Intelligent order receiving and dynamic settlement method and system based on multi-dimensional risk identification

Abstract

The invention relates to the field of order receiving and settlement, in particular to an intelligent order receiving and dynamic settlement method and system based on multi-dimensional risk identification. The method comprises the following steps: acquiring a multi-source auxiliary information set used by a scientific instrument, and based on the multi-source auxiliary information set, carrying out fusion quantitative analysis on scientific research behavior credibility of a researcher related to the request, an internal risk of an experimental scheme and a matching degree of a sample and a target instrument to generate a scientific research credibility evaluation result set; on the basis, order receiving risk income tradeoff and multi-dimensional composite settlement strategy construction are carried out, and an order receiving decision and dynamic settlement scheme set fusing graded order receiving conditions, a composite pricing unit and a risk hedging mechanism is generated; and on the basis, resource locking, permission configuration and contract generation operations are executed, and a structured order receiving and settlement report loaded with assessment traceability, process constraint and settlement terms is generated. In order receiving and settlement processes, win-win of the platform and researchers is realized, and standardized and large-scale development of the industry is facilitated.

Description

Technical Field

[0001] This application relates to the field of order acceptance and settlement, and in particular to intelligent order acceptance and dynamic settlement methods and systems based on multi-dimensional risk identification. Background Technology

[0002] In the field of scientific instrument sharing and leasing, the open sharing of high-value scientific research instruments has become a key support for accelerating scientific and technological innovation and optimizing resource allocation. The accuracy of order acceptance decisions and the rationality of settlement models are directly related to the platform's operational security, asset utilization efficiency, and the healthy development of the scientific research collaboration ecosystem. It is a core service link for promoting the deep integration of industry, academia, and research and the efficient utilization of scientific research infrastructure.

[0003] However, the existing order-acceptance decision-making and settlement methods of instrument sharing platforms lack a mechanism for integrating, quantifying, and dynamically weighing multi-dimensional information when dealing with scientific research service requests that are highly heterogeneous and have complex risk factors. This not only makes it difficult to achieve intelligent decision-making that optimizes risk and return, but may also lead to problems such as increased instrument wear and tear and higher dispute rates due to insufficient evaluation, thus restricting the platform's service capabilities and level of intelligence. Summary of the Invention

[0004] This application provides a method and system for intelligent order acceptance and dynamic settlement based on multi-dimensional risk identification to solve the above-mentioned technical problems.

[0005] Firstly, this application provides an intelligent order acceptance and dynamic settlement method based on multi-dimensional risk identification. The method includes: acquiring a multi-source auxiliary information set on the use of scientific instruments; based on the multi-source auxiliary information set on the use of scientific instruments, performing a fusion quantitative analysis on the credibility of the researcher's research behavior involved in this request, the inherent risk of the experimental plan, and the matching degree between the sample and the target instrument, generating a research credibility assessment result set; based on the research credibility assessment result set, performing a risk-reward trade-off for order acceptance and constructing a multi-dimensional composite settlement strategy, generating an order acceptance decision and dynamic settlement scheme set that integrates hierarchical order acceptance conditions, composite pricing units, and risk hedging mechanisms; based on the order acceptance decision and dynamic settlement scheme set, performing resource locking, permission configuration, and contract generation operations, generating a structured order acceptance and settlement report containing assessment traceability, process constraints, and settlement terms.

[0006] Through the above technical solutions, multi-dimensional assessments improve decision-making accuracy and reduce operational and safety risks; a dynamic settlement system matches risks and benefits, ensuring fairness; standardized processes regulate resource allocation and cooperation constraints, reducing disputes; ultimately, the platform's operational efficiency and service quality are improved, researchers' trust is enhanced, the efficient sharing of scientific instrument resources is promoted, a win-win situation for the platform and researchers is achieved, and the industry's standardized and large-scale development is facilitated.

[0007] Optionally, the generation of the scientific research credibility assessment result set includes: the multi-source auxiliary information set for scientific instrument use includes researchers' historical research records, structured experimental protocol data, and sample characteristics and instrument specifications data; guided by supporting the construction of the order acceptance risk-reward trade-off and multi-dimensional composite settlement strategy, based on the multi-source auxiliary information set for scientific instrument use, quantitative assessment of scientific research integrity, analysis of experimental protocol risk and feasibility, and sample-instrument suitability analysis are performed respectively, thereby generating a scientific research integrity score, protocol risk level, and sample-instrument fit index; based on the scientific research integrity score, the protocol risk level, and the sample-instrument fit index, the scientific research credibility assessment result set is generated, serving as a key quantitative basis for forming the order acceptance decision and dynamic settlement plan.

[0008] Optionally, the quantitative assessment of research integrity includes: extracting publicly published academic achievements and historical instrument usage records on the shared platform based on the researcher's historical research record data; analyzing academic misconduct and controversies in the academic achievement records to identify negative academic events, including paper retractions, corrections, and controversial comments, and analyzing the weight of negative academic events based on event type, journal authority, and time decay factor; analyzing the compliance and operational norms of the historical instrument usage records to assess whether the researcher complies with platform operating procedures, ends instrument usage on time, and uploads and records original experimental data truthfully, generating an operational compliance score; integrating the weight of the negative academic events and the operational compliance score to weight and correct the researcher's academic reputation index, generating the research integrity score.

[0009] Optionally, the risk and feasibility analysis of the experimental scheme includes: based on the structured experimental scheme data, using natural language processing technology to parse the scheme text, extracting key experimental steps, reagents and consumables used, and preset experimental parameter information; matching the structured experimental scheme data with a scientific experimental knowledge graph to identify procedures involving high-risk chemicals, extreme physical conditions, or potential damage to instruments, and classifying the risks according to the degree of hazard and probability of occurrence to generate an experimental operation risk list; comparing the key experimental steps with similar standard operating procedure databases and classic literature methods to identify defects or contradictions in the experimental scheme in terms of methodological logic, step completeness, or parameter rationality, and generating a methodological feasibility assessment conclusion; and combining the experimental operation risk list and the methodological feasibility assessment conclusion to generate the scheme risk level.

[0010] Optionally, the sample-instrument compatibility analysis includes: based on the sample characteristics and instrument specifications, determining whether the sample's concentration, purity, stability, and physical dimensions are within the instrument's effective measurable range according to the target instrument's preset optimal operating parameter range; based on the sample's pre-screening data characteristics, predicting whether the final experimental data can meet the accuracy, resolution, or publishability standards required to satisfy the user's declared research objectives through data quality and signal-to-noise ratio analysis; for the sample's corrosive, radioactive, or highly polluting properties, predicting the cumulative damage that this experiment may cause to the instrument based on the material and durability parameters of the instrument's core components, and relating it to maintenance costs and lifespan reduction; and generating a quantitative sample-instrument compatibility index by combining the determination results of the effective measurable range, the prediction results of data compliance, and the prediction results of instrument cumulative damage.

[0011] Optionally, the process of generating the order acceptance decision and dynamic settlement scheme set includes: based on the scientific research credibility assessment result set as input, optimizing the order acceptance decision conditions and generating dynamic settlement strategies respectively: in terms of order acceptance decision, jointly determining the scientific research integrity score and the scheme risk level to generate an intelligent order acceptance decision tree containing different order acceptance states; in terms of dynamic settlement, based on the sample-instrument matching index and combined with the correlation impact of the scheme risk level on potential losses and maintenance costs, generating a composite settlement strategy that integrates composite pricing units and dynamic rate adjustment mechanisms; and constructing the order acceptance decision and dynamic settlement scheme set based on the intelligent order acceptance decision tree and the composite settlement strategy.

[0012] Optionally, the generation of an intelligent order-accepting decision tree containing different order-accepting states includes: setting channel judgment thresholds associated with the research integrity score and the scheme risk level, and performing logical judgment and channel classification on the channel judgment thresholds of the current request: automatic approval channel, corresponding to the combination of a high research integrity score range and a low scheme risk level range, whose logic is to automatically accept orders and allocate resources for requests that meet this combination condition; condition review channel, corresponding to the combination of one or more of the research integrity score and the scheme risk level being in the medium risk range, whose logic is to accept orders only after attaching performance guarantees or operational supervision clauses to the request; risk assessment and referral channel, corresponding to the combination of an extremely low research integrity score range or an extremely high scheme risk level range and insufficient scheme innovation, whose logic is to generate processing opinions containing clear rejection suggestions and alternative scheme recommendations; matching the assessment result of the current request with the combination conditions of each channel, classifying it into the corresponding processing channel according to the matching result, and constructing the intelligent order-accepting decision tree.

