Data processing method and device

Abstract

The embodiment of the invention provides a data processing method and device, and the method comprises the steps: determining a strategy set related to a target object in response to an object evaluation instruction submitted for the target object, and determining a mapping relation table related to the strategy set; and acquiring multi-source data corresponding to the strategy set according to the mapping relation table, and triggering automatic acquisition of the multi-source data by the object evaluation instruction. And determining policy association data and policy revenue data of a target policy in the policy set based on the multi-source data. On the basis of automatic acquisition of the multi-source data, intelligent analysis is performed on the multi-source data, the strategy associated data and the strategy income data are obtained, and a streamline for performing automatic evaluation on the target object is constructed, so that the efficiency of evaluating the target object is improved, and the labor cost is reduced.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of computer technology, and in particular to data processing methods and apparatus. Background Technology

[0002] In scenarios such as corporate decision-making, risk control, performance management, and intelligent recommendation, the scientific and efficient comprehensive evaluation of target objects (such as products, projects, users, or strategic solutions) is a crucial link in supporting high-quality decision-making. Traditional evaluation methods typically rely on manual collection of multi-source data (such as business logs, financial indicators, user feedback, and external public opinion) scattered across different systems. This process is cumbersome, time-consuming, and prone to errors. Existing evaluation processes are mostly linear and fragmented operational steps, unable to automatically trigger end-to-end data collection, fusion, and analysis based on evaluation instructions. This results in delayed evaluation results, strong subjectivity, and high labor costs. Therefore, there is an urgent need for a more effective data processing method to solve the above problems. Summary of the Invention

[0003] In view of the above, embodiments of this specification provide a data processing method. One or more embodiments of this specification also relate to a data processing apparatus, a computing device, a computer-readable storage medium, and a computer program product, to address the technical deficiencies existing in the prior art.

[0004] According to a first aspect of the embodiments of this specification, a data processing method is provided, comprising: In response to an object evaluation instruction submitted for a target object, determine a set of strategies associated with the target object, and determine a mapping table associated with the set of strategies; Obtain the multi-source data corresponding to the strategy set according to the mapping relationship table; Based on the multi-source data, determine the strategy association data and strategy benefit data of the target strategy in the strategy set.

[0005] Optionally, after obtaining the multi-source data corresponding to the strategy set in parallel according to the mapping table, the method further includes: Determine the object evaluation index associated with the target object, and determine the confidence interval corresponding to the object evaluation index; Obtain environmental and business data associated with the target object; The confidence interval is updated based on the environmental data and / or the business data to obtain the target confidence interval.

[0006] Optionally, after updating the confidence interval based on the environmental data and / or the business data to obtain the target confidence interval, the method further includes: Based on the multi-source data, abnormal policies are determined in the policy set, and abnormal data corresponding to the abnormal policies are detected based on the target confidence interval to obtain abnormal policy detection data. The anomaly policy is updated based on the anomaly detection data.

[0007] Optionally, determining the set of strategies associated with the target object in response to an object evaluation instruction submitted for the target object includes: Receive the object evaluation instruction submitted for the target object, parse the object evaluation instruction, and obtain the object identifier; Determine at least one policy associated with the object identifier in the policy database, and construct the policy set based on the at least one policy.

[0008] Optionally, obtaining the multi-source data corresponding to the strategy set based on the mapping table includes: Determine at least one object strategy contained in the strategy set, and determine the mapping relationship information corresponding to each object strategy in the mapping relationship table; According to the mapping relationship information corresponding to each object strategy, read the object strategy data corresponding to the at least one object strategy from the database; The object policy data corresponding to each of the at least one object policy is used as the multi-source data corresponding to the policy set.

[0009] Optionally, obtaining the object policy data corresponding to any target object policy includes: Determine the target mapping relationship information corresponding to the target object strategy; At least one data source is determined from the target mapping relationship information, and the object policy data corresponding to the target object policy is obtained in parallel from the at least one data source.

[0010] Optionally, determining the strategy association data and strategy benefit data of the target strategy in the strategy set based on the multi-source data includes: The target strategy is determined in the strategy set, and the target sub-object associated with the target strategy is determined. Based on the multi-source data, the strategy-associated data associated with the target sub-object is determined. Based on the multi-source data, the decision data corresponding to the target sub-object is determined, and the strategy benefit data corresponding to the decision data is calculated.

[0011] Optionally, after determining the strategy association data and strategy return data of the target strategy in the strategy set based on the multi-source data, the method further includes: Based on the strategy association data and the strategy benefit data, construct a strategy view corresponding to the target strategy, and generate object evaluation data corresponding to the target object based on the strategy association data and the strategy benefit data.

[0012] According to a second aspect of the embodiments of this specification, a data processing apparatus is provided, comprising: The first determining module is configured to determine a set of strategies associated with the target object in response to an object evaluation instruction submitted for the target object, and to determine a mapping table associated with the set of strategies; The acquisition module is configured to acquire multi-source data corresponding to the strategy set based on the mapping relationship table; The second determining module is configured to determine the strategy association data and strategy benefit data of the target strategy in the strategy set based on the multi-source data.

[0013] According to a third aspect of the embodiments of this specification, a computing device is provided, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the above-described data processing method.

[0014] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores computer-executable instructions, which, when executed by a processor, implement the steps of the data processing method described above.

[0015] According to a fifth aspect of the embodiments of this specification, a computer program product is provided, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described data processing method.

[0016] This specification provides a data processing method in one embodiment that, in response to an object evaluation instruction submitted for a target object, determines a set of strategies associated with the target object and a mapping table associated with the strategy set. Multi-source data corresponding to the strategy set is obtained based on the mapping table, and the automatic collection of multi-source data is triggered by the object evaluation instruction. Based on the multi-source data, strategy-related data and strategy benefit data of the target strategy in the strategy set are determined. On the basis of automatic acquisition of multi-source data, intelligent analysis is performed on the multi-source data to obtain strategy-related data and strategy benefit data, constructing a pipeline for automated evaluation of target objects, improving the efficiency of target object evaluation, and reducing labor costs. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a data processing method provided in one embodiment of this specification; Figure 2 This is a schematic representation of a mapping relationship for a data processing method provided in one embodiment of this specification; Figure 3 This is a flowchart illustrating the processing procedure of a data processing method provided in one embodiment of this specification. Figure 4 This is a schematic diagram of the structure of a data processing apparatus provided in one embodiment of this specification; Figure 5 This is a structural block diagram of a computing device provided in one embodiment of this specification. Detailed Implementation

[0018] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0019] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a,” “described,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0020] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0021] Furthermore, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0022] First, the terms and concepts used in one or more embodiments of this specification will be explained.

[0023] Heatmap: A visualization tool that visually displays the distribution of data density, intensity, or attention through color changes. While the effectiveness and value of heatmaps vary slightly across different fields (such as web analytics, advertising, user behavior research, and geographic information systems), their core objective remains the same: to transform abstract data into spatial patterns easily recognizable to the human eye.

[0024] This specification provides a data processing method, and also relates to a data processing apparatus, a computing device, a computer-readable storage medium, and a computer program product, which will be described in detail in the following embodiments.

[0025] See Figure 1 , Figure 1 A flowchart of a data processing method according to an embodiment of this specification is shown, which specifically includes the following steps.

[0026] Step 102: In response to the object evaluation instruction submitted for the target object, determine the strategy set associated with the target object, and determine the mapping table associated with the strategy set.

[0027] Specifically, the target object can be an object in scenarios such as enterprise decision-making, risk control, performance management, and intelligent recommendation. In a risk control scenario, the target object can be an insurance product in the insurance industry. The object assessment instruction can be a computer instruction submitted by business personnel through a client, used to request the server to conduct a risk assessment of the target object. The strategy set is a collection of multiple insurance strategies corresponding to the target object. The mapping table stores each strategy contained in the strategy set, as well as data items such as the corresponding database table, fields, or API interfaces.

[0028] Based on this, the server receives an object evaluation command submitted by the client for the target object. In response to the object evaluation command, the server determines a policy set associated with the target object. This policy set contains at least one policy related to the target object. A mapping table associated with the policy set is then determined. This mapping table stores multiple records, each corresponding to a policy. Each record in the mapping table corresponding to a policy contains at least one data source, field, API interface, etc., for that policy.

[0029] Furthermore, considering that in a business scenario, there are multiple business objects, each associated with a different strategy, and the strategies corresponding to multiple business objects are stored in a strategy database, when retrieving the strategy set corresponding to a target object among multiple business objects, at least one strategy can be determined from the strategy database based on the object identifier of the target object to construct the strategy set. The specific implementation is as follows: The system receives the object evaluation instruction submitted for the target object, parses the object evaluation instruction to obtain the object identifier, determines at least one policy associated with the object identifier in the policy database, and constructs the policy set based on the at least one policy.

[0030] Specifically, the object identifier can be an ID or other identifier that uniquely identifies a target object. The policy database stores the policies associated with the business scenario to which the target object belongs.

[0031] Based on this, the system receives object evaluation instructions submitted for the target object, and parses these instructions to obtain an object identifier representing the target object's ID. It then determines at least one policy associated with the object identifier in the policy database and constructs a policy set based on this policy. Each policy in the policy database can be associated with at least one business object within the business scenario.

