Data writing method and device, computer equipment and storage medium

Abstract

The embodiment of the invention provides a data writing method and device, computer equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring original underwriting data and data source information of the original underwriting data; obtaining original data configuration information of the original underwriting data according to the data source information and a preset data configuration rule; screening out a target data verification rule from the candidate data verification rules according to the original data configuration information; performing rule verification on the original underwriting data according to the target data verification rule and the original data configuration information to obtain data verification information; screening out target underwriting data from the original underwriting data according to the data verification information, and obtaining original data configuration information of the target underwriting data to obtain target data configuration information; and writing the target underwriting data into the target system according to the target data configuration information. According to the embodiment of the invention, the manpower for writing the underwriting data can be saved, and the data error problem is reduced.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to a data writing method and apparatus, computer equipment and storage medium. Background Art

[0002] In the financial field, a large amount of financial data will be generated. For subsequent financial services, the financial data needs to be written into the system as a data reference for financial services. However, in the related technologies, the writing of financial data mainly relies on manual input, which not only takes a lot of time, but also the direct manual input does not verify the financial data, which is prone to data writing errors, affecting financial services. For example, in the insurance field, for insurance claim assessment, underwriting data needs to be written into the system. However, direct manual writing not only takes a lot of time, but also easily leads to the writing of wrong data, resulting in insurance claims not being in line with the actual situation and causing complaints. Therefore, how to save manpower for writing underwriting data and reduce data errors has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a data writing method and apparatus, a computer device and a storage medium, aiming to save manpower in writing underwriting data and reduce data error problems.

[0004] To achieve the above object, a first aspect of an embodiment of the present application provides a data writing method, the method comprising:

[0005] Obtaining original underwriting data and data source information of the original underwriting data;

[0006] Acquire data configuration information of the original underwriting data according to the data source information and preset data configuration rules to obtain original data configuration information;

[0007] Filtering out target data validation rules from preset candidate data validation rules according to the original data configuration information;

[0008] Perform rule verification on the original underwriting data according to the target data verification rule and the original data configuration information to obtain data verification information; wherein the data verification information indicates whether the original underwriting data complies with or does not comply with the target data verification rule;

[0009] Filtering target underwriting data from the original underwriting data according to the data verification information, and acquiring original data configuration information of the target underwriting data to obtain target data configuration information; wherein the target underwriting data complies with the target data verification rule;

[0010] The target underwriting data is written into a preset target system according to the target data configuration information.

[0011] In some embodiments, the original data configuration information includes: data application scenario information, data item name information, data acquisition channel information and data acquisition method information; the candidate data verification rules include: candidate eligibility verification rules and candidate adoption degree verification rules; the target data verification rules include: target eligibility verification rules and target adoption degree verification rules;

[0012] The step of selecting a target data validation rule from preset candidate data validation rules according to the original data configuration information includes:

[0013] Filtering the target eligibility verification rule from the candidate eligibility verification rules according to the data application scenario information and the data item name information;

[0014] The target adoption degree verification rule is selected from the candidate adoption degree verification rules according to the data acquisition channel information and the data acquisition method information.

[0015] In some embodiments, the original data configuration information further includes: data availability measurement data, the data availability measurement data characterizing the availability of the original underwriting data; the data verification information includes: eligibility verification information and adoption degree verification information;

[0016] The performing rule verification on the original underwriting data according to the target data verification rule and the original data configuration information to obtain data verification information includes:

[0017] Performing qualification verification on the original underwriting data according to the target qualification verification rule, the data application scenario information and the data item name information to obtain the qualification verification information;

[0018] Acquire the data availability measurement data according to the data acquisition channel information and the data acquisition method information to obtain target availability measurement data;

[0019] The original underwriting data is subjected to adoption degree verification according to the target adoption degree verification rule and the target availability measurement data to obtain the adoption degree verification information.

[0020] In some embodiments, the original underwriting data includes: historical underwriting data and current underwriting data; the target adoption verification rule includes at least one of the following: data credibility verification rule, data importance verification rule and data similarity verification rule; the target availability measurement data includes at least one of the following: data credibility and data importance measurement data; the adoption verification information includes at least one of the following: data credibility verification information, data importance verification information and data similarity verification information;

[0021] The step of performing adoption degree verification on the original underwriting data according to the target adoption degree verification rule and the target availability measurement data to obtain the adoption degree verification information comprises at least one of the following steps:

[0022] Performing credibility verification on the original underwriting data according to the data credibility verification rule and the data credibility to obtain the data credibility verification information;

[0023] Performing importance verification on the original underwriting data according to the data importance verification rule and the data importance measurement data to obtain the data importance verification information;

[0024] The historical underwriting data and the current underwriting data are subjected to similarity measurement to obtain data similarity measurement data, and the data similarity verification information is determined according to the data similarity verification rule, the data similarity measurement data and a preset similarity threshold.

[0025] In some embodiments, the data source information includes: historical source information of the historical underwriting data and current source information of the current underwriting data; the original data configuration information includes: historical data configuration information and current data configuration information;

[0026] The step of acquiring the data configuration information of the original underwriting data according to the data source information and the preset data configuration rules to obtain the original data configuration information includes:

[0027] Filtering out historical data configuration rules from the preset data configuration rules according to the historical source information;

[0028] Acquire data configuration information of the historical underwriting data according to the historical data configuration rule to obtain the historical data configuration information;

[0029] Filtering a current data configuration rule from the preset data configuration rules according to the current source information;

[0030] The data configuration information of the current underwriting data is obtained according to the current data configuration rule to obtain the current data configuration information.

[0031] In some embodiments, after filtering out target underwriting data from the original underwriting data according to the data verification information and acquiring original data configuration information of the target underwriting data to obtain the target data configuration information, the method further includes:

[0032] Obtaining the data source information of the original underwriting data to obtain original source information;

[0033] Acquire the data source information of the target underwriting data to obtain target source information;

[0034] Performing an adoption rate evaluation on the original underwriting data according to the target source information and the original source information to obtain adoption rate evaluation data;

[0035] The original data configuration information is updated according to the adoption rate evaluation data.

