CN111340246A - Processing method and device for enterprise intelligent decision analysis and computer equipment - Google Patents
Processing method and device for enterprise intelligent decision analysis and computer equipment Download PDFInfo
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Abstract
The application relates to a processing method, a processing device, computer equipment and a storage medium for enterprise intelligent decision analysis. The method comprises the following steps: acquiring to-be-processed data corresponding to the enterprise identification; extracting the features of the data to be processed to obtain a feature vector of the data to be processed; determining a logical relationship between the feature vectors through a logical layer of a pre-trained machine learning model; analyzing the feature vector by using the logical relationship through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification; and processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data. By adopting the method, the accuracy of processing for enterprise intelligent decision analysis can be improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a processing method and apparatus, a computer device, and a storage medium for enterprise intelligent decision analysis.
Background
With the development of artificial intelligence technology, the artificial intelligence technology is more and more emphasized by enterprises, and the artificial intelligence technology is widely applied to data processing. The enterprise data are processed through an artificial intelligence technology, so that the data form considerable value, namely the health state of the enterprise can be known by processing the enterprise intelligent decision analysis and calculating related numerical values such as enterprise risks, but the health state of the enterprise at risk mainly depends on the related data of the enterprise and the related data of the enterprise.
However, the modern data warehouse technology, the online analysis processing technology, and the data mining and data presentation technology mainly adopted in the current processing for the intelligent decision analysis of the enterprise can only process the data related to the enterprise, which results in low accuracy of the processing for the intelligent decision analysis of the enterprise.
Disclosure of Invention
In view of the above, it is necessary to provide a processing method, an apparatus, a computer device and a storage medium for an enterprise intelligent decision analysis, which can improve the accuracy of processing for the enterprise intelligent decision analysis.
A processing method for enterprise intelligent decision analysis, the method comprising:
acquiring to-be-processed data corresponding to the enterprise identification;
extracting the features of the data to be processed to obtain a feature vector of the data to be processed;
determining a logical relationship between the feature vectors through a logical layer of a pre-trained machine learning model;
analyzing the feature vector by using the logical relationship through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification;
and processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data.
In one embodiment, the evaluation value is an enterprise financial risk value, and the processing of the evaluation value by a decision layer of the machine learning model to obtain corresponding decision data includes:
and processing the enterprise financial risk value through a decision layer of the machine learning model, and obtaining business financial risk warning information when the enterprise financial risk value is greater than a financial risk threshold value.
In one embodiment, the evaluating value is a benchmarking index, and the processing of the evaluating value by a decision layer of the machine learning model to obtain corresponding decision data includes:
processing the benchmarking indexes from different dimensions through a decision layer of the machine learning model, and obtaining decision data corresponding to the benchmarking indexes when the benchmarking indexes are smaller than a benchmarking index threshold value; the benchmarking index at least comprises one of profit margin, return on investment, product period, sales volume of each employee and product cost.
In one embodiment, the evaluation value is an enterprise health value, and the processing of the evaluation value by a decision layer of the machine learning model to obtain corresponding decision data includes:
and processing the enterprise health value through a decision layer of the machine learning model, and obtaining a corresponding enterprise health data report when the enterprise health value is smaller than an enterprise health threshold value.
In one embodiment, the method further comprises:
and taking the evaluation value and the decision data as new enterprise sample data, and training the machine learning model according to the new enterprise sample data.
In one embodiment, the training process of the machine learning model includes:
acquiring sample data and a knowledge graph corresponding to the sample data; the sample data is stored in a production database in a formatted form;
extracting features of the sample data to obtain a training feature vector;
learning the logic relation between the training feature vectors according to the knowledge graph through a logic layer of the machine learning model;
analyzing the training characteristic vector by using the logical relation through a data analysis layer of the machine learning model to obtain a training evaluation value;
generating training decision data corresponding to the training evaluation value through a decision layer of the machine learning model;
calculating a loss value between the training solution strategy data and a decision label;
and adjusting parameters of each network layer in the machine learning model by using the loss values until preset conditions are met, so as to obtain the trained machine learning model.
In one embodiment, the sample data includes at least global macro-economic data, enterprise industry data, and case data; the enterprise internal data at least comprises enterprise target management behavior data, enterprise historical financial data, enterprise scene question and answer data and network public opinion data; the knowledge graph at least comprises a global macroscopic economy knowledge graph, an industrial structure classification knowledge graph and a financial accounting examination.
A processing apparatus for enterprise intelligent decision analysis, the apparatus comprising:
the acquisition module is used for acquiring the data to be processed corresponding to the enterprise identification;
the characteristic extraction module is used for extracting the characteristics of the data to be processed to obtain a characteristic vector of the data to be processed;
a determining module, configured to determine a logical relationship between the feature vectors through a logic layer of a pre-trained machine learning model;
the data analysis module is used for analyzing the characteristic vectors by utilizing the logical relation through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification;
and the decision module is used for processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring to-be-processed data corresponding to the enterprise identification;
extracting the features of the data to be processed to obtain a feature vector of the data to be processed;
determining a logical relationship between the feature vectors through a logical layer of a pre-trained machine learning model;
analyzing the feature vector by using the logical relationship through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification;
and processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring to-be-processed data corresponding to the enterprise identification;
extracting the features of the data to be processed to obtain a feature vector of the data to be processed;
determining a logical relationship between the feature vectors through a logical layer of a pre-trained machine learning model;
analyzing the feature vector by using the logical relationship through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification;
and processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data.
The processing method, the processing device, the computer equipment and the storage medium for the enterprise intelligent decision analysis acquire the data to be processed corresponding to the enterprise identification; performing feature extraction on the data to be processed to obtain a feature vector of the data to be processed; determining a logical relationship between the feature vectors through a logical layer of a pre-trained machine learning model; analyzing the characteristic vectors by using a logical relation through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification; and processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data.
Drawings
FIG. 1 is a diagram of an application environment for a processing method for enterprise intelligent decision analysis, in one embodiment;
FIG. 2 is a schematic flow diagram that illustrates a processing method for enterprise intelligent decision analysis, according to an embodiment;
FIG. 3 is a schematic flow chart diagram illustrating a method for machine learning model training in one embodiment;
FIG. 4 is a schematic flow chart diagram illustrating a processing method for enterprise intelligent decision analysis in another embodiment;
FIG. 5 is an application scenario of a processing method for enterprise intelligent decision analysis in one embodiment;
FIG. 6 is a block diagram of a processing device for enterprise intelligent decision analysis, in one embodiment;
FIG. 7 is a block diagram of a processing device for enterprise intelligent decision analysis in another embodiment;
FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The processing method for enterprise intelligent decision analysis provided by the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The terminal 102 acquires data to be processed corresponding to the enterprise identifier from the server 104; performing feature extraction on the data to be processed to obtain a feature vector of the data to be processed; determining a logical relationship between the feature vectors through a logical layer of a pre-trained machine learning model; analyzing the characteristic vectors by using a logical relation through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification; and processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a processing method for enterprise intelligent decision analysis is provided, which is described by taking the method as an example applied to the terminal in fig. 1, and includes the following steps:
The enterprise identification is used for distinguishing marks of different enterprises; the enterprise identification can be, but is not limited to, a character string combination such as a number, a letter combination and the like. The data to be processed can be the data to be processed of an entity, and the entity can be a region, an industry, an enterprise commodity and the like. The to-be-processed data of the entity enterprise can comprise enterprise external big data and enterprise internal data; the enterprise external big data is data of an enterprise external environment, and the enterprise external data can comprise global macro economic data, industrial data, marketing company financial data, case data and the like; the global macro economic data can be used for representing the comparison between the global macro economic index and economic indexes of each country/region; the industry data can comprise industry basic data, industry market data, industry financial data and the like; the listed company financial data can comprise company financial statement data, financial index data and the like; the financial index data may include repayment capacity index data, operational capacity index data, profitability index data, and enterprise development capacity index data.
The enterprise internal data is operation, scene and financial data inside the decision-making enterprise and comment data of the external part on the enterprise. The internal data of the enterprise can comprise enterprise target management behavior data, enterprise historical financial data, enterprise scene question and answer data, network public opinion data and the like.
Specifically, the terminal acquires an enterprise identifier, and acquires data to be processed corresponding to the enterprise identifier from a database of the server through a communication interface, wherein the data to be processed is formatted data; the database can be composed of a data table and a document, and the formatted data to be processed can be stored in the data table or the data document of the database; the data tables may characterize dimensional relationships between data. The database may be a production database, which may be an oracle database, a DB2 database, a MySQL database, a Sybase database, a MSSQL Server database, or the like.
And 204, performing feature extraction on the data to be processed to obtain a feature vector of the data to be processed.
Specifically, the terminal selects a data processing algorithm corresponding to the data to be processed from the machine learning algorithm library according to the type of the data to be processed, and performs feature vector extraction on the data to be processed to obtain a feature vector of the data to be processed. The characteristic vector can comprise a characteristic vector of global macro economic data, a characteristic vector of industrial data, a characteristic vector of enterprise target management behavior data, a characteristic vector of enterprise historical financial data, a characteristic vector of enterprise scene question and answer data, a characteristic vector of network public opinion data extraction information and the like. And different data processing algorithms corresponding to different types of data to be processed. The data processing algorithm in the machine learning algorithm library can be a statistical analysis algorithm, a non-statistical analysis algorithm, a deep learning algorithm and the like; wherein, the statistical analysis algorithm can comprise correlation analysis, natural regression, variability analysis, factor analysis, center trend analysis, variance analysis, multivariate analysis and the like; the non-statistical analysis algorithm may include a support vector machine, a genetic algorithm, a decision tree, a random forest algorithm, a random gradient algorithm, a gradient integration, an iterative algorithm, etc.; the deep learning algorithm may include a maximum likelihood estimation method, a maximum expectation algorithm, and the like.
At step 206, the logic layer of the pre-trained machine learning model determines the logical relationship between the feature vectors.
Specifically, the logical relationship may be an upstream-downstream relationship, a stockholder relationship, an investment invested relationship, and the like that exist between enterprises. The logic layer of the pre-trained machine learning model can determine the logic relationship among each kind of characteristic vectors such as the characteristic vector of global macro-economic data of an enterprise, the characteristic vector of industrial data, the characteristic vector of enterprise target management behavior data, the characteristic vector of enterprise historical financial data, the characteristic vector of enterprise scene question and answer data, the characteristic vector of information extracted by network public opinion data and the like. When the data are the feature vectors, the logic relation between the feature vectors is determined through the logic layer of the machine learning model trained in advance.
And step 208, analyzing the feature vectors by using the logical relation through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification.
And step 210, processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data.
Specifically, the evaluation value is processed through a decision layer of the machine learning model, and when the evaluation value is detected to be not within an evaluation threshold range preset in the decision layer of the machine learning model, corresponding decision data is obtained; the decision data may be displayed in the user interface in the form of a table, a graph, a video, etc.
In the processing method for enterprise intelligent decision analysis, to-be-processed data corresponding to an enterprise identifier is obtained; performing feature extraction on the data to be processed to obtain a feature vector of the data to be processed; determining a logical relationship between the feature vectors through a logical layer of a pre-trained machine learning model; analyzing the characteristic vectors by using a logical relation through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification; and processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data. Processing the characteristic vector of the data to be processed through a trained machine learning model to obtain a logical relationship between the data to be processed; and the machine learning model performs multi-dimensional analysis according to the data to be processed of the logical relationship, so that the accuracy of processing for enterprise intelligent decision analysis is improved.
In one embodiment, as shown in fig. 3, a machine learning model training method is provided, which is described by taking the method as an example applied to the terminal in fig. 1, and includes the following steps:
Wherein the sample data is sample data of a machine learning model used for training. The sample data at least comprises global macro-economic data, enterprise industrial data and case data; the enterprise internal data at least comprises enterprise target management behavior data, enterprise historical financial data, enterprise scene question and answer data and network public opinion data.
The knowledge graph is used for representing the relationship among various entities existing in the real world, and the entities can be distinguishable and independent things, such as enterprises, industries, people, commodities and the like; the upstream and downstream relationship, stockholder relationship, investment invested relationship and the like exist between enterprises, the stockholder relationship, legal relationship, supervisor relationship and the like exist between enterprises and persons, and the relativity relationship exists between persons and the like. And performing shortest path discovery between enterprises, enterprise association analysis and growth states of the enterprises according to the relationship between the entities. Each entity has corresponding attribute and attribute value, namely mapping relation exists among the entities, the attributes and the attributes; the mapping relation among the entities, the attributes and the attribute values can be stored through an RDF (resource description framework) triple storage format; such as "entity 1-relationship-entity 2" and "entity-attribute value", etc.
Specifically, data collected in real time are stored in a data warehouse, the collected real-time data are preprocessed in the data warehouse, and the preprocessed data are used as sample data; and acquiring a corresponding knowledge graph according to the sample data. The preprocessing comprises the steps of cleaning data and deleting dirty data in the data; the data warehouse can be used for storing different types of data (such as structured data, semi-structured data and unstructured data), and the data can be data such as offline pictures and documents; the data warehouse supports the data addition and deletion and does not support the data change.
And step 304, performing feature extraction on the sample data to obtain a training feature vector.
Optionally, different data processing algorithms correspond to different types of sample data. The data processing algorithm in the machine learning algorithm library can be a statistical analysis algorithm, a non-statistical analysis algorithm, a deep learning algorithm and the like; wherein, the statistical analysis algorithm can comprise correlation analysis, natural regression, variability analysis, factor analysis, center trend analysis, variance analysis, multivariate analysis and the like; the non-statistical analysis algorithm may include a support vector machine, a genetic algorithm, a decision tree, a random forest algorithm, a random gradient algorithm, a gradient integration, an iterative algorithm, etc.; the deep learning algorithm may include a maximum likelihood estimation method, a maximum expectation algorithm, and the like. The training feature vectors comprise feature vectors of global macro-economic data, feature vectors of industrial data, feature vectors of enterprise target management behavior data, feature vectors of enterprise historical financial data, feature vectors of enterprise scene question and answer data and feature vectors of network public opinion data extraction information.
And step 306, learning the logic relation among the training feature vectors according to the knowledge graph through the logic layer of the machine learning model.
And 308, analyzing the training characteristic vectors by using the logical relation through a data analysis layer of the machine learning model to obtain a training evaluation value.
And 310, generating training decision data corresponding to the training evaluation value through a decision layer of the machine learning model.
In step 312, a loss value between the training solution strategy data and the decision label is calculated.
And step 314, adjusting parameters of each network layer in the machine learning model by using the loss values until preset conditions are met, and obtaining the trained machine learning model.
In the embodiment, the sample data and the knowledge graph corresponding to the sample data are obtained; the sample data is stored in a production database in a formatted form; extracting the characteristics of the sample data to obtain a training characteristic vector; learning a logic relation between training feature vectors according to a knowledge graph through a logic layer of a machine learning model; analyzing the training characteristic vector by using a logical relation through a data analysis layer of a machine learning model to obtain a training evaluation value; generating training decision data corresponding to a training evaluation value through a decision layer of a machine learning model; calculating a loss value between the training solution strategy data and the decision label; the parameters of each network layer in the machine learning model are adjusted by using the loss values until preset conditions are met, the well-trained machine learning model is obtained, the accuracy of data processing of the machine learning model is improved by expanding the training depth and the training breadth of the machine learning model, the health state and the development trend of an enterprise are predicted according to a data processing result, and meanwhile, the utilization rate of the data is improved.
In another embodiment, as shown in fig. 4, a processing method for enterprise intelligent decision analysis is provided, which is described by taking the method as an example applied to the terminal in fig. 1, and includes the following steps:
and 402, acquiring to-be-processed data corresponding to the enterprise identification.
And step 404, performing feature extraction on the data to be processed to obtain a feature vector of the data to be processed.
At step 406, the logic layer of the pre-trained machine learning model determines the logical relationship between the feature vectors.
The logical relationship refers to an association relationship between different data. The pre-trained machine learning model is based on
And 408, analyzing the feature vectors by using the logical relation through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification.
And step 410, processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data.
And step 412, taking the evaluation value and the decision data as new enterprise sample data, and training the machine learning model according to the new enterprise sample data.
Specifically, when the preset time point is reached, the sample data is updated through the evaluation value and the decision data, and the machine learning model is updated according to the updated sample data.
In one embodiment, the evaluation value is an enterprise financial risk value, and the evaluation value is processed by a decision layer of a machine learning model to obtain corresponding decision data, including:
and processing the financial risk value of the enterprise through a decision layer of the machine learning model, and obtaining business financial risk warning information when the financial risk value of the enterprise is greater than a financial risk threshold value.
Optionally, the business and financial risk warning information may include historical risk warning information of the enterprise on a time axis, total risk warning information of the enterprise and the same region, the same industry and enterprises of similar scale, risk factor risk warning information of the enterprise and the same region, the same industry and enterprises of similar scale, and the like. Processing the enterprise financial risk value through a decision layer of the machine learning model, and obtaining business financial risk warning information when the enterprise financial risk value is greater than a financial risk threshold value; the development state of the enterprise can be acquired in real time.
In one embodiment, the evaluation value is a benchmarking index, and the evaluation value is processed by a decision layer of a machine learning model to obtain corresponding decision data, including:
processing the benchmarking indexes from different dimensions through a decision layer of the machine learning model, and obtaining decision data corresponding to the benchmarking indexes when the benchmarking indexes are smaller than a benchmarking index threshold value; the benchmarking index includes at least one of profit margin, return on investment, product period, sales volume of each employee, and product cost.
Specifically, the benchmarking indexes are processed from different dimensions through a decision layer of a machine learning model, and when the benchmarking indexes are smaller than a benchmarking index threshold value, decision data corresponding to the benchmarking indexes are obtained; the decision data can comprise the decision data of the industry benchmarking enterprise of the enterprise and the benchmarking department inside the enterprise.
In one embodiment, the evaluation value is an enterprise health value, and the evaluation value is processed by a decision layer of a machine learning model to obtain corresponding decision data, including:
and processing the enterprise health value through a decision layer of the machine learning model, and obtaining a corresponding enterprise health data report when the enterprise health value is smaller than an enterprise health threshold value.
Optionally, the enterprise health data report may include health status scores of the various functional departments of the enterprise, health indexes of the various functional departments of the enterprise, and industry enterprise comparison data; the functional departments include human resources, marketing, customer support, sales and distribution, financial accounting, research and development, administrative management, production, company operation, procurement, technical support, and legal affairs. The health index of each functional department of the enterprise can be an assessment index of the enterprise, such as a household electronic business marketing department, and the assessment index can be sales volume, cost of each sale, life-long value of a client, website click conversion rate, conversion rate of a clue to the client, natural flow of an official website, social media flow and conversion rate, mobile phone access flow and conversion rate and the like. The enterprise health value is processed through a decision layer of the machine learning model, when the enterprise health value is smaller than an enterprise health threshold value, a corresponding enterprise health data report is obtained, the enterprise health can be diagnosed, and the enterprise health state is obtained.
In one embodiment, the evaluation values are current equity and reputation value of the enterprise, and the evaluation values are processed by a decision layer of a machine learning model to obtain corresponding decision data, including:
and processing the current equity value and the enterprise reputation value of the enterprise through a decision layer of the machine learning model, and obtaining a correction coefficient index of the enterprise valuation when the current equity value and the enterprise reputation value of the enterprise are respectively smaller than a current equity threshold and a current reputation value threshold of the enterprise.
In the processing method for enterprise intelligent decision analysis, to-be-processed data corresponding to an enterprise identifier is obtained; performing feature extraction on the data to be processed to obtain a feature vector of the data to be processed; determining a logical relationship between the feature vectors by a logical layer of a pre-trained machine learning model; analyzing the characteristic vectors by using a logical relation through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification; processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data; and taking the evaluation value and the decision data as new enterprise sample data, and training the machine learning model according to the new enterprise sample data. After enterprise data are processed, the machine learning model continues to be trained through the processed data, updating of the machine learning model is achieved, and accuracy and efficiency of data processing are improved.
An application scenario for the process of enterprise intelligent decision analysis is as shown in fig. 5, in which the machine learning model is an artificial intelligent decision-making superconcephalon obtained by training global macro-economic data, industrial data, financial data of listed companies and real case best practice data through technologies such as deep learning, multi-agent joint learning, federal learning and the like and knowledge maps of various fields. Processing the data to be processed corresponding to the enterprise identification in a production database to obtain the data to be processed in a formatted form; performing feature extraction on the data to be processed to obtain a feature vector of the data to be processed; determining a logic relation between the feature vectors through a logic layer of a pre-trained artificial intelligence decision-making superconcephalon; analyzing the characteristic vectors by utilizing a logic relation through an artificial intelligence decision-making superconcephalon data analysis layer to obtain an evaluation value corresponding to the enterprise identification; and processing the evaluation value through a decision layer of an artificial intelligence decision-making superconcephalon to obtain corresponding decision data. The data to be processed comprises enterprise external data and enterprise internal data, wherein the enterprise external data comprises global macro economic data, industrial data, marketing company financial data and case data; the enterprise internal data comprises enterprise target management behavior data, enterprise historical financial data, enterprise scene question and answer data, network public opinion data and the like. The decision data comprises financial wind control early warning data, standard case analysis data, enterprise problem diagnosis data, resource assessment data, strategic target management data and performance assessment and assessment data.
It should be understood that although the various steps in the flow charts of fig. 2-4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-4 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 6, there is provided a processing apparatus for enterprise intelligent decision analysis, including: an obtaining module 602, a feature extraction module 604, a determining module 606, a data analysis module 608, and a decision module 610, wherein:
the obtaining module 602 is configured to obtain to-be-processed data corresponding to the enterprise identifier.
The feature extraction module 604 is configured to perform feature extraction on the data to be processed to obtain a feature vector of the data to be processed.
A determining module 606 for determining a logical relationship between the feature vectors through a logical layer of a pre-trained machine learning model.
And the data analysis module 608 is configured to analyze the feature vector by using a logical relationship through a data analysis layer of the machine learning model, so as to obtain an evaluation value corresponding to the enterprise identifier.
And the decision module 610 is configured to process the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data.
The processing device for enterprise intelligent decision analysis obtains data to be processed corresponding to an enterprise identifier through the obtaining module 602; the feature extraction module 604 performs feature extraction on the data to be processed to obtain a feature vector of the data to be processed; determining logical relationships between the feature vectors by a determination module 606 in a logical layer of the pre-trained machine learning model; analyzing the feature vectors by using the logical relationship of the data analysis module 608 through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification; the evaluation value is processed by a decision module 610 in a decision layer of the machine learning model to obtain corresponding decision data. Processing the characteristic vector of the data to be processed through a trained machine learning model to obtain a logical relationship between the data to be processed; and the machine learning model performs multi-dimensional analysis according to the data to be processed of the logical relationship, so that the accuracy of processing for enterprise intelligent decision analysis is improved.
In another embodiment, as shown in fig. 7, a processing apparatus for enterprise intelligent decision analysis is provided, which comprises, in addition to an acquisition module 602, a feature extraction module 604, a determination module 606, a data analysis module 608 and a decision module 610: the update module 612 trains the module 614, the calculation module 616, and the parameter adjustment module 618, wherein:
in one embodiment, the decision module 610 is further configured to process the enterprise financial risk value through a decision layer of the machine learning model, and obtain the business financial risk warning information when the enterprise financial risk value is greater than the financial risk threshold.
In one embodiment, the decision module 610 is further configured to process the benchmarking index from different dimensions through a decision layer of the machine learning model, and obtain decision data corresponding to the benchmarking index when the benchmarking index is smaller than a benchmarking index threshold; the benchmarking index includes at least one of profit margin, return on investment, product period, sales volume of each employee, and product cost.
In one embodiment, the decision module 610 is further configured to process the business health value through a decision layer of the machine learning model, and obtain a corresponding business health data report when the business health value is less than the business health threshold.
And the updating module 612 is configured to use the evaluation values and the decision data as new enterprise sample data, and train the machine learning model according to the new enterprise sample data.
In one embodiment, the obtaining module 602 is further configured to obtain sample data and a knowledge graph corresponding to the sample data; the sample data is stored in a production database in a formatted form.
In one embodiment, the feature extraction module 604 is further configured to perform feature extraction on the sample data to obtain a training feature vector.
The training module 614 is used for learning the logical relationship between the training feature vectors according to the knowledge graph through the logical layer of the machine learning model; analyzing the training characteristic vector by using a logical relation through a data analysis layer of a machine learning model to obtain a training evaluation value; and generating training decision data corresponding to the training evaluation value through a decision layer of the machine learning model.
A calculating module 616, configured to calculate a loss value between the training solution strategy data and the decision label.
And the parameter adjusting module 618 is configured to adjust parameters of each network layer in the machine learning model by using the loss value until a preset condition is met, so as to obtain a trained machine learning model.
In one embodiment, by obtaining sample data and a knowledge graph corresponding to the sample data; the sample data is stored in a production database in a formatted form; extracting the characteristics of the sample data to obtain a training characteristic vector; learning a logic relation between training feature vectors according to a knowledge graph through a logic layer of a machine learning model; analyzing the training characteristic vector by using a logical relation through a data analysis layer of a machine learning model to obtain a training evaluation value; generating training decision data corresponding to a training evaluation value through a decision layer of a machine learning model; calculating a loss value between the training solution strategy data and the decision label; and adjusting parameters of each network layer in the machine learning model by using the loss values until preset conditions are met, so as to obtain the trained machine learning model.
Acquiring to-be-processed data corresponding to the enterprise identification; performing feature extraction on the data to be processed to obtain a feature vector of the data to be processed; determining a logical relationship between the feature vectors through a logical layer of a pre-trained machine learning model; analyzing the characteristic vectors by using a logical relation through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification; and processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data. Processing the characteristic vector of the data to be processed through a trained machine learning model to obtain a logical relationship between the data to be processed; and the machine learning model performs multi-dimensional analysis according to the data to be processed of the logical relationship, so that the accuracy of processing for enterprise intelligent decision analysis is improved.
For specific limitations of the processing apparatus for enterprise intelligent decision analysis, reference may be made to the above limitations of the processing method for enterprise intelligent decision analysis, which are not described herein again. The modules in the processing device for enterprise intelligent decision analysis can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a processing method for enterprise intelligent decision analysis. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring to-be-processed data corresponding to the enterprise identification;
performing feature extraction on the data to be processed to obtain a feature vector of the data to be processed;
determining a logical relationship between the feature vectors through a logical layer of a pre-trained machine learning model;
analyzing the characteristic vectors by using a logical relation through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification;
and processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and processing the financial risk value of the enterprise through a decision layer of the machine learning model, and obtaining business financial risk warning information when the financial risk value of the enterprise is greater than a financial risk threshold value.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
processing the benchmarking indexes from different dimensions through a decision layer of the machine learning model, and obtaining decision data corresponding to the benchmarking indexes when the benchmarking indexes are smaller than a benchmarking index threshold value; the benchmarking index includes at least one of profit margin, return on investment, product period, sales volume of each employee, and product cost.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and processing the enterprise health value through a decision layer of the machine learning model, and obtaining a corresponding enterprise health data report when the enterprise health value is smaller than an enterprise health threshold value.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and taking the evaluation value and the decision data as new enterprise sample data, and training the machine learning model according to the new enterprise sample data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring sample data and a knowledge graph corresponding to the sample data; the sample data is stored in a production database in a formatted form;
extracting the characteristics of the sample data to obtain a training characteristic vector;
learning a logic relation between training feature vectors according to a knowledge graph through a logic layer of a machine learning model;
analyzing the training characteristic vector by using a logical relation through a data analysis layer of a machine learning model to obtain a training evaluation value;
generating training decision data corresponding to a training evaluation value through a decision layer of a machine learning model;
calculating a loss value between the training solution strategy data and the decision label;
and adjusting parameters of each network layer in the machine learning model by using the loss values until preset conditions are met, so as to obtain the trained machine learning model.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
the sample data at least comprises global macro-economic data, enterprise industrial data and case data; the enterprise internal data at least comprises enterprise target management behavior data, enterprise historical financial data, enterprise scene question and answer data and network public opinion data; the knowledge graph at least comprises a global macroscopic economy knowledge graph, an industrial structure classification knowledge graph and a financial accounting examination.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring to-be-processed data corresponding to the enterprise identification;
performing feature extraction on the data to be processed to obtain a feature vector of the data to be processed;
determining a logical relationship between the feature vectors through a logical layer of a pre-trained machine learning model;
analyzing the characteristic vectors by using a logical relation through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification;
and processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and processing the financial risk value of the enterprise through a decision layer of the machine learning model, and obtaining business financial risk warning information when the financial risk value of the enterprise is greater than a financial risk threshold value.
