Data asset evaluation method and device, computer equipment and storage medium
By acquiring and calculating the correlation between the target transaction information of data assets and the reference transaction information, the problems of low efficiency and high cost of data asset appraisal in the prior art are solved, and an accurate and automated data asset price appraisal is achieved.
Patent Information
- Application Number
- CN202411787675.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-06
- Publication Date
- 2025-04-08
AI Technical Summary
Existing data asset valuation methods are inefficient, costly, and cannot fully utilize the related information between assets, making it difficult to achieve accurate and automated data asset price evaluation.
By obtaining the target transaction information of the data assets to be evaluated, filtering the reference transaction information of the reference data assets from the data asset information database, calculating the correlation information between the two, and performing data asset evaluation based on this to obtain the evaluation results.
A comprehensive and accurate price evaluation of data assets is achieved, which improves evaluation efficiency, reduces costs, and makes full use of the related information between assets.
Smart Images
Figure CN119784503A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of big data technology, and in particular to a data asset evaluation method, device, computer equipment and storage medium. Background Art
[0002] Data assets refer to data sets that are generated, collected, stored and have economic value during the operation of an enterprise or organization. Due to the 6V characteristics of data assets, namely data volume, velocity, diversity, accuracy, connectivity and value, the data asset pricing process is difficult to rely on manual review one by one, but will mainly rely on the collaboration between humans and machines, and to a large extent rely on machines to run various algorithms for inspection and verification.
[0003] Existing data asset valuation methods are often based on the analytic hierarchy process, which constructs a tree-structured scoring dimension and corresponding weight value, and then applies the scoring method to the actual asset evaluation process. However, this method has strong domain characteristics and is closely tied to specific industries or business models. The analytic hierarchy process scoring matrices defined in different industries and companies vary greatly, making it difficult to generalize to general application scenarios. In the actual asset valuation process, multiple experts use the analytic hierarchy process to combine their experience and subjective judgment to score and estimate the price of a single asset. This process is not only inefficient, resulting in a high cost for the asset valuation process, but it can only target a single asset at a time and cannot fully utilize the correlation information between assets.
[0004] Therefore, how to accurately and automatically evaluate data assets and obtain accurate and automatic price evaluation results of data assets has become an urgent problem to be solved. Summary of the invention
[0005] Based on this, the present invention provides a data asset evaluation method, apparatus, computer equipment and storage medium to solve the problem of how to accurately and automatically evaluate data assets and to accurately and automatically obtain price evaluation results of data assets.
[0006] In a first aspect, an embodiment of the present invention provides a data asset evaluation method, comprising: Acquire the data asset to be evaluated, and filter the target transaction information corresponding to the data asset to be evaluated from the data asset information library; According to the target transaction information, perform full data calculation on all data assets in the data asset information library to obtain reference data assets, and filter reference transaction information corresponding to the reference data assets from the data asset information library; Calculating the correlation between the reference transaction information and the target transaction information to obtain correlation information; The data asset to be evaluated is evaluated according to the reference transaction information and the correlation information to obtain an evaluation result.
[0007] In a second aspect, an embodiment of the present invention provides a data asset evaluation device, including: An information screening module is used to obtain the data assets to be evaluated and screen the target transaction information corresponding to the data assets to be evaluated from the data asset information library; A screening module, configured to perform full data calculation on all data assets in the data asset information library according to the target transaction information, obtain reference data assets, and screen reference transaction information corresponding to the reference data assets from the data asset information library; A correlation calculation module, used to calculate the correlation between the reference transaction information and the target transaction information to obtain correlation information; The evaluation module is used to evaluate the data assets to be evaluated based on the reference transaction information and the correlation information to obtain an evaluation result.
[0008] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the data asset evaluation method described in the first aspect when executing the computer program.
[0009] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the data asset evaluation method described in the first aspect is implemented.
[0010] Compared with the prior art, the present invention has the following beneficial effects: by obtaining the data asset to be evaluated, the target transaction information corresponding to the data asset to be evaluated is screened from the data asset information library, and according to the target transaction information, all data assets in the data asset information library are calculated to obtain the reference data asset, and the reference transaction information corresponding to the reference data asset is screened from the data asset information library, and the correlation between the reference transaction information and the target transaction information is calculated to obtain the correlation information, and the data asset to be evaluated is evaluated according to the reference transaction information and the correlation information to obtain the evaluation result. By evaluating the data asset to be evaluated with reference to the transaction information and the correlation information, the price evaluation result of the data asset to be evaluated is obtained comprehensively and accurately. BRIEF DESCRIPTION OF THE DRAWINGS
[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.
