Risk supervision and early warning method and device based on data monitoring

Abstract

The application relates to the technical field of data governance, and discloses a risk supervision and early warning method and device based on data monitoring, which comprises the following steps: obtaining business scene data; determining a target risk early warning condition according to the business scene data; judging whether the obtained business processing data meets the target risk early warning condition; when it is judged that the business processing data meets the target risk early warning condition, generating a target early warning parameter according to the business processing data and the target risk early warning condition, so as to trigger an early warning operation matched with the target early warning parameter. It can be seen that the application can improve the determination efficiency and accuracy of the target risk early warning condition, and the generation efficiency and accuracy of the target early warning parameter, is beneficial to improving the execution efficiency and accuracy of the early warning operation, improving the instantaneity and accuracy of risk supervision and early warning, and is beneficial to timely protecting and correcting the misconduct of organization members and timely preventing and resolving the risks of the organization.

Description

Technical Field

[0001] This invention relates to the field of data governance technology, and in particular to a risk monitoring and early warning method and apparatus based on data monitoring. Background Technology

[0002] As an important system to assist organizational management, the discipline inspection and supervision system can help discover and correct violations of discipline by organizational members, safeguard the organization's image and interests, and promote the organization's stable operation along the right track.

[0003] In the actual implementation of the discipline inspection and supervision system, as the main business of each department within an organization changes constantly at different stages of development, the operational risks of each department also change accordingly. However, practice has shown that due to the different sizes of different organizations, the efficiency of the replacement of operational risks in each discipline inspection and supervision department also varies, resulting in most cases of violations being discovered after the fact. This is not only detrimental to timely protection and correction of the violations of organizational members, but also to timely prevention and resolution of organizational risks.

[0004] Therefore, it is particularly important to propose a technical solution to improve the timeliness of risk monitoring and early warning. Summary of the Invention

[0005] This invention provides a risk monitoring and early warning method and device based on data monitoring, which can help improve the timeliness of risk monitoring and early warning.

[0006] To address the aforementioned technical problems, the first aspect of this invention discloses a risk monitoring and early warning method based on data monitoring, the method comprising:

[0007] Obtain business scenario data;

[0008] Based on the business scenario data, determine the target risk warning conditions;

[0009] Determine whether the acquired business processing data meets the target risk warning conditions. If it is determined that the business processing data meets the target risk warning conditions, then generate target warning parameters based on the business processing data and the target risk warning conditions to trigger the execution of a warning operation that matches the target warning parameters.

[0010] As an optional implementation, in the first aspect of the present invention, determining the target risk warning conditions based on the business scenario data includes:

[0011] Based on the business scenario data, a target object is determined, and the target object is used to process the business scenario data;

[0012] Based on the predetermined first attribute data of the target object, at least one risk item of the business scenario data is determined;

[0013] For each risk item, the weight value of the risk item in the business scenario data is calculated based on the preset second attribute data of the determined risk item. The weight value is used to represent the degree of influence of the risk item on the processing of the business scenario data.

[0014] Among all the weight values, at least one target weight value is determined, and the risk items of all the target weight values ​​are determined as target risk items, wherein the target weight value is greater than or equal to a preset first weight threshold;

[0015] Based on all the aforementioned target risk items, determine the target risk early warning conditions.

[0016] As an optional implementation, in the first aspect of the present invention, determining the target object based on the business scenario data includes:

[0017] Retrieve multiple pre-stored historical business data;

[0018] For each piece of historical business data, a first business feature data is determined for that historical business data; based on the first business feature data and the determined second business feature data of the business scenario data, a matching value is calculated between the historical business data and the business scenario data, wherein the first business feature data corresponds to the second business feature data; it is determined whether the matching value is greater than or equal to a preset first matching threshold; when it is determined that the matching value is greater than or equal to the preset first matching threshold, the processing object of the historical business data is determined as the target object;

[0019] When it is determined that all the matching values ​​are less than the preset first matching threshold, a target matching value is determined among all the matching values, and a target object is determined according to the historical business data corresponding to the target matching value. The target matching value is greater than or equal to a preset second matching threshold, and the preset second matching threshold is less than the preset first matching threshold.

[0020] As an optional implementation, in the first aspect of the present invention, determining at least one risk item of the business scenario data based on the predetermined first attribute data of the determined target object includes:

[0021] Based on the business scenario data, at least one expected indicator data is determined;

[0022] For each expected indicator data, based on the expected indicator data and the preset first attribute data of the target object, the predicted indicator data of the target object corresponding to the expected indicator data is calculated; it is determined whether the predicted indicator data matches the expected indicator data; if it is determined that the predicted indicator data does not match the expected indicator data, at least one risk item of the business scenario data is determined based on the expected indicator data.

[0023] When it is determined that all the predicted indicator data matches the corresponding expected indicator data, then based on the preset indicator weight of each expected indicator data, at least one risk item of the business scenario data is determined from all the expected indicator data, wherein the preset indicator weight is greater than or equal to a preset second weight threshold.

[0024] As an optional implementation, in the first aspect of the present invention, the expected indicator data includes target business workload, and the step of calculating the predicted indicator data of the target object corresponding to the expected indicator data based on the expected indicator data and the preset first attribute data of the target object includes:

[0025] Based on the predetermined first attribute data of the target object and the target business workload, calculate the predicted business processing efficiency value of the target;

[0026] And, the step of determining whether the predicted indicator data matches the expected indicator data includes:

[0027] Determine the target business processing efficiency value based on the target business workload;

[0028] Determine whether the predicted business processing efficiency value is less than the target business processing efficiency value. If it is determined that the predicted business processing efficiency value is less than the target business processing efficiency value, then it is determined that the predicted indicator data does not match the expected indicator data.

[0029] When it is determined that the predicted business processing efficiency value is greater than or equal to the target business processing efficiency value, the predicted indicator data is determined to match the expected indicator data.