[0036] In some embodiments, after writing the target underwriting data into a preset target system according to the target data configuration information, the method further includes:

[0037] Obtaining the number of data revisions of the target underwriting data;

[0038] If the number of data corrections is greater than a preset correction threshold, a data prompt message is generated;

[0039] Receive the revised underwriting data based on the data prompt information feedback, and write the revised underwriting data into the target system.

[0040] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a data writing device, the device comprising:

[0041] A data acquisition module, used to acquire original underwriting data and data source information of the original underwriting data;

[0042] A configuration acquisition module, used to acquire data configuration information of the original underwriting data according to the data source information and preset data configuration rules, so as to obtain original data configuration information;

[0043] A rule screening module, used to screen out target data validation rules from preset candidate data validation rules according to the original data configuration information;

[0044] A rule verification module, used to perform rule verification on the original underwriting data according to the target data verification rule and the original data configuration information to obtain data verification information; wherein the data verification information indicates whether the original underwriting data complies with or does not comply with the target data verification rule;

[0045] A data screening module, used to screen out target underwriting data from the original underwriting data according to the data verification information, and obtain original data configuration information of the target underwriting data to obtain target data configuration information; wherein the target underwriting data complies with the target data verification rule;

[0046] A data writing module is used to write the target underwriting data into a preset target system according to the target data configuration information.

[0047] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes a computer device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0048] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0049] The data writing method and device, computer equipment and storage medium proposed in the present application automatically obtain the original underwriting data, and obtain the original data configuration information of the target underwriting data according to the data source information of the original underwriting data and the preset data configuration rules, and select the target data verification rules that meet the data verification of the target underwriting data from the candidate data verification rules according to the original data configuration information. Then, the original underwriting data is subjected to rule verification according to the target data verification rules and the original data configuration information to determine whether the original underwriting data meets the target data verification rules, thereby realizing automatic verification of the original underwriting data. Finally, the target underwriting data that meets the target data verification rules is written into the target system according to the target data configuration information. Therefore, not only is the automatic writing of the underwriting data into the target system realized, but the underwriting data is also verified before writing, and the underwriting data that meets the target data verification rules is selected and then written into the target system, so that the underwriting data written into the target system is more accurate and data error problems are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of a data writing method provided in an embodiment of the present application;

[0051] Figure 2 yes Figure 1 Flow chart of step S102 in FIG.

[0052] Figure 3 yes Figure 1 Flow chart of step S103 in FIG.

[0053] Figure 4 yes Figure 1Flow chart of step S104 in FIG.

[0054] Figure 5 It is a structural diagram of rule verification in the data writing method provided in an embodiment of the present application;

[0055] Figure 6 yes Figure 4 Flow chart of step S403 in FIG.

[0056] Figure 7 is a flow chart of a data writing method provided by another embodiment of the present application;

[0057] Figure 8 is a flow chart of a data writing method provided by another embodiment of the present application;

[0058] Fig. 9 is a structural schematic diagram of a data writing device provided in an embodiment of the present application;

[0059] Fig.10 It is a schematic diagram of the hardware structure of the computer device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0063] First, some nouns involved in this application are analyzed:

[0064] Artificial intelligence (AI) is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. AI is a branch of computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robots, language recognition, image recognition, natural language processing and expert systems. AI can simulate the information process of human consciousness and thinking. AI is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0065] Natural language processing (NLP): NLP uses computers to process, understand and apply human languages ​​(such as Chinese and English). NLP is a branch of artificial intelligence and an interdisciplinary subject between computer science and linguistics. It is often referred to as computational linguistics. Natural language processing includes grammatical analysis, semantic analysis, and text understanding. Natural language processing is often used in technical fields such as machine translation, handwritten and printed character recognition, speech recognition and text-to-speech conversion, information intent recognition, information extraction and filtering, text classification and clustering, public opinion analysis and opinion mining. It involves data mining related to language processing, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research, and linguistic research related to language computing.

[0066] OCR (Optical Character Recognition): is a text recognition technology that can convert different types of documents (such as scanned paper documents, PDF files or photos) into editable and searchable data. This technology can recognize text, numbers and symbols and convert them into machine-encoded text.

[0067] Automatic writing is a technology that uses computer technology to automatically collect, process and write data. It can reduce manual participation in data writing and improve the efficiency and accuracy of data writing.

[0068] In the financial field, financial data is generally written into the system manually to serve as a data reference for subsequent financial services. For example, in the insurance scenario, when underwriting is required, staff are required to manually write the underwriting data filled in by the policyholder into the system. Since the underwriting process requires a lot of data to be filled in, manual writing takes a lot of time, resulting in a long underwriting cycle. At the same time, in the process of writing underwriting data, since staff directly write underwriting data, it is easy to make mistakes or write wrong data, which will affect the accuracy of subsequent underwriting and the service effect of insurance claims.

[0069] Based on this, the embodiment of the present application provides a data writing method and device, a computer device and a storage medium, which are intended to automatically collect the original underwriting data and the data source information of the original underwriting data, and determine the original data configuration information of the original underwriting data according to the data source information and the preset data configuration rules, so as to select the target data verification rule that meets the original underwriting data from the candidate data verification rules according to the original data configuration information. The original underwriting data is rule-verified by the target data verification rule and the original data configuration information to obtain data verification information. Therefore, after automatically acquiring the underwriting data, the underwriting data is automatically rule-verified to determine whether the underwriting data meets the corresponding data verification rule. Then, the data verification information is characterized as the original underwriting data that meets the target data verification rule as the target underwriting data, and then the target underwriting data is written into the target system. Therefore, the embodiment of the present application automatically acquires data, automatically performs rule verification on the data, and writes the data that passes the rule verification into the target system, which not only realizes the fully automated writing of the underwriting data, saves manpower, but also ensures the accuracy of the written underwriting data, and reduces the situation of errors in writing the underwriting data.

[0070] The data writing method and apparatus, computer equipment and storage medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the data writing method in the embodiments of the present application is described.

[0071] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0072] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0073] The data writing method provided in the embodiment of the present application relates to the field of financial technology. The data writing method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the data writing method, etc., but is not limited to the above forms.

