A Method and Device for Predicting Monopoly Situation Based on Digital Twin

Abstract

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

[0001] The present invention relates to the technical field of digital twins, and particularly relates to a method and device for predicting monopoly situations based on digital twins. Background Art

[0002] In the new economic era, since the transformation speed of new technologies into the market has significantly accelerated, and a global marketing and information network has been formed, the speed at which new products enter the market and break the original market monopoly has greatly accelerated. Generally speaking, a market monopoly position can be proven by a large proportion of the share that a company occupies in the relevant market, or by indirect evidence such as the ability to control prices or exclude competition. However, it usually relies on manual data sorting and evidence collection. Limited by objective factors such as manpower and material resources, the judgment efficiency and result accuracy are both relatively low. There is also no algorithm model applicable to the anti-monopoly field in the existing technology, and it is impossible to form a quantitative analysis and prediction result based on actual data, with poor intelligence level and inability to adapt to the rapid development of the new economic era. Summary of the Invention

[0003] Aiming at the technical problems existing in the prior art, the present invention provides a method for predicting monopoly situations based on digital twins. Based on digital twin technology and combined with an intelligent algorithm model, it can identify and evaluate monopoly behaviors in the competition of market entities, and can effectively predict monopoly situations.

[0004] In a first aspect, an embodiment of the present invention provides a method for predicting monopoly situations based on digital twins, and the method includes:

[0005] Step 1: Obtain real data of market competition behaviors in multiple industries and multiple platforms, extract key features using text mining data, create a monopoly behavior feature library and a monopoly behavior evaluation index system, and set weight values for each index in the evaluation index system;

[0006] Step 2: Generate simulated data of market competition behaviors according to the monopoly behavior feature library and the monopoly behavior evaluation index system constructed in Step 1, and input it into a generative adversarial network to adjust the parameters of the simulated data to obtain the simulated data that conforms to the monopoly behavior feature distribution, and construct a monopoly behavior data range in the digital space;

[0007] Step 3: Use a data-driven modeling method to construct a digital twin prediction model, simulate market competition behaviors in the monopoly behavior data range, input the simulated data into the digital twin prediction model to obtain a monopoly situation prediction result, and use the real data for external verification, and adjust the operating parameters of the digital twin prediction model based on the verification result;

[0008] Step 4: Obtain the information of the target economic entity to be monitored and the information of the target economic environment of the industry where it is located, preprocess the information of the target economic entity and the information of the target economic environment, perform dynamic monitoring using the digital twin prediction model, and output the evaluation result of the monopoly situation. Send the evaluation result to the supervision platform through the communication module.

[0009] In a possible implementation manner of the present invention, the method provided by the embodiments of the present invention further includes:

[0010] Set weight values for each index in the evaluation index system, specifically including:

[0011] Use the expert scoring method to set the initial weights for each evaluation index in the evaluation index system, and then adjust and optimize the initial weights through the entropy weight method to obtain the final weight values of each evaluation index.

[0012] In a possible implementation manner of the present invention, the method provided by the embodiments of the present invention further includes:

[0013] The monopoly behavior data range in the digital space includes a core data warehouse part, a simulation data generation part, and a digital twin visualization part. The core data warehouse is used to collect, analyze, and clean the original data to generate the original data layer, data integration layer, and feature data layer of the core data warehouse; the simulation data generation part is used to learn the data features and build models according to the features; the digital twin visualization part is used to visually display the data features and the effects of the models.

[0014] In a possible implementation manner of the present invention, the method provided by the embodiments of the present invention further includes:

[0015] Preprocess the information of the target economic entity and the information of the target economic environment, specifically including: after denoising and cleaning the information of the target economic entity and the information of the target economic environment, extract the parameters corresponding to the key features according to the monopoly behavior feature library, and perform quantization processing to obtain the feature vectors.

[0016] In a possible implementation manner of the present invention, the method provided by the embodiments of the present invention further includes:

[0017] The supervision platform includes one or more of the following: the supervision platform of the target economic entity, the supervision platform of the market supervision department, and the third-party supervision platform.

