Enterprise computer informationization construction system and method

Abstract

The application discloses an enterprise computer informationization construction system and method, which comprises data collection, distributed processing, intelligent routing transmission, data storage and data application modules. The data collection module converts business data into a unified format; the distributed processing module divides data according to transmission requirements and processes the data in parallel; the intelligent routing module dynamically selects an optimal path through an improved algorithm, supports multi-channel parallel transmission and real-time path switching; the distributed storage module realizes data sharding storage, backup recovery and security guarantee; and the data application module provides query, report and analysis services.

Description

Technical Field

[0001] This invention relates to the field of enterprise computer information construction, specifically to an enterprise computer information construction system and method. Background Technology

[0002] As businesses expand and become increasingly complex, the development of enterprise computer information systems has become crucial. Currently, most enterprises employ traditional systems based on centralized databases and linear data transmission architectures when implementing computer information systems. These systems often suffer from inefficiency and error-proneness during data transmission. For example, in large manufacturing enterprises, multiple departments such as production, warehousing, and sales frequently need to transmit production, inventory, and sales data. Traditional systems transmit data sequentially; if a data transmission delay or error occurs at any stage, the entire data transmission chain is interrupted, affecting subsequent departments' data acquisition and business processing, thus reducing overall enterprise operational efficiency. Furthermore, with large data volumes, the processing pressure on centralized databases increases dramatically, and data transmission response times become longer, making it difficult to meet the real-time and efficient business needs of enterprises. Summary of the Invention

[0003] The purpose of this invention is to provide an enterprise computer information construction system and method to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a data acquisition module, a distributed data processing module, an intelligent routing transmission module, a data storage module, and a data application module; Data acquisition module: Used to collect business data generated by various departments of the enterprise, and to perform preliminary format conversion and verification on the collected business data, converting it into a unified data format recognized by the system to obtain the collected data; Distributed data processing module: Adopting a distributed computing architecture, it receives the collected data after processing by the data acquisition module, divides the collected data according to preset rules, including the division dimensions related to data transmission requirements, and processes the divided data in parallel, specifically performing data cleaning and data analysis operations on the divided data to obtain processed data. Intelligent routing transmission module: Using an improved intelligent routing algorithm, it selects the optimal transmission path for processing data based on the type and urgency of the data being processed, as well as the real-time load of each transmission channel; and monitors the data transmission status in real time during transmission, automatically switching to a backup path when a transmission path fails or experiences a delay, ensuring the continuity of data transmission. Data storage module: It adopts a distributed storage system to receive and store data processed by the distributed data processing module, and also has data backup and recovery functions to ensure data security and integrity; Data Application Module: Connects to the data storage module, providing data query, report generation, and data analysis application services for various departments within the enterprise, enabling each department to obtain the data it needs in a timely manner and assist in business decision-making.

[0005] Preferably, the segmented data carries clear business attributes or transmission requirement tags, providing a basis for path selection in the intelligent routing transmission module.

