CN115034654A - Asset assessment method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to the technical field of big data and discloses an asset assessment method, device, equipment and storage medium. The method comprises the following steps: the asset information of the target object is obtained, and the assets to be evaluated of the target object, which need to be subjected to value evaluation, are determined from the asset information; determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated; determining an asset evaluation task processing sequence according to a preset rule dependency relationship; acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data; and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated. According to the invention, the asset value is evaluated by associating different data sources of the target asset for comprehensive calculation, so that the technical problem is solved.
Description
Technical Field
The invention relates to the technical field of big data, in particular to an asset assessment method, device, equipment and storage medium.
Background
Asset management, which is a process of analyzing and managing the assets of target users to make further investment strategies. In asset management, the assessment of assets is an important ring. Only if the value of the asset is correctly assessed, further investment strategies can be subsequently accurately formulated to maximize the user's asset profitability.
In the existing asset management, generally, a special institution or a special evaluator follows legal or fair standards and procedures, a scientific method is used to evaluate assets in units of currency, and a next investment strategy is made by combining the evaluation value of the assets and market trends of manual analysis. Therefore, how to provide multidimensional inspection portfolio performance for fund management organizations through comprehensive calculation by associating different data sources becomes a technical problem to be solved by technical personnel in the field.
Disclosure of Invention
The invention mainly aims to evaluate the asset value and solve the technical problem by associating different data sources for comprehensive calculation.
A first aspect of the present invention provides an asset assessment method, comprising: acquiring asset information of a target object, and determining assets to be evaluated of the target object, which need to be subjected to value evaluation, from the asset information; determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated; determining the asset evaluation task processing sequence according to a preset rule dependency relationship; acquiring text data corresponding to the assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data; and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated.
Optionally, in a first implementation manner of the first aspect of the present invention, the obtaining asset information of the target object includes: acquiring financial data of a target object; and screening the financial data according to a preset asset judgment rule to obtain corresponding assets belonging to the asset transaction category, and taking the assets as asset information of the target object.
Optionally, in a second implementation manner of the first aspect of the present invention, the determining an asset assessment rule corresponding to the asset to be assessed includes: asset publishing data acquired from a preset asset management authority; and analyzing the asset publishing data based on a preset data analysis algorithm to obtain asset evaluation rules corresponding to the assets to be evaluated so as to determine asset evaluation rules corresponding to different types of assets.
Optionally, in a third implementation manner of the first aspect of the present invention, the analyzing the asset publishing data based on a preset data analysis algorithm to obtain the asset assessment rules corresponding to the assets to be assessed, so as to determine the asset assessment rules corresponding to the assets of different categories includes: determining asset values corresponding to different asset classes in the asset publishing data in a historical period; according to the asset value, determining asset value evaluation tables corresponding to different asset categories; and determining asset evaluation rules corresponding to different asset categories according to the asset value evaluation table.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the determining, according to a preset rule dependency relationship, the asset evaluation task processing order includes: generating a first task corresponding to the asset to be evaluated according to the asset to be evaluated and the evaluation time of the asset to be evaluated; dividing the first task into tasks to be evaluated according to the algorithm to be called corresponding to the first task; generating a corresponding processing result according to the task to be evaluated and storing the task to be evaluated to a preset cache; and determining the asset evaluation task processing sequence according to the processing result and the budget rule dependency relationship.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the obtaining text data corresponding to the asset to be evaluated, and performing recognition and classification on the text data through a preset classification model to obtain the classified target text data includes: acquiring text data corresponding to the assets to be evaluated; performing word segmentation processing on the text data based on an entity dictionary to obtain a vector matrix corresponding to the text data; and inputting the vector matrix into a preset classification model, and identifying and classifying the text data according to the classification model to obtain classified target text data.
Optionally, in a sixth implementation manner of the first aspect of the present invention, the executing the asset assessment task according to the asset assessment rule and the asset assessment task processing order to achieve assessing the target text data, and obtaining the current asset value of the asset to be assessed includes: determining the asset class of the asset to be evaluated; determining an asset value evaluation table corresponding to the assets to be evaluated according to the asset types; and evaluating the assets to be evaluated according to the asset value evaluation table to obtain the current asset value of the assets to be evaluated.
A second aspect of the present invention provides an asset evaluation device comprising: the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring asset information of a target object and determining assets to be evaluated of the target object, which need to be subjected to value evaluation, from the asset information; the generation module is used for determining an asset evaluation rule corresponding to the asset to be evaluated and generating an asset evaluation task corresponding to the asset to be evaluated; the determining module is used for determining the asset evaluation task processing sequence according to a preset rule dependency relationship; the classification module is used for acquiring text data corresponding to the assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data; and the evaluation module is used for executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence so as to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated.
Optionally, in a first implementation manner of the second aspect of the present invention, the obtaining module is specifically configured to: acquiring financial data of a target object; and screening the financial data according to a preset asset judgment rule to obtain corresponding assets belonging to the asset transaction category, and taking the assets as asset information of the target object.
