Multi-system collaborative data cross validation management system

Abstract

The invention discloses a multi-system collaborative data cross validation management system, and relates to the technical field of logistics data management, and the system comprises an initial credibility grading module which is used for obtaining a data stream and a preset static source reliability factor, and calculating an initial credibility score; the evidence weight dynamic quantification module is used for calculating a timeliness attenuation factor, matching a current service scene, distributing a service rule adjustment factor and determining a final evidence weight score of the data point; the gold record automatic judgment module is used for sequencing the data points; obtaining a highest score and a second high score; and the judgment confidence evaluation module is used for calculating a judgment confidence margin between the highest score and the second highest score, and automatically routing the conflict data set to a manual auditing queue or automatically confirming a golden record. Through quantitative evaluation, dynamic weighting and automatic judgment, the credible gold record is efficiently generated, and refined risk control and non-tampering decision traceability are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of logistics data management, in particular to a data cross-validation management system for multi-system cooperation. BACKGROUND

[0002] In modern logistics and supply chain management, especially when it comes to the operation of recyclable packaging assets, data management plays a core role. The operation process spans multiple stakeholders, including the sender, the carrier, the receiver and the asset owner. To manage these complex interactions, enterprises widely deploy various information systems, such as warehouse management systems, transportation management systems, Internet of Things monitoring platforms and back-end financial settlement systems. Each system generates a large amount of heterogeneous data streams, such as warehouse management system delivery records, transportation management system signing records, Internet of Things platform real-time location and status data, etc. These data from different sources together constitute the basis for settlement and responsibility definition. Therefore, before the final financial settlement or asset inventory, it is necessary to effectively aggregate and cross-validate these multi-party data.

[0003] However, the existing technology faces serious challenges in dealing with these heterogeneous data. Since the data comes from different stakeholders, and the data collection methods and timeliness are different, the data between systems frequently appear inconsistent and conflicting, such as the number of shipments and the number of sign-offs do not match. Traditional solutions highly rely on manual intervention for reconciliation and adjudication. This approach not only is inefficient and costly, but also lacks objective and unified adjudication standards, making it difficult to define responsibilities and seriously affecting the settlement period. In addition, the existing data verification mechanism is usually too simple and cannot dynamically quantify the credibility of different evidence. For example, they cannot distinguish the inherent reliability differences of data sources, cannot evaluate the integrity of the data evidence chain, and do not consider the erosion of data timeliness on evidence value. When conflicts occur, the system lacks intelligent adjudication mechanisms and risk assessment capabilities, resulting in a large number of low-risk conflicts that require manual intervention, and also cannot provide traceable and undeniable audit logs for the final adjudication results, which brings hidden dangers to subsequent business dispute handling and financial audit. SUMMARY

[0004] The purpose of the present application is to provide a multi-system cooperative data cross-validation management system to solve the problems in the background art.

[0005] To solve the above technical problems, the present application provides a multi-system cooperative data cross-validation management system, comprising: An initial credibility grading module is used to obtain data streams from heterogeneous data sources and obtain preset static source reliability factors associated with the heterogeneous data sources; the integrity of the evidence chain of the data points in the data stream is evaluated in real time, and an evidence chain integrity score is generated. The initial credibility score of the data point is calculated by combining the static source reliability factor and the evidence chain completeness score; The evidence weight dynamic quantification module is configured to calculate a timeliness decay factor based on the initial credibility score, according to the event physical occurrence timestamp and the data arrival system timestamp of the data point; in response to detecting a data conflict, and according to a preset business rule matrix, match a current business scenario, and assign a business rule adjustment factor; and determine the final evidence weight score of the data point by comprehensively considering the initial credibility score, the timeliness decay factor, and the business rule adjustment factor; The golden record automatic adjudication module is configured to aggregate multiple data points into a conflict data set through an association key; perform sorting on the data points in the conflict data set according to the final evidence weight score; select the data point with the highest final evidence weight score in the sorting; obtain the data value carried by the data point with the highest final evidence weight score, and adopt the data value as the golden record; obtain the final evidence weight score of the data point with the second highest final evidence weight score in the sorting as the second highest score; The adjudication confidence evaluation module is configured to calculate the adjudication confidence margin between the highest score and the second highest score based on the highest score and the second highest score; compare the adjudication confidence margin with a preset manual intervention threshold; when the adjudication confidence margin is less than the manual intervention threshold, automatically route the conflict data set to a manual review queue; and when the adjudication confidence margin is greater than or equal to the manual intervention threshold, automatically confirm the golden record.