[0006] Preferably, the specific implementation logic of the intelligent routing transmission module is as follows: Step 1: Data Feature Extraction and Quantization: Obtain processed data from the distributed data processing module. Based on the different business attributes and transmission requirements of the processed data, perform multi-dimensional feature extraction and quantization to generate multi-dimensional feature vectors. ,in This represents the extracted features, for example, Represents data type encoding, The level of urgency is indicated and quantified through preset business rules. It refers to the size of the data, used by improved intelligent routing algorithms to evaluate whether different channels can handle the transmission of that data. Indicates timeliness requirements, Represents the density of data value; Step 2, Transmission Channel Status Awareness: Constructing the Channel Status Matrix: ,in Number of available channels Indicates the first The bandwidth utilization of a channel is used to indicate the current busy level of the channel; It is the first The average delay of each channel; Representing the Packet loss rate of each channel; For the first The reliability score of each channel is derived based on the channel's historical transmission success rate; Step 3, Improved Path Evaluation Function: This function organically combines the above data characteristics with channel status, calculating the comprehensive score for each channel based on specific data using the following formula: ,in: Data fit score , ,and The weighting coefficients are dynamically adjusted based on the enterprise's business priorities. Channel performance rating: ,in , To ensure the scientific validity and rationality of the scoring, weight coefficients are derived by training with historical data. Interaction optimization items: When data timeliness is critical, i.e. At that time, the amount of data introduced With remaining bandwidth of the channel The ratio evaluation avoids delays in high-time data transmission due to insufficient channel bandwidth; Step 4: Dynamic Weight Adjustment Mechanism: A dynamic weight adjustment mechanism is introduced to adjust the weights in the intelligent routing algorithm to adapt to changes in the enterprise's business environment and network conditions. Specifically, the weights in the path evaluation function are dynamically adjusted using the following formula: ,in Indicates the current time step. This indicates the weight value of the previous time step, i.e., the previous shift or time period, which serves as the basis for the current adjustment; For the first The actual transmission success rate of the data is within the range of [value missing]. ; For time window The average success rate within; To control the learning rate, adjust the step size; Display window The number of data entries processed within; The difference determines the direction of weight adjustment; Step 5, Optimal Path Selection and Execution: After completing the comprehensive evaluation of all available channels... After calculation, the channel with the highest score is selected as the optimal transmission path using the following formula. To ensure that data can be transmitted in the best way under current conditions; where when When this occurs, it indicates that a single channel may not be able to meet the data transmission requirements, triggering a multi-channel parallel transmission strategy. The preset threshold; Step 6, Real-time Monitoring and Adaptive Adjustment: Continuously monitor the actual transmission delay during the data transmission process along the optimal path. Packet loss rate ,when or ,in If a problem is detected in the transmission path based on the preset transmission delay value, the path switching mechanism will be triggered immediately, that is, steps 1-5 will be executed again to select a new path.

[0007] Preferably, the multi-channel parallel transmission strategy is implemented using the following steps: Data partitioning: Dividing the processed data into portions according to the channel bandwidth ratio. Sub-block ,satisfy ; Redundant coding: Parity check is used to add check information to each sub-block, where the check block is... ; Parallel transmission: via One available channel can transmit both sub-blocks and parity blocks simultaneously, utilizing the remaining bandwidth of the channel. Improve transmission efficiency.

[0008] Preferably, the processed data is divided into segments based on the channel bandwidth ratio. Sub-block The specific implementation steps are as follows: The intelligent routing transmission module first filters out the set of available channels that meet the criteria. And obtain the remaining bandwidth ratio of each channel, where ,in For channel bandwidth utilization Indicates the remaining available bandwidth of the channel; Calculate the data allocation for each channel: based on the total amount of data processed. Calculate the size of each sub-block based on the proportion of remaining bandwidth in each channel: ; Perform data splitting: from raw data Starting from the initial position, according to the calculated Divide the data into sub-blocks in sequence: Sub-block Includes data processing before Bytes, sub-blocks The following contains data Byte, ..., Sub-block Includes remaining data byte; Multi-channel parallel transmission: dividing the sub-blocks Through their respective channels Transmission efficiency is improved by making full use of the available bandwidth of each channel.

[0009] A preferred method for enterprise computer information system construction includes the following steps: Data Acquisition: The data acquisition module collects business data from the business systems of various departments of the enterprise according to the preset acquisition cycle and acquisition rules, and performs format conversion and verification processing on the data; Data segmentation and parallel processing: The data acquisition module transmits the processed data to the distributed data processing module, which segments the data and starts multiple processing nodes to process the segmented data in parallel. Intelligent routing transmission: The distributed data processing module transmits the processed data to the intelligent routing transmission module, which selects the optimal transmission path for the data based on the improved intelligent routing algorithm and transmits the data to the data storage module. Data storage and application: The data storage module receives and stores data, while the data application module retrieves data from the data storage module and provides corresponding data application services to various departments of the enterprise.