Optionally, in a second implementation manner of the second aspect of the present invention, the generating module includes: the system comprises an acquisition unit, a storage unit and a processing unit, wherein the acquisition unit is used for acquiring asset publishing data from a preset asset management mechanism; and the analysis unit is used for analyzing the asset publishing data based on a preset data analysis algorithm to obtain the asset evaluation rule corresponding to the asset to be evaluated so as to determine the asset evaluation rules corresponding to the assets of different categories.
Optionally, in a third implementation manner of the second aspect of the present invention, the analysis unit is specifically configured to: determining asset values corresponding to different asset classes in the asset publishing data in a historical period; according to the asset value, determining asset value evaluation tables corresponding to different asset types; and determining asset evaluation rules corresponding to different asset categories according to the asset value evaluation table.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the determining module is specifically configured to: generating a first task corresponding to the asset to be evaluated according to the asset to be evaluated and the evaluation time of the asset to be evaluated; dividing the first task into tasks to be evaluated according to the algorithm to be called corresponding to the first task; generating a corresponding processing result according to the task to be evaluated and storing the task to be evaluated to a preset cache; and determining the asset evaluation task processing sequence according to the processing result and the budget rule dependency relationship.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the classification module is specifically configured to: acquiring text data corresponding to the assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data, wherein the classified target text data comprises the following steps: acquiring text data corresponding to the assets to be evaluated; performing word segmentation processing on the text data based on an entity dictionary to obtain a vector matrix corresponding to the text data; and inputting the vector matrix into a preset classification model, and identifying and classifying the text data according to the classification model to obtain classified target text data.
Optionally, in a sixth implementation manner of the second aspect of the present invention, the evaluation module is specifically configured to: determining the asset class of the asset to be evaluated; determining an asset value evaluation table corresponding to the assets to be evaluated according to the asset types; and evaluating the assets to be evaluated according to the asset value evaluation table to obtain the current asset value of the assets to be evaluated.
A third aspect of the present invention provides an asset evaluation device comprising: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;
the at least one processor invokes the instructions in the memory to cause the asset assessment device to perform the steps of the asset assessment method described above.
A fourth aspect of the present invention provides a computer-readable storage medium having stored therein instructions, which, when run on a computer, cause the computer to perform the steps of the asset assessment method described above.
In the technical scheme provided by the invention, the asset information of the target object is obtained, and the assets to be evaluated of the target object, which need to be subjected to value evaluation, are determined from the asset information; determining an asset assessment rule corresponding to the asset to be assessed, and generating an asset assessment task corresponding to the asset to be assessed; determining an asset evaluation task processing sequence according to a preset rule dependency relationship; acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data; and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated. According to the invention, the asset value is evaluated by comprehensively calculating different data sources of the associated target asset, so that the technical problem is solved.
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FIG. 1 is a schematic diagram of a first embodiment of an asset assessment method provided by the present invention;
FIG. 2 is a schematic diagram of a second embodiment of an asset assessment method provided by the present invention;
FIG. 3 is a schematic diagram of a third embodiment of an asset assessment method provided by the present invention;
FIG. 4 is a schematic diagram of a fourth embodiment of an asset assessment method provided by the present invention;
FIG. 5 is a schematic diagram of a fifth embodiment of the asset assessment method provided by the present invention;
FIG. 6 is a schematic diagram of a first embodiment of an asset assessment device provided in accordance with the present invention;
FIG. 7 is a schematic view of a second embodiment of an asset assessment device provided by the present invention;
FIG. 8 is a schematic diagram of an embodiment of an asset assessment device provided by the present invention.
Detailed Description
According to the asset assessment method, the asset assessment device, the asset assessment equipment and the storage medium, the asset information of the target object is obtained, and the assets to be assessed, needing to be subjected to value assessment, of the target object are determined from the asset information; determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated; determining an asset evaluation task processing sequence according to a preset rule dependency relationship; acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data; and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated. According to the invention, the asset value is evaluated by comprehensively calculating different data sources of the associated target asset, so that the technical problem is solved.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Moreover, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, a detailed flow of an embodiment of the present invention is described below, with reference to fig. 1, a first embodiment of an asset assessment method in an embodiment of the present invention includes:
101. acquiring asset information of a target object, and determining assets to be evaluated of the target object, which need to be subjected to value evaluation, from the asset information;
in this embodiment, asset information of the target object is acquired, and assets to be evaluated of the target object, which need to be subjected to value evaluation, are determined from the asset information.
102. Determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated;
in this embodiment, the asset assessment rules are used to determine asset assessment rules corresponding to different asset classes. Optionally, the asset valuation rules may include, but are not limited to, asset price, asset valuation rate, asset valuation probability, and asset value trend parameters corresponding to the asset class.
In this embodiment, the processing the task to be evaluated according to the processing sequence and storing the processing result in a preset cache, so that the subsequent processing the task to be evaluated, which has an association relationship with the task to be evaluated according to the processing result obtained from the preset cache, includes: and processing the task to be evaluated by using the algorithm to be called corresponding to the task to be evaluated, generating a corresponding processing result and storing the corresponding processing result in the preset cache, so that the task to be evaluated having the association relation with the task to be evaluated is processed by using the algorithm to be called corresponding to the task to be evaluated having the association relation according to the processing result obtained from the preset cache.
103. Determining an asset evaluation task processing sequence according to a preset rule dependency relationship;
in this embodiment, when the prices of all assets in a portfolio need to be calculated, if the portfolio to be evaluated includes 100 assets, the calculation service layer may divide the portfolio to be evaluated into 100 tasks to be evaluated for calculating the prices. When the combined profitability of a bond combination is calculated, the profitability and the base point value of each asset in the combination need to be calculated, the combined profitability is calculated by weighting average, and if 100 assets to be evaluated are included, the calculation service layer can divide the assets to be evaluated into tasks to be evaluated for calculating the profitability by 100, tasks to be evaluated for calculating the base point value by 100 and tasks to be evaluated by weighted average.
The service layer does not need to partition tasks as the computing function layer can provide functionality to directly compute the combined profitability. If the function of directly calculating the combined profitability provided by the calculation function layer cannot meet the requirement of a certain request, the service layer can also decompose the request and organize a plurality of calculation functions to complete the calculation. According to the association relationship among the preset rules, the computing service layer can determine the execution sequence and the mutual association relationship of each task to be evaluated.
104. Acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data;
in this embodiment, the classification model is a probability-based classification model, which may include a classification model based on a combination of a two-way length memory unit and a convolutional neural network, a classification model based on a combination of a two-way length memory unit and a deep neural network, and a classification model based on a combination of a convolutional neural network and a deep neural network.
When training the classification model, the text data is used as input, the classification of the text data is used as output, and the classification model is trained. And the training target of the classification model is a loss function of the minimum text classification model, and parameters of the model are adjusted according to the loss function of the initial classification model to obtain the target classification model.
105. And executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated.
In the embodiment, data is prepared in a centralized manner after the tasks are decomposed, the list of the tasks to be evaluated provides the overall situation of data requirements, repeated loading can be effectively avoided, data loading on the same database table can be combined and executed in one SQL, and the possibility of loading data more efficiently can be provided. When a task to be evaluated does not have an association relationship, the calculation function can calculate to obtain a corresponding processing result according to the parameters extracted from the calculation environment through a corresponding algorithm to be called. When a task to be evaluated with an association relationship exists in a certain task to be evaluated, the calculation function can calculate to obtain a corresponding processing result according to the parameters extracted from the calculation environment and the processing result of the task to be evaluated, which is stored in the preset cache and has the association relationship with the parameter, through a corresponding algorithm to be called.
In the embodiment, the target assets can be automatically marked from the asset information of the target object, and the assets are automatically evaluated based on the asset evaluation rule.
In the embodiment of the invention, the asset information of the target object is obtained, and the asset to be evaluated of the target object, which needs to be subjected to value evaluation, is determined from the asset information; determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated; determining an asset evaluation task processing sequence according to a preset rule dependency relationship; acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data; and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated. According to the invention, the asset value is evaluated by comprehensively calculating different data sources of the associated target asset, so that the technical problem is solved.
Referring to fig. 2, a second embodiment of an asset assessment method according to an embodiment of the present invention comprises:
201. acquiring financial data of a target object;
in this embodiment, the target object may be a personal object or an enterprise object, further, the personal object may be a person with certain economic strength of different nationalities, and the enterprise object may be a domestic enterprise or a foreign enterprise, which is not limited in the present invention.
In this embodiment, the target assets may include, but are not limited to, one or both of physical assets and intangible assets. Alternatively, the physical assets may include, but are not limited to, automobile property rights, land property rights, antique property rights, calligraphy and painting property rights, etc. assets having substantial asset ontologies. Alternatively, intangible assets may include, but are not limited to, game assets, virtual items, intellectual property, music copyrights, creditor revenue rights, and the like, assets that do not have a substantial asset body.
In this embodiment, the mark of the target asset may be used for tracking and recording the target asset, and by this mark, when data processing is performed subsequently, corresponding data processing operation may be performed by setting automatic identification of the mark. For example, when the target property is marked for sale/purchase, and the corresponding processing system recognizes the mark, the corresponding sale/purchase operation can be directly executed
202. Screening the financial data according to a preset asset judgment rule to obtain corresponding assets belonging to the asset transaction category, and taking the assets as asset information of a target object;
in this embodiment, the financial data of the target object may be directly derived from the financial system of the target object, or may be obtained from a database of a related public affairs management authority. Optionally, the financial records of the asset types belonging to the asset transaction types in the financial data are screened through a preset text analysis algorithm, for example, a keyword detection module related to asset transactions is preset in an algorithm model, and the financial data is input into the algorithm model for processing, so that the screened financial records can be directly output subsequently by the algorithm model.
