CN114118793B - A local exchange risk warning method, device and equipment - Google Patents
A local exchange risk warning method, device and equipment Download PDFInfo
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Abstract
本发明涉及计算机信息技术领域，公开了一种地方交易所风险预警方法、装置及设备，方法包括：接收用户终端发送的风险分析请求；基于风险分析请求获取目标客户的监管数据，其中，监管数据包括目标客户在地方交易所的基础数据以及目标客户在地方交易所之外的外部分析数据；对监管数据进行预处理，得到与预设风险预测模型包含的各监控指标所对应的指标值；根据指标值与预设风险预测模型确定指标值所对应的风险等级；基于风险等级执行相应的风险预警动作。本发明解决了现有技术中地方交易所的风险预警中所存在的风险指标件无关联、预警指标单一且使用数据为单一汇总数据的技术问题，实现了提高地方交易所风险预警的准确性和正确率的技术效果。
The present invention relates to the field of computer information technology, and discloses a method, device and equipment for risk warning of local exchanges, the method comprising: receiving a risk analysis request sent by a user terminal; obtaining regulatory data of target customers based on the risk analysis request, wherein the regulatory data includes basic data of the target customers in the local exchange and external analysis data of the target customers outside the local exchange; preprocessing the regulatory data to obtain indicator values corresponding to each monitoring indicator included in a preset risk prediction model; determining the risk level corresponding to the indicator value according to the indicator value and the preset risk prediction model; and executing corresponding risk warning actions based on the risk level. The present invention solves the technical problems in the prior art that the risk indicators in the risk warning of local exchanges are unrelated, the warning indicators are single, and the data used is a single summary data, and achieves the technical effect of improving the accuracy and correctness of risk warnings of local exchanges.
Description
Technical Field
The embodiment of the invention relates to the technical field of computer information, in particular to a local exchange risk early warning method, device and equipment.
Background
The prior risk early warning process of various exchanges in places is as follows (1) processing and processing the collected data, basically formatting and standardizing the data, and cleaning the abnormal data. And then storing the processed data into a structured or unstructured database. (2) For different types of data, the risk early warning system adopts different calculation rules to extract features from the data, calculates indexes and stores the indexes in a structured database. (3) And collecting the calculation results of the indexes to form indexes, and judging risk identification of various local trading places by utilizing the relation between the indexes and the threshold value.
However, the current risk early warning model identification method for local exchanges has certain limitations, including (1) calculation of a single index. And (2) the risk is generally mainly based on single-index early warning, the recognition mode is relatively fixed, the direction needing early warning is regulated, but the business forms of various local transaction places are different, the risk forms are various, and the timely early warning effect is difficult to achieve through a certain single index of universality. (3) The data used for risk early warning is generally summarized data rather than detail data, and has a certain influence on early warning accuracy and pertinence.
Disclosure of Invention
The embodiment of the invention provides a risk early warning method, device and equipment for a local exchange, which solve the technical problems that in the prior art, risk indicators are uncorrelated, early warning indicators are single and use data are single summary data in the risk early warning of the local exchange.
In a first aspect, an embodiment of the present application provides a local exchange risk early warning method, where the method includes:
receiving a risk analysis request sent by a user terminal;
Acquiring supervision data of a target client based on the risk analysis request, wherein the supervision data comprises basic data of the target client at a local exchange and external analysis data of the target client outside the local exchange;
Preprocessing the supervision data to obtain index values corresponding to all monitoring indexes contained in a preset risk prediction model;
determining a risk level corresponding to the index value according to the index value and the preset risk prediction model;
and executing corresponding risk early warning actions based on the risk grade.
In a second aspect, an embodiment of the present application provides a risk early warning device for a local exchange, where the risk early warning device includes:
The request receiving unit is used for receiving a risk analysis request sent by the user terminal;
A data acquisition unit configured to acquire, based on the risk analysis request, regulatory data of a target customer, where the regulatory data includes basic data of the target customer at a local exchange and external analysis data of the target customer outside the local exchange;
the preprocessing unit is used for preprocessing the supervision data to obtain index values corresponding to all monitoring indexes contained in a preset risk prediction model;
The grade determining unit is used for determining a risk grade corresponding to the index value according to the index value and the preset risk prediction model;
And the risk early-warning unit is used for executing corresponding risk early-warning actions based on the risk grade.