[0030] As an optional implementation, in the first aspect of the present invention, the target risk warning condition includes at least one target risk threshold interval, each target risk threshold interval has a corresponding target risk item, each target risk item corresponds to a business category, and the step of determining whether the acquired business processing data meets the target risk warning condition includes:

[0031] Obtain business processing data and determine the business category corresponding to the business processing data;

[0032] Determine whether the business processing data is within the target risk threshold range corresponding to the business category. If it is determined that the business processing data is within the target risk threshold range corresponding to the business category, then the business processing data is determined to meet the target risk warning condition.

[0033] As an optional implementation, in the first aspect of the present invention, generating target warning parameters based on the business processing data and the target risk warning conditions includes:

[0034] Based on the business category corresponding to the business processing data, at least one of the target risk items is determined;

[0035] Based on the target weight values ​​of all the target risk items, generate target early warning parameters;

[0036] And, generating target early warning parameters based on the target weight values ​​of all the target risk items includes:

[0037] For each of the target risk items, preliminary early warning parameters are generated based on the target weight value of that target risk item;

[0038] Based on all the aforementioned preliminary warning parameters and preset calculation algorithms, target warning parameters are generated.

[0039] A second aspect of the present invention discloses a risk monitoring and early warning device based on data monitoring, the device comprising:

[0040] The acquisition module is used to acquire business scenario data;

[0041] The determination module is used to determine the target risk warning conditions based on the business scenario data;

[0042] The judgment module is used to determine whether the acquired business processing data meets the target risk warning conditions;

[0043] The generation module is used to generate target warning parameters based on the business processing data and the target risk warning conditions when the judgment module determines that the business processing data meets the target risk warning conditions, so as to trigger the execution of a warning operation that matches the target warning parameters.

[0044] As an optional implementation, in the second aspect of the present invention, the specific method by which the determining module determines the target risk warning conditions based on the business scenario data includes:

[0045] Based on the business scenario data, a target object is determined, and the target object is used to process the business scenario data;

[0046] Based on the predetermined first attribute data of the target object, at least one risk item of the business scenario data is determined;

[0047] For each risk item, the weight value of the risk item in the business scenario data is calculated based on the preset second attribute data of the determined risk item. The weight value is used to represent the degree of influence of the risk item on the processing of the business scenario data.

[0048] Among all the weight values, at least one target weight value is determined, and the risk items of all the target weight values ​​are determined as target risk items, wherein the target weight value is greater than or equal to a preset first weight threshold;

[0049] Based on all the aforementioned target risk items, determine the target risk early warning conditions.

[0050] As an optional implementation, in a second aspect of the present invention, the determining module determines the target object based on the business scenario data in the following specific ways:

[0051] Retrieve multiple pre-stored historical business data;

[0052] For each piece of historical business data, a first business feature data is determined for that historical business data; based on the first business feature data and the determined second business feature data of the business scenario data, a matching value is calculated between the historical business data and the business scenario data, wherein the first business feature data corresponds to the second business feature data; it is determined whether the matching value is greater than or equal to a preset first matching threshold; when it is determined that the matching value is greater than or equal to the preset first matching threshold, the processing object of the historical business data is determined as the target object;

[0053] When it is determined that all the matching values ​​are less than the preset first matching threshold, a target matching value is determined among all the matching values, and a target object is determined according to the historical business data corresponding to the target matching value. The target matching value is greater than or equal to a preset second matching threshold, and the preset second matching threshold is less than the preset first matching threshold.

[0054] As an optional implementation, in a second aspect of the present invention, the specific method by which the determining module determines at least one risk item of the business scenario data based on the predetermined first attribute data of the determined target object includes:

[0055] Based on the business scenario data, at least one expected indicator data is determined;

[0056] For each expected indicator data, based on the expected indicator data and the preset first attribute data of the target object, the predicted indicator data of the target object corresponding to the expected indicator data is calculated; it is determined whether the predicted indicator data matches the expected indicator data; if it is determined that the predicted indicator data does not match the expected indicator data, at least one risk item of the business scenario data is determined based on the expected indicator data.

[0057] When it is determined that all the predicted indicator data matches the corresponding expected indicator data, then based on the preset indicator weight of each expected indicator data, at least one risk item of the business scenario data is determined from all the expected indicator data, wherein the preset indicator weight is greater than or equal to a preset second weight threshold.

[0058] As an optional implementation, in the second aspect of the present invention, the expected indicator data includes the target business workload, and the specific method by which the determining module calculates the predicted indicator data of the target object corresponding to the expected indicator data based on the expected indicator data and the preset first attribute data of the target object is as follows:

[0059] Based on the predetermined first attribute data of the target object and the target business workload, calculate the predicted business processing efficiency value of the target;

[0060] Furthermore, the specific methods by which the determining module determines whether the predicted indicator data matches the expected indicator data include:

[0061] Determine the target business processing efficiency value based on the target business workload;

[0062] Determine whether the predicted business processing efficiency value is less than the target business processing efficiency value. If it is determined that the predicted business processing efficiency value is less than the target business processing efficiency value, then it is determined that the predicted indicator data does not match the expected indicator data.

[0063] When it is determined that the predicted business processing efficiency value is greater than or equal to the target business processing efficiency value, the predicted indicator data is determined to match the expected indicator data.

[0064] As an optional implementation, in the second aspect of the present invention, the target risk warning condition includes at least one target risk threshold interval, each target risk threshold interval has a corresponding target risk item, each target risk item corresponds to a business category, and the specific method by which the judgment module determines whether the acquired business processing data meets the target risk warning condition includes:

[0065] Obtain business processing data and determine the business category corresponding to the business processing data;

[0066] Determine whether the business processing data is within the target risk threshold range corresponding to the business category. If it is determined that the business processing data is within the target risk threshold range corresponding to the business category, then the business processing data is determined to meet the target risk warning condition.