[0032] For example, in an insurance business scenario, the target audience is the insurance products within the insurance business. The strategies in the strategy database are atomic rules, which can be formulated based on specific behaviors of users corresponding to the target audience. For example, claims exceeding 10,000 yuan in the past year. Strategies can be associated with specific actions, such as blacklisting someone for refusing coverage. Strategy sets can be constructed according to dimensions, such as wealth or credit dimensions. Different insurance products can reuse the same strategy set. Different insurance products emphasize different dimensions; for example, adult / elderly accident insurance places greater emphasis on credit, while children's accident insurance is not suitable.

[0033] In summary, by determining at least one policy associated with the object identifier in the policy database and constructing the policy set based on the at least one policy, the policy set can be automatically constructed or determined in response to object evaluation instructions, thereby improving the efficiency of policy set determination.

[0034] Step 104: Obtain the multi-source data corresponding to the strategy set according to the mapping relationship table.

[0035] Specifically, after determining the strategy set associated with the target object in response to the object evaluation instruction submitted for the target object, and determining the mapping relationship table associated with the strategy set, the multi-source data corresponding to the strategy set can be obtained according to the mapping relationship table. Here, the multi-source data corresponding to the strategy set refers to data related to the strategies contained in the strategy set collected from multiple data sources. In the insurance industry, multi-source data includes, but is not limited to, claims details, underwriting data, channel investment, market benchmarks, and other data.

[0036] Based on this, after determining the strategy set associated with the target object in response to the object evaluation command submitted for the target object, and determining the mapping table associated with the strategy set, the multi-source data corresponding to the strategy set is obtained according to the mapping table, and the multi-source data associated with the strategy set is obtained in parallel from multiple data sources. The object evaluation command can automatically trigger the collection of multi-source data, which can significantly improve the efficiency of multi-source data collection.

[0037] Furthermore, after obtaining multi-source data, to ensure the accuracy of the confidence intervals corresponding to the object evaluation indicators, environmental data and business data can be obtained, and the confidence intervals can be updated based on the environmental data and business data. The specific implementation is as follows: Determine the object evaluation index associated with the target object, and determine the confidence interval corresponding to the object evaluation index; obtain the environmental data and business data associated with the target object; update the confidence interval based on the environmental data and / or the business data to obtain the target confidence interval.

[0038] Specifically, target evaluation metrics can be key indicators of the target target, such as claims rate and complaint rate. The confidence interval for each target evaluation metric can be an interception rate confidence interval, used to determine if the metric is abnormal. If the metric falls within the confidence interval, it indicates no abnormality; if it falls outside, it indicates an abnormality. Environmental data includes, but is not limited to, climate data, seasonal data, and other data representing the natural environment. Business data includes, but is not limited to, time and business volume data. Updating the confidence interval based on environmental and / or business data can be done dynamically; that is, the confidence interval is dynamically updated in real time at fixed intervals based on environmental and / or business data, enabling intelligent identification of target evaluation metric anomalies.

[0039] Based on this, target evaluation indicators such as claims rate and complaint rate are identified, and corresponding confidence intervals are determined. These confidence intervals can be calculated based on multi-source data. Environmental and business data of the target targets are acquired at fixed intervals. The confidence intervals are automatically updated based on the environmental and / or business data to obtain the target confidence intervals, achieving intelligent updating of the confidence intervals and ensuring more accurate identification of anomalies in target evaluation indicators.

[0040] Continuing with the previous example, after determining the multi-source data, the upper and lower limits of the confidence interval for each key indicator (such as claims rate and complaint rate) are calculated in real time based on the multi-source data. These key indicators are the target assessment indicators. The confidence interval is dynamically adjusted with factors such as time, season, and business volume, more intelligently identifying true outliers rather than ordinary fluctuations corresponding to the target assessment indicators. For example, a strategy might have a normal interception rate of 1% at a certain point in time, but increase to 200% at another point, showing a significant increase. Such an indicator is considered an outlier. During the peak flu season in spring, the upper limit of the confidence interval needs to be appropriately increased to allow the confidence interval to adjust with seasonal changes.

[0041] In summary, updating the confidence interval based on environmental and / or business data yields the target confidence interval, and updating the confidence interval based on the impact of environmental and business data on the confidence interval improves the accuracy of the target confidence interval.

[0042] Furthermore, when the confidence interval is adjusted, the anomaly strategy is also affected by the update of the confidence interval and needs to be updated synchronously to ensure the accuracy of anomaly warnings. The specific implementation is as follows: Based on the multi-source data, anomaly policies are determined in the policy set, and anomaly data corresponding to the anomaly policies are detected based on the target confidence interval to obtain anomaly policy detection data; the anomaly policies are updated based on the anomaly policy detection data.

[0043] Specifically, anomaly strategies can be strategies with abnormal business values ​​within a strategy set, such as a claims strategy with a claims ratio significantly higher than a claims ratio threshold. The abnormal data corresponding to anomaly strategies are the business values ​​calculated based on those strategies. Anomaly strategy detection data represents the detection results obtained by performing anomaly detection on the business values, i.e., the detection conclusion of whether the data is abnormal or not.

[0044] Based on this, anomalous policies are identified in the policy set using multi-source data, and anomalous data corresponding to these policies are detected based on target confidence intervals to obtain anomalous policy detection data. This anomalous policy detection data represents the detection conclusion of whether a policy is anomalous or not. If the anomalous policy detection data indicates anomaly, the policy is updated; otherwise, it is not necessary to update the policy.

[0045] Continuing with the previous example, when the target is outpatient insurance, claims settlement and interception strategies are considered as abnormal strategies for anomaly detection. Abnormal data includes claims rate and interception rate. If both the claims rate and interception rate show a significant increase, the anomaly strategy detection data is considered normal. If the difference between the rate of increase in claims rate and the rate of increase in interception rate exceeds a threshold, an anomaly is indicated, and the anomaly strategy detection data is deemed abnormal. Therefore, an alert needs to be issued, and the claims settlement and / or interception strategies need to be updated to adapt to changes in seasonality, time, and business volume.

[0046] In summary, updating anomaly strategies based on anomaly detection data improves the accuracy of anomaly warnings.

[0047] Furthermore, considering that the policy set contains at least one object policy, and the multi-source data corresponding to the policy set is usually stored in a distributed manner, retrieving data from each data source individually would consume a lot of time. Therefore, after determining the policy set, multi-source data can be automatically retrieved in parallel. The specific implementation is as follows: Determine at least one object policy contained in the policy set, and determine the mapping relationship information corresponding to each object policy in the mapping relationship table; read the object policy data corresponding to the at least one object policy from the database according to the mapping relationship information corresponding to each object policy; use the object policy data corresponding to the at least one object policy as the multi-source data corresponding to the policy set.

[0048] Specifically, the mapping table stores fields such as strategy type, strategy name, strategy status, applicable business type, application personnel, third-party data source, and data source call ratio. The mapping information corresponding to an object strategy can include the database table, fields, and API interface information of the object strategy, used to achieve multi-source data collection for the object strategy. Object strategy data is the multi-source data of the object strategy, containing data obtained from at least one data source.

[0049] Based on this, at least one object policy is identified within the policy set, and the mapping relationship information corresponding to each object policy is determined in the mapping relationship table. This clarifies the data mapping information for each object policy, facilitating subsequent collection of object policy data based on the data mapping information. The object policy data corresponding to at least one object policy is read from the database according to the mapping relationship information for each object policy. After reading the object policy data corresponding to each object policy, the object policy data corresponding to at least one object policy can be used as the multi-source data corresponding to the policy set.

[0050] Continuing with the previous example, the mapping table is a built-in data mapping table in the insurance business system, used to associate each policy with its corresponding database table, field, or API interface in the data platform. See [link to mapping table]. Figure 2 Data tables in, such as Figure 2 As shown, the mapping table includes fields such as strategy type, strategy name, strategy status, applicable business type, application personnel, third-party data source, and data source call ratio. Each field records data corresponding to different strategies. Strategy types include strategy sets and strategies. Strategy names include fraud rule sets, high coverage amounts, claims history, and personal allowances. Strategy status can be active or inactive. Application personnel can be policyholders or insured persons. Third-party data sources include, but are not limited to, feature platforms, and the data source call ratio can be expressed as a percentage. For each object strategy in the strategy set, object strategy data such as claims details, underwriting data, channel investment, and market benchmarks can be obtained.

[0051] In summary, by reading the object strategy data corresponding to at least one object strategy from the database according to the mapping relationship information of each object strategy, and after completing the reading of the object strategy data corresponding to each object strategy, the object strategy data corresponding to at least one object strategy can be used as the multi-source data corresponding to the strategy set. This enables parallel acquisition of data from multiple data sources for at least one object strategy, improving the efficiency of multi-source data acquisition and shortening the acquisition time of multi-source data.

[0052] Furthermore, considering that each strategy in the strategy set can correspond to at least one data source, collecting data from each data source individually would consume a significant amount of time and manpower. Therefore, parallel data acquisition can be performed on at least one data source corresponding to the target object's strategy. The specific implementation is as follows: Determine the target mapping relationship information corresponding to the target object strategy; determine at least one data source from the target mapping relationship information, and acquire the object strategy data corresponding to the target object strategy in parallel from the at least one data source.

[0053] Specifically, the target mapping information refers to the mapping relationship information corresponding to the target object strategy in the mapping relationship table. The data source refers to the data source related to the target object strategy; the data source can be a database, a data storage platform, or an external data source.

[0054] Based on this, the target mapping relationship information corresponding to the target object strategy is determined. Within this target mapping relationship information, at least one data source is identified, including a database, a data storage platform, and an external data source. Object strategy data corresponding to the target object strategy is then acquired in parallel from at least one data source, achieving automated and parallel data collection. After determining the target object strategy, there is no need to manually write SQL queries for data extraction; the collection of object strategy data can be automatically triggered. This replaces the tedious process of repeatedly writing query statements, waiting for data export, and manually merging data using Excel, reducing the time for determining object strategy data to the second level.

[0055] In summary, by identifying at least one data source from the target mapping relationship information, and by acquiring the object policy data corresponding to the target object policy in parallel from at least one data source, the acquisition time of object policy data is shortened and the acquisition efficiency of object policy data is improved.

[0056] Step 106: Determine the strategy association data and strategy benefit data of the target strategy in the strategy set based on the multi-source data.

[0057] Specifically, after obtaining the multi-source data corresponding to the strategy set based on the mapping table, the strategy-related data and strategy revenue data of the target strategy in the strategy set can be determined based on the multi-source data. The target strategy can be any strategy in the strategy set. Strategy-related data refers to data from multiple dimensions related to the target strategy, such as product type, statistical data, and guiding factors. Strategy revenue data refers to the business revenue corresponding to the target strategy.

[0058] Based on this, after obtaining the multi-source data corresponding to the strategy set according to the mapping relationship table, the strategy association data and strategy benefit data of the target strategy in the strategy set are determined based on the multi-source data, so as to realize automatic statistical analysis of the target strategy and improve the efficiency of analysis of the target strategy.

[0059] Furthermore, after collecting multi-source data corresponding to the strategy set, cost-benefit attribution analysis can be performed on the target strategy based on the multi-source data to obtain strategy correlation data and strategy benefit data. The specific implementation is as follows: The target strategy is determined in the strategy set, and the target sub-object associated with the target strategy is determined. The strategy-related data associated with the target sub-object is determined based on the multi-source data. The decision data corresponding to the target sub-object is determined based on the multi-source data, and the strategy benefit data corresponding to the decision data is calculated.

[0060] Specifically, strategy-related data can be automatically calculated using calculation templates configured in the actuarial model, covering both product and strategy dimensions corresponding to the target object. The product dimension is used to assess the overall risk control contribution, while the strategy dimension is used to measure the value output of individual rules. Strategy revenue data is determined using a revenue verification mechanism, which can correspond to traffic retention strategies. For example, 10% of people who should be rejected outright are allowed to proceed, to verify the effectiveness of the strategy. If the allowed group experiences high payouts (e.g., income of 1 million, claims of 5 million), the potential losses across all scenarios are extrapolated to estimate the overall loss reduction benefits, thus enhancing the persuasiveness of the assessment results.

[0061] Based on this, the target strategy is determined in the strategy set, and the target sub-objects associated with the target strategy are identified. Strategy-related data associated with the target sub-objects are determined based on multi-source data. Decision data corresponding to the target sub-objects is determined based on multi-source data, and the strategy benefit data corresponding to the decision data is calculated. Based on the target strategy and multi-source data, risk control actions (cost items) are automatically and accurately correlated and attributed to their benefits (such as reduced claims expenditures, lower complaint rates, and improved customer retention rates), automatically achieving cost-benefit calculation.

[0062] Continuing with the previous example, after determining the multi-source data and target strategy, cost-benefit calculations can be performed automatically, and risk control actions can be accurately correlated and attributed to their benefits. After determining the target strategy, it's possible to identify which accident insurance products are included with it. For accident insurance, we would examine which accident insurance products are included with these strategies, then analyze claims payments and identify complaint information related to these products; the resulting information constitutes the strategy-related data. If, based on multi-source data, the product's interception rate is determined to be 5%, we further determine why it's 5% and what benefits this 5% interception rate brings to the business; the resulting data constitutes the strategy benefit data.

[0063] In summary, the system determines the strategy-related data associated with the target sub-object based on multi-source data; it also determines the decision data corresponding to the target sub-object based on multi-source data and calculates the strategy benefit data corresponding to the decision data, thus achieving automatic cost-benefit calculation. Based on the target strategy, it automatically and accurately correlates and attributes risk control actions (cost items) with their resulting benefits.

[0064] Furthermore, considering that the volume of strategy correlation data and strategy return data is usually large, resulting in low readability and difficulty in identifying the key indicators contained in the data, visualization processing can be performed after the strategy correlation data and strategy return data are determined. The specific implementation is as follows: Based on the strategy association data and the strategy benefit data, construct a strategy view corresponding to the target strategy, and generate object evaluation data corresponding to the target object based on the strategy association data and the strategy benefit data.

[0065] Specifically, strategy views include, but are not limited to, heatmaps, point density maps, attention maps, and animated heatmaps. A heatmap is a visualization tool that visually displays the distribution of data density, intensity, or attention through color changes, used to transform abstract strategy-related data and strategy benefit data into spatial patterns easily recognizable to the human eye. Object evaluation data can be an evaluation report generated based on strategy-related data and strategy benefit data, assessing the target strategy. The evaluation report includes key indicators and evaluation conclusions for evaluating the target treatment.

[0066] Based on this, a strategy view corresponding to the target strategy is constructed using strategy correlation data and strategy benefit data. This strategy view can be a heatmap, used to indicate which products or product categories yield higher returns for the target strategy. Object evaluation data corresponding to the target object is then generated based on the strategy correlation data and strategy benefit data. This object evaluation data represents the benefit performance of the target strategy over a period of time.

[0067] Following the previous example, after determining the strategy-related data and strategy revenue data, heatmaps and evaluation reports (object evaluation data) will be generated using these two dimensions of data. The heatmap primarily uses visualization to show which strategies yield higher returns on which products or product categories. For example, accident insurance, health insurance, and outpatient insurance have different strategy distributions; by drawing heatmaps, the strategies with higher returns can be quickly identified. Evaluation reports can be generated periodically to show the revenue performance of each strategy. Evaluation reports will also be generated periodically for each business unit, such as the outpatient insurance business unit and the accident insurance business unit, to clarify key products, key indicators, and evaluation results.

[0068] In summary, constructing a strategy view corresponding to the target strategy based on strategy correlation data and strategy benefit data improves the readability of these data through visualization. Generating object evaluation data corresponding to the target object based on the strategy correlation data and strategy benefit data facilitates the identification of key indicators contained in the strategy correlation data and strategy benefit data, enabling rapid determination of evaluation conclusions for the target object.

[0069] This specification provides a data processing method in one embodiment that, in response to an object evaluation instruction submitted for a target object, determines a set of strategies associated with the target object and a mapping table associated with the strategy set. Multi-source data corresponding to the strategy set is obtained based on the mapping table, and the automatic collection of multi-source data is triggered by the object evaluation instruction. Based on the multi-source data, strategy-related data and strategy benefit data of the target strategy in the strategy set are determined. On the basis of automatic acquisition of multi-source data, intelligent analysis is performed on the multi-source data to obtain strategy-related data and strategy benefit data, constructing a pipeline for automated evaluation of target objects, improving the efficiency of target object evaluation, and reducing labor costs.

[0070] The following is in conjunction with the appendix Figure 3 Taking the application of the data processing method provided in this specification to insurance strategies as an example, the data processing method will be further explained. Figure 3 A flowchart illustrating the processing procedure of a data processing method according to an embodiment of this specification is shown, specifically including the following steps.

[0071] Step 302: Receive the object evaluation instruction submitted for the target object, parse the object evaluation instruction, and obtain the object identifier.

[0072] Step 304: Determine at least one policy for the associated object identifier in the policy database, construct a policy set based on the at least one policy, and determine the mapping relationship table associated with the policy set.

[0073] Step 306: Determine at least one object strategy contained in the strategy set, and determine the mapping relationship information corresponding to each object strategy in the mapping relationship table.

[0074] Step 308: Read at least one object strategy data corresponding to each object strategy from the database according to the mapping relationship information corresponding to each object strategy.

[0075] Step 310: Use the object policy data corresponding to at least one object policy as the multi-source data corresponding to the policy set.

[0076] Determine the object evaluation metrics associated with the target object and the corresponding confidence intervals for those metrics. Obtain the environmental and business data of the associated target object. Update the confidence intervals based on the environmental and / or business data to obtain the target confidence interval. Determine abnormal policies in the policy set based on multi-source data, and detect abnormal data corresponding to these policies based on the target confidence interval to obtain abnormal policy detection data. Update the abnormal policies based on the abnormal policy detection data.

[0077] Step 312: Determine the strategy association data and strategy benefit data of the target strategy in the strategy set based on multi-source data.

[0078] Step 314: Construct a strategy view corresponding to the target strategy based on strategy association data and strategy benefit data.

[0079] Step 316: Generate object evaluation data corresponding to the target object based on strategy association data and strategy benefit data.

[0080] This specification provides a data processing method in one embodiment that, in response to an object evaluation instruction submitted for a target object, determines a set of strategies associated with the target object and a mapping table associated with the strategy set. Multi-source data corresponding to the strategy set is obtained based on the mapping table, and the automatic collection of multi-source data is triggered by the object evaluation instruction. Based on the multi-source data, strategy-related data and strategy benefit data of the target strategy in the strategy set are determined. On the basis of automatic acquisition of multi-source data, intelligent analysis is performed on the multi-source data to obtain strategy-related data and strategy benefit data, constructing a pipeline for automated evaluation of target objects, improving the efficiency of target object evaluation, and reducing labor costs.

[0081] Corresponding to the above method embodiments, this specification also provides data processing apparatus embodiments. Figure 4 A schematic diagram of the structure of a data processing apparatus according to one embodiment of this specification is shown. Figure 4 As shown, the device includes: The first determining module 402 is configured to determine a set of strategies associated with the target object in response to an object evaluation instruction submitted for the target object, and to determine a mapping table associated with the set of strategies; The acquisition module 404 is configured to acquire multi-source data corresponding to the strategy set based on the mapping relationship table. The second determining module 406 is configured to determine the strategy association data and strategy benefit data of the target strategy in the strategy set based on the multi-source data.

[0082] In an optional embodiment, the acquisition module 404 is further configured to: Determine the object evaluation index associated with the target object, and determine the confidence interval corresponding to the object evaluation index; Obtain environmental and business data associated with the target object; The confidence interval is updated based on the environmental data and / or the business data to obtain the target confidence interval.

[0083] In an optional embodiment, the acquisition module 404 is further configured to: Based on the multi-source data, abnormal policies are determined in the policy set, and abnormal data corresponding to the abnormal policies are detected based on the target confidence interval to obtain abnormal policy detection data. The anomaly policy is updated based on the anomaly detection data.

[0084] In an optional embodiment, the first determining module 402 is further configured to: Receive the object evaluation instruction submitted for the target object, parse the object evaluation instruction, and obtain the object identifier; Determine at least one policy associated with the object identifier in the policy database, and construct the policy set based on the at least one policy.

[0085] In an optional embodiment, the acquisition module 404 is further configured to: Determine at least one object strategy contained in the strategy set, and determine the mapping relationship information corresponding to each object strategy in the mapping relationship table; According to the mapping relationship information corresponding to each object strategy, read the object strategy data corresponding to the at least one object strategy from the database; The object policy data corresponding to each of the at least one object policy is used as the multi-source data corresponding to the policy set.

[0086] In an optional embodiment, the acquisition module 404 is further configured to: Determine the target mapping relationship information corresponding to the target object strategy; At least one data source is determined from the target mapping relationship information, and the object policy data corresponding to the target object policy is obtained in parallel from the at least one data source.

[0087] In an optional embodiment, the second determining module 406 is further configured to: The target strategy is determined in the strategy set, and the target sub-object associated with the target strategy is determined. Based on the multi-source data, the strategy-associated data associated with the target sub-object is determined. Based on the multi-source data, the decision data corresponding to the target sub-object is determined, and the strategy benefit data corresponding to the decision data is calculated.

[0088] In an optional embodiment, the second determining module 406 is further configured to: Based on the strategy association data and the strategy benefit data, construct a strategy view corresponding to the target strategy, and generate object evaluation data corresponding to the target object based on the strategy association data and the strategy benefit data.

[0089] One embodiment of this specification provides a data processing apparatus that, in response to an object evaluation instruction submitted for a target object, determines a set of strategies associated with the target object and a mapping table associated with the strategy set. Based on the mapping table, it acquires multi-source data corresponding to the strategy set, and the object evaluation instruction triggers automatic collection of the multi-source data. Based on the multi-source data, it determines the strategy-related data and strategy benefit data of the target strategy in the strategy set. On the basis of automatic acquisition of multi-source data, it performs intelligent analysis on the multi-source data to obtain the strategy-related data and strategy benefit data, constructing a pipeline for automated evaluation of target objects, improving the efficiency of target object evaluation, and reducing labor costs.

[0090] The above is an illustrative scheme of a data processing apparatus according to this embodiment. It should be noted that the technical solution of this data processing apparatus and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the data processing apparatus, please refer to the description of the technical solution of the data processing method described above.

[0091] Figure 5 A structural block diagram of a computing device 500 according to one embodiment of this specification is shown. The components of the computing device 500 include, but are not limited to, a memory 510 and a processor 520. The processor 520 is connected to the memory 510 via a bus 530, and a database 550 is used to store data.