[0006] Preferably, the initial credibility grading module determines the evidence chain completeness score, including: obtaining a preset total number of evidence attributes and a preset weight of each evidence attribute; detecting in real time whether the data point contains each evidence attribute to determine a Boolean value of the existence of the evidence attribute; quantitatively generating the evidence chain completeness score by performing weighted summation of the preset weight of each evidence attribute on the Boolean value of the existence of the evidence attribute.

[0007] Preferably, the evidence weight dynamic quantification module calculates the timeliness decay factor, including: obtaining the event physical occurrence timestamp and the data arrival system timestamp; obtaining a preset decay constant set according to business requirements; calculating the timeliness decay factor by using an exponential decay model based on the event physical occurrence timestamp, the data arrival system timestamp, and the preset decay constant.

[0008] Preferably, the evidence weight dynamic quantification module assigns the business rule adjustment factor, including: loading a preset business rule matrix; In response to detecting the data conflict, matching the current business scenario according to a preset business rule matrix; Based on the matched current business scenario, extracting a business rule adjustment factor from the preset business rule matrix.

[0009] Preferably, the human intervention threshold is a preset adjustable parameter according to the risk exposure of the settlement amount.

[0010] Preferably, it further comprises: The adjudication explainability audit log generation module is configured to: In response to the generation of the golden record, automatically reconstructing the decision-making process; Retrieving all relevant original data points and complete calculation path parameters in the conflict data set, the calculation path parameters including static source reliability factors, evidence chain completeness scores, timeliness decay factors, business rule adjustment factors, final evidence weight scores, and adjudication confidence margins; Format the original data points and calculation path parameters into a human-readable audit tracking report.

[0011] Preferably, the adjudication explainability audit log generation module is further configured to: Calculate the combined hash value of the golden record and the human-readable audit tracking report; Anchor the combined hash value to an unalterable timestamp service or a distributed ledger.

[0012] Preferably, it further comprises: The core performance parameter calculation module is configured to: Monitor the total number of events and the total number of cross-system data inconsistency events of automatic adjudication, and calculate the automatic adjudication rate; Monitor the generation timestamp of the golden record and the timestamp of the arrival of the last relevant party data in the conflict set, and calculate the golden record generation delay.

[0013] Compared with the prior art, the present application has the following beneficial effects: A standardized heterogeneous data credibility evaluation method is provided, which no longer considers data values in isolation, but first comprehensively evaluates the static reliability of the data source and the dynamic evidence chain completeness of the data point itself. Through this multi-dimensional quantitative evaluation, an objective and comparable initial credibility benchmark score can be calculated for data from different sources with varying quality, laying a solid data foundation for subsequent intelligent adjudication.

[0014] The dynamic situational weight adjustment mechanism is introduced, which can automatically calculate the timeliness decay factor according to the time delay from the physical event to the recording of the system by the data, so that the weight of the obsolete data is correspondingly reduced, which is closer to the requirement of the business for real-time, and when the data conflict is detected, the weight of the evidence of each party can be adjusted strategically according to the preset business rule matrix, so that the final evidence weight not only reflects the objective quality of the data, but also combines the timeliness and the game logic in the specific business scene, which significantly improves the rationality of the weight scoring.

[0015] The automatic conflict resolution based on quantified weight is realized, which can automatically generate a unified and reliable golden record as the settlement basis, greatly improving the settlement efficiency, and the resolution confidence evaluation mechanism is introduced, which can intelligently judge the reliability of the resolution result by comparing the weight difference between the winning evidence and the suboptimal evidence, and only the conflicts with low confidence and risks are automatically routed to manual review, so as to ensure the automation efficiency while realizing the fine risk control, and effectively balancing the efficiency and safety.