[0013] Optionally, the generation of the composite settlement strategy integrating the composite pricing unit and the dynamic rate adjustment mechanism includes: generating a dynamic pricing strategy based on the sample-instrument fit index and the scheme risk level; and adding structured transaction and incentive terms to the initial settlement quote constructed by the dynamic pricing strategy to generate a complete composite settlement strategy. The dynamic pricing strategy includes: a basic pricing unit corresponding to the standard machine time rate of the target instrument, forming the settlement basis; a risk adjustment factor positively correlated with the scheme risk level and the instrument wear intensity predicted based on sample attributes, used to generate risk-added fees on top of the basic pricing unit; and a value incentive factor negatively correlated with the innovation coefficient of the experimental scheme and the expected data quality index predicted based on the sample pre-screening data characteristics, used to generate incentive fee deductions in the total settlement amount.

[0014] Optionally, the structured transaction and incentive terms include: additional service packages, selectively integrating value-added services such as technical guidance, sample preprocessing, or data analysis, along with corresponding fees, based on the risk level of the solution or user requests; data rights exchange options, providing an alternative settlement path to offset part of the settlement fees by disclosing desensitized non-core experimental data within a specified scope and period permitted by the user; and performance reward terms, stipulating that if the user's experimental process is compliant and the quality of the output data is assessed to meet a preset excellent standard, a partial refund of risk-added fees or an increase in future credit limits may be obtained.

[0015] Secondly, this application provides an intelligent order acceptance and dynamic settlement system based on multi-dimensional risk identification. The system includes: a credibility assessment module, used to acquire a multi-source auxiliary information set on the use of scientific instruments, and based on the multi-source auxiliary information set on the use of scientific instruments, to perform a fusion quantitative analysis on the credibility of the researcher's research behavior involved in this request, the inherent risk of the experimental plan, and the matching degree between the sample and the target instrument, generating a research credibility assessment result set; an order acceptance and settlement module, used to, based on the research credibility assessment result set, to perform a risk-reward trade-off for order acceptance and construct a multi-dimensional composite settlement strategy, generating an order acceptance decision and dynamic settlement scheme set that integrates hierarchical order acceptance conditions, composite pricing units, and risk hedging mechanisms; and a report generation module, used to, based on the order acceptance decision and dynamic settlement scheme set, to perform resource locking, permission configuration, and contract generation operations, generating a structured order acceptance and settlement report containing assessment traceability, process constraints, and settlement terms. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application; Figure 2 A flowchart illustrating an embodiment of this application of an intelligent order acceptance and dynamic settlement method based on multi-dimensional risk identification; Figure 3 This is a schematic diagram of the structure of an intelligent order-taking and dynamic settlement system based on multi-dimensional risk identification, provided as an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0020] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0021] The existing order-acceptance decision-making and settlement methods of instrument sharing platforms lack a mechanism for integrating, quantifying, and dynamically weighing multi-dimensional information when dealing with research service requests that are highly heterogeneous and have complex risk factors. This not only makes it difficult to achieve intelligent decision-making that optimizes risk and return, but may also lead to problems such as increased instrument wear and tear and higher dispute rates due to insufficient evaluation, thus restricting the service capabilities and intelligence level of the platform.

[0022] Based on this, this application provides an intelligent order acceptance and dynamic settlement method and system based on multi-dimensional risk identification. First, by aggregating researchers' historical records, experimental protocols, and sample instrument data, a multi-source information set is constructed. This set is then integrated and quantitatively analyzed to assess research credibility, protocol risk, and suitability, generating a structured evaluation result. Based on this result, the decision engine weighs the risks and benefits, dynamically generating a personalized contract scheme that integrates tiered order acceptance conditions, composite pricing units (such as base fees, risk surcharges, and innovation discounts), and risk hedging mechanisms. Finally, the system automatically executes corresponding instrument resource locking and operation permission configuration, generating a structured electronic report containing assessment traceability, process constraints, and settlement terms, which is then output to platform personnel, completing a closed loop from intelligent decision-making to contract performance management. Multi-dimensional evaluation improves decision-making accuracy and reduces operational and safety risks; the dynamic settlement system achieves risk-benefit matching, ensuring fairness; standardized processes regulate resource allocation and cooperation constraints, reducing disputes; ultimately, it improves platform operational efficiency and service quality, enhances researcher trust, promotes efficient sharing of scientific instrument resources, achieves a win-win situation for both the platform and researchers, and contributes to the standardized and large-scale development of the industry.

[0023] Figure 1 This is a schematic diagram illustrating an application scenario provided by this application. In the order acceptance and settlement process, this application utilizes the method provided to promote the efficient sharing of scientific instrument resources, achieving a win-win situation for both the platform and researchers, and contributing to the standardization and large-scale development of the industry.

[0024] Specifically, the method of this application is applied to any server that communicates with an intelligent operation platform. Through this server, the system obtains a multi-source auxiliary information set on scientific instrument usage provided by the intelligent operation platform. First, by aggregating researcher historical records, experimental protocols, and sample instrument data, a multi-source information set is constructed. This set is then fused and quantitatively analyzed to determine its research credibility, protocol risk, and suitability, generating a structured evaluation result. Based on this result, the decision engine weighs the risks and benefits, dynamically generating a personalized contract scheme that integrates tiered order acceptance conditions, composite pricing units (such as base fees, risk surcharges, and innovation discounts), and risk hedging mechanisms. Finally, the system automatically executes the corresponding instrument resource locking and operation permission configuration, generating a structured electronic report containing evaluation traceability, process constraints, and settlement terms, and outputting it to platform personnel, completing the closed loop from intelligent decision-making to contract performance management. Specific implementation methods can be found in the following embodiments.

[0025] Figure 2 This is a flowchart illustrating an embodiment of an intelligent order-taking and dynamic settlement method based on multi-dimensional risk identification provided in this application. The method of this embodiment can be applied to servers in the above scenarios. Figure 2 As shown, the method includes: S201. Obtain a multi-source auxiliary information set on the use of scientific instruments. Based on the multi-source auxiliary information set on the use of scientific instruments, conduct a fusion and quantitative analysis on the credibility of the researcher's research behavior, the inherent risks of the experimental plan, and the matching degree between the sample and the target instrument involved in this request, and generate a research credibility assessment result set.

[0026] Scientific instruments utilize multi-source auxiliary information sets, which are collections of relevant information from multiple channels that support research credibility assessment. These include researchers' historical research records, structured experimental protocol data, and sample characteristics and instrument specifications data, sourced from corresponding intelligent operating platforms. Researcher credibility is a comprehensive evaluation indicator of whether researchers follow standardized procedures, possess compliant operational capabilities, and have engaged in any dishonest behavior during research activities; it is one of the core dimensions of research credibility assessment. The inherent risk of an experimental protocol refers to the potential adverse consequences arising from problems in the design rationality, process safety, and resource consumption of the experimental protocol, such as instrument damage, experimental failure, and safety accidents. The sample-to-target-instrument matching degree refers to the degree of compatibility between the characteristics of the sample to be tested or used and the applicable scope, technical parameter requirements, and operating conditions of the target instrument, directly affecting the validity of experimental data and the operational safety of the instrument. The research credibility assessment result set is an information set formed through the fusion and quantitative analysis of multi-source auxiliary information, including a research credibility score, the inherent risk level of the experimental protocol, the sample-to-target-instrument matching coefficient, and a comprehensive assessment conclusion.

[0027] Specifically, in the scientific instrument sharing and leasing (non-sale) model, the core challenge for the platform lies in how to effectively manage operational risks (such as equipment damage, wasted time, and legal disputes) caused by instrument misuse, abuse, or user default while providing open services and promoting resource sharing. Existing technologies typically rely on manual review of order requests or simply match credit scores (such as deposit amounts) with time, resulting in limitations such as a single evaluation dimension, strong subjectivity, and delayed response. For example, it cannot effectively distinguish between a high-risk experimental protocol submitted by a researcher with good credit and a request from someone with a mediocre track record but a highly innovative protocol and a high sample matching degree. This solution addresses this core issue by systematically aggregating the aforementioned multi-source heterogeneous data through an intelligent operation platform and proposing a fusion quantitative analysis of three key and interrelated dimensions: the credibility of research behavior (reflecting the researcher's subjective integrity and willingness to fulfill obligations), the inherent risks of experimental protocols (reflecting the potential harm of objective experimental operations to instruments and the environment), and the sample-instrument matching degree (reflecting the technical feasibility of the task and the degree of equipment wear and tear). The necessity of this step lies in the fact that it transforms the traditionally vague and qualitative order acceptance judgment into a calculable and comparable quantitative evaluation process based on multi-dimensional evidence, providing an irreplaceable and structured intermediate result (i.e., a set of scientific research credibility evaluation results) for subsequent intelligent and refined risk-return decision-making.

[0028] S202. Based on the scientific research credibility assessment result set, conduct order acceptance risk-return trade-off and construct multi-dimensional composite settlement strategies to generate a set of order acceptance decision and dynamic settlement schemes that integrate hierarchical order acceptance conditions, composite pricing units and risk hedging mechanisms.