In this embodiment, further, after determining the assets corresponding to the screened financial incoming records as the asset information of the target object, the current total holding amount of the assets of the target object may be determined according to all the financial incoming records corresponding to the assets, so as to lay a data foundation for the subsequent calculation of the value of the assets.
Therefore, by implementing the optional embodiment, the financial data of the target object can be screened according to the preset asset judgment rule, so that the asset information of the target object can be determined, the asset information of the target object can be directly acquired through the financial data of the target object, the acquisition efficiency of the asset information of the target object is improved, and a data basis is provided for subsequent asset management operation. .
203. Asset publishing data acquired from a preset asset management authority;
in this embodiment, the asset publishing data includes asset publishing data corresponding to different asset categories. The asset publication data may include, but is not limited to, one or more of asset start rates, asset deal rates, or asset valuation. Alternatively, the asset management entity may include, but is not limited to, an auction entity, a house authority, a cultural relic authority, or an asset evaluation entity.
In this embodiment, the acquisition of the asset publishing data may directly capture data in an information document periodically published on the network by the asset management mechanism through a crawler algorithm, or may directly interface with a data system of the asset management mechanism, and perform data acquisition by using a preset data interface and a data rule.
Therefore, the optional embodiment is implemented to obtain the asset publishing data of the asset management mechanism, and the asset publishing data is analyzed to obtain the asset evaluation rule based on the preset data analysis algorithm to determine the asset evaluation rules corresponding to different asset classes, so that the asset evaluation rules corresponding to different asset classes can be accurately obtained through the asset publishing data of the asset management mechanism, the determination efficiency of the asset evaluation rules is improved, and a data basis is provided for subsequent asset management operations. .
204. Analyzing the asset publishing data based on a preset data analysis algorithm to obtain asset evaluation rules corresponding to assets to be evaluated so as to determine asset evaluation rules corresponding to different types of assets;
in this embodiment, asset publishing data within a target history period corresponding to different asset classes in the asset publishing data is first determined; and determining asset evaluation rules corresponding to different asset types according to the asset publishing data in the target history period corresponding to the different asset types in the asset publishing data.
According to the asset publishing data corresponding to different asset types in the target historical period, a relational expression or a relational curve model of the asset publishing data and time corresponding to different asset types is calculated through a data fitting algorithm, so that asset value evaluation tables corresponding to different asset types are obtained.
Optionally, in the above step, the asset evaluation rules corresponding to different asset classes are determined according to the asset publishing data in the target history period corresponding to the different asset classes in the asset publishing data, or an average or median of the asset publishing data in the target history period corresponding to the different asset classes is calculated, and the average or median is determined as the asset evaluation rules corresponding to the different asset classes.
Optionally, in the foregoing step, asset assessment rules corresponding to different asset classes are determined according to asset publishing data within a target history period corresponding to different asset classes in the asset publishing data, or asset publishing data within the target history period corresponding to different asset classes may be input into the neural network model to train the neural network model, and the trained neural network model corresponding to different asset classes is used as the asset assessment rules corresponding to different asset classes. Subsequently, the trained neural network model can be used for evaluating the current asset value of the target asset.
205. Determining an asset evaluation task processing sequence according to a preset rule dependency relationship;
206. acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data;
207. and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated.
Step 205-207 in the present embodiment is similar to step 103-105 in the first embodiment, and will not be described herein again.
In the embodiment of the invention, assets to be evaluated of the target object needing value evaluation are determined from the asset information by acquiring the asset information of the target object; determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated; determining an asset evaluation task processing sequence according to a preset rule dependency relationship; acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data; and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated. According to the invention, the asset value is evaluated by comprehensively calculating different data sources of the associated target asset, so that the technical problem is solved.
Referring to fig. 3, a third embodiment of an asset assessment method according to the present invention comprises:
301. acquiring asset information of a target object, and determining assets to be evaluated of the target object, which need to be subjected to value evaluation, from the asset information;
302. asset publishing data acquired from a preset asset management authority;
303. determining asset values corresponding to different asset categories in the asset publishing data in a historical period;
in this embodiment, a relational expression or a relational curve model of the asset publishing data and time corresponding to different asset classes can be calculated by a data fitting algorithm according to the asset publishing data in the target history period corresponding to the different asset classes, so as to obtain asset value evaluation tables corresponding to the different asset classes.
304. According to the asset value, determining asset value evaluation tables corresponding to different asset types;
in this embodiment, optionally, in the above step, the asset evaluation rules corresponding to different asset classes are determined according to the asset publishing data in the target history period corresponding to the different asset classes in the asset publishing data, or an average or median of the asset publishing data in the target history period corresponding to the different asset classes is calculated, and the average or median is determined as the asset evaluation rules corresponding to the different asset classes.
305. According to the asset value evaluation table, determining asset evaluation rules corresponding to different asset categories;
in this embodiment, in the above steps, asset evaluation rules corresponding to different asset classes are determined according to asset publishing data within a target history period corresponding to the different asset classes in the asset publishing data, the asset publishing data within the target history period corresponding to the different asset classes may also be input into the neural network model to train the neural network model, and the trained neural network model corresponding to the different asset classes is used as the asset evaluation rules corresponding to the different asset classes. Subsequently, the trained neural network model can be used for evaluating the current asset value of the target asset.
306. Determining an asset evaluation task processing sequence according to a preset rule dependency relationship;
307. acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data;
308. and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated.
The steps 306-308 in this embodiment are similar to the steps 101, 103-105 in the first embodiment, and are not described herein again.
In the embodiment of the invention, assets to be evaluated of the target object needing value evaluation are determined from the asset information by acquiring the asset information of the target object; determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated; determining an asset evaluation task processing sequence according to a preset rule dependency relationship; acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data; and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated. According to the invention, the asset value is evaluated by comprehensively calculating different data sources of the associated target asset, so that the technical problem is solved.
Referring to fig. 4, a fourth embodiment of an asset assessment method according to the present invention comprises:
401. acquiring asset information of a target object, and determining assets to be evaluated of the target object, which need to be subjected to value evaluation, from the asset information;
402. determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated;
403. generating a first task corresponding to the asset to be evaluated according to the asset to be evaluated and the evaluation time of the asset to be evaluated;
in this embodiment, the model service layer may generate a calculation target matrix according to the assets to be evaluated and the corresponding rules to be evaluated, where each calculation target includes a single asset to be evaluated and a corresponding rule to be evaluated, and then generate the first task according to the calculation target matrix and the time to be evaluated.
Each first task represents a rule to be evaluated of a computing object at a certain time point. According to the preset computing service provided by each computing function, a plurality of first tasks can be divided into one task to be evaluated, and each task to be evaluated corresponds to one call of a corresponding computing function.
For example, when a user needs to calculate the risk value (VaR) of a portfolio using historical observation, the price of each historical scenario needs to be calculated for each asset in the portfolio first, and then the VaR rule is obtained through statistics. In the case of a large amount of data, the former step needs to be decomposed into a parallel computing framework for completion, and the latter step needs to assemble the computing results of the former step onto a single node for completion. If there are 100 assets in the portfolio and the historical observation interval corresponding to the time to be evaluated is 250 working days, the previous step includes 100 x 250-25000 first tasks. When a computing function is available that calculates successive date prices for a single asset, then the one computing function may resolve 250 subtasks, which is equivalent to the service layer dividing 25000 first tasks into 100 tasks to be evaluated.
404. Dividing the first task into tasks to be evaluated according to a to-be-called algorithm corresponding to the first task;
in this embodiment, when the calculation function layer can provide a function of directly calculating the combined profitability, the service layer does not need to divide the tasks. If the function of directly calculating the combined profitability provided by the calculation function layer cannot meet the requirement of a certain request, the service layer can also decompose the request and organize a plurality of calculation functions to complete the calculation.
According to the association relationship between the preset indexes, the computing service layer can determine the execution sequence and the association relationship of each task to be evaluated. .
405. Generating a corresponding processing result according to the task to be evaluated and storing the task to be evaluated to a preset cache;
in this embodiment, when a task to be evaluated does not have an association relationship, the computing function may compute, according to a parameter extracted from the computing environment, a corresponding processing result through a corresponding algorithm to be called. When a task to be evaluated with an association relation exists in a certain task to be evaluated, the calculation function can calculate to obtain a corresponding processing result according to the parameters extracted from the calculation environment and the processing result of the task to be evaluated, which is stored in the preset cache and has the association relation with the parameter, through a corresponding algorithm to be called.
406. Determining the asset evaluation task processing sequence according to the processing result and the budget rule dependency relationship;
in this embodiment, a particle task corresponding to the asset assessment request is generated according to the time to be assessed, the asset to be assessed and the index to be assessed; dividing the particle tasks into tasks to be evaluated according to algorithms to be called corresponding to the particle tasks; and processing the task to be evaluated by using the algorithm to be called corresponding to the task to be evaluated, generating a corresponding processing result and storing the corresponding processing result in the preset cache, so that the task to be evaluated having the association relation with the task to be evaluated is processed by using the algorithm to be called corresponding to the task to be evaluated having the association relation according to the processing result obtained from the preset cache.
407. Acquiring text data corresponding to assets to be evaluated;
in this embodiment, the text data of the asset to be evaluated may be asset price publishing data, including asset publishing data corresponding to different asset categories. The asset publication data may include, but is not limited to, one or more of asset start prices, asset deal prices, or asset valuation. Alternatively, the asset management authority may include, but is not limited to, an auction authority, a house authority, a cultural relic authority, or an asset evaluation authority.
408. Performing word segmentation processing on the text data based on the entity dictionary to obtain a vector matrix corresponding to the text data;
in this embodiment, the text data is subjected to word segmentation, and a vector matrix corresponding to the text data is obtained. The word segmentation processing of the text based on the entity dictionary comprises the steps of performing word segmentation on text data based on a medical entity dictionary, performing word vectorization processing on word segmentation results, and finally performing splicing processing on word vectors to obtain a vector matrix of the text data. The word segmentation processing, word vectorization processing and concatenation processing performed in this step are the same as the processing performed on the text in the training sample in the above step, and are not described herein again.
409. Inputting the vector matrix into a preset classification model, and identifying and classifying the text data according to the classification model to obtain classified target text data;
in this embodiment, the obtained vector matrix corresponding to the text data is input into a classification model obtained by pre-training, and a classification result of the text data is obtained according to the output of the classification model.
Specifically, the classification result of the text data obtained by the classification model in this step includes two parts: one part is the classification result of the text data obtained by the first classification model, and the other part is the similarity between the text data obtained by the second classification model and the medical field expert data corresponding to the classification of the text data.
Therefore, the method can be used for accurately classifying the texts in different fields and can be used for automatically classifying the texts in different fields.
410. And executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated.
The steps 401, 402, 410 in this embodiment are similar to the steps 101, 102, 105 in the first embodiment, and are not described herein again.
In the embodiment, asset information of a target object is obtained, and assets to be evaluated of the target object, which need to be subjected to value evaluation, are determined from the asset information; determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated; determining an asset evaluation task processing sequence according to a preset rule dependency relationship; acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data; and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated. According to the invention, the asset value is evaluated by comprehensively calculating different data sources of the associated target asset, so that the technical problem is solved.
Referring to fig. 5, a fifth embodiment of an asset assessment method according to the present invention comprises:
501. acquiring asset information of a target object, and determining assets to be evaluated of the target object, which need to be subjected to value evaluation, from the asset information;
502. determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated;
503. determining an asset evaluation task processing sequence according to a preset rule dependency relationship;
504. acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data;
505. determining the asset type of the asset to be evaluated;
in the embodiment, financial data of a target object is obtained; the financial data includes a plurality of financial incoming and outgoing records and corresponding asset categories; and screening financial incoming records of which the asset types belong to the asset transaction types in the financial data according to a preset asset judgment rule, and determining assets corresponding to the screened financial incoming records as asset information of the target object.
In this embodiment, the financial data of the target object may be directly derived from the financial system of the target object, or may be obtained from a database of a related public affairs management authority. Optionally, the financial records of the asset types belonging to the asset transaction types in the financial data are screened through a preset text analysis algorithm, for example, a keyword detection module related to asset transactions is preset in an algorithm model, and the financial data is input into the algorithm model for processing, so that the screened financial records can be directly output subsequently by the algorithm model.
506. Determining an asset value evaluation table corresponding to the assets to be evaluated according to the asset types;
in the embodiment, asset publishing data in a target history period corresponding to different asset types in asset price publishing information is determined; and determining asset evaluation rules corresponding to different asset types according to asset publishing data corresponding to different asset types in the asset price publishing information in the target history period.
In this embodiment, a relational expression or a relational curve model of the asset publishing data and time corresponding to different asset classes can be calculated by a data fitting algorithm according to the asset publishing data in the target history period corresponding to the different asset classes, so as to obtain the asset publishing data-time relation corresponding to the different asset classes.
Optionally, in the above step, the asset valuation parameters corresponding to different asset classes are determined according to the asset publishing data in the target history period corresponding to the different asset classes in the asset price publishing information, or an average or median of the asset publishing data in the target history period corresponding to the different asset classes is calculated, and the average or median is determined as the asset valuation parameters corresponding to the different asset classes.
507. And evaluating the assets to be evaluated according to the asset value evaluation table to obtain the current asset value of the assets to be evaluated.
In this embodiment, the selection of the target time period and the target history period may follow a certain rule, that is, when the target time period corresponding to the current asset value of the target asset to be evaluated is determined, the corresponding target history period is selected by analyzing the characteristics of the target time period. Further, the target historical time period may be a time period having the same time period characteristics as the target time period. Optionally, the time period characteristics include, but are not limited to, one or more of a month of belongings, a quarter of belongings, a number of specific economic activities within a period, and a number of trading activities of the target object within a period.
Step 501-504 in the present embodiment is similar to step 101-104 in the first embodiment, and will not be described herein again.
In the embodiment of the invention, assets to be evaluated of the target object needing value evaluation are determined from the asset information by acquiring the asset information of the target object; determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated; determining an asset evaluation task processing sequence according to a preset rule dependency relationship; acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data; and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated. According to the invention, the asset value is evaluated by comprehensively calculating different data sources of the associated target asset, so that the technical problem is solved.
With reference to fig. 6, the asset assessment method according to the embodiment of the present invention is described above, and the asset assessment apparatus according to the embodiment of the present invention is described below, where the first embodiment of the asset assessment apparatus according to the embodiment of the present invention includes:
an obtaining module 601, configured to obtain asset information of a target object, and determine, from the asset information, an asset to be evaluated of the target object, where the asset needs to be evaluated;
a generating module 602, configured to determine an asset assessment rule corresponding to the asset to be assessed, and generate an asset assessment task corresponding to the asset to be assessed;
a determining module 603, configured to determine a processing order of the asset assessment tasks according to a preset rule dependency relationship;
the classification module 604 is configured to obtain text data corresponding to the asset to be evaluated, and perform recognition and classification on the text data through a preset classification model to obtain classified target text data;
and the evaluation module 605 is configured to execute the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence, so as to evaluate the target text data and obtain the current asset value of the asset to be evaluated.
In the embodiment of the invention, assets to be evaluated of the target object needing value evaluation are determined from the asset information by acquiring the asset information of the target object; determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated; determining an asset evaluation task processing sequence according to a preset rule dependency relationship; acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data; and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated. According to the invention, the asset value is evaluated by comprehensively calculating different data sources of the associated target asset, so that the technical problem is solved.
Referring to fig. 7, a second embodiment of the asset assessment apparatus according to the embodiment of the present invention specifically includes:
an obtaining module 601, configured to obtain asset information of a target object, and determine, from the asset information, an asset to be evaluated of the target object, where the asset needs to be evaluated;
a generating module 602, configured to determine an asset assessment rule corresponding to the asset to be assessed, and generate an asset assessment task corresponding to the asset to be assessed;
a determining module 603, configured to determine a processing order of the asset evaluation tasks according to a preset rule dependency relationship;
the classification module 604 is configured to obtain text data corresponding to the asset to be evaluated, and perform recognition and classification on the text data through a preset classification model to obtain classified target text data;
and the evaluation module 605 is configured to execute the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence, so as to evaluate the target text data and obtain the current asset value of the asset to be evaluated.
In this embodiment, the obtaining module 601 is specifically configured to:
acquiring financial data of a target object;
and screening the financial data according to a preset asset judgment rule to obtain corresponding assets belonging to the asset transaction category, and taking the assets as asset information of the target object.
In this embodiment, the generating module 602 includes:
an obtaining unit 6021, configured to obtain asset publishing data from a preset asset management authority;
the analysis unit 6022 is configured to analyze the asset publishing data based on a preset data analysis algorithm to obtain an asset assessment rule corresponding to the asset to be assessed, so as to determine asset assessment rules corresponding to different types of assets.
In this embodiment, the analysis unit 6022 is specifically configured to:
determining asset values corresponding to different asset classes in the asset publishing data in a historical period;
according to the asset value, determining asset value evaluation tables corresponding to different asset types;
and determining asset evaluation rules corresponding to different asset categories according to the asset value evaluation table.
In this embodiment, the determining module 603 is specifically configured to:
generating a first task corresponding to the asset to be evaluated according to the asset to be evaluated and the evaluation time of the asset to be evaluated;
dividing the first task into tasks to be evaluated according to the algorithm to be called corresponding to the first task;
generating a corresponding processing result according to the task to be evaluated and storing the task to be evaluated to a preset cache;
and determining the asset evaluation task processing sequence according to the processing result and the budget rule dependency relationship.
In this embodiment, the classification module 604 is specifically configured to:
acquiring text data corresponding to the assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data, wherein the classified target text data comprises the following steps:
acquiring text data corresponding to the assets to be evaluated;
performing word segmentation processing on the text data based on an entity dictionary to obtain a vector matrix corresponding to the text data;
and inputting the vector matrix into a preset classification model, and identifying and classifying the text data according to the classification model to obtain classified target text data.
In this embodiment, the evaluation module 605 is specifically configured to:
determining the asset class of the asset to be evaluated;
determining an asset value evaluation table corresponding to the assets to be evaluated according to the asset types;
and evaluating the assets to be evaluated according to the asset value evaluation table to obtain the current asset value of the assets to be evaluated.
In the embodiment of the invention, the asset information of the target object is obtained, and the asset to be evaluated of the target object, which needs to be subjected to value evaluation, is determined from the asset information; determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated; determining an asset evaluation task processing sequence according to a preset rule dependency relationship; acquiring text data corresponding to assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data; and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated. According to the invention, the asset value is evaluated by comprehensively calculating different data sources of the associated target asset, so that the technical problem is solved.
Fig. 6 and 7 describe the asset assessment device in the embodiment of the present invention in detail from the perspective of the modular functional entity, and the asset assessment apparatus in the embodiment of the present invention is described in detail from the perspective of the hardware processing.
