namely, a conventional consulting type identifier or a complex consulting type identifier. This dual-dimensional coupling judgment mode takes into account both the economic value attributes of the business and the enterprise's performance capacity attributes, ensuring that capacity resources are prioritized for high-quality businesses while accurately identifying complex businesses with performance barriers.

[0071] Furthermore, different operations are performed according to the type of business information to be tested, as detailed below: When identified as a regular consultation type, the system retrieves standardized business data, technical specifications, and process solutions from the preset information database, matches the current business information to be tested to generate standardized consultation response information, and pushes the standardized information to the supplier's terminal device. When identified as a complex consultation type, the system collects all parameters of the business information to be tested, the business value level, and the carrying verification results, integrates them to generate a complete business processing work order, and pushes the business processing work order to the corresponding business specialist's backend.

[0072] For services deemed routine consulting, given that their performance requirements perfectly match the company's capacity, the business processes are standardized, there is no risk of exceeding performance limits, and the revenue level is controllable, the platform activates a fully automated intelligent response mechanism without human intervention. First, the request parsing and routing module extracts structured features from the business information to be tested, such as core consulting keywords, business scenario tags, process requirements, and delivery quality inspection standards. These features are then used as precise search matching factors and simultaneously pushed to the supply chain-specific information retrieval module. Second, the supply chain-specific information retrieval module, relying on its internally stored standardized knowledge base, conducts multi-dimensional precise matching searches, specifically retrieving standardized process specifications, routine quality inspection procedures, compliant delivery guidelines, standardized cooperation and settlement schemes, and industry-standard technical Q&A materials highly relevant to the consulting scenario.

[0073] After the search is completed, the system removes redundant information and integrates, simplifies, and optimizes the retrieved data based on the specific needs of this consultation, generating a standardized and customized response report. Finally, the completed response is directly pushed to the supplier's front-end terminal through the client interaction module, while simultaneously retaining consultation records and response logs, completing the entire process for this routine consultation.

[0074] For services deemed complex, a specialized, manually customized handling process is initiated. The system first automatically collects all core data across all dimensions of the service, comprehensively summarizing the original consulting parameters, capacity requirements parameters, enterprise capacity boundary comparison details, service value level assessment results, risk coefficient matching data, service revenue calculation data, and other ledger information.

[0075] Subsequently, the request parsing and routing module integrates and encapsulates the collected full-dimensional business data into standardized special handling work orders. These work orders clearly indicate the dimensions exceeding business limits, the difficulties in the demand, the business value level, the performance risk points, and preliminary handling suggestions. They are then precisely pushed to the corresponding dedicated business specialist's backend via the manual processing reminder module, simultaneously triggering pop-up reminders and message pushes to ensure timely receipt and processing. After receiving the work order, the business specialist, relying on the complete ledger information of the work order, and combining it with the company's actual capacity allocation capabilities, customized process improvement level, flexible delivery solutions, and special quality inspection standards, conducts manual review and solution analysis. For the dimensions exceeding limits, targeted customized performance solutions are developed. Through customized operations such as manual interaction, demand consultation, process optimization, delivery schedule adjustment, and quality inspection standard adaptation, the special handling and Q&A for complex consulting business are completed.

[0076] Regardless of whether an automated or manually customized handling method is used for a single consultation, all business data, consultation content, matching materials, response results, and manual handling logs are synchronized and archived to the data storage module in real time after completion, forming a complete business data repository. The big data statistical analysis module periodically collects archived data according to a preset cycle, statistically analyzes high-frequency routine consultation questions, complex consultation bottlenecks, business value distribution patterns, and capacity matching pain points, and outputs standardized supply chain business optimization reports. The platform relies on continuous data updates, iterative analysis, and rule optimization to dynamically refine consultation judgment standards and handling mechanisms.

[0077] This embodiment filters out complex inquiries from interested customers that cannot be resolved by the information database's automatic responses by judging the type of information, thereby reducing the amount of invalid information processed manually and improving the efficiency of human customer service.

[0078] The processes described above with reference to the flowcharts in the embodiments disclosed in this invention can be implemented as computer software programs. The embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wire segments, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical fibers, RF, etc., or any suitable combination thereof.

[0079] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. 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 indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated 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 the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0080] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and explained in the embodiments. Without departing from the stated principles, the implementation of the present invention may have any variations or modifications.