In a third aspect, an embodiment of the present application provides a local exchange risk early-warning device, where the local exchange risk early-warning device includes:
One or more processors;
A storage means for storing one or more programs;
The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method for risk early warning of local exchange as described in any of the first aspects of the embodiments of the present application.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for risk early warning of a local exchange as in any of the first aspects of the embodiments of the present application.
The embodiment of the invention discloses a local exchange risk early warning method, device and equipment, which comprise the steps of receiving a risk analysis request sent by a user terminal, acquiring supervision data of a target client based on the risk analysis request, wherein the supervision data comprise basic data of the local exchange of the target client and external analysis data of the target client outside the local exchange, preprocessing the supervision data to obtain index values corresponding to monitoring indexes contained in a preset risk prediction model, determining a risk grade corresponding to the index values according to the index values and the preset risk prediction model, and executing corresponding risk early warning actions based on the risk grade. The method solves the technical problems that in the prior art, the risk index piece is irrelevant, the early warning index is single and the use data is single summary data in the risk early warning of the local exchange, and achieves the technical effect of improving the accuracy and the accuracy of the risk early warning of the local exchange.
Drawings
FIG. 1 is a flow chart of a local exchange risk early warning method provided by an embodiment of the invention;
FIG. 2 is a flow chart of another method for risk early warning of a local exchange according to an embodiment of the present invention;
FIG. 3 is a flowchart of another risk early warning method for a local exchange according to an embodiment of the present invention;
FIG. 4 is a flowchart of another risk early warning method for a local exchange according to an embodiment of the present invention;
FIG. 5 is a block diagram of a risk early warning device for a local exchange provided by an embodiment of the invention;
fig. 6 is a schematic structural diagram of a risk early warning device for a local exchange according to an embodiment of the present invention.
Detailed Description
The local exchange refers to various transaction places which are approved for advancing rights (such as equity, property rights and the like) and commodity market development in various areas and are engaged in property right transaction, cultural artwork transaction, long-term transaction in bulk commodity and the like, various financial business innovation states are greatly promoted along with the full use of new generation information technology in the financial industry, various traditional off-line financial business behaviors are continuously transferred and fused on line, various local exchanges are outstanding and rapidly developed, business innovation layers are endless, and local financial supervision is used as a system arrangement provided by government for maintaining economic order of market and is faced with new challenges and higher standard requirements.
In order to meet higher standard requirements and achieve more accurate and correct risk early warning of local exchanges, the invention provides a method, a device and equipment for risk early warning of local exchanges.
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and in the drawings are used for distinguishing between different objects and not for limiting a particular order. The following embodiments of the present invention may be implemented individually or in combination with each other, and the embodiments of the present invention are not limited thereto.
Fig. 1 is a flowchart of a local exchange risk early warning method according to an embodiment of the present invention. The risk early warning method for the local exchange is applicable to the situation that risk early warning is needed to be carried out on all local exchanges. The local exchange risk early warning method can be executed by a local exchange risk early warning device, and the device can be realized in a hardware and/or software mode and can be generally integrated in a server.
As shown in fig. 1, the risk early warning method for the local exchange specifically includes the following steps:
S101, receiving a risk analysis request sent by a user terminal.
Specifically, the user terminal is generally an upper computer, a mobile terminal or a corresponding man-machine interaction unit installed on the upper computer and the mobile terminal of the supervision department, the man-machine interaction unit can be in the form of an APP (Application program) or other devices capable of realizing sending risk analysis requests, the target client to be monitored is all local trading places and branch institutions thereof in the supervision department district, and the supervision department personnel sends risk analysis requests of target local exchanges needing to predict risks to the local exchange risk early warning devices through the user terminal.
S102, acquiring supervision data of the target client based on the risk analysis request, wherein the supervision data comprises basic data of the local exchange of the target client and external analysis data of the target client outside the local exchange.
Optionally, S102, acquiring the supervision data of the target client based on the risk analysis request comprises acquiring the supervision data of the target client from a plurality of preset dimensions based on the risk analysis request in a centralized acquisition and continuous acquisition mode, wherein the preset dimensions at least comprise transaction events, transaction institutions, fund flows, product information, transaction states, account straggles, industry trends, event reporting states, event troubleshooting information, event related personnel information, advertisements, rewards and reports.