Fig. 8 is a schematic structural diagram of an asset assessment device 800 according to an embodiment of the present invention, where the asset assessment device 800 may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 810 (e.g., one or more processors) and a memory 820, and one or more storage media 830 (e.g., one or more mass storage devices) storing an application 833 or data 832. Memory 820 and storage medium 830 may be, among other things, transient or persistent storage. The program stored in the storage medium 830 may include one or more modules (not shown), each of which may include a series of instructions operating on the asset assessment device 800. Still further, the processor 810 may be configured to communicate with the storage medium 830 and execute a series of instruction operations in the storage medium 830 on the asset assessment device 800 to implement the steps of the asset assessment method provided by the above-described method embodiments.
The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, and which may also be a volatile computer-readable storage medium, having stored therein instructions, which, when executed on a computer, cause the computer to perform the steps of the above-described asset assessment method.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention, which is substantially or partly contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An asset assessment method, characterized in that said asset assessment method comprises:
acquiring asset information of a target object, and determining assets to be evaluated of the target object, which need to be subjected to value evaluation, from the asset information;
determining an asset evaluation rule corresponding to the asset to be evaluated, and generating an asset evaluation task corresponding to the asset to be evaluated;
determining the asset evaluation task processing sequence according to a preset rule dependency relationship;
acquiring text data corresponding to the assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data;
and executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated.
2. The asset assessment method according to claim 1, wherein said obtaining asset information of a target object comprises:
acquiring financial data of a target object;
and screening the financial data according to a preset asset judgment rule to obtain corresponding assets belonging to the asset transaction category, and taking the assets as asset information of the target object.
3. The asset assessment method according to claim 1, wherein said determining asset assessment rules corresponding to said assets to be assessed comprises:
asset publishing data obtained from a pre-set asset management authority;
and analyzing the asset publishing data based on a preset data analysis algorithm to obtain asset evaluation rules corresponding to the assets to be evaluated so as to determine asset evaluation rules corresponding to different types of assets.
4. The asset assessment method according to claim 3, wherein the analyzing the asset publishing data based on a preset data analysis algorithm to obtain the asset assessment rules corresponding to the assets to be assessed so as to determine the asset assessment rules corresponding to the assets of different categories comprises:
determining asset values corresponding to different asset classes in the asset publishing data in a historical period;
according to the asset value, determining asset value evaluation tables corresponding to different asset types;
and determining asset evaluation rules corresponding to different asset categories according to the asset value evaluation table.
5. The asset assessment method according to claim 1, wherein said determining said asset assessment task processing order according to a preset rule dependency comprises:
generating a first task corresponding to the asset to be evaluated according to the asset to be evaluated and the evaluation time of the asset to be evaluated;
dividing the first task into tasks to be evaluated according to the algorithm to be called corresponding to the first task;
generating a corresponding processing result according to the task to be evaluated and storing the task to be evaluated to a preset cache;
and determining the asset evaluation task processing sequence according to the processing result and the budget rule dependency relationship.
6. The asset assessment method according to claim 1, wherein the acquiring text data corresponding to the asset to be assessed, and performing recognition classification on the text data through a preset classification model to obtain the classified target text data comprises:
acquiring text data corresponding to the assets to be evaluated;
performing word segmentation processing on the text data based on an entity dictionary to obtain a vector matrix corresponding to the text data;
and inputting the vector matrix into a preset classification model, and identifying and classifying the text data according to the classification model to obtain classified target text data.
7. The asset assessment method according to claim 1, wherein said performing the asset assessment tasks according to the asset assessment rules and the asset assessment task processing order to achieve assessment of the target textual data to obtain the current asset worth value of the asset to be assessed comprises:
determining the asset class of the asset to be evaluated;
determining an asset value evaluation table corresponding to the assets to be evaluated according to the asset types;
and evaluating the assets to be evaluated according to the asset value evaluation table to obtain the current asset value of the assets to be evaluated.
8. An asset assessment apparatus, characterized in that said asset assessment apparatus comprises:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring asset information of a target object and determining assets to be evaluated of the target object, which need to be subjected to value evaluation, from the asset information;
the generation module is used for determining an asset evaluation rule corresponding to the asset to be evaluated and generating an asset evaluation task corresponding to the asset to be evaluated;
the determining module is used for determining the asset evaluation task processing sequence according to a preset rule dependency relationship;
the classification module is used for acquiring text data corresponding to the assets to be evaluated, and identifying and classifying the text data through a preset classification model to obtain classified target text data;
and the evaluation module is used for executing the asset evaluation task according to the asset evaluation rule and the asset evaluation task processing sequence so as to realize the evaluation of the target text data and obtain the current asset value of the asset to be evaluated.
9. An asset assessment device, characterized in that said asset assessment device comprises: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;
the at least one processor invoking the instructions in the memory to cause the asset assessment device to perform the steps of the asset assessment method of any of claims 1-7.
10. A computer-readable storage medium, having a computer program stored thereon, which, when being executed by a processor, carries out the steps of the asset assessment method according to any one of claims 1-7.
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