Claims

1. A method for retrieving and consulting technical information, characterized in that, The method includes: Receive business inquiry information, verify the completeness of the business inquiry information, and use the complete business inquiry information as the business information to be tested; Based on the business information to be tested, and combined with the dynamic risk coefficient determined according to the supplier credit gradient, the net income is calculated through the net income calculation formula, and the unit time income is calculated in combination with the business cycle. The revenue per unit time is compared with the two-level threshold determined based on the revenue per unit time to determine the business value level. Based on the business carrying requirements in the business information to be tested, the carrying capacity is verified by combining relevant information in the preset information database to obtain the carrying verification result. Based on the business value level and business carrying requirements, the type of business information to be tested is determined. For business information under test classified as routine consultation, standardized information is generated based on the content in the information database and pushed to the supplier's terminal. For business information under test classified as complex consultation, manual processing work orders are generated and pushed to the corresponding business specialists.

2. The technical information retrieval and consultation method according to claim 1, characterized in that, The specific process for verifying the integrity of the business consultation information is as follows: Pre-build a standardized dataset of required parameters adapted to supply chain consulting scenarios, and standardize the parameter dimensions corresponding to various business consulting. Based on the standardized required parameter dataset, the received business consultation information is compared one by one to complete the full field matching screening, and the business consultation information with complete fields and compliant format is selected and output as the business information to be tested. Identify and intercept business inquiry information with missing fields or abnormal parameters, and terminate the current data flow chain.

3. The technical information retrieval and consultation method according to claim 2, characterized in that, The process for determining the dynamic risk coefficient is as follows: Pre-configure supplier risk benchmark coefficients, divide supplier credit gradient ranges into multiple tiers, and bind corresponding differentiated dynamic risk coefficients to each gradient range. Calculate the supplier's real-time credit score based on transaction data, delivery data, and after-sales performance data after each order is fulfilled. Based on the real-time credit score, the corresponding supplier credit gradient range is matched to obtain the supplier's current dynamic risk coefficient.

4. The technical information retrieval and consultation method according to claim 3, characterized in that, The calculation process for the net income is as follows: Extract order revenue data, comprehensive cost data, and prepayment data from the business information to be tested, and calculate the business risk loss value by combining dynamic risk coefficients. Based on the net profit calculation formula, the cost deduction, risk loss calculation and deposit hedging calculation are completed in sequence, and the net profit data of the order is output.

5. The technical information retrieval and consultation method according to claim 4, characterized in that, The process for determining the business value level is as follows: Collect unit-time revenue data for all compliant historical transactions, perform anomaly cleaning on the original revenue sample, and construct a unit-time revenue statistical data set; Based on the statistical data set of revenue per unit time, hierarchical quantile calculations are performed to iteratively generate two levels of thresholds; Based on real-time revenue data per unit time, numerical comparisons and matching are performed with two levels of thresholds in sequence, and the corresponding business value levels are determined according to the threshold matching results.

6. The technical information retrieval and consultation method according to claim 5, characterized in that, The process of obtaining the bearer verification result is as follows: Extract the service carrying requirement feature parameters from the service information to be tested, retrieve the carrying boundary parameter set stored in the preset information database, and compare and match the service carrying requirement feature parameters with the carrying boundary parameter set field by field to obtain the overall matching overlap and over-limit situation. Based on the overall matching overlap and over-limit situation, generate the corresponding load compliance verification result or load over-limit verification result, and output the load verification identifier.

7. The technical information retrieval and consultation method according to claim 6, characterized in that, The process of determining the type of the service information to be tested is as follows: Based on the business value level and the verification results, the preset layered coupling discrimination rule matrix is ​​retrieved. According to the combination relationship between the level and the compliance status of the carrier, the standardized classification judgment logic is executed to obtain the business information type identifier to be tested. The business information type identifier to be tested includes the conventional consultation type identifier and the complex consultation type identifier.

8. The technical information retrieval and consultation method according to claim 7, characterized in that, Different operations are performed according to the type of the service information to be tested, and the specific process is as follows: When identified as a regular consultation type, the system retrieves standardized business data, technical specifications, and process solutions from the preset information database, matches them with the current business information to be tested, generates standardized consultation response information, and pushes the standardized information to the supplier's terminal device. When identified as a complex consultation type, the system collects all parameters of the business information to be tested, the business value level, and the verification results, integrates them to generate a complete business processing work order, and pushes the business processing work order to the corresponding business specialist's backend.

9. A technical information retrieval and consulting service platform, characterized in that, The service platform is used to execute the technical information retrieval and consultation method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which is executed by a processor to implement the technical information retrieval and consultation method according to any one of claims 1-8.

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