WO2021103492A1 - 一种企业经营风险预测方法和*** - Google Patents
一种企业经营风险预测方法和*** Download PDFInfo
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- WO2021103492A1 WO2021103492A1 PCT/CN2020/096192 CN2020096192W WO2021103492A1 WO 2021103492 A1 WO2021103492 A1 WO 2021103492A1 CN 2020096192 W CN2020096192 W CN 2020096192W WO 2021103492 A1 WO2021103492 A1 WO 2021103492A1
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- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
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- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
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- This application relates to a method and system for predicting business operation risks; it belongs to the technical field of data processing.
- Enterprise business risk management helps reduce the probability of making mistakes in the enterprise, avoid losses, and relatively increase the added value of the enterprise itself.
- the normal and effective management of various risks is conducive to the enterprise to make correct decisions; it is conducive to protecting the safety and integrity of corporate assets; it is conducive to achieving the business objectives of the enterprise. Therefore, it is of great significance to the enterprise to predict the business risk of the enterprise;
- the current technologies have certain defects, which are mainly manifested in several aspects.
- the data sources in the existing schemes are still limited to the relevant data within the enterprise.
- external Internet data has gradually become an important part of business risk warning, such as corporate Internet public opinion data, policy data, competitor bidding data, and business data. Therefore, the construction of a complete risk model depends not only on the internal data of the enterprise, but also on external data.
- Second: The rule engine can meet the risk prediction of certain programs in simple scenarios. However, with the increase of data scale, the continuous growth of application scenarios, and the continuous changes of business logic, the limitations of rules become more obvious.
- Knowledge graph is a new generation of semantic-based structured information organization method proposed by Google in 2012. Different from the existing mainstream relational data model, the knowledge graph focuses on describing the concepts and their relationships in the physical world in symbolic form. Its basic unit is the "entity-relation-entity" triplet, as well as entities and their related attributes. -Value pairs, entities are connected to each other through relationships, forming a networked knowledge structure. Through the knowledge graph, business data can realize the transformation from information to knowledge, which is especially suitable for organizing large-scale and strongly related business concept entities. In this patent application, the knowledge graph is used to organize and manage the massive data of internal and external risks of the enterprise, which can provide a high-quality data basis for subsequent risk feature extraction, risk identification and risk cause interpretation.
- This patent proposes a method and system for enterprise operation risk prediction based on deep learning based on the fusion of internal and external data of the enterprise.
- a method for predicting business risks including the following steps:
- S14 Collect business risk feature data information; use deep learning methods to obtain risk feature tag words that are the same or similar to the preset risk feature words from the knowledge map, and then use the deep learning method to find the business risk feature data information for extraction;
- the collected information and data of the required enterprises and their associated enterprises to construct and draw the knowledge graph includes:
- S21 Use natural language processing technology to perform named entity identification on the associated entity information data of different enterprises, and then obtain the associated entity information data of the enterprise that needs to conduct business risk prediction; and perform entity disambiguation to obtain a clear name indication Specific entity information data of the generation;
- pre-set risk feature tag words use deep learning methods to mine similar or identical risk feature words from the knowledge graph, and use risk feature words to extract risk feature codes for subsequent models Construct.
- the business risk characteristic data information is used as a deep learning training set to construct a business risk model, and the process of constructing a business risk model includes the following steps:
- the fitting verification set compares the data in the constructed model
- the method further includes:
- the risk management prediction model is continuously updated iteratively, thereby obtaining an optimal prediction model, and the business risk of the enterprise is predicted through the optimal prediction model.
- the internal information data includes all formats of financial data, accounting information data, contract information data, HR data, bidding data, and other internal office electronic documents related to the enterprise;
- the external data includes corporate Internet public opinion Data, external public bidding information data, business information data, policy information data, and other corporate-related web data
- the prediction system includes a data acquisition module, a knowledge map module, a model construction module and a prediction module;
- the data collection module uses web crawler scripts to obtain internal and external data of relevant enterprises on different platforms; then data fusion is performed on the obtained data;
- the knowledge graph module uses the data acquired on the data acquisition module to construct a knowledge graph, uses the graph to cluster and risk the events in the graph, and imports related data into the model building module;
- the model construction module uses the information data in the graph constructed by the graph to construct a training set and a test set, so as to perform continuous iterative training to obtain an optimal model;
- the prediction module can analyze and predict the future risks of the required enterprise by using the optimal model obtained in the model section and the current relevant data of the enterprise to be predicted.
- This application proposes a method and system for business operation risk prediction based on the fusion of internal and external data based on deep learning, and for the first time proposed modeling based on the fusion of internal and external data of the enterprise, which can significantly improve the accuracy of model prediction.
- This application proposes a semantic network of corporate knowledge graphs composed of concepts, entities, and relationships between entities, which can visually present information such as the knowledge context of business risks and provide strong support for model construction.
- Model construction based on deep learning through the recognition of text semantics, to make up for the problems of complex information, unclear background, and unclear rules that cannot be handled by traditional solutions, and improve the accuracy of risk models.
- the present application has strong robustness and adaptability while improving measurement accuracy. At the same time, it has the ability to self-learn and continuous improvement.
- Figure 1 is a schematic diagram of the overall process provided by an embodiment of the application.
- FIG. 2 is a diagram of the steps of constructing a knowledge graph provided by an embodiment of the application.
- FIG. 3 is a diagram of the construction steps of a deep learning model provided by an embodiment of the application.
- Fig. 4 is a schematic diagram of a system for predicting business risks of an enterprise provided by an embodiment of the application.
- Figure 1 is a schematic diagram of the overall process provided by an embodiment of the application. The process is used to provide a method for predicting business risks of an enterprise. As shown in Figure 1, it includes the following steps:
- the web crawler technology is used to collect the enterprise information data required for risk prediction and the information data of its associated enterprises; and the internal information data and external information data of the enterprise for risk prediction are collected.
- the internal information data includes all formats of financial data, accounting information data, contract information data, HR data, bidding data, and other internal office electronic documents related to the enterprise.
- the external data includes enterprise Internet public opinion data, external public bidding information data, industrial and commercial information data, policy information data, and other enterprise-related web page data.
- data fusion processing is performed on the collected internal information data and external information data of the enterprise, and the unstructured or semi-structured data is converted into structured data, and the unstructured or semi-structured data is converted into structured data.
- the data is stored in the neo4j database.
- the knowledge graph is constructed and drawn based on the collected enterprise information data required for risk prediction and the information data of its associated enterprises.
- Deep learning is a method of machine learning that learns targets through the results of neural networks in multiple hidden layers. Through end-to-end machine learning, it reduces the difficulty of people's understanding of the entire knowledge system and avoids tedious manual feature extraction; in addition to more accurate predictions, deep learning can also automatically learn from each layer of network structure while predicting Different characteristics. Therefore, deep learning can also be used to generate features.
- This method of generating features is also called Embedding in the field of deep learning. It refers to finding a mapping to map the space where the original feature is located to another space to generate an expression in the new space; this step is to set a Appropriate target value, through the deep network learning the target value to automatically find this mapping relationship.
- GEM Network Representation Learning
- NRL Network Representation Learning
- Use low-dimensional, dense, real-valued vectors to represent the network Nodes can also project heterogeneous information into the same low-dimensional space to facilitate downstream calculations, meaning that complex information is simplified to represent, downstream calculations are the deep learning model receiving dimensionality-reduced data for training or prediction; Convert the relationship between the graphs into the word sequence class in word2vec to mine the relationship between the graph nodes; Word2vec represents a specific technical processing method in natural language processing, turning the relationship between two words into two The distance of the vector; for example, the two words like/favored are relatively close in word2vec; the same or similar risk word tags are selected, and the above-mentioned relationship search method between words is used.
- the business risk model used to predict the future business risk situation of the enterprise is constructed through the business risk characteristic data information.
- the enterprise information data required for risk prediction is input into the risk management model for prediction.
- S21 Use natural language processing technology to perform named entity identification on the associated entity information data of different enterprises, and then obtain the associated entity information data of the enterprise that needs to conduct business risk prediction; and perform entity disambiguation to obtain a clear name indication Specific entity information data of the generation;
- the business risk feature data information (also called business risk feature code) is used as a deep learning training set to construct a business risk model, and the construction of business risk
- the model process includes the following steps:
- the fitting verification set compares the data in the constructed model, which can eliminate the data errors in the model and the original data that has not undergone data preprocessing;
- S36 Predict the real data and adjust the parameters about the business risk model in the training set and the test set according to the results, so as to perform continuous iterative training to obtain an optimal model.
- the method for predicting business risk in the embodiments of this application inputs the acquired current business information data into the risk business prediction model; through the fusion processing of the internal and external information data of the enterprise that needs risk prediction, and the structured according to the knowledge graph
- the enterprise information data required for risk prediction while using deep learning technology to construct a risk management prediction model, and continuously iteratively update the risk management prediction model, so as to obtain an optimal prediction model to predict the business risk of the enterprise.
- the embodiment of the present application also provides a system for predicting business risk of an enterprise.
- the predicting system includes a data collection module 401, a knowledge graph module 402, a model construction module 403, and a prediction module 404;
- the data collection module 401 uses web crawler scripts to obtain relevant internal and external information data of the enterprise on different platforms; then data fusion is performed on the obtained information data to convert unstructured and semi-structured information data into structured data Information data
- the knowledge graph module 402 uses the information data obtained from the data acquisition module to construct a knowledge graph, uses the graph to cluster and risk the events in the graph, and imports related data into the model building module ;
- the model construction module 403 uses the in-map information data constructed by the atlas to construct a training set and a test set, so as to perform continuous iterative training to obtain an optimal model;
- the prediction module 404 can analyze and predict the future risks of the required enterprise by using the optimal model obtained in the model section and the current relevant data of the enterprise to be predicted.
- each module in the enterprise operation risk prediction system can be understood with reference to the relevant description of the aforementioned enterprise operation risk prediction method.
- each module in the business risk prediction system of the enterprise can be implemented by a processor, such as a central processing unit (CPU), a digital signal processor (DSP), and a micro control unit (Microcontroller Unit). , MCU) or programmable gate array (Field-Programmable Gate Array, FPGA).
- the embodiments of the present application also provide a computer-readable storage medium for storing computer programs.
- the computer-readable storage medium can be applied to the system for predicting business risks in the embodiments of the present application, and the computer program enables the computer to execute the various methods in the embodiments of the present application that are implemented by the system for predicting business risks For the sake of brevity, the corresponding process will not be repeated here.
- the computer-readable storage medium can be applied to the mobile terminal/terminal device in the embodiment of the present application, and the computer program enables the computer to execute the corresponding process implemented by the mobile terminal/terminal device in each method of the embodiment of the present application For the sake of brevity, I won’t repeat it here.
- the embodiments of the present application also provide a computer program product, including computer program instructions.
- the computer program product can be applied to the system for predicting business risks in the embodiments of this application, and the computer program instructions cause the computer to execute the corresponding methods implemented by the system for predicting business risks in the embodiments of this application.
- the process will not be repeated here.
- the computer program product can be applied to the mobile terminal/terminal device in the embodiment of the present application, and the computer program instructions cause the computer to execute the corresponding process implemented by the mobile terminal/terminal device in each method of the embodiment of the present application, For the sake of brevity, I will not repeat them here.
- the embodiment of the present application also provides a computer program.
- the computer program can be applied to the enterprise operation risk prediction system in the embodiment of this application.
- the computer program can execute the prediction of the enterprise operation risk in the various methods of the embodiment of this application.
- the corresponding process of the system implementation will not be repeated here.
- the computer program can be applied to the mobile terminal/terminal device in the embodiment of the present application.
- the computer program runs on the computer, the computer executes each method in the embodiment of the present application. For the sake of brevity, the corresponding process will not be repeated here.
- the disclosed system, device, and method can be implemented in other ways.
- the device embodiments described above are only illustrative.
- the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or It can be integrated into another system, or some features can be ignored or not implemented.
- the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
- the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
- the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
- the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.
- the technical solution of this application essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application.
- the aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory,) ROM, random access memory (Random Access Memory, RAM), magnetic disks or optical disks and other media that can store program codes. .
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- 一种企业经营风险预测方法,所述企业经营风险预测方法包括:利用网络爬虫技术采集所需进行风险预测的企业信息数据以及其关联企业的信息数据;采集进行风险预测的企业内部信息数据以及外部信息数据;对于所收集到的企业内部信息数据和外部信息数据进行数据融合处理,将非结构化或者半结构化数据转化为结构化的数据并且储存至图形数据库内;根据所收集到的所需进行风险预测的企业信息数据和其关联企业的信息数据进行知识图谱构建及绘制;采集经营风险特征数据信息;使用深度学习方法从知识图谱里获取与预先设定的风险特征词相同或者类似的风险特征标签词,然后利用深度学习方法找到经营风险特征数据信息进行提取;通过所述经营风险特征数据信息构建用于预测企业未来的经营风险情况的经营风险模型;把所需进行风险预测的企业信息数据输入风险经营模型进行预测。
- 根据权利要求1所述的一种企业经营风险预测方法,其中,所述的所收集到的所需企业以及其关联企业的信息数据进行知识图谱构建及绘制,包括:利用自然语言处理技术对不同企业的相关联的实体信息数据进行命名实体识别,进而得到与所需进行经营风险预测的企业的相关联实体信息数据;并进行实体消歧,获得明确名称指代的具体实体信息数据;对实体之间的语义关系进行提取,从而获得不同实体间的实体关系信息数据;整合所述实体信息数据以及所述实体关系信息数据进行知识图谱的 构建以及绘制通过图的方式表达实体与实体之间的复杂关系。
- 根据权利要求1所述的一种企业经营风险预测方法,其中,将所述的经营风险特征数据信息作为深度学习的训练集,构建经营风险模型,所述构建经营风险模型的过程包含以下步骤:利用损失函数来评估模型的预测值与真实值不一致的程度;初始化所述经营风险模型架构;拟合训练集对构建的模型进行训练;拟合验证集对构建的模型内的数据进行对比;检验测试集性能,对构建的模型进行数据比对测试和评估;预测真实数据并根据结果调整训练集以及测试集。
- 根据权利要求1所述的一种企业经营风险预测方法,其中,所述方法还包括:将获取的当前的企业信息数据输入风险经营预测模型中;通过对所需进行风险预测的企业的内部以及外部信息数据的融合处理和根据知识图谱结构化所需进行风险预测的企业信息数据,同时采用深度学习技术构建风险经营预测模型;持续迭代更新所述风险经营预测模型,从而得出最优预测模型,通过所述最优预测模型对企业经营风险进行预测。
- 根据权利要求1至4中任一项所述的一种企业经营风险预测方法,其中,所述的内部信息数据包括所有格式的财务数据、报账信息数据、合同信息数据、HR数据、招投标数据以及其他企业内部相关的办公电子文档;所述的外部数据包括了企业互联网舆情数据、外部公开招投标信息数据、工商信息数据、政策信息数据以及其他与企业相关的网页数据。
- 一种企业经营风险的预测***,所述的预测***包括了数据采集模块,知识图谱模块,模型构建模块以及预测模块;所述的数据采集模块,利用网络爬虫脚本获取不同平台上的相关企业内外部数据;再将所获取的数据进行数据融合和整合;所述的知识图谱模块,利用数据采集模块上所获取的数据构建知识图谱,利用所述的图谱对图谱内的事件进行聚类和风险等级划分,并把相关的数据导入模型构建模块中;所述的模型构建模块,利用所述图谱构建的图内信息数据,构建训练集和测试集,从而进行持续的迭代训练,得出最优模型;所述的预测模块,利用所述模型板块内得到的最优模型以及将要预测的企业当前相关数据,能够对所需企业的未来风险进行分析预测。
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