[0029] The risk-reward trade-off in order acceptance involves comprehensively considering the potential risks reflected in the research credibility assessment (such as instrument damage, experimental failure, and resource waste) and the potential benefits after acceptance (such as instrument usage fees, additional revenue from consumables, and long-term cooperation value) during the order acceptance decision-making process, thereby determining the order priority and cooperation conditions. A multi-dimensional, composite settlement strategy can be a settlement rule system integrating three core elements: tiered order acceptance conditions, composite pricing units, and risk hedging mechanisms. This system can be dynamically adjusted based on differences in risk levels, resource consumption, and service requirements of different orders. Tiered order acceptance conditions can be differentiated order acceptance standards set for orders with different risk levels and credibility levels based on the research credibility assessment results, including categories such as priority acceptance, acceptance with additional review conditions, and rejection. Composite pricing units can be diversified charging calculation units that integrate multiple pricing factors such as instrument usage time, consumable consumption, instrument wear coefficient, and risk level coefficient, differing from traditional single-dimensional pricing models. A risk hedging mechanism can be a protective system established to address potential risks and losses during order execution, including margin systems, special insurance linkages, and risk loss sharing clauses. An order acceptance decision and dynamic settlement solution set can be a structured set of solutions that directly guide order execution and fee settlement, including core elements such as tiered order acceptance conditions, composite pricing units, and risk hedging mechanisms.

[0030] Specifically, after obtaining the quantitative assessment results, the core decision-making issues facing the platform become: whether to accept the order? And under what price and terms? Existing leasing platforms mostly use fixed rates or simple segmented pricing models, resulting in a disconnect between price and risk. This fails to cover potential losses from high-risk experiments with high returns, nor can it incentivize high-quality, low-risk users with preferential treatment, leading to an imbalance in the platform's risk-return structure. The necessity of this step lies in its creative introduction of the concepts of "risk pricing" and "contract design" from the financial field into the scientific instrument leasing sector. It establishes a mechanism that directly and dynamically maps risk assessment results into business decisions and terms. This step serves as the core decision-making bridge connecting "risk identification" and "final action."

[0031] S203. Based on the order acceptance decision and dynamic settlement scheme set, perform resource locking, permission configuration and contract generation operations to generate a structured order acceptance and settlement report containing evaluation traceability, process constraints and settlement terms.

[0032] Structured order acceptance and settlement reports can be standardized reports that integrate assessment and traceability information, process constraints, and settlement terms, forming a clear logic, complete elements, and traceability. They serve as an important basis for collaboration between the platform and researchers for execution and subsequent traceability.

[0033] Specifically, decision-making solutions only generate value if they are faithfully and accurately executed. Existing technologies often suffer from a disconnect between decision-making and execution at this stage. For example, after an order is accepted, there is a lack of effective means to constrain user behavior, or settlement terms exist only in the form of a simple order, making it impossible to trace the original decision-making basis when disputes arise. The necessity of this step lies in ensuring that the generated intelligent decisions can be implemented securely, reliably, and auditably. It completes the final mile of the transition from "digital decision-making" to "physical world service delivery and the establishment of legal relationships." Without this rigorous and structured execution and report generation process, the value of all preceding intelligent analysis and decision-making will be significantly diminished, and may even lead to new risks due to execution deviations.

[0034] The method provided in this embodiment first constructs a multi-source information set by aggregating researchers' historical records, experimental protocols, and sample instrument data. This set is then quantitatively analyzed to assess research credibility, protocol risk, and suitability, generating a structured evaluation result. Based on this result, the decision engine weighs the risks and benefits, dynamically generating a personalized contract scheme that integrates tiered order acceptance conditions, composite pricing units (such as base fees, risk surcharges, and innovation discounts), and risk hedging mechanisms. Finally, the system automatically locks corresponding instrument resources, configures operating permissions, generates a structured electronic report containing evaluation traceability, process constraints, and settlement terms, and outputs it to platform personnel, completing a closed loop from intelligent decision-making to contract performance management. Multi-dimensional evaluation improves decision-making accuracy and reduces operational and safety risks; the dynamic settlement system matches risks and benefits, ensuring fairness; standardized processes regulate resource allocation and cooperation constraints, reducing disputes; ultimately, it improves platform operational efficiency and service quality, enhances researcher trust, promotes efficient sharing of scientific instrument resources, achieves a win-win situation for both the platform and researchers, and contributes to the standardized and large-scale development of the industry.

[0035] In some embodiments, scientific instruments utilize a multi-source auxiliary information set, including researchers' historical research records, structured experimental protocol data, and sample characteristics and instrument specifications data. Guided by the goal of supporting risk-benefit trade-offs in order acceptance and the construction of multi-dimensional composite settlement strategies, based on the multi-source auxiliary information set used by scientific instruments, quantitative assessments of research integrity, analysis of experimental protocol risks and feasibility, and sample-instrument compatibility analysis are conducted, thereby generating research integrity scores, protocol risk levels, and sample-instrument fit indices. Based on the research integrity scores, protocol risk levels, and sample-instrument fit indices, a research credibility assessment result set is generated, serving as a key quantitative basis for forming order acceptance decisions and dynamic settlement plans.

[0036] Quantitative assessment of research integrity can be based on researchers' historical research records, using quantitative analysis of dimensions such as academic conduct compliance and operational adherence to standards to arrive at an assessment process that objectively reflects the researcher's level of research integrity. Experimental protocol risk and feasibility analysis can utilize technologies such as natural language processing and knowledge graph matching to deeply analyze structured experimental protocol data, identify experimental operational risks, assess methodological rationality, and ultimately determine the risk level of the experimental protocol. Sample-instrument compatibility analysis can be based on sample characteristics and instrument specifications, comprehensively evaluating aspects such as sample measurability, experimental data compliance, and the impact of instrument wear and tear to generate a quantitative index of the degree of sample-instrument fit. Research integrity score can be a quantitative numerical value derived from the quantitative assessment of research integrity, used to characterize the researcher's level of research integrity. The value range can be set from 0 to 100 points, with higher scores representing higher integrity levels. Protocol risk level can be a classification result determined through experimental protocol risk and feasibility analysis, reflecting the degree of potential risk of the experimental protocol, typically divided into three levels: low risk, medium risk, and high risk. The sample-instrument fit index is an indicator that quantifies the degree of fit between sample characteristics and target instruments, derived from sample-instrument fit analysis. The value ranges from 0 to 1, with the index being closer to 1 indicating better fit.

[0037] Specifically, traditional research credibility assessment suffers from limitations such as simplistic and subjective approaches. Relying solely on static qualifications like academic credentials and professional titles, or simple manual reviews, it fails to comprehensively address researcher integrity shortcomings, hidden risks in experimental protocols, and sample-instrument compatibility issues. This leads to distorted assessment results and ineffective risk control. For instance, a researcher with a doctoral degree might have a history of academic misconduct, including the retraction of a core journal article three years prior. Furthermore, their submitted experimental protocol might involve highly hazardous chemicals like concentrated sulfuric acid without clearly defined protective procedures. Traditional assessment methods might overlook dynamic integrity records and hidden risks in the protocol, ultimately leading to safety incidents such as corrosion of core instrument components. Similarly, researchers with a history of frequent violations such as exceeding time limits and failing to upload accurate data, where traditional methods fail to quantify performance risks, result in wasted instrument usage time and missing data after accepting the order. To address the above issues, this step first obtains the structured experimental protocols uploaded by researchers through a scientific instrument sharing platform (including experimental steps, reagent lists such as 0.5 mol / L sodium hydroxide, preset parameters such as reaction temperature 80℃), and sample characteristic descriptions (such as purity 98%, weak corrosivity, and physical size 20 μm). Simultaneously, it connects to academic databases to obtain researchers' publicly published papers, patents, and other academic achievements. It also retrieves researchers' past instrument usage records from the platform's historical database (such as 5 uses and 2 instances of exceeding time limits in the past year). Natural language processing technology is used to parse the experimental protocol text, extracting key experimental steps and parameters. This is then matched against a scientific experimental knowledge graph covering a list of high-risk chemicals and prohibited instrument operations to identify and classify high-risk procedures (e.g., using highly toxic reagents without protective measures is considered high-risk). Simultaneously, the experimental steps are compared with similar standard operating procedure databases and classic... Literature comparison revealed a missing instrument calibration step, leading to a methodological feasibility conclusion that "optimization is needed." Based on academic record data, negative events such as paper retractions and controversial comments were identified (e.g., corrections of ordinary journal articles from two years ago, weighted at 0.2 after time decay). Combined with historical operational compliance (e.g., 3 compliant operations, 2 violations, compliance score of 70), a research integrity score of 78 was generated using preset weights. By comparing sample characteristics with instrument specifications (e.g., sample particle size 20μm, instrument effective measurable range 5-50μm), predicted data compliance (e.g., pre-review data signal-to-noise ratio 30dB, meeting publication standards), and instrument wear (estimated maintenance cost increases by 10% for weakly corrosive samples), a sample-instrument fit index of 0.8 was generated. Finally, the research integrity score, scheme risk level (medium risk), fit index, and detailed analysis results were integrated to form a structured research credibility assessment result set.

