A business evaluation method, device, equipment and storage medium thereof

Abstract

The embodiment of the present application belongs to the field of financial technology and is applied to claims business scenarios. It relates to a business evaluation method, device, equipment and storage medium thereof, which obtains multi-dimensional data for target business evaluation; uses the improved Halstead method to calculate the complexity of the multi-dimensional data; determines the complexity level corresponding to the multi-dimensional data according to the complexity calculation result; based on the complexity level, screens out the pre-trained business evaluation model corresponding to the corresponding level; inputs the multi-dimensional data into the business evaluation model to obtain the business evaluation result. By calculating the complexity of the multi-dimensional data for target business evaluation, the business evaluation model corresponding to different complexity levels is selected for business evaluation, and the business evaluation is graded and refined, thereby improving the efficiency of the business evaluation.

Description

Technical Field

[0001] The present application relates to the field of financial technology and is applied in claims settlement business scenarios, and in particular to a business assessment method, apparatus, device and storage medium thereof. Background Art

[0002] Traditional complex claims analysis algorithms consume significant manpower and time, and are prone to misjudgment. Traditional complex claims analysis relies primarily on expert experience and case analysis, resulting in low efficiency and accuracy.

[0003] To address the above issues, existing claims assessments are mainly based on expert experience and case analysis to train large assessment models. However, the training of larger assessment models often involves a large amount of data. Moreover, in terms of claims cases, the data dimensions involved in different claims cases often vary greatly, which will result in a higher overall complexity of the assessment model. When the model complexity is too high, it is easy to cause slow program execution and low assessment efficiency. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to propose a business evaluation method, device, equipment and storage medium thereof to solve the problems that the existing business evaluation methods have certain limitations, resulting in a high overall complexity of the evaluation model, which easily leads to slow program execution and low evaluation efficiency.

[0005] In order to solve the above technical problems, the embodiments of the present application provide a service evaluation method, which adopts the following technical solutions:

[0006] A business evaluation method comprises the following steps:

[0007] Obtain multi-dimensional data for target business evaluation;

[0008] Using an improved Halstead method to perform complexity calculation on the multi-dimensional data;

[0009] Determining the complexity level corresponding to the multi-dimensional data according to the complexity calculation result;

[0010] Based on the complexity level, selecting a pre-trained business evaluation model corresponding to the corresponding level;

[0011] The multi-dimensional data is input into the business evaluation model to obtain a business evaluation result.

[0012] Furthermore, the target business includes claims settlement business, and the step of obtaining multi-dimensional data for target business evaluation specifically includes:

[0013] Acquire multi-dimensional data uploaded in advance by a user from a preset cache space, wherein the multi-dimensional data includes image data, text data, and video data;

[0014] Calculating the amount of data for the multi-dimensional data according to a preset data amount statistical rule;

[0015] The step of using the improved Halstead method to calculate the complexity of the multi-dimensional data specifically includes:

[0016] According to the preset improved Halstead method: Vol=L×V×σ, the complexity of the multidimensional data is calculated, where Vol represents the complexity of the multidimensional data, L represents the data volume of the multidimensional data, V represents the number of operation instructions involved in the multidimensional data, and σ represents a positive correlation adjustment factor. The larger the data volume of the multidimensional data, the greater the number of operation instructions involved in the multidimensional data, and L, V and σ are all greater than 0.

[0017] Furthermore, the step of calculating the data volume of the multi-dimensional data according to a preset data volume statistical rule specifically includes:

[0018] If the multi-dimensional data includes picture data, all picture data are filtered out;

[0019] Using the Haar detection algorithm, the claim damage areas contained in all the image data are detected, the total area of ​​the claim damage areas contained in all the image data is counted, and the total area of ​​the claim damage areas is set as the data volume of all the image data;

[0020] If the multi-dimensional data contains text data, all text data are filtered out;

[0021] Performing word segmentation processing on all the text data according to a preset vocabulary table, and counting the total amount of the word segments, and setting the total amount of the word segments as the data amount of all the text data;

[0022] If the multi-dimensional data includes video data, filtering out all video data;

[0023] Performing frame processing on all video data according to a preset frame interval, counting the total number of pictures after the frame processing, and using the total number of pictures as the data volume of all video data;

[0024] Obtain the data volume summary weights pre-set for image data, text data, and video data respectively;

[0025] According to the data volume aggregation weights corresponding to the image data, text data and video data respectively, the data volume of all the image data, the data volume of all the text data and the data volume of all the video data are cumulatively added and calculated to obtain the data volume corresponding to the multi-dimensional data.

[0026] Furthermore, before executing the step of determining the complexity level corresponding to the multi-dimensional data based on the complexity calculation result, the method further includes:

[0027] Different complexity levels are pre-set for different complexity intervals, wherein, when setting, the greater the complexity value corresponding to the complexity interval, the higher the corresponding complexity level.

[0028] Furthermore, before executing the step of screening out the pre-trained business evaluation model corresponding to the corresponding level based on the complexity level, the method further includes:

[0029] Obtain multi-dimensional data uploaded by historical batch claim users for claim assessment as training sample data;

[0030] Performing complexity calculation on the multi-dimensional data corresponding to each claim settlement user in the training sample data;

[0031] Determine the complexity level corresponding to each claim user based on the complexity calculation results;

[0032] Summarize and organize the multi-dimensional data of claim users with the same complexity level to obtain training sub-sample data corresponding to all complexity levels;

[0033] Constructing a number of claims business assessment models to be trained equal to the number of complexity levels, wherein all the claims business assessment models to be trained have the same model structure;

[0034] The training sub-sample data corresponding to different complexity levels are input into different claims business evaluation models to be trained, and model training is performed to obtain pre-trained claims business evaluation models corresponding to different complexity levels.

[0035] Furthermore, before executing the step of constructing a number of claims business assessment models to be trained equal to the number of complexity levels, the method further includes:

[0036] Obtaining the claim result data of the historical batch claim users;

[0037] Summarize and organize the claim result data of users with the same complexity level to obtain the summary data of claim results corresponding to all complexity levels;

[0038] After executing the step of constructing a number of claims business assessment models to be trained equal to the number of complexity levels, the method further includes:

[0039] Establish a mapping relationship between the complexity level and the claims business assessment model to be trained.

[0040] Furthermore, the step of inputting the training subsample data corresponding to different complexity levels into different claims business assessment models to be trained, performing model training, and obtaining pre-trained claims business assessment models corresponding to different complexity levels specifically includes:

[0041] According to the mapping relationship between the complexity level and the claims business evaluation model to be trained, the training sub-sample data corresponding to different complexity levels are input into the corresponding claims business evaluation model to be trained;

[0042] According to the mapping relationship between the complexity level and the claims business evaluation model to be trained, the summary data of the claims results corresponding to different complexity levels are input into the corresponding claims business evaluation model to be trained;

[0043] Through different claims business evaluation models to be trained, a comprehensive analysis method is used to construct the evaluation and prediction relationship between training sub-sample data of different complexity levels and claim result summary data;

[0044] The claims business evaluation model constructed with the evaluation prediction relationship is used as the claims business evaluation model pre-trained at the corresponding complexity level.

[0045] In order to solve the above technical problems, the embodiment of the present application also provides a service evaluation device, which adopts the following technical solution:

[0046] A service evaluation device, comprising:

[0047] A business evaluation data acquisition module is used to obtain multi-dimensional data for target business evaluation;

[0048] A complexity calculation module, configured to perform complexity calculation on the multi-dimensional data using an improved Halstead method;

[0049] A complexity level determination module, configured to determine the complexity level corresponding to the multi-dimensional data according to the complexity calculation result;

[0050] A business evaluation model screening module is used to screen out pre-trained business evaluation models corresponding to the corresponding level based on the complexity level;

[0051] The business evaluation execution module is used to input the multi-dimensional data into the business evaluation model to obtain a business evaluation result.

[0052] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0053] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the above-mentioned business evaluation method when executing the computer-readable instructions.

[0054] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0055] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the business evaluation method described above.

[0056] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0057] The business evaluation method described in the embodiment of the present application obtains multi-dimensional data for target business evaluation; uses the improved Halstead method to perform complexity calculation on the multi-dimensional data; determines the complexity level corresponding to the multi-dimensional data based on the complexity calculation result; based on the complexity level, screens out the pre-trained business evaluation model corresponding to the corresponding level; inputs the multi-dimensional data into the business evaluation model to obtain a business evaluation result. By calculating the complexity of the multi-dimensional data for target business evaluation, the business evaluation model corresponding to the different complexity levels is selected for business evaluation, and the business evaluation is graded and refined, thereby improving the efficiency of the business evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the solutions in this application, a brief introduction will be given below to the drawings required for use in the description of the embodiments of this application. Obviously, the drawings described below are some embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0059] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0060] Figure 2 is a flow chart of an embodiment of a business evaluation method according to the present application;

[0061] Figure 3 yes Figure 2 A flowchart of a specific embodiment of step 201 is shown;

[0062] Figure 4 yes Figure 3 A flowchart of a specific embodiment of step 302 is shown;

[0063] Figure 5 This is a flowchart of a specific embodiment of processing the claims assessment data required for training the business assessment model in the business assessment method described in this application;

[0064] Figure 6 yes Figure 5 A flowchart of a specific embodiment of step 506 is shown;

[0065] Figure 7 is a structural diagram of an embodiment of a service evaluation device according to the present application;

[0066] Figure 8 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0068] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0069] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0070] like Figure 1As shown, system architecture 100 may include a terminal device 101, a network 102, and a server 103. Terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. Network 102 is a medium for providing a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0071] The user can use the terminal device 101 to interact with the server 103 via the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0072] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 1012 or mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer and a desktop computer, etc.

[0073] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0074] It should be noted that the service evaluation method provided in the embodiment of the present application is generally executed by a server, and accordingly, the service evaluation device is generally set in the server.

[0075] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0076] Continue to refer Figure 2 , shows a flow chart of an embodiment of a business evaluation method according to the present application. The business evaluation method comprises the following steps:

[0077] Step 201: Acquire multi-dimensional data for target business evaluation.

[0078] In this embodiment, the target business includes insurance claims business, such as automobile insurance claims business, and the multi-dimensional data includes text data, image data, or video data provided by various parties involved in the automobile insurance accident.

[0079] In the auto insurance claims business, the more parties involved in the auto insurance accident, the more text data, image data or video data, the more factors that need to be considered in the claims, and the more complicated the claims processing.

[0080] By obtaining multi-dimensional data for the target business assessment, it is possible to fully conduct a claims assessment in combination with the multi-dimensional data. At the same time, the complexity of the claims can also be assessed using the multi-dimensional data.

[0081] Step 202: Perform complexity calculation on the multi-dimensional data using an improved Halstead method.

[0082] The Halstead method is used in software metrics to assess program complexity, development effort, and difficulty. It can help developers optimize code structure and improve code maintainability and readability.

[0083] The Halstead method uses the operators and operands in a program to jointly characterize the complexity of the program. Generally speaking, the more operators and operands involved, the greater the complexity of the program. At this time, it is necessary to consider optimizing the program.

[0084] The improvement proposed in this application is to calculate the claim assessment complexity based on the amount of data and the number of operational instructions involved in the multi-dimensional data. This can be improved using the Halstead method, whereby the greater the amount of data and the number of operational instructions involved in the multi-dimensional data, the higher the claim assessment complexity. A corresponding positive correlation factor is introduced to ensure computer readability.

[0085] Step 203: Determine the complexity level corresponding to the multi-dimensional data according to the complexity calculation result.

[0086] Step 204: Based on the complexity level, select the pre-trained business evaluation model corresponding to the corresponding level.

[0087] In this embodiment, business assessment models are trained separately for different complexity levels, so that when multi-dimensional data corresponding to the target claims business is subsequently obtained, it is only necessary to first determine the complexity of the multi-dimensional data, and select the pre-trained claims assessment model corresponding to the corresponding level to perform claims assessment. Claims assessment is performed in a targeted manner based on data complexity, thereby improving claims assessment efficiency.

[0088] Step 205: Input the multi-dimensional data into the business evaluation model to obtain a business evaluation result.

[0089] In this embodiment, multidimensional data for target business evaluation is obtained; the complexity of the multidimensional data is calculated using an improved Halstead method; the complexity level corresponding to the multidimensional data is determined based on the complexity calculation result; based on the complexity level, a pre-trained business evaluation model corresponding to the corresponding level is screened out; the multidimensional data is input into the business evaluation model to obtain a business evaluation result. By calculating the complexity of the multidimensional data for target business evaluation, business evaluation models corresponding to different complexity levels are selected for business evaluation, and hierarchical processing and refinement are performed on the business evaluation, thereby improving the efficiency of business evaluation.

[0090] In this embodiment, the target business includes claims settlement business.

[0091] Continue to refer Figure 3 , in some optional implementations, Figure 3 yes Figure 2 The flowchart of a specific embodiment of step 201 shown includes the following steps:

[0092] Step 301: Acquire multi-dimensional data uploaded in advance by a user from a preset cache space, wherein the multi-dimensional data includes image data, text data, and video data;

[0093] Step 302: Calculate the data volume of the multi-dimensional data according to a preset data volume statistical rule.

[0094] By calculating the data volume of the multi-dimensional data according to a preset data volume statistical rule, it is convenient to calculate the complexity of the multi-dimensional data.

[0095] In this embodiment, the step of using the improved Halstead method to calculate the complexity of the multidimensional data specifically includes: calculating the complexity of the multidimensional data according to the preset improved Halstead method: Vol=L×V×σ, wherein Vol represents the complexity of the multidimensional data, L represents the data volume of the multidimensional data, V represents the number of operation instructions involved in the multidimensional data, and σ represents a positive correlation adjustment factor. The larger the data volume of the multidimensional data, the greater the number of operation instructions involved in the multidimensional data, and L, V and σ are all greater than 0.

[0096] Specifically, the number of operation instructions involved in the multi-dimensional data can be obtained by counting the number of execution instructions in the program.

[0097] Continue to refer Figure 4, in some optional implementations, Figure 4 yes Figure 3 The flowchart of a specific embodiment of step 302 shown includes the following steps:

[0098] Step 401: If the multi-dimensional data includes picture data, all picture data are filtered out;

[0099] Step 402: Using a Haar detection algorithm, detect the claim damage areas contained in all the image data, calculate the total area of ​​the claim damage areas contained in all the image data, and set the total area of ​​the claim damage areas as the data volume of all the image data;

[0100] Step 403: If the multi-dimensional data includes text data, all text data are filtered out;

[0101] Step 404: performing word segmentation processing on all the text data according to a preset vocabulary table, and counting the total number of the segmented words, and setting the total number of the segmented words as the data size of all the text data;

[0102] Step 405: If the multi-dimensional data includes video data, all video data are filtered out;

[0103] Step 406: performing frame processing on all video data according to a preset frame interval, counting the total number of pictures after the frame processing, and using the total number of pictures as the data volume of all video data;

[0104] Step 407: Obtain the data volume summary weights pre-set for the image data, text data, and video data, respectively;

[0105] Step 408: Based on the weights of the data volumes corresponding to the image data, text data, and video data, respectively, the data volumes of all image data, the data volumes of all text data, and the data volumes of all video data are cumulatively added and calculated to obtain the data volumes corresponding to the multi-dimensional data.

[0106] By respectively calculating the data volume of data in different formats and then performing cumulative sum operations, the data volume corresponding to the multi-dimensional data is obtained, so as to obtain the complexity of the multi-dimensional data.

[0107] In this embodiment, before executing the step of determining the complexity level corresponding to the multidimensional data based on the complexity calculation result, the method also includes: pre-setting different complexity levels for different complexity intervals, wherein, when setting, the larger the complexity value corresponding to the complexity interval, the higher the corresponding complexity level.

[0108] Continue to refer Figure 5In some optional implementations, before step 204, a step of processing the claims assessment data required for training the business assessment model is also included. Figure 5 This is a flowchart of a specific embodiment of processing the claims assessment data required for training the business assessment model in the business assessment method described in this application, including the following steps:

[0109] Step 501: Acquire multi-dimensional data uploaded by historical batch claim users for claim assessment as training sample data;

[0110] Step 502: performing complexity calculation on the multi-dimensional data corresponding to each claim settlement user in the training sample data;

[0111] Step 503: Determine the complexity level corresponding to each claim user based on the complexity calculation result;

[0112] Step 504: Summarize and organize the multi-dimensional data of claim users with the same complexity level to obtain training subsample data corresponding to all complexity levels;

[0113] Step 505: constructing a number of claims business evaluation models to be trained equal to the number of complexity levels, wherein all the claims business evaluation models to be trained have the same model structure;

[0114] Step 506: Input the training subsample data corresponding to different complexity levels into different claims business assessment models to be trained, perform model training, and obtain pre-trained claims business assessment models corresponding to different complexity levels.

[0115] By calculating the complexity of the multi-dimensional data uploaded by historical batch claims users for claims assessment, and aggregating the multi-dimensional data of the same complexity level together, we train pre-trained claims business assessment models corresponding to different complexity levels. This makes it easier to select the corresponding level of claims business assessment model for the multi-dimensional data of different complexity levels uploaded by claims users during the subsequent actual claims assessment.

[0116] In this embodiment, before executing the step of constructing a number of claims business evaluation models to be trained equal to the number of complexity levels, the method also includes: obtaining the claims result data of the historical batch claims users; summarizing and collating the claims result data of claims users of the same complexity level to obtain the claims result summary data corresponding to all complexity levels.

[0117] By summarizing and organizing the claim result data of claim users with the same complexity level, we can obtain the summary data of claim results corresponding to all complexity levels, so that when training the model, we can obtain the evaluation and prediction relationship between the multi-dimensional data for claim evaluation at different complexity levels and the corresponding claim result summary data.

[0118] In this embodiment, after executing the step of constructing a number of claims business assessment models to be trained equal to the number of complexity levels, the method further includes: establishing a mapping relationship between the complexity levels and the claims business assessment models to be trained.

[0119] Specifically, the mapping relationship between the complexity level and the claim business evaluation model to be trained is established by numbering different claim business evaluation models to be trained so that each number corresponds to a complexity level.

[0120] Continue to refer Figure 6 , in some optional implementations, Figure 6 yes Figure 5 The flowchart of a specific embodiment of step 506 includes the following steps:

[0121] Step 601: Input the training subsample data corresponding to different complexity levels into the corresponding claims business evaluation model to be trained based on the mapping relationship between the complexity level and the claims business evaluation model to be trained;

[0122] Step 602: Based on the mapping relationship between the complexity level and the claims business evaluation model to be trained, the summary data of the claims results corresponding to the different complexity levels are input into the corresponding claims business evaluation model to be trained;

[0123] Step 603: Using different claims business evaluation models to be trained, a comprehensive analysis method is used to construct evaluation and prediction relationships between training subsample data of different complexity levels and claim result summary data;

[0124] In step 604, the claim business evaluation model constructed based on the evaluation prediction relationship is used as the claim business evaluation model pre-trained at the corresponding complexity level.

[0125] In this embodiment, the step of inputting the multi-dimensional data into the business evaluation model to obtain the business evaluation result specifically includes: performing claim evaluation and prediction on the multi-dimensional data according to the evaluation prediction relationship possessed by the business evaluation model to obtain the claim evaluation prediction result.

[0126] In this embodiment, business assessment models are trained separately for different complexity levels, so that when multi-dimensional data corresponding to the target claims business is subsequently obtained, it is only necessary to first determine the complexity of the multi-dimensional data, and select the pre-trained claims assessment model corresponding to the corresponding level to perform claims assessment. Claims assessment is performed in a targeted manner based on data complexity, thereby improving claims assessment efficiency.

[0127] This application obtains multi-dimensional data for target business evaluation; uses the improved Halstead method to calculate the complexity of the multi-dimensional data; determines the complexity level corresponding to the multi-dimensional data based on the complexity calculation result; based on the complexity level, screens out the pre-trained business evaluation model corresponding to the corresponding level; inputs the multi-dimensional data into the business evaluation model to obtain the business evaluation result. By calculating the complexity of the multi-dimensional data for target business evaluation, the business evaluation model corresponding to different complexity levels is selected for business evaluation, and the business evaluation is graded and refined, thereby improving the efficiency of business evaluation.

[0128] 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 achieve optimal results.

[0129] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0130] In an embodiment of the present application, by obtaining multidimensional data for target business evaluation; using the improved Halstead method to perform complexity calculation on the multidimensional data; determining the complexity level corresponding to the multidimensional data based on the complexity calculation result; based on the complexity level, screening out the pre-trained business evaluation model corresponding to the corresponding level; inputting the multidimensional data into the business evaluation model to obtain a business evaluation result. By calculating the complexity of the multidimensional data for target business evaluation, the business evaluation model corresponding to the different complexity levels is selected for business evaluation, and the business evaluation is graded and refined, thereby improving the efficiency of the business evaluation.

[0131] Further references Figure 7 , as a response to the above Figure 2 In order to realize the method shown in FIG, the present application provides an embodiment of a service evaluation device. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0132] like Figure 7 As shown, the service evaluation device 700 of this embodiment includes: a service evaluation data acquisition module 701, a complexity calculation module 702, a complexity level determination module 703, a service evaluation model screening module 704 and a service evaluation execution module 705. Among them:

[0133] The business evaluation data acquisition module 701 is used to obtain multi-dimensional data for target business evaluation;

[0134] A complexity calculation module 702 is configured to perform complexity calculation on the multi-dimensional data using an improved Halstead method;

[0135] A complexity level determination module 703 is configured to determine the complexity level corresponding to the multi-dimensional data according to the complexity calculation result;

[0136] A business evaluation model screening module 704 is configured to screen out pre-trained business evaluation models corresponding to the corresponding level based on the complexity level;

[0137] The business evaluation execution module 705 is used to input the multi-dimensional data into the business evaluation model to obtain a business evaluation result.

[0138] This application obtains multi-dimensional data for target business evaluation; uses the improved Halstead method to calculate the complexity of the multi-dimensional data; determines the complexity level corresponding to the multi-dimensional data based on the complexity calculation result; based on the complexity level, screens out the pre-trained business evaluation model corresponding to the corresponding level; inputs the multi-dimensional data into the business evaluation model to obtain the business evaluation result. By calculating the complexity of the multi-dimensional data for target business evaluation, the business evaluation model corresponding to different complexity levels is selected for business evaluation, and the business evaluation is graded and refined, thereby improving the efficiency of business evaluation.

[0139] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware via computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0140] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0141] To solve the above technical problems, the present application also provides a computer device. Figure 8 , Figure 8 This is a basic structural block diagram of the computer device in this embodiment.

[0142] The computer device 8 includes a memory 8a, a processor 8b, and a network interface 8c that are interconnected via a system bus. Figure 8 Only a computer device 8 having components such as a memory 8a, a processor 8b, and a network interface 8c is shown. However, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead. It should be understood by those skilled in the art that a computer device herein is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0143] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0144] The memory 8a includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, a magnetic disk, an optical disk, etc. In some embodiments, the memory 8a may be an internal storage unit of the computer device 8, such as the hard disk or memory of the computer device 8. In other embodiments, the memory 8a may also be an external storage device of the computer device 8, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the computer device 8. Of course, the memory 8a may also include both the internal storage unit of the computer device 8 and its external storage device. In this embodiment, the memory 8a is generally used to store the operating system and various application software installed on the computer device 8, such as computer-readable instructions of a service evaluation method. In addition, the memory 8a can also be used to temporarily store various types of data that have been output or are to be output.

[0145] In some embodiments, the processor 8b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 8b is generally used to control the overall operation of the computer device 8. In this embodiment, the processor 8b is used to execute computer-readable instructions stored in the memory 8a or process data, such as computer-readable instructions for executing the business evaluation method.

[0146] The network interface 8c may include a wireless network interface or a wired network interface. The network interface 8c is generally used to establish a communication connection between the computer device 8 and other electronic devices.

[0147] The computer device proposed in this embodiment belongs to the field of financial technology and is used in claims business scenarios. This application obtains multi-dimensional data for target business evaluation; uses the improved Halstead method to calculate the complexity of the multi-dimensional data; determines the complexity level corresponding to the multi-dimensional data according to the complexity calculation result; based on the complexity level, screens out the pre-trained business evaluation model corresponding to the corresponding level; inputs the multi-dimensional data into the business evaluation model to obtain the business evaluation result. By calculating the complexity of the multi-dimensional data for target business evaluation, the business evaluation models corresponding to different complexity levels are selected for business evaluation, and the business evaluation is graded and refined, thereby improving the efficiency of the business evaluation.

[0148] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by a processor to enable the processor to perform the steps of the business evaluation method as described above.

[0149] The computer-readable storage medium proposed in this embodiment belongs to the field of financial technology and is applied to claims business scenarios. This application obtains multi-dimensional data to be evaluated for the target business; uses the improved Halstead method to calculate the complexity of the multi-dimensional data; determines the complexity level corresponding to the multi-dimensional data according to the complexity calculation result; based on the complexity level, screens out the pre-trained business evaluation model corresponding to the corresponding level; inputs the multi-dimensional data into the business evaluation model to obtain the business evaluation result. By calculating the complexity of the multi-dimensional data to be evaluated for the target business, the business evaluation models corresponding to different complexity levels are selected for business evaluation, and the business evaluation is graded and refined, thereby improving the efficiency of the business evaluation.

[0150] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0151] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present application specification and the accompanying drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present application.

Claims

1. A business evaluation method, characterized in that: The steps include: Obtain multidimensional data for target business evaluation, wherein the target business includes claims settlement business. The step of obtaining the multidimensional data for target business evaluation specifically includes: obtaining multidimensional data pre-uploaded by a user from a preset cache space, specifically, the multidimensional data includes image data, text data, and video data; performing data volume calculation on the multidimensional data according to a preset data volume statistical rule. Specifically, the step of performing data volume calculation on the multidimensional data according to the preset data volume statistical rule includes: If the multi-dimensional data includes image data, all image data are filtered out; a Haar detection algorithm is used to detect the claim damage areas respectively contained in all image data, and the total area of ​​the claim damage areas contained in all image data is counted, and the total area of ​​the claim damage areas is set as the data volume of all image data; If the multidimensional data includes text data, all text data are filtered out; all text data are segmented according to a preset vocabulary form, and the total amount of segmented words is counted, and the total amount of segmented words is set as the data amount of all text data; If the multi-dimensional data includes video data, all video data are filtered out; all video data are framed according to a preset frame interval, and the total number of pictures after the frame processing is counted, and the total number of pictures is used as the data volume of all video data; Obtain the data volume summary weights pre-set for image data, text data, and video data respectively; According to the data amount aggregation weights corresponding to the image data, text data and video data respectively, the data amount of all the image data, the data amount of all the text data and the data amount of all the video data are cumulatively added and calculated to obtain the data amount corresponding to the multi-dimensional data; The improved Halstead method is used to perform complexity calculation on the multidimensional data, wherein the step of performing complexity calculation on the multidimensional data using the improved Halstead method specifically includes: according to a preset improved Halstead method: ,Calculate the complexity of the multi-dimensional data, specifically, represents the complexity of the multi-dimensional data, represents the data volume of the multi-dimensional data, Indicates the number of operation instructions involved in the multi-dimensional data, represents a positive correlation adjustment factor. The larger the amount of the multi-dimensional data, the more operation instructions the multi-dimensional data involves. 、 and are all greater than 0; Determining the complexity level corresponding to the multi-dimensional data according to the complexity calculation result; Based on the complexity level, selecting a pre-trained business evaluation model corresponding to the corresponding level; The multi-dimensional data is input into the business evaluation model to obtain a business evaluation result.

2. The business evaluation method according to claim 1, characterized in that: Before executing the step of determining the complexity level corresponding to the multi-dimensional data according to the complexity calculation result, the method further includes: Different complexity levels are pre-set for different complexity intervals, wherein, when setting, the greater the complexity value corresponding to the complexity interval, the higher the corresponding complexity level.

3. The business evaluation method according to claim 1, characterized in that: Before executing the step of screening out the pre-trained business evaluation model corresponding to the corresponding level based on the complexity level, the method further includes: Obtain multi-dimensional data uploaded by historical batch claim users for claim assessment as training sample data; Performing complexity calculation on the multi-dimensional data corresponding to each claim settlement user in the training sample data; Determine the complexity level corresponding to each claim user based on the complexity calculation results; Summarize and organize the multi-dimensional data of claim users with the same complexity level to obtain training sub-sample data corresponding to all complexity levels; Constructing a number of claims business assessment models to be trained equal to the number of complexity levels, wherein all the claims business assessment models to be trained have the same model structure; The training sub-sample data corresponding to different complexity levels are input into different claims business evaluation models to be trained, and model training is performed to obtain pre-trained claims business evaluation models corresponding to different complexity levels.

4. The business evaluation method according to claim 3, characterized in that: Before executing the step of constructing a number of claims business assessment models to be trained equal to the number of complexity levels, the method further includes: Obtaining the claim result data of the historical batch claim users; Summarize and organize the claim result data of users with the same complexity level to obtain the summary data of claim results corresponding to all complexity levels; After executing the step of constructing a number of claims business assessment models to be trained equal to the number of complexity levels, the method further includes: Establish a mapping relationship between the complexity level and the claims business assessment model to be trained.

5. The business evaluation method according to claim 4, characterized in that: The step of inputting the training subsample data corresponding to different complexity levels into different claims business assessment models to be trained, performing model training, and obtaining pre-trained claims business assessment models corresponding to different complexity levels specifically includes: According to the mapping relationship between the complexity level and the claims business evaluation model to be trained, the training sub-sample data corresponding to different complexity levels are input into the corresponding claims business evaluation model to be trained; According to the mapping relationship between the complexity level and the claims business evaluation model to be trained, the summary data of the claims results corresponding to different complexity levels are input into the corresponding claims business evaluation model to be trained; Through different claims business evaluation models to be trained, a comprehensive analysis method is used to construct the evaluation and prediction relationship between training sub-sample data of different complexity levels and claim result summary data; The claims business evaluation model constructed with the evaluation prediction relationship is used as the claims business evaluation model pre-trained at the corresponding complexity level.

6. A service evaluation device, characterized in that: The service evaluation device is used to implement the steps of the service evaluation method according to any one of claims 1 to 5, and the service evaluation device includes: The business evaluation data acquisition module is used to obtain multi-dimensional data for target business evaluation, wherein the target business includes claims business. The step of obtaining the multi-dimensional data for target business evaluation specifically includes: obtaining multi-dimensional data pre-uploaded by the user from a preset cache space, specifically, the multi-dimensional data includes image data, text data and video data; calculating the data volume of the multi-dimensional data according to a preset data volume statistical rule. Specifically, the step of calculating the data volume of the multi-dimensional data according to the preset data volume statistical rule includes: If the multi-dimensional data includes image data, all image data are filtered out; a Haar detection algorithm is used to detect the claim damage areas respectively contained in all image data, and the total area of ​​the claim damage areas contained in all image data is counted, and the total area of ​​the claim damage areas is set as the data volume of all image data; If the multidimensional data includes text data, all text data are filtered out; all text data are segmented according to a preset vocabulary form, and the total amount of segmented words is counted, and the total amount of segmented words is set as the data amount of all text data; If the multi-dimensional data includes video data, all video data are filtered out; all video data are framed according to a preset frame interval, and the total number of pictures after the frame processing is counted, and the total number of pictures is used as the data volume of all video data; Obtain the data volume summary weights pre-set for image data, text data, and video data respectively; According to the data amount aggregation weights corresponding to the image data, text data and video data respectively, the data amount of all the image data, the data amount of all the text data and the data amount of all the video data are cumulatively added and calculated to obtain the data amount corresponding to the multi-dimensional data; A complexity calculation module is used to perform complexity calculation on the multi-dimensional data using an improved Halstead method, wherein the step of performing complexity calculation on the multi-dimensional data using the improved Halstead method specifically includes: according to a preset improved Halstead method: ,Calculate the complexity of the multi-dimensional data, specifically, represents the complexity of the multi-dimensional data, represents the data volume of the multi-dimensional data, Indicates the number of operation instructions involved in the multi-dimensional data, represents a positive correlation adjustment factor. The larger the amount of the multi-dimensional data, the more operation instructions the multi-dimensional data involves. 、 and are all greater than 0; A complexity level determination module, configured to determine the complexity level corresponding to the multi-dimensional data according to the complexity calculation result; A business evaluation model screening module is used to screen out pre-trained business evaluation models corresponding to the corresponding level based on the complexity level; The business evaluation execution module is used to input the multi-dimensional data into the business evaluation model to obtain a business evaluation result.

7. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the service evaluation method according to any one of claims 1 to 5 when executing the computer-readable instructions.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the service evaluation method according to any one of claims 1 to 5.

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