Insurance industry data processing method and device and electronic equipment

Abstract

The invention provides an insurance industry data processing method and device and electronic equipment, and relates to the field of data processing. In the method, indexes of a plurality of insurance service systems are managed in a unified manner by constructing an index map, an index disturbance potential function is established based on historical field fluctuation and a real-time update state, potential instability index nodes are identified, and reverse dependence deduction is executed to generate an instability path set. The method comprises the following steps of: stripping an original writing process into a push channel to generate a pre-written data snapshot, executing corrected writing after data arrives, dynamically adjusting an identification threshold and channel resource configuration, and ensuring causal structure closure and writing sequence consistency. By implementing the technical scheme provided by the invention, the stability of data processing in the insurance industry is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to an insurance industry data processing method and device and electronic equipment. BACKGROUND

[0002] In the actual operation process of the insurance industry, a multi-class index system constructed for premium structure indexes, claim payment data indexes, policy activity indexes and customer dimension indexes often depends on asynchronous data streams from multiple heterogeneous business systems, including a collection and payment system, a policy management system, a claim settlement system and a customer relationship system. In order to support accurate description of complex business behaviors, index calculation usually involves high-coupling logic paths across systems, fields and time windows.

[0003] However, the existing data processing mechanism is based on explicit state features such as whether data arrives, whether load is balanced or whether tasks are accumulated to execute scheduling or write-back strategies, and lacks systematic modeling and pre-control capabilities for the integrity, stability and future disturbance trend of the cause-effect chain in the index graph structure. Especially in the scene of multi-source data update timing disorder, field state lag or frequent business logic adjustment, the situation of index node triggering failure, cause-effect relationship rupture and even batch out-of-order writing of indexes in the whole dependent path often occurs due to the failure of timely updating of key indexes, forming chain write-back collapse, which seriously affects the stability of insurance industry data processing.

[0004] Therefore, there is an urgent need for an insurance industry data processing method, device and electronic equipment. SUMMARY

[0005] The present application provides an insurance industry data processing method, device and electronic equipment, which facilitates to improve the stability of insurance industry data processing.

[0006] The first aspect of the application provides an insurance industry data processing method, the method comprising: obtaining premium structure indicators, claim data indicators, policy activity indicators and customer dimension indicators involved in a plurality of insurance business systems as index nodes, obtaining field dependency relationships, index calculation logic and time sequence to establish index dependency paths, and forming an index map; obtaining the field variation frequency, update delay characteristics and dependency strength parameters of the data sources relied on by the index nodes in the historical period, and combining the update state of each field in the current data stream to establish an index disturbance potential function; identifying the index nodes with the index disturbance potential function greater than the preset threshold as potential instability index nodes in the index map, and performing reverse dependency deduction based on the index dependency path where the potential instability index nodes are located to construct a potential instability path set; separating the potential instability index nodes in the potential instability path set from the original write process and classifying them into a potential instability index pushing channel to generate a corresponding pre-write data snapshot; if it is determined that the actual data required by the potential instability index nodes has arrived, performing a correction write on the pre-write data snapshot, and dynamically adjusting the identification threshold and channel resource configuration to maintain the causal structure closure and index write sequence consistency of the index map.

[0007] Optionally, the obtaining of the premium structure indicators, claim data indicators, policy activity indicators and customer dimension indicators involved in a plurality of insurance business systems as index nodes, the obtaining of field dependency relationships, index calculation logic and time sequence to establish index dependency paths, and the forming of an index map specifically comprise: obtaining structured definition information of the premium structure indicators, claim data indicators, policy activity indicators and customer dimension indicators from a premium collection and payment system, a policy system, a claim settlement system and a customer service system; analyzing the original data fields relied on by each index according to the calculation formula of each index; and establishing a dependency mapping relationship between the index nodes and the fields. For index nodes with field sharing or formula derivation relationships between a plurality of the index nodes, the index dependency paths are constructed based on the field dependency path intersection. The index nodes and the index dependency paths are constructed into a directional graph structure, and a time sequence labeling operation is performed in combination with the calculation period information of each index node to form an index map with field dependency structure and time logic constraints.

[0008] Optionally, the field variation frequency, update delay feature and dependency strength parameter of the data source relied on by the indicator node in the historical period are acquired, and the update state of each field in the current data stream is combined to establish an indicator disturbance potential function, specifically including: extracting each original data field relied on by the indicator node, acquiring the field variation frequency of the original data field in the historical period, the field variation frequency being used to describe the fluctuation intensity of the field value; acquiring the update delay feature of the original data field in the historical period, the update delay feature including the time interval mean, maximum value and standard deviation from generation to storage of the field, the update delay feature being used to describe the timeliness uncertainty of field update; calculating the dependency strength parameter between the original data field and the indicator node; combining the real-time update state of each original data field in the current data stream, constructing a nonlinear weighted model based on the field variation frequency, the update delay feature and the dependency strength parameter, and generating an indicator disturbance potential function representing the local structural instability of the indicator node.

[0009] Optionally, the indicator node in the indicator graph whose indicator disturbance potential function is greater than a preset threshold is identified as a potential unstable indicator node, and reverse dependency deduction is performed based on the indicator dependency path where the potential unstable indicator node is located to construct a potential unstable path set, specifically including: traversing the indicator nodes in the indicator graph, acquiring the indicator disturbance potential function value corresponding to each indicator node, and identifying the indicator node greater than the preset threshold as the potential unstable indicator node; taking each potential unstable indicator node as a starting point, performing reverse dependency deduction along the indicator dependency path in the indicator graph, recursively tracing the upstream indicator nodes relied on by the potential unstable indicator node, recording the formed dependency path and constituting a path set; aggregating the path set to obtain the potential unstable path set, the potential unstable path set being used to represent the indicator dependency structure region causing structural cascade instability due to the abnormal indicator disturbance potential function.

[0010] Optionally, the potential instability indicator nodes in the potential instability path set are isolated from the original write process and drawn into a potential instability indicator push channel, and a corresponding pre-write data snapshot is generated, specifically comprising: identifying each potential instability indicator node in the potential instability path set, and determining whether each potential instability indicator node is in a regular write process triggered based on data arrival; if in the regular write process, the potential instability indicator node is isolated from the regular write process, and the state record of the potential instability indicator node in the regular scheduling queue is cancelled; each isolated potential instability indicator node is allocated the potential instability indicator push channel, and a scheduling context corresponding to the potential instability indicator push channel is established in the scheduling control structure; based on the corresponding indicator dependency path, the arrived original data field and the indicator calculation logic of each potential instability indicator node in the indicator graph, the pre-write data snapshot is generated, which includes the filling value of the current available field, the indicator calculation logic path identifier and the indicator node structure state information; the pre-write data snapshot is written into the indicator cache structure, and the pre-write data snapshot is marked as in the future mapping to be corrected state.

[0011] Optionally, if it is determined that the actual data required by the potential instability indicator node has arrived, the pre-write data snapshot is executed for correction writing, and the identification threshold and channel resource configuration are dynamically adjusted, specifically comprising: monitoring the arrival state of the original data field corresponding to the potential instability indicator node in the current scheduling period, and if the original data field has arrived and the field value is valid, marking the potential instability indicator node as correctable; calling the indicator calculation logic corresponding to the potential instability indicator node in the indicator graph, performing structure consistency check and value correction operation on the pre-write data snapshot stored in the indicator cache structure, and generating a corrected indicator result; writing the corrected indicator result into the indicator storage structure, and updating the write state of the potential instability indicator node in the indicator graph to structure closed completed; according to the change trend of the disturbance potential function in the continuous scheduling period, the identification threshold of the potential instability indicator node is dynamically adjusted, and according to the number of potential instability indicator nodes and the disturbance concentration degree, the scheduling concurrency degree, the cache capacity and the priority weight of the potential instability indicator push channel are dynamically adjusted.

[0012] Optionally, after the indicator graph is constructed, the calculation period of each indicator node is uniformly standardized; the daily calculation period, the weekly calculation period, the monthly calculation period and the quarterly calculation period are mapped to a unified time dimension reference; based on the time dimension reference, the time sequence of each indicator node in the indicator dependency path is marked to ensure the time consistency of the cause-effect path in the indicator graph and the periodical coordination of the calculation scheduling.

[0013] In a second aspect of the present application, an insurance industry data processing device is provided, the device comprising an acquisition module and a processing module, wherein the acquisition module is configured to acquire premium structure indicators, claim payment data indicators, policy activity indicators and customer dimension indicators involved in a plurality of insurance business systems as indicator nodes, acquire field dependency relationships, indicator calculation logic and time sequence to establish indicator dependency paths, and form an indicator atlas; the acquisition module is further configured to acquire field variation frequency, update delay characteristics and dependency strength parameters of data sources relied on by the indicator nodes in a historical period, and establish an indicator disturbance potential function in combination with the update state of each field in the current data stream; the processing module is configured to identify an indicator node with an indicator disturbance potential function greater than a preset threshold as a potential instability indicator node in the indicator atlas, and perform reverse dependency deduction based on an indicator dependency path where the potential instability indicator node is located to construct a potential instability path set; the processing module is further configured to separate the potential instability indicator nodes in the potential instability path set from the original write process and classify them into a potential instability indicator pushing channel to generate a corresponding pre-write data snapshot; and the processing module is further configured to, if it is determined that actual data required by the potential instability indicator node arrives, perform a correction write on the pre-write data snapshot, and dynamically adjust the identification threshold and channel resource configuration to maintain the causal structure closure and indicator write sequence consistency of the indicator atlas.

[0014] In a third aspect of the present application, an electronic device is provided, the electronic device comprising a processor, a memory, a user interface and a network interface, the memory being configured to store instructions, the user interface and the network interface both being configured to communicate with other devices, and the processor being configured to execute the instructions stored in the memory to enable the electronic device to perform the method described above.

[0015] In a fourth aspect of the present application, a computer readable storage medium is provided, the computer readable storage medium storing instructions which, when executed, perform the method described above.

[0016] In summary, one or more technical solutions provided in the present application have at least the following technical effects or advantages: 1. The indicator atlas structure is introduced to carry the field dependency and time logic between highly coupled indicators; the disturbance potential function is constructed to realize the forward-looking evaluation of future structural stability; the potential instability paths are identified through reverse dependency deduction and the indicators are pushed in advance to break the traditional scheduling paradigm; the pre-write, future mapping and structural closure maintenance under the condition of data non-arrival are realized. Therefore, the stability of the insurance industry data processing is facilitated.

[0017] 2. The construction dimension of the disturbance potential function includes field volatility, update delay and dependence strength, and a nonlinear weighting modeling mechanism is adopted, which helps to highlight the accuracy and prediction of the disturbance quantification model, and to strengthen the discrimination effectiveness of the index instability risk. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A flowchart of an insurance industry data processing method provided by an embodiment of the present application is shown in the figure. Figure 2 A module diagram of an insurance industry data processing device provided by an embodiment of the present application is shown in the figure. Figure 3 A structural diagram of an electronic device provided by an embodiment of the present application is shown in the figure.

[0019] Explanation of reference numerals: 21, acquisition module; 22, processing module; 31, processor; 32, communication bus; 33, user interface; 34, network interface; 35, memory. DETAILED DESCRIPTION

[0020] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in conjunction with the drawings in the embodiments of the specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments.

[0021] In the description of the embodiments of the present application, the words such as "for example" or "for instance" are used to represent an example, illustration or description. Any embodiment or design scheme described as "for example" or "for instance" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words such as "for example" or "for instance" are intended to present the relevant concept in a specific manner.

[0022] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are used for description purposes only, and should not be interpreted as indicating or implying relative importance or implicitly indicating the indicated technical features. Therefore, the features defined with "first" and "second" can explicitly or implicitly include one or more of the features. The terms "include", "contain", "have" and their variants mean "include but are not limited to", unless otherwise specifically emphasized.

[0023] To solve the above technical problems, the present application provides an insurance industry data processing method, which is described with reference to Figure 1 , Figure 1A flowchart of an insurance industry data processing method provided by an embodiment of the present application. The method is applied to a server and includes steps S110 to S150, which are as follows: S110, obtaining premium structure indicators, claim data indicators, policy activity indicators, and customer dimension indicators involved in a plurality of insurance business systems as indicator nodes, obtaining field dependency relationships, indicator calculation logic, and time sequence to establish indicator dependency paths, and forming an indicator graph.

[0024] Specifically, the server is a data processing node or a scheduling engine that undertakes the task of constructing the indicator graph. It is usually deployed in a distributed data platform or a cloud computing environment and has the ability to access multiple business systems, analyze data structures, and construct logical relationships. In this scenario, the server is responsible for obtaining indicator structure definitions and related dependency information from various insurance business systems and completing the construction of the indicator graph. The plurality of insurance business systems refers to a collection of data systems that constitute the core business processes of an insurance company, typically including: a premium collection and payment system that records premium collection and claim payment data; a policy management system that manages policy issuance, cancellation, and renewal processes; a claim processing system that records various types of claim cases and progress; and a customer service system that records customer basic information, contact information, and interaction behavior. Each of the above systems generates asynchronous, structured, or semi-structured data sets and forms cross-references at the indicator level.

[0025] The premium structure indicators refer to descriptive statistical indicators constructed based on premium income, used to reflect the characteristics of premium composition of different products, different channels, or different policy types. For example: channel-specific annual premium payment proportion; first-year premium payment and renewal premium proportion; monthly premium income by type of insurance. The claim data indicators refer to dynamic indicators constructed based on claim amount, claim frequency, or claim period as core variables, reflecting the time evolution characteristics of claim business. For example: monthly claim amount in the last 12 months; average claim period; average claim rate for critical illness insurance. The policy activity indicators refer to indicators constructed based on policy state changes, premium payment frequency, and policy action events, used to measure the usage frequency and life status of policies. For example: quarterly policy state change frequency; average premium payment interval; active policy proportion. The customer dimension indicators refer to statistical indicators constructed based on customer attributes and customer behavior, used to identify customer group characteristics and operational risks. For example: number of policies held by customers of different ages; customer contact success rate; customer churn warning score.

[0026] An index node refers to a structural node constructed in an index map in units of each independently defined index. Each index node corresponds to a set of explicit calculation logic, a group of dependent fields, and a group of time attributes, and is a basic unit in the map. For example, "monthly channel premium growth rate" is an index node, the calculation logic of which is the difference between this month's channel premium and last month's divided by last month's premium, and the dependent fields include premium amount, premium date, and channel code. Field dependency relationship refers to the mapping relationship between the original data fields required in the calculation process of a certain index node and its data source fields. This relationship determines which underlying data the index node depends on, and thus establishes the connection path between nodes. For example, if the "claim ratio" index depends on the "claim amount" and "effective premium" fields, and the "claim amount" field is provided by the claims settlement system and the "effective premium" field is provided by the collection and payment system, a field dependency relationship is formed between the index node and these two fields. Index calculation logic refers to the specific calculation formula, aggregation method, and dimension limitation required when each index node is generated. For example, the calculation logic of the "customer monthly average premium" index is to sum all premium records in a month on a certain customer dimension and divide by the monthly time span. The calculation logic defines the processing rules of the index node, determines the data aggregation method and processing conditions.

[0027] Time sequence refers to the dependent order of the calculation of each index node in the time dimension, reflecting the periodic sequence relationship in the calculation scheduling. For example, "monthly claim ratio" depends on "monthly claim amount" and "monthly effective premium", while "quarterly claim data" depends on the "monthly claim ratio" of the last three months, so there is a dependent sequence in the time sequence. Index dependency path refers to the path structure formed between index nodes due to field sharing, calculation logic reference, or time aggregation relationship, which is used to represent the complete chain required for the generation of a certain index. For example, the "quarterly customer renewal premium growth rate" node depends on the "monthly customer renewal premium" node, which in turn depends on the "policy renewal amount" field, thus forming a dependent path along the field layer to the index layer. Index map refers to a graph structure with causal relationship, directionality, and time logic formed by connecting multiple index nodes through index dependency paths, which is used to express the structural coupling relationship of the index system in the insurance business as a whole. The map supports path tracking, stability analysis, and push scheduling, and is the structural basis for subsequent disturbance identification and index rewriting scheduling.

[0028] For example, when constructing the "quarterly loss ratio trend" index graph, the server extracts the "loss amount" field from the claim system and the "effective premium" field from the premium collection and payment system, takes the "loss ratio" as the index node, identifies its calculation logic as the loss amount divided by the effective premium, and then aggregates the "monthly loss ratio" nodes in chronological order to form the "quarterly loss ratio" node and establish path connections in the graph. If a "customer risk score" node is constructed, which references the "quarterly loss ratio" and "customer age segment dimension indicators" in its calculation logic, a multi-layer nested dependency path is formed between this node and all previously mentioned index nodes, and the entire structure is included in the index graph.

[0029] Through the above definitions and examples, this paragraph describes a data structure centered on the index graph, which is used to construct a structured index system with field dependencies, calculation logic, and chronological order in the multi-source data environment of the insurance industry.

[0030] In one possible implementation, the premium structure indicators, loss data indicators, policy activity indicators, and customer dimension indicators involved in multiple insurance business systems are obtained as index nodes, the field dependency relationships between the indicators, the index calculation logic, and the chronological order are obtained to establish the index dependency path, and the index graph is formed. Specifically, the structured definition information of the premium structure indicators, loss data indicators, policy activity indicators, and customer dimension indicators is obtained from the premium collection and payment system, policy system, claim system, and customer service system, respectively. The dependent mapping relationship between the index nodes and the fields is established according to the calculation formula of each index. For index nodes that have field sharing or formula derivation relationships between multiple index nodes, the index dependency path is constructed based on the intersection of the field dependency paths. The index nodes and the index dependency paths are constructed into a directional graph structure, and the time sequence is labeled based on the calculation period information of each index node to form an index graph with field dependency structure and time logic constraints.

[0031] Specifically, first, the structured definition information of various indicators is extracted from the premium collection and payment system, policy system, claim system, and customer service system, and the premium structure indicators, loss data indicators, policy activity indicators, and customer dimension indicators are extracted as index nodes. Each index node corresponds to a structured definition unit, which includes the calculation formula, calculation period, dependent field list, and data domain identifier of the index. For example, for the "year-on-year renewal premium growth rate" premium structure indicator, the structured definition information includes: the calculation formula is "the difference between the current period renewal premium and the comparison period renewal premium divided by the comparison period renewal premium", the calculation period is monthly, the dependent fields include "renewal premium amount" and "policy payment date", and the data domain identifier is "premium collection and payment system".

[0032] Secondly, for each index node, the calculation formula recorded in its structured definition unit is parsed to determine the set of original data fields on which the index node depends, and a one-to-one mapping relationship between the index node and the original data fields is established to form an index field dependency mapping structure. The dependency mapping relationship can be represented as:

[0033] wherein, represents the index node depends on the field set, represents each original data field of the index, such as “renewal premium amount”, “premium payment timestamp”, “currency”, etc. The structure is used to support subsequent field cross-analysis between index nodes.

[0034] Subsequently, the field dependency relationships between multiple index nodes are cross-compared. If two or more index nodes depend on the same original data field, or the calculation formula of one index node references the output result of another index node, it is determined that there is a field sharing or formula derivation relationship between them. On this basis, the system connects the dependency paths between these index nodes according to the field sharing intersection to construct a set of index dependency paths. Let index nodes and depend on field sets and respectively, if:

[0035] or there is:

[0036] then an index dependency path from index node to index node is established, indicating that the generation of depends on the calculation result or field content of .

[0037] After the index dependency paths are constructed, all index nodes and their index dependency paths are organized into a directional graph structure, i.e., an index graph, where the nodes in the graph are index nodes, the edges are index dependency paths, and the direction represents the dependency flow direction. Each edge in the graph points from the dependent node to the depended node, forming a directed graph with logical structure and direction wherein is the set of all index nodes, is the set of all index dependency paths.

[0038] Finally, combined with the calculation period information of each index node, the system performs a time sequence labeling operation on the index map, that is, each node is relatively positioned on the time axis according to its minimum calculation period. For example, the calculation period of the “monthly claim ratio” node is 30 days, and the calculation period of the “quarterly claim data” node is 90 days, so the system marks the “monthly claim ratio” as a front layer node on the time axis, and the latter as a dependent node for aggregation operation. This time sequence labeling mechanism is used to maintain the causal logic consistency of all index nodes in the index map, ensuring that there is no time reversal or path misplacement when performing subsequent scheduling and pushing operations, thereby forming an index map with field dependency structure and time logic constraints.

[0039] S120, acquire the field variation frequency, update delay characteristics and dependency strength parameters of the data source relied on by the index node in the historical period, and establish an index disturbance potential function combined with the update state of each field in the current data stream.

[0040] Specifically, the historical period refers to the time period selected for statistical modeling and state evaluation, usually 30 days, 90 days, 180 days or other whole period units, used to quantify field fluctuation behavior and system update characteristics. For example, when analyzing the “monthly claim ratio” index, the system may select the past 12 months of claim and premium data as the historical period to extract the fluctuation and time delay rules of the index dependent fields. Data source refers to the business system or database table structure to which the original field relied on by the index node belongs. Common data sources in the insurance industry include the premium payment record table in the premium payment system, the policy status table in the policy system, and the case registration table in the claim system.

[0041] Field variation frequency refers to the number of value changes of a certain original data field in the historical period, which is used to measure the volatility and input instability of the field. The field variation frequency affects the continuity and consistency of index calculation. For example, if the “claim amount” field is updated three times in a day, and the “customer label level” field is updated once a week, the former has a high variation frequency and is sensitive. Update delay characteristics refer to the time delay attribute of an original data field from the occurrence of a real business event to being recorded into the database, which is used to describe the timeliness and reliability of the field. Update delay characteristics include: average update delay: the average time lag of the field in multiple events; maximum update delay: the latest time lag of the occurrence; update delay standard deviation: used to measure the update volatility. For example, in the claim system, the “claim settlement date” field may have a large delay due to manual processing, while the “claim amount” field is usually generated early, and the delay characteristics of the two are significantly different.

[0042] The dependency strength parameter refers to the influence degree of the original data field on the calculation result of a certain index node, reflecting whether the change of the field will significantly change the output of the index. The dependency strength parameter can be obtained through sensitivity analysis or structural regression modeling. If an index is highly dependent on the "premium amount" field and not sensitive to the "payment method" field, the former has a higher dependency strength. For example, in the index "annual premium growth rate", the change of the premium amount directly affects the index output, and its dependency strength is much higher than that of the payment channel or payment frequency. The update state of each field in the current data stream refers to whether the original data field has been updated, whether the updated value is valid, whether there is a mutation trend, and other information in the current scheduling period, which is used to dynamically evaluate the real-time availability of the field. For example, if the "policy status" field is covered multiple times in the current period, it means that the field is experiencing a period of unstable state, which will affect the accuracy of the index that depends on it. The index disturbance potential function refers to modeling the field change frequency, update delay characteristics, and dependency strength parameters as function variables to construct a function expression that represents the risk of calculation failure or path disorder caused by input abnormalities, time delay fluctuations, or structural instability of a certain index node in the future time window. Its essence is a comprehensive quantitative expression of index stability, which is used to identify potential unstable nodes in the graph.

[0043] For example, when evaluating the disturbance potential of the "monthly customer renewal rate" index node, the server will analyze the historical data of the fields it depends on, such as "policy renewal status", "customer payment time", and "renewal amount". In the past 90 days, if the "policy renewal status" field has changed every day, the fluctuation frequency is high, and there is an average delay of 3 days in the database, and this field is a decision branch variable in the index formula, with high dependency strength. In the current period, the field has not been updated for two consecutive days, triggering a field inconsistency state, so the system will input the above factors into the disturbance potential function, and the result shows that the index node is in a high disturbance risk state, and needs to enter the subsequent pre-push mechanism to maintain the structural closure.

[0044] Therefore, the above content is based on the historical statistics and real-time update trend of the original field to establish a quantitative prediction model of future uncertainty and instability trend at the index level, forming the core input quantity for scheduling and structure intervention in the index graph.

[0045] In a possible implementation, the field variation frequency, update delay feature and dependency strength parameter of the data source relied on by the indicator node in the historical period are acquired, and the update state of each field in the current data stream is combined to establish an indicator disturbance potential function, specifically including: extracting the original data field relied on by each indicator node, acquiring the field variation frequency of the original data field in the historical period, and the field variation frequency is used to describe the fluctuation strength of the field value; the update delay feature of the original data field in the historical period is acquired, and the update delay feature includes the time interval mean, maximum value and standard deviation from generation to storage of the field, and the update delay feature is used to describe the timeliness uncertainty of field update; the dependency strength parameter between the original data field and the indicator node is calculated; the real-time update state of each original data field in the current data stream is combined, a nonlinear weighted model is constructed based on the field variation frequency, the update delay feature and the dependency strength parameter, and an indicator disturbance potential function representing the local structural instability of the indicator node is generated.

[0046] Specifically, first, the server traverses each indicator node in the indicator graph, extracts all original data fields recorded in the structured definition of the indicator node, and forms a field dependency set. Taking the "quarterly claim rate fluctuation" indicator node as an example, its original data fields include "claim amount", "premium amount", "claim confirmation time", "policy status code" and the like, and the server takes these original data fields as modeling input basis, and fully calls the records of the original data fields in the selected historical period (such as the past 90 days).

[0047] Subsequently, the field variation frequency of each original data field is calculated to measure the value fluctuation degree of the field value in the historical period. The server sorts the historical values of each field in time sequence, and counts the number of times the value changes. The field variation frequency is defined as:

[0048] Wherein, represents the original data field in the historical period, represents the total length of the time sequence, represents the value of the field at the time point , and is a logical discrimination function, which is 1 if the previous and subsequent values are not equal, indicating that fluctuation occurs. The higher the field variation frequency, the more susceptible the indicator node corresponding to the field to input disturbance.

[0049] Then, the server calculates the update delay feature of each raw data field to characterize the timeliness and uncertainty of the field in the data link. The update delay of each raw data field is defined as the interval from the time of the business event (field generation time) to the time of writing into the database. The delay feature includes the following three statistics:

[0050] wherein, is the set of all historical delay values of field is the average delay, is the delay standard deviation, is the maximum delay, and the three jointly constitute the update delay feature of the field, which is used to quantify the stability and lag of the field in the database.

[0051] After that, the dependence strength parameter between each raw data field and the corresponding index node is calculated, which represents the influence of the field variation on the index value. This parameter is obtained by modeling the sensitivity analysis of historical data, and is defined as:

[0052] wherein, represents the sensitivity of field to index node , which is equivalent to the partial derivative of the index value with respect to the field, reflecting the change amplitude caused by the unit field disturbance on the index output. If the field is a multiplication weight item or an impact calculation denominator, its dependence strength is generally high.

[0053] In combination with the above static features, the server further obtains the update state of each field in the current scheduling period, including whether to update, whether the update is successful, whether the field value is stable, etc., and constructs a dynamic state vector , which is defined as:

[0054] wherein, represents whether field has a new value in the current period, taking the value of 0 or 1; represents whether the field value in the current period has a mutation compared with the last period, also taking the value of 0 or 1. The state vector is used to dynamically adjust the disturbance evaluation result.

[0055] Finally, the server combines the field variation frequency , the update delay feature , the dependence strength parameter , and the current data stream state vector ​The joint input nonlinear weighting model is used to generate an index disturbance potential function. The function is used to evaluate the risk level of structural instability of a certain index node in the current scheduling period and a future time window, and is defined as:

[0056] wherein, is the disturbance potential function value of the index node ; is the weight of the field in the index formula; is an empirical adjustment coefficient used to balance the influence of volatility, delay and dependence strength; is a dynamic adjustment factor that sensitively weights the current period state. The larger the function value, the higher the risk of input disturbance that the index node is currently subjected to, and the index node should enter the pre-scheduling priority processing process.

[0057] Through the above steps, the system can comprehensively quantify the future potential instability of the index node and identify the index region that may cause structural cascade instability at the atlas level, so as to provide accurate input for the subsequent push channel allocation and structure closure control.

[0058] S130, identifying an index node with an index disturbance potential function greater than a preset threshold value in the index atlas as a potential instability index node, and performing reverse dependence deduction based on the index dependence path where the potential instability index node is located to construct a potential instability path set.

[0059] Specifically, the preset threshold value refers to the risk tolerance boundary determined by the system through experience setting or historical training, which is used to determine whether a certain index disturbance potential function reaches a instability threshold value. If the function value exceeds the threshold value, it means that the current structure state may trigger index calculation failure, result misplacement or path breakage in the future period. For example, if the system sets the threshold value to 0.65, and the disturbance potential function value of a certain index node is 0.78, the node is marked as a potential instability index node.

[0060] The potential instability index node refers to an index node that is identified as having a high structural risk according to the evaluation result of the index disturbance potential function in the current scheduling period. Such nodes may fail to complete normal calculation due to unattainable upstream fields, non-closed dependence paths or time logic disorder, and are unstable trigger sources in the atlas structure. For example, the "monthly high compensation alert index" node depends on the input fields of multiple systems, and if any of the fields is not updated in time, the index is invalid, and it is identified as a potential instability index node because its disturbance potential function is continuously higher than the threshold value.

[0061] ​The index dependency path refers to a path structure constituting a relationship between nodes in the index graph. Each index dependency path is composed of a starting index node, a terminal index node, and a logical connection on the path. The path direction represents the dependency flow direction, and the path structure can reflect the calculation flow, field reference, and time sequence aggregation relationship. For example, the index node "customer activity score" depends on "monthly login times", and the latter depends on the "login event" field of the "interaction record table". The path is: login event → monthly login times → customer activity score. Reverse dependency deduction refers to taking the identified potential unstable index node as the starting point, recursively tracing all upstream index nodes along the index dependency path in reverse direction, and identifying which nodes' changes may be the source of the current node's instability, thereby restoring the complete dependency chain of the node in the graph. Reverse deduction not only includes path traversal operations, but also needs to record the path structure for subsequent structural intervention operations. For example, after identifying the "quarterly risk score" node as a potential unstable index node, the system deduces along its path upwards and finds that it depends on two intermediate index nodes "monthly claim rate" and "customer contact success rate", and "customer contact success rate" depends on "contact record update time", finally forming a complete reverse path set.

[0062] The potential unstable path set refers to a path set formed by performing reverse dependency deduction on all potential unstable index nodes, used to represent the path area in the index graph that has a logical conduction instability risk in the current scheduling period. The paths covered by this set are the key objects for subsequent scheduling, pushing, and structure correction. For example, if three potential unstable index nodes are related to the claim index chain, the customer index chain, and the policy status chain, respectively, nine paths are obtained after reverse dependency deduction, forming a potential unstable path set, which is used to identify high-risk structure areas that need to be scheduled in advance.

[0063] In summary, the server dynamically identifies high-risk index nodes in the index graph based on the index disturbance potential function, and takes these nodes as the starting point to perform reverse recursive tracing of the index dependency path, constructing a path structure set from the high disturbance source to each instability root cause, to support subsequent scheduling priority rearrangement and structure stability repair. It is the logical entrance and key identification process of the entire disturbance control mechanism.

[0064] In a possible implementation, in the index graph, an index node with an index disturbance potential function greater than a preset threshold is identified as a potential instability index node, and based on an index dependence path in which the potential instability index node is located, reverse dependence deduction is performed to construct a potential instability path set, specifically including: traversing the index nodes in the index graph, obtaining the index disturbance potential function value corresponding to each index node, and identifying an index node with a value greater than a preset threshold as a potential instability index node; taking each potential instability index node as a starting point, performing reverse dependence deduction along the index dependence path in the index graph, recursively tracing upstream index nodes on which the potential instability index node depends, recording the dependence path formed and constituting a path set; aggregating the path set to obtain a potential instability path set, the potential instability path set being used to represent an index dependence structure region that causes structural cascade instability due to an abnormal index disturbance potential function.

[0065] Specifically, first, the server performs a traversal operation on all index nodes in the index graph, and sequentially calls the index disturbance potential function value corresponding to each index node in the current scheduling period. The index disturbance potential function is a nonlinear comprehensive function constructed based on the field variation frequency, the field update delay feature, the dependence strength parameter between the field and the index, and the current field update state. The higher the value, the greater the instability risk faced by the index node in the current structure. The server compares the disturbance potential function value of each index node extracted in the traversal process with the system preset threshold. If the disturbance potential function value corresponding to a certain index node satisfies:

[0066] the index node is marked as a potential instability index node, where represents the disturbance potential function value of the index node , and is a global threshold value used for structure stability evaluation.

[0067] Subsequently, taking each identified potential instability index node as a starting point, the server performs a reverse dependence deduction operation in the index graph based on the index dependence path established by the potential instability index node, that is, traces the upstream index nodes on which the current potential instability index node depends in the direction of the dependence path. The tracing process is performed in the reverse direction of the directed edge in the graph structure, and each upstream index node in the path has a conductive effect on the current potential instability index node through field dependence or formula call relationship. For each potential instability index node , the following path deduction logic is performed:

[0068] wherein represents the upstream index nodes on which the potential instability index node An entire dependent path derived by upward inference, each node in the path satisfies and there is a directed edge , wherein denotes a set of index nodes in the graph, denotes a set of dependent paths of the index.

[0069] During the path inference process, the server records each successfully established dependent path and aggregates all the paths into a set of path collection:

[0070] , wherein denotes a set of upstream dependent paths corresponding to all potential unstable index nodes, is the total number of potential unstable index nodes identified in the current scheduling period.

[0071] Finally, the server performs structure merging and path merging operations on the path collection to eliminate overlapping segments and uniformly represent the potential unstable path set. The potential unstable path set constitutes a highly coupled dependent section in the current index graph that is most likely to collapse, representing a structural cascade instability region that may be triggered by abnormal increases in potential function values due to perturbations of multiple index nodes. This set will provide key inputs in subsequent scheduling priority adjustment, push channel reconstruction, and index write control mechanisms, and is the path-level data structure foundation supporting feedforward stability maintenance.

[0072] S140, the potential unstable index nodes in the potential unstable path set are separated from the original write process and classified into the potential unstable index push channel to generate the corresponding pre-write data snapshot.

[0073] Specifically, the original write process refers to the standard write-back process followed by index nodes in normal state, usually triggered by data arrival state, dependent field readiness flag, and calculation completion flag. In this process, all index nodes are written to the database or index cache area in sequence according to the timing path in the graph, with strict sequence control and data integrity protection. Separation refers to removing a potential unstable index node from its original write process, so that it no longer participates in passive scheduling write based on data readiness state, but enters an independent control push mechanism, avoiding the node triggering a structural cascade failure of the graph path due to data not arriving, logic mismatch, or timing imbalance. The potential unstable index push channel is a separate scheduling channel designed specifically for handling potential unstable index nodes. This channel bypasses the original state-based write-back trigger mechanism and uses disturbance priority-driven scheduling. Its design goal is to occupy the scheduling window in advance, ensuring the causal closure and path integrity of the index graph structure. The push channel has higher resource priority, cache capacity, and pre-write protection mechanism.

[0074] Pre-write data snapshot refers to the storage of intermediate state indicator results generated based on ready fields and structure paths when the potential instability indicator node has not yet met the complete write condition (such as partial absence of data fields, partial closure of dependent paths). This snapshot is used for placeholder pre-writing, waiting for subsequent data to arrive to complete the structure and correct the results, which is an important intermediate object for breakpoint compensation mechanism and push channel write-back closure. The pre-write data snapshot includes the following contents: the filling value of the current available field; the calculation formula identifier of the indicator node; the structure placeholder mark of the unready field; the current indicator dependent path structure hash; the logical label identifying the "future mapping to be corrected state".

[0075] For example: In a certain scheduling period, the server identifies that the "customer dimension claim frequency index" is a potential instability indicator node, because its dependent field "claim occurrence date" has continuous delay, and the disturbance potential function value is higher than the threshold. The server separates it from the original regular write process of "data ready -> calculation -> write-back", and classifies it into the potential instability indicator push channel. Since part of the field has not been updated, the system constructs a pre-write data snapshot based on the current arrived fields such as "customer number" "policy number" and part of the time series information, writes the snapshot into the indicator cache structure and marks it as "to be corrected", and waits for the subsequent field data to be filled before performing logical closure and formal writing.

[0076] In summary, after identifying the risk of structure instability, the server actively transfers high-risk indicator nodes to an independent channel under the structure defense mechanism, and performs partial write snapshot generation to ensure the path continuity and causal closure of the indicator graph as a whole, avoiding structure conduction failure and result cascade disorder.

[0077] In a possible implementation, a potential instability indicator node in a potential instability path set is separated from an original write process and is classified into a potential instability indicator pushing channel, and a corresponding pre-write data snapshot is generated, specifically including: identifying each potential instability indicator node in the potential instability path set, judging whether each potential instability indicator node is in a regular write process based on data arrival triggering; if in the regular write process, separating the potential instability indicator node from the regular write process, and canceling the state record of the potential instability indicator node in the regular scheduling queue; allocating a potential instability indicator pushing channel for each separated potential instability indicator node, and establishing a scheduling context corresponding to the potential instability indicator pushing channel in the scheduling control structure; generating a pre-write data snapshot based on the corresponding indicator dependency path, the arrived original data field, and the indicator calculation logic of each potential instability indicator node in the indicator graph, the pre-write data snapshot including the filling value of the current available field, the indicator calculation logic path identifier, and the indicator node structure state information; writing the pre-write data snapshot into the indicator cache structure, and marking the pre-write data snapshot as in a future mapping to be corrected state.

[0078] Specifically, first, the server traverses the potential instability path set obtained by the pre-sequence deduction, and identifies each potential instability indicator node contained in the path set. The server checks the scheduling state of each potential instability indicator node one by one according to the node unique identifier in the indicator graph, and judges whether it is still in the regular write process based on data arrival triggering. The judgment standard is: whether the indicator node has been mounted in the scheduling queue driven by the data readiness, and whether it is in the standard scheduling path waiting for triggering write after the dependent field is complete.

[0079] If it is determined that a certain potential instability indicator node is still in the regular write process, the server immediately separates it from the process, and the specific operation includes: canceling the readiness flag of the indicator node in the scheduling controller, canceling its queuing position in the current period scheduling task table, and releasing its original write-back lock resource, so that the node is separated from the write process driven by the data arrival.

[0080] Next, the server allocates an independent potential instability indicator pushing channel for each separated potential instability indicator node. The pushing channel is a channel resource reserved in the scheduling control structure, which has the ability of high priority, feed-forward triggering, and structure closed control. At the same time of channel allocation, the server creates a dedicated scheduling context for the indicator node in the scheduling control structure, and the scheduling context records the following contents: indicator node number, indicator dependency path hash, current arrived field set, missing field list, path depth, and upstream state mapping structure.

[0081] After the push channel configuration is completed, the server generates a pre-write data snapshot based on the index dependency path structure corresponding to the potential instability index node in the index graph, in combination with the current arrived data field and the calculation formula of the index node. The pre-write data snapshot is composed of the following three main components: the filling value of the current available field: that is, the field data set successfully pulled from the data source and standardized in the current scheduling period.

[0082] The index calculation logic path identifier: that is, the formula structure and its upstream node dependency path relied on by the index node calculation, encoded in a hash structure, denoted as:

[0083] Among them, is the calculation logic expression of the index node , and is its dependency path in the graph.

[0084] The index node structure state information: records the current calculation state of the index node in the graph structure, including the dependency field state, the upstream index readiness flag and the timing closure, defined as a state tuple:

[0085] Among them, is the available state of the field , is the completion flag of the upstream index node , is the current timestamp.

[0086] The server writes the pre-write data snapshot constructed into the index cache structure. The index cache structure is a multi-version parallel write intermediate layer storage component, used to persist the intermediate results of the index that have not completed structure closure. The server logically labels the snapshot as "future mapping to be corrected state", which will trigger the backfill calculation mechanism and the correction write process in the subsequent data arrival and structure closure judgment process.

[0087] Through the above steps, the server realizes the structure isolation, resource redirection and data pre-write of the potential instability index node without interrupting the overall write scheduling process of the index graph, which occupies the integrity of the index graph path and lays a stable structure foundation for the subsequent causal closure and timing consistency.

[0088] S150, if it is determined that the actual data required by the potential instability index node has arrived, the pre-write data snapshot is executed for correction write, and the identification threshold and channel resource configuration are dynamically adjusted to maintain the causal structure closure of the index graph and the consistency of the index write sequence.

[0089] In particular, actual data arrival means that all original data fields on which the potential instability indicator node depends have been successfully synchronized from various business systems to the current scheduling period, and have reached a state that can be used for calculation through data cleaning, standardization, and time alignment processes. The judgment criteria include field existence, value validity, field timestamp consistency with the current period window. Pre-written data snapshots refer to intermediate indicator result structures generated by the server when the fields are not yet fully available, used for placeholder and subsequent structure closure calculation. Its content includes the filling value of the arrived field, the calculation logic path identifier, and the structure state marker. The core function of the snapshot is to maintain the continuity of the path structure and provide a basis for subsequent correction. For example, in the snapshot structure generated when the field "policy status code" is missing, the field bit is marked as an empty bit occupied by the "future mapping to be corrected" state control.

[0090] Correction writing means that after the actual data arrives, the server performs structure completion and result update operations on the pre-written data snapshot according to the calculation logic recorded in the indicator map, and formally writes the calculation value to the indicator storage structure. Correction writing contains two parts: one is calculation closed-loop update, and the other is state synchronization writing. The execution logic is as follows: cover the actual field value in the snapshot; call the indicator node calculation logic to re-execute the indicator function; replace the old value in the snapshot; mark the indicator node as "write completion" state. The recognition threshold refers to the disturbance potential function critical value used to determine whether a certain indicator node belongs to a potential instability node. This threshold affects the server's recognition granularity, and a too low threshold will result in too many indicators being stripped, and a too high threshold will miss potential structure breakage risk nodes. Dynamically adjusting the recognition threshold means that the server can adjust the threshold up or down in time to improve the system's adaptive ability according to the system's running load and map instability frequency. For example, if the system finds that the push channel load is too high for three consecutive periods, the server can appropriately adjust the recognition threshold θ\thetaθ, thereby shrinking the set of indicator nodes identified as potential instability.

[0091] Channel resource configuration refers to the number of concurrent dispatches, cache capacity, priority queue weight, and other system resources allocated to the potential instability indicator pushing channel. Dynamic adjustment of channel resource configuration refers to the server adjusting relevant parameters in real time based on the current number of potential instability indicator nodes, the degree of scheduling conflict, and the number of snapshot accumulations to avoid channel resource bottlenecks or scheduling delays. For example, if there are a large number of potential instability indicator nodes in the customer dimension path in the current period, the server can increase the concurrency of the pushing channel of this path from 3 to 6 and increase its priority in the write queue to speed up closure. The causal structure closure of the indicator graph refers to the fact that in the indicator graph, each indicator node on a dependent path can complete its own calculation based on the calculation results and time sequence of its upstream nodes, forming a complete logical chain from top to bottom, and there are no nodes with broken dependencies, suspended paths, or closure failures. The modified write operation ensures that the nodes stripped from the pushing channel in the path structure are re-integrated into the causal chain of the graph, ensuring that their output can be used for downstream indicator calculation. The consistency of the indicator write order refers to the fact that during multi-indicator writing, the system strictly follows the dependency order and time period defined in the indicator graph to avoid timing disorder phenomena such as early writing of downstream indicators and late writing of upstream indicators. The indicator nodes after the modified write need to synchronize their timestamps and graph scheduling sequences to ensure that the indicator results in the entire graph are completely consistent in terms of semantics, time, and logic.

[0092] For example, the server identifies "quarterly loss ratio risk score" as a potential instability indicator node in scheduling period T, and the field "loss amount" is delayed. The server generates a pre-write data snapshot for the node and transfers it to the pushing channel; by period T+2, the field data arrives, and the server immediately recalculates the indicator value using the indicator formula, updates the snapshot structure to the official result, and writes it to the indicator storage structure, while marking the node as "structure closure completed". Due to the decrease in pushing channel processing rate in the current period, the server automatically increases the pushing channel concurrency from 5 to 8 and increases the disturbance identification threshold from 0.65 to 0.72 to narrow the identification range and relieve resource pressure, thereby maintaining the causal structure closure of the indicator graph and the write order consistency of the entire path.

[0093] In summary, after the data is completed, the server completes the closure correction of the pre-write data snapshot and dynamically adjusts the identification and scheduling strategy based on the scheduling state to ensure the continuity of all dependent paths in the graph and the global consistency of the indicator output, which is the core mechanism to achieve structural stability and logical control.

[0094] In one possible implementation, if the actual data required for a potential unstable indicator node to arrive is determined, a correction write is performed on the pre-written data snapshot. Simultaneously, the identification threshold and channel resource configuration are dynamically adjusted. Specifically, this includes: monitoring the arrival status of the original data field corresponding to the potential unstable indicator node within the current scheduling cycle; if the original data field has arrived and its value is valid, the potential unstable indicator node is marked as correctable; invoking the indicator calculation logic corresponding to the potential unstable indicator node in the indicator graph, performing structural consistency verification and numerical correction operations on the pre-written data snapshot stored in the indicator cache structure, and generating a corrected indicator result; writing the corrected indicator result into the indicator storage structure and updating the write status of the potential unstable indicator node in the indicator graph to indicate that structural closure is complete; dynamically adjusting the identification threshold of the potential unstable indicator node based on the changing trend of the disturbance potential function within continuous scheduling cycles, and dynamically adjusting the scheduling concurrency, cache capacity, and priority weight of the potential unstable indicator push channel based on the number of potential unstable indicator nodes and the degree of disturbance concentration.

[0095] Specifically, firstly, the server continuously monitors the arrival status of all raw data fields upon which each potentially unstable indicator node depends in the current scheduling cycle. This includes whether the data fields have been stored in the database, whether the field values ​​conform to the field definition domain, and whether they have valid timestamps that fall within the allowable range of the scheduling cycle. Only when all raw data fields meet the above conditions does the server mark the potentially unstable indicator node as correctable. This marking signifies that the indicator node has the conditions to complete the calculation and execution of corrective writes, and can transition from a write-ahead state to a structurally closed state.

[0096] Secondly, the server invokes the indicator calculation logic recorded in the indicator graph to perform structural consistency checks and numerical correction operations on the pre-written data snapshot stored in the indicator cache structure. The structural consistency check includes verifying whether the field structure in the pre-written data snapshot matches the field dependencies in the indicator calculation logic, whether the field values ​​are complete, and whether the formula structure is correctly parsed. If the check passes, the correction calculation operation is performed, that is, the field values ​​are re-substituted into the indicator function for calculation, replacing the temporary calculated values ​​in the snapshot, and generating the corrected indicator result. The indicator result is represented in the following form:

[0097] in, Indicator Node The final calculation results, This is the calculation logic function for the indicator node. This refers to the original data fields that this node depends on.

[0098] Further, the server writes the modified index result into an index storage structure. The index storage structure is a structured storage area in a persistent database, which has multiple attributes such as index value, writing time, calculation state, field version, etc. After the writing is completed, the server updates the writing state of the potential unstable index node in the index graph, changes the state from “future mapping to be modified” to “structure closed and completed”, and releases the cache resource and scheduling context of the potential unstable index node in the push channel.

[0099] Subsequently, the server dynamically adjusts the identification threshold according to the change trend of the disturbance potential function in the continuous scheduling period. The definition of the disturbance potential function is as follows:

[0100] wherein, is the disturbance potential function value of the index node is the change frequency of the field is the update delay feature of the field is the dependence strength between the field and the index, , , is a nonlinear weighting coefficient.

[0101] If the number of potential unstable index nodes identified in continuous multiple scheduling periods continues to rise, it means that the identification mechanism is too sensitive, and the server will appropriately increase the threshold to reduce false positives. Conversely, if the number of identified potential unstable index nodes is small, the threshold will be lowered to improve the identification coverage.

[0102] Finally, the server dynamically adjusts the scheduling concurrency, cache capacity, and priority weight of the potential unstable index push channel according to the number of potential unstable index nodes in the current period and the aggregation degree of the path in the graph. If multiple potential unstable index nodes are found to be concentrated in a certain dependent path, the server will preferentially increase the concurrency and resource allocation weight of the channel to which the path belongs to ensure the closure priority of the path, thereby avoiding the formation of intra-path cascading collapse.

[0103] The above steps form a closed-loop processing mechanism, which ensures that the data conditions are met immediately and the writing is modified at the same time, and dynamically adjusts the parameters to ensure the stability of the overall graph structure and the optimization of resource allocation, providing stable operation guarantee for the complex, high-asynchronous, and strong-dependent index calculation environment of the insurance industry.

[0104] ​​​In a possible implementation, after the index graph is constructed, the calculation periods of the index nodes are standardized; the daily, weekly, monthly and quarterly calculation periods are mapped to a unified time dimension reference; the time sequence of the index nodes in the index dependency path is marked based on the time dimension reference, to ensure the time consistency of the cause-effect path and the period coordination of the calculation scheduling in the index graph.

[0105] Specifically, after the index graph is constructed, the server first needs to standardize the calculation periods of all the index nodes in the graph, to eliminate the time scale differences caused by different index calculation frequencies. Specifically, the server extracts the calculation period attribute marked in the original definition of each index node, and the common period types include daily, weekly, monthly and quarterly calculation periods. These discrete period attributes are mapped to a unified time dimension reference, to construct a time scale system with strong comparability and hierarchical division capability. For example, all the calculation period units are converted to the smallest time unit "day", and the integer multiple reference scale weights are specified for each period type: the daily calculation period is set to , the weekly calculation period is set to , the monthly calculation period is set to , and the quarterly calculation period is set to . The above period conversion enables the time granularity of all the index nodes to be linearly compared and sequentially sorted in the unified dimension, providing a basis for subsequent scheduling control and cause-effect path tracing.

[0106] Subsequently, the server marks the time sequence of each index node in the index dependency path based on the time dimension reference. Taking each index dependency path as a unit, the standardized period scale values of all the nodes in the path are extracted, sorted in ascending order of the period scale values, and then the time cause-effect sequence along each path in the index graph is determined. For example, if the index node A in the path is a daily calculation period, the node B is a weekly calculation period, and the node C is a monthly calculation period, according to the above period weights, we have:

[0107] The time sequence of the index dependency path is A→B→C, and the server will mark the scheduling sequence and time index of the nodes in sequence, to construct the following identification vector:

[0108] Wherein represents the day when the index node X should be scheduled and calculated after the current period t, and i is obtained by accumulating the period difference.

[0109] ​Through the above time sequence annotation, the server can explicitly distinguish the time sequence constraint relationship between different index nodes. Even if there is a field dependency relationship between two nodes, as long as the time sequence is inconsistent (the downstream index node calculation period is earlier than the upstream node), the system can also determine it as a non-closed path through annotation, thereby avoiding structural errors caused by early scheduling.

[0110] Finally, the server performs consistency verification on the entire index graph to ensure that the index nodes involved in each index dependency path in the graph have coordination in the time dimension reference, that is, the time annotations of all index nodes do not appear to be backtracking or overlapping. If it is found that there are multiple nodes in the path that share fields but the calculation period is seriously misaligned (such as a daily index depending on a quarterly index), the system will issue a period conflict warning and automatically adjust the node scheduling order or request index definition adjustment to achieve period coordination.

[0111] In summary, establishing a unified time dimension reference, performing time sequence annotation, and verifying calculation period consistency are key steps to achieve stable operation of the index graph, avoiding scheduling chaos, data misalignment, and causal logic disorder, and are important guarantees for building a graph structure with cross-period calculation capability and scheduling safety.

[0112] The present application also provides an insurance industry data processing device, referring to Figure 2 , Figure 2 A module schematic diagram of an insurance industry data processing device provided by an embodiment of the present application. The device is a server, which includes an acquisition module 21 and a processing module 22. The acquisition module 21 acquires premium structure indicators, claim payment data indicators, policy activity indicators, and customer dimension indicators involved in a plurality of insurance business systems as index nodes, acquires field dependency relationships between the indicators, index calculation logic, and time sequence to establish index dependency paths, and forms an index graph. The acquisition module 21 acquires the field variation frequency, update delay characteristics, and dependency strength parameters of the data sources relied on by the index nodes in the historical period, and establishes an index disturbance potential function in combination with the update state of each field in the current data stream. The processing module 22 identifies index nodes with an index disturbance potential function greater than a preset threshold as potential unstable index nodes in the index graph, and performs reverse dependency deduction based on the index dependency path in which the potential unstable index nodes are located to construct a potential unstable path set. The processing module 22 separates the potential unstable index nodes in the potential unstable path set from the original write-in process and classifies them into a potential unstable index push channel to generate a corresponding pre-write-in data snapshot. The processing module 22 performs a correction write-in on the pre-write-in data snapshot if the actual data required by the potential unstable index nodes arrives, and dynamically adjusts the identification threshold and channel resource configuration to maintain the causal structure closure and index write-in sequence consistency of the index graph.

[0113] It should be noted that the apparatus provided in the above embodiments is only used as an example for dividing the above functional modules to achieve their functions, and in actual applications, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0114] The application also provides an electronic device, referring to Figure 3 , Figure 3 A structural schematic diagram of an electronic device provided by the application is shown. The electronic device can include at least one processor 31, at least one network interface 34, a user interface 33, a memory 35, and at least one communication bus 32.

[0115] The communication bus 32 is used to realize the connection and communication between the components.

[0116] The user interface 33 can include a display screen (Display) and a camera (Camera). Optionally, the user interface 33 can also include a standard wired interface and a wireless interface.

[0117] The network interface 34 can optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface).

[0118] The processor 31 can include one or more processing cores. The processor 31 connects various parts of the server through various interfaces and lines, executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 35, and calling data stored in the memory 35. Optionally, the processor 31 can be realized in at least one of the hardware forms of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 31 can integrate a combination of one or more of central processing units (Central Processing Unit, CPU), graphics processing units (Graphics Processing Unit, GPU), and modems. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 31, but can be realized by a separate chip.

[0119] The memory 35 can include a Random Access Memory (RAM) and a Read-Only Memory (ROM). Optionally, the memory 35 includes a non-transitory computer-readable storage medium. The memory 35 can be configured to store instructions, programs, codes, code sets, or instruction sets. The memory 35 can include a program storage area and a data storage area. The program storage area can be configured to store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the methods described above, etc. The data storage area can be configured to store data related to the methods described above, etc. The memory 35 can optionally be at least one storage device located away from the processor 31. As shown, the memory 35, as a computer storage medium, can include an operating system, a network communication module, a user interface module, and an application program of an insurance industry data processing method. Figure 3

[0120] As shown in the electronic device, the user interface 33 can be configured to provide an interface for user input and obtain data input by the user. The processor 31 can be configured to call the application program of the insurance industry data processing method stored in the memory 35, and when executed by one or more processors, cause the electronic device to perform the method(s) of one or more of the above embodiments. Figure 3

[0121] It should be noted that, for the above-mentioned method embodiments, in order to simply describe, they are all described as a series of action combinations, but those skilled in the art should know that the present application is not limited to the action sequence described, because according to the present application, some steps can be performed in other sequences or at the same time. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0122] The present application also provides a computer-readable storage medium having instructions stored therein. When executed by one or more processors, the electronic device performs the method(s) of one or more of the above embodiments.

[0123] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0124] ​​In several embodiments provided in the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the division of the apparatus embodiments is merely illustrative, and the division of units can be changed according to actual conditions, such as a combination or integration of some units, or a deletion of some features, or an addition of some features. In addition, the coupling or direct coupling or communication connection between the shown or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0125] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0126] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0127] If the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, magnetic disk or optical disk, etc. Various program codes that can store program codes.

[0128] The above is only exemplary embodiments of the present disclosure, which cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be easily obtained by those skilled in the art after considering the specification and the true disclosure. The present application is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional techniques in the art that are not described in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An insurance industry data processing method, characterized by, The method comprises: obtaining premium structure indicators, claim data indicators, policy activity indicators and customer dimension indicators involved in a plurality of insurance business systems as index nodes, obtaining field dependency relationships, index calculation logic and time sequence among the indicators to establish index dependency paths, and forming an index graph; obtaining the field variation frequency, update delay characteristics and dependency strength parameters of the data sources relied on by the index nodes in the historical period, and combining the update state of each field in the current data stream to establish an index disturbance potential function; identifying the index nodes with the index disturbance potential function greater than a preset threshold in the index graph as potential instability index nodes, and performing reverse dependency deduction based on the index dependency paths where the potential instability index nodes are located to construct a potential instability path set; separating the potential instability index nodes in the potential instability path set from the original write process and classifying them into a potential instability index pushing channel to generate a corresponding pre-write data snapshot; if it is determined that the actual data required by the potential instability index nodes arrives, performing a correction write on the pre-write data snapshot, and dynamically adjusting the identification threshold and channel resource configuration to maintain the causal structure closure and index write sequence consistency of the index graph.

2. The insurance industry data processing method of claim 1, wherein, The method comprises: obtaining the structured definition information of the premium structure indicators, claim data indicators, policy activity indicators and customer dimension indicators from the premium collection and payment system, policy system, claim settlement system and customer service system, analyzing the original data fields relied on by each index according to the calculation formula of each index, and establishing the dependency mapping relationship between the index nodes and the fields; for the index nodes with field sharing or formula derivation relationship among a plurality of the index nodes, constructing the index dependency paths based on the field dependency path intersection; constructing the index nodes and the index dependency paths into a directional graph structure, combining the calculation period information of each index node, performing time sequence labeling operation, to form an index graph with field dependency structure and time logic constraint.

3. The insurance industry data processing method of claim 1, wherein, The method comprises: extracting the original data fields relied on by each index node, obtaining the field variation frequency of the original data fields in the historical period, and the field variation frequency is used to describe the fluctuation intensity of the field value; obtaining the update delay characteristics of the original data fields in the historical period, the update delay characteristics include the mean, maximum value and standard deviation of the time interval from generation to storage of the field, and the update delay characteristics are used to describe the timeliness uncertainty of field update; calculating the dependency strength parameters between the original data fields and the index nodes; The nonlinear weighting model is constructed based on the field variation frequency, the update delay feature and the dependence strength parameter in combination with the real-time update state of each original data field in the current data stream, and an index disturbance potential function representing local structure instability of the index node is generated.

4. The insurance industry data processing method of claim 1, wherein, The index nodes in the index graph whose index disturbance potential functions are greater than a preset threshold are identified as potential instability index nodes, and reverse dependence deduction is performed based on the index dependence path in which the potential instability index nodes are located to construct a potential instability path set, specifically including: Each index node in the index graph is traversed to obtain the index disturbance potential function value corresponding to each index node, and the index nodes whose index disturbance potential function values are greater than the preset threshold are identified as the potential instability index nodes; Starting from each potential instability index node, reverse dependence deduction is performed along the index dependence path in the index graph, upstream index nodes on which the potential instability index nodes depend are recursively traced, and the dependence path formed is recorded to form a path set; The path set is summarized to obtain the potential instability path set, which represents an index dependence structure region that may trigger structure cascade instability due to abnormal index disturbance potential function.

5. The insurance industry data processing method of claim 1, wherein, The potential instability index nodes in the potential instability path set are peeled off from the original write process and divided into a potential instability index pushing channel to generate a corresponding pre-write data snapshot, specifically including: Each potential instability index node in the potential instability path set is identified to determine whether each potential instability index node is in a regular write process triggered based on data arrival; If the potential instability index node is in the regular write process, the potential instability index node is peeled off from the regular write process, and the state record of the potential instability index node in the regular scheduling queue is cancelled; Each peeled potential instability index node is allocated the potential instability index pushing channel, and a scheduling context corresponding to the potential instability index pushing channel is established in the scheduling control structure; The pre-write data snapshot is generated based on the index dependence path corresponding to each potential instability index node in the index graph, the arrived original data field and the index calculation logic, and the pre-write data snapshot includes the filling value of the currently available field, the index calculation logic path identifier and the index node structure state information; The pre-write data snapshot is written into the index cache structure, and the pre-write data snapshot is marked as in a future mapping to be corrected state.

6. The insurance industry data processing method of claim 1, wherein, If it is determined that the actual data required by the potential instability index node has arrived, the pre-write data snapshot is corrected and written, and the identification threshold and the channel resource configuration are dynamically adjusted, specifically including: The arrival state of the original data field corresponding to the potential instability index node in the current scheduling period is monitored, and if the original data field has arrived and the field value is valid, the potential instability index node is marked as correctable; Call the index calculation logic corresponding to the potential instability index node in the index graph, perform structural consistency check and value correction operation on the pre-written data snapshot stored in the index cache structure, and generate the corrected index result; Write the corrected index result into the index storage structure, and update the write state of the potential instability index node in the index graph to structural closure completion; According to the change trend of the disturbance potential function in the continuous scheduling period, dynamically adjust the identification threshold of the potential instability index node, and dynamically adjust the scheduling concurrency, cache capacity and priority weight of the potential instability index pushing channel according to the number of potential instability index nodes and the concentration degree of disturbance.

7. The insurance industry data processing method of claim 1, wherein, The method further comprises: After the index graph is constructed, uniformly standardize the calculation period of each index node; Map the daily calculation period, weekly calculation period, monthly calculation period and quarterly calculation period to a unified time dimension reference; Based on the time dimension reference, mark the time sequence of each index node in the index dependency path to ensure the time consistency of the causal path and the period coordination of the calculation scheduling in the index graph.

8. An insurance industry data processing apparatus characterized by comprising: The device comprises an acquisition module (21) and a processing module (22), wherein, The acquisition module (21) is configured to acquire premium structure indicators, claim payment data indicators, policy activity indicators and customer dimension indicators involved in a plurality of insurance business systems as index nodes, acquire field dependency relationships between the indicators, index calculation logic and time sequence to establish an index dependency path, and form an index graph; The acquisition module (21) is further configured to acquire the field variation frequency, update delay feature and dependency strength parameter of the data source relied on by the index nodes in a historical period, and establish an index disturbance potential function in combination with the update state of each field in the current data stream; The processing module (22) is configured to identify the index nodes with the index disturbance potential function greater than a preset threshold as potential instability index nodes in the index graph, and perform reverse dependency deduction based on the index dependency path where the potential instability index nodes are located to construct a potential instability path set; The processing module (22) is further configured to separate the potential instability index nodes in the potential instability path set from the original write process and divide them into a potential instability index pushing channel to generate a corresponding pre-written data snapshot; The processing module (22) is further configured to perform correction writing on the pre-written data snapshot if the actual data required by the potential instability index node arrives, and dynamically adjust the identification threshold and channel resource configuration to maintain the causal structural closure and index write sequence consistency of the index graph.

9. An electronic device, comprising: The electronic device comprises a processor (31), a memory (35) for storing instructions, a user interface (33) and a network interface (34) for communicating with other devices, and the processor (31) is configured to execute the instructions stored in the memory (35) to cause the electronic device to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores instructions which, when executed, perform the method of any one of claims 1 to 7.

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