[0038] The method provided in this embodiment integrates multi-source auxiliary information to conduct multi-dimensional fusion quantitative analysis, systematically identifies these hidden risks, accurately characterizes the authenticity and credibility of scientific research projects, and provides a scientific basis for subsequent order acceptance decisions and settlement strategies, fundamentally solving the core pain points of traditional assessment's one-sidedness and insufficient risk identification.

[0039] In some embodiments, based on researchers' historical research records, publicly published academic achievements and historical instrument usage records on the sharing platform are extracted. Academic misconduct and controversy events are analyzed in the academic achievement records to identify negative academic events, including paper retractions, corrections, and controversial comments. The weight of negative academic events is analyzed based on event type, journal authority, and time decay factor. Performance and operational compliance analysis is performed on historical instrument usage records to assess whether researchers adhere to platform operating procedures, end instrument usage on time, and accurately upload and record original experimental data, generating an operational compliance score. The weight of negative academic events and the operational compliance score are integrated to weight and correct the researchers' academic reputation indicators, generating a research integrity score.

[0040] Academic achievement records can include publicly published academic outputs such as papers, books, patents, and research project completion reports by researchers in various academic journals and conferences. Historical instrument usage records can be the complete process records generated by researchers during the reservation and use of instruments on the scientific instrument sharing platform, including usage time, operational compliance, time commitment, and experimental data upload records. The time decay factor is a quantitative coefficient used to adjust the timeliness of the impact of negative academic events; that is, the degree of impact of a negative event on the current integrity assessment gradually decreases over time. This factor is determined through fitting analysis of historical assessment data and ranges from 0 to 1. The negative academic event weight is a quantitative value representing the degree of impact of the negative event on the researcher's integrity level, derived by comprehensively considering the severity of the event type, the journal's authority, and the time decay factor. It ranges from 0 to 1, with a larger value indicating a more significant impact. The performance and operational compliance analysis is a process of systematically evaluating whether researchers have complied with platform rules and fulfilled cooperation agreements during instrument use, based on their historical instrument usage records on the sharing platform. Timely completion of instrument use refers to whether researchers complete experiments within the reserved instrument usage period and do not exceed the allotted time. Operational compliance score is a quantitative indicator reflecting a researcher's level of platform cooperation compliance, calculated by quantifying their historical instrument usage behavior and operational standardization. The maximum score is 100 points, with higher scores indicating better compliance.

[0041] Specifically, traditional research integrity assessments rely on static qualifications such as academic qualifications and professional titles, which are subjective, one-sided, and lack a systematic quantitative framework. They fail to integrate negative events related to researchers' academic achievements with platform operation and performance records, leading to missed assessments of integrity risks. For example, a researcher with a professorship may have had a paper retracted from an SCI core journal two years ago due to data fabrication. Furthermore, the researcher had exceeded the platform's time limit three times and failed to upload experimental data accurately twice. Traditional assessments failed to identify these dynamic risks and yet still judged the researcher as having high integrity. After accepting an order, the researcher's violation of regulations resulted in damage to core instrument components, incurring high repair costs. To address the above issues, this step first connects to academic databases such as Web of Science and the platform's historical operation database to obtain researchers' publicly published academic achievements (e.g., 4 papers) and historical instrument usage records (e.g., 6 usage records). Natural language processing technology is used to analyze the academic achievements, identifying negative events such as retractions and corrections (e.g., corrections of ordinary journal papers from one year ago, with an event type weight of 0.3, a journal weight of 0.6, a time decay factor of 0.7, and a single item weight of 0.126). Operational performance is scored according to a standardized system: 30 points for compliance with procedures, 28 points for timely completion of work, and 38 points for accurate data upload, resulting in a compliance score of 96 points. A weighted correction is applied using 30% for negative academic events and 70% for compliance scores, resulting in a score of (1-0.126)×96×0.7+96×0.3=89.5 points, generating a research integrity score, which is then synchronized to the order-acceptance decision module in the microservice architecture.

[0042] The research integrity score generated through the method provided in this embodiment can be directly used as a core indicator for risk level determination in order acceptance decision-making. It also provides a basis for setting risk adjustment factors in dynamic settlement strategies, helping the platform to achieve differentiated management of "those with high integrity enjoy convenience, and those with low integrity are constrained", improving the platform's operational efficiency and risk control capabilities. At the same time, it guides researchers to standardize their academic behavior and cooperation performance, and promotes the healthy and sustainable development of the scientific instrument sharing industry.

[0043] In some embodiments, based on structured experimental protocol data, natural language processing technology is used to parse the protocol text, extract key experimental steps, reagents and consumables used, and preset experimental parameters. The structured experimental protocol data is matched with a scientific experimental knowledge graph to identify procedures involving high-risk chemicals, extreme physical conditions, or potential damage to instruments. Risks are then classified according to their degree of hazard and probability of occurrence, generating an experimental operation risk list. Key experimental steps are compared with similar standard operating procedure databases and classic literature methods to identify defects or contradictions in the experimental protocol's methodological logic, step completeness, or parameter rationality, generating a methodological feasibility assessment conclusion. Finally, the experimental operation risk list and the methodological feasibility assessment conclusion are combined to generate a protocol risk level.

[0044] Natural Language Processing (NLP) technology can be an artificial intelligence technology used to parse, extract, and understand human natural language text information, including functions such as text segmentation, keyword extraction, and semantic analysis, used to extract core information from experimental protocol texts. Structured experimental protocol data can be a standardized collection of information submitted by researchers according to the format specified by the scientific instrument sharing platform, containing core experimental elements, including experimental objectives, key steps, reagent and consumable lists, preset parameters, and safety precautions. A scientific experimental knowledge graph can be a structured knowledge network constructed by the platform, covering information such as experimental safety regulations, chemical properties, instrument operation contraindications, and standard experimental procedures, including core content such as a list of high-risk chemicals, safety thresholds for extreme physical conditions, and instrument damage risk association rules. An experimental operation risk list can be a structured risk record document that integrates identified high-risk procedures, hazard levels, probability of occurrence, and prevention and control recommendations. Methodological feasibility assessment conclusions can be qualitative judgments on the scientific design and operational feasibility of experimental protocols, including three categories: "fully feasible," "requires optimization and improvement," and "infeasible."

[0045] Specifically, traditional experimental protocol review relies on subjective human judgment, which has fatal flaws such as one-sided risk identification, insufficient methodological assessment, and low efficiency, failing to meet the refined risk management needs of scientific instrument sharing platforms. For example, in an organic synthesis experimental protocol submitted by a researcher, the use of highly corrosive hydrochloric acid at a concentration of 6 mol / L without mentioning protective measures, the preset reaction temperature of 160℃ exceeding the target instrument's rated upper limit of 130℃, and the omission of sample pretreatment purification steps, the human review focused only on the completeness of the experimental procedure and overlooked these critical issues, resulting in corrosion of the instrument's detection components during the experiment and experimental failure due to data distortion; another protocol set the detection wavelength of a certain spectrometer to 180nm, which was not covered by the instrument, and the human review failed to notice the parameter contradiction, causing the instrument to malfunction and shut down after startup, wasting valuable time. To address the above issues, this step first obtains the structured experimental protocol uploaded by researchers through the platform (including parameters such as 0.5 mol / L potassium chloride as reagent, 4 MPa reaction pressure, and 500 r / min stirring speed). Natural language processing (NLP) is used to segment and semantically analyze the protocol text, extracting key experimental steps (such as "sample digestion" and "chromatographic separation"), reagent and consumable lists, and preset parameter information. The extracted protocol data is then matched with the scientific experimental knowledge graph constructed by the platform (including a list of high-risk chemicals and instrument rated parameter ranges) to identify high-risk processes such as the use of highly corrosive reagents and operation beyond rated parameters. These processes are graded according to their severity (severe) and probability of occurrence (medium) to generate an experimental operation risk list. Next, the key steps are compared with similar standard operating procedure databases and classic literature methods. Missing blank control steps are identified, indicating methodological deficiencies, and an assessment conclusion of "needs optimization and improvement" is generated. Finally, combining the experimental operation risk list with the methodological feasibility conclusion, the protocol is classified as medium risk according to standardized rules and synchronized to the research credibility assessment module in the microservice architecture, providing core data support for the generation of the research credibility assessment result set.

[0046] The method provided in this embodiment systematically identifies high-risk situations and assesses the feasibility of methodologies based on technical means, accurately quantifies the risk level of solutions, and fundamentally solves the core pain points of traditional review, such as missed judgments, misjudgments, and strong subjectivity, providing a reliable basis for subsequent order acceptance decisions and risk control.