[0067] As an optional implementation, in the second aspect of the present invention, the specific method by which the generation module generates the target warning parameters based on the business processing data and the target risk warning conditions includes:

[0068] Based on the business category corresponding to the business processing data, at least one of the target risk items is determined;

[0069] Based on the target weight values ​​of all the target risk items, generate target early warning parameters;

[0070] Furthermore, the specific method by which the generation module generates target early warning parameters based on the target weight values ​​of all the target risk items includes:

[0071] For each of the target risk items, preliminary early warning parameters are generated based on the target weight value of that target risk item;

[0072] Based on all the aforementioned preliminary warning parameters and preset calculation algorithms, target warning parameters are generated.

[0073] A third aspect of the present invention discloses another risk monitoring and early warning device based on data monitoring, the device comprising:

[0074] Memory containing executable program code;

[0075] A processor coupled to the memory;

[0076] The processor calls the executable program code stored in the memory to execute the risk monitoring and early warning method based on data monitoring disclosed in the first aspect of the present invention.

[0077] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the risk monitoring and early warning method based on data monitoring disclosed in the first aspect of the present invention.

[0078] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0079] In this embodiment of the invention, business scenario data is acquired; target risk warning conditions are determined based on the business scenario data; it is determined whether the acquired business processing data meets the target risk warning conditions. When it is determined that the business processing data meets the target risk warning conditions, target warning parameters are generated based on the business processing data and the target risk warning conditions to trigger the execution of a warning operation matching the target warning parameters. Therefore, implementing this invention can determine target risk warning conditions based on the acquired business scenario data, improving the efficiency and accuracy of determining target risk warning conditions, achieving a one-to-one match between target risk warning conditions and business scenario data. Thus, when it is determined that the acquired business processing data meets the target risk warning conditions, target warning parameters are generated based on the business processing data and the target risk warning conditions to trigger the execution of a matching warning operation, improving the efficiency and accuracy of target warning parameter generation, achieving a one-to-one match between target warning parameters and business processing data, thereby improving the execution efficiency and accuracy of warning operations, enhancing the timeliness and accuracy of risk monitoring and warning, and facilitating timely protection and correction of organizational members' misconduct and timely prevention and mitigation of organizational risks. Attached Figure Description

[0080] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0081] Figure 1 This is a flowchart illustrating a risk monitoring and early warning method based on data monitoring disclosed in an embodiment of the present invention;

[0082] Figure 2 This is a flowchart illustrating another risk monitoring and early warning method based on data monitoring disclosed in an embodiment of the present invention;

[0083] Figure 3 This is a schematic diagram of the structure of a risk monitoring and early warning device based on data monitoring disclosed in an embodiment of the present invention;

[0084] Figure 4 This is a schematic diagram of another risk monitoring and early warning device based on data monitoring disclosed in an embodiment of the present invention. Detailed Implementation

[0085] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0086] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0087] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0088] This invention discloses a risk monitoring and early warning method and apparatus based on data monitoring. It can determine target risk early warning conditions based on acquired business scenario data, improving the efficiency and accuracy of determining these conditions. It achieves a one-to-one match between target risk early warning conditions and business scenario data. When the acquired business processing data is determined to meet the target risk early warning conditions, target early warning parameters are generated based on the business processing data and the target risk early warning conditions to trigger matching early warning operations. This improves the efficiency and accuracy of target early warning parameter generation and achieves a one-to-one match between target early warning parameters and business processing data. Consequently, it enhances the execution efficiency and accuracy of early warning operations, improves the timeliness and accuracy of risk monitoring and early warning, and facilitates timely protection and correction of organizational members' misconduct and timely prevention and mitigation of organizational risks. Detailed descriptions follow.

[0089] Example 1

[0090] Please see Figure 1 , Figure 1 This is a flowchart illustrating a risk monitoring and early warning method based on data monitoring, as disclosed in an embodiment of the present invention. Figure 1The described data-based risk monitoring and early warning method can be applied to organizational monitoring / early warning devices, as well as to intelligent devices related to these devices, such as data processing devices. These data processing devices include, but are not limited to, one or more of cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. This invention does not limit the application of these methods. Figure 1 As shown, this data-based risk monitoring and early warning method may include the following operations:

[0091] 101. Obtain business scenario data.

[0092] In this embodiment of the invention, the aforementioned business scenario data includes one or more of business plan data, business type data, and business allocation data.

[0093] 102. Determine the target risk warning conditions based on business scenario data.

[0094] In this embodiment of the invention, as an optional implementation, determining the target risk warning conditions based on business scenario data may include the following operations:

[0095] Based on the business scenario data, the target object is determined, and the target object is used to process the business scenario data.

[0096] Based on the predefined first attribute data of the identified target object, at least one risk item in the business scenario data is determined.

[0097] For each risk item, the weight value of the risk item in the business scenario data is calculated based on the pre-defined second attribute data of the risk item. The weight value is used to represent the degree of influence of the risk item on the processing of business scenario data.

[0098] Among all weight values, at least one target weight value is determined, and the risk items of all target weight values ​​are determined as target risk items, with the target weight value being greater than or equal to a preset first weight threshold.

[0099] Based on all target risk items, determine the target risk early warning conditions.

[0100] In this optional embodiment, the preset first attribute data of the target object may include, but is not limited to, at least one of the following: scale data, human resources data, financial data, disposable income data, credit data, identification data, historical business processing data, historical business delivery data, historical business type data, and cooperation object data.

[0101] Further optionally, the aforementioned preset second attribute data may include, but is not limited to, at least one of the aforementioned preset priority data corresponding to the risk item and the aforementioned preset first attribute data.

[0102] As can be seen, implementing this optional embodiment can determine the target object based on business scenario data, and then determine at least one risk item in the business scenario data based on the preset first attribute data of the determined target object. Then, based on the preset second attribute data of each risk item, the weight value of each risk item in the business scenario data is calculated to determine the target risk item, and then the target risk warning condition is determined. This improves the accuracy and flexibility of determining the target risk warning condition, and is conducive to further improving the generation efficiency and accuracy of target warning parameters.

