Integrated management system for enterprise collaborative management and innovative services

Abstract

The invention discloses an integrated management system for enterprise collaborative management and innovative services, which belongs to the technical field of management systems, and aims to solve the problem that product innovation and service optimization lack systematic methods and tools, and it is difficult to meet the needs of enterprises. The invention obtains internal and external data of the enterprise through a data collection unit, and processes and analyzes the data through a data analysis unit. The trend prediction module is based on data analysis and model operation. It first performs quantum state data encoding to increase data representation and potential information content, and then constructs a fusion neural network, combines multiple neural networks, and realizes sub-network information transmission and fusion through quantum entanglement and quantum gate operations to process different types of data and tasks. Then, a quantum heuristic optimization algorithm is used to optimize the fusion neural network parameters to achieve a balance between prediction accuracy, complexity and data diversity. Finally, dynamic trend prediction is used to predict future trends in real time or periodically, so as to realize the integration of enterprise collaborative management and innovative services and meet the innovation needs of enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of management systems, and in particular to an integrated management system for enterprise collaborative management and innovative services. Background Art

[0002] In today's fast-paced business environment, companies are facing many complex challenges. Market trends change rapidly, and accurately predicting future trends is crucial to the survival and development of companies. However, traditional trend forecasting systems often rely on limited data sources and a single analysis model. Product innovation and service optimization lack systematic methods and tools, making it difficult to meet ever-changing customer needs and capture complex and changing market dynamics. Summary of the invention

[0003] The purpose of the present invention is to provide an integrated management system for enterprise collaborative management and innovative services, which solves the problem that product innovation and service optimization in the background technology lack systematic methods and tools and are difficult to meet the needs of enterprises.

[0004] To achieve the above objectives, the present invention provides the following technical solutions: an integrated management system for enterprise collaborative management and innovative services, comprising:

[0005] Data collection unit: including internal data collection module and external data collection module. The internal data collection module obtains various data generated in the enterprise's own operation process, including sales data, production data and financial data. Sales data can reflect the market performance of products and customer purchasing behavior; production data helps to monitor production processes and optimize production efficiency; financial data provides a basis for the company's capital management and decision-making. The external data collection module covers relevant information captured from the Internet, including user purchase history, browsing history and social interaction data, and also includes cooperation with third-party data suppliers to obtain more professional market research data. Market research data includes user portrait feature data and industry trend data;

[0006] Data analysis unit: The data analysis unit includes a data preprocessing module, a behavior pattern analysis module, a preference analysis module, and a trend prediction module. After cooperating with a third-party data supplier to obtain more professional market research data, the data preprocessing module, the behavior pattern analysis module, and the preference analysis module and their steps can be omitted. The data preprocessing module is used to remove duplicate, erroneous, and invalid data and perform standardization processing. The behavior pattern analysis module uses a clustering algorithm to group user behavior data, and extracts and describes the behavior pattern characteristics. The preference analysis module establishes a collaborative filtering model based on users and items to calculate the user preference degree, and extracts and describes the behavior pattern characteristics. The trend prediction module is used to predict user behavior trends;

[0007] Innovation Service Unit: used to determine the company's product innovation and service optimization;

[0008] Collaborative management unit: used to improve efficient communication and collaboration between departments within the enterprise;

[0009] Decision support unit: used to provide data support and analysis results for corporate decision-making;

[0010] System management unit: used to set up, maintain and manage the entire integrated management system;

[0011] The trend prediction module predicts market trends, industry development directions and user demand changes based on data analysis and model calculations, and provides reference for the strategic decision-making of enterprises. The prediction steps of the trend prediction module include:

[0012] S1. Quantum state data encoding: The collected data is quantum-encoded so that each data point uses a quantum gate to realize the state transformation and entanglement of the quantum bit. A data feature is represented as where α and β are complex coefficients, satisfying α 2 +|β 2 =1;

[0013] S2. Construct a fusion neural network: Design a fusion neural network composed of multiple sub-networks. Each sub-network is a different type of neural network. The convolutional neural network is used to process image data, the recurrent neural network is used to process time series data, and the graph neural network is used to process relational data. The sub-networks are connected through the quantum entanglement mechanism, so that different sub-networks can share information and influence each other. Quantum gate operations are used to realize information transmission and fusion between sub-networks.

[0014] S3. Quantum-inspired optimization: quantum-inspired optimization algorithm is used to optimize the parameters of the fusion neural network;

[0015] S4. Dynamic trend prediction: Use the optimized fusion neural network to predict future trends. The prediction process is real-time or periodic, and is adjusted according to actual needs. During the prediction process, data and model parameters are continuously updated to adapt to the ever-changing market environment and data distribution. The online learning method is used to allow the model to be quickly updated and adjusted when new data arrives, thereby improving the timeliness and accuracy of the prediction.

[0016] In step S1, for a data point x=(x 1 ,x 2 ,…,x b ), encoding it into a quantum state where |i> is the quantum state represented by an n-bit binary number, α i is a complex coefficient, satisfying Each feature of a data point corresponds to a quantum bit. Through the superposition of different quantum states, the data point is in multiple possible states at the same time, thereby increasing the representation ability and potential information content of the data. The coefficient α i Represents the probability amplitude of the data point being in the i-th quantum state; the Hadamard gate is used to transform and entangle the quantum bit state. For a single quantum bit, the effect of the Hadamard gate is expressed as When a quantum bit is in state |0>, after the Hadamard gate, it will be transformed into When the quantum bit is in the |1> state, after the Hadamard gate, it will be transformed into superposition state of ;

[0017] The formula of the fusion neural network in step S2 is The fusion neural network consists of m sub-networks, and the output of the pth sub-network is y p , where w p is the weight coefficient of the p-th sub-network, satisfying The output of the fusion neural network is the weighted sum of the outputs of each sub-network. Each sub-network is optimized for different types of data or tasks. p , controls the contribution of each sub-network to the final output, and the sum of the weight coefficients is 1, ensuring that the output is a reasonable combination of the outputs of each sub-network;

[0018] The quantum-inspired optimization algorithm in step S3 is: Let the objective function be F(θ), where θ is the parameter vector of the fusion neural network, and the objective function is a combination of multiple objectives such as prediction accuracy, model complexity, and data diversity, F(θ) = w 1 E(θ)+w 2 C(θ)+w 3 D(θ), where E(θ) is the prediction error function, C(θ) is the model complexity function, D(θ) is the data diversity function, and w 1 ,w 2 ,w 3 is the weight coefficient; the prediction error function E(θ) measures the prediction accuracy of the model, using the root mean square error or mean absolute error indicator; the model complexity function C(θ) measures the complexity of the model, including the number of parameters and layers of the neural network; the data diversity function D(θ) measures the diversity of the data, including the entropy and variance of the data, w 1 ,w 2 ,w 3 The weight coefficient is used to adjust the importance of each target in the objective function. By optimizing the objective function, a set of optimal model parameters is found so that the model achieves a balance between prediction accuracy, complexity and data diversity.

[0019] Furthermore, the data preprocessing module uses Python’s pandas library for data cleaning and organization, including processing missing values ​​using the file na() method, removing outliers and combining the statistical characteristics and visualization of the data for post-processing judgment. For data standardization, the StandardSca ler in the scikit-learn library is used for Z-score standardization to convert the data into a distribution with a mean of 0 and a standard deviation of 1, so that data with different characteristics have the same scale in subsequent analysis.

[0020] Furthermore, the behavior pattern analysis module uses the K-Means clustering algorithm in the scikit-learn library to perform cluster analysis on user behavior data to identify user groups with similar behavior patterns. At the same time, it combines the association rule mining algorithm and uses the April algorithm in the mlxtend library of Python to mine frequent item sets in user behavior data and discover the correlation between different behaviors, so as to better understand user behavior patterns.

[0021] Furthermore, the preference analysis module uses the user-based collaborative filtering algorithm in Python's Surprise library to infer user preferences by analyzing users' historical behavior data. At the same time, it combines the NLTK (Natural Language Tool kit) library to perform sentiment analysis and keyword extraction on open-ended questions in user feedback to further enrich the understanding of user preferences.

[0022] Furthermore, the specific process of dynamic trend prediction in step S4 is as follows:

[0023] S41. Data update: obtain new data from the internal data acquisition module and the external data acquisition module, integrate the new data with the original data, ensure the integrity and consistency of the data, and store the updated data in the database for subsequent use;

[0024] S42. Model parameter update: Use the root mean square error or mean absolute error indicator to calculate the prediction error of the model on the new data. Based on the prediction error, calculate the gradient of the model parameters through the back propagation algorithm. Based on the gradient information, use the stochastic gradient descent algorithm to adjust the parameters of the model so that the prediction error of the model on the new data gradually decreases. Repeat the above process continuously. As new data continues to arrive, continuously update the model parameters to adapt to the ever-changing market environment and data distribution.