Specifically, the supervision data is mainly divided into status data and detail data, wherein the basic data of the target client in the local exchange is the status data, and the external analysis data of the target client outside the local exchange is the detail data.
The basic data mainly comprise all non-empty effective data of the local exchange, are automatically obtained periodically in a centralized acquisition mode, and comprise government administrative data, internet public data, daily supervision report data and other supplementary data which are disclosed by government departments such as local exchange, business data, financial data, public opinion data, public law and the like, such as transaction contracts, client information, member management information and the like, wherein the basic data can be current data of the local exchange and also comprise historical transaction data of the local exchange, and when the supervision data are stored and/or processed, the basic data meet relevant regulations of national laws and regulations.
Optionally, the external analysis data of the target client outside the local exchange comprises at least one of relevant data of the target client pushed by an external data exchange center, relevant government affair data collected by a government data resource sharing exchange center and relevant data generated based on various data dimensions, wherein the data dimensions comprise an organization dimension, a matter dimension, a natural person dimension and an administrative organization dimension.
S103, preprocessing the supervision data to obtain index values corresponding to all monitoring indexes contained in the preset risk prediction model.
Specifically, after each item of supervision data is obtained, preprocessing the supervision data includes converting the supervision data into a unified format, extracting feature vectors from the supervision data in the unified format, and obtaining corresponding cross correlation features and weights of the feature vectors based on the feature vectors, wherein the feature vectors, the cross correlation features and the weights of the feature vectors are index values.
The method comprises the following steps of collecting a large amount of supervision data, constructing a supervision machine learning label and a training sample according to the condition of the existing supervision data, wherein the training sample comprises a training set, a testing set and a verification sample, the relevant data of a historic risk enterprise is extracted as the training sample, the relevant data of the existing enterprise is extracted as the testing sample, and the relevant data of the enterprise with the existing enterprise supervision rating A and the supervision rating D is extracted as the verification sample.
After the sample is divided, business, fund and business data corresponding to local transaction are extracted according to object types and industry attributes, data classification is carried out, risk index processing is carried out according to data extracted in different analysis dimensions, and the dimensions are business operation, financial risk, public opinion risk and the like. When the model is initialized, the model features are extracted by utilizing the definition of relevant industry experts according to indexes processed by the supervision data, and the cross features, the deviation value features and the hidden features existing in the actual business scene of the association relation are constructed. For example, if the index value of the external litigation of debt is large in index processing, a difficult feature of enterprise management is extracted, and the difficult feature of the operation is one of the model features.
Specifically, the feature cross is a composite feature formed by combining two or more features, the dimension of the feature is increased by a feature combination mode to obtain a better training effect, therefore, the cross feature is mainly used for solving the nonlinear problem, the data are separated by establishing the feature combination, the deviation feature is data for observing the test environment and the system stability, the smaller the feature value of the deviation feature is, the more stable the environment is, the more effective the measured data are, otherwise, the environment is unstable if the feature value of the deviation feature is too large, the measured data have no value under the condition, so that the feature value of the deviation feature is small during testing, the corresponding feature with the larger feature value of the deviation feature is gradually removed during training, the obscure feature is the feature which is required to be judged by using the practical business experience only from the data condition, for example, all the data of an enterprise are stable, but the enterprise is single stock, when other enterprises with stock risks appear, the environment is unstable, the measured data have no value under the condition, the condition is required to be judged by the enterprise supervision, and the situation of the enterprise is required to be fake in advance, so that the situation that the financial situation is required to be discovered.
And training a preset risk prediction model based on a machine learning algorithm construction basis according to the training samples, and performing model tuning in a test set. Optionally, the verification sample is used for verifying the prediction accuracy of the preset risk prediction model. And carrying out model generalization performance verification on the basic preset risk prediction model according to the verification sample, and continuously training the model to return results to improve the accuracy of the basic preset risk prediction model.