[0103] In this optional embodiment, as an optional implementation method, determining the target object based on business scenario data may include the following operations:

[0104] Retrieve multiple pre-stored historical business data.

[0105] In this optional embodiment, the aforementioned historical business data may be business data processed by the execution entity of this embodiment of the invention.

[0106] For each historical business data, determine the first business feature data of the historical business data; based on the first business feature data and the second business feature data of the determined business scenario data, calculate the matching value between the historical business data and the business scenario data, with the first business feature data corresponding to the second business feature data; determine whether the matching value is greater than or equal to a preset first matching threshold, and when the matching value is determined to be greater than or equal to the preset first matching threshold, then determine the processing object of the historical business data as the target object.

[0107] When it is determined that all matching values ​​are less than the preset first matching threshold, the target matching value is determined among all matching values, and the target object is determined based on the historical business data corresponding to the target matching value. The target matching value is greater than or equal to the preset second matching threshold, and the preset second matching threshold is less than the preset first matching threshold.

[0108] In this optional embodiment, the first business feature data and the second business feature data mentioned above can each include at least one of the preset first attribute data of the corresponding processing object and the preset second attribute data of the historical risk item.

[0109] As can be seen, implementing this optional embodiment can determine the first business characteristic data of each historical business data and the second business characteristic data of the business scenario data, and then determine the above-mentioned matching value to identify the target object. This can improve the efficiency of target object identification, which in turn helps to improve the efficiency of risk item identification, target risk item identification, and ultimately improve the efficiency of target risk warning conditions identification, and improve the timeliness of risk supervision and warning.

[0110] In this optional embodiment, as another optional implementation, determining at least one risk item of the business scenario data based on the preset first attribute data of the determined target object may include the following operations:

[0111] Based on business scenario data, determine at least one expected metric.

[0112] In this optional embodiment, the expected indicator data may include one or more of the following: expected workload data, expected processing efficiency data, expected processing cost data, and expected processing quality data.

[0113] For each expected indicator data, based on the expected indicator data and the preset first attribute data of the target object, the predicted indicator data of the target object corresponding to the expected indicator data is calculated; it is determined whether the predicted indicator data matches the expected indicator data; if it is determined that the predicted indicator data does not match the expected indicator data, at least one risk item of the business scenario data is determined based on the expected indicator data.

[0114] When it is determined that all predicted indicator data match the corresponding expected indicator data, then based on the preset indicator weight of each expected indicator data, at least one risk item of the business scenario data is determined from all expected indicator data, and the preset indicator weight is greater than or equal to the preset second weight threshold.

[0115] As can be seen, implementing this optional embodiment can determine at least one expected indicator data based on business scenario data. Then, based on the expected indicator data and the aforementioned preset first attribute data of the determined target object, the predicted indicator data of the target object corresponding to the expected indicator data is calculated. Furthermore, by judging whether the predicted indicator data matches the expected indicator data, at least one risk item is determined, improving the accuracy and flexibility of risk item determination. In particular, when it is determined that all predicted indicator data matches the corresponding expected indicator data, at least one risk item of the business scenario data is determined from all expected indicator data according to the preset indicator weight of each expected indicator data. The preset indicator weight is greater than or equal to the preset second weight threshold, which further improves the efficiency of risk item determination and replacement, and is conducive to timely protection and correction of organizational members' misconduct and timely prevention and mitigation of organizational risks.

[0116] In an optional embodiment, the aforementioned expected indicator data includes the target business workload. The calculation of the predicted indicator data for the target object corresponding to the expected indicator data, based on the expected indicator data and the predetermined first attribute data of the target object, may include the following operations:

[0117] Based on the predetermined first attribute data of the identified target object and the target business workload, calculate the predicted business processing efficiency value of the target.

[0118] Furthermore, determining whether the predicted indicator data matches the expected indicator data may include the following operations:

[0119] Determine the target business processing efficiency value based on the target business workload.

[0120] Determine whether the predicted business processing efficiency value is less than the target business processing efficiency value. If it is determined that the predicted business processing efficiency value is less than the target business processing efficiency value, then it is determined that the predicted indicator data does not match the expected indicator data.

[0121] When it is determined that the predicted business processing efficiency value is greater than or equal to the target business processing efficiency value, the predicted indicator data is determined to match the expected indicator data.

[0122] As can be seen, implementing this optional embodiment can specifically calculate the predicted business processing efficiency value of the target based on the preset first attribute data of the determined target object and the target business workload, thereby improving the feasibility of determining the predicted business processing efficiency value. Furthermore, by combining the target business processing efficiency value, it can be determined whether the predicted business processing efficiency value is less than the target business processing efficiency value, thereby determining whether the predicted indicator data matches the expected indicator data, which is beneficial to improving the accuracy and flexibility of risk item determination.

[0123] Furthermore, the aforementioned forecasting indicators may include, but are not limited to, one or more of the following: forecasting financial borrowing / settlement efficiency, forecasting business budget, etc., and the corresponding expected indicators may include, but are not limited to, one or more of the following: target financial borrowing / settlement efficiency, target business budget, etc.

[0124] 103. Determine whether the acquired business processing data meets the target risk warning conditions.

[0125] 104. When it is determined that the business processing data meets the target risk warning conditions, target warning parameters are generated based on the business processing data and the target risk warning conditions to trigger the execution of warning operations that match the target warning parameters.

[0126] In this embodiment of the invention, the above-mentioned warning operation may include a prompting operation, wherein the prompting operation may take the form of at least one of sound, light, image, and message.