[0016] A perfect explainability and traceability mechanism is constructed, which can automatically reconstruct the complete decision-making process for each resolution, especially the automatically generated golden record, including all original evidence, adopted calculation parameters and final weight score, and through the generation of human-readable audit tracking report and the use of cryptography hash and distributed ledger technology to ensure its non-tamperability, the problems of opaque traditional resolution process and easily tampered results are solved, which provides high-confidence evidence support for business dispute processing and financial audit BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, a brief introduction will be made to the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Figure 1 The logical block diagram of the system of the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0019] Please refer to Figure 1The application provides a multi-system collaborative data cross-validation management system, comprising: an initial credibility grading module, configured to acquire data streams from heterogeneous data sources and acquire preset static source reliability factors associated with the heterogeneous data sources; a real-time evaluation data point evidence chain integrity degree in the data streams is generated; The initial credibility score of the data point is calculated by combining the static source reliability factor and the evidence chain integrity degree score; An evidence weight dynamic quantification module is configured to calculate a timeliness decay factor based on the initial credibility score, according to the event physical occurrence timestamp and the data arrival system timestamp of the data point; in response to detecting data conflicts, and according to a preset business rule matrix, a business rule adjustment factor is allocated by matching the current business scenario; the initial credibility score, the timeliness decay factor and the business rule adjustment factor are integrated to determine the final evidence weight score of the data point; A golden record automatic adjudication module is configured to aggregate multiple data points into a conflict data set through an association key; the data points in the conflict data set are sorted according to the final evidence weight score; the data point with the highest final evidence weight score in the sorting is selected; the data value carried by the data point with the highest final evidence weight score is acquired, and the data value is adopted as the golden record; the final evidence weight score of the data point with the highest final evidence weight score is acquired as the highest score; the final evidence weight score of the data point with the second highest final evidence weight score in the sorting is acquired as the second highest score; An adjudication confidence evaluation module is configured to calculate the adjudication confidence margin between the highest score and the second highest score based on the highest score and the second highest score; the adjudication confidence margin is compared with a preset artificial intervention threshold value; when the adjudication confidence margin is less than the artificial intervention threshold value, the conflict data set is automatically routed to an artificial review queue; when the adjudication confidence margin is greater than or equal to the artificial intervention threshold value, the golden record is automatically confirmed.

[0020] The embodiment provides a multi-system collaborative data cross-validation management system; aims to solve the technical problems of frequent data conflicts, unclear responsibility definition and low artificial settlement efficiency in the circulation packaging logistics due to the involvement of multiple heterogeneous data sources such as warehouse management systems, transportation management systems, Internet of Things platforms and financial systems; the system builds a multi-module collaborative quantification adjudication mechanism, realizes standardized evaluation, dynamic scenario weighting, automatic conflict adjudication and controllable artificial intervention of data credibility, and finally generates a reliable golden record as a settlement basis; Specifically, it comprises: An initial credibility grading module, which aims to make a standardized baseline credibility assessment on the raw data from various sources entering the system; in this embodiment, the module acquires data streams from heterogeneous data sources, such as shipping data from warehouse management systems, signed data from transportation management systems, and in-transit scanning data from Internet of Things platforms; the module acquires a preset static source reliability factor associated with the heterogeneous data sources ; Here refers to the quantification of the inherent properties of the data source, which is to reflect the innate reliability of the data source; its source is a scalar between 0 and 1 pre-configured by an expert system according to the technical characteristics of the data source, such as Internet of Things automatic collection and historical performance, such as historical error rate; the static source reliability factor It can be obtained by the following exemplary formula:

[0021] : the historical data error rate of the data source ( ), which is obtained by calculating the proportion of the number of times the data source is judged as incorrect data to the total number of data in the historical settlement period; : data source type weight ( ), which is pre-set by an expert system according to the inherent reliability of the data collection method; Internet of Things automatic collection source , enterprise resource planning system integration source , and manual web entry source ; This way makes the setting of no longer rely solely on pure expert experience, but has a quantifiable basis for calculation; This design aims to reflect the business rule that the Internet of Things scanning data of the receiving party is more important than the manual entry data of the shipping party; The module assesses the completeness of the evidence chain of the data points in the data stream in real time; the evidence chain here refers to the set of auxiliary information necessary to constitute a data point, such as timestamp, geolocation stamp, operator ID, and previous node confirmation status; after the assessment, a , a scalar between 0 and 1.0, is generated; The module combines the static source reliability factor and the evidence chain completeness score to calculate the initial credibility score of the data point; this calculation aims to integrate static source credibility and dynamic data integrity; its calculation formula is:

[0022] : initial credibility score, dimensionless score, output of this step, as input of subsequent dynamic quantification; : static source reliability factor, 0-1 scalar, preset by expert system as mentioned before; : evidence chain completeness score, 0-1 scalar, evaluated by this module in real time; Through this calculation, the system ensures that data with unreliable source or low evidence chain completeness will have a lower initial credibility score , providing a standardized and comparable benchmark input for subsequent dynamic decision-making; the initial credibility score can also be calculated using a weighted average model to balance the contributions of source reliability and evidence completeness:

[0023] and are preset weight coefficients for static source reliability factor and evidence chain completeness score respectively, and ; they can be set to , , indicating that the system places more emphasis on the evidence completeness of data points themselves; This approach avoids the problem of an overall score being too low due to a low score in the pure multiplication model, enhancing the robustness of the model; The evidence weight dynamic quantification module aims to dynamically correct the weight of evidence based on the initial credibility score , making it more in line with business reality; in the embodiments of the present application, this module is designed to be called by the golden record automatic decision-making module after detecting data conflicts; this module processes conflict data sets pre-aggregated by the golden record automatic decision-making module and determined to have data inconsistencies; ; this module performs subsequent dynamic quantification calculations based on the initial credibility score of each data point in ; This module addresses the timeliness issue based on the initial credibility score ; it calculates the timeliness decay factor based on the event physical occurrence timestamp of the data point and the data arrival system timestamp ; Here it refers to the time when the physical event such as Internet of Things scanning actually occurs, and its source is the evidence chain attribute of the data point; ​Here refers to the time when the data is received and recorded by the system, which is derived from the system time; Here refers to the correction factor for the decrease in credibility caused by data delay, with a value range of 0 ≤1.0; This module responds to the detection of data conflicts, such as discrepancies between the shipping party's warehouse management system and the receiving party's transportation management system, and assigns business rule adjustment factors according to the pre-set business rule matrix that matches the current business situation, such as in-transit loss or discrepancy in the number of signatures; Here refers to the strategic adjustment of data weight in specific business conflict games; it is derived from the pre-set business rule library and can be greater than 1.0 (enhancing weight) or less than 1.0 (weakening weight); This module integrates the initial credibility score , the timeliness decay factor , and the business rule adjustment factor to determine the final evidence weight score of the data point; this calculation is a dynamic evolution that integrates time and business dimension corrections; its calculation formula is:

[0024] Final evidence weight score, dimensionless score, calculated output of this step, used as the basis for decision-making; Initial credibility score, dimensionless score, from the initial credibility classification module; Timeliness decay factor, 0-1 scalar, calculated by this module; Business rule adjustment factor, scalar, matched by this module according to the rule matrix ; Through this calculation, the system evolves static into dynamic, strongly related to business context , so that the decision-making is no longer based on isolated data, but on scenario-based evidence weight; Golden record automatic decision-making module, whose purpose is to aggregate and detect conflicts in data streams, and use the score calculated by the previous modules to automatically resolve data conflicts and generate unified and reliable settlement basis; In this embodiment, the workflow of this module first starts, it aggregates multiple data points from heterogeneous data sources that point to the same business event into a data set through the association key After aggregation, the module immediately detects... Are there any data value conflicts? If If no data value conflict is found, the value can be directly confirmed; if a conflict is detected, this module calls the evidence weight dynamic quantification module to request a change in the value. For each data point, calculate its final weighted evidence score. ; In obtaining the data calculated and returned by the evidence weight dynamic quantization module. After scoring, this module continues with its subsequent adjudication steps; For conflict datasets The data points in the data are scored according to the final weighted evidence. The system performs a sorting process; it selects the data point with the highest final weight of evidence score in the sorting. ; The system acquires the data points with the highest final evidence weight score. The data value it carries, for example, is 100 signed-for items, and this data value is adopted as the gold record. ; To support subsequent confidence assessment, this module also acquires the data point with the highest final evidence weight score. The final evidence weight score, as the highest score And obtain the final evidence weight score of the data point with the second highest final evidence weight score in the ranking, as the second highest score. ; This adjudication mechanism is a direct mathematical implementation of the maximum weight priority rule; its selection logic can be expressed as:

[0025]

[0026] Gold record, specific data value, such as quantity or status, the final ruling output of this module; : Conflict dataset, a set of data points, is aggregated in this module; : Final evidence weight score, dimensionless score, derived from the evidence weight dynamic quantification module; This operation refers to selecting... Largest data point ; This operation refers to extraction. The data value it carries; This formula consumes the preceding steps. As a result, the only basis for decision-making is achieved, realizing the logical closed loop from quantitative scoring to automatic ruling; The ruling confidence assessment module aims to assess the reliability of the automatic ruling result, intelligently balance automation efficiency and settlement risk, and minimize unnecessary manual intervention. The module is based on the highest score And the second highest score Both are provided by the golden record automatic ruling module, and the ruling confidence margin between the two is calculated The margin is used to quantify the degree of winning evidence leading to failure evidence; Its formula is:

[0027] : ruling confidence margin, dimensionless score, calculated in this step; : highest score, dimensionless score, from the golden record automatic ruling module; : second highest score, dimensionless score, from the golden record automatic ruling module; The formula is a quantitative assessment of the confidence of The result; The greater, the more explicit the ruling result, the lower the risk; The module compares the ruling confidence margin With the preset manual intervention threshold ; When the ruling confidence margin Is less than the manual intervention threshold , it means that the winning party is close to the second place score, the ruling risk is high, and the system automatically routes the conflict data set To the manual review queue; When the ruling confidence margin Is greater than or equal to the manual intervention threshold , it means that the ruling result is reliable, and the system automatically confirms the golden record ; Through the cooperative work of the above four modules, the system builds a complete technical closed loop from heterogeneous data standardization evaluation, i.e. initial confidence grading, dynamic scenario weighting, i.e. evidence weight dynamic quantization, automatic conflict ruling, i.e. golden record generation, and risk intelligent control, i.e. ruling confidence assessment; It solves the technical problems of settlement difficulties and low efficiency of manual review caused by inconsistent data from multiple systems, such as warehouse management system, transportation management system, and Internet of Things data conflicts in the circulation packaging logistics; The invention realizes automatic, efficient, and risk-controllable data cross-validation and golden record generation based on quantitative evidence weight.