[0074] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer computer devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0075] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0076] Figure 1 is an optional flow chart of the data writing method provided in the embodiment of the present application, Figure 1 The method may include but is not limited to steps S101 to S106.

[0077] Step S101, obtaining original underwriting data and data source information of the original underwriting data;

[0078] Step S102, obtaining data configuration information of original underwriting data according to data source information and preset data configuration rules, and obtaining original data configuration information;

[0079] Step S103, selecting a target data validation rule from preset candidate data validation rules according to the original data configuration information;

[0080] Step S104, performing rule verification on the original underwriting data according to the target data verification rule and the original data configuration information to obtain data verification information; wherein the data verification information indicates whether the original underwriting data complies with or does not comply with the target data verification rule;

[0081] Step S105, filtering out target underwriting data from the original underwriting data according to the data verification information, and acquiring original data configuration information of the target underwriting data to obtain target data configuration information; wherein the target underwriting data complies with the target data verification rules;

[0082] Step S106, writing the target underwriting data into a preset target system according to the target data configuration information.

[0083] Steps S101 to S106 shown in the embodiment of the present application are to automatically obtain the original underwriting data and the data source information of the original underwriting data, and obtain the original data configuration information of the original underwriting data according to the data source information and the data configuration rules. According to the original data configuration information, the target data verification rules for verifying the original underwriting data are screened out from the candidate data verification rules, and the original underwriting data is subjected to rule verification according to the target data verification rules and the original data configuration information to obtain data verification information, so as to determine whether the original underwriting data complies with the target data verification rules. Then, according to the data verification information, the target underwriting data that complies with the target data verification rules is extracted from the original underwriting data, and the target underwriting data is written into the target system. Therefore, by automatically obtaining the underwriting data, and performing rule verification on the underwriting data, the underwriting data that passes the rule verification is automatically written into the target system, which not only realizes the automatic writing of the underwriting data and saves manpower, but also performs verification before writing into the target system, thereby reducing the writing of erroneous underwriting data.

[0084] In step S101 of some embodiments, the original underwriting data is used as reference data for underwriting and as a reference indicator for insurance claims, and the original underwriting data includes: historical underwriting data and current underwriting data. The historical underwriting data is the underwriting data of the target user in the target system or other external system, and the historical underwriting data specifically includes: historical user information, historical insurance purchase data, historical claims data, historical claims auxiliary data, and historical identification data. The current underwriting data includes: current user information, current insurance purchase data, current claims data, current claims auxiliary data, and current identification data. It should be noted that both the historical claims auxiliary data and the current claims auxiliary data are data used to assist underwriting, which are data actively provided by the target user and can be used for claims verification. The data source information represents the acquisition channel of the original underwriting data, and the availability of the original underwriting data obtained from different acquisition channels is different. It is necessary to update the acquisition channel regularly to obtain more credible original underwriting data.

[0085] In some embodiments, data source information includes: historical source information of historical underwriting data and current source information of current underwriting data; original data configuration information includes: historical data configuration information and current data configuration information. It should be noted that historical source information represents the acquisition channel of historical underwriting data, and the acquisition channel can be the target system or other external system, so the historical source information is the target system information of the target system and / or the external system information of the external system. Current source information represents the acquisition channel of current underwriting data, and the acquisition channel can also be the target system and the external system.

[0086] Specifically, the current underwriting data can be obtained by OCR identifying image data uploaded by the target user, or by the target user inputting voice data through the underwriting software and performing semantic extraction on the voice data. This embodiment does not impose specific restrictions on the method of obtaining the current underwriting data.

[0087] It should be noted that the historical data configuration information is the information that needs to be configured for the historical underwriting data, and is used to guide the rule verification of the historical underwriting data. The current data configuration information is the information that needs to be configured for the current underwriting data, and is used to guide the rule verification of the current underwriting data.

[0088] See also Figure 2 In some embodiments, step S102 may include but is not limited to steps S201 to S204:

[0089] Step S201, filtering out historical data configuration rules from preset data configuration rules according to historical source information;

[0090] Step S202, obtaining data configuration information of historical underwriting data according to historical data configuration rules to obtain historical data configuration information;

[0091] Step S203, filtering out the current data configuration rule from the preset data configuration rules according to the current source information;

[0092] Step S204, obtaining data configuration information of current underwriting data according to current data configuration rules to obtain current data configuration information.

[0093] In step S201 of some embodiments, the historical data configuration rule is a configuration rule for historical underwriting data, and different data sources correspond to different configuration rules. Therefore, the historical data configuration rule corresponding to the historical source information is selected from the preset data configuration rules.

[0094] In step S202 of some embodiments, historical data configuration information of historical underwriting data is obtained according to historical data configuration rules, and data items that need to be configured for the historical underwriting data are filled in according to the historical data configuration rules as historical data configuration information.

[0095] In steps S203 to S204 of some embodiments, the current data configuration rules corresponding to the current source information are filtered out from the preset data configuration rules to determine which data items need to be configured for the current underwriting data, and the data items that need to be configured for the current underwriting data are obtained according to the current data configuration rules to obtain the current data configuration information.

[0096] In steps S201 to S204 shown in this embodiment, corresponding data configuration rules are selected according to different data sources, and then data configuration information corresponding to the underwriting data is determined according to the data configuration rules, so that rule verification is performed on different underwriting data according to the data configuration information, so that the underwriting data rule verification is more accurate.

[0097] In some embodiments, the original data configuration information includes: data application scenario information, data item name information, data acquisition channel information, data acquisition method information, and data availability measurement data. It should be noted that the data application scenario represents the scenario in which the original underwriting data is used, the data item name information represents the name of the data item to which the original underwriting data belongs, the data acquisition channel information is the channel for obtaining the original underwriting data, the data acquisition method information is the method by which the original underwriting data is uploaded, and the data availability measurement data represents the availability of the original underwriting data.