[0127] As can be seen, implementing the embodiments of the present invention can determine the target risk warning conditions based on the acquired business scenario data, improve the efficiency and accuracy of determining the target risk warning conditions, and achieve a one-to-one match between the target risk warning conditions and the business scenario data. Therefore, when it is determined that the acquired business processing data meets the target risk warning conditions, target warning parameters are generated based on the business processing data and the target risk warning conditions to trigger the execution of matching warning operations. This improves the efficiency and accuracy of target warning parameter generation, achieves a one-to-one match between the target warning parameters and the business processing data, and thus helps improve the execution efficiency and accuracy of warning operations, enhances the timeliness and accuracy of risk supervision and warning, and facilitates timely protection and correction of organizational members' violations and timely prevention and mitigation of organizational risks.

[0128] Example 2

[0129] Please see Figure 2 , Figure 2 This is a flowchart illustrating a risk monitoring and early warning method based on data monitoring, as disclosed in an embodiment of the present invention. Figure 2 The described data-based risk monitoring and early warning method can be applied to organizational monitoring / early warning devices, as well as to intelligent devices related to these devices, such as data processing devices. These data processing devices include, but are not limited to, one or more of cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. This invention does not limit the application of these methods. Figure 2 As shown, this data-based risk monitoring and early warning method may include the following operations:

[0130] 201. Obtain business scenario data.

[0131] 202. Based on business scenario data, determine the target risk warning conditions. The target risk warning conditions include at least one target risk threshold range. Each target risk threshold range has a corresponding target risk item, and each target risk item corresponds to a business category.

[0132] In this embodiment of the invention, the above-mentioned business categories may include, but are not limited to, one or more of the following: financial category, technology development category, post-production category, management category, etc. Furthermore, they may include the specific transaction categories in each of the above categories, such as one or more of the following: tabulation category, programming category, and work tool management category. This embodiment of the invention does not specifically limit these categories.

[0133] 203. Obtain business processing data.

[0134] In this embodiment of the invention, the aforementioned business processing data is used to represent the data generated when the target object processes the aforementioned business scenario data.

[0135] 204. Determine the business category corresponding to the business processing data.

[0136] In this embodiment of the invention, the business category corresponding to the above-mentioned business processing data corresponds to a business category corresponding to the above-mentioned target risk item.

[0137] The business category corresponding to the above-mentioned business processing data may include at least one.

[0138] 205. Determine whether the business processing data is within the target risk threshold range for the corresponding business category.

[0139] In this embodiment of the invention, the above-mentioned judgment process can be a process of judging multiple business categories one by one.

[0140] 206. When it is determined that the business processing data is within the target risk threshold range of the corresponding business category, the business processing data is deemed to meet the target risk warning conditions.

[0141] 207. When it is determined that the business processing data meets the target risk warning conditions, target warning parameters are generated based on the business processing data and the target risk warning conditions to trigger the execution of warning operations that match the target warning parameters.

[0142] In this embodiment of the invention, for the supplementary explanations of steps 201, 202, and 207, please refer to the supplementary explanations of steps 101, 102, and 104 in Embodiment 1. This embodiment of the invention will not repeat these details.

[0143] As can be seen, implementing this embodiment of the invention can determine target risk warning conditions based on the acquired business scenario data, improving the efficiency and accuracy of determining target risk warning conditions, and achieving a one-to-one match between target risk warning conditions and business scenario data. Specifically, the target risk warning conditions include at least one target risk threshold interval, each target risk threshold interval has a corresponding target risk item, and each target risk item corresponds to a business category. Furthermore, by determining the business category corresponding to the business processing data, it is determined whether the business processing data is within the target risk threshold interval of the corresponding business category, thereby determining whether the business processing data meets the target risk warning conditions. When it is determined that the acquired business processing data meets the target risk warning conditions, target warning parameters are generated based on the business processing data and the target risk warning conditions to trigger the execution of matching warning operations. This improves the efficiency and accuracy of target warning parameter generation, achieving a one-to-one match between target warning parameters and business processing data. This, in turn, helps improve the execution efficiency and accuracy of warning operations, enhances the timeliness and accuracy of risk monitoring and warning, and facilitates timely protection and correction of organizational members' misconduct and timely prevention and mitigation of organizational risks.

[0144] In this embodiment of the invention, as an optional implementation, generating target warning parameters based on business processing data and target risk warning conditions may include the following operations:

[0145] Based on the business category corresponding to the business processing data, identify at least one target risk item.

[0146] Target early warning parameters are generated based on the target weight values ​​of all target risk items.

[0147] Furthermore, generating target early warning parameters based on the target weight values ​​of all target risk items can include the following operations:

[0148] For each target risk item, preliminary early warning parameters are generated based on the target weight value of that target risk item.

[0149] Based on all the preliminary warning parameters and the preset calculation algorithm, the target warning parameters are generated.

[0150] In this optional embodiment, the preset calculation algorithm mentioned above includes, but is not limited to, a combination of one or more underlying algorithms such as variance, mean square error, logarithmic operation, and calculus.

[0151] As can be seen, implementing this optional embodiment can determine at least one target risk item based on the business category corresponding to the business processing data, and then generate target early warning parameters based on the target weight values ​​of all target risk items. Furthermore, it generates preliminary early warning parameters based on the target weight values ​​of the target risk items, and then generates the target early warning parameters based on all preliminary early warning parameters and a preset calculation algorithm. This can improve the accuracy and flexibility of target early warning parameter generation, achieve one-to-one matching between target early warning parameters and business processing data, thereby improving the execution efficiency and accuracy of early warning operations, enhancing the timeliness and accuracy of risk supervision and early warning, and facilitating timely protection and correction of organizational members' disciplinary violations and timely prevention and mitigation of organizational risks.

[0152] In an optional embodiment, after determining the business category corresponding to the business processing data, the method may further include the following operations:

[0153] Determine whether the business processing data matches the target risk threshold data corresponding to the business category. If the business processing data matches the target risk threshold data corresponding to the business category, then the business processing data meets the target risk warning conditions.

[0154] When it is determined that the business processing data does not match the target risk threshold data corresponding to the business category, the business processing data is then used to determine whether it matches the risk warning feature information corresponding to the business category. When it is determined that the business processing data matches the risk warning feature information corresponding to the business category, the business processing data is determined to meet the target risk warning conditions.

[0155] As can be seen, implementing this optional embodiment can provide another way to analyze whether business processing data meets the target risk warning conditions. Specifically, when the analysis shows that the business processing data matches the target risk threshold data corresponding to the business category, it is determined that the business processing data meets the target risk warning conditions. When the analysis shows that the business processing data does not match the target risk threshold data corresponding to the business category, it is further determined whether the business processing data matches the risk warning feature information corresponding to the business category. When it is determined that the business processing data matches the risk warning feature information corresponding to the business category, it is determined that the business processing data meets the target risk warning conditions. This can further improve the accuracy and flexibility of determining the target risk warning conditions, which is conducive to improving the accuracy and diversity of target warning parameter generation, improving the timeliness and accuracy of risk supervision and warning, and timely protecting and correcting the disciplinary behavior of organizational members and timely preventing and resolving organizational risks.

[0156] Example 3

[0157] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a risk monitoring and early warning device based on data monitoring, as disclosed in an embodiment of the present invention. Figure 3 The described data-based risk monitoring and early warning device can be applied to organizational monitoring / early warning equipment, or to intelligent devices related to organizational monitoring / early warning equipment, such as data processing devices. These data processing devices include, but are not limited to, one or more of cloud devices, edge computing devices, relay devices, base station devices, urban management devices, and intelligent connected devices. This invention does not limit the application of these devices. Figure 3 As shown, the risk monitoring and early warning device based on data monitoring may include:

[0158] The acquisition module 301 is used to acquire business scenario data.

[0159] The determination module 302 is used to determine the target risk warning conditions based on business scenario data.

[0160] The judgment module 303 is used to determine whether the acquired business processing data meets the target risk warning conditions.

[0161] The generation module 304 is used to generate target warning parameters based on the business processing data and the target risk warning conditions when the judgment module 303 determines that the business processing data meets the target risk warning conditions, so as to trigger the execution of warning operations that match the target warning parameters.

[0162] As can be seen, implementing the embodiments of the present invention can determine the target risk warning conditions based on the acquired business scenario data, improve the efficiency and accuracy of determining the target risk warning conditions, and achieve a one-to-one match between the target risk warning conditions and the business scenario data. Therefore, when it is determined that the acquired business processing data meets the target risk warning conditions, target warning parameters are generated based on the business processing data and the target risk warning conditions to trigger the execution of matching warning operations. This improves the efficiency and accuracy of target warning parameter generation, achieves a one-to-one match between the target warning parameters and the business processing data, and thus helps improve the execution efficiency and accuracy of warning operations, enhances the timeliness and accuracy of risk supervision and warning, and facilitates timely protection and correction of organizational members' violations and timely prevention and mitigation of organizational risks.

[0163] In this embodiment of the invention, as an optional implementation, the specific method by which the determining module 302 determines the target risk warning conditions based on business scenario data includes:

[0164] Based on the business scenario data, the target object is determined, and the target object is used to process the business scenario data.

[0165] Based on the predefined first attribute data of the identified target object, at least one risk item in the business scenario data is determined.

[0166] For each risk item, the weight value of the risk item in the business scenario data is calculated based on the pre-defined second attribute data of the risk item. The weight value is used to represent the degree of influence of the risk item on the processing of business scenario data.

[0167] Among all weight values, at least one target weight value is determined, and the risk items of all target weight values ​​are determined as target risk items, with the target weight value being greater than or equal to a preset first weight threshold.

[0168] Based on all target risk items, determine the target risk early warning conditions.

[0169] As can be seen, implementing this optional embodiment can determine the target object based on business scenario data, and then determine at least one risk item in the business scenario data based on the preset first attribute data of the determined target object. Then, based on the preset second attribute data of each risk item, the weight value of each risk item in the business scenario data is calculated to determine the target risk item, and then the target risk warning condition is determined. This improves the accuracy and flexibility of determining the target risk warning condition, and is conducive to further improving the generation efficiency and accuracy of target warning parameters.

[0170] In this optional embodiment, as an optional implementation method, the determination module 302 determines the target object based on business scenario data in the following ways:

[0171] Retrieve multiple pre-stored historical business data.

[0172] For each historical business data, determine the first business feature data of the historical business data; based on the first business feature data and the second business feature data of the determined business scenario data, calculate the matching value between the historical business data and the business scenario data, with the first business feature data corresponding to the second business feature data; determine whether the matching value is greater than or equal to a preset first matching threshold, and when the matching value is determined to be greater than or equal to the preset first matching threshold, then determine the processing object of the historical business data as the target object.

[0173] When it is determined that all matching values ​​are less than the preset first matching threshold, the target matching value is determined among all matching values, and the target object is determined based on the historical business data corresponding to the target matching value. The target matching value is greater than or equal to the preset second matching threshold, and the preset second matching threshold is less than the preset first matching threshold.

[0174] As can be seen, implementing this optional embodiment can determine the first business characteristic data of each historical business data and the second business characteristic data of the business scenario data, and then determine the above-mentioned matching value to identify the target object. This can improve the efficiency of target object identification, which in turn helps to improve the efficiency of risk item identification, target risk item identification, and ultimately improve the efficiency of target risk warning conditions identification, and improve the timeliness of risk supervision and warning.

[0175] In this optional embodiment, as another optional implementation, the specific method by which the determining module 302 determines at least one risk item of the business scenario data based on the preset first attribute data of the determined target object includes:

[0176] Based on business scenario data, determine at least one expected metric.