[0028] The initial credibility grading module determines the evidence chain integrity score, including: Obtain the preset total number of evidence attributes and the preset weight of each evidence attribute; Real-time detection of whether data points contain various evidence attributes, and determination of the Boolean value of the existence of evidence attributes; The completeness score of the evidence chain is quantified by summing the Boolean values ​​of the existence of evidence attributes with preset weights for each attribute.

[0029] This embodiment explains how the initial credibility grading module determines the completeness of the chain of evidence. It has been specifically defined; its purpose is to provide a quantifiable and configurable... The computational method aims to address the technical challenge of not only comparing data values ​​but also evaluating the chain of evidence constituting those data values ​​in real time. In this embodiment, the module obtains a preset total number of evidence attributes. and the preset weights of each evidence attribute ; This refers to the total number of predefined evidentiary attributes that are important to settlement transactions, such as timestamps and geolocation stamps. This refers to the preset weight of the i-th evidence attribute, and all The sum of these values ​​is 1, and their function is to reflect the dependence of business rules on this attribute; their source is the expert system's pre-configuration based on settlement business rules, such as geolocation stamps. Higher than operator ID ; Module real-time detection data points Whether each evidentiary attribute is included, and the Boolean value used to determine the existence of the evidentiary attributes. ; This refers to a Boolean value, 1 or 0, which originates from real-time detection data points. The result of whether the i-th attribute is included is 1 if it is included and 0 if it is missing. The module uses Boolean values ​​to determine the existence of evidence attributes. Preset weights for each evidence attribute. The weighted summation quantifies the completeness of the evidence chain. The formula for calculating the weighted sum is:

[0030] Completeness score of the chain of evidence, scalar value of 0.0-1.0, is the output of this step. : Total number of evidence attributes, integer, expert system preset; The preset weight of the i-th evidence attribute, a scalar, and a preset value for the expert system. ; : Boolean value for the existence of evidence attributes, 1 or 0, real-time detection result; This embodiment quantifies the completeness of evidence using this formula; one data point Included, and business weight The higher the evidentiary value, the better. The more, the more The closer the score is to 1.0; Through the weighted summation model described above, this invention transforms an abstract concept of a complete chain of evidence into a precise, configurable, and computable quantitative indicator. This makes the initial credibility... The calculations are more objective and precise, and can be performed through... The configuration directly reflects the different requirements of business rules for the integrity of evidence, which significantly improves the accuracy of benchmark credibility assessment.

[0031] The evidence weight dynamic quantification module calculates the timeliness decay factor, including: Get the physical occurrence timestamp of the event and the arrival timestamp of the data in the system; Obtain the preset attenuation constant set according to business requirements; Based on the physical occurrence timestamp of the event, the arrival timestamp of the data in the system, and the preset decay constant, the timeliness decay factor is calculated using an exponential decay model.

[0032] This embodiment explains how the evidence weight dynamic quantification module calculates the time-sensitivity decay factor. Specific limitations were imposed; the purpose was to introduce a mathematical model to simulate the erosion of the credibility of evidence by the time difference between the physical and digital worlds, and to solve the technical problem of the decline in the value of evidence due to data delays. The module also obtains the preset attenuation constant set according to business requirements. ; This refers to the time-dependent decay constant, whose dimensions are: Its function is to adjust the severity of the penalty for delays; its source is adjustable parameters set according to business needs, such as the settlement cycle; for example, the shorter the settlement cycle, the more... The larger the value, the heavier the penalty for delay; The module is based on the physical occurrence timestamp of the event. Data arrival system timestamp With preset attenuation constant The time-related decay factor is calculated using an exponential decay model. ; the exponential decay model is a standard model for simulating such time-dependent value, whose formula is:

[0033] : time-dependent decay factor, 0 ≤1.0 scalar, the output of this step; : natural constant; : preset decay constant, dimensionless , preset according to business needs; : data arrival system timestamp, time, system time; : event physical occurrence timestamp, time, from data point evidence chain; In this model, the data delay is larger, the value of tends to 0, so that the weight of the data is punished and attenuated when calculating the maximum weight By introducing the exponential decay model, the final evidence weight can dynamically reflect the freshness of the data; this ensures that data with a delay much larger than , even if its initial credibility is high, its maximum weight will be significantly reduced; this is more in line with the reality of the priority of timely evidence in logistics settlement business, and improves the rationality of dynamic weight quantification.

[0034] The evidence weight dynamic quantification module assigns a business rule adjustment factor, including: loading a preset business rule matrix; in response to detecting data conflict, matching the current business situation according to the preset business rule matrix; based on the matched current business situation, extracting a business rule adjustment factor from the preset business rule matrix.

[0035] This embodiment specifically defines how the evidence weight dynamic quantification module assigns a business rule adjustment factor ; its purpose is to provide a mechanism so that the system can introduce preset business logic and game strategy when detecting data conflict, to strategically enhance or weaken the weight of the conflicting party; In this embodiment, the module loads a preset business rule matrix ; This refers to a pre-defined rule base or lookup table that stores different business scenario keys and their corresponding adjustment factors. The mapping relationship of Value; In response to the detection of data conflicts, such as discrepancies between the shipper's warehouse management system and the recipient's transportation management system regarding the quantity received, the system will respond according to a preset business rule matrix. Match the current business scenario; Based on the current business scenario, from the preset business rule matrix Extract business rule adjustment factors ; For example, in a scenario where the number of items signed for does not match, the rule matrix... Possible stipulations: For the recipient, such as a transportation management system or an IoT data point, its A weight of 1.2 indicates enhanced weighting; for data points in the shipping party's warehouse management system, its... A value of 0.9 indicates a weakened weight. The specific values ​​are derived from the preset values ​​of business experts based on historical game experience; This invention injects static business logic into a dynamic algorithm; this makes the final weight... This not only reflects the objective quality of the data and timeliness It also reflects the business game theory aspect under specific conflict scenarios; this makes the ruling more closely aligned with complex commercial settlement rules, enhancing the business rationale of the ruling; to further clarify, the business rule matrix In this embodiment, it is specifically implemented as a multidimensional key-value pair lookup structure, rather than a matrix in the mathematical sense; its keys are determined by the business scenario type. and data point source It is composed of multiple factors; its value is the corresponding business rule adjustment factor. ; When a data conflict is detected, the first step is to identify the conflict. Then iterate through the conflict dataset. Each data point in , obtain its and through Combination keys from Query the corresponding Values; for example, rule bases It can be defined as: ; If a certain scenario or source is in If no predefined value is specified, the default value can be used. This implementation method clarifies the specific technical path for matching and allocation.

[0036] The threshold for manual intervention is an adjustable parameter preset based on the risk exposure of the settlement amount.

[0037] This embodiment specifies the manual intervention threshold used in the decision confidence assessment module. Specific limitations were imposed; its purpose was to provide The settings provide clear technical basis and adjustability, enabling them to match the risk control requirements of different settlement scenarios; In this embodiment, the threshold for manual intervention These are adjustable parameters preset based on the risk exposure of the settlement amount; Specifically, The risk level is pre-set by the risk control department or expert system based on the risk level of the business; for example, for orders involving high-value assets or high-value settlements, which have a large risk exposure, the system will configure a higher risk level for them. For example, a value of 0.5; conversely, for low-value, routine transactions, The value can be set relatively low, for example, 0.2; In assessing the confidence level of the ruling, a higher This means the system requires proof of victory. The evidence must be better than the second one. More than that Only then can automatic confirmation be achieved; this will trigger more stringent manual review, in accordance with the control requirements for high-risk businesses. By Linked to the core business indicator of risk exposure in settlement amount, this invention achieves an intelligent balance between the efficiency of automated systems and the rigid constraints of financial settlement; the system no longer uses a one-size-fits-all threshold, but instead implements differentiated, risk-based automatic adjudication and manual intervention strategies, significantly improving the system's practicality and security in complex financial environments; manual intervention threshold It can be used with a settlement amount The relevant functions are dynamically calculated, rather than simply pre-defined in segments; a bounded sigmoid function can be used for mapping to achieve smooth risk control.

[0038] Minimum threshold; Maximum threshold; : The reference midpoint for risk amount; : Slope factor, used to control the sensitivity of the threshold to changes in amount, its dimension is the reciprocal of currency; and Adjustable parameters preset according to business needs; This way makes it possible to make smooth and automatic adjustments according to changes, achieving more refined risk control.

[0039] Further comprising: A ruling explainability audit log generation module for: automatically reconstructing the decision-making process in response to the generation of the golden record; retrieving all relevant original data points and complete calculation path parameters in the conflict data set, including static source reliability factors, evidence chain completeness scores, timeliness decay factors, business rule adjustment factors, final evidence weight scores, and ruling confidence margins; formatting the original data points and calculation path parameters into a human-readable audit trail report.

[0040] The embodiment further comprises a ruling explainability audit log generation module; its purpose is to meet the rigid constraints of terminality and traceability of logistics settlement business, and to generate a complete, interpretable, and auditable evidence chain for each golden record , especially after automatic ruling ; The module is automatically triggered in response to the generation of the golden record ; the module performs the operation of automatically reconstructing the decision-making process; it retrieves all relevant original data points from the conflict data set , including winning and all failed data points, and complete calculation path parameters; Calculation path parameters include: all original data values, respective static source reliability factors , evidence chain completeness scores , timeliness decay factors , business rule adjustment factors , final evidence weight scores , i.e., all participants , and ruling confidence margins and the threshold of human intervention used at the time of ruling ; The module formats the retrieved original data points and the above calculation path parameters into a human-readable audit trail report ; the report may be an XML or JSON document that clearly shows all original evidence, adopted ruling rules, scores of each evidence, and final decision-making logic.By adding this module, the system realizes the explainability and traceability of the decision; it is no longer a black box decision system; when a commercial dispute occurs or financial audit is needed, the audit tracking report generated by this module can provide complete decision basis and all intermediate parameters, and accurately trace back how the decision is made; this greatly enhances the legal validity and commercial credibility of the system as an automated settlement basis.

[0041] The decision explainability audit log generation module is also used for: calculating the combined hash value of the golden record and the human-readable audit tracking report; anchoring the combined hash value to an unalterable timestamp service or distributed ledger.

[0042] This embodiment further limits the function of the decision explainability audit log generation module to enhance the security of the audit log; its purpose is to provide technical support for the audit log that is undeniable and tamper-proof, meeting the highest security requirements of the settlement business for evidence finality; In this embodiment, the decision explainability audit log generation module is also used for: calculating the combined hash value of the golden record and its corresponding human-readable audit tracking report ; ; Here it refers to the unique digital fingerprint generated by combining the contents of the golden record and the audit log using a standard cryptographic hash function such as SHA-256; or any minor changes will cause the value to change greatly; The module anchors the combined hash value to an unalterable timestamp service or distributed ledger; for example, the system can write this hash value as metadata of a transaction into a consortium blockchain or enterprise-level distributed ledger, thereby permanently fixing the decision evidence by taking advantage of the unalterable and chronological characteristics of the blockchain; Through hash calculation and on-chain or timestamp anchoring, the invention provides cryptographic-level irrefutability for the golden record and its decision logic Once the hash value is anchored, no one, including the system administrator, can tamper with the historical decision record without being detected; this completely solves the pain point of the audit log being easily tampered with in traditional databases, making the golden record generated by the system have the highest level of evidence validity as a judicial and financial audit.

[0043] Also included are: a core performance parameter calculation module for: monitoring the total number of events of automatic adjudication and the total number of cross-system data inconsistency events, and calculating the automatic adjudication rate; monitoring the generation timestamp of the golden record and the timestamp of the arrival of the last related party data in the conflict set, and calculating the golden record generation delay.

[0044] The embodiment further includes a core performance parameter calculation module; the purpose is to provide quantitative and real-time technical indicators for continuously monitoring and evaluating the automation efficiency and real-time performance of the system, and providing data support for continuous optimization of the system; The module monitors the total number of events of automatic adjudication and the total number of cross-system data inconsistency events , and calculates the automatic adjudication rate ; the rate is used to measure the automation efficiency of the system; the calculation formula is:

[0045] : automatic adjudication rate, percentage, output of this step; : total number of events of automatic adjudication, integer, from the event count in the module three, i.e., the golden record automatic adjudication module, that meets ; : total number of cross-system data inconsistency events, integer, total number of conflict sets detected by the system ; The module monitors the generation timestamp of the golden record and the timestamp of the arrival of the last related party data in the conflict set , and calculates the golden record generation delay ; the delay is used to measure the real-time performance of the system; the calculation formula is:

[0046] : golden record generation delay, time unit, such as seconds or milliseconds, output of this step; : golden record generation timestamp, time, from the system time generated by the module three, i.e., the golden record automatic adjudication module ; : timestamp of the arrival of the last related party data, time, from the detection of the module one, i.e., the initial credibility classification module, that constitutes ​​the last data point of the arrival time of ; By adding this module, the system realizes self-measurement of performance; and Real-time calculation of these two core technical parameters enables system operation and maintenance personnel to intuitively evaluate system health status; for example, if decreases, it may mean that the threshold is not reasonable or the data quality decreases; if increases, it may indicate that there is a performance bottleneck in the calculation module; this provides closed-loop data feedback for parameter tuning and algorithm iteration of the system.

[0047] The above is only a preferred embodiment of the present application, and does not limit the present application in other forms. Any person skilled in the art can use the disclosed technical content to make changes or modifications into equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made on the basis of the technical essence of the present application to the above embodiments without departing from the technical solution content of the present application still belongs to the protection scope of the technical solution of the present application.

Claims

1. A multi-system coordinated data cross-validation management system, characterized in that, The method comprises the following steps: An initial credibility grading module is configured to obtain a data stream from a heterogeneous data source and obtain a preset static source reliability factor associated with the heterogeneous data source; A real-time evaluation of the completeness of the evidence chain of the data points in the data stream is performed to generate an evidence chain completeness score; The initial credibility score of the data points is calculated by combining the static source reliability factor and the evidence chain completeness score; An evidence weight dynamic quantification module is configured to calculate a timeliness decay factor based on the initial credibility score, the event physical occurrence timestamp of the data points, and the data arrival system timestamp; In response to detecting a data conflict, a business rule adjustment factor is assigned according to a preset business rule matrix matched with the current business scenario; The final evidence weight score of the data points is determined by combining the initial credibility score, the timeliness decay factor, and the business rule adjustment factor; A golden record automatic adjudication module is configured to aggregate multiple data points into a conflict data set through an association key, perform sorting on the data points in the conflict data set according to the final evidence weight score, select the data point with the highest final evidence weight score in the sorting, obtain the data value carried by the data point with the highest final evidence weight score, and adopt the data value as the golden record, and obtain the final evidence weight score of the data point with the highest final evidence weight score as the highest score; The final evidence weight score of the data point with the second-highest final evidence weight score in the sorting is obtained as the second-highest score; An adjudication confidence evaluation module is configured to calculate the adjudication confidence margin between the highest score and the second-highest score based on the highest score and the second-highest score; The adjudication confidence margin is compared with a preset manual intervention threshold value; When the adjudication confidence margin is less than the manual intervention threshold value, the conflict data set is automatically routed to a manual review queue; and when the adjudication confidence margin is greater than or equal to the manual intervention threshold value, the golden record is automatically confirmed.

2. The multi-system coordinated data cross-validation management system of claim 1, wherein, The initial credibility grading module determines the evidence chain completeness score, which comprises the following steps: A preset total number of evidence attributes and a preset weight of each evidence attribute are obtained; It is detected in real time whether the data points contain each evidence attribute to determine the Boolean value of the existence of the evidence attribute; The evidence chain completeness score is quantitatively generated by performing a weighted summation of the preset weights of each evidence attribute on the Boolean value of the existence of the evidence attribute.

3. The multi-system coordinated data cross-validation management system of claim 1, wherein, The evidence weight dynamic quantification module calculates the timeliness decay factor, which comprises the following steps: The event physical occurrence timestamp and the data arrival system timestamp are obtained; A preset decay constant is set according to business needs; Based on the event physical occurrence timestamp, the data arrival system timestamp, and the preset decay constant, an exponential decay model is used to calculate the timeliness decay factor.

4. The multi-system coordinated data cross-validation management system of claim 1, wherein, The evidence weight dynamic quantification module assigns the business rule adjustment factor, which comprises the following steps: A preset business rule matrix is loaded; In response to detecting a data conflict, the current business scenario is matched according to the preset business rule matrix; Based on the matched current business scenario, a business rule adjustment factor is extracted from the preset business rule matrix.

5. The multi-system coordinated data cross-validation management system of claim 1, wherein, The manual intervention threshold value is a preset adjustable parameter according to the risk exposure of the settlement amount.

6. The multi-system coordinated data cross-validation management system of claim 1, wherein, Further comprising: An adjudication explainability audit log generation module is configured to: In response to the generation of the golden record, the decision-making process is automatically reconstructed. retrieve all relevant raw data points and complete calculation path parameters in the conflict data set, the calculation path parameters including static source reliability factor, evidence chain completeness score, timeliness decay factor, business rule adjustment factor, final evidence weight score, and adjudication confidence margin; format the raw data points and calculation path parameters into human-readable audit trail reports.

7. The multi-system coordinated data cross-validation management system of claim 6, wherein, The adjudication explainability audit log generation module is further configured to: calculate a combined hash value of the golden record and the human-readable audit trail report; anchor the combined hash value to an unalterable timestamp service or distributed ledger.

8. The multi-system coordinated data cross-validation management system of claim 1, wherein, Further comprising: a core performance parameter calculation module configured to: monitor the total number of events and the total number of cross-system data inconsistency events of automatic adjudication, and calculate the automatic adjudication rate; monitor the generation timestamp of the golden record and the timestamp of the arrival of the last relevant party data in the conflict set, and calculate the golden record generation delay.

Images