Data asset value evaluation method and system, electronic equipment and storage medium

Abstract

The invention discloses a data asset value evaluation method and system, electronic equipment and a storage medium, and relates to the technical field of data asset value evaluation.The data asset value evaluation method comprises the steps that a target model corresponding to to-be-evaluated data is determined in all preset value evaluation models; evaluating the to-be-evaluated data based on the target model to obtain an evaluation report; the evaluation report is scored through a preset scoring model, a score corresponding to the evaluation report is obtained, when it is detected that the score is larger than or equal to a preset score threshold value, the evaluation report serves as a data asset value evaluation result of the to-be-evaluated data, and the score threshold value is used for judging whether the evaluation report is qualified or not. The technical scheme of the invention aims to solve the technical problem of how to improve the efficiency of data asset value evaluation.

Description

Technical Field

[0001] The present application relates to the technical field of data asset value assessment, and in particular to a data asset value assessment method, system, electronic device and storage medium. Background Art

[0002] Data assets are a new type of asset that has high economic value for enterprises. Therefore, it is necessary to conduct a value assessment on data assets.

[0003] Nowadays, data asset evaluation business mainly relies on manual consulting services to determine the value of customer or enterprise data assets. This process usually involves offline in-depth analysis of the composition, processing flow and collection path of the other party's actual data, and then using the cost method to estimate the required costs, or using the income method in combination with income data to predict potential income. However, this highly manual operation method requires a lot of time, resulting in low efficiency of asset evaluation.

[0004] Therefore, how to improve the efficiency of data asset value assessment is a technical problem that needs to be solved by technical personnel in this field. Summary of the invention

[0005] The main purpose of this application is to provide a data asset value assessment method, system, electronic device and storage medium, aiming to solve the technical problem of how to improve the efficiency of data asset value assessment.

[0006] To achieve the above objectives, this application proposes a data asset value assessment method, which includes:

[0007] Determine the target model corresponding to the data to be evaluated in each preset value evaluation model;

[0008] Evaluate the data to be evaluated based on the target model to obtain an evaluation report;

[0009] The evaluation report is scored using a preset scoring model to obtain a score corresponding to the evaluation report, and when it is detected that the score is greater than or equal to a preset score threshold, the evaluation report is used as a data asset value evaluation result of the data to be evaluated, wherein the score threshold is used to determine whether the evaluation report is qualified.

[0010] In one embodiment, the step of determining the target model corresponding to the data to be evaluated in each preset value evaluation model includes:

[0011] In response to a selection instruction output by a user, taking a value assessment model corresponding to the selection instruction among the value assessment models as a target model corresponding to the data to be assessed;

[0012] or,

[0013] The data to be evaluated is analyzed by the big model, and based on the analysis results output by the big model, the target model corresponding to the data to be evaluated is determined in each preset value evaluation model.

[0014] In one embodiment, the step of determining the target model corresponding to the data to be evaluated in each preset value evaluation model further includes:

[0015] Obtain each data indicator in the data to be evaluated, and based on the indicator type in each data indicator and the number of indicators corresponding to each indicator type, determine the target model corresponding to the data to be evaluated in each preset value evaluation model, wherein the data indicator is used to characterize the quality level of the data to be evaluated.

[0016] In one embodiment, the method further comprises:

[0017] Obtaining each evaluation index corresponding to the target model, and determining whether there is an index value corresponding to each of the evaluation indexes in the data to be evaluated;

[0018] If an evaluation indicator without an indicator value is detected, the evaluation indicator without an indicator value is used as an indicator to be filled, and the indicator value of the indicator to be filled is filled;

[0019] After the indicators to be filled are filled, the step of evaluating the data to be evaluated based on the target model to obtain an evaluation report is performed.

[0020] In one embodiment, the step of filling the indicator value of the indicator to be filled includes:

[0021] Input the data to be evaluated into a preset missing value evaluation model to obtain the target missing value output by the missing value evaluation model, wherein the missing value evaluation model is a model trained by asset data of various types and fields for outputting missing values ​​of indicators;

[0022] The target missing value is output, and after receiving a value confirmation instruction, the target missing value is used as the indicator value of the indicator to be filled.

[0023] In one embodiment, before the step of determining the target model corresponding to the to-be-evaluated data in each preset value evaluation model, the method further includes:

[0024] In response to the model adding instruction, output a model information receiving page;

[0025] The model information is received through the model information receiving page, and a new value assessment model is obtained based on the model information.

[0026] In one embodiment, the method further comprises:

[0027] When it is detected that the score is less than the score threshold, receiving the reported defect information output by the scoring model, and outputting the reported defect information;

[0028] When it is detected that the indicator value of the defect indicator in the reported defect information is updated, the step of evaluating the data to be evaluated based on the target model to obtain an evaluation report is returned to be executed.

[0029] In addition, to achieve the above purpose, the present application also proposes a data asset value assessment system, which includes:

[0030] A model selection module is used to determine the target model corresponding to the data to be evaluated from various preset value evaluation models;

[0031] An automatic evaluation module, used to evaluate the data to be evaluated based on the target model to obtain an evaluation report;

[0032] The model scoring module is used to score the evaluation report through a preset scoring model to obtain a score corresponding to the evaluation report, and when it is detected that the score is greater than or equal to a preset score threshold, use the evaluation report as the data asset value evaluation result of the data to be evaluated, wherein the score threshold is used to determine whether the evaluation report is qualified.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the data asset value assessment method as described above.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the data asset value assessment method described above are implemented.

[0035] In an embodiment of the present application, by determining the target model corresponding to the data to be evaluated in each preset value assessment model, a model that meets the characteristics of the current data to be evaluated can be selected from each pre-built-in value assessment model; then, the data to be evaluated is evaluated based on the target model to obtain an evaluation report, and the asset value can be automatically evaluated according to the selected model to obtain an evaluation report; then, the evaluation report is scored using a preset scoring model to obtain a score corresponding to the evaluation report, and when it is detected that the score is greater than or equal to a preset score threshold, the evaluation report is used as the data asset value evaluation result of the data to be evaluated, wherein the score threshold is used to determine whether the evaluation report is qualified, thereby ensuring the stability of the evaluation result on the basis of automatic asset value evaluation.

[0036] It can be seen that the present application improves the evaluation efficiency by automatically determining the target model and automatically generating the evaluation report based on the target model, thereby solving the problem of low efficiency of manual evaluation. In addition, the present application also ensures the stability of the evaluation results by scoring the evaluation report through the scoring model. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0039] Figure 1 A flowchart of the first embodiment of the data asset value assessment method of this application is provided;

[0040] Figure 2 A flowchart of the second embodiment of the data asset value assessment method of this application is provided;

[0041] Figure 3 A flowchart of the third embodiment of the data asset value assessment method of this application is provided;

[0042] Figure 4 This is a schematic diagram of the module structure of the data asset value assessment system according to an embodiment of the present application;

[0043] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the data asset value assessment method in the embodiment of the present application.

[0044] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0045] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0046] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0047] Data assets are a new type of asset that has high economic value for enterprises. Therefore, it is necessary to conduct a value assessment on data assets.

[0048] Nowadays, data asset evaluation business mainly relies on manual consulting services to determine the value of customer or enterprise data assets. This process usually involves offline in-depth analysis of the composition, processing flow and collection path of the other party's actual data, and then using the cost method to estimate the required costs, or using the income method in combination with income data to predict potential income. However, this highly manual operation method requires a lot of time, resulting in low efficiency of asset evaluation.

[0049] Therefore, how to improve the efficiency of data asset value assessment is a technical problem that needs to be solved by technical personnel in this field.

[0050] In order to solve the above problems, the present application determines the target model corresponding to the data to be evaluated in each preset value assessment model, and can select a model that meets the characteristics of the current data to be evaluated in each pre-built-in value assessment model; then the data to be evaluated is evaluated based on the target model to obtain an assessment report, and the asset value can be automatically evaluated according to the selected model to obtain an assessment report; then the assessment report is scored by a preset scoring model to obtain a score corresponding to the assessment report, and when it is detected that the score is greater than or equal to a preset score threshold, the assessment report is used as the data asset value assessment result of the data to be evaluated, wherein the score threshold is used to determine whether the assessment report is qualified, thereby ensuring the stability of the assessment result on the basis of automatic asset value assessment.