When the accuracy of the basic early warning model does not reach a preset accuracy threshold, namely the accuracy, the precision and the recall rate are not reached, the basic preset risk prediction model is required to be trained again. The method comprises two training modes, namely checking whether parameter configuration in the construction process of a preset risk prediction model is unreasonable, if the parameter configuration is unreasonable, readjusting a cross characteristic association mode and evidence weight conversion among indexes, and checking whether a selected characteristic field has a condition which does not accord with economic significance, or checking whether a sample structure in the construction process of the preset risk prediction model is reasonable, wherein the sample splitting comprises label definition, sample number and sample splitting, and the sample splitting comprises splitting of training samples and verification samples and splitting of a training set and a test set in the training samples. If an unreasonable part exists, the supervision data is required to be rearranged and matched to generate a new training sample, and the basic preset risk prediction model is trained again according to the new training sample.
And finally, if the newly-added supervision data are read and accumulated, constructing an optimized preset risk prediction model by using the newly-added supervision data, and performing model fine tuning by taking the newly-added supervision data as a sample to obtain the optimized preset risk prediction model. And comparing the basic preset risk prediction model with the optimized preset risk prediction model to obtain a final preset risk prediction model, wherein the preset risk prediction model of the local trading place is based on the application of the final preset risk prediction model.
S104, determining the risk level corresponding to the index value according to the index value and a preset risk prediction model.
Specifically, assuming that all the index values are risk indexes capable of causing transaction risks of local exchanges, but the probability of causing risks is different, ranking the importance of each index value according to expert experience, and then performing grade assessment on each index value according to the importance ranking, preference of each expert and a preset risk prediction model to finally obtain the risk grade corresponding to the index value.
S105, executing corresponding risk early warning actions based on the risk level.
Specifically, after the risk level is obtained, risk early warning is carried out on each supervision object according to the risk level.
In summary, in the embodiment of the present invention, the latest data is obtained each time the supervision data is obtained, and the optimization is performed according to the reading of the newly added data. After all the data are normalized according to the supervision data standard, the collected supervision data are processed into supervision indexes uniformly, risk characteristic analysis is carried out on index parameters, risk characteristic quantity is extracted, and measurement and analysis are carried out according to the risk characteristic quantity. If a risk event occurs, judging the reasons, positions and possibly other risk conduction situations of the risk occurrence so as to make corresponding risk early warning actions.
The method solves the technical problems that in the prior art, the risk index piece is irrelevant, the early warning index is single and the use data is single summary data in the risk early warning of the local exchange, and achieves the technical effect of improving the accuracy and the accuracy of the risk early warning of the local exchange.
Based on the above technical solutions, fig. 2 is a flowchart of another risk early warning method for a local exchange according to an embodiment of the present invention, as shown in fig. 2, where step S103 specifically includes:
S201, converting the supervision data into a unified format.
Specifically, after corresponding supervision data is acquired, the original supervision data is stored, and then the supervision data is converted according to a specified unified standard.
S202, extracting the supervision data with the unified format based on preset dimension characteristics to obtain corresponding characteristic vectors.
Specifically, the supervision data is subjected to preset dimension feature extraction, namely transaction events, transaction mechanisms, fund flow directions, product information, transaction states, account fluctuation, industry trend, event reporting states, event investigation information, event related personnel information, advertisements, prompt receipts, reports and the like in preset dimensions are taken as corresponding targets, and the respective acquired supervision data under each target are subjected to feature extraction to obtain corresponding feature vectors.
S203, creating cross domain classification based on the feature vector, and determining corresponding cross correlation features based on the cross domain classification.
In particular, with the analytical impact of the early stage on the feature space, one or more conditions that must be met before returning a value from a source object may be specified when creating a cross-domain classification, conditions may be defined for the source object and the target object, and if the conditions are met, the cross-domain classification is automatically created. After determining the cross-domain classification, corresponding cross-domain correlation characteristics may be determined based on the cross-domain classification.
And S204, determining the weight of the feature vector based on a preset risk prediction model, wherein the weight of the feature vector, the cross-correlation feature and the feature vector is an index value.
Optionally, the preset risk prediction model comprises a training sample and a verification sample, the training sample comprises a training set and a testing set, and the step S204 of determining the weight of the feature vector based on the preset risk prediction model comprises the steps of training the feature vector through the training set to respectively obtain classifiers lacking an nth feature vector, wherein n is the number of the feature vector, n is more than or equal to 1, respectively testing the classification effect of each classifier through the testing set, counting the error classification number of each classifier, and carrying out normalization processing on the error classification number to obtain the weight of the feature vector.
Based on the above technical solutions, fig. 3 is a flowchart of another risk early warning method for a local exchange according to an embodiment of the present invention, as shown in fig. 3, where step S202 specifically includes:
S301, performing principal component analysis on the supervision data based on a preset dimension, and forming a feature space by using feature vectors corresponding to the maximum feature value obtained by analysis;
S302, taking the influence of each supervision data in the respective feature space as a classification basis of the feature vector;
and S303, extracting the characteristics of the supervision data based on the classification basis to obtain corresponding characteristic vectors.
Specifically, the method comprises the steps of taking preset dimensions and the like as corresponding targets, carrying out principal component analysis on the supervision data collected under each target, forming a feature space by using feature vectors corresponding to the maximum feature values obtained by analysis, taking the influence of each target data (namely the supervision data collected under each target) in the respective feature space as a classification basis of the feature vectors, and carrying out feature extraction on the supervision data based on the classification basis to obtain the corresponding feature vectors.
Based on the above technical solutions, fig. 4 is a flowchart of another risk early warning method for a local exchange according to an embodiment of the present invention, as shown in fig. 4, where step S104 specifically includes:
s401, taking the index value as a risk index, and sorting the importance of the risk index according to expert experience, wherein the number of the expert experience is more than 1, and the number of the importance sorting of the obtained risk index is more than 1;
s402, performing risk assessment on the importance sequences according to a preset risk prediction model to obtain corresponding risk grades.
Illustratively, among the index values, it is assumed that all the index values are risk indexes that can cause local exchanges, the index values are taken as risk indexes, and then the risk indexes are ranked in order of index importance according to expert experience. Assuming that n possible risk indexes exist, defining a complete risk index set A, defining n possible risk indexes as U 1,u2,……,un, wherein the complete risk index set is expressed as A= { U 1,u2,……,un }, ordering the importance of all the risk indexes by each expert in advance, and forming the preference of an expert group according to the preference of each expert, thereby obtaining a key factor set U= { U 1,u2,……,um }, wherein m < n. Meanwhile, the established risk assessment set is a set of possible assessment results, denoted as v= { V 1,v2,……,vm }, wherein each element represents various possible assessment results.
In local exchange risk assessment, classification can be adopted by referring to convention, fuzzy judgment sets are classified into stages, and the stages are respectively represented by v 1-v 9, namely AAA, AA, A, BB, B, CC, C, DD and D. In order to facilitate quantitative calculation, the five levels are assigned with a value of V= {0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09} by referring to other risk level data, and finally, corresponding risk levels are obtained based on a preset risk prediction model and the level assignment of risk assessment.
Fig. 5 is a structural diagram of a local exchange risk early warning device provided by an embodiment of the present invention, as shown in fig. 5, the local exchange risk early warning device includes:
A request receiving unit 51, configured to receive a risk analysis request sent by a user terminal;
a data acquisition unit 52 for acquiring, based on the risk analysis request, the supervision data of the target client, wherein the supervision data includes basic data of the target client at the local exchange and external analysis data of the target client outside the local exchange;
A preprocessing unit 53, configured to preprocess the supervision data to obtain index values corresponding to each monitoring index included in the preset risk prediction model;
the level determining unit 54 is configured to determine a risk level corresponding to the index value according to the index value and a preset risk prediction model;
the risk early-warning unit 55 is configured to perform a corresponding risk early-warning action based on the risk level.
Optionally, the data obtaining unit 52 is specifically configured to obtain, by means of centralized obtaining and continuous obtaining, the supervision data of the target client from a plurality of preset dimensions based on the risk analysis request, where the preset dimensions at least include two of transaction event, transaction mechanism, fund flow direction, product information, transaction status, account transaction, industry trend, event reporting status, event investigation information, event related personnel information, advertisement, collection and report.
Optionally, the preprocessing unit 53 includes:
the conversion subunit is used for converting the supervision data into a unified format;
The feature extraction subunit is used for extracting the supervision data in the unified format based on the preset dimension feature to obtain a corresponding feature vector;
A feature determination subunit configured to create a cross-domain classification based on the feature vector, and determine a corresponding cross-correlation feature based on the cross-domain classification;
And the weight determining subunit is used for determining the weights of the feature vectors based on a preset risk prediction model, wherein the weights of the feature vectors, the cross-correlation features and the feature vectors are index values.