[0012] Figure 1 This is a schematic diagram of an application environment of a data asset evaluation method provided in the first embodiment of the present invention; Figure 2 It is a flowchart of a data asset evaluation method provided in Embodiment 2 of the present invention; Figure 3 It is a flowchart of a data asset evaluation method provided in Embodiment 3 of the present invention; Figure 4 It is a flowchart of a data asset evaluation method provided by Embodiment 4 of the present invention; Figure 5 This is a flowchart of a data asset evaluation method provided in Embodiment 5 of the present invention; Figure 6 It is a flowchart of a data asset evaluation method provided in Embodiment 6 of the present invention; Figure 7 It is a flowchart of a data asset evaluation method provided in Embodiment 7 of the present invention; Figure 8 It is a flowchart of a data asset evaluation method provided in Embodiment 8 of the present invention; Fig. 9 It is a structural diagram of a data asset evaluation device provided by Embodiment 9 of the present invention; Fig.10 It is a structural diagram of a computer device provided in Embodiment 10 of the present invention. DETAILED DESCRIPTION
[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0014] like Figure 1As shown, it is a schematic diagram of the application environment of a data asset evaluation method provided in the first embodiment of the present invention, wherein the client and the server are connected to communicate, and the user can provide the server with conditions, requirements and operation instructions for data asset evaluation by operating the client, and the server is used to execute the data asset evaluation method of the present invention according to the relevant content sent by the client. Among them, the client includes but is not limited to various personal computers, laptops, smart phones, tablet computers, portable wearable devices and other computer devices. The computer device corresponding to the server can be implemented with an independent server or a server cluster composed of multiple servers.
[0015] like Figure 2 FIG. 1 is a flow chart of a data asset evaluation method provided in Embodiment 2 of the present invention, wherein the data asset evaluation method is applied in Figure 1 The data asset evaluation method may include the following steps: Step S201, obtaining the data assets to be evaluated, and screening the target transaction information corresponding to the data assets to be evaluated from the data asset information library.
[0016] The target transaction information includes but is not limited to the data assets to be evaluated, which include but are not limited to data structure, metadata, transaction price, transaction time, transaction conditions, activity, download volume, usage satisfaction, transaction frequency, and the scoring dimensions and corresponding weight value information of the hierarchical analysis method used in the data asset price evaluation process. Collect as much of the above information as possible to achieve a sufficient number of comparable references and improve the accuracy of the valuation.
[0017] The target transaction information of the data assets to be evaluated can be connected to the data asset information repository through database connection, application programming interface (API) call, etc., and can be queried and obtained from the data asset information repository using structured query language (SQL) query, API request and other methods.
[0018] Step S202: perform full data calculation on all data assets in the data asset information library according to the target transaction information to obtain reference data assets, and filter reference transaction information corresponding to the reference data assets from the data asset information library.
[0019] Among them, the reference transaction information includes but is not limited to reference assets. The reference assets have certain similarities and correlations with the data assets to be evaluated. In the process of selecting reference data assets, priority is given to data assets that are relatively close to the asset valuation date. For example, data assets within 3 to 6 months are selected as reference assets. The reference asset selection method includes but is not limited to the following: one is to use all the collected target transaction information, use the data in all the asset information libraries for calculations, collect historical data of all target transactions and detailed information on related assets, integrate the collected data into a unified data set to ensure the integrity and consistency of the data, and use but are not limited to statistics, machine learning or deep learning models to calculate the integrated data to determine that the information available for the reference assets is more comprehensive.
[0020] Another preferred method is to place the target transaction information into a data asset information library, and then use a clustering algorithm to cluster all data assets in the data asset information library to obtain several clusters, and then select all data assets in the cluster where the data asset to be evaluated is located except for the data asset to be evaluated as reference data assets for pricing. The selection range can also be expanded to select data assets in several clusters that are relatively close to the cluster where the data asset to be evaluated is located as reference assets, or different clustering algorithms or multiple different clustering parameters of the same cluster can be used to obtain multiple clustering results about the data asset to be evaluated. The reference assets in multiple clustering results can all be used as reference data assets. After the results of multiple selections of reference assets are aggregated, duplicate data assets need to be removed to ensure uniqueness in the reference data asset set.
[0021] Among them, the dimensions used for clustering include but are not limited to the data structure, metadata, transaction price, transaction time, transaction conditions, activity, download volume, usage satisfaction, transaction frequency of the data assets to be evaluated, as well as the scoring dimensions and corresponding weight values of the hierarchical analysis method used in the data asset price evaluation process. This can greatly reduce the unnecessary calculation process caused by unrelated data assets, and at the same time can improve the correlation of reference assets used for pricing, reduce the interference of unrelated data assets, and improve the accuracy of valuation.