[0047] In some embodiments, based on sample characteristics and instrument specifications, and according to the preset optimal operating parameter range of the target instrument, it is determined whether the sample's concentration, purity, stability, and physical size are within the instrument's effective measurable range. Based on the sample's pre-screening data characteristics, through data quality and signal-to-noise ratio analysis, it is predicted whether the final experimental data can meet the accuracy, resolution, or publishability standards required to satisfy the user's declared research objectives. For the sample's corrosive, radioactive, or highly polluting properties, based on the material and durability parameters of the instrument's core components, it is predicted that the experiment may cause cumulative damage to the instrument, and this is related to maintenance costs and lifespan reduction. By combining the judgment results of the effective measurable range, the prediction results of data compliance, and the prediction results of cumulative instrument damage, a quantitative sample-instrument fit index is generated.

[0048] The preset optimal operating parameter range can be the optimal parameter interval set during the design of the target instrument to ensure detection accuracy and operational safety, including concentration adaptation range, purity requirement threshold, physical size compatibility range, and operating temperature and humidity range. The effective measurable range can be the parameter range within which the target instrument can accurately detect samples and output reliable data; it is a core subset of the preset optimal operating parameter range and is determined by the platform after calibration using actual instrument operating data. Pre-review data characteristics can be the basic data attributes of the samples submitted by researchers during preprocessing or pre-experiments, including data signal-to-noise ratio, signal strength, and fluctuation amplitude. Data quality and signal-to-noise ratio analysis can be an analytical process that assesses data reliability by quantifying the signal strength and noise interference ratio of the sample pre-review data; the signal-to-noise ratio is the ratio of signal strength to noise intensity, and a higher ratio indicates better data quality. Publishability standards can be experimental data quality specifications recognized by academic journals and other institutions, including data accuracy thresholds and resolution lower limits. Cumulative damage can be the gradual, cumulative damage to the instrument caused by sample attributes during the experiment, including component corrosion, decreased accuracy, and shortened lifespan; it is a result predicted based on the matching degree between sample characteristics and instrument materials.

[0049] Specifically, traditional sample-instrument compatibility assessment relies on subjective human experience and lacks systematic quantitative analysis. This leads to fatal flaws such as misjudgment of the effective measurable range, inadequate prediction of data compliance, and neglect of instrument wear and tear assessment, resulting in frequent experimental failures and instrument damage. For example, a researcher submitted a heavy metal sample with a concentration of 4 mg / L, while the target instrument's effective measurable range was 0.1-3 mg / L. Human error in misjudging the compatibility resulted in severely distorted experimental data and delays in research projects. In another instance, a weakly acidic sample (pH=3) was used on an instrument whose core components were made of ordinary carbon steel (tolerant to pH=4-10). The risk of corrosion was not assessed manually, leading to corrosion of the instrument's internal chambers after the experiment, resulting in high repair costs. To address the above issues, this step first obtains the sample characteristic data uploaded by the researcher (e.g., concentration 2 mg / L, purity 98%, weakly acidic pH=5, physical size 25 μm) and target instrument specifications (e.g., measurable concentration 0.5-3 mg / L, core component is polytetrafluoroethylene, pH tolerance 2-12, accuracy 0.001 mg / L); the sample characteristics are compared with the instrument's preset optimal operating parameters to determine if the sample concentration, physical size, etc., are within the effective measurable range; and the sample pre-screening data (e.g., signal-to-noise ratio 28 dB) is then used for further analysis. Data quality and signal-to-noise ratio analysis are performed to predict whether the data can meet research objectives (such as the 0.01 mg / L accuracy required by core journals); the corrosivity of the sample and the compatibility of the instrument material are assessed to predict slight instrument wear (an 8% increase in maintenance costs); the sample-instrument fit index of 0.91 is generated by weighting the effective measurable range (40%), data compliance (30%), and instrument wear (30%), i.e., 1×0.4+0.9×0.3+0.8×0.3=0.91, and synchronized to the research credibility assessment module.

[0050] The method provided in this embodiment generates a fit index based on multi-dimensional quantitative analysis, which can fundamentally solve the problems of traditional subjectivity and one-sidedness, ensure the quality of experimental data and the safety of instruments, and provide a scientific basis for subsequent order acceptance decisions and dynamic settlement.

[0051] In some embodiments, based on the research credibility assessment result set as input, the order acceptance decision conditions are optimized and the dynamic settlement strategy is generated respectively: In terms of order acceptance decision, the research integrity score and the solution risk level are jointly determined to generate an intelligent order acceptance decision tree containing different order acceptance states; In terms of dynamic settlement, based on the sample-instrument matching index and combined with the correlation between the solution risk level and potential losses and maintenance costs, a composite settlement strategy integrating composite pricing units and dynamic rate adjustment mechanisms is generated; Based on the intelligent order acceptance decision tree and the composite settlement strategy, an order acceptance decision and dynamic settlement scheme set is constructed.

[0052] Optimizing order acceptance decision conditions can be a process of setting differentiated and refined order acceptance criteria based on core quantitative indicators from research credibility assessment results, and optimizing the order acceptance process logic. This aims to improve the scientific rigor of order acceptance decisions and the accuracy of risk management. Dynamic settlement strategies can be a process of constructing a flexible and adjustable pricing rule system by combining dynamic factors such as sample-instrument compatibility and solution risk level, replacing the traditional fixed pricing model. The core is to achieve a match between "risk-return-value." Intelligent order acceptance decision trees can be order acceptance decision models with a logical branching structure, formed by using research integrity scores and solution risk levels as core judgment dimensions and dividing different order acceptance channels by setting thresholds. This can automatically match orders to the corresponding processing channels. Dynamic rate adjustment mechanisms can be rules for adjusting the weights and values ​​of various pricing factors based on indicators such as solution risk level and sample-instrument compatibility index, ensuring that rates match the actual situation of the order.

[0053] Specifically, traditional order acceptance decisions rely on subjective human judgment or a single indicator, and settlement uses a fixed rate. Furthermore, these two approaches are disconnected, resulting in fatal flaws such as an imbalance between risk and return, insufficient fairness, and low efficiency. For example, a researcher with a research integrity score of 88, a low-risk experimental protocol, and a sample-instrument fit index of 0.95 still requires multiple layers of manual review in the traditional model, delaying the experimental progress. On the other hand, for high-risk (high-risk protocol) and low-fit (fit index 0.6) orders, the fixed rate does not cover the cost of instrument depreciation, leading to platform losses. To address the above issues, this step first extracts core quantitative indicators from the research credibility assessment results: research integrity score (e.g., 88 points), scheme risk level (low risk), and sample-instrument fit index (0.95). For order acceptance decisions, a channel judgment threshold is set (integrity ≥ 85 points + low risk for automatic approval channel, 60-84 points + medium risk for conditional review channel, < 60 points + high risk for referral channel). The current order is then jointly determined to be placed in the automatic approval channel, generating an intelligent order acceptance decision tree. For dynamic settlement, the standard machine time rate of the target instrument (200 yuan / hour) is used as the basic pricing unit, and a risk adjustment factor is set based on the scheme risk level. For the low-risk (0.1) component, the risk-added fee is calculated as "basic cost × (1 + risk adjustment factor)". Then, based on the innovation coefficient of the experimental scheme (0.9) and the expected data quality index (0.98), the value incentive factor (0.2) is set. The deductible fee is calculated as "(basic cost + risk-added fee) × incentive factor". The initial price is 200 × 1.1 - 220 × 0.2 = 176 yuan / hour, with an additional data rights exchange clause (10% deduction for publicly anonymized data). Finally, the intelligent order-accepting decision tree and composite settlement strategy are integrated to generate a structured set of order-accepting decisions and dynamic settlement schemes, which are synchronized to the execution module in the microservice architecture.

[0054] The intelligent order-taking decision tree and composite settlement strategy provided in this embodiment are both built on clear rules and quantitative indicators, avoiding subjective errors caused by human intervention, ensuring consistency between decision-making and settlement results, integrating traceability information throughout the entire process, providing a complete basis for subsequent problem investigation and dispute resolution, and reducing management costs and legal risks.

[0055] In some embodiments, channel judgment thresholds are set that are associated with research integrity scores and scheme risk levels. Logical judgment and channel classification are then performed on the current request's channel judgment thresholds: Automatic approval channels correspond to combinations of high research integrity scores and low scheme risk levels, where requests meeting these conditions are automatically accepted and resources are allocated; condition review channels correspond to combinations where one or more of the research integrity score and scheme risk level fall within the medium-risk range, where requests are accepted only after attaching performance guarantees or operational supervision clauses; risk assessment and referral channels correspond to combinations of extremely low research integrity scores or extremely high scheme risk levels with insufficient scheme innovation, where processing opinions are generated that include explicit rejection suggestions and alternative recommendations. The assessment results of the current request are matched with the combination conditions of each channel, and based on the matching results, the request is classified into the corresponding processing channel, thus constructing an intelligent order acceptance decision tree.

