CN119168699A - Monopoly situation prediction method and device based on digital twinning - Google Patents
Monopoly situation prediction method and device based on digital twinning Download PDFInfo
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Abstract
本发明公开了一种基于数字孪生的垄断态势预测方法及装置，基于数字孪生技术，结合智能化的算法模型，构建数字空间的垄断行为数据靶场，通过将模拟数据和真实数据结合，对市场主体竞争中的垄断行为进行识别和评估，并能有效预测垄断态势，实现对垄断违法行为的早期预警。
The present invention discloses a method and device for predicting monopoly situation based on digital twins. Based on digital twin technology and combined with an intelligent algorithm model, a monopoly behavior data target range in digital space is constructed. By combining simulated data and real data, the monopoly behavior in the competition among market entities is identified and evaluated, and the monopoly situation can be effectively predicted, thereby achieving early warning of monopoly violations.
Description
Technical Field
The invention relates to the technical field of digital twinning, in particular to a monopoly situation prediction method and device based on digital twinning.
Background
In the new economic age, the speed of new products entering the market and breaking the monopoly of the original market is greatly accelerated because the conversion speed of new technology to the market is obviously accelerated and the global marketing and information network is already formed. In general, market monopoly can be demonstrated by a large proportion of a company in related market, and also can be demonstrated by indirect evidence such as price control or competitive exclusion capability, but is usually limited by manual data carding and evidence collection, and is limited by objective factors such as manpower, material resources and the like, so that judging efficiency and result accuracy are low, an algorithm model suitable for the field of monopoly is not formed in the prior art, quantitative analysis and prediction results cannot be formed according to actual data, the intelligent level is poor, and the method cannot be suitable for rapid development of new economic age.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a monopoly situation prediction method based on digital twinning, which is based on the digital twinning technology, combines an intelligent algorithm model to identify and evaluate monopoly behaviors in main market competition and can effectively predict monopoly situations.
In a first aspect, an embodiment of the present invention provides a monopoly situation prediction method based on digital twinning, where the method includes:
Step one, acquiring market competition real data of multiple industries and multiple platforms, extracting key features by utilizing text mining data, creating a monopoly behavior feature library and a monopoly behavior evaluation index system, and setting weight values for various indexes in the evaluation index system;
Step two, generating market competition behavior simulation data according to the monopoly behavior feature library and the monopoly behavior evaluation index system constructed in the step one, inputting the market competition behavior simulation data into a generated countermeasure network, adjusting parameters of the simulation data to obtain the simulation data conforming to monopoly behavior feature distribution, and constructing a monopoly behavior data target range of a digital space;
Step three, a digital twin prediction model is constructed by utilizing a data driving modeling method, market competition behaviors are simulated in the monopoly behavior data shooting range, the simulation data are input into the digital twin prediction model to obtain monopoly situation prediction results, external verification is conducted by utilizing the real data, and operation parameters of the digital twin prediction model are adjusted based on the verification results;
And step four, acquiring target economic main body information to be monitored and target economic environment information of industries where the target economic main body information and the target economic environment information are located, preprocessing the target economic main body information and the target economic environment information, dynamically monitoring by using the digital twin prediction model, outputting monopoly situation assessment results, and sending the assessment results to a supervision platform through a communication module.
In one possible implementation manner of the present invention, the method provided by the embodiment of the present invention further includes:
setting weight values for all indexes in the evaluation index system, specifically comprising:
And setting initial weights for all evaluation indexes in the evaluation index system by adopting an expert evaluation method, and adjusting and optimizing the initial weights by adopting an entropy value weighting method to obtain final weight values of all the evaluation indexes.
In one possible implementation manner of the present invention, the method provided by the embodiment of the present invention further includes:
The monopoly behavior data shooting range of the digital space comprises a core data warehouse part, a simulation data generation part and a digital twin visualization part, wherein the core data warehouse is used for collecting, analyzing and cleaning original data to generate an original data layer, a data integration layer and a characteristic data layer of the core data warehouse, the simulation data generation part is used for learning data characteristics and modeling according to the characteristics, and the digital twin visualization part is used for carrying out visual display on the characteristics of the data and the effects of the model.
In one possible implementation manner of the present invention, the method provided by the embodiment of the present invention further includes:
The target economic main body information and the target economic environment information are preprocessed, and specifically the method comprises the steps of denoising and cleaning the target economic main body information and the target economic environment information, extracting parameters corresponding to key features according to a monopoly behavior feature library, and carrying out quantization processing to obtain feature vectors.
In one possible implementation manner of the present invention, the method provided by the embodiment of the present invention further includes:
The supervision platform comprises one or more of a supervision platform of the target economic body, a supervision platform of a market supervision department and a third party supervision platform.