[0022] Step S203, calculating the correlation between the reference transaction information and the target transaction information to obtain correlation information.
[0023] The correlation information may include similarity, gain, and other information that can characterize the degree of correlation.
[0024] Among them, the correlation between the reference transaction information and the target transaction information can be measured using similarity and gain. The similarity between the reference transaction information and the target transaction information uses, but is not limited to, the data structure, metadata, transaction price, transaction time, transaction conditions, activity, download volume, usage satisfaction, transaction frequency, and the scoring dimensions and corresponding weight value information of the hierarchical analysis method used in the data asset price evaluation process. The similarity between the target transaction information and the reference transaction information is calculated by the similarity calculation method. The measurement methods that can be used include cosine similarity, Euclidean distance, Manhattan distance, Chebyshev distance, Minkowski distance, etc.
[0025] Similarity and gain are different measurement methods. Generally, similarity calculation is simpler, which is to directly calculate the similarity of assets. Similarity is sometimes defined between [0,1], while gain is a ratio. It can be considered that gain can better measure assets with huge improvement effects, for example, improving the algorithm accuracy from 30% to 95%.
[0026] Gain is used to measure the degree of change or improvement from one data point to another, and is used to evaluate the effectiveness of a model or algorithm. For example, the degree of quality improvement in key business indicators brought about by the current data asset to be evaluated relative to the reference asset.
[0027] Step S204: Evaluate the data assets to be evaluated based on the reference transaction information and the correlation information to obtain an evaluation result.
[0028] Among them, the price of the data asset to be evaluated is estimated based on the price of reference transaction information that has certain similarities and correlations with the target transaction information, and the evaluation results are summarized and analyzed to generate a final evaluation report. The evaluation report may include the market reference price of the data asset to be evaluated, the predicted buying price and selling price, and the scores of various indicators.
[0029] In the embodiment of the present application, by obtaining the data asset to be evaluated, the target transaction information corresponding to the data asset to be evaluated is screened from the data asset information library, and according to the target transaction information, the full amount of data calculation is performed on all data assets in the data asset information library to obtain the reference data asset, and the reference transaction information corresponding to the reference data asset is screened from the data asset information library, and the correlation between the reference transaction information and the target transaction information is calculated to obtain the correlation information, and the data asset to be evaluated is evaluated according to the reference transaction information and the correlation information to obtain the evaluation result. By evaluating the data asset to be evaluated with reference to the transaction information and the correlation information, the price evaluation result of the data asset to be evaluated can be obtained more accurately.
[0030] like Figure 3As shown, it is a flow chart of a data asset evaluation method provided by the third embodiment of the present invention. The correlation information in step S203 includes similarity and gain. In step S203, the correlation between the reference transaction information and the target transaction information is calculated to obtain the correlation information, which may include the following steps: Step S301: Process the target transaction information and the reference transaction information in a fusion manner to obtain a fused data set.
[0031] Among them, the target transaction information and the reference transaction information are fused to generate a new data set so that key information can be extracted in subsequent steps. The methods for extracting key information include, but are not limited to, deep learning models, data mining algorithms, machine learning algorithms, deep learning algorithms, topic models, etc. First, the target transaction information and reference transaction information are read from the database or file, and feature columns useful for evaluating the value of data assets are selected. These features may include buying price, selling price, data quality, data volume, transaction frequency, data usage, etc. The data is standardized to ensure that the numerical range of the features is consistent. The reference transaction information and the target transaction information are merged by row to generate a fused data set. It is ensured that the names of the merged feature columns do not conflict, and they can be distinguished by adding prefixes to the feature column names.
[0032] Among them, the fusion methods include but are not limited to horizontal fusion, vertical fusion and multimodal fusion. Horizontal fusion can refer to the process of expanding the features of data with the same sample identifier in the data assets or data sets to be evaluated, aligning them with new data, expanding the feature dimensions, and forming data assets or data sets with higher dimensional features. This process only increases the data feature dimensions and does not change the original number.
[0033] Vertical integration can refer to the process of aligning new data with the same or similar data features in the data assets or data sets to be evaluated to form a larger number of data assets or data sets. This process only increases the amount of data but does not increase the feature dimension.
[0034] Multimodal fusion refers to the process of extracting and representing features of data assets or data sets of different modalities, aligning them in time and space, making the data features of different modalities have consistent forms of expression, and then constructing new data assets or data sets through feature fusion so that the data therein has high-level expression dimensions or abstract features.
[0035] Step S302: extract key information of the reference data asset to obtain a first key indicator.
[0036] Step S303: extract key information of the fused data set to obtain a second key indicator.
[0037] Step S304: combining the first key indicator and the second key indicator, performing ratio calculation to obtain a gain between the fused data set and the reference transaction information.