[0056] Channel determination thresholds can be quantitative judgment standards set by the platform based on operational strategies, risk tolerance, and historical data fitting results, and are related to research integrity scores and solution risk levels. These standards are used to delineate the boundaries of different order-accepting channels, including high, medium, and low threshold ranges for research integrity scores and corresponding matching thresholds for solution risk levels. Logical judgment and channel classification can be the process of classifying orders into corresponding order-accepting channels according to preset logical rules, based on the order's research integrity score and solution risk level, and in accordance with the channel determination thresholds. Automatic approval channels can be fast order-accepting channels set up for orders with high research integrity levels and low experimental solution risks. The core logic is that order acceptance and resource allocation are completed automatically without additional manual review. Conditional review channels can be restrictive order-accepting channels set up for orders with research integrity scores or solution risk levels in the medium range. The core logic is that order acceptance can only be completed after attaching specific performance guarantee clauses. Risk assessment and referral channels can be order rejection processing channels set up for orders with extremely low research integrity levels, extremely high experimental solution risks, and lack of innovation. The core logic is to explicitly refuse order acceptance and provide alternative solutions.

[0057] Specifically, traditional order acceptance decisions rely on subjective human judgment, lacking standardized channel divisions and clear thresholds, resulting in fatal flaws such as low efficiency, unbalanced risk control, and poor user experience. For example, a researcher with a research integrity score of 92 (high integrity) and a low-risk experimental plan still requires multiple layers of manual review in the traditional model, delaying the experimental progress; another example is an order with a research integrity score of 48 (extremely low integrity) and a high-risk, uninnovative plan, which was mistakenly accepted due to the lack of clear rejection criteria, ultimately leading to damage to core instrument components due to improper operation. To address these issues, this step first extracts the research integrity score (e.g., 92 points) and the plan risk level (low risk) from the research credibility assessment results. Combining this with historical platform data and risk preferences, channel judgment thresholds are set: research integrity scores of 85-100 are high, 60-84 are medium, and 0-59 are extremely low, with plan risk levels categorized into low, medium, and high. The conditions for each channel combination are clearly defined: the automatic approval channel corresponds to high integrity and low risk; the condition review channel corresponds to medium integrity or medium risk; and the risk assessment and referral channel corresponds to extremely low integrity or high risk and a lack of innovative solutions. The order is determined according to the priority logic of "automatic approval → condition review → risk referral." For example, a score of 92+ (low risk) is assigned to the automatic approval channel, generating an "automatic order acceptance + priority resource allocation" result; a score of 75+ (medium risk) is assigned to the condition review channel, with an additional "payment of a 25% performance bond" clause; and a score of 52+ (high risk and lack of innovation) is assigned to the referral channel, generating rejection suggestions and alternative solutions. Finally, the thresholds, combination conditions, and judgment logic are integrated to construct a tree-structured intelligent order acceptance decision tree, which is synchronized to the order execution module in the microservice architecture.

[0058] The intelligent order-accepting decision tree, as provided in this embodiment, automates order-accepting decisions, significantly reducing manual review costs and enabling efficient handling of ever-increasing order volumes. Simultaneously, the traceability of the decision-making process provides data support for the platform to subsequently optimize threshold settings and improve channel rules, helping the platform continuously enhance its operational capabilities.

[0059] In some embodiments, a dynamic pricing strategy is generated based on the sample-instrument fit index and the scheme risk level. A complete composite settlement strategy is generated by adding structured transaction and incentive clauses to the initial settlement quote constructed by the dynamic pricing strategy. The dynamic pricing strategy includes: a basic pricing unit, corresponding to the standard machine time rate of the target instrument, forming the settlement basis; a risk adjustment factor, positively correlated with the scheme risk level and the instrument wear intensity predicted based on sample attributes, used to generate risk-added fees on top of the basic pricing unit; and a value incentive factor, negatively correlated with the experimental scheme's innovation coefficient and the expected data quality index predicted based on sample pre-screening data characteristics, used to generate incentive fee deductions in the total settlement amount.

[0060] Dynamic pricing strategies, based on dynamic indicators such as sample-instrument fit indices and solution risk levels, integrate three core elements: basic pricing, risk adjustment, and value incentives. This forms a flexible and adjustable fee calculation rule system and is a core component of composite settlement strategies. Structured transactions and incentive terms can be additional rules designed to enrich settlement models and balance the interests of the platform and users, including supplementary service packages, data rights exchange options, and performance reward terms. The basic pricing unit can be a fundamental fee standard based on the standard machine-hour rate of the target instrument. It serves as the settlement basis for dynamic pricing strategies, reflecting the operating costs (such as depreciation, energy consumption, and basic maintenance) under normal instrument usage. The risk adjustment factor can be a quantitative coefficient positively correlated with the solution risk level and the instrument wear intensity predicted by sample attributes, used to calculate the additional risk costs of high-risk orders. Risk surcharges can be additional charges calculated using the basic pricing unit and risk adjustment factor, used to cover potential losses such as excessive maintenance costs from high-risk orders. The value incentive factor can be a quantitative coefficient negatively correlated with the experimental design innovation coefficient and the expected data quality index. It is used to provide incentive fee deductions for high-quality orders, encouraging researchers to submit high-value and highly feasible experimental projects. The incentive fee deduction can be a fee reduction amount calculated by combining the basic pricing unit, risk-added fees, and the value incentive factor, representing a positive incentive from the platform for high-quality orders.

[0061] Specifically, the traditional settlement model uses a single fixed rate, charging only based on instrument usage time, completely ignoring differences in order risk, sample loss, and research value. This results in fatal flaws such as insufficient cost coverage, lack of fairness, and absence of incentive mechanisms. For example, a high-risk order (high-risk scheme) has highly corrosive samples with a sample-instrument compatibility index of only 0.6. After the experiment, the core components of the instrument need to be replaced, incurring repair costs of tens of thousands of yuan. However, the platform still charges a fixed rate of 200 yuan / hour, making the order revenue far from enough to cover the costs. In addition, high-quality orders (innovation coefficient 0.9, compatibility index 0.95) receive no incentives, leading to the loss of valuable users. To address the above issues, this step first extracts the core indicators: sample-instrument fit index (0.95), scheme risk level (low risk), experimental scheme innovation coefficient (0.9), and expected data quality index (0.98). The basic pricing unit is set as the target instrument standard machine time rate (200 yuan / hour). Risk adjustment factors are set based on historical risk data (0.1 for low risk, 0.3 for medium risk, and 0.5 for high risk). A value incentive factor is set based on the incentive objectives (0.2 for both high innovation and high data quality). The initial settlement price is calculated: if the experiment lasts 5 hours, the basic cost = 200 × 5. =1000 yuan, risk surcharge =1000×0.1=100 yuan, incentive fee deduction =(1000+100)×0.2=220 yuan, initial quote =1000+100-220=880 yuan; additional structured transaction and incentive terms: optional technical guidance service (500 yuan / time), data rights exchange (15% fee deduction for publicly anonymized data), performance reward (50% risk surcharge refund for compliant performance); finally, integrate the dynamic pricing strategy and additional terms to generate a complete composite settlement strategy, and synchronize it to the order acceptance decision and settlement scheme set construction module in the microservice architecture.

[0062] The dynamic rate adjustment mechanism provided in this embodiment can quickly respond to changes in instrument maintenance costs, market conditions, etc. By adjusting parameters such as the basic pricing unit and risk adjustment factor, it ensures that the settlement strategy always remains reasonable and competitive, avoiding the rigidity problem of traditional fixed rates.

[0063] In some embodiments, the additional service package selectively integrates value-added services and corresponding fees, including technical guidance, sample preprocessing, or data analysis, depending on the risk level of the solution or user request; the data rights exchange option allows for the disclosure of de-identified non-core experimental data within a specified scope and period permitted by the user, serving as an alternative settlement path to offset part of the settlement fees; and the performance reward clause stipulates that if the user's experimental process is compliant and the quality of the output data is assessed to meet a preset excellent standard, a partial refund of the risk-added fees or an increase in future credit limits may be obtained.

[0064] Additional service packages can be selectively integrated combinations of value-added services and corresponding pricing standards based on the risk level of the solution or explicit user requests. Core services include technical guidance, sample preprocessing, and data analysis, providing support for scientific research. Data rights exchange options can be alternative settlement paths where researchers agree to publicly disclose anonymized non-core experimental data within a specific scope and timeframe to offset part of the settlement fee; this serves as an incentive mechanism to promote the sharing of scientific research data. Anonymized non-core experimental data can be data that has undergone privacy information removal and core parameter encryption of the original experimental data, ensuring it does not affect the researcher's core research results and can be publicly shared. Performance incentive clauses can be incentive clauses that stipulate researchers can receive a risk-added fee refund or future credit limit increases when their experimental process is compliant and the quality of the output data meets standards; these clauses are used to guide researchers to fulfill their obligations in a standardized manner.

[0065] Specifically, the traditional settlement model only provides basic instrument usage fees, lacking value-added services, data sharing incentives, and compliance rewards. It suffers from fatal flaws such as limited service offerings, data silos, and insufficient guidance. For example, a researcher submitting a high-risk experimental protocol but lacking operational experience urgently needs technical guidance, but the traditional model offers no corresponding services, leading to instrument damage due to operational errors during the experiment. Another researcher produces high-quality data that meets the standards of core journals, but is unwilling to share it due to the lack of incentive mechanisms, resulting in a waste of data resources. Meanwhile, compliant and high-quality users do not receive any preferential treatment and lack the motivation to continue operating in a standardized manner. To address the above issues, this step first designs three additional service packages based on the risk level of the solution (e.g., high risk) and user requests: technical guidance (500 RMB / session, including 2 hours of operation guidance), sample pretreatment (300 RMB / sample, including purification and concentration calibration), and data analysis (800 RMB / set, including data cleaning and visualization), which users can choose independently. Next, data rights exchange rules are established: researchers are required to publicly disclose anonymized non-core data (masking sensitive parameters), with a sharing period of 1-3 years (optional). Those with excellent data quality will have 15% of the settlement fee deducted. Simultaneously, performance reward clauses are set: requiring compliant experimental procedures (no violations, timely completion of testing time) and data compliance (meeting core journal standards). Those who meet the standards will receive a 50% risk surcharge refund and an increased platform credit limit (20% discount on subsequent order risk surcharges). Finally, these three clauses are integrated with a composite settlement strategy, clarifying applicable conditions and operational procedures, and synchronized to the contract generation module in the microservice architecture.

[0066] The structured terms and conditions provided in this embodiment clearly define the service content, pricing standards, incentive rules, and operational procedures, achieving standardized management of transactions and incentives. At the same time, the precise matching of terms and conditions with order risk levels and user needs demonstrates the platform's refined operational capabilities, providing a solid foundation for subsequent service optimization and rule iteration.

[0067] Figure 3This is a schematic diagram of the structure of an intelligent order-taking and dynamic settlement system based on multi-dimensional risk identification provided in an embodiment of this application, as shown below. Figure 3 As shown, the intelligent order acceptance and dynamic settlement system 300 based on multi-dimensional risk identification in this embodiment includes: a credibility assessment module 301, an order acceptance and settlement module 302, and a report generation module 303.

[0068] The credibility assessment module 301 is used to acquire a multi-source auxiliary information set on the use of scientific instruments. Based on the multi-source auxiliary information set on the use of scientific instruments, it performs a fusion quantitative analysis on the credibility of the researcher's research behavior involved in this request, the inherent risk of the experimental plan, and the matching degree between the sample and the target instrument, generating a research credibility assessment result set. The order acceptance and settlement module 302 is used to construct a multi-dimensional composite settlement strategy based on the research credibility assessment result set, and generate an order acceptance decision and dynamic settlement scheme set that integrates hierarchical order acceptance conditions, composite pricing units, and risk hedging mechanisms. The report generation module 303 is used to perform resource locking, permission configuration, and contract generation operations based on the order acceptance decision and dynamic settlement scheme set, and generate a structured order acceptance and settlement report containing assessment traceability, process constraints, and settlement terms.

[0069] Optionally, when generating the research credibility assessment result set, the credibility assessment module 301 is specifically used for: the scientific instrument using a multi-source auxiliary information set including researchers' historical research records, structured experimental protocol data, and sample characteristics and instrument specifications data; guided by supporting the construction of the order acceptance risk-reward trade-off and multi-dimensional composite settlement strategy, based on the scientific instrument using a multi-source auxiliary information set, conducting quantitative assessment of research integrity, analysis of experimental protocol risk and feasibility, and sample-instrument compatibility analysis, thereby generating a research integrity score, protocol risk level, and sample-instrument fit index respectively; and generating the research credibility assessment result set based on the research integrity score, the protocol risk level, and the sample-instrument fit index, as a key quantitative basis for forming the order acceptance decision and dynamic settlement plan.

[0070] Optionally, the credibility assessment module 301, when conducting the quantitative assessment of research integrity, is specifically used for: extracting publicly published academic achievements and historical instrument usage records on the shared platform based on the researcher's historical research record data; analyzing academic misconduct and controversies in the academic achievement records to identify negative academic events, including paper retractions, corrections, and controversial comments, and analyzing the weight of negative academic events based on event type, journal authority, and time decay factor; analyzing the compliance and operational norms of the historical instrument usage records to assess whether the researcher complies with platform operating procedures, ends machine time on time, and truthfully uploads and records original experimental data, generating an operational compliance score; integrating the negative academic event weights and the operational compliance score to weight and correct the researcher's academic reputation indicators, generating the research integrity score.

[0071] Optionally, the credibility assessment module 301, when analyzing the risks and feasibility of the experimental scheme, is specifically used for: parsing the scheme text using natural language processing technology based on the structured experimental scheme data, extracting key experimental steps, reagents and consumables used, and preset experimental parameter information; matching the structured experimental scheme data with a scientific experimental knowledge graph, identifying procedures involving high-risk chemicals, extreme physical conditions, or potential damage to instruments, and classifying the risks according to the degree of hazard and probability of occurrence, generating an experimental operation risk list; comparing the key experimental steps with a database of similar standard operating procedures and classic literature methods, identifying defects or contradictions in the experimental scheme in terms of methodological logic, step completeness, or parameter rationality, generating a methodological feasibility assessment conclusion; and combining the experimental operation risk list with the methodological feasibility assessment conclusion to generate the scheme risk level.

[0072] Optionally, the credibility assessment module 301, when performing the sample-instrument compatibility analysis, is specifically used for: determining whether the sample's concentration, purity, stability, and physical size are within the instrument's effective measurable range based on the sample characteristics and instrument specifications, according to the target instrument's preset optimal operating parameter range; predicting whether the final experimental data can meet the accuracy, resolution, or publishability standards required to satisfy the user's declared research objectives through data quality and signal-to-noise ratio analysis, based on the sample's corrosive, radioactive, or highly polluting properties; predicting the cumulative damage that the experiment may cause to the instrument, and relating it to maintenance costs and lifespan reduction, based on the material and durability parameters of the instrument's core components; and generating a quantitative sample-instrument compatibility index by combining the judgment results of the effective measurable range, the prediction results of data compliance, and the prediction results of instrument cumulative damage.

[0073] Optionally, the order acceptance and settlement module 302, during the generation process of the order acceptance decision and dynamic settlement scheme set, is specifically used for: optimizing the order acceptance decision conditions and generating dynamic settlement strategies based on the scientific research credibility assessment result set as input; regarding the order acceptance decision, jointly determining the scientific research integrity score and the scheme risk level to generate an intelligent order acceptance decision tree containing different order acceptance states; regarding the dynamic settlement, based on the sample-instrument fit index and combined with the correlation between the scheme risk level and potential losses and maintenance costs, generating a composite settlement strategy that integrates a composite pricing unit and a dynamic rate adjustment mechanism; and constructing the order acceptance decision and dynamic settlement scheme set based on the intelligent order acceptance decision tree and the composite settlement strategy.

[0074] Optionally, when generating an intelligent order-accepting decision tree containing different order-accepting states, the order-accepting and settlement module 302 is specifically used to: set a channel judgment threshold associated with the scientific research integrity score and the scheme risk level, and perform logical judgment and channel classification on the channel judgment threshold of the current request: automatic approval channel, corresponding to the combination of high scientific research integrity score and low scheme risk level, whose logic is to automatically accept orders and allocate resources for requests that meet this combination condition; condition review channel, corresponding to the combination of one or more of the scientific research integrity score and the scheme risk level being in the medium risk range, whose logic is to accept orders only after attaching performance guarantees or operation supervision clauses to the request; risk assessment and referral channel, corresponding to the combination of extremely low scientific research integrity score or extremely high scheme risk level and insufficient scheme innovation, whose logic is to generate processing opinions containing clear rejection suggestions and alternative scheme recommendations; match the assessment result of the current request with the combination conditions of each channel, classify it into the corresponding processing channel according to the matching result, and construct the intelligent order-accepting decision tree.

[0075] Optionally, when the order receiving and settlement module 302 generates a composite settlement strategy based on the integrated composite pricing unit and dynamic rate adjustment mechanism, it is specifically used to: generate a dynamic pricing strategy based on the sample-instrument fit index and the scheme risk level; and add structured transaction and incentive terms to the initial settlement quotation constructed by the dynamic pricing strategy to generate a complete composite settlement strategy. The dynamic pricing strategy includes: a basic pricing unit, corresponding to the standard machine time rate of the target instrument, forming the settlement basis; a risk adjustment factor, positively correlated with the scheme risk level and the instrument wear intensity predicted based on sample attributes, used to generate risk-added fees on top of the basic pricing unit; and a value incentive factor, negatively correlated with the innovation coefficient of the experimental scheme and the expected data quality index predicted based on the sample pre-screening data characteristics, used to generate incentive fee deductions in the total settlement amount.