[0010] Compared with the prior art, the beneficial effects of the present invention are: Traditional systems use fixed-path sequential transmission, and single-channel failures can easily lead to link interruptions (as in the case of a manufacturing company whose production schedule was affected by transmission delays, as mentioned in the background). This invention uses an intelligent routing algorithm to analyze data characteristics (such as urgency and data volume) and channel status (bandwidth utilization, latency, and packet loss rate) in real time, dynamically selecting the optimal path to improve transmission efficiency. When a single-channel score is insufficient, a multi-channel parallel transmission strategy is triggered, dividing the data according to the channel bandwidth ratio and transmitting it synchronously using redundant coding (such as parity checking), which also improves transmission efficiency and solves the problem of "single-channel failure causing overall interruption."

[0011] Real-time monitoring and path self-healing: Traditional systems cannot detect transmission anomalies in real time, and fault recovery relies on manual intervention. This invention continuously monitors transmission latency and packet loss rate. When an anomaly occurs (such as latency exceeding 1.2 times the expected value or packet loss rate > 5%), it automatically switches to a backup path within 100ms without manual intervention, ensuring data transmission continuity. It is particularly suitable for production monitoring scenarios with high real-time requirements.

[0012] Overcoming the performance bottleneck of centralized processing: Distributed computing architecture: Traditional centralized databases are slow to respond when processing massive amounts of data (e.g., monthly data processing for large enterprises in the background technology takes several hours). This invention uses a distributed data processing module to divide data into multiple sub-blocks according to business dimensions (such as production stage and timeliness), and utilizes multiple nodes to process data cleaning and analysis tasks in parallel.

[0013] Business-oriented intelligent segmentation: Traditional systems lack the ability to perceive data transmission needs (e.g., the mixed transmission of data with different priorities in the background technology leads to delays in critical data). This invention segments data through preset rules (e.g., by timeliness level, business priority), enabling the separate processing of real-time data (e.g., equipment monitoring) and batch data (e.g., monthly reports). Real-time data is preferentially allocated to high-performance nodes, while batch data can be transmitted during network idle periods, making full use of resources. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2This is a schematic diagram of the working logic of the intelligent routing module of the present invention; Figure 3 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0015] 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.

[0016] Example 1 Please see Figure 1-2 The present invention provides a technical solution: an enterprise computer information construction system, including a data acquisition module, a distributed data processing module, an intelligent routing transmission module, a data storage module, and a data application module; Data acquisition module: Used to collect business data generated by various departments of the enterprise, and to perform preliminary format conversion and verification on the collected business data, converting it into a unified data format recognized by the system to obtain the collected data; Distributed data processing module: Adopting a distributed computing architecture, it receives the collected data after processing by the data acquisition module, segments the collected data according to preset rules, including segmentation dimensions related to data transmission requirements, and processes the segmented data in parallel. Specifically, it performs data cleaning and data analysis operations on the segmented data to obtain processed data. The segmented data carries clear business attributes or transmission requirement tags, providing a basis for path selection in the intelligent routing transmission module.

[0017] Here, we will illustrate the implementation steps by segmenting the collected data according to preset rules: The collected data is segmented according to preset rules: based on the data transmission characteristics (such as real-time, data volume, priority) and channel load, for example, the following segmentation dimensions are preset: segmentation by data size: suitable for large data volume transmission, avoiding single channel congestion (such as dividing 1GB log data into 10 100MB sub-blocks).

[0018] Data is segmented by timeliness level: real-time data (such as equipment monitoring) is separated from batch data (such as monthly reports), and real-time data is processed first and selected through low-latency channels.

[0019] Data is segmented by business priority: high-priority data (such as order data) is separated from low-priority data (such as employee attendance data) to ensure that critical data is transmitted first; Perform data splitting operation Taking "segmentation by timeliness level" as an example, the steps are as follows: Parse the timeliness field of the data: Extract the timeliness level from the collected data. (Real-time data) Batch data Grouping rules: Real-time data subset Batch data subset: Data volume balancing adjustment: If the real-time data volume is too large, further divide it according to data volume. Ensure that each sub-block is suitable for single-channel transmission; Parallel processing configuration: Assigning processing nodes: Assigning independent processing nodes to each subset of data, for example: Assigned to node A to perform real-time data cleaning (such as filtering invalid device signals); Assigned to node B to perform batch data analysis (such as sales trend statistics); Task priority: Adjust node resource allocation according to data priority, such as giving node A priority to obtain CPU computing power to ensure that real-time data processing latency is less than 50ms; Transmission requirements are linked to segmentation results: Real-time data subsets: The processed sub-data blocks need to be marked with a "high timeliness" label to trigger the low-latency channel selection strategy of the intelligent routing module (such as prioritizing the use of dedicated lines or 5G channels). Batch data subsets: Marked with a "low timeliness" label, allowing the use of channels with sufficient bandwidth but lower cost (such as Wi-Fi), and can be transmitted during network idle periods; Parallel processing and transmission coordination: Pipeline operation: After the processing node completes data cleaning, it directly pushes the sub-data blocks to the intelligent routing module without waiting for all data to be processed, shortening the end-to-end time; Load balancing: Monitor the load of each processing node. If the backlog of data on a node exceeds the threshold, the subsequent sub-blocks will be automatically redistributed to idle nodes (such as node C).

[0020] Intelligent routing transmission module: Utilizing an improved intelligent routing algorithm, it selects the optimal transmission path for processed data based on the type and urgency of the data, as well as the real-time load of each transmission channel. Furthermore, it monitors the data transmission status in real time during transmission, automatically switching to a backup path when a transmission path fails or experiences a delay, ensuring continuous data transmission. The specific implementation logic is as follows: Step 1: Data Feature Extraction and Quantization: Obtain processed data from the distributed data processing module. Based on the different business attributes and transmission requirements of the processed data, perform multi-dimensional feature extraction and quantization to generate multi-dimensional feature vectors. ,in This represents the extracted features, for example, The encoding represents the data type, such as production data being encoded as 1, equipment data as 2, and quality data as 3. Through this encoding method, the algorithm can quickly distinguish the business source of the data, because different types of data have different requirements for channel stability, bandwidth, etc. during transmission. The urgency level is indicated and quantified through preset business rules. Taking order data from a large manufacturing enterprise as an example, assuming the remaining time for order delivery is... The total production cycle is The urgency level quantification value The higher the value, the more urgent the order, and this type of data needs to be transmitted first to ensure timely production and delivery. The data size (unit: MB) directly affects the bandwidth resources required for transmission. Quantifying it into a specific value makes it easier for algorithms to evaluate whether different channels can carry the transmission of the data. The timeliness requirement is indicated by a value of 1 for real-time data, 0.7 for near real-time data, and 0.3 for batch data. Real-time data, such as the real-time operating parameters of the device, has extremely high requirements for transmission latency and should be transmitted through a low-latency channel. Batch data, on the other hand, can be transmitted when the channel load is relatively low. Representing data value density, critical business data is assigned a value of 1, while auxiliary data is assigned a value of 0.5. Data value density reflects the importance of data to enterprise business decisions. The loss or delayed transmission of critical business data may affect the normal operation of the enterprise, and therefore needs to be given priority consideration during route selection. Through these feature extraction and quantification processes, the data is transformed into a multi-dimensional feature vector that can be processed by algorithms, providing a data foundation for subsequent path selection.

[0021] Step 2, Transmission Channel Status Awareness: Constructing the Channel Status Matrix: ,in Number of available channels; Indicates the first The bandwidth utilization of a channel reflects the current busyness of the channel. If the bandwidth utilization of a channel is too high, continuing to transmit a large amount of data may lead to congestion and delays. It is the first The average latency of a channel (unit: ms) directly affects the timeliness of data transmission. For latency-sensitive data, such as real-time monitoring data, channels with low average latency must be selected. Representing the The packet loss rate of a channel is related to the integrity of data transmission. A high packet loss rate can lead to data errors or loss, affecting subsequent business processing. For the first The reliability score of each channel is derived based on its historical transmission success rate, reflecting the long-term transmission stability of the channel. By continuously and in real-time collecting these indicators to form a channel status matrix, the algorithm can accurately grasp the real-time operation of each channel, providing an objective basis for path evaluation.

[0022] Step 3, Improved Path Evaluation Function: This function organically combines the above data characteristics with channel status, calculating the comprehensive score for each channel based on specific data using the following formula: ,in: Data fit score , ,and The weighting coefficients are dynamically adjusted based on the company's business priorities; for example, during peak production seasons, the urgency of order data is weighted. This will improve accordingly, ensuring that order data can be prioritized for transmission through fast and stable channels. The formula assesses the compatibility between data and channels at the business needs level by weighting the data's urgency, timeliness requirements, and value density.

[0023] Channel performance rating: ,in , To ensure the scientific validity and rationality of the scoring, weight coefficients are derived by training with historical data. It should be noted that, The step of training and deriving weight coefficients from historical data will be explained here. The specific implementation logic is as follows: Historical Data Collection and Organization: In the actual operation of large manufacturing enterprises, relevant data for each transmission channel is continuously collected over a period of time (e.g., the past 3 months), including bandwidth utilization, average latency, packet loss rate, and reliability score. Simultaneously, the characteristics of data transmitted through these channels and the transmission results (e.g., successful or failed transmission; if failed, the specific reason is recorded) are recorded. This data is then organized into a historical dataset. Each data record includes information such as channel number, performance index values, type of transmitted data, urgency level, timeliness requirement, data size, and transmission result. For example, recording that when a piece of equipment monitoring data was transmitted through channel 1, channel 1 had a bandwidth utilization of 60%, an average latency of 30ms, a packet loss rate of 1%, a reliability score of 0.9, the data type was equipment data, the urgency level was quantified as 0.8, the timeliness requirement was real-time (value 1), the data size was 5MB, and the transmission result was successful. Then utilize For each piece of data in the historical dataset, a channel transmission performance score is calculated. Set optimization objectives and algorithms: Using the actual transmission results of data in the historical dataset as a reference, set the optimization objective to make the model score highly correlated with the actual transmission quality. For example, for data that is successfully transmitted and of high quality (e.g., no latency, no packet loss), the corresponding channel score should be higher; for data that is transmitted but has failed or is of poor quality, the channel score should be lower. Choose an appropriate optimization algorithm, such as gradient descent. Gradient descent continuously adjusts the weights, updating them in the direction that reduces the objective function (e.g., the sum of squares of the difference between the predicted score and the actual transmission quality), to gradually optimize the model. Iterative weight optimization: Divide the historical dataset into training and test sets (e.g., in a 7:3 ratio). Using the training set data, optimize the weights in the initial evaluation model using gradient descent. The process involves iterative updates. In each iteration, the difference between the model's predicted score and the actual transmission quality is calculated. The gradient of the weights is then calculated based on this difference, and the weights are adjusted according to the set learning rate. After multiple iterations, the optimized model is evaluated using test set data to check its generalization ability, i.e., to assess its performance on data not used in training. If the test results are unsatisfactory, parameters such as the learning rate and number of iterations are adjusted, and iterative optimization continues until the model accurately reflects the channel transmission performance on the test set, yielding the final weight values. Weight Validation and Application: The final weight values ​​are applied to new real-world data transmission scenarios, and the model's evaluation performance and data transmission status are continuously monitored. If a significant deviation is found between the model's evaluation results and the actual transmission performance, new historical data is collected, the above training steps are repeated, and the weights are optimized and adjusted again to ensure the scientific and reasonable nature of the channel transmission performance evaluation, providing reliable support for the intelligent routing algorithm to select the optimal transmission path.

[0024] Interaction optimization items: When data timeliness is critical, i.e. At that time, the amount of data introduced With remaining bandwