Data processing system and method based on innovation chain

Abstract

The invention provides a data processing system and method based on an innovation chain, and relates to the field of data processing. The data processing system based on the innovation chain comprises a data acquisition module used for acquiring data from various data sources in an industrial chain, a supply chain and the innovation chain, and the data sources comprise an enterprise internal database, a supplier database, market research data, user feedback and a partner system; the data storage module is used for storing the collected data, supporting real-time data updating and historical data query, and supporting a distributed architecture to meet the requirements of large-scale data storage and processing; and the data processing module is used for cleaning, converting and analyzing the stored data. According to the system, data in an industrial chain, a supply chain and an innovation chain can be comprehensively collected, stored, processed and utilized, the dispersity of the data is broken, integrated management of the data is achieved, and powerful data support is provided for innovation activities, industrial chain collaboration and supply chain management of enterprises.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a data processing system and method based on an innovation chain. Background Technology

[0002] The term "innovation chain" is generally used to describe a series of interconnected innovation activities and processes that work together to drive the development and commercialization of new products, services, or business models. The concept of an innovation chain emphasizes the entire process from the generation of an idea to the realization of the final product or service, including research and development, design, prototyping, testing, production, marketing, and sales. In a broader sense, an innovation chain may also include supply chain management, partnerships, intellectual property management, fundraising, and regulatory compliance.

[0003] In the current wave of digital transformation and industrial collaborative development, the deep integration of the industrial chain, supply chain, and innovation chain has become the core engine for enterprise upgrading and industrial competitiveness enhancement. However, existing data management models generally face the following challenges: Data silos are prominent, with data from various links in the industrial chain, supply chain participants, and innovation chain entities scattered across independent systems, exhibiting heterogeneous formats and inconsistent standards, making cross-domain data sharing difficult and hindering the formation of synergistic effects; Insufficient data value mining: Traditional data processing often remains at the level of simple storage and statistics, lacking the ability to deeply clean, correlate, and intelligently predict massive amounts of data, thus failing to provide accurate support for innovation decisions and supply chain optimization; Low efficiency of cross-organizational collaboration: Collaboration between various links in the innovation chain and the industrial and supply chains relies on manual intervention, resulting in delayed data flow, extended R&D cycles, resource waste, and difficulty in adapting to rapidly changing market demands; Data security and scalability bottlenecks: With the explosive growth of data scale, centralized storage architectures face problems such as insufficient storage capacity and limited concurrent processing capabilities. Simultaneously, privacy protection and access control mechanisms in cross-organizational data sharing are not yet perfect, restricting the secure flow of data elements. Therefore, those skilled in the art provide a data processing system and method based on the innovation chain to solve the problems mentioned in the background. Summary of the Invention

[0004] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a data processing system and method based on the innovation chain, which solves the problems of prominent data silos, insufficient data value mining, and low efficiency of cross-organizational collaboration.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a data processing system based on an innovation chain, comprising: The data acquisition module is used to collect data from various data sources in the industrial chain, supply chain and innovation chain. The data sources include the company's internal database, supplier database, market research data, user feedback and partner system. The data storage module is used to store the collected data, supports real-time data updates and historical data queries, and supports a distributed architecture to meet the needs of large-scale data storage and processing. The data processing module is used to clean, transform, and analyze the stored data. The cleaning process includes removing duplicate data and correcting erroneous data, and the transformation process includes data format conversion and data aggregation. The data management module is used to distribute the processed data to various links in the innovation chain, support innovation activities such as idea generation and R&D decision-making, and support cross-organizational collaboration to ensure the synergy of innovation processes among all participants in the industrial chain and supply chain. The collaboration module is used to enable data sharing and collaborative optimization among all participants; The intelligent analytics module is used to analyze and predict data using machine learning and artificial intelligence technologies. The analysis results are used to support enterprise decision-making, innovation process optimization, and collaborative optimization of the industrial chain and supply chain.

[0006] Preferably, the data acquisition module acquires data through interface calls, data crawling, or file import.

[0007] Preferably, the data storage module uses a distributed file system to achieve distributed data storage, uses a message queue to achieve real-time data updates, and uses a data index to achieve historical data queries.

[0008] Preferably, when performing data cleaning, the data processing module determines and removes duplicate data by comparing key fields of the data, and identifies and corrects erroneous data by using data verification rules.

[0009] Preferably, the management module enables cross-organizational collaboration by establishing a cross-organizational collaborative platform.

[0010] Preferably, in the collaborative module, suppliers adjust production plans based on market demand data, and enterprises optimize inventory management based on supply chain data.

[0011] Preferably, the intelligent analysis module uses machine learning algorithms such as linear regression, decision tree, and random forest to establish a prediction module, and uses natural language processing technology to analyze unstructured data.