[0098] For example, in the auto insurance underwriting scenario, the original underwriting data is used as the underwriting data for vehicle accident claims, then the data application scenario information is the claims investigation scenario, the data item name information is the vehicle frame number, the data acquisition channel information is the OCR automatic entry channel, the data acquisition method information is the OCR interface, and the data availability measurement data is 4. Therefore, through the original data configuration information, it can be known that during auto insurance underwriting, the data availability of the vehicle frame number automatically entered through OCR is 4.

[0099] It should be noted that the data acquisition method information can be determined by the data source information, and the original underwriting data will carry a data availability label when it is uploaded, and the data availability label is obtained after the availability assessment of the original underwriting data is done in advance. It should be noted that the data availability label can be set manually or according to the credibility of the acquisition channel. This embodiment does not impose specific restrictions on the data availability label.

[0100] Among them, the candidate data verification rules include: candidate eligibility verification rules and candidate adoption degree verification rules; the target data verification rules include: target eligibility verification rules and target adoption degree verification rules. It should be noted that the original data is mainly verified from two aspects, one is eligibility verification, and the other is adoption degree verification, in order to select qualified and usable underwriting data.

[0101] See also Figure 3 In some embodiments, step S103 may include but is not limited to steps S301 to S302:

[0102] Step S301, selecting a target eligibility verification rule from candidate eligibility verification rules according to data application scenario information and data item name information;

[0103] Step S302: Filter out a target adoption degree verification rule from candidate adoption degree verification rules according to the data acquisition channel information and the data acquisition method information.

[0104] In step S301 of some embodiments, different application scenarios and data item configurations have different eligibility verification rules, so a target eligibility verification rule that matches the data application scenario information and the data item name information is selected from the candidate eligibility verification rules. Specifically, candidate scenario labels and candidate data item labels are set on the candidate eligibility verification rules, and the target scenario label is first selected from the candidate scenario labels based on the data application scenario information, and then the target data item label is filtered out from the candidate data item labels based on the data item name information, and then the candidate eligibility verification rules corresponding to the target scenario label and the target data item label are used as the target eligibility verification rules. It should be noted that the target eligibility verification rules are used to verify whether the expression of the original underwriting data is correct, reduce the amount of underwriting data with data expression errors directly written to the target system, and improve the accuracy of the underwriting data written to the target system.

[0105] In step S302 of some embodiments, the candidate adoption degree verification rule mainly verifies whether the original underwriting data can be used, and the candidate adoption degree verification rules for different data acquisition channels and data acquisition methods are different. Specifically, the candidate adoption degree verification rule carries the candidate data acquisition channel label and the candidate data acquisition method label, selects the target data acquisition channel label from the candidate data acquisition channel labels according to the data acquisition channel information, selects the target data acquisition method label from the candidate data acquisition method labels according to the data acquisition method information, and finally uses the candidate adoption degree verification rule corresponding to the target data acquisition channel label and the target data acquisition method label as the target adoption degree verification rule, so that the target adoption degree verification rule is accurately screened.

[0106] In steps S301 to S302 shown in this embodiment, a target eligibility verification rule that meets the data application scenario information and the data item name information is selected from the candidate eligibility verification rules, and then a target adoption degree verification rule that matches the data acquisition channel information and the data acquisition method information is selected from the candidate adoption degree verification rules, so as to select the target eligibility verification rule and the target adoption degree verification rule that meet the original underwriting data. Then, the eligibility verification and adoption degree verification of the original underwriting data based on the target eligibility verification rule and the target adoption degree verification rule are more accurate, thereby achieving precise verification of different underwriting data.

[0107] See also Figure 4 In some embodiments, step S104 may include but is not limited to steps S401 to S403:

[0108] Step S401, performing qualification verification on the original underwriting data according to the target qualification verification rules, data application scenario information and data item name information to obtain qualification verification information;

[0109] Step S402, acquiring data availability measurement data according to the data acquisition channel information and the data acquisition method information to obtain target availability measurement data;

[0110] Step S403, performing adoption degree verification on the original underwriting data according to the target adoption degree verification rules and the target availability measurement data to obtain adoption degree verification information.

[0111] In step S401 of some embodiments, the data application scenario information is the application scenario of the original underwriting data, and the data item name information is the data item name of the original underwriting data. The specific operation of the qualification verification of the original data is determined by the data application scenario information and the data item name information.

[0112] Specifically, if the data application scenario information is a motor vehicle insurance underwriting scenario, and the data item name information is a frame number, then the original underwriting data is the frame number, and the target eligibility verification rule is the verification rule for the frame number, which is used to verify whether the expression of the frame number is correct. For example, the target eligibility verification rule sets the verification expression of the frame number as: province Chinese characters + city letters + numbers. The target eligibility verification rule is used to verify whether the expression of the frame number meets the verification expression. If it meets, the eligibility verification information indicates that the original underwriting data meets the target eligibility verification rule; if it does not meet, the eligibility verification information indicates that the original underwriting data does not meet the target eligibility verification rule.

[0113] It should be noted that the target eligibility verification rules can be used to verify whether the expression of the original underwriting data conforms to the verification expression, and can also be used to verify whether the data content of the original underwriting data contains garbled characters, missing characters, etc. This embodiment does not impose specific restrictions on the use of the target eligibility verification rules.

[0114] In step S402 of some embodiments, different data acquisition channels and data acquisition methods correspond to different data availability metrics, so the data availability metrics that match the data acquisition channel information and the data acquisition method information are used as the target availability metrics. It should be noted that the target availability metrics are used to measure whether the target underwriting data can be written, so as to reduce the underwriting data obtained by unavailable data acquisition channels and data acquisition methods from being directly written into the target system, which affects the accuracy of underwriting.

[0115] In step S403 of some embodiments, because different original underwriting data require different target availability measurement data to be written into the target system, the original underwriting data is verified for adoption according to the target adoption verification rules and the target availability measurement data to determine whether the original underwriting data is available, and then determine whether the original underwriting data can be written into the target system, to ensure that the target system writes qualified and available underwriting data.