[0018] In a second aspect, an embodiment of the present invention provides a monopoly situation prediction device based on digital twin, including a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the above-mentioned monopoly situation prediction method based on digital twin is implemented.

[0019] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0020] 1. The present invention makes anti-monopoly law enforcement professional and refined, and greatly improves the credibility and effectiveness of anti-monopoly work.

[0021] 2. The present invention strengthens and innovates market price supervision.

[0022] 3. The present invention helps to timely discover problems existing in the enterprise operation process, and effectively reduces the cost of correcting violations.

[0023] 4. The present invention ensures the utilization efficiency of data, verifies the effectiveness of anti-monopoly detection work, enriches the monitoring dimensions, and effectively improves the reliability of anti-monopoly monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0025] Figure 1 is a flowchart of a method for predicting a monopoly situation based on digital twin shown according to an exemplary embodiment.

[0026] Figure 2 is a structure diagram of a monopoly behavior data range in the digital space shown according to an exemplary embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] Now, the exemplary embodiments will be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the embodiments set forth herein; 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 their repeated description will be omitted.

[0028] In addition, 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 the embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present disclosure.

[0029] The block diagrams shown in the drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0030] The flowcharts shown in the drawings are only exemplary illustrations and do not necessarily include all the contents and operations / steps, nor are they necessarily executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0031] Embodiment 1

[0032] Figure 1 It is a method flowchart of a monopoly situation prediction method based on digital twin shown according to an exemplary embodiment. The monopoly situation prediction method based on digital twin at least includes steps S1 to S4.

[0033] Step S1, obtain the real data of market competition behaviors in multiple industries and multiple platforms, extract key features using text mining data, create a monopoly behavior feature library and a monopoly behavior evaluation index system, and set corresponding weights for each index in the evaluation index system.

[0034] Market competition behaviors mainly refer to suspected monopolistic behaviors such as suspected monopoly agreements, failure to declare business concentration in accordance with the law, unfair prices, sales below cost, and differential treatment. Information on economic entities involved in suspected monopolistic behaviors and the economic environment of the industries they belong to can be obtained from regulatory authorities or public historical monopoly cases. The information on economic entities and the economic environment refers to relevant information used to determine whether the business operations of economic entities are suspected of monopolistic behaviors. For example, enterprise basic information data, tax data, sales data, market share, as well as market price discrimination, market concentration, product differentiation degree, price fluctuations, industry growth rate, and consumer demand, etc. Text mining techniques are used to extract key features from them to construct a monopoly behavior feature library. The monopoly behavior feature library is verified using the real data of market competition behaviors. The effectiveness and reliability of the monopoly behavior feature library are verified based on the matching degree between the real data of market competition behaviors and the monopoly behavior feature library, and the monopoly behavior feature library is adjusted according to the verification results to finally obtain a true and reliable monopoly behavior feature library.

[0035] Construct a monopoly behavior evaluation index system based on the existing rules for judging monopoly behaviors. Exemplarily, this application gives a way to establish a monopoly behavior evaluation index system. For example, a three-level evaluation index can be constructed. Taking the monopolization of factual standards as the target layer of the index system, and taking the five levels of technology, market, management, policy, and law as the criterion layer of the index system, and taking the problems corresponding to the five levels respectively as the element layer of the index system, as shown in Table 1.

[0036] Those skilled in the art should be aware that, in addition to the above methods, other various methods can also be used to establish an evaluation index system according to actual needs. The established evaluation index system can be a two-level system, or a three-level or four-level system. The present invention does not limit this.

[0037] Table 1 Monopoly Behavior Evaluation Index System

[0038]

[0039] Based on expert experience, the expert scoring method is used to set initial weights for each index in the evaluation index system, and then the entropy weight method is used to adjust and optimize the initial weights to obtain the final weight values of each evaluation index.

[0040] Step S2: Based on the monopoly behavior feature library and the monopoly behavior evaluation index system constructed in step S1, generate simulated data of market competition behaviors and input it into the generative adversarial network. Adjust the parameters of the simulated data to obtain simulated data that conforms to the monopoly behavior feature distribution, and construct a monopoly behavior data range in the digital space.

[0041] The generative adversarial network consists of a generator G and a discriminator D. During the entire training process, G and D are the two sides of the "game". The generator G captures the distribution of sample data, and the discriminator D is a binary classifier used to judge the probability that the input result comes from the training data. Both G and D are non-linear mapping functions, which are multi-layer perceptrons or convolutional neural networks. During the training process, the goal of the generator G is to generate results as close as possible to the original data to deceive the discriminator D. The goal of D is to distinguish the results generated by G from the real data as much as possible. G and D form a dynamic "game process", and finally obtain simulated data with a feature distribution extremely similar to the real data, which serves as the data foundation of the monopoly behavior data range.

[0042] As Figure 2 shown, the monopoly behavior data range in the digital space can be divided into the core data warehouse part, the simulation data generation part, and the digital twin visualization part according to data, models, and applications. The core data warehouse part aims to collect, analyze, and clean the original data to generate the original data layer of the core data warehouse. Then, the comparison and mapping work of data from different data sources are completed to generate the data integration layer of the core data warehouse. Finally, the feature processing of the data is carried out to generate the feature data layer of the core data warehouse.

[0043] The simulation data generation part is a process of learning data features and modeling according to the features. First step, through the feature engineering processing of the data in the feature data layer, a training data set available for model training is generated. Second step, select appropriate models and parameters for feature learning and the generation of simulation data. Third step, conduct a comparative evaluation of the simulation data generated by the model, analyze the model performance, and optimize the model. Fourth step, publish the trained model. The process of model training requires repeated iteration and optimization. The results of model test evaluation provide a basis for the selection of different models, prompting the selection of continuously optimized training models.

[0044] The digital twin visualization part is a process of publishing the previous model capabilities and monopoly behavior data capabilities. Through the digital twin visualization module, the simulation data model can be used to generate monopoly behavior simulation data. The monopoly behavior simulation data can also be stored in the feature data layer of the data warehouse to enrich the data accumulation. Based on the accumulated feature data, the simulation of monopoly behavior can be carried out in the visualization part, and the features of the data and the effects of the models can be visually displayed.

[0045] Step S3, use the data-driven modeling method to construct a digital twin prediction model, simulate the competitive behaviors of market players in the data range, input the simulated data into the digital twin prediction model to obtain the monopoly situation prediction result, and use the real data for external verification. Adjust the operating parameters of the digital twin prediction model based on the verification results.

[0046] Digital twin refers to making full use of data such as physical models, sensors, and operation history, integrating multi-disciplinary and multi-scale simulation processes. As a mirror image of the physical product in the virtual space, it reflects the entire life cycle process of the corresponding physical entity product. The essence of digital twin is to create a twin model of the physical entity, use the twin model as the basic model for simulation, reflect the real operating conditions of the physical entity in real time, and adjust the operating parameters of the physical entity through the feedback of the twin model to achieve the optimization effect. The twin model has two significant characteristics: the twin model is basically the same as the object it is intended to reflect in appearance (geometric dimensions and shape), content (structural composition and its macroscopic / microscopic physical properties), and nature (functions and performance); it allows mirroring / reflection of the real operating conditions / status through means such as simulation.

[0047] The data-driven modeling method uses data mining techniques to find useful information between data, establish a more specific and clear function expression form to describe the relationship between input variables and output variables, aims to fit the samples, has a fixed input-output relationship, can construct a parameter optimization function, select a suitable data-driven model and a suitable model structure from known data-driven models, and establish a mathematical relationship expression for the corresponding model. Generally, BP neural network models, response surface models, support vector machines, etc. are often selected.

[0048] Using the data-driven modeling method to construct a digital twin prediction model, taking the market entity as the physical entity, identifying its current state, and being able to predict its future state effectively improves the reliability of the monitoring results of monopoly behaviors.

[0049] Step S4, obtain the information of the target economic entity to be monitored and the information of the target economic environment of the industry it belongs to, preprocess the target economic entity information and the target economic environment information, use the digital twin prediction model for dynamic monitoring, and output the monopoly situation assessment result, and send the assessment result to the regulatory platform through the communication module.

[0050] In practical applications, obtain the target object to be detected, which can be, for example, an important business entity in a key industry, obtain the information of the target economic entity and the information of the target economic environment of the industry it belongs to, after denoising and cleaning the obtained information, extract the parameters corresponding to the key features according to the monopoly behavior feature library, and perform quantization processing to obtain a feature vector, then use the digital twin prediction model to obtain the monopoly situation assessment result, and provide the assessment result to the regulatory platform.

[0051] The regulatory platform includes the regulatory platforms of target economic entities, the regulatory platforms of market supervision departments, and third-party regulatory platforms. Target economic entities can conduct self-inspections and rectifications on their own business operations based on the evaluation results. Market supervision departments can intervene in potential monopoly behaviors in the market in advance according to the evaluation results, or effectively supervise monopoly behaviors that have already occurred.

[0052] In the process of dynamically monitoring target economic entities using the digital twin prediction model, visual parameter settings can be performed to visually display the actual operation process in the digital space.

[0053] Embodiment 2

[0054] The embodiment of the present invention also provides a monopoly situation prediction device based on digital twin, including a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the above-mentioned monopoly situation prediction method based on digital twin is implemented.

[0055] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0056] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: various media such as ROM, RAM, magnetic disk or optical disk that can store program codes.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A monopoly situation prediction method based on digital twins, characterized in that: The following steps are involved: Step 1: Obtain real data on market competition behaviors in multiple industries and platforms, extract key features using text mining data, create a monopoly behavior feature library and a monopoly behavior evaluation index system, and set weight values ​​for each indicator in the evaluation index system; Step 2: Based on the monopolistic behavior feature library and the monopolistic behavior evaluation index system constructed in step 1, generate market competition behavior simulation data, and input them into the generative adversarial network, adjust the parameters of the simulation data, obtain the simulation data that conforms to the distribution of monopolistic behavior characteristics, and construct a monopolistic behavior data target range in the digital space; Step 3: Use the data-driven modeling method to build a digital twin prediction model, simulate market competition behavior in the monopoly behavior data target range, input the simulated data into the digital twin prediction model to obtain the monopoly situation prediction result, and use the real data for external verification, and adjust the operating parameters of the digital twin prediction model based on the verification result; Step 4: Obtain the target economic entity information to be monitored and the target economic environment information of the industry in which it is located, pre-process the target economic entity information and the target economic environment information, use the digital twin prediction model to perform dynamic monitoring, and output the monopoly situation assessment results, and send the assessment results to the supervision platform through the communication module; The generative adversarial network is composed of a generator G and a discriminator D, and both the generator G and the discriminator D are nonlinear mapping functions; The monopoly behavior data range in the digital space includes a core data warehouse part, a simulation data generation part, and a digital twin visualization part. The core data warehouse is used to collect, analyze and clean up the original data, and generate the original data layer, data integration layer and feature data layer of the core data warehouse; the simulation data generation part is used to learn data features and model according to the features; The digital twin visualization part is used to visualize the characteristics of the data and the effects of the model.

2. The method according to claim 1, characterized in that Setting weight values ​​for each indicator in the evaluation indicator system specifically includes: The expert scoring method is used to set initial weights for each evaluation indicator in the evaluation indicator system, and then the entropy weighting method is used to adjust and optimize the initial weights to obtain the final weight values ​​of each evaluation indicator.

3. The method according to claim 1, characterized in that Preprocessing the target economic entity information and the target economic environment information specifically includes: after denoising and cleaning the target economic entity information and the target economic environment information, extracting parameters corresponding to key features based on the monopoly behavior feature library, and performing quantization processing to obtain feature vectors.

4. The method according to claim 3, characterized in that: The regulatory platform includes one or more of the following: the regulatory platform of the target economic entity, the regulatory platform of the market regulatory department, and the third-party regulatory platform.

5. A monopoly situation prediction device based on digital twins, comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, the monopoly situation prediction method based on digital twins as described in any one of claims 1 to 4 is implemented.

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