[0076] Optionally, the order receiving and settlement module 302, based on the structured transaction and incentive terms, is specifically used for: additional service packages, selectively integrating value-added service items and corresponding fees, including technical guidance, sample preprocessing, or data analysis, according to the risk level of the solution or user requests; data rights exchange options, disclosing desensitized non-core experimental data within the agreed scope and period allowed by the user as an alternative settlement path to offset part of the settlement fees; and performance reward terms, stipulating that if the user's experimental process is compliant and the quality of the output data is evaluated to reach a preset excellent standard, then a partial refund of risk-added fees or an increase in future credit limits can be obtained.

[0077] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. A smart order acceptance and dynamic settlement method based on multi-dimensional risk identification, characterized in that, include: Obtain a multi-source auxiliary information set on the use of scientific instruments. Based on the multi-source auxiliary information set on the use of scientific instruments, perform a fusion quantitative analysis on the credibility of the researcher's research behavior, the inherent risk of the experimental plan, and the matching degree between the sample and the target instrument involved in this request, and generate a research credibility assessment result set. Based on the research credibility assessment result set, a risk-reward trade-off for order acceptance and a multi-dimensional composite settlement strategy are constructed, generating a set of order acceptance decision and dynamic settlement schemes that integrate tiered order acceptance conditions, composite pricing units and risk hedging mechanisms. Based on the order acceptance decision and dynamic settlement scheme set, resource locking, permission configuration and contract generation operations are performed to generate a structured order acceptance and settlement report containing evaluation traceability, process constraints and settlement terms.

2. The method according to claim 1, characterized in that, The generated set of research credibility assessment results includes: The scientific instruments use a multi-source auxiliary information set, including researchers' historical research records, structured experimental protocol data, and sample characteristics and instrument specifications data. Guided by the goal of supporting the risk-reward trade-off of order acceptance and the construction of a multi-dimensional composite settlement strategy, based on the use of multi-source auxiliary information sets for the scientific instruments, we conduct quantitative assessment of scientific research integrity, analysis of experimental scheme risk and feasibility, and analysis of sample-instrument compatibility, thereby generating scientific research integrity scores, scheme risk levels, and sample-instrument compatibility indices. Based on the research integrity score, the risk level of the plan, and the sample-instrument fit index, the research credibility assessment result set is generated, which serves as the key quantitative basis for forming the order acceptance decision and dynamic settlement plan.

3. The method according to claim 2, characterized in that, The quantitative assessment of research integrity includes: Based on the researchers' historical research records, extract the researchers' publicly published academic achievements and historical instrument usage records on the shared platform; The academic achievement records are analyzed for academic misconduct and controversy, identifying negative academic events including paper retractions, corrections, and controversial comments, and the weight of negative academic events is analyzed based on event type, journal authority, and time decay factor. The historical instrument usage records are analyzed for compliance and operational norms to assess whether the researchers have complied with the platform's operating procedures, ended the instrument time on time, and uploaded and recorded the original experimental data accurately, thereby generating an operational compliance score. By integrating the weights of the negative academic events with the operational compliance score, the researcher's academic reputation index is weighted and corrected to generate the research integrity score.

4. The method according to claim 2, characterized in that, The risk and feasibility analysis of the experimental plan includes: Based on the structured experimental scheme data, natural language processing technology is used to parse the scheme text and extract key experimental steps, reagents and consumables used, and preset experimental parameters. The structured experimental scheme data is matched with a scientific experimental knowledge graph to identify processes involving high-risk chemicals, extreme physical conditions, or potential damage to instruments. The risks are then classified according to the degree of hazard and the probability of occurrence to generate an experimental operation risk list. The key experimental steps are compared with similar standard operating procedure databases and classic literature methods to identify defects or contradictions in the experimental scheme in terms of methodological logic, step completeness or parameter rationality, and to generate a methodological feasibility assessment conclusion. Based on the combined experimental operation risk list and the methodological feasibility assessment conclusions, the risk level of the proposed scheme is generated.

5. The method according to claim 2, characterized in that, The sample-instrument compatibility analysis includes: Based on the sample characteristics and instrument specifications, and according to the preset optimal operating parameter range of the target instrument, it is determined whether the sample concentration, purity, stability, and physical size are within the effective measurable range of the instrument. Based on the characteristics of the sample's pre-screening data, data quality and signal-to-noise ratio analysis are used to predict whether the final experimental data can meet the accuracy, resolution, or publishability standards required to satisfy the research objectives declared by the user. Based on the corrosive, radioactive, or highly contaminating properties of the samples, and according to the material and durability parameters of the instrument's core components, the cumulative damage that this experiment may cause to the instrument is predicted, and this is related to maintenance costs and lifespan reduction. Based on the combined results of the determination of the effective measurable interval, the prediction of data compliance, and the prediction of cumulative instrument wear, a quantitative sample-instrument fit index is generated.

6. The method according to claim 5, characterized in that, The process of generating the order acceptance decision and dynamic settlement scheme set includes: Based on the research credibility assessment result set as input, the order acceptance decision conditions are optimized and the dynamic settlement strategy is generated respectively: Regarding the order acceptance decision, the scientific research integrity score and the risk level of the plan are jointly determined to generate an intelligent order acceptance decision tree that includes different order acceptance states. Regarding the dynamic settlement, based on the sample-instrument compatibility index and combined with the correlation between the scheme risk level and potential losses and maintenance costs, a composite settlement strategy integrating composite pricing units and dynamic rate adjustment mechanisms is generated. Based on the intelligent order acceptance decision tree and the composite settlement strategy, the order acceptance decision and dynamic settlement scheme set is constructed.

7. The method according to claim 6, characterized in that, The generation of the intelligent order-accepting decision tree, which includes different order-accepting states, includes: Set a channel determination threshold that is associated with the research integrity score and the risk level of the scheme, and perform logical determination and channel classification on the currently requested channel determination threshold: The automatic approval channel corresponds to the combination of high scientific research integrity score range and low scheme risk level range. Its logic is to automatically accept orders and allocate resources for requests that meet the conditions of this combination. The condition review channel corresponds to a combination of one or more of the research integrity score and the scheme risk level being in the medium risk range. The logic is that the order can only be accepted after the request is attached with performance guarantee or operation supervision clause. The risk assessment and referral channel is for combinations that fall within the range of extremely low research integrity scores or extremely high risk levels and lack innovation. Its logic is to generate processing opinions that include clear rejection suggestions and alternative recommendations. The evaluation result of the current request is matched with the combination conditions of each channel, and the request is classified into the corresponding processing channel based on the matching result to construct the intelligent order acceptance decision tree.

8. The method according to claim 6, characterized in that, The composite settlement strategy that generates and integrates the composite pricing unit and the dynamic rate adjustment mechanism includes: Based on the sample-instrument fit index and the risk level of the solution, a dynamic pricing strategy is generated. On the basis of the initial settlement quotation constructed by the dynamic pricing strategy, structured transactions and incentive clauses are added to generate a complete composite settlement strategy. The dynamic pricing strategy includes: The basic pricing unit, corresponding to the standard machine time rate of the target instrument, forms the basis for settlement. A risk adjustment factor, which is positively correlated with the risk level of the scheme and the instrument wear intensity predicted based on sample attributes, is used to generate risk surcharges on top of the basic pricing unit. The value incentive factor, which is negatively correlated with the innovation coefficient of the experimental scheme and the expected data quality index predicted based on the characteristics of the sample pre-screening data, is used to generate incentive expense deductions in the total settlement amount.

9. The method according to claim 8, characterized in that, The structured transaction and incentive terms include: Additional service packages may be selectively integrated, including value-added services such as technical guidance, sample pretreatment, or data analysis, and corresponding fees, depending on the risk level of the proposed solution or user requests. The data rights exchange option allows users to disclose their de-identified non-core experimental data within a specified scope and timeframe, providing an alternative settlement path to offset part of the settlement fees. The performance reward terms stipulate that if a user's experimentation process is compliant and the quality of the output data is assessed to meet the preset excellent standards, the user can receive a partial refund of the risk-added fees or an increase in future credit limits.

10. An intelligent order-taking and dynamic settlement system based on multi-dimensional risk identification, characterized in that: The method applied to any one of claims 1-9 includes: The credibility assessment module is used to acquire a multi-source auxiliary information set on the use of scientific instruments. Based on the multi-source auxiliary information set on the use of scientific instruments, it performs a fusion quantitative analysis on the credibility of the researcher's research behavior, the inherent risk of the experimental plan, and the matching degree between the sample and the target instrument involved in this request, and generates a research credibility assessment result set. The order acceptance and settlement module is used to weigh the risks and benefits of order acceptance and construct a multi-dimensional composite settlement strategy based on the scientific research credibility assessment result set, and generate a set of order acceptance decision and dynamic settlement schemes that integrate hierarchical order acceptance conditions, composite pricing units and risk hedging mechanisms. The report generation module is used to perform resource locking, permission configuration and contract generation operations based on the order acceptance decision and dynamic settlement scheme set, and generate a structured order acceptance and settlement report containing evaluation traceability, process constraints and settlement terms.

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