[0116] Please refer to Figure 5 , the qualification verification and adoption degree verification of the original underwriting data are mainly realized through the policy executor, which obtains the original underwriting data sent by multiple data channels, and the original underwriting data includes: user information, underwriting data, claims data and claims auxiliary data. The policy executor performs qualification verification and adoption degree verification on the user information, underwriting data, claims data and claims auxiliary data one by one according to the original data configuration information, and finally outputs the qualification verification information and adoption degree verification information. The qualification verification information and adoption degree verification information can be used to determine whether the original underwriting data can be written into the target system, thereby reducing the errors in the underwriting data written into the target system.

[0117] In steps S401 to S403 shown in this embodiment, the eligibility of the original underwriting data is verified by using the target eligibility verification rules, data application scenario information and data item name information to obtain eligibility verification information, and then the original underwriting data is subjected to adoption degree verification according to the target adoption degree verification rules and target availability measurement data to obtain adoption degree verification information. Therefore, by performing eligibility verification and adoption degree verification on the original underwriting data, it is possible to more accurately evaluate whether the original underwriting data can be accurately written into the target system, thereby reducing the amount of erroneous underwriting data written into the target system.

[0118] In some embodiments, the target adoption degree verification rules include at least one of the following: data credibility verification rules, data importance verification rules, and data similarity verification rules; the target availability measurement data includes at least one of the following: data credibility and data importance measurement data; the adoption degree verification information includes at least one of the following: data credibility verification information, data importance verification information, and data similarity verification information. It should be noted that the judgment of whether the original underwriting data is adopted is mainly based on three aspects, namely data credibility, data importance, and data similarity, to ensure that the data written into the target system is not only accurate, but also reduces the situation of duplicate writing. Specifically, the adoption degree verification information corresponding to the data credibility verification rule is the data credibility verification information, the adoption degree verification information corresponding to the data importance verification rule is the data importance verification information, and the adoption degree verification information corresponding to the data similarity verification rule is the data similarity verification information.

[0119] See also Figure 6 In some embodiments, step S403 may include but is not limited to at least one of steps S601 to S603:

[0120] Step S601, performing credibility verification on the original underwriting data according to the data credibility verification rules and data credibility, and obtaining data credibility verification information;

[0121] Step S602, performing importance verification on the original underwriting data according to the data importance verification rules and the data importance measurement data to obtain data importance verification information;

[0122] Step S603, performing similarity measurement on the historical underwriting data and the current underwriting data to obtain data similarity measurement data, and determining data similarity verification information according to the data similarity verification rules, the data similarity measurement data and a preset similarity threshold.

[0123] In step S601 of some embodiments, credibility verification is used to determine whether the original underwriting data is trusted. It should be noted that different original underwriting data originate from different data acquisition channels, and the credibility requirements of different original underwriting data are different, so the data credibility verification rule is the credibility threshold corresponding to different original underwriting data. The data credibility of each original underwriting data is compared with the corresponding credibility threshold. If the data credibility is greater than or equal to the credibility threshold, the data credibility verification information of the original underwriting data is determined to be that the original underwriting data meets the target adoption degree verification rule, that is, the data acquisition channel of the original underwriting data is credible, and the original underwriting data with the highest data credibility is written into the target system as the target underwriting data. If the data credibility is less than the credibility threshold, the data credibility verification information of the original underwriting data is determined to be that the original underwriting data does not meet the target adoption degree verification rule, indicating that the data acquisition channel corresponding to the original underwriting data is untrustworthy, and the original underwriting data is not written into the target system.

[0124] In step S602 of some embodiments, importance verification also verifies whether the original underwriting data can be trusted, measured from the perspective of data importance. It should be noted that the higher the data importance measurement data, the higher the credibility of the original underwriting data, and different original underwriting data require different data importance measurement data. Among them, the data importance verification rules record the importance thresholds of different original underwriting data, by comparing the data importance measurement data with the corresponding importance threshold; if the data importance measurement data is greater than or equal to the corresponding importance threshold, it is determined that the data importance verification information of the original underwriting data is characterized as the original underwriting data complying with the data importance verification rules; if the data importance measurement data is less than the corresponding importance threshold, it is determined that the data importance verification information of the original underwriting data is characterized as the original underwriting data not complying with the data importance verification rules.

[0125] Specifically, the data importance measurement data is the importance ratio, and the higher the importance ratio, the higher the write priority of the original underwriting data. The importance ratio is compared with the importance threshold to select the original underwriting data with an importance ratio greater than the importance threshold as the target underwriting data.

[0126] In step S603 of some embodiments, the similarity verification is mainly to determine whether the original underwriting data is correct or repeated, and mainly to determine whether the current underwriting data is correct or repeated. Therefore, the current underwriting data and the historical underwriting data are first measured for similarity to obtain similarity measurement data, and the similarity measurement data represents whether the current underwriting data is written into the target system as accurate underwriting data. The similarity verification rule is a similarity threshold, which is determined by whether the similarity measurement data is greater than the similarity threshold. If the similarity measurement data is less than the similarity threshold, it indicates that the current underwriting data is inaccurate, and the historical underwriting data is used as the target underwriting data. If the similarity measurement data is greater than the similarity threshold, it indicates that the current underwriting data is accurate, and the current underwriting data is used as the target underwriting data. It can be seen that the data similarity verification information is used to determine whether the current underwriting data or the historical underwriting data is used as the target underwriting data, thereby improving the accuracy of the target underwriting data screening.

[0127] In steps S601 to S603 shown in this embodiment, by setting data credibility verification, importance verification and similarity verification, the original underwriting data can be verified from at least one of the three aspects, so as to select more accurate target underwriting data from the original underwriting data, improve the accuracy of underwriting data written into the target system, and enhance subsequent underwriting effects.

[0128] See also Figure 7 In some embodiments, after step S104, the data writing method further includes but is not limited to steps S701 to S704:

[0129] Step S701, obtaining data source information of original underwriting data to obtain original source information;

[0130] Step S702, obtaining data source information of target underwriting data to obtain target source information;

[0131] Step S703, performing adoption rate evaluation on the original underwriting data according to the target source information and the original source information to obtain adoption rate evaluation data;

[0132] Step S704: updating the original data configuration information according to the adoption rate evaluation data.

[0133] In steps S701 to S702 of some embodiments, the original source information represents the data acquisition channel of the original underwriting data, and the target source information represents the data acquisition channel of the target underwriting data. The collected underwriting data and the data acquisition channels that can actually be written to the target system can be analyzed through the original source information and the target source information to determine which data acquisition channels have a higher adoption rate.

[0134] In step S703 of some embodiments, there are multiple underwriting data corresponding to the same data source information, so the adoption rate is evaluated based on the target source information and the original source information. Specifically, the data volume of the same original source information is obtained to obtain the original data volume, and then the data volume of the same target source information is obtained to obtain the target data volume. The adoption rate evaluation data of the same data acquisition channel is obtained by obtaining the ratio between the target data volume and the original data volume. It should be noted that the higher the adoption rate evaluation data, the higher the credibility of the data acquisition channel. When the underwriting business is subsequently executed, the underwriting data of the data acquisition channel will be obtained first, so that the underwriting data written into the target system is more accurate.

[0135] For example, if the amount of data from data acquisition channel A is 20, but the amount of data that can be entered into the target system by data acquisition channel A is 10, then the adoption rate evaluation data of data acquisition channel A is determined to be 50%. If the amount of data from data acquisition channel B is 15, but the amount of data that can be entered into the target system by data acquisition channel B is 2, then the adoption rate evaluation data of data acquisition channel B is determined to be 13.3%. Therefore, it can be seen that the credibility of data acquisition channel A is higher than that of data acquisition channel B.

[0136] In step S704 of some embodiments, the original data configuration information is updated according to the adoption rate evaluation data. Specifically, the data availability metric data in the original data configuration information is updated according to the adoption rate evaluation data of each data acquisition channel. First, the data acquisition channel of the adoption rate evaluation data is obtained, the target channel information is obtained, and the data availability metric data in the original data configuration information is updated according to the target channel information and the data acquisition channel information in the original data configuration information. It should be noted that if the adoption rate evaluation data is high, the data availability metric data of the corresponding data acquisition channel will be increased, and conversely, if the adoption rate evaluation data is low, the data availability metric data of the corresponding data acquisition channel will be lowered, so as to facilitate the subsequent target underwriting data screening to select the underwriting data with higher credibility from the data acquisition channel, so that the underwriting data entered into the target system is more accurate.

[0137] In steps S701 to S704 shown in this embodiment, by obtaining the target source information of the target underwriting data entered into the target system, and then obtaining the original source information of the original underwriting data, the adoption rate evaluation data of each data acquisition channel is determined by comparing the target source information with the original source information. Finally, the data availability measurement data in the original data configuration information is updated according to the adoption rate evaluation data, so that the underwriting data with higher credibility can be collected according to the data availability measurement data in the future, so that the underwriting data written into the target system is more accurate.

[0138] In step S105 of some embodiments, the data verification information includes qualification verification information and adoption verification information, and the adoption verification information includes at least one of the following: data credibility verification information, data importance verification information, and data similarity verification information. In screening out the target underwriting data, preliminary underwriting data will be screened out from the original underwriting data based on the qualification verification information, and then the target underwriting data will be screened out from the preliminary underwriting data based on at least one of the data credibility verification information, the data importance verification information, and the data similarity verification information. It should be noted that the preliminary underwriting data is data that meets the target qualification verification rules, and the target underwriting data is data that meets at least one of the data credibility verification rules, the data importance verification rules, and the data similarity verification rules.

[0139] Specifically, the accuracy requirements of different underwriting data are different, so the number of data credibility verification rules, data importance verification rules, and data similarity verification rules that the underwriting data needs to meet is also different. For example, if the accuracy requirements of the screened target underwriting data are high, it is necessary to screen out the underwriting data that meets the data credibility verification rules, data importance verification rules, and data similarity verification rules from the preliminary underwriting data as the target underwriting data. If the accuracy requirements for the target underwriting data are not high, it is only necessary to screen out the data that meets any one of the data credibility verification rules, data importance verification rules, and data similarity verification rules from the preliminary underwriting data as the target underwriting data.

[0140] In step S106 of some embodiments, the target underwriting data is written into the target system according to the data application scenario information and the data item name information of the target data configuration information, so as to achieve orderly writing of the target underwriting data and facilitate clearer analysis of the target underwriting data during subsequent underwriting.

[0141] See also Figure 8 In some embodiments, after step S106, the data writing method may further include but is not limited to steps S801 to S803:

[0142] Step S801, obtaining the number of data revisions of target underwriting data;

[0143] Step S802, if the number of data corrections is greater than a preset correction threshold, generate data prompt information;

[0144] Step S803, receiving the revised underwriting data according to the data prompt information feedback, and writing the revised underwriting data into the target system.

[0145] In step S801 of some embodiments, after the target underwriting data is written into the target system, it does not mean that the target underwriting data written into the target system is completely accurate, and the target underwriting data needs to be monitored. The number of data revisions is obtained by monitoring the number of revisions of each target underwriting data.

[0146] In step S802 of some embodiments, if the number of data corrections is greater than the correction threshold, it indicates that the error in the target underwriting data cannot be automatically repaired, and a data prompt message needs to be generated. It should be noted that the data prompt message indicates that the target underwriting data is unavailable and needs to be updated.

[0147] In step S803 of some embodiments, if revised underwriting data is received from the target user according to the data prompt information feedback, the target underwriting data of the target system is replaced according to the revised underwriting data, and the revised underwriting data is written into the target system.

[0148] For example, if the target underwriting data is the vehicle frame number, and the number of data corrections for the same vehicle frame number exceeds the correction threshold, the generated data prompt message is "There is an error in the frame number", and the target user will provide feedback on the corrected frame number, so that subsequent vehicle insurance underwriting based on the frame number will be more accurate and reduce underwriting errors.

[0149] In steps S801 to S803 shown in this embodiment, after the target underwriting data is written into the target system, it is necessary to monitor the number of corrections to the target underwriting data. If the number of corrections is too many, feedback will be given to the target user so that the target user can correct the erroneous target underwriting data again, so that the underwriting data written into the target system is more accurate.

[0150] In this embodiment, the current underwriting data input by the target user in the target system is first obtained, and the input method of the current underwriting data can be at least one of voice input, picture input and text data. At the same time, in order to make the underwriting more accurate, it is also necessary to obtain the historical underwriting data of the target user in other external systems and the target system, and the historical underwriting data includes: historical user information, historical insurance purchase data, historical claims data and historical underwriting auxiliary data, etc. In order to evaluate whether the current underwriting data and historical underwriting data can be written to the target system, it is necessary to first obtain the data source information of the current underwriting data and the historical underwriting data, and obtain the original data configuration information of the current underwriting data and the historical underwriting data according to the data source information and the preset data configuration rules. Among them, the original data configuration information includes: data application scenario information, data item name information, data acquisition channel information, data acquisition method information and data availability measurement data. First, select the target scenario label from the candidate scenario labels according to the data application scenario information, and then filter out the target data item label from the candidate data item labels according to the data item name information, and then use the candidate eligibility verification rules corresponding to the target scenario label and the target data item label as the target eligibility verification rules. Then, the target data acquisition channel label is selected from the candidate data acquisition channel labels according to the data acquisition channel information, and the target data acquisition method label is selected from the candidate data acquisition method labels according to the data acquisition method information. Finally, the candidate adoption degree verification rules corresponding to the target data acquisition channel label and the target data acquisition method label are used as the target adoption degree verification rules. The data application scenario information and data item name information of the current underwriting data and the historical underwriting data are verified by the target qualification verification rules to obtain the qualification verification information of the current underwriting data and the historical underwriting data, and then the data availability measurement data is obtained according to the data acquisition channel information and the data acquisition method information as the target availability measurement data, so as to perform adoption degree verification on the original underwriting data according to the target adoption degree verification rules and the target availability measurement data to obtain the adoption degree verification information of the current underwriting data and the historical underwriting data. Among them, the target adoption degree verification rules include at least one of the following: data credibility verification rules, data importance verification rules and data similarity verification rules, and the adoption degree verification includes: data credibility verification, importance verification and similarity verification. The data credibility verification is specifically as follows: the data credibility verification rules are the credibility thresholds corresponding to different original underwriting data, and the data credibility of the current underwriting data and the historical underwriting data are compared with the corresponding credibility thresholds to obtain the data credibility verification information. The importance verification is as follows: the data importance verification rules record the importance thresholds of different original underwriting data, and the data importance verification information is obtained by comparing the data importance measurement data with the corresponding importance thresholds. The similarity verification is as follows: the current underwriting data and the historical underwriting data are similarly measured to obtain similarity measurement data, and the similarity measurement data is compared with the preset similarity threshold to determine the data similarity verification information.

[0151] Finally, the eligibility verification information is characterized as the current underwriting data and the historical underwriting data complying with the target eligibility verification rules, and the data credibility verification information is characterized as the current underwriting data and the historical underwriting data complying with the data credibility verification rules, the data importance verification information is characterized as the current underwriting data and the historical underwriting data complying with the data importance verification rules, and the data similarity verification information is characterized as the current underwriting data and the historical underwriting data complying with the data similarity verification rules. The underwriting data is taken as the target underwriting data, and then the target underwriting data is written into the target system according to the target data configuration information.

[0152] In summary, by automatically acquiring underwriting data and performing qualification verification, credibility verification, importance verification, and similarity verification on the underwriting data, the underwriting data that meets the four verifications can be written into the target system. On the one hand, the underwriting data can be automatically written, saving manpower and writing time; on the other hand, the target system can write accurate underwriting data, improving the accuracy of subsequent underwriting.

[0153] See also Fig. 9 The embodiment of the present application further provides a data writing device, which can implement the above data writing method, and the device includes:

[0154] Data acquisition module 901, used to acquire original underwriting data and data source information of the original underwriting data;

[0155] Configuration acquisition module 902, used to acquire data configuration information of original underwriting data according to data source information and preset data configuration rules, and obtain original data configuration information;

[0156] A rule screening module 903 is used to screen out target data validation rules from preset candidate data validation rules according to the original data configuration information;

[0157] A rule verification module 904 is used to perform rule verification on the original underwriting data according to the target data verification rule and the original data configuration information to obtain data verification information; wherein the data verification information indicates whether the original underwriting data complies with or does not comply with the target data verification rule;

[0158] The data screening module 905 is used to screen out target underwriting data from the original underwriting data according to the data verification information, and obtain the original data configuration information of the target underwriting data to obtain the target data configuration information; wherein the target underwriting data meets the target data verification rules;

[0159] The data writing module 906 is used to write the target underwriting data into a preset target system according to the target data configuration information.

[0160] The specific implementation of the data writing device is substantially the same as the specific implementation of the above-mentioned data writing method, and will not be described in detail herein.

[0161] The embodiment of the present application also provides a computer device, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the above data writing method when executing the computer program. The computer device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0162] See also Fig.10 , Fig.10 The hardware structure of a computer device according to another embodiment is shown, and the computer device includes:

[0163] The processor 1001 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0164] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1002, and the processor 1001 calls and executes the data writing method of the embodiment of this application;

[0165] Input / output interface 1003, used to implement information input and output;

[0166] The communication interface 1004 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WI FI, Bluetooth, etc.);

[0167] A bus 1005 , which transmits information between various components of the device (e.g., the processor 1001 , the memory 1002 , the input / output interface 1003 , and the communication interface 1004 );

[0168] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0169] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned data writing method is implemented.

[0170] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0171] The data writing method and device, computer equipment and storage medium provided in the embodiments of the present application automatically obtain the original underwriting data, and obtain the original data configuration information of the target underwriting data according to the data source information of the original underwriting data and the preset data configuration rules, so as to screen out the target data verification rules that meet the target underwriting data for data verification from the candidate data verification rules according to the original data configuration information. Then, the original underwriting data is subjected to rule verification according to the target data verification rules and the original data configuration information to determine whether the original underwriting data meets the target data verification rules, thereby realizing automatic verification of the original underwriting data. Finally, the target underwriting data that meets the target data verification rules is written into the target system according to the target data configuration information. Therefore, not only is the automatic writing of the underwriting data into the target system realized, but the underwriting data is also verified before writing, so as to select the underwriting data that meets the target data verification rules and write them into the target system, so that the underwriting data written into the target system is more accurate and reduces data error problems.

[0172] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0173] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0174] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0175] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0176] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0177] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0178] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0179] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0180] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0181] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0182] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A data writing method, characterized in that: The method comprises: Obtaining original underwriting data and data source information of the original underwriting data; Acquire data configuration information of the original underwriting data according to the data source information and preset data configuration rules to obtain original data configuration information; Filtering out target data validation rules from preset candidate data validation rules according to the original data configuration information; Perform rule verification on the original underwriting data according to the target data verification rule and the original data configuration information to obtain data verification information; wherein the data verification information indicates whether the original underwriting data complies with or does not comply with the target data verification rule; Filtering target underwriting data from the original underwriting data according to the data verification information, and acquiring original data configuration information of the target underwriting data to obtain target data configuration information; wherein the target underwriting data complies with the target data verification rule; The target underwriting data is written into a preset target system according to the target data configuration information.

2. The method according to claim 1, characterized in that The original data configuration information includes: data application scenario information, data item name information, data acquisition channel information and data acquisition method information; the candidate data verification rules include: candidate eligibility verification rules and candidate adoption degree verification rules; the target data verification rules include: target eligibility verification rules and target adoption degree verification rules; The step of selecting a target data validation rule from preset candidate data validation rules according to the original data configuration information includes: Filtering the target eligibility verification rule from the candidate eligibility verification rules according to the data application scenario information and the data item name information; The target adoption degree verification rule is selected from the candidate adoption degree verification rules according to the data acquisition channel information and the data acquisition method information.

3. The method according to claim 2, characterized in that The original data configuration information also includes: data availability measurement data, the data availability measurement data characterizes the availability of the original underwriting data; the data verification information includes: eligibility verification information and adoption verification information; The performing rule verification on the original underwriting data according to the target data verification rule and the original data configuration information to obtain data verification information includes: Performing qualification verification on the original underwriting data according to the target qualification verification rule, the data application scenario information and the data item name information to obtain the qualification verification information; Acquire the data availability measurement data according to the data acquisition channel information and the data acquisition method information to obtain target availability measurement data; The original underwriting data is subjected to adoption degree verification according to the target adoption degree verification rule and the target availability measurement data to obtain the adoption degree verification information.

4. The method according to claim 3, characterized in that The original underwriting data includes: historical underwriting data and current underwriting data; the target adoption verification rule includes at least one of the following: data credibility verification rule, data importance verification rule and data similarity verification rule; the target availability measurement data includes at least one of the following: data credibility and data importance measurement data; the adoption verification information includes at least one of the following: data credibility verification information, data importance verification information and data similarity verification information; The step of performing adoption degree verification on the original underwriting data according to the target adoption degree verification rule and the target availability measurement data to obtain the adoption degree verification information comprises at least one of the following steps: Performing credibility verification on the original underwriting data according to the data credibility verification rule and the data credibility to obtain the data credibility verification information; Performing importance verification on the original underwriting data according to the data importance verification rule and the data importance measurement data to obtain the data importance verification information; The historical underwriting data and the current underwriting data are subjected to similarity measurement to obtain data similarity measurement data, and the data similarity verification information is determined according to the data similarity verification rule, the data similarity measurement data and a preset similarity threshold.

5. The method according to claim 4, characterized in that The data source information includes: the historical source information of the historical underwriting data and the current source information of the current underwriting data; the original data configuration information includes: historical data configuration information and current data configuration information; The step of acquiring the data configuration information of the original underwriting data according to the data source information and the preset data configuration rules to obtain the original data configuration information includes: Filtering out historical data configuration rules from the preset data configuration rules according to the historical source information; Acquire data configuration information of the historical underwriting data according to the historical data configuration rule to obtain the historical data configuration information; Filtering a current data configuration rule from the preset data configuration rules according to the current source information; The data configuration information of the current underwriting data is obtained according to the current data configuration rule to obtain the current data configuration information.

6. The method according to any one of claims 1 to 5, characterized in that: After the target underwriting data is screened out from the original underwriting data according to the data verification information, and the original data configuration information of the target underwriting data is acquired to obtain the target data configuration information, the method further includes: Obtaining the data source information of the original underwriting data to obtain original source information; Acquire the data source information of the target underwriting data to obtain target source information; Performing an adoption rate evaluation on the original underwriting data according to the target source information and the original source information to obtain adoption rate evaluation data; The original data configuration information is updated according to the adoption rate evaluation data.

7. The method according to any one of claims 1 to 5, characterized in that: After writing the target underwriting data into a preset target system according to the target data configuration information, the method further includes: Obtaining the number of data revisions of the target underwriting data; If the number of data corrections is greater than a preset correction threshold, a data prompt message is generated; Receive the revised underwriting data based on the data prompt information feedback, and write the revised underwriting data into the target system.

8. A data writing device, characterized in that: The device comprises: A data acquisition module, used to acquire original underwriting data and data source information of the original underwriting data; A configuration acquisition module, used to acquire data configuration information of the original underwriting data according to the data source information and preset data configuration rules, so as to obtain original data configuration information; A rule screening module, used to screen out target data validation rules from preset candidate data validation rules according to the original data configuration information; A rule verification module, used to perform rule verification on the original underwriting data according to the target data verification rule and the original data configuration information to obtain data verification information; wherein the data verification information indicates whether the original underwriting data complies with or does not comply with the target data verification rule; A data screening module, used to screen out target underwriting data from the original underwriting data according to the data verification information, and obtain original data configuration information of the target underwriting data to obtain target data configuration information; wherein the target underwriting data complies with the target data verification rule; A data writing module is used to write the target underwriting data into a preset target system according to the target data configuration information.

9. A computer device, characterized in that: The computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the data writing method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the data writing method according to any one of claims 1 to 7 is implemented.

Images