In a second aspect, an embodiment of the present invention provides a digital twin-based monopoly situation prediction apparatus, including a processor, a memory, and a program or an instruction stored in the memory and executable on the processor, where the program or the instruction implements the digital twin-based monopoly situation prediction method when executed by the processor.
In the technical scheme, the invention has the technical effects and advantages that:
1. The invention makes the monopoly law enforcement specialized and refined, and greatly improves the credibility and effectiveness of monopoly work.
2. The invention enhances and innovates market price supervision.
3. The method is beneficial to timely finding out problems in the enterprise operation process, and effectively reduces the cost of correcting the illegal behaviors.
4. The invention ensures the utilization efficiency of data, verifies the effectiveness of anti-monopoly detection work, enriches the monitoring dimension and effectively improves the reliability of the anti-monopoly monitoring result.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a method flow diagram illustrating a digital twinning-based monopoly situation prediction method, according to an example embodiment.
FIG. 2 is a block diagram of monopoly behavior data range of a digital space shown according to an exemplary embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Example 1
FIG. 1 is a method flow diagram illustrating a digital twinning-based monopoly situation prediction method, according to an example embodiment. The monopoly situation prediction method based on digital twinning at least comprises steps S1 to S4.
Step S1, acquiring market competition behavior real data of multiple industries and multiple platforms, extracting key features by utilizing text mining data, creating a monopoly behavior feature library and a monopoly behavior evaluation index system, and setting corresponding weights for various indexes in the evaluation index system.
The market competition behavior mainly refers to suspected monopolizing agreements, operators centralizing non-legal declarations, unfair prices, lower than cost sales, differential treatment and other suspected monopolizing behaviors, economic main body information of the suspected monopolizing behaviors and economic environment information of industries where the economic main body information and the economic environment information are located can be obtained from administrative departments or published historical monopolizing cases, the economic main body information and the economic environment information refer to related information for judging whether the operation behaviors of the economic main body are suspected to be monopolizing behaviors or not, such as enterprise basic information data, tax data, sales data, market share, market price discrimination, market centralization degree, product differentiation degree, price fluctuation, industry growth rate, consumer demands and the like, key features are extracted from the monopolizing behavior feature libraries by means of text mining technology, the obtained market competition behavior real data are utilized to verify the monopolizing behavior feature libraries, the effectiveness and the reliability of the monopolizing behavior feature libraries are verified according to the matching degree of the market competition behavior real data and the monopolizing behavior feature libraries, and the real reliable behavior feature libraries are finally obtained.
The application provides a mode for establishing the monopoly behavior evaluation index system according to the existing rule for judging monopoly behavior, for example, a three-level evaluation index can be established, the fact standard monopoly is used as a target layer of the index system, five layers of technology, market, management, policy and law are used as criterion layers of the index system, and the problems corresponding to the five layers are used as element layers of the index system, as shown in table 1.
Those skilled in the art should appreciate that, in addition to the above manner, other various manners may be used to establish the evaluation index system according to actual needs, and the established evaluation index system may be a secondary system or a tertiary or quaternary system, which is not limited by the present invention.
TABLE 1 monopoly behavior evaluation index system
According to expert experience, setting initial weights for all indexes in an evaluation index system by adopting an expert scoring method, and then adjusting and optimizing the initial weights by adopting an entropy value weighting method to obtain final weight values of all evaluation indexes.
And S2, generating market competition behavior simulation data according to the monopoly behavior feature library and the monopoly behavior evaluation index system constructed in the step S1, inputting the market competition behavior simulation data into a generated countermeasure network, adjusting parameters of the simulation data to obtain simulation data conforming to monopoly behavior feature distribution, and constructing a monopoly behavior data target range of a digital space.
The generating type countermeasure network consists of a generator G and a discriminator D, wherein the generator G captures the distribution of sample data in the whole training process, the discriminator D is a classifier used for judging the probability that an input result is from training data, the generator G and the discriminator D are nonlinear mapping functions and are multi-layer perceptron or convolutional neural networks, in the training process, the generator G aims at generating the result approaching to the original data as much as possible to deception the discriminator D, the generator D aims at distinguishing the generated result from real data as much as possible, the generator G and the discriminator D form a dynamic game process, and finally, the simulated data with the characteristic distribution very similar to the real data are obtained and are used as data bases of monopoly behavior data ranges.
As shown in fig. 2, the monopolizing behavior data range of the digital space can be divided into a core data warehouse part, a simulation data generation part and a digital twin visualization part according to data, a model and an application, wherein the core data warehouse part is used for collecting, analyzing and cleaning original data, and generating an original data layer of the core data warehouse. And then, the comparison and mapping work of different data source data is completed, and a data integration layer of the core data warehouse is generated. And finally, processing the characteristics of the data to generate a characteristic data layer of the core data warehouse.
The simulation data generating section is a process of learning data features, modeling from the features. First, a training data set for model training is generated by feature engineering processing of feature data layer data. And secondly, selecting a proper model and parameters, and performing feature learning and simulation data generation. And thirdly, comparing and evaluating simulation data generated by the model, analyzing the efficiency of the model, and optimizing the model. And fourthly, publishing the trained model. The model training process needs repeated iteration and optimization, and the result of model test evaluation provides basis for selection of different models so as to promote selection of continuously optimized training models.
The digital twin visualization part is a process of publishing the early model capability and monopoly behavior data capability. The simulation data model can be used by the digital twin visualization module to generate monopoly behavior simulation data, the monopoly behavior simulation data can be stored in a characteristic data layer of a data warehouse, and accumulation of data is enriched. Through the accumulated characteristic data, the monopoly behavior can be simulated in the visual part, and the visual display of the characteristics and the effects of the model can be performed.
And S3, constructing a digital twin prediction model by using a data-driven modeling method, simulating competitive behaviors of a market subject in a data target range, inputting simulation data into the digital twin prediction model to obtain monopoly situation prediction results, carrying out external verification by using real data, and adjusting operation parameters of the digital twin prediction model based on the verification results.
The digital twin is to fully utilize data such as a physical model, a sensor, an operation history and the like, integrate a multidisciplinary and multiscale simulation process, and reflect the full life cycle process of a corresponding physical entity product as a mirror image of the entity product in a virtual space. The essence of digital twinning is to create a twinning model of a physical entity, simulate the twinning model as a basic model, reflect the real running condition of the physical entity in real time, and adjust the running parameters of the physical entity through the feedback of the twinning model so as to achieve the effect of optimization. The twin model has two remarkable characteristics that the twin model is basically the same as an object to be reflected by the twin model in appearance (geometric size and shape), content (structural composition and macroscopic/microscopic physical characteristics thereof) and properties (functions and performances), and allows the real running condition/state to be mirrored/reflected by a simulation mode or the like.
The data driving modeling method is to use a data mining technology to find useful information between data to establish a more specific and more definite function expression form to describe the relation between input variables and output variables, to fit a sample as a target, to have a fixed input-output relation, to construct a parameter optimization function, to select a proper data driving model and a proper model structure from known data driving models, to establish a mathematical relation expression of a corresponding model, and to generally select a BP neural network model, a response surface model, a support vector machine and the like.
The digital twin prediction model is built by using the data-driven modeling method, the market main body is used as a physical entity, the current state of the market main body is identified, the future state can be predicted, and the reliability of monopoly behavior monitoring results is effectively improved.
S4, acquiring target economic main body information to be monitored and target economic environment information of industries where the target economic main body information and the target economic environment information are located, preprocessing the target economic main body information and the target economic environment information, dynamically monitoring by using a digital twin prediction model, outputting monopoly situation assessment results, and sending the assessment results to a supervision platform through a communication module.
In practical application, the target object to be detected is obtained, for example, an important operation subject in key industries, target economic subject information and target economic environment information of the industries in which the target economic subject information is located are obtained, the obtained information is denoised and cleaned, parameters corresponding to key features are extracted according to a monopoly behavior feature library, quantitative processing is carried out to obtain feature vectors, then a monopoly situation assessment result is obtained by utilizing a digital twin prediction model, and the assessment result is provided for a supervision platform.
The supervision platform comprises a supervision platform of a target economic body, a supervision platform of a market supervision department and a third party supervision platform, the target economic body can carry out self-checking and rectifying on own business behaviors according to the evaluation result, the market supervision department can intervene in advance on potential monopoly behaviors of the market according to the evaluation result, and the market supervision department can effectively supervise the monopoly behaviors which have occurred.
In the process of dynamically monitoring the target economic main body by utilizing the digital twin prediction model, visual parameter setting can be carried out, and visual display can be carried out on the actual running process of the digital space.
Example two
The embodiment of the invention also provides a monopoly situation prediction device based on the digital twin, which comprises a processor, a memory and a program or an instruction stored in the memory and capable of running on the processor, wherein the program or the instruction realizes the monopoly situation prediction method based on the digital twin when being executed by the processor.
It is noted that relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one.," does not exclude that an additional identical element is present in a process, method, article, or apparatus that comprises the element.
It will be appreciated by those of ordinary skill in the art that implementing all or part of the steps of the above method embodiments may be accomplished by hardware associated with program instructions, and that the above program may be stored in a computer readable storage medium which, when executed, performs the steps comprising the above method embodiments, where the above storage medium includes various media that may store program code, such as ROM, RAM, magnetic or optical disks.
It should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention, and not for limiting the same, and although the present invention has been described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that the technical solution described in the above-mentioned embodiments may be modified or some technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solution of the embodiments of the present invention.
Claims (9)
1. The monopoly situation prediction method based on digital twinning is characterized by comprising the following steps of:
Step one, acquiring market competition real data of multiple industries and multiple platforms, extracting key features by utilizing text mining data, creating a monopoly behavior feature library and a monopoly behavior evaluation index system, and setting weight values for various indexes in the evaluation index system;
Step two, generating market competition behavior simulation data according to the monopoly behavior feature library and the monopoly behavior evaluation index system constructed in the step one, inputting the market competition behavior simulation data into a generated countermeasure network, adjusting parameters of the simulation data to obtain the simulation data conforming to monopoly behavior feature distribution, and constructing a monopoly behavior data target range of a digital space;
Step three, a digital twin prediction model is constructed by utilizing a data driving modeling method, market competition behaviors are simulated in the monopoly behavior data shooting range, the simulation data are input into the digital twin prediction model to obtain monopoly situation prediction results, external verification is conducted by utilizing the real data, and operation parameters of the digital twin prediction model are adjusted based on the verification results;
And step four, acquiring target economic main body information to be monitored and target economic environment information of industries where the target economic main body information and the target economic environment information are located, preprocessing the target economic main body information and the target economic environment information, dynamically monitoring by using the digital twin prediction model, outputting monopoly situation assessment results, and sending the assessment results to a supervision platform through a communication module.
2. The method according to claim 1, characterized in that the weighting values are set for the individual indicators in the evaluation indicator system, in particular comprising:
And setting initial weights for all evaluation indexes in the evaluation index system by adopting an expert evaluation method, and adjusting and optimizing the initial weights by adopting an entropy value weighting method to obtain final weight values of all the evaluation indexes.
3. The method of claim 1, wherein the monopolizing behavioral data range of the digital space comprises a core data warehouse part, a simulation data generation part and a digital twin visualization part, wherein the core data warehouse is used for collecting, analyzing and cleaning raw data to generate a raw data layer, a data integration layer and a characteristic data layer of the core data warehouse, the simulation data generation part is used for learning data characteristics and modeling according to the characteristics, and the digital twin visualization part is used for visually displaying the effects of the characteristics and the models of the data.
4. The method of claim 3, wherein preprocessing the target economic main body information and the target economic environment information specifically comprises denoising and cleaning the target economic main body information and the target economic environment information, extracting parameters corresponding to key features according to a monopoly behavior feature library, and performing quantization processing to obtain feature vectors.
5. The method of claim 4, wherein the regulatory platform comprises one or more of a regulatory platform of the target economic subject, a regulatory platform of a market regulatory authority, and a third party regulatory platform.
6. A digital twinning-based monopoly situation prediction apparatus comprising a processor, a memory and a program or instruction stored on the memory and executable on the processor, which when executed by the processor implements the digital twinning-based monopoly situation prediction method of any one of claims 1 to 5.
7. A digital twinning-based prediction method, comprising the steps of:
Step one, acquiring data A, extracting key features by utilizing text mining data, creating a feature library and an evaluation index system, and setting weight values for various indexes in the evaluation index system;
Step two, generating analog data according to the feature library and the evaluation index system constructed in the step one, inputting the analog data into a generated type countermeasure network, adjusting parameters of the analog data, and constructing a data target range of a digital space;
And thirdly, constructing a digital twin prediction model by using a data driving modeling method, inputting the simulation data into the digital twin prediction model to obtain a monopoly situation prediction result, carrying out external verification by using the data A, and adjusting the operation parameters of the digital twin prediction model based on the verification result.
8. The method according to claim 7, wherein the data A is multi-industry and multi-platform market competition behavior real data, the feature library is monopoly behavior feature library, the evaluation index system is monopoly behavior evaluation index system, and the data target range is monopoly behavior data target range.
9. The method according to claim 7, wherein the prediction method further comprises the steps of obtaining target economic main body information to be monitored and target economic environment information of industries where the target economic main body information and the target economic environment information are located, preprocessing the target economic main body information and the target economic environment information, dynamically monitoring by using the digital twin prediction model, outputting monopoly situation assessment results, and sending the assessment results to a supervision platform through a communication module.
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