A classification system based on a neural network model
Patent Information
- Application Number
- CN202410893923.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-04
- Publication Date
- 2026-10-30
AI Technical Summary
[0005]为了解决上述技术问题，本发明提供一种基于神经网络模型的分类系统，以解决现有技术中传统的业绩分析预测结果不够准确等问题
[0027]与现有技术相比，本发明具有如下有益效果：通过先进的神经网络模型，能够有效准确识别并分类公司业绩数据中的复杂趋势和模式；不仅提高了业绩预测的精确度，还使得公司能够更快速地做出基于数据的决策，实现了高效、准确的数据分析，从而为公司的业绩管理和决策提供了有力支持；同时，系统的用户友好性设计，如图形、报表和文字描述的输出方式，使得结果更加直观易懂，极大地提升了用户体验。
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Figure CN123012686A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of neural network models, specifically a classification system based on neural network models. Background Technology
[0002] A neural network model is a mathematical model that simulates the signal transmission process between neurons in the human brain. It is used to solve various problems, such as classification, regression, image recognition, and natural language processing.
[0003] Performance analysis is the process of thoroughly analyzing the business results of an organization, department, individual, or project over a specific period. Its main purpose is to evaluate past performance, identify successes and shortcomings, and provide data support for future business decisions.
[0004] Existing traditional performance analysis methods cannot accurately process large amounts of complex company performance data, resulting in inaccurate predictions. At the same time, traditional performance analysis systems lack intuitive user interfaces and user-friendly output methods, making them difficult for users to understand and use. Summary of the Invention
[0005] To address the aforementioned technical problems, this invention provides a classification system based on a neural network model, which solves the problem that traditional performance analysis and prediction results are not accurate enough in the prior art.
[0006] A classification system based on a neural network model, comprising:
[0007] a. Data input module, used to receive company performance data to be classified and predicted, including historical performance data, market trend data and macroeconomic data, and convert this data into a format that can be processed by neural network models;
[0008] b. The neural network model module is trained to identify and classify specific trends and patterns in company performance data, and to make performance predictions; the prediction process is as follows:
[0009] y p red = f(x; θ)
[0010] Among them, y p red is the prediction result, x is the input data, f is the neural network model function, and θ is the model parameters;
[0011] c. A classification and prediction processing module, connected to the neural network model module, is used to receive classification and prediction results from the neural network model module and classify and predict company performance based on these results;
[0012] d. Output module, connected to the classification and prediction processing module, for outputting the classification and prediction results in a user-readable manner, including graphics, reports and text descriptions.
[0013] Preferably, the neural network model module further includes:
[0014] a. Training data acquisition unit, used to acquire historical company performance data for training neural network models;
[0015] b. A training unit, connected to the training data acquisition unit, is used to train the neural network model based on the historical company performance data; the training process typically minimizes the loss function between the prediction error and the true value as follows:
[0016] Loss=∑(y t rue–y p red) 2
[0017] Where Loss is the value of the loss function, y t rue is the true value, y p red is the predicted value;
[0018] c. A verification unit, connected to the training unit, for verifying the classification and prediction performance of the trained neural network model.
[0019] Preferably, the neural network model adopts a deep learning architecture, including convolutional neural networks, recurrent neural networks, long short-term memory networks, and transformer models, to adapt to different forms of company performance data.
[0020] Preferably, it also includes a data preprocessing module for cleaning, transforming and standardizing the raw data before the data input module to ensure data quality and model performance.
[0021] Preferably, it also includes a user interaction module that allows users to input specific parameters and queries in order to more accurately customize and obtain classification and prediction results.
[0022] Preferably, it also includes a feedback module for receiving user feedback on classification and prediction results, and for adjusting the parameters of the neural network model and retraining the neural network model to improve prediction accuracy.
[0023] Preferably, it also includes a hardware accelerator connected to the neural network model module to accelerate the computation process of the neural network model and improve the speed and efficiency of classification and prediction.
[0024] Preferably, it also includes a future planning module, which generates strategic planning suggestions for the company's future development based on the prediction results of the neural network model, including product strategy, market strategy and investment strategy.
[0025] Preferably, the future planning module can perform multi-objective optimization on the prediction results of the neural network model based on the company's long-term goals and constraints to generate the best future planning recommendations.
[0026] Preferably, the output module can be integrated with the company's internal management system to achieve automated performance forecasting, classification, and future planning, providing support for the company's decision-making and allowing the company to make timely strategic adjustments based on the forecast results and planning recommendations.
[0027] Compared with existing technologies, this invention has the following advantages: through an advanced neural network model, it can effectively and accurately identify and classify complex trends and patterns in company performance data; it not only improves the accuracy of performance forecasting, but also enables companies to make data-driven decisions more quickly, achieving efficient and accurate data analysis, thereby providing strong support for the company's performance management and decision-making; at the same time, the system's user-friendly design, such as the output methods of graphics, reports, and text descriptions, makes the results more intuitive and easy to understand, greatly enhancing the user experience. Attached Figure Description
[0028] Figure 1 This is a schematic diagram of the overall system of the present invention. Detailed Implementation
[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0030] like Figure 1 As shown:
[0031] Example 1: This invention provides a classification system based on a neural network model, comprising:
[0032] a. Data input module, used to receive company performance data to be classified and predicted, including historical performance data, market trend data and macroeconomic data, and convert this data into a format that can be processed by neural network models;
[0033] b. The neural network model module is trained to identify and classify specific trends and patterns in company performance data, and to make performance predictions; the prediction process is as follows:
[0034] y p red = f(x; θ)
[0035] Among them, y p red is the prediction result, x is the input data, f is the neural network model function, and θ is the model parameters;
[0036] c. A classification and prediction processing module, connected to the neural network model module, is used to receive classification and prediction results from the neural network model module and classify and predict company performance based on these results;
[0037] d. Output module, connected to the classification and prediction processing module, for outputting the classification and prediction results in a user-readable manner, including graphics, reports and text descriptions;
[0038] This system, a company performance classification system based on neural networks, receives and processes company performance data, including historical performance, market trends, and macroeconomic data, to identify key trends and patterns within the data. It consists of data input, a neural network model, classification and prediction processing, and an output module, ensuring data format compatibility, model recognition accuracy, and intuitive result presentation. The core of the system lies in using a trained neural network model for performance classification and prediction, ultimately presenting the results in a user-friendly manner through graphs, reports, and text descriptions, helping users intuitively understand company performance trends and providing support for decision-making.
[0039] Specifically, the neural network model module further includes:
[0040] a. Training data acquisition unit, used to acquire historical company performance data for training neural network models;
[0041] b. A training unit, connected to the training data acquisition unit, is used to train the neural network model based on the historical company performance data; the training process typically minimizes the loss function between the prediction error and the true value as follows:
[0042] Loss=∑(y t rue-y p red) 2
[0043] Where Loss is the value of the loss function, y t rue is the true value, y p red is the predicted value;
[0044] c. A verification unit, connected to the training unit, for verifying the classification and prediction performance of the trained neural network model.
[0045] Specifically, the neural network model adopts a deep learning architecture, including convolutional neural networks, recurrent neural networks, long short-term memory networks, and transformer models, to adapt to different forms of company performance data.
[0046] Specifically, it also includes a data preprocessing module, which cleans, transforms, and standardizes the raw data before the data input module to ensure data quality and model performance.
[0047] Specifically, it also includes a user interaction module that allows users to input specific parameters and queries in order to more accurately customize and obtain classification and prediction results.
[0048] Specifically, it also includes a feedback module, which receives user feedback on classification and prediction results, and adjusts the parameters of the neural network model and retrains the neural network model to improve prediction accuracy.
[0049] Among them, the hardware accelerator connected to the neural network model module plays a significant role in the system, specifically in the following aspects:
[0050] Accelerating the computation process: Hardware accelerators can significantly speed up the computation process of neural network models. Compared with traditional general-purpose computing hardware, hardware accelerators such as GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units) are specifically designed for neural network computation, enabling them to process a large number of computational tasks in parallel and greatly improving computational efficiency.
[0051] Improved speed and efficiency: By using hardware accelerators, the classification and prediction speed of neural network models is significantly improved. This means that the system can process large amounts of company performance data faster, providing users with more timely performance predictions and classification results.
[0052] Reduced energy consumption and operating costs: Hardware accelerators, through specialized architecture design and algorithm optimization, can perform computing tasks with higher energy efficiency, reducing power consumption and operating costs. This is of significant economic importance for large-scale neural network applications.
[0053] Support for large-scale data and real-time decision-making: Thanks to the high performance of the hardware accelerator, the system is able to process larger datasets and support real-time decision-making scenarios, which is especially important for companies in market environments that deal with large amounts of data and require rapid response.
[0054] Specifically, it also includes a hardware accelerator connected to the neural network model module to accelerate the computation process of the neural network model and improve the speed and efficiency of classification and prediction.
[0055] As described above, the system receives and processes company performance data, including historical performance, market trends, and macroeconomic data, through the data input module. The neural network model module is trained using deep learning architectures (such as convolutional neural networks and recurrent neural networks) to identify key trends and patterns in the data and make performance predictions accordingly. The classification and prediction processing module transforms the model output into classification and prediction results, which are then presented to the user in the form of graphs, reports, and text descriptions through the output module. The system also includes a data preprocessing module to improve data quality, a user interaction module to support personalized queries, a feedback module to optimize model performance, and a hardware accelerator to improve processing speed. Through this system, users can intuitively understand company performance trends, providing support for decision-making. At the same time, the system can continuously learn and optimize based on user feedback to improve prediction accuracy.
[0056] Example 2: This example is basically the same as the previous example, except that it also includes a future planning module, which generates strategic planning suggestions for the company's future development based on the prediction results of the neural network model, including product strategy, market strategy and investment strategy.
[0057] Specifically, the future planning module can perform multi-objective optimization on the prediction results of the neural network model based on the company's long-term goals and constraints to generate the best future planning recommendations.
[0058] Specifically, the output module can be integrated with the company's internal management system to achieve automated performance forecasting, classification, and future planning, providing support for the company's decision-making and allowing the company to make timely strategic adjustments based on forecast results and planning recommendations.
[0059] As shown above, this system can provide companies with customized future development strategic plans based on the prediction results of neural network models. This module not only considers the company's long-term goals, but also performs multi-objective optimization of the prediction results in combination with constraints to generate the best product, market, and investment strategies. In addition, the output module is tightly integrated with the company's internal management system to realize automated performance forecasting, classification, and future planning, which greatly facilitates the company's decision-making process. In this way, the company can make strategic adjustments more quickly based on the prediction results and planning suggestions, thereby improving business efficiency and competitiveness.
[0060] All standard parts used in this invention can be purchased commercially, and irregularly shaped parts can be customized according to the description and drawings. The specific connection methods for each part all employ conventional methods such as bolts, rivets, and welding, which are mature technologies in the prior art. The machinery, parts, and equipment all use conventional models in the prior art, and the circuit connections also use conventional connection methods in the prior art, which will not be detailed here. Any content not described in detail in this specification belongs to the prior art known to those skilled in the art.
[0061] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.
[0062] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.
[0063] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.
[0064] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0065] The accompanying drawings of the embodiments disclosed in this invention only involve structures relevant to the embodiments disclosed in this invention. Other structures can be referred to with common designs. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.
[0066] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A classification system based on a neural network model, characterized in that, include: a. Data input module, used to receive company performance data to be classified and predicted, including historical performance data, market trend data and macroeconomic data, and convert this data into a format that can be processed by neural network models; b. The neural network model module is trained to identify and classify specific trends and patterns in company performance data, and to make performance predictions; the prediction process is as follows: y p red=f(x;θ) Among them, y p red is the prediction result, x is the input data, f is the neural network model function, and θ is the model parameters; c. A classification and prediction processing module, connected to the neural network model module, is used to receive classification and prediction results from the neural network model module and classify and predict company performance based on these results: d. Output module, connected to the classification and prediction processing module, for outputting the classification and prediction results in a user-readable manner, including graphics, reports and text descriptions.
2. The classification system based on a neural network model as described in claim 1, characterized in that, The neural network model module further includes: a. Training data acquisition unit, used to acquire historical company performance data for training neural network models; b. A training unit, connected to the training data acquisition unit, is used to train the neural network model based on the historical company performance data; the training process typically minimizes the loss function between the prediction error and the true value as follows: Loss=∑(y t rue-y p red) 2 Where Loss is the value of the loss function, y t rue is the true value, y p red is the predicted value; c. A verification unit, connected to the training unit, for verifying the classification and prediction performance of the trained neural network model.
3. The classification system based on a neural network model as described in claim 2, characterized in that, The neural network model employs a deep learning architecture, including convolutional neural networks, recurrent neural networks, long short-term memory networks, and transformer models, to adapt to different forms of company performance data.
4. The classification system based on a neural network model as described in claim 3, characterized in that, It also includes a data preprocessing module, which cleans, transforms, and standardizes the raw data before the data input module to ensure data quality and model performance.
5. The classification system based on a neural network model as described in claim 4, characterized in that, It also includes a user interaction module that allows users to input specific parameters and queries in order to more accurately customize and obtain classification and prediction results.
6. The classification system based on a neural network model as described in claim 5, characterized in that, It also includes a feedback module, which receives user feedback on classification and prediction results, and adjusts the parameters of the neural network model and retrains the neural network model to improve prediction accuracy.
7. The classification system based on a neural network model as described in claim 6, characterized in that, It also includes a hardware accelerator connected to the neural network model module to accelerate the computation process of the neural network model and improve the speed and efficiency of classification and prediction.
8. The classification system based on a neural network model as described in claim 6, characterized in that, It also includes a future planning module, which generates strategic planning suggestions for the company's future development based on the prediction results of neural network models, including product strategy, market strategy and investment strategy.
9. The classification system based on a neural network model as described in claim 8, characterized in that, The future planning module can perform multi-objective optimization on the prediction results of the neural network model based on the company's long-term goals and constraints to generate the best future planning recommendations.
10. The classification system based on a neural network model as described in claim 1, characterized in that, The output module can be integrated with the company's internal management system to automate performance forecasting, classification, and future planning, providing support for the company's decision-making and allowing the company to make timely strategic adjustments based on forecast results and planning recommendations.