[0051] It can be seen that the present application improves the evaluation efficiency by automatically determining the target model and automatically generating the evaluation report based on the target model, thereby solving the problem of low efficiency of manual evaluation. In addition, the present application also ensures the stability of the evaluation results by scoring the evaluation report through the scoring model.

[0052] It should be noted that the execution subject of the data asset value assessment method of the present application can be an electronic device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a server, etc. The following takes a computer as an example to illustrate various embodiments of the data asset value assessment method of the present application.

[0053] Based on this, the present application embodiment provides a data asset value assessment method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the data asset value assessment method of the present application.

[0054] In this embodiment, the data asset value assessment method includes steps S10 to S30:

[0055] Step S10, determining the target model corresponding to the data to be evaluated from each preset value evaluation model;

[0056] It should be noted that the value assessment model is predefined by the user. Specifically, the value assessment model may include an income approach model, a market approach model, a cost approach model, etc. The data to be assessed refers to the data that needs to be assessed for asset value, and the target model refers to the value assessment model corresponding to the data to be assessed.

[0057] In this embodiment, after the data to be evaluated is obtained, a model matching the characteristics of the data to be evaluated can be determined from a plurality of pre-built-in value evaluation models.

[0058] In a feasible implementation manner, the above step S10 includes:

[0059] Step S101, in response to a selection instruction output by a user, taking a value assessment model corresponding to the selection instruction among various value assessment models as a target model corresponding to the data to be assessed;

[0060] It is understandable that the target model can be determined through manual experience. Therefore, the selection instruction output by the user can be received based on a specific page on the computer, and then the value assessment model corresponding to the selection instruction output by the user can be used as the target model of the data currently to be assessed.

[0061] Step S102: Analyze the data to be evaluated by the big model, and determine the target model corresponding to the data to be evaluated in each preset value evaluation model based on the analysis result output by the big model.

[0062] It is understandable that, in addition to determining the target model based on manual experience, the target model can also be determined through a large model.

[0063] Specifically, the big model is a model with powerful data processing and analysis capabilities. Therefore, in this embodiment, the big model can be used to mine and understand the characteristics, patterns, relationships, etc. of the data to be evaluated, thereby obtaining the analysis results corresponding to the data to be evaluated. Then, the target model can be determined in each value evaluation model based on the analysis results.

[0064] For example, when the analysis results characterize that the characteristics of the current data to be evaluated are inclined towards the application scenario of the cost method, the target model may be a cost method model; when the analysis results characterize that the characteristics of the current data to be evaluated are inclined towards the application scenario of the market method, the target model may be a market method model; when the analysis results characterize that the characteristics of the current data to be evaluated are inclined towards the application scenario of the income method, the target model may be an income method model.

[0065] In a feasible implementation manner, the above step S10 further includes:

[0066] Step S103, obtaining each data indicator in the data to be evaluated, and based on the indicator type in each data indicator and the number of indicators corresponding to each indicator type, determining the target model corresponding to the data to be evaluated in each preset value evaluation model, wherein the data indicator is used to characterize the quality level of the data to be evaluated.

[0067] It is understandable that data indicators may include: completeness, standardization, accuracy, uniqueness, consistency, timeliness, remaining income period, risk discount rate, etc. When the value assessment model includes the cost method model, the income method model and the market method model, the indicator types include the indicators required by the cost method, the indicators required by the income method and the indicators required by the market method. When the value assessment model also includes other models, other indicator types can also be obtained.

[0068] In this embodiment, the model corresponding to the indicator type with the largest number of indicators may be used as the target model, thereby achieving the effect of automatically selecting the target model.

[0069] For example, assuming that the number of automatically collected indicators is 5, 3 of which are indicators required by the cost method model and 2 are indicators required by the income method model, the cost method model can be used to evaluate the data to be evaluated.

[0070] It is understandable that the more indicators that are automatically collected, the closer the model determined based on the number of indicators will be to the actual situation of the data to be evaluated, thereby improving the accuracy of value evaluation.

[0071] Step S20, evaluating the data to be evaluated based on the target model to obtain an evaluation report;

[0072] It is understandable that after obtaining the data to be evaluated, an evaluation can be performed based on each data indicator and the target model in the data to be evaluated to obtain an evaluation report. Specifically, the indicator value of each data indicator can be input into the target model, so that the value of the data to be evaluated can be calculated according to the target model. Then, each indicator value and the calculated value can be filled into a preset template to obtain an evaluation report.

[0073] In a feasible implementation, the evaluation report may include basic evaluation information, evaluation model basis, evaluation result details, etc., and historical evaluation reports support review and report download.

[0074] Step S30, scoring the evaluation report through a preset scoring model to obtain a score corresponding to the evaluation report, and when it is detected that the score is greater than or equal to a preset score threshold, using the evaluation report as a data asset value evaluation result of the data to be evaluated, wherein the score threshold is used to determine whether the evaluation report is qualified.

[0075] It should be noted that the scoring model can be a deep learning model, a machine learning model, a large model, etc., and this application does not limit this. In the present embodiment, the scoring model is pre-trained. Specifically, the sample report can be manually scored, and the index values ​​and indicators of defects in the sample report can be marked. Then, the sample report can be used as the model input data, and the score of the sample report, the index value and indicator of defects in the sample report can be used as the model training label to train the scoring model, so that the scoring model has the ability to automatically score the evaluation report and automatically mark the defect indicators and defect indicator values. Then, the evaluation report can be scored based on the trained scoring model to obtain the score corresponding to the evaluation report.

[0076] In this embodiment, when it is detected that the score of the evaluation report is greater than or equal to the score threshold, it indicates that the evaluation report is qualified. Therefore, the evaluation report obtained by the target model can be used as the data asset value evaluation result of the data to be evaluated.

[0077] In a feasible implementation manner, the method further includes:

[0078] Step S40, when it is detected that the score is less than the score threshold, receiving the reported defect information output by the scoring model, and outputting the reported defect information;

[0079] It is understandable that when it is detected that the score of the evaluation report is less than the score threshold, it indicates that the evaluation report is unqualified. Therefore, it is necessary to modify the indicator value in the data to be evaluated based on the report defect information.

[0080] Step S50, when it is detected that the indicator value of the defect indicator in the reported defect information is updated, return to the step of evaluating the data to be evaluated based on the target model to obtain an evaluation report.

[0081] In this embodiment, after the defect report information is output, the indicator value of the defect indicator input by the user after browsing the defect report information may be received so as to re-evaluate.

[0082] In this embodiment, the present application determines the target model corresponding to the data to be evaluated in each preset value assessment model, and can select a model that meets the characteristics of the current data to be evaluated from each pre-built-in value assessment model; then the data to be evaluated is evaluated based on the target model to obtain an assessment report, and the asset value can be automatically evaluated according to the selected model to obtain an assessment report; then the assessment report is scored by a preset scoring model to obtain a score corresponding to the assessment report, and when it is detected that the score is greater than or equal to a preset score threshold, the assessment report is used as the data asset value assessment result of the data to be evaluated, wherein the score threshold is used to determine whether the assessment report is qualified, thereby ensuring the stability of the assessment result on the basis of automatic asset value assessment.

[0083] It can be seen that the present application improves the evaluation efficiency by automatically determining the target model and automatically generating the evaluation report based on the target model, thereby solving the problem of low efficiency of manual evaluation. In addition, the present application also ensures the stability of the evaluation results by scoring the evaluation report through the scoring model.

[0084] Furthermore, based on the above-mentioned first embodiment of the data asset value assessment method of the present application, a second embodiment of the data asset value assessment method of the present application is proposed.

[0085] Please refer to Figure 2 In this embodiment, the method further includes:

[0086] Step S100, obtaining each evaluation index corresponding to the target model, and determining whether there is an index value corresponding to each evaluation index in the data to be evaluated;

[0087] It is understandable that the above data indicators refer to the indicators of the parameters to be evaluated, while the evaluation indicators specifically refer to the parameters in the data indicators that are required for the target model to calculate the asset value. Since some evaluation indicators may be provided when the data asset information is entered, they can be automatically collected from the input data asset information without filling in. For example, when the data element has been evaluated for quality in the data vault, the quality score indicator can be automatically collected. However, when there are null values ​​in the evaluation indicators required by the target model, the target model cannot operate. Therefore, when performing value evaluation through the target model, it is also necessary to judge whether there are indicator values ​​for the evaluation indicators.

[0088] Specifically, a method for determining whether an evaluation indicator has an indicator value may be to determine whether the value corresponding to the evaluation indicator is empty; if the value corresponding to the evaluation indicator is empty, it indicates that the evaluation indicator does not have an indicator value; if the value corresponding to the evaluation indicator is not empty, it indicates that the evaluation indicator has an indicator value.

[0089] Step S200, if an evaluation indicator without an indicator value is detected, the evaluation indicator without an indicator value is used as an indicator to be filled, and the indicator value of the indicator to be filled is filled;

[0090] Step S300, after the to-be-filled indicators are filled, the step of evaluating the to-be-evaluated data based on the target model to obtain an evaluation report is performed.

[0091] It is understandable that after detecting the existence of indicators to be filled, the information of each indicator to be filled can be output through a preset page, so as to receive the filling value input by the user from the page. Thus, the filling value of the indicator to be filled can be filled according to the filling value input by the user, and the asset value can be evaluated through the target model after the filling is completed.

[0092] In this embodiment, the present application avoids the problem of incorrect evaluation results caused by incorrect indicator values ​​of the evaluation indicators by judging the values ​​of the evaluation indicators.

[0093] Furthermore, based on the above-mentioned second embodiment of the data asset value assessment method of the present application, a third embodiment of the data asset value assessment method of the present application is proposed.

[0094] Please refer to Figure 3 In a feasible implementation manner, the above step S200 includes:

[0095] Step S2001, inputting the data to be evaluated into a preset missing value evaluation model to obtain the target missing value output by the missing value evaluation model, wherein the missing value evaluation model is a model for outputting missing values ​​of indicators trained by asset data of various types and fields;

[0096] It should be noted that the missing value assessment model can be a machine learning model, a large model, or a neural network model, and this application does not impose any restrictions on this.

[0097] Step S2002, output the target missing value, and after receiving a value confirmation instruction, use the target missing value as the indicator value of the indicator to be filled.

[0098] It should be noted that the numerical confirmation instruction is an operation instruction input by the user, which is used to confirm the target missing value output by the missing value assessment model.

[0099] In this embodiment, in addition to relying on manual filling of indicator values, automatic filling can also be performed through the model, thereby further improving the efficiency of data asset value assessment. In addition, in order to improve the accuracy of the assessment while ensuring the efficiency of asset value assessment, after the missing value assessment model outputs the target missing value, it can also be manually verified.

[0100] Furthermore, based on the above-mentioned various embodiments of the data asset value assessment method of the present application, a fourth embodiment of the data asset value assessment method of the present application is proposed.

[0101] In this embodiment, before the above step S10, the method further includes:

[0102] Step S400, in response to the model adding instruction, output a model information receiving page;

[0103] It should be noted that the model adding instruction is an operation instruction input by the user, and the model information receiving page is a pre-set page corresponding to the model adding instruction.

[0104] Step S500: receiving model information through the model information receiving page, and obtaining a new value assessment model based on the model information.

[0105] It should be noted that the model information includes the model name, the algorithm formula of the model and the evaluation index. After receiving the model information, the model name, the algorithm formula of the model and the evaluation index can be associated and stored, so that a new value evaluation model can be obtained according to the information input by the user.

[0106] In this embodiment, the present application can obtain a new value assessment model based on the model new addition instruction and the model information receiving page, so that the user can perform asset value assessment on the data to be assessed based on a customized or latest value assessment model, thereby improving the universality of the present application.

[0107] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the data asset value assessment method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0108] This application also provides a data asset value assessment system, please refer to Figure 4 , the data asset value assessment system includes:

[0109] The model selection module 10 is used to determine the target model corresponding to the data to be evaluated from various preset value evaluation models;

[0110] An automatic evaluation module 20 is used to evaluate the data to be evaluated based on the target model to obtain an evaluation report;

[0111] The model scoring module 30 is used to score the evaluation report through a preset scoring model to obtain a score corresponding to the evaluation report, and when it is detected that the score is greater than or equal to a preset score threshold, use the evaluation report as the data asset value evaluation result of the data to be evaluated, wherein the score threshold is used to determine whether the evaluation report is qualified.

[0112] In one embodiment, the model selection module 10 is further configured to:

[0113] In response to a selection instruction output by a user, taking a value assessment model corresponding to the selection instruction among the value assessment models as a target model corresponding to the data to be assessed;

[0114] The data to be evaluated is analyzed by the big model, and based on the analysis results output by the big model, the target model corresponding to the data to be evaluated is determined in each preset value evaluation model.

[0115] In one embodiment, the model selection module 10 is further configured to:

[0116] Obtain each data indicator in the data to be evaluated, and based on the indicator type in each data indicator and the number of indicators corresponding to each indicator type, determine the target model corresponding to the data to be evaluated in each preset value evaluation model, wherein the data indicator is used to characterize the quality level of the data to be evaluated.

[0117] In one embodiment, the data asset value assessment system further includes:

[0118] An indicator value judgment module is used to obtain each evaluation indicator corresponding to the target model, and judge whether there is an indicator value corresponding to each evaluation indicator in the data to be evaluated;

[0119] A value filling module is used for, if an evaluation indicator without an indicator value is detected, taking the evaluation indicator without an indicator value as an indicator to be filled, and filling the indicator value of the indicator to be filled;

[0120] The first switching module is used to execute the step of evaluating the data to be evaluated based on the target model to obtain an evaluation report after the indicators to be filled are filled.

[0121] In one embodiment, the value filling module is further used to:

[0122] Input the data to be evaluated into a preset missing value evaluation model to obtain the target missing value output by the missing value evaluation model, wherein the missing value evaluation model is a model trained by asset data of various types and fields for outputting missing values ​​of indicators;

[0123] The target missing value is output, and after receiving a value confirmation instruction, the target missing value is used as the indicator value of the indicator to be filled.

[0124] In one embodiment, the data asset value assessment system further includes:

[0125] A page output module, used to output a model information receiving page in response to a model new adding instruction;

[0126] The model adding module is used to receive model information through the model information receiving page and obtain a new value assessment model based on the model information.

[0127] In one embodiment, the data asset value assessment system further includes:

[0128] A defect information output module, configured to receive the reported defect information output by the scoring model and output the reported defect information when detecting that the score is less than the score threshold;

[0129] The second switching module is used to return to the step of evaluating the data to be evaluated based on the target model to obtain an evaluation report when it is detected that the indicator value of the defect indicator in the reported defect information is updated.

[0130] The data asset value assessment system provided by this application adopts the data asset value assessment method in the above embodiment, which can solve the technical problem of how to improve the efficiency of data asset value assessment. Compared with the prior art, the beneficial effects of the data asset value assessment system provided by this application are the same as the beneficial effects of the data asset value assessment method provided by the above embodiment, and the other technical features in the data asset value assessment system are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0131] The present application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the data asset value assessment method in the above-mentioned embodiment one.

[0132] Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic devices in the embodiments of the present application may include but are not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0133] like Figure 5 As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0134] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0135] The electronic device provided by this application adopts the data asset value assessment method in the above embodiment, which can solve the technical problem of how to improve the efficiency of data asset value assessment. Compared with the prior art, the beneficial effects of the electronic device provided by this application are the same as the beneficial effects of the data asset value assessment method provided by the above embodiment, and the other technical features in the electronic device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0136] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0137] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0138] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, the computer-readable program instructions being used to execute the data asset value assessment method in the above-mentioned embodiment.

[0139] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0140] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0141] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the electronic device: determines the target model corresponding to the data to be evaluated in each preset value assessment model; evaluates the data to be evaluated based on the target model to obtain an assessment report; scores the assessment report through a preset scoring model to obtain a score corresponding to the assessment report, and when it is detected that the score is greater than or equal to a preset score threshold, uses the assessment report as the data asset value assessment result of the data to be evaluated, wherein the score threshold is used to determine whether the assessment report is qualified.

[0142] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0143] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0144] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0145] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned data asset value assessment method, and can solve the technical problem of how to improve the efficiency of data asset value assessment. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the data asset value assessment method provided in the above-mentioned embodiment, and will not be repeated here.

[0146] The present application also provides a computer program product, including a computer program, which implements the steps of the data asset value assessment method as described above when executed by a processor.

[0147] The computer program product provided by this application can solve the technical problem of how to improve the efficiency of data asset value assessment. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the data asset value assessment method provided by the above embodiment, which will not be repeated here.

[0148] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A data asset value assessment method, characterized in that: The data asset value assessment method includes: Determine the target model corresponding to the data to be evaluated in each preset value evaluation model; Evaluate the data to be evaluated based on the target model to obtain an evaluation report; The evaluation report is scored using a preset scoring model to obtain a score corresponding to the evaluation report, and when it is detected that the score is greater than or equal to a preset score threshold, the evaluation report is used as a data asset value evaluation result of the data to be evaluated, wherein the score threshold is used to determine whether the evaluation report is qualified.

2. The data asset value assessment method according to claim 1, characterized in that: The step of determining the target model corresponding to the data to be evaluated in each preset value evaluation model includes: In response to a selection instruction output by a user, taking a value assessment model corresponding to the selection instruction among the value assessment models as a target model corresponding to the data to be assessed; or, The data to be evaluated is analyzed by the big model, and based on the analysis results output by the big model, the target model corresponding to the data to be evaluated is determined in each preset value evaluation model.

3. The data asset value assessment method according to claim 1, characterized in that: The step of determining the target model corresponding to the data to be evaluated in each preset value evaluation model also includes: Obtain each data indicator in the data to be evaluated, and based on the indicator type in each data indicator and the number of indicators corresponding to each indicator type, determine the target model corresponding to the data to be evaluated in each preset value evaluation model, wherein the data indicator is used to characterize the quality level of the data to be evaluated.

4. The data asset value assessment method according to claim 1, characterized in that: The method further comprises: Obtaining each evaluation index corresponding to the target model, and determining whether there is an index value corresponding to each of the evaluation indexes in the data to be evaluated; If an evaluation indicator without an indicator value is detected, the evaluation indicator without an indicator value is used as an indicator to be filled, and the indicator value of the indicator to be filled is filled; After the indicators to be filled are filled, the step of evaluating the data to be evaluated based on the target model to obtain an evaluation report is performed.

5. The data asset value assessment method according to claim 4, characterized in that: The step of filling the indicator value of the indicator to be filled includes: Input the data to be evaluated into a preset missing value evaluation model to obtain the target missing value output by the missing value evaluation model, wherein the missing value evaluation model is a model for outputting missing values ​​of indicators trained by asset data of various types and fields; The target missing value is output, and after receiving a value confirmation instruction, the target missing value is used as the indicator value of the indicator to be filled.

6. The data asset value assessment method according to claim 1, characterized in that: Before the step of determining the target model corresponding to the to-be-evaluated data in each preset value evaluation model, the method further includes: In response to the model adding instruction, output a model information receiving page; The model information is received through the model information receiving page, and a new value assessment model is obtained based on the model information.

7. The data asset value assessment method according to claim 1, characterized in that: The method further comprises: When it is detected that the score is less than the score threshold, receiving the reported defect information output by the scoring model, and outputting the reported defect information; When it is detected that the indicator value of the defect indicator in the reported defect information is updated, the step of evaluating the data to be evaluated based on the target model to obtain an evaluation report is returned to be executed.

8. A data asset value assessment system, characterized in that: The data asset value assessment system includes: A model selection module is used to determine the target model corresponding to the data to be evaluated from various preset value evaluation models; An automatic evaluation module, used to evaluate the data to be evaluated based on the target model to obtain an evaluation report; The model scoring module is used to score the evaluation report through a preset scoring model to obtain a score corresponding to the evaluation report, and when it is detected that the score is greater than or equal to a preset score threshold, use the evaluation report as the data asset value evaluation result of the data to be evaluated, wherein the score threshold is used to determine whether the evaluation report is qualified.

9. An electronic device, characterized in that: The electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the data asset value assessment method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the data asset value assessment method according to any one of claims 1 to 7 are implemented.

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