[0025] Furthermore, the innovation service unit includes:

[0026] Product innovation module: Displays various types of enterprise data to help decision makers quickly understand the meaning of the data. It displays the distribution of market share through pie charts and provides data analysis tools to help decision makers analyze data in depth and discover the patterns and problems behind the data. Based on data visualization and analysis results, it provides decision support to decision makers to help them make more informed decisions.

[0027] Service optimization module: Analyze the problems and deficiencies in the current service by collecting user feedback and monitoring service indicators. By analyzing user complaints and suggestions, it is found that there are cumbersome links in the service process, which lead to poor user experience. According to the analysis results of service problems, design specific service optimization plans, be responsible for implementing the service optimization plans, and track the implementation results;

[0028] Marketing strategy formulation module: Based on the results of user portraits and behavioral analysis, the market is segmented into different sub-markets. Each sub-market has similar needs and characteristics. Personalized marketing strategies are formulated for different market segments. The effectiveness of marketing strategies is evaluated through monitoring and data analysis of marketing activities, and marketing strategies are adjusted in a timely manner based on the evaluation results.

[0029] Furthermore, the collaborative management unit includes:

[0030] Communication and collaboration module: provides real-time text, voice and video communication tools between employees within the enterprise, as well as file sharing, task allocation and progress tracking tools to facilitate information exchange and collaboration between employees;

[0031] Task and project management module: used to clearly assign the company's tasks to relevant employees or teams, ensure that the task responsibilities are assigned to individuals, assist the company in project planning, track the execution progress of tasks and projects in real time, and promptly identify problems and take measures to solve them;

[0032] Document management module: used to establish an enterprise document library to centrally store various documents of the enterprise, support multiple people to edit documents online at the same time, and improve team collaboration efficiency.

[0033] Furthermore, the decision support unit includes:

[0034] Data visualization module: used to display various types of enterprise data, so that decision makers can quickly understand the meaning of the data. It provides data analysis tools to help decision makers analyze the data in depth and discover the patterns and problems behind the data. Based on data visualization and analysis results, it provides decision support for decision makers to help them make more informed decisions.

[0035] Report generation module: automatically generates regular data analysis reports based on preset templates and data, and generates special analysis reports based on needs;

[0036] Decision-making recommendation module: interprets the data analysis results to help decision makers understand the meaning and impact of the data. Based on the data analysis and interpretation results, it provides specific decision-making recommendations to decision makers. By establishing a decision model and simulating the effects of different decision-making plans, it helps decision makers evaluate the feasibility and risks of decisions.

[0037] Furthermore, the system management unit includes:

[0038] User management module: responsible for user registration and login management, ensuring that only authorized users can access the system, setting different user permissions based on user roles and responsibilities, ensuring that users can only access and operate functions and data within their own permissions, and managing basic user information;

[0039] Data security management module: Use encryption technology to encrypt the data in the system for storage and transmission, ensure the security and confidentiality of the data, establish a strict access control mechanism to limit users' access to sensitive data, back up the data in the system regularly, and ensure that the data can be quickly restored when it is lost or damaged;

[0040] System settings module: used to configure various system parameters and set the system notification method and content.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The integrated management system for enterprise collaborative management and innovation services provided by the present invention obtains internal and external data of the enterprise through a data collection unit, and processes and analyzes the data by a data analysis unit, including preprocessing, behavior pattern analysis, preference analysis and trend prediction. The trend prediction module is based on data analysis and model operation. It first performs quantum state data encoding to increase data representation and potential information content, and then constructs a fusion neural network, combines multiple neural networks and realizes sub-network information transmission fusion through quantum entanglement and quantum gate operations to process different types of data and tasks. Then, a quantum heuristic optimization algorithm is used to optimize the fusion neural network parameters to achieve a balance between prediction accuracy, complexity and data diversity. Finally, dynamic trend prediction is used to predict future trends in real time or periodically, and data and model parameters are continuously updated to adapt to market changes. This module provides an important reference for enterprise strategic decision-making, helps predict market trends, industry development directions and changes in user needs, and the system integrates data collection, analysis and innovation service functions to realize the integration of enterprise collaborative management and innovation services. The trend prediction module provides forward-looking support for enterprise decision-making to meet the innovation needs of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] In order to solve the technical problems of lack of systematic methods and tools for product innovation and service optimization, it is difficult to meet the needs of enterprises, such as Figure 1 As shown, the following preferred technical solutions are provided:

[0046] Integrated management system for enterprise collaborative management and innovative services, including:

[0047] Data collection unit: including internal data collection module and external data collection module. The internal data collection module obtains various data generated in the enterprise's own operation process, including sales data, production data and financial data. Sales data can reflect the market performance of products and customer purchasing behavior; production data helps to monitor production processes and optimize production efficiency; financial data provides a basis for the company's capital management and decision-making. The external data collection module covers relevant information captured from the Internet, including user purchase history, browsing history and social interaction data, and also includes cooperation with third-party data suppliers to obtain more professional market research data. Market research data includes user portrait feature data and industry trend data;

[0048] Data analysis unit: The data analysis unit includes a data preprocessing module, a behavior pattern analysis module, a preference analysis module, and a trend prediction module. After cooperating with a third-party data supplier to obtain more professional market research data, the data preprocessing module, the behavior pattern analysis module, and the preference analysis module and their steps can be omitted. The data preprocessing module is used to remove duplicate, erroneous, and invalid data and perform standardization processing. The behavior pattern analysis module uses a clustering algorithm to group user behavior data, and extracts and describes the behavior pattern characteristics. The preference analysis module establishes a collaborative filtering model based on users and items to calculate the user preference degree, and extracts and describes the behavior pattern characteristics. The trend prediction module is used to predict user behavior trends;

[0049] Innovation Service Unit: used to determine the company's product innovation and service optimization;

[0050] Collaborative management unit: used to improve efficient communication and collaboration between departments within the enterprise;

[0051] Decision support unit: used to provide data support and analysis results for corporate decision-making;

[0052] System management unit: used to set up, maintain and manage the entire integrated management system;

[0053] The trend forecasting module predicts market trends, industry development directions and changes in user needs based on data analysis and model calculations, providing reference for the company's strategic decision-making. The forecasting steps of the trend forecasting module include:

[0054] S1. Quantum state data encoding: The collected data is quantum-encoded so that each data point uses a quantum gate to realize the state transformation and entanglement of the quantum bit. A data feature can be expressed as where α and β are complex coefficients, satisfying α 2 +|β 2 =1;

[0055] S2. Construct a fusion neural network: Design a fusion neural network composed of multiple sub-networks. Each sub-network is a different type of neural network. Convolutional neural networks are used to process image data, recurrent neural networks are used to process time series data, and graph neural networks are used to process relational data. The sub-networks are connected through the quantum entanglement mechanism, so that different sub-networks can share information and influence each other. Quantum gate operations are used to realize information transmission and fusion between sub-networks.

[0056] S3. Quantum-inspired optimization: quantum-inspired optimization algorithm is used to optimize the parameters of the fusion neural network;

[0057] S4. Dynamic trend prediction: Use the optimized fusion neural network to predict future trends. The prediction process is real-time or periodic and adjusted according to actual needs. During the prediction process, data and model parameters are continuously updated to adapt to the ever-changing market environment and data distribution. Online learning is used to allow the model to be quickly updated and adjusted when new data arrives, thereby improving the timeliness and accuracy of the prediction.

[0058] The data preprocessing module uses Python's pandas library for data cleaning and organization, including the processing of missing values ​​using the fill na() method, removing outliers and combining the statistical characteristics and visualization of the data for post-processing judgment. For data standardization, the StandardSca l er in the sc ik it-l earn library is used for Z-score standardization to convert the data into a distribution with a mean of 0 and a standard deviation of 1, so that data with different characteristics have the same scale in subsequent analysis.

[0059] The behavior pattern analysis module uses the K-Means clustering algorithm in the scik it-l earn library to perform cluster analysis on user behavior data to identify user groups with similar behavior patterns. At the same time, it combines the association rule mining algorithm and uses the April algorithm in the mlxtend library of Python to mine frequent item sets in user behavior data and discover the correlation between different behaviors, so as to better understand user behavior patterns.

[0060] The preference analysis module uses the user-based collaborative filtering algorithm in Python's Surprise library to infer user preferences by analyzing users' historical behavior data. It also combines the NLTK (Natural Language Tool kit) library to perform sentiment analysis and keyword extraction on open-ended questions in user feedback to further enrich our understanding of user preferences.

[0061] In step S1, for a data point x=(x 1 ,x 2 ,…,x b ), which can be encoded as a quantum state where |i> is the quantum state represented by an n-bit binary number, α i is a complex coefficient, satisfying Each feature of a data point corresponds to a quantum bit. Through the superposition of different quantum states, the data point can be in multiple possible states at the same time, thereby increasing the representation ability and potential information content of the data. The coefficient α i Represents the probability amplitude of the data point being in the i-th quantum state; the Hadamard gate is used to transform and entangle the quantum bit state. For a single quantum bit, the effect of the Hadamard gate can be expressed as When a quantum bit is in state |0>, after the Hadamard gate, it will be transformed into When the quantum bit is in the |1> state, after the Hadamard gate, it will be transformed into superposition state of ;