[0092] The computing device 500 also includes an access device 540, which enables the computing device 500 to communicate via one or more networks 560. Examples of these networks include Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or combinations of communication networks such as the Internet. The access device 540 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Wi-MAX (Worldwide Interoperability for Microwave Access) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0093] In one embodiment of this specification, the above-described components of the computing device 500 and Figure 5 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 5 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0094] The computing device 500 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 500 can also be a mobile or stationary server.

[0095] The processor 520 is configured to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the above-described data processing method.

[0096] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the data processing method described above.

[0097] An embodiment of this specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described data processing method.

[0098] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the data processing method described above.

[0099] An embodiment of this specification also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described data processing method.

[0100] The above is an illustrative scheme of a computer program product according to this embodiment. It should be noted that the technical solution of this computer program product and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the computer program product, please refer to the description of the technical solution of the data processing method described above.

[0101] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0102] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added or removed according to the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0103] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0104] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0105] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A data processing method, characterized in that, include: In response to an object evaluation instruction submitted for a target object, determine a set of strategies associated with the target object, and determine a mapping table associated with the set of strategies; Obtain the multi-source data corresponding to the strategy set based on the mapping relationship table; Based on the multi-source data, determine the strategy association data and strategy benefit data of the target strategy in the strategy set.

2. The data processing method according to claim 1, characterized in that, After obtaining the multi-source data corresponding to the strategy set in parallel according to the mapping table, the method further includes: Determine the object evaluation index associated with the target object, and determine the confidence interval corresponding to the object evaluation index; Obtain environmental and business data associated with the target object; The confidence interval is updated based on the environmental data and / or the business data to obtain the target confidence interval.

3. The data processing method according to claim 2, characterized in that, After updating the confidence interval based on the environmental data and / or the business data to obtain the target confidence interval, the method further includes: Based on the multi-source data, abnormal policies are determined in the policy set, and abnormal data corresponding to the abnormal policies are detected based on the target confidence interval to obtain abnormal policy detection data. The anomaly policy is updated based on the anomaly detection data.

4. The data processing method according to claim 1, characterized in that, The step of determining the set of strategies associated with the target object in response to an object evaluation instruction submitted for the target object includes: Receive the object evaluation instruction submitted for the target object, parse the object evaluation instruction, and obtain the object identifier; Determine at least one policy associated with the object identifier in the policy database, and construct the policy set based on the at least one policy.

5. The data processing method according to claim 1, characterized in that, The step of obtaining the multi-source data corresponding to the strategy set according to the mapping relationship table includes: Determine at least one object strategy contained in the strategy set, and determine the mapping relationship information corresponding to each object strategy in the mapping relationship table; According to the mapping relationship information corresponding to each object strategy, read the object strategy data corresponding to the at least one object strategy from the database; The object policy data corresponding to each of the at least one object policy is used as the multi-source data corresponding to the policy set.

6. The data processing method according to claim 5, characterized in that, The acquisition of object policy data corresponding to any target object policy includes: Determine the target mapping relationship information corresponding to the target object strategy; At least one data source is determined from the target mapping relationship information, and the object policy data corresponding to the target object policy is obtained in parallel from the at least one data source.

7. The data processing method according to claim 1, characterized in that, The step of determining the strategy association data and strategy benefit data of the target strategy in the strategy set based on the multi-source data includes: The target strategy is determined in the strategy set, and the target sub-object associated with the target strategy is determined. Based on the multi-source data, the strategy-associated data associated with the target sub-object is determined. Based on the multi-source data, the decision data corresponding to the target sub-object is determined, and the strategy benefit data corresponding to the decision data is calculated.

8. The data processing method according to claim 1, characterized in that, After determining the strategy association data and strategy return data of the target strategy in the strategy set based on the multi-source data, the method further includes: Based on the strategy association data and the strategy benefit data, construct a strategy view corresponding to the target strategy, and generate object evaluation data corresponding to the target object based on the strategy association data and the strategy benefit data.

9. A data processing apparatus, characterized in that, include: The first determining module is configured to determine a set of strategies associated with the target object in response to an object evaluation instruction submitted for the target object, and to determine a mapping table associated with the set of strategies; The acquisition module is configured to acquire multi-source data corresponding to the strategy set based on the mapping relationship table; The second determining module is configured to determine the strategy association data and strategy benefit data of the target strategy in the strategy set based on the multi-source data.

10. A computing device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the data processing method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the data processing method according to any one of claims 1 to 8.

12. A computer program product, characterized in that, It includes a computer program or instructions that, when executed by a processor, implement the steps of the data processing method according to any one of claims 1 to 8.

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