In one embodiment, the computer program when executed by the processor further performs the steps of:
processing the benchmarking indexes from different dimensions through a decision layer of the machine learning model, and obtaining decision data corresponding to the benchmarking indexes when the benchmarking indexes are smaller than a benchmarking index threshold value; the benchmarking index includes at least one of profit margin, return on investment, product period, sales volume of each employee, and product cost.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and processing the enterprise health value through a decision layer of the machine learning model, and obtaining a corresponding enterprise health data report when the enterprise health value is smaller than an enterprise health threshold value.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and taking the evaluation value and the decision data as new enterprise sample data, and training the machine learning model according to the new enterprise sample data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring sample data and a knowledge graph corresponding to the sample data; the sample data is stored in a production database in a formatted form;
extracting the characteristics of the sample data to obtain a training characteristic vector;
learning a logic relation between training feature vectors according to a knowledge graph through a logic layer of a machine learning model;
analyzing the training characteristic vector by using a logical relation through a data analysis layer of a machine learning model to obtain a training evaluation value;
generating training decision data corresponding to a training evaluation value through a decision layer of a machine learning model;
calculating a loss value between the training solution strategy data and the decision label;
and adjusting parameters of each network layer in the machine learning model by using the loss values until preset conditions are met, so as to obtain the trained machine learning model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
the sample data at least comprises global macro-economic data, enterprise industrial data and case data; the enterprise internal data at least comprises enterprise target management behavior data, enterprise historical financial data, enterprise scene question and answer data and network public opinion data; the knowledge graph at least comprises a global macroscopic economy knowledge graph, an industrial structure classification knowledge graph and a financial accounting examination.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A processing method for enterprise intelligent decision analysis, the method comprising:
acquiring to-be-processed data corresponding to the enterprise identification;
extracting the features of the data to be processed to obtain a feature vector of the data to be processed;
determining a logical relationship between the feature vectors through a logical layer of a pre-trained machine learning model;
analyzing the feature vector by using the logical relationship through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification;
and processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data.
2. The method of claim 1, wherein the assessment value is a corporate financial risk value, and the processing of the assessment value by a decision layer of the machine learning model to obtain corresponding decision data comprises:
and processing the enterprise financial risk value through a decision layer of the machine learning model, and obtaining business financial risk warning information when the enterprise financial risk value is greater than a financial risk threshold value.
3. The method of claim 1, wherein the evaluation value is a benchmarking indicator, and the processing of the evaluation value by a decision layer of the machine learning model to obtain corresponding decision data comprises:
processing the benchmarking indexes from different dimensions through a decision layer of the machine learning model, and obtaining decision data corresponding to the benchmarking indexes when the benchmarking indexes are smaller than a benchmarking index threshold value; the benchmarking index at least comprises one of profit margin, return on investment, product period, sales volume of each employee and product cost.
4. The method of claim 1, wherein the evaluation value is an enterprise health value, and the processing of the evaluation value by a decision layer of the machine learning model to obtain corresponding decision data comprises:
and processing the enterprise health value through a decision layer of the machine learning model, and obtaining a corresponding enterprise health data report when the enterprise health value is smaller than an enterprise health threshold value.
5. The method of any one of claims 1 to 4, further comprising:
and taking the evaluation value and the decision data as new enterprise sample data, and training the machine learning model according to the new enterprise sample data.
6. The method of claim 1, wherein the training process of the machine learning model comprises:
acquiring sample data and a knowledge graph corresponding to the sample data; the sample data is stored in a production database in a formatted form;
extracting features of the sample data to obtain a training feature vector;
learning the logic relation between the training feature vectors according to the knowledge graph through a logic layer of the machine learning model;
analyzing the training characteristic vector by using the logical relation through a data analysis layer of the machine learning model to obtain a training evaluation value;
generating training decision data corresponding to the training evaluation value through a decision layer of the machine learning model;
calculating a loss value between the training solution strategy data and a decision label;
and adjusting parameters of each network layer in the machine learning model by using the loss values until preset conditions are met, so as to obtain the trained machine learning model.
7. The method of claim 6, wherein the sample data includes at least global macro-economic data, business industry data, and case data; the enterprise internal data at least comprises enterprise target management behavior data, enterprise historical financial data, enterprise scene question and answer data and network public opinion data; the knowledge maps at least comprise a global macroscopic economy knowledge map, an industrial structure classification knowledge map, a financial accounting audit knowledge map, an enterprise industry classification, an organization structure and a strategic target knowledge map.
8. A processing apparatus for enterprise intelligent decision analysis, the apparatus comprising:
the acquisition module is used for acquiring the data to be processed corresponding to the enterprise identification;
the characteristic extraction module is used for extracting the characteristics of the data to be processed to obtain a characteristic vector of the data to be processed;
a determining module, configured to determine a logical relationship between the feature vectors through a logic layer of a pre-trained machine learning model;
the data analysis module is used for analyzing the characteristic vectors by utilizing the logical relation through a data analysis layer of the machine learning model to obtain an evaluation value corresponding to the enterprise identification;
and the decision module is used for processing the evaluation value through a decision layer of the machine learning model to obtain corresponding decision data.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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