[0038] Step S305: Evaluate the data asset to be evaluated based on the reference transaction information and the gain degree to obtain an evaluation result.
[0039] Among them, the methods for extracting key information include, but are not limited to, deep learning models, data mining algorithms, machine learning algorithms, deep learning algorithms, topic models, etc. Before extracting key information, it is necessary to ensure that the format of input data is consistent, for example, converting the output into one-dimensional features, standardizing the output, etc. The extracted key information is added to the reference transaction information for use in subsequent steps.
[0040] The gain calculation methods include but are not limited to the following two methods: Method 1: Use the same machine learning, deep learning and data mining models, such as support vector machine (SVM), decision tree, dual-tower deep neural network model (DNN model), single shot multi-box detection (SSD), logistic regression, etc., apply the evaluation data assets and reference assets respectively, and then compare the ratio of key indicators as the gain. For example, key indicators can include but are not limited to precision, recall, F1 score, etc.
[0041] Method 2: A new data set is formed by fusing the target transaction information and the reference transaction information. Then, the reference assets and the new data set are applied to the same machine learning, deep learning, and data mining models to obtain the corresponding key indicators R1 (reference transaction information) and R2 (fused data set). The ratio of R2 / R1 is used as the gain.
[0042] The evaluation results can be combined with the gain and the prices in the reference transaction information to calculate the weighted average buying price and selling price, and generate an evaluation report, including the market reference price of the data assets to be evaluated, the most similar reference transaction information, etc.
[0043] In this embodiment, a fusion data set of target transaction information and reference transaction information is obtained by fusion, and the first key indicator of the reference data asset and the second key indicator of the fusion data set are extracted. The gain between the fusion data set and the reference transaction information is obtained by the first key indicator and the second key indicator, and the data asset to be evaluated is evaluated to obtain the evaluation result. In this way, the value of the target data asset can be evaluated more comprehensively and accurately, its performance in the market can be identified, and strong support can be provided for data trading and pricing.
[0044] like Figure 4 As shown, it is a flow chart of a data asset evaluation method provided by the fourth embodiment of the present invention. In step S203, the correlation between the reference transaction information and the target transaction information is calculated to obtain the correlation information, which may include the following steps: Step S401 , based on the reference transaction information and the target transaction information, a correlation degree calculation is performed by using a measurement method to obtain the similarity between the reference transaction information and the target transaction information.
[0045] Step S402: Evaluate the data asset to be evaluated based on the reference transaction information and the similarity to obtain an evaluation result.
[0046] Among them, by calculating the similarity between the reference transaction information and the target transaction information, we can identify which reference transaction information is most similar to the target transaction information, providing a basis for subsequent evaluation. Through a variety of measurement methods (such as Euclidean distance, cosine similarity, Jaccard similarity (English full name Jaccard Index)), we comprehensively evaluate the similarity to improve the accuracy of the evaluation. Combined with the similarity information, we calculate the weighted average buying and selling prices of the reference transaction information. The higher the similarity of the reference transaction information, the greater its weight, and the greater its impact on the evaluation results of the target transaction information. A detailed evaluation report is generated, including the market reference price of the target data asset, the most similar reference transaction information, etc.
[0047] In this embodiment, the similarity between the reference transaction information and the target transaction information is obtained by measuring, and the data asset to be evaluated is evaluated according to the reference transaction information and the similarity to obtain an evaluation result, thereby providing an important basis for subsequent data asset evaluation.
[0048] like Figure 5 FIG. 2 is a flow chart of a data asset evaluation method provided in Embodiment 5 of the present invention. The evaluation result in step S204 includes a similarity evaluation result and a gain evaluation result. Step S204 evaluates the data asset to be evaluated based on the reference transaction information and the association information to obtain the evaluation result, which may include the following steps: Step S501, obtaining a preset first smoothed data period value, and performing data aggregation according to the preset first smoothed data period value in combination with reference transaction information to obtain first aggregate data.
[0049] Among them, a smooth data cycle value is preset according to business needs and data characteristics. For example, if the transaction frequency of data assets is high and the market volatility is large, a shorter cycle value (such as 3 days or 5 days) can be selected; if the transaction frequency is low and the market is relatively stable, a longer cycle value (such as 10 days or 30 days) can be selected.
[0050] The selection of the period value usually requires a combination of historical data performance and expert opinions to ensure that the data can be effectively smoothed. The preset smoothed data period value is recorded for use in subsequent steps. This value can be stored in a configuration file, database, or program constant. The time series data used for smoothing is determined, usually the transaction date or timestamp. The smoothed price data is merged with the original reference transaction information to generate the first set of data. The first set of data contains the original transaction information and the smoothed price data, which can be used for subsequent analysis and evaluation.
[0051] Step S502: Based on the similarity between the target transaction information and the reference transaction information and the first set of data, an exponential moving average method is used to calculate to obtain first reference price trend information.
[0052] Among them, the exponential moving average (EMA) is a commonly used time series smoothing method. It gives higher weights to the most recent data points and gradually reduces the weights of earlier data points. When calculating the exponential moving average, the similarity between the target transaction information and the reference transaction information is combined, so that the reference transaction information that is more similar to the target transaction information contributes more to the price trend, and a suitable smoothing factor is determined. The smoothing factor is usually selected based on data characteristics and business needs. For example, if the market fluctuates greatly, a smaller smoothing factor (such as 0.2) can be selected, and if the market is relatively stable, a larger smoothing factor (such as 0.8) can be selected. Each time point in the first set of data is traversed in chronological order, and the EMA value of each time point is calculated using the above formula. When calculating the EMA value, the similarity information is used as the weight, and the smoothing value of each time point is weighted. The calculated weighted EMA value is added to the first set of data to generate a data set containing the first reference price trend information.
[0053] Step S503: Evaluate the data asset to be evaluated according to the similarity and the first reference price trend information to obtain a first similarity evaluation result.
[0054] The similarity between the target transaction information and the reference transaction information obtained in steps S501 and S502 and the first reference price trend information are calculated for the data asset to be evaluated through an overall calculation method.
[0055] The formula for calculating the appraisal price is as follows: In the formula, is the evaluation price of the data asset to be evaluated, E is the target transaction information, A is the reference transaction information, there are n elements in total, that is, there are n reference data assets, Similarity is the similarity between two data assets or data sets, the value range is [0,1], EMA is the exponential moving average, t is the first smoothing data period value, For reference data assets.
[0056] In this embodiment, the first set of data is obtained by using the first smoothed data period value and the reference transaction information, and the first reference price trend information is obtained by using the exponential moving average method, and the data asset to be evaluated is evaluated by the similarity and the first reference price trend information to obtain the first similarity evaluation result, thereby determining the performance of the data asset to be evaluated in the market and its similarity with the reference transaction information.
[0057] like Figure 6 As shown, it is a flow chart of a data asset evaluation method provided by Embodiment 6 of the present invention. In step S204, the data asset to be evaluated is evaluated according to the reference transaction information and the correlation information to obtain the evaluation result, which may include the following steps: Step S601, obtaining a preset second smoothed data period value, and performing data aggregation according to the preset second smoothed data period value in combination with reference transaction information to obtain second aggregate data.
[0058] Step S602, based on the gain between the target transaction information and the reference transaction information and the second set of data, an exponential moving average method is used to calculate to obtain second reference price trend information.
[0059] Step S603: Evaluate the data asset to be evaluated according to the gain degree and the second reference price trend information to obtain a first gain degree evaluation result.
[0060] The acquisition of the second smoothed data period value may refer to the content of the above step S501, and the calculation of the second reference price trend information may refer to the content of the above step S502.
[0061] The gain between the target transaction information and the reference transaction information and the second reference price trend information obtained from steps S601 and S602 are calculated for the data asset to be evaluated through an overall calculation method.
[0062] The formula for calculating the appraisal price is as follows: In the formula, is the evaluation price of the data asset to be evaluated, E is the target transaction information, A is the reference transaction information, there are n elements in total, that is, there are n reference data assets, Gain is the gain brought to the business system relationship indicator after the target transaction information and the reference transaction information are integrated, and its meaningful value range is (1, +∞], EMA is the exponential moving average, t is the second smoothing data period value, For reference data assets.
[0063] In this embodiment, the gain degree of the data asset to be evaluated is calculated according to the gain degree to perform gain degree evaluation, and a gain degree evaluation result is obtained, thereby obtaining the potential value and gain potential of the data asset to be evaluated.
[0064] like Figure 7 As shown, it is a flow chart of a data asset evaluation method provided by Embodiment 7 of the present invention. In step S204, the data asset to be evaluated is evaluated according to the reference transaction information and the correlation information to obtain the evaluation result, which may include the following steps: Step S701, obtaining a preset third smoothed data period value, and performing data aggregation according to the preset third smoothed data period value in combination with reference data asset information to obtain third aggregate data.
[0065] Step S702, based on the similarity between the target transaction information and the reference transaction information and the third set of data, an exponential moving average method is used to calculate to obtain third reference price trend information.
[0066] The acquisition of the third smoothed data period value may refer to the content of the above step S501, and the calculation of the third reference price trend information may refer to the content of the above step S502.
[0067] Step S703: obtaining the number of preset first reference transaction information, comparing the similarity with the number of first reference transaction information, and obtaining an average similarity.
[0068] Step S704: Evaluate the data asset to be evaluated according to the average similarity and the third reference price trend information to obtain a second similarity evaluation result.
[0069] The number of preset first reference transaction information is determined. Usually, this number is set based on market research and historical data. An appropriate value can be selected, for example, 100 reference transaction information.
[0070] The similarity values between the target transaction information and each first reference transaction information are added to obtain the sum of similarities, and the sum of similarities is divided by the number of preset first reference transaction information to obtain the average similarity. From the average similarity value obtained in step S703, some key evaluation indicators are selected, such as price trend similarity, price volatility, transaction frequency, data quality and data quantity. According to these indicators and the average similarity, the weighted value of each indicator is calculated, and all weighted values are summarized to obtain a comprehensive second similarity evaluation result. The data assets to be evaluated are calculated through the partial sum calculation method.
[0071] The formula for calculating the appraisal price is as follows: In the formula, is the evaluation price of the data asset to be evaluated, E is the target transaction information, A is the reference transaction information, there are n elements in total, that is, there are n reference data assets, Similarity is the similarity between two data assets or data sets, the value range is [0,1], EMA is the exponential moving average, t is the third smoothing data period value, is the reference data asset, and m is the number of first reference transaction information.
[0072] In this embodiment, the average similarity is obtained by obtaining the number and similarity of the first reference transaction information, and the second similarity evaluation result is obtained according to the average similarity and the third reference price trend information. Thus, the similarity evaluation result is obtained by the partial sum calculation method, so that the evaluation result is more accurate.
[0073] like Figure 8 FIG. 2 is a flow chart of a data asset evaluation method provided in Embodiment 8 of the present invention. In step S204, based on reference transaction information and association information, the data asset to be evaluated is evaluated to obtain an evaluation result, which may include the following steps: Step S801, obtaining a preset fourth smoothed data period value, and performing data aggregation according to the preset fourth smoothed data period value in combination with reference transaction information to obtain fourth aggregate data.
[0074] Step S802, according to the gain between the target transaction information and the reference transaction information and the fourth set of data, an exponential moving average method is used to calculate to obtain fourth reference price trend information.
[0075] The acquisition of the fourth smoothed data period value may refer to the content of the above step S501, and the calculation of the fourth reference price trend information may refer to the content of the above step S502.
[0076] Step S803: Obtain the number of preset second reference data assets, compare the gain degree with the number of second reference data assets, and obtain an average gain degree.
[0077] Step S804: Evaluate the data asset to be evaluated based on the average gain and the fourth reference price trend information to obtain a second gain evaluation result.
[0078] The calculation of the average gain may refer to the contents of the above-mentioned steps S703 and S704, and the data assets to be evaluated are calculated by the partial sum calculation method.
[0079] The formula for calculating the appraisal price is as follows: In the formula, is the evaluation price of the data asset to be evaluated, E is the target transaction information, A is the reference transaction information, there are n elements in total, that is, there are n reference data assets, Gain is the gain brought to the business system relationship indicator after the target transaction information and the reference transaction information are integrated, and its meaningful value range is (1, +∞], t is the fourth smoothing data period value, is the reference data asset, and m is the number of second reference transaction information.
[0080] In this embodiment, the average gain is obtained by obtaining the number and gain of the second reference transaction information, and the second gain evaluation result is obtained according to the average similarity and the fourth reference price trend information. Thus, the gain evaluation result is obtained by the partial sum calculation method, so that the evaluation result is more accurate.
[0081] like Fig. 9 As shown, it is a schematic diagram of a data asset evaluation device provided in the ninth embodiment of the present invention, and the data asset evaluation device corresponds to the data asset evaluation method in the above embodiment. The data asset evaluation device includes an information screening module 91, a screening module 92, a correlation calculation module 93 and an evaluation module 94. The functional modules are described in detail as follows: The information screening module 91 is used to obtain the data assets to be evaluated and screen the target transaction information corresponding to the data assets to be evaluated from the data asset information library; The screening module 92 is used to perform full data calculation on all data assets in the data asset information base according to the target transaction information, obtain reference data assets, and screen reference transaction information corresponding to the reference data assets from the data asset information base; A correlation calculation module 93, used to calculate the correlation between the reference transaction information and the target transaction information to obtain correlation information; The evaluation module 94 is used to evaluate the data assets to be evaluated based on the reference transaction information and the correlation information to obtain an evaluation result.
[0082] Optionally, the above-mentioned correlation calculation module 93 includes: The data fusion unit is used to process the target transaction information and the reference transaction information in a fusion manner to obtain a fused data set.
[0083] The first key indicator acquisition unit is used to extract key information of the reference data asset to obtain the first key indicator.
[0084] The second key indicator acquisition unit is used to extract key information of the fused data set to obtain a second key indicator.
[0085] The gain calculation unit is used to combine the first key indicator and the second key indicator to perform ratio calculation to obtain the gain between the fused data set and the reference transaction information.
[0086] The asset evaluation unit is used to evaluate the data assets to be evaluated based on the reference transaction information and the gain degree to obtain the evaluation result.
[0087] Optionally, the above-mentioned correlation calculation module 93 further includes: The similarity calculation unit is used to calculate the relevance by measuring the reference transaction information and the target transaction information to obtain the similarity between the reference transaction information and the target transaction information.
[0088] The asset similarity evaluation unit is used to evaluate the data assets to be evaluated based on the reference transaction information and similarity to obtain the evaluation result.
[0089] Optionally, the evaluation module 94 includes: The first set data acquisition unit is used to acquire a preset first smoothed data period value, and perform data aggregation according to the preset first smoothed data period value in combination with reference transaction information to obtain first set data.
[0090] The first reference trend acquisition unit is used to obtain first reference price trend information by calculating using an exponential moving average method according to the similarity between the target transaction information and the reference transaction information and the first set data.
[0091] The first similarity evaluation unit evaluates the data asset to be evaluated according to the similarity and the first reference price trend information to obtain a first similarity evaluation result.
[0092] Optionally, the evaluation module 94 further includes: The second set data acquisition unit is used to acquire a preset second smoothed data period value, and perform data aggregation according to the preset second smoothed data period value combined with reference transaction information to obtain second set data.
[0093] The second reference trend acquisition unit is used to obtain second reference price trend information by calculating the gain between the target transaction information and the reference transaction information and the second set of data using an exponential moving average method.
[0094] The first gain evaluation unit is used to evaluate the data asset to be evaluated according to the gain and the second reference price trend information to obtain a first gain evaluation result.
[0095] Optionally, the evaluation module 94 further includes: The third set data acquisition unit is used to acquire a preset third smoothed data period value, and perform data aggregation according to the preset third smoothed data period value in combination with reference data asset information to obtain third set data.
[0096] The third reference trend acquisition unit is used to obtain third reference price trend information by calculating using an exponential moving average method according to the similarity between the target transaction information and the reference transaction information and the third set of data.
[0097] The average similarity calculation unit is used to obtain the number of preset first reference transaction information, compare the similarity with the number of first reference transaction information, and obtain the average similarity.
[0098] The second similarity evaluation unit is used to evaluate the data asset to be evaluated according to the average similarity and the third reference price trend information to obtain a second similarity evaluation result.
[0099] Optionally, the evaluation module 94 further includes: The fourth set data acquisition unit is used to acquire a preset fourth smoothed data period value, and perform data aggregation according to the preset fourth smoothed data period value in combination with reference transaction information to obtain fourth set data.
[0100] The fourth reference trend acquisition unit is used to obtain fourth reference price trend information by calculating the gain between the target transaction information and the reference transaction information and the fourth set of data using an exponential moving average method.
[0101] The average gain calculation unit is used to obtain the number of preset second reference data assets, compare the gain with the number of second reference data assets, and obtain the average gain.
[0102] The second gain evaluation unit is used to evaluate the data asset to be evaluated according to the average gain and the fourth reference price trend information to obtain a second gain evaluation result.
[0103] For the specific definition of the data asset evaluation device, please refer to the definition of the data asset evaluation method above, which will not be repeated here. Each module in the above-mentioned data asset evaluation device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
[0104] like Fig.10 As shown, it is a schematic diagram of the structure of a computer device provided in Embodiment 10 of the present invention. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a data asset evaluation method is implemented.
[0105] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the data asset evaluation method in the above embodiment is implemented, for example Figures 2 to 8 Alternatively, when the processor executes the computer program, the functions of each module / unit in the embodiment of the data asset evaluation device are realized, for example Fig. 9 The functions of the information screening module 91 shown are not described here in detail to avoid repetition.
[0106] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the data asset evaluation method in the above embodiment is implemented. Figures 2 to 8 Alternatively, when the computer program is executed by a processor, the functions of each module / unit in the above-mentioned data asset evaluation device are realized, for example Fig. 9 The functions of the correlation calculation module 93 are not described here in detail to avoid repetition. The computer-readable storage medium may be non-volatile or volatile.
[0107] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0108] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.
[0109] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.
Claims
1. A data asset evaluation method, characterized in that: include: Acquire the data asset to be evaluated, and filter the target transaction information corresponding to the data asset to be evaluated from the data asset information library; According to the target transaction information, perform full data calculation on all data assets in the data asset information library to obtain reference data assets, and filter reference transaction information corresponding to the reference data assets from the data asset information library; Calculating the correlation between the reference transaction information and the target transaction information to obtain correlation information; The data asset to be evaluated is evaluated according to the reference transaction information and the correlation information to obtain an evaluation result.
2. The data asset evaluation method according to claim 1, characterized in that: The association information includes similarity and gain; The calculating the correlation between the reference transaction information and the target transaction information to obtain correlation information includes: According to the target transaction information and the reference transaction information, a fusion method is used to process the target transaction information and the reference transaction information to obtain a fusion data set; Extracting key information of the reference data asset to obtain a first key indicator; Extracting key information of the fused data set to obtain a second key indicator; Combining the first key indicator and the second key indicator, performing ratio calculation to obtain a gain between the fused data set and the reference transaction information; The data asset to be evaluated is evaluated according to the reference transaction information and the gain degree to obtain an evaluation result.
3. The data asset evaluation method according to claim 2, characterized in that: The calculating the correlation between the reference transaction information and the target transaction information to obtain correlation information includes: According to the reference transaction information and the target transaction information, a correlation degree calculation is performed by using a measurement method to obtain a similarity between the reference transaction information and the target transaction information; The data asset to be evaluated is evaluated according to the reference transaction information and the similarity to obtain an evaluation result.
4. The data asset evaluation method according to claim 3, characterized in that: The evaluation results include similarity evaluation results and gain evaluation results; The step of evaluating the data asset to be evaluated based on the reference transaction information and the correlation information to obtain an evaluation result includes: Obtaining a preset first smoothed data period value, and performing data aggregation according to the preset first smoothed data period value combined with the reference transaction information to obtain first aggregate data; According to the similarity between the target transaction information and the reference transaction information and the first set of data, an exponential moving average method is used to calculate to obtain first reference price trend information; The data asset to be evaluated is evaluated according to the similarity and the first reference price trend information to obtain a first similarity evaluation result.
5. The data asset evaluation method according to claim 4, characterized in that: According to the reference transaction information and the correlation information, the data asset to be evaluated is evaluated to obtain an evaluation result, including: Obtaining a preset second smoothed data period value, and performing data aggregation according to the preset second smoothed data period value combined with the reference transaction information to obtain second aggregate data; According to the gain between the target transaction information and the reference transaction information and the second set of data, an exponential moving average method is used to calculate to obtain second reference price trend information; The data asset to be evaluated is evaluated according to the gain degree and the second reference price trend information to obtain a first gain degree evaluation result.
6. The data asset evaluation method according to claim 4, characterized in that: The step of evaluating the data asset to be evaluated based on the reference transaction information and the correlation information to obtain an evaluation result includes: Obtaining a preset third smoothed data period value, and performing data aggregation according to the preset third smoothed data period value in combination with the reference data asset information to obtain third aggregate data; According to the similarity between the target transaction information and the reference transaction information and the third set of data, an exponential moving average method is used to calculate to obtain third reference price trend information; Acquire the number of preset first reference transaction information, compare the similarity with the number of the first reference transaction information, and obtain an average similarity; The data asset to be evaluated is evaluated according to the average similarity and the third reference price trend information to obtain a second similarity evaluation result.
7. The data asset evaluation method according to claim 4, characterized in that: The step of evaluating the data asset to be evaluated based on the reference transaction information and the correlation information to obtain an evaluation result includes: Obtaining a preset fourth smoothed data period value, and performing data aggregation according to the preset fourth smoothed data period value in combination with the reference transaction information to obtain fourth aggregate data; According to the gain between the target transaction information and the reference transaction information and the fourth set of data, an exponential moving average method is used to calculate to obtain fourth reference price trend information; Acquire the number of preset second reference data assets, compare the gain degree with the number of the second reference data assets, and obtain an average gain degree; The data asset to be evaluated is evaluated according to the average gain and the fourth reference price trend information to obtain a second gain evaluation result.
8. A data asset evaluation device, characterized in that: include: An information screening module is used to obtain the data assets to be evaluated and screen the target transaction information corresponding to the data assets to be evaluated from the data asset information library; A screening module, configured to perform full data calculation on all data assets in the data asset information library according to the target transaction information, obtain reference data assets, and screen reference transaction information corresponding to the reference data assets from the data asset information library; A correlation calculation module, used to calculate the correlation between the reference transaction information and the target transaction information to obtain correlation information; The evaluation module is used to evaluate the data assets to be evaluated based on the reference transaction information and the correlation information to obtain an evaluation result.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the data asset evaluation method according to any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the data asset evaluation method according to any one of claims 1 to 7 is implemented.