[0062] The formula of the fusion neural network in step S2 is The fusion neural network consists of m sub-networks, and the output of the pth sub-network is y i , where w p is the weight coefficient of the p-th sub-network, satisfying The output of the fusion neural network is the weighted sum of the outputs of each sub-network. Each sub-network is optimized for different types of data or tasks. i, the contribution of each sub-network to the final output can be controlled, and the sum of the weight coefficients is 1, ensuring that the output is a reasonable combination of the outputs of each sub-network;

[0063] The quantum-inspired optimization algorithm in step S3 is: Let the objective function be F(θ), where θ is the parameter vector of the fusion neural network, and the objective function is a combination of multiple objectives such as prediction accuracy, model complexity, and data diversity, F(θ) = w 1 E(θ)+w 2 C(θ)+w 3 D(θ), where E(θ) is the prediction error function, C(θ) is the model complexity function, D(θ) is the data diversity function, and w 1 ,w 2 ,w 3 is the weight coefficient; the prediction error function E(θ) measures the prediction accuracy of the model, using the root mean square error or mean absolute error indicator; the model complexity function C(θ) measures the complexity of the model, including the number of parameters and layers of the neural network; the data diversity function D(θ) measures the diversity of the data, including the entropy and variance of the data, w 1 ,w 2 ,w 3 The weight coefficient is used to adjust the importance of each target in the objective function. By optimizing the objective function, a set of optimal model parameters is found so that the model achieves a balance between prediction accuracy, complexity and data diversity.

[0064] The specific process of dynamic trend prediction in step S4 is:

[0065] S41. Data update: obtain new data from the internal data acquisition module and the external data acquisition module, integrate the new data with the original data, ensure the integrity and consistency of the data, and store the updated data in the database for subsequent use;

[0066] S42. Model parameter update: Use the root mean square error or mean absolute error indicator to calculate the prediction error of the model on the new data. Based on the prediction error, calculate the gradient of the model parameters through the back propagation algorithm. Based on the gradient information, use the stochastic gradient descent algorithm to adjust the parameters of the model so that the prediction error of the model on the new data gradually decreases. Repeat the above process continuously. As new data continues to arrive, continuously update the model parameters to adapt to the ever-changing market environment and data distribution.

[0067] Innovation service units include:

[0068] Product innovation module: Displays all kinds of enterprise data in intuitive forms such as charts and graphs, so that decision makers can quickly understand the meaning of the data. For example, a bar chart can be used to display the trend of sales data, and a pie chart can be used to display the distribution of market share. Data analysis tools are provided to help decision makers analyze data in depth and discover the patterns and problems behind the data. For example, user behavior data can be analyzed through data mining algorithms to find out the user's purchasing preferences and behavior patterns. Based on data visualization and analysis results, decision support is provided to decision makers to help them make more informed decisions. For example, based on the analysis results of sales data, it is recommended that decision makers adjust product strategies or market strategies.

[0069] Service optimization module: Analyze the problems and deficiencies in the current service by collecting user feedback and monitoring service indicators. By analyzing user complaints and suggestions, it is found that there are cumbersome links in the service process, which leads to poor user experience. According to the analysis results of service problems, design specific service optimization plans, including simplifying service processes, improving service quality, and increasing personalized services. Be responsible for implementing the service optimization plan and tracking the implementation effect.

[0070] Marketing strategy formulation module: Based on the results of user portraits and behavioral analysis, the market is segmented into different sub-markets, each with similar needs and characteristics; for example, through the preference analysis module and the behavior pattern analysis module, users are divided into different groups, such as price-sensitive users, quality-seeking users, etc.; for different market segments, personalized marketing strategies are formulated, including product positioning, pricing strategies, promotional activities, channel selection, etc. The effectiveness of the marketing strategy is evaluated through monitoring and data analysis of marketing activities, and the marketing strategy is adjusted in a timely manner according to the evaluation results. For example, by analyzing sales data, market share, user feedback and other indicators, it is determined whether the marketing strategy has achieved the expected goals. If not, the strategy is adjusted in a timely manner to improve the marketing effect.

[0071] The collaborative management units include:

[0072] Communication and collaboration module: provides real-time text, voice and video communication tools between employees within the enterprise, as well as file sharing, task allocation and progress tracking tools to facilitate information exchange and collaboration between employees;

[0073] Task project management module: used to clearly assign the company's tasks to relevant employees or teams, ensure that the task responsibilities are assigned to individuals, assist the company in project planning, including setting project goals, project plans, project budgets, etc., track the execution progress of tasks and projects in real time, and promptly identify problems and take measures to solve them;

[0074] Document management module: used to establish an enterprise document library to centrally store various enterprise documents, such as contracts, reports, plans, etc., support multiple people to edit documents online at the same time, and improve team collaboration efficiency.

[0075] The decision support unit includes:

[0076] Data visualization module: used to display various types of enterprise data in intuitive forms such as charts and graphs, so that decision makers can quickly understand the meaning of the data. It provides data analysis tools to help decision makers analyze the data in depth and discover the patterns and problems behind the data. Based on data visualization and analysis results, it provides decision support for decision makers to help them make more informed decisions.

[0077] Report generation module: automatically generates regular data analysis reports such as weekly, monthly, and quarterly reports based on preset templates and data, and generates special analysis reports such as market research reports and project evaluation reports based on demand;

[0078] Decision-making recommendation module: interprets the data analysis results to help decision makers understand the meaning and impact of the data. Based on the data analysis and interpretation results, it provides specific decision-making recommendations to decision makers. By establishing a decision model and simulating the effects of different decision-making plans, it helps decision makers evaluate the feasibility and risks of decisions.

[0079] The system management unit includes:

[0080] User management module: responsible for user registration and login management, ensuring that only authorized users can access the system, setting different user permissions based on user roles and responsibilities, ensuring that users can only access and operate functions and data within their permissions, and managing basic user information such as name, department, position, etc.;

[0081] Data security management module: Use encryption technology to encrypt the data in the system for storage and transmission, ensure the security and confidentiality of the data, establish a strict access control mechanism to limit users' access to sensitive data, back up the data in the system regularly, and ensure that the data can be quickly restored when it is lost or damaged;

[0082] System settings module: used to configure various system parameters, such as server address, port number, database connection parameters, etc., and set the system notification method and notification content, such as email notification, SMS notification, etc.

[0083] Specifically, various data inside and outside the enterprise are obtained through the data collection unit, and the data analysis unit processes and analyzes these data, including data preprocessing, behavior pattern analysis, preference analysis and trend prediction; among them, the trend prediction module is based on data analysis and model calculation, and converts data into quantum state representation through quantum state data encoding, thereby increasing the data's representation capability and potential information content. Next, a fusion neural network is constructed, combining convolutional neural networks, recurrent neural networks and graph neural networks. Information transmission and fusion between sub-networks are realized through quantum entanglement mechanism and quantum gate operation, so that different types of data and tasks can be processed. The parameters of the fusion neural network are optimized using quantum heuristic optimization algorithm to find the optimal model parameters so that the model can achieve a balance between prediction accuracy, complexity and data diversity. Finally, through dynamic trend prediction, the optimized fusion neural network is used to make real-time or periodic predictions of future trends, and the data and model parameters are continuously updated to adapt to the ever-changing market environment and data distribution. The trend prediction module provides an important reference for the strategic decision-making of enterprises, helping enterprises predict market trends, industry development directions and changes in user needs. The system realizes the integration of enterprise collaborative management and innovation services by integrating functions such as data collection, analysis and innovation services, and the trend prediction module provides forward-looking support for enterprise decision-making, helping enterprises better respond to market changes and meet user needs.

[0084] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0085] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. The integrated management system of enterprise collaborative management and innovation services is characterized by: include: Data collection unit: including internal data collection module and external data collection module. The internal data collection module obtains various data generated during the enterprise's own operation, including sales data, production data and financial data. Sales data can reflect the market performance of products and customer purchasing behavior; Production data helps monitor production processes and optimize production efficiency; financial data provides a basis for the company's fund management and decision-making. The external data collection module covers relevant information captured from the Internet, including user purchase history, browsing history and social interaction data, and also includes cooperation with third-party data suppliers to obtain more professional market research data, including user portrait feature data and industry trend data; Data analysis unit: The data analysis unit includes a data preprocessing module, a behavior pattern analysis module, a preference analysis module, and a trend prediction module. After cooperating with a third-party data supplier to obtain more professional market research data, the data preprocessing module, the behavior pattern analysis module, and the preference analysis module and their steps can be omitted. The data preprocessing module is used to remove duplicate, erroneous, and invalid data and perform standardization processing. The behavior pattern analysis module uses a clustering algorithm to group user behavior data, and extracts and describes the behavior pattern characteristics. The preference analysis module establishes a collaborative filtering model based on users and items to calculate the user preference degree, and extracts and describes the behavior pattern characteristics. The trend prediction module is used to predict user behavior trends; Innovation Service Unit: used to determine the company's product innovation and service optimization; Collaborative management unit: used to improve efficient communication and collaboration between departments within the enterprise; Decision support unit: used to provide data support and analysis results for corporate decision-making; System management unit: used to set up, maintain and manage the entire integrated management system; The trend prediction module predicts market trends, industry development directions and user demand changes based on data analysis and model calculations, and provides reference for the strategic decision-making of enterprises. The prediction steps of the trend prediction module include: S1. Quantum state data encoding: The collected data is quantum-encoded so that each data point uses a quantum gate to realize the state transformation and entanglement of the quantum bit. A data feature is represented as where α and β are complex coefficients, satisfying α 2 +|β 2 =1; S2. Construct a fusion neural network: Design a fusion neural network composed of multiple sub-networks. Each sub-network is a different type of neural network. The convolutional neural network is used to process image data, the recurrent neural network is used to process time series data, and the graph neural network is used to process relational data. The sub-networks are connected through the quantum entanglement mechanism, so that different sub-networks can share information and influence each other. Quantum gate operations are used to realize information transmission and fusion between sub-networks. S3. Quantum-inspired optimization: quantum-inspired optimization algorithm is used to optimize the parameters of the fusion neural network; S4. Dynamic trend prediction: Use the optimized fusion neural network to predict future trends. The prediction process is real-time or periodic, and is adjusted according to actual needs. During the prediction process, data and model parameters are continuously updated to adapt to the ever-changing market environment and data distribution. The online learning method is used to allow the model to be quickly updated and adjusted when new data arrives, thereby improving the timeliness and accuracy of the prediction. In step S1, for a data point x=(x1,x2,…,x b ), encoding it into a quantum state where |i> is the quantum state represented by an n-bit binary number, α i is a complex coefficient, satisfying Each feature of a data point corresponds to a quantum bit. Through the superposition of different quantum states, the data point is in multiple possible states at the same time, thereby increasing the representation ability and potential information content of the data. The coefficient α i Represents the probability amplitude of the data point being in the i-th quantum state; the Hadamard gate is used to transform and entangle the quantum bit state. For a single quantum bit, the effect of the Hadamard gate is expressed as When a quantum bit is in state |0>, after the Hadamard gate, it will be transformed into When the quantum bit is in the |1> state, after the Hadamard gate, it will be transformed into superposition state of ; The formula of the fusion neural network in step S2 is The fusion neural network consists of m sub-networks, and the output of the pth sub-network is y p , where w p is the weight coefficient of the p-th sub-network, satisfying The output of the fusion neural network is the weighted sum of the outputs of each sub-network. Each sub-network is optimized for different types of data or tasks. p , controls the contribution of each sub-network to the final output, and the sum of the weight coefficients is 1, ensuring that the output is a reasonable combination of the outputs of each sub-network; The quantum heuristic optimization algorithm in step S3 is: let the objective function be F(θ), where θ is the parameter vector of the fusion neural network, the objective function is a combination of multiple objectives such as prediction accuracy, model complexity and data diversity, F(θ)=w1E(θ)+w2C(θ)+w3D(θ), where E(θ) is the prediction error function, C(θ) is the model complexity function, D(θ) is the data diversity function, and w1, w2, w3 are weight coefficients; the prediction error function E(θ) measures the prediction accuracy of the model, using the root mean square error or mean absolute error indicator, the model complexity function C(θ) measures the complexity of the model, including the number of parameters and layers of the neural network, the data diversity function D(θ) measures the diversity of the data, including the entropy and variance of the data, and the w1, w2, w3 weight coefficients are used to adjust the importance of each objective in the objective function, and by optimizing the objective function, find a set of optimal model parameters so that the model achieves a balance between prediction accuracy, complexity and data diversity.

2. The integrated management system for enterprise collaborative management and innovative services as claimed in claim 1, characterized in that: The data preprocessing module uses Python's pandas library for data cleaning and organization, including processing missing values ​​using the fillna() method, removing outliers and combining the statistical characteristics and visualization of the data for post-processing judgment. For data standardization, the StandardScaler in the scikit-learn library is used for Z-score standardization to convert the data into a distribution with a mean of 0 and a standard deviation of 1, so that data with different characteristics have the same scale in subsequent analysis.

3. The integrated management system for enterprise collaborative management and innovative services as claimed in claim 1, characterized in that: The behavior pattern analysis module uses the K-Means clustering algorithm in the scikit-learn library to perform cluster analysis on user behavior data to identify user groups with similar behavior patterns. At the same time, it combines the association rule mining algorithm and uses the Apriori algorithm in the mlxtend library of Python to mine frequent item sets in user behavior data and discover the correlation between different behaviors, so as to better understand user behavior patterns.

4. The integrated management system for enterprise collaborative management and innovative services as claimed in claim 1, characterized in that: The preference analysis module uses the user-based collaborative filtering algorithm in Python's Surprise library to infer user preferences by analyzing users' historical behavior data. It also combines the NLTK (Natural Language Toolkit) library to perform sentiment analysis and keyword extraction on open-ended questions in user feedback to further enrich our understanding of user preferences.

5. The integrated management system for enterprise collaborative management and innovative services as claimed in claim 1, characterized in that: The specific process of dynamic trend prediction in step S4 is: S41. Data update: obtain new data from the internal data acquisition module and the external data acquisition module, integrate the new data with the original data, ensure the integrity and consistency of the data, and store the updated data in the database for subsequent use; S42. Model parameter update: Use the root mean square error or mean absolute error indicator to calculate the prediction error of the model on the new data. Based on the prediction error, calculate the gradient of the model parameters through the back propagation algorithm. Based on the gradient information, use the stochastic gradient descent algorithm to adjust the parameters of the model so that the prediction error of the model on the new data gradually decreases. Repeat the above process continuously. As new data continues to arrive, continuously update the model parameters to adapt to the ever-changing market environment and data distribution.

6. The integrated management system for enterprise collaborative management and innovative services as claimed in claim 1, characterized in that: The innovation service unit includes: Product innovation module: Displays various types of enterprise data to help decision makers quickly understand the meaning of the data. It displays the distribution of market share through pie charts and provides data analysis tools to help decision makers analyze data in depth and discover the patterns and problems behind the data. Based on data visualization and analysis results, it provides decision support to decision makers to help them make more informed decisions. Service optimization module: Analyze the problems and deficiencies in the current service by collecting user feedback and monitoring service indicators. By analyzing user complaints and suggestions, it is found that there are cumbersome links in the service process, which lead to poor user experience. According to the analysis results of service problems, design specific service optimization plans, be responsible for implementing the service optimization plans, and track the implementation results; Marketing strategy formulation module: Based on the results of user portraits and behavioral analysis, the market is segmented into different sub-markets. Each sub-market has similar needs and characteristics. Personalized marketing strategies are formulated for different market segments. The effectiveness of marketing strategies is evaluated through monitoring and data analysis of marketing activities, and marketing strategies are adjusted in a timely manner based on the evaluation results.

7. The integrated management system for enterprise collaborative management and innovative services as claimed in claim 1, characterized in that: The collaborative management unit comprises: Communication and collaboration module: provides real-time text, voice and video communication tools between employees within the enterprise, as well as file sharing, task allocation and progress tracking tools to facilitate information exchange and collaboration between employees; Task and project management module: used to clearly assign the company's tasks to relevant employees or teams, ensure that the task responsibilities are assigned to individuals, assist the company in project planning, track the execution progress of tasks and projects in real time, and promptly identify problems and take measures to solve them; Document management module: used to establish an enterprise document library to centrally store various documents of the enterprise, support multiple people to edit documents online at the same time, and improve team collaboration efficiency.

8. The integrated management system for enterprise collaborative management and innovative services as claimed in claim 1, characterized in that: The decision support unit comprises: Data visualization module: used to display various types of enterprise data, so that decision makers can quickly understand the meaning of the data. It provides data analysis tools to help decision makers analyze the data in depth and discover the patterns and problems behind the data. Based on data visualization and analysis results, it provides decision support for decision makers to help them make more informed decisions. Report generation module: automatically generates regular data analysis reports based on preset templates and data, and generates special analysis reports based on needs; Decision-making recommendation module: interprets the data analysis results to help decision makers understand the meaning and impact of the data. Based on the data analysis and interpretation results, it provides specific decision-making recommendations to decision makers. By establishing a decision model and simulating the effects of different decision-making plans, it helps decision makers evaluate the feasibility and risks of decisions.

9. The integrated management system for enterprise collaborative management and innovative services as claimed in claim 1, characterized in that: The system management unit includes: User management module: responsible for user registration and login management, ensuring that only authorized users can access the system, setting different user permissions based on user roles and responsibilities, ensuring that users can only access and operate functions and data within their own permissions, and managing basic user information; Data security management module: Use encryption technology to encrypt the data in the system for storage and transmission, ensure the security and confidentiality of the data, establish a strict access control mechanism to limit users' access to sensitive data, back up the data in the system regularly, and ensure that the data can be quickly restored when it is lost or damaged; System settings module: used to configure various system parameters and set the system notification method and content.

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