[0177] For each expected indicator data, based on the expected indicator data and the preset first attribute data of the target object, the predicted indicator data of the target object corresponding to the expected indicator data is calculated; it is determined whether the predicted indicator data matches the expected indicator data; if it is determined that the predicted indicator data does not match the expected indicator data, at least one risk item of the business scenario data is determined based on the expected indicator data.

[0178] When it is determined that all predicted indicator data match the corresponding expected indicator data, then based on the preset indicator weight of each expected indicator data, at least one risk item of the business scenario data is determined from all expected indicator data, and the preset indicator weight is greater than or equal to the preset second weight threshold.

[0179] As can be seen, implementing this optional embodiment can determine at least one expected indicator data based on business scenario data. Then, based on the expected indicator data and the aforementioned preset first attribute data of the determined target object, the predicted indicator data of the target object corresponding to the expected indicator data is calculated. Furthermore, by judging whether the predicted indicator data matches the expected indicator data, at least one risk item is determined, improving the accuracy and flexibility of risk item determination. In particular, when it is determined that all predicted indicator data matches the corresponding expected indicator data, at least one risk item of the business scenario data is determined from all expected indicator data according to the preset indicator weight of each expected indicator data. The preset indicator weight is greater than or equal to the preset second weight threshold, which further improves the efficiency of risk item determination and replacement, and is conducive to timely protection and correction of organizational members' misconduct and timely prevention and mitigation of organizational risks.

[0180] In this optional embodiment, as another optional implementation, the aforementioned expected indicator data includes the target business workload. The specific method by which the determining module 302 calculates the predicted indicator data of the target object corresponding to the expected indicator data based on the expected indicator data and the predetermined first attribute data of the determined target object includes:

[0181] Based on the predetermined first attribute data of the identified target object and the target business workload, calculate the predicted business processing efficiency value of the target.

[0182] Furthermore, the specific methods by which the determination module 302 determines whether the predicted indicator data matches the expected indicator data include...

[0183] Determine the target business processing efficiency value based on the target business workload.

[0184] Determine whether the predicted business processing efficiency value is less than the target business processing efficiency value. If it is determined that the predicted business processing efficiency value is less than the target business processing efficiency value, then it is determined that the predicted indicator data does not match the expected indicator data.

[0185] When it is determined that the predicted business processing efficiency value is greater than or equal to the target business processing efficiency value, the predicted indicator data is determined to match the expected indicator data.

[0186] As can be seen, implementing this optional embodiment can specifically calculate the predicted business processing efficiency value of the target based on the preset first attribute data of the determined target object and the target business workload, thereby improving the feasibility of determining the predicted business processing efficiency value. Furthermore, by combining the target business processing efficiency value, it can be determined whether the predicted business processing efficiency value is less than the target business processing efficiency value, thereby determining whether the predicted indicator data matches the expected indicator data, which is beneficial to improving the accuracy and flexibility of risk item determination.

[0187] In an optional embodiment, the above-mentioned target risk warning conditions include at least one target risk threshold interval, each target risk threshold interval has a corresponding target risk item, and each target risk item corresponds to a business category. The specific method by which the judgment module 303 judges whether the acquired business processing data meets the target risk warning conditions includes:

[0188] Obtain business processing data and determine the business category corresponding to the business processing data.

[0189] Determine whether the business processing data is within the target risk threshold range for the corresponding business category. If it is determined that the business processing data is within the target risk threshold range for the corresponding business category, then the business processing data is determined to meet the target risk warning conditions.

[0190] As can be seen, implementing this embodiment of the invention can determine target risk warning conditions based on the acquired business scenario data, improving the efficiency and accuracy of determining target risk warning conditions, and achieving a one-to-one match between target risk warning conditions and business scenario data. Specifically, the target risk warning conditions include at least one target risk threshold interval, each target risk threshold interval has a corresponding target risk item, and each target risk item corresponds to a business category. Furthermore, by determining the business category corresponding to the business processing data, it is determined whether the business processing data is within the target risk threshold interval of the corresponding business category, thereby determining whether the business processing data meets the target risk warning conditions. When it is determined that the acquired business processing data meets the target risk warning conditions, target warning parameters are generated based on the business processing data and the target risk warning conditions to trigger the execution of matching warning operations. This improves the efficiency and accuracy of target warning parameter generation, achieving a one-to-one match between target warning parameters and business processing data. This, in turn, helps improve the execution efficiency and accuracy of warning operations, enhances the timeliness and accuracy of risk monitoring and warning, and facilitates timely protection and correction of organizational members' misconduct and timely prevention and mitigation of organizational risks.

[0191] In another optional embodiment, the generation module 304 generates the target warning parameters based on the business processing data and the target risk warning conditions in the following specific ways:

[0192] Based on the business category corresponding to the business processing data, identify at least one target risk item.

[0193] Target early warning parameters are generated based on the target weight values ​​of all target risk items.

[0194] Furthermore, the specific methods by which the aforementioned generation module 304 generates target early warning parameters based on the target weight values ​​of all target risk items include:

[0195] For each target risk item, preliminary early warning parameters are generated based on the target weight value of that target risk item.

[0196] Based on all the preliminary warning parameters and the preset calculation algorithm, the target warning parameters are generated.

[0197] As can be seen, implementing this optional embodiment can determine at least one target risk item based on the business category corresponding to the business processing data, and then generate target early warning parameters based on the target weight values ​​of all target risk items. Furthermore, it generates preliminary early warning parameters based on the target weight values ​​of the target risk items, and then generates the target early warning parameters based on all preliminary early warning parameters and a preset calculation algorithm. This can improve the accuracy and flexibility of target early warning parameter generation, achieve one-to-one matching between target early warning parameters and business processing data, thereby improving the execution efficiency and accuracy of early warning operations, enhancing the timeliness and accuracy of risk supervision and early warning, and facilitating timely protection and correction of organizational members' disciplinary violations and timely prevention and mitigation of organizational risks.

[0198] Example 4

[0199] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of another risk monitoring and early warning device based on data monitoring disclosed in an embodiment of the present invention. Figure 4 As shown, the risk monitoring and early warning device based on data monitoring may include:

[0200] Memory 401 that stores executable program code.

[0201] Processor 402 coupled to memory 401.

[0202] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the risk supervision and early warning method based on data monitoring as described in Embodiment 1 or Embodiment 2 of the present invention.

[0203] Example 5

[0204] This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the risk monitoring and early warning method based on data monitoring described in Embodiment 1 or Embodiment 2 of this invention.

[0205] Example 6

[0206] This invention discloses a computer program product, which includes a non-transitory computer read storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the risk supervision and early warning method based on data monitoring described in Embodiment 1 or Embodiment 2.

[0207] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0208] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0209] Finally, it should be noted that the risk monitoring and early warning method and apparatus based on data monitoring disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A risk monitoring and early warning method based on data monitoring, characterized in that, The method is applied to an organizational monitoring / early warning device, or the method is applied to a smart device associated with the organizational monitoring / early warning device, and the method includes: Obtain business scenario data; Based on the business scenario data, a target object and at least one expected indicator data are determined. The target object is used to process the business scenario data, and the expected indicator data includes one or more of the following: target financial loan / settlement efficiency, target business budget, expected processing quality data, and target business workload. For each expected indicator data, based on the expected indicator data and the preset first attribute data of the target object, the predicted indicator data of the target object corresponding to the expected indicator data is calculated; it is determined whether the predicted indicator data matches the expected indicator data; if it is determined that the predicted indicator data does not match the expected indicator data, at least one risk item of the business scenario data is determined based on the expected indicator data. When it is determined that all the predicted indicator data match the corresponding expected indicator data, then according to the preset indicator weight of each expected indicator data, at least one risk item of the business scenario data is determined among all the expected indicator data, and the preset indicator weight is greater than or equal to the preset second weight threshold. For each risk item, a weight value for the risk item in the business scenario data is calculated based on the determined preset second attribute data of the risk item. The weight value is used to represent the degree of influence of the risk item on the processing of the business scenario data. The preset first attribute data includes at least one of scale data, human resources data, financial data, disposable income data, credit data, identification data, historical business processing data, historical business delivery data, historical business type data, and partner data. The preset second attribute data includes at least one of the preset priority data corresponding to the risk item and the preset first attribute data. Among all the weight values, at least one target weight value is determined, and the risk items of all the target weight values ​​are determined as target risk items, wherein the target weight value is greater than or equal to a preset first weight threshold; Based on all the target risk items, target risk warning conditions are determined. The target risk warning conditions include at least one target risk threshold interval. Each target risk threshold interval has a corresponding target risk item. Each target risk item corresponds to a business category. Obtain business processing data and determine the business category corresponding to the business processing data; Determine whether the business processing data is within the target risk threshold range corresponding to the business category. If it is determined that the business processing data is within the target risk threshold range corresponding to the business category, then the business processing data is determined to meet the target risk warning condition. If it is determined that the business processing data meets the target risk warning condition, then at least one target risk item is determined according to the business category corresponding to the business processing data. For each of the target risk items, preliminary early warning parameters are generated based on the target weight value of that target risk item; Based on all the aforementioned pre-warning parameters and preset calculation algorithms, target warning parameters are generated to trigger the execution of warning operations that match the target warning parameters. The preset calculation algorithms include a combination of variance, mean square error, logarithmic operation, calculus, or one or more underlying algorithms. And, when the expected indicator data includes the target business workload, the step of calculating the predicted indicator data of the target object corresponding to the expected indicator data based on the expected indicator data and the preset first attribute data of the target object includes: Based on the predetermined first attribute data of the target object and the target business workload, calculate the predicted business processing efficiency value of the target; And, the step of determining whether the predicted indicator data matches the expected indicator data includes: Determine the target business processing efficiency value based on the target business workload; Determine whether the predicted business processing efficiency value is less than the target business processing efficiency value. If it is determined that the predicted business processing efficiency value is less than the target business processing efficiency value, then it is determined that the predicted indicator data does not match the expected indicator data. When it is determined that the predicted business processing efficiency value is greater than or equal to the target business processing efficiency value, the predicted indicator data is determined to match the expected indicator data.

2. The risk monitoring and early warning method based on data monitoring according to claim 1, characterized in that, The step of determining the target object based on the business scenario data includes: Retrieve multiple pre-stored historical business data; For each piece of historical business data, a first business feature data is determined for that historical business data; based on the first business feature data and the determined second business feature data of the business scenario data, a matching value is calculated between the historical business data and the business scenario data, wherein the first business feature data corresponds to the second business feature data; it is determined whether the matching value is greater than or equal to a preset first matching threshold; when it is determined that the matching value is greater than or equal to the preset first matching threshold, the processing object of the historical business data is determined as the target object; When it is determined that all the matching values ​​are less than the preset first matching threshold, a target matching value is determined among all the matching values, and a target object is determined according to the historical business data corresponding to the target matching value. The target matching value is greater than or equal to a preset second matching threshold, and the preset second matching threshold is less than the preset first matching threshold.

3. A risk monitoring and early warning device based on data monitoring, characterized in that, The device is used to execute the risk monitoring and early warning method based on data monitoring as described in claim 1 or 2, and the device includes: The acquisition module is used to acquire business scenario data; The determination module is used to determine the target risk warning conditions based on the business scenario data; The judgment module is used to determine whether the acquired business processing data meets the target risk warning conditions; The generation module is used to generate target warning parameters based on the business processing data and the target risk warning conditions when the judgment module determines that the business processing data meets the target risk warning conditions, so as to trigger the execution of a warning operation that matches the target warning parameters.

4. A risk monitoring and early warning device based on data monitoring, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the risk monitoring and early warning method based on data monitoring as described in claim 1 or 2.

5. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the risk monitoring and early warning method based on data monitoring as described in claim 1 or 2.

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