[0012] A data processing method based on the innovation chain includes the following steps: S1. Use the data acquisition module to collect data from various data sources in the industrial chain, supply chain and innovation chain. The data sources include internal enterprise databases, supplier databases, market research data, user feedback and partner systems. Select appropriate acquisition methods according to the characteristics of different data sources to ensure the integrity and accuracy of the collected data sources. S2. Data storage module, used to store the collected data, supports real-time data updates and historical data queries, and supports a distributed architecture to meet the needs of large-scale data storage and processing; S3. Data processing module, used to clean, transform and analyze stored data. The cleaning process includes removing duplicate data and correcting erroneous data. The transformation process includes data format conversion and data aggregation. Through these processing steps, the quality and usability of the data are improved. S4. Data Management Module: This module distributes processed data to various stages of the innovation chain, supports innovation activities such as idea generation and R&D decision-making, and supports cross-organizational collaboration to ensure the synergy of innovation processes among all participants in the industry chain and supply chain. S5. Collaboration Module: This module enables data sharing and collaborative optimization among all participants. Based on the analysis of market demand data, supply chain data, supplier adjustments to production plans, and enterprise inventory management optimization, it improves the collaborative efficiency of the industrial chain and supply chain. The S6 Intelligent Analysis Module utilizes machine learning and artificial intelligence technologies to analyze and predict data. The analysis results support enterprise decision-making, innovation process optimization, and collaborative optimization of the industrial and supply chains, providing data-driven decision support for the development of enterprises and industries.

[0013] (III) Beneficial Effects This invention provides a data processing system and method based on the innovation chain. It has the following beneficial effects: 1. This invention enables comprehensive collection, storage, processing, and utilization of data in the industrial chain, supply chain, and innovation chain, breaking the data fragmentation and realizing integrated data management, thus providing strong data support for enterprise innovation activities, industrial chain collaboration, and supply chain management.

[0014] 2. In this invention, by cleaning, transforming and analyzing the data, the quality and usability of the data are improved, enabling the data to better serve the enterprise's decision-making and operations.

[0015] 3. In this invention, the establishment of the innovation chain management module and the industrial chain and supply chain collaboration module promotes cross-organizational collaboration and innovation process coordination, which is conducive to improving the innovation capabilities of enterprises and the level of coordinated industrial development.

[0016] 4. In this invention, the intelligent analysis module utilizes machine learning and artificial intelligence technologies to analyze and predict data, enabling it to identify market trends and potential problems in advance, providing forward-looking support for enterprise decision-making and helping enterprises gain an advantage in market competition. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the overall system structure of the present invention. Detailed Implementation

[0018] 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.

[0019] Example 1: like Figure 1 As shown, this embodiment of the invention provides a data processing system based on an innovation chain, comprising: The data acquisition module is used to collect data from various data sources in the industry chain, supply chain, and innovation chain. These data sources include internal enterprise databases, supplier databases, market research data, user feedback, and partner systems. Data is read and collected through API interfaces. For relational databases, SQL statements can be used for data querying and export; for non-relational databases, data is collected according to their corresponding operation interfaces. For supplier databases, data interface specifications are determined through negotiation with suppliers, and relevant supplier data, including but not limited to product inventory data and production progress data, is obtained periodically or in real-time via interface calls. For market research data, if the market research report is provided by a third-party organization, the data can be imported into the system via file import; if it is an independently conducted market research, the data obtained from the research is entered into the system through a data entry interface. For user feedback, user feedback information is collected into the system in real-time through user feedback entry points set up on the company's official website, APP, and other channels. For partner systems, relevant partner data, such as progress data of cooperative projects and shared market data, is obtained by establishing data sharing channels with partners and using secure data transmission protocols such as HTTPS. The data storage module stores the collected data, supports real-time data updates and historical data queries, and features a distributed architecture to meet the needs of large-scale data storage and processing. It employs a distributed file system to achieve distributed data storage, distributing data across multiple data nodes. The NameNode manages the file system's namespace and client access to files, while DataNodes store the actual data blocks. For real-time data updates, a message queue is used to send real-time data to the queue. The data storage module retrieves data from the queue and updates it to the storage system promptly, ensuring data timeliness. For historical data queries, a data index is established. For time-series data, a time index is created to facilitate quick historical data queries based on time ranges. For other data types, corresponding indexes are created based on key data attributes to improve the efficiency of historical data queries. The data processing module is used to clean, transform, and analyze the stored data. The cleaning process includes removing duplicate data and correcting erroneous data, while the transformation process includes data format conversion and data aggregation. To remove duplicate data, we compare key fields of the data, such as the unique identifier and content summary, to determine if the data is duplicated. For duplicate data, we keep only one record. For user feedback data, we use the user ID and the content summary of the feedback as key fields. If both of these fields are the same in two feedback reports, the data is considered duplicate and one of them is removed. To correct erroneous data, data validation rules are used to identify erroneous data. For numerical data, it is determined whether the data is within a reasonable range. If it is outside the range, it is considered erroneous data. Then, the erroneous data is corrected by comparing it with the surrounding information or other relevant data, or the erroneous data is marked to remind humans to review and correct it. Data format conversion transforms data from different formats into a unified format, facilitating subsequent processing and analysis. Existing data conversion tools can be used, or a self-developed conversion program can be used to achieve format conversion. Based on business needs, relevant data can be aggregated, which can be achieved through SQL aggregation functions or other data processing algorithms. Data analysis employs statistical analysis methods, such as descriptive statistics, to analyze the basic characteristics of data and calculate the mean, variance, maximum or minimum values. It can also use data mining algorithms, such as association rule mining, to discover the relationships between data and provide a reference for the execution of corporate marketing strategies. The data management module is used to distribute the processed data to various links in the innovation chain, support innovation activities such as idea generation and R&D decision-making, and support cross-organizational collaboration to ensure the synergy of innovation processes among all participants in the industrial chain and supply chain. Creative Generation Stage: Market trend data and user demand data obtained from the analysis are provided to the creative generation team. The team can use this data to brainstorm and generate new product ideas, service ideas, etc. For example, if market research data shows that users have a high demand for a certain function, the creative generation team can design new product ideas around that function. R&D decision-making stage: Provide the R&D team with product technical parameters, market competition data, etc. The R&D team evaluates the feasibility and technical difficulty of the R&D project based on this data, and makes R&D decisions, such as whether to carry out the R&D project and the priority of the R&D. Cross-organizational collaboration: Establish cross-organizational collaboration platforms to share and distribute relevant data from different organizations. For example, in an industry alliance, innovation data and production data of various enterprises can be shared. Through workflow management, it can be ensured that enterprises work together in the innovation process. For example, in joint R&D projects, R&D progress data and results data of different enterprises can be shared in a timely manner, which facilitates the adjustment of R&D plans and resource allocation. The collaboration module is used to enable data sharing and collaborative optimization among all participants; Suppliers adjust their production plans based on market demand data: The system sends the analyzed market demand forecast data to suppliers, who then adjust their production plans accordingly, such as increasing or decreasing the production quantity of a certain product or adjusting the production schedule, to meet changes in market demand. Enterprises optimize inventory management based on supply chain data: Enterprises acquire data from each link in the supply chain, such as supplier delivery time data and logistics data during transportation, and combine it with their own sales data to use inventory optimization algorithms, such as the Economic Order Quantity (EOQ) model, to optimize inventory levels and reduce the risk of inventory backlog and stockouts. The intelligent analysis module is used to analyze and predict data using machine learning and artificial intelligence technologies. The analysis results are used to support enterprise decision-making, innovation process optimization, and collaborative optimization of the industrial chain and supply chain. Machine learning algorithms, such as linear regression, decision trees, and random forests, are used to train historical data and build predictive models, such as predicting product sales volume and market growth rate. Taking product sales volume prediction as an example, historical sales data, marketing data, and competitor data are used as input features to train the model, and then the trained model is used to predict future sales volume. By leveraging artificial intelligence technologies, such as natural language processing, we can analyze unstructured data such as user feedback and market research texts to extract key information and sentiment. For example, by analyzing positive and negative reviews of products in user feedback, we can understand user needs and dissatisfactions and provide direction for product improvement.

[0020] The data acquisition module collects data through API calls, data crawling, or file import.

[0021] The data storage module uses a distributed file system to achieve distributed data storage, a message queue to achieve real-time data updates, and a data index to achieve historical data queries.

[0022] When cleaning data, the data processing module identifies and removes duplicate data by comparing key fields of the data, and identifies and corrects erroneous data by using data validation rules.

[0023] The management module enables cross-organizational collaboration by establishing a cross-organizational collaborative platform.

[0024] In the collaboration module, suppliers adjust their production plans based on market demand data, and enterprises optimize inventory management based on supply chain data.

[0025] The intelligent analysis module uses machine learning algorithms such as linear regression, decision trees, and random forests to build a prediction module, and employs natural language processing technology to analyze unstructured data.