Optionally, the feature extraction subunit is specifically configured to:
performing principal component analysis on the supervision data based on a preset dimension, and forming a feature space by using a feature vector corresponding to the maximum feature value obtained by analysis;
taking the influence of each supervision data in the respective feature space as a classification basis of the feature vector;
and extracting the characteristics of the supervision data based on the classification basis to obtain corresponding characteristic vectors.
Optionally, the preset risk prediction model includes a training sample and a verification sample, the training sample includes a training set and a test set, and the weight determining subunit is specifically configured to:
Training the feature vectors through a training set to respectively obtain classifiers lacking an nth feature vector, wherein n is the number of the feature vectors, and n is more than or equal to 1;
testing the classification effect of each classifier through the test set and counting the error classification number of each classifier;
And carrying out normalization processing on the error classification number to obtain the weight of the feature vector.
Alternatively, the rank determination unit 54 is specifically configured to:
taking the index value as a risk index, and sorting the importance of the risk index according to expert experience, wherein the number of the expert experience is more than 1, and the number of the importance sorting of the obtained risk index is more than 1;
And performing risk assessment on the importance sequences according to a preset risk prediction model to obtain corresponding risk grades.
The device provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned.
The risk early warning device for the local exchange provided by the embodiment of the invention has the same technical characteristics as the risk early warning method for the local exchange provided by the embodiment, so that the same technical problems can be solved, and the same technical effects can be achieved.
Fig. 6 is a schematic structural diagram of a local exchange risk early warning device according to an embodiment of the present invention, where the local exchange risk early warning device includes a processor 61, a memory 62, an input device 63 and an output device 64, where the number of the processors 61 in the local exchange risk early warning device may be one or more, in fig. 6, one processor 61 is taken as an example, and the processor 61, the memory 62, the input device 63 and the output device 64 in the local exchange risk early warning device may be connected by a bus or other manners, and in fig. 6, the connection is taken as an example by a bus.
The memory 62 is used as a computer readable storage medium, and may be used to store a software program, a computer executable program, and modules, such as program instructions/modules corresponding to the local exchange risk early warning method in the embodiment of the present invention (for example, the request receiving unit 51, the data acquiring unit 52, the preprocessing unit 53, the level determining unit 54, and the risk early warning unit 55 in the local exchange risk early warning device). The processor 61 executes various functional applications and data processing of the local exchange risk early warning device by running software programs, instructions and modules stored in the memory 62, that is, implements the above-described local exchange risk early warning method.
The memory 62 may mainly include a storage program area that may store an operating system, application programs required for at least one function, and a storage data area that may store data created according to the use of the terminal, etc. In addition, memory 62 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, memory 62 may further include memory remotely located with respect to processor 61, which may be connected to local exchange risk early warning devices via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 63 may be used to receive entered numerical or character information and to generate key signal inputs related to user settings and function control of the local exchange risk warning device. The output device 64 may include a display device such as a display screen.
Embodiments of the present invention also provide a storage medium containing computer-executable instructions for performing a local exchange risk early warning method when executed by a computer processor.
Specifically, the local exchange risk early warning method comprises the following steps:
receiving a risk analysis request sent by a user terminal;
Acquiring supervision data of the target client based on the risk analysis request, wherein the supervision data comprises basic data of the local exchange of the target client and external analysis data of the target client outside the local exchange;
Preprocessing the supervision data to obtain index values corresponding to all monitoring indexes contained in a preset risk prediction model;
determining a risk level corresponding to the index value according to the index value and a preset risk prediction model;
and executing corresponding risk early warning actions based on the risk level.
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present invention is not limited to the above-mentioned method operations, and may also perform the related operations in the local exchange risk early warning method provided in any embodiment of the present invention.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk, or an optical disk of a computer, etc., and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present invention.
It should be noted that, in the above embodiment of the search device, each unit and module included are only divided according to the functional logic, but not limited to the above division, as long as the corresponding functions can be implemented, and the specific names of the functional units are only for convenience of distinguishing each other, and are not used for limiting the protection scope of the present invention.
In describing embodiments of the present invention, unless explicitly stated or limited otherwise, the terms "mounted," "connected," "coupled," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected, mechanically connected, electrically connected, directly connected, indirectly connected via an intervening medium, or in communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Finally, it should be noted that the foregoing description is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (9)
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