[0026] A data processing method based on the innovation chain includes the following steps: S1. Use the data acquisition module to collect data from various data sources in the industrial chain, supply chain and innovation chain. Data sources include internal enterprise databases, supplier databases, market research data, user feedback and partner systems. Select appropriate acquisition methods according to the characteristics of different data sources to ensure the integrity and accuracy of the collected data sources. S2. Data storage module, used to store the collected data, supports real-time data updates and historical data queries, and supports a distributed architecture to meet the needs of large-scale data storage and processing; S3. Data processing module, used to clean, transform and analyze stored data. The cleaning process includes removing duplicate data and correcting erroneous data, and the transformation process includes data format conversion and data aggregation. Through these processing steps, the quality and usability of the data are improved. S4. Data Management Module: This module distributes processed data to various stages of the innovation chain, supports innovation activities such as idea generation and R&D decision-making, and supports cross-organizational collaboration to ensure the synergy of innovation processes among all participants in the industry chain and supply chain. S5. Collaboration Module: This module enables data sharing and collaborative optimization among all participants. Based on the analysis of market demand data, supply chain data, supplier adjustments to production plans, and enterprise inventory management optimization, it improves the collaborative efficiency of the industrial chain and supply chain. The S6 Intelligent Analysis Module utilizes machine learning and artificial intelligence technologies to analyze and predict data. The analysis results support enterprise decision-making, innovation process optimization, and collaborative optimization of the industrial and supply chains, providing data-driven decision support for the development of enterprises and industries.

[0027] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A data processing system based on the innovation chain, characterized in that: include: The data acquisition module is used to collect data from various data sources in the industrial chain, supply chain and innovation chain. The data sources include the company's internal database, supplier database, market research data, user feedback and partner system. The data storage module is used to store the collected data, supports real-time data updates and historical data queries, and supports a distributed architecture to meet the needs of large-scale data storage and processing. The data processing module is used to clean, transform, and analyze the stored data. The cleaning process includes removing duplicate data and correcting erroneous data, and the transformation process includes data format conversion and data aggregation. The data management module is used to distribute the processed data to various links in the innovation chain, support innovation activities such as idea generation and R&D decision-making, and support cross-organizational collaboration to ensure the synergy of innovation processes among all participants in the industrial chain and supply chain. The collaboration module is used to enable data sharing and collaborative optimization among all participants; The intelligent analytics module is used to analyze and predict data using machine learning and artificial intelligence technologies. The analysis results are used to support enterprise decision-making, innovation process optimization, and collaborative optimization of the industrial chain and supply chain.

2. The data processing system based on the innovation chain according to claim 1, characterized in that: The data acquisition module collects data through interface calls, data crawling, or file import.

3. The data processing system based on the innovation chain according to claim 1, characterized in that: The data storage module uses a distributed file system to achieve distributed data storage, a message queue to achieve real-time data updates, and a data index to achieve historical data queries.

4. A data processing system based on an innovation chain according to claim 1, characterized in that: When cleaning data, the data processing module compares key fields of the data to identify and remove duplicate data, and identifies and corrects erroneous data through data verification rules.

5. A data processing system based on an innovation chain according to claim 1, characterized in that: The management module enables cross-organizational collaboration by establishing a cross-organizational collaborative platform.

6. A data processing system based on an innovation chain according to claim 1, characterized in that: In the collaborative module, suppliers adjust their production plans based on market demand data, and enterprises optimize inventory management based on supply chain data.

7. A data processing system based on an innovation chain according to claim 1, characterized in that: The intelligent analysis module uses machine learning algorithms such as linear regression, decision trees, and random forests to build a prediction module, and uses natural language processing technology to analyze unstructured data.

8. A data processing method based on the innovation chain, characterized in that: Includes the following steps: S1. Use the data acquisition module to collect data from various data sources in the industrial chain, supply chain and innovation chain. The data sources include internal enterprise databases, supplier databases, market research data, user feedback and partner systems. Select appropriate acquisition methods according to the characteristics of different data sources to ensure the integrity and accuracy of the collected data sources. S2. Data storage module, used to store the collected data, supports real-time data updates and historical data queries, and supports a distributed architecture to meet the needs of large-scale data storage and processing; S3. Data processing module, used to clean, transform and analyze stored data. The cleaning process includes removing duplicate data and correcting erroneous data. The transformation process includes data format conversion and data aggregation. Through these processing steps, the quality and usability of the data are improved. S4. Data Management Module: This module distributes processed data to various stages of the innovation chain, supports innovation activities such as idea generation and R&D decision-making, and supports cross-organizational collaboration to ensure the synergy of innovation processes among all participants in the industry chain and supply chain. S5. Collaboration Module: This module enables data sharing and collaborative optimization among all participants. Based on the analysis of market demand data, supply chain data, supplier adjustments to production plans, and enterprise inventory management optimization, it improves the collaborative efficiency of the industrial chain and supply chain. The S6 Intelligent Analysis Module utilizes machine learning and artificial intelligence technologies to analyze and predict data. The analysis results support enterprise decision-making, innovation process optimization, and collaborative optimization of the industrial and supply chains, providing data-driven decision support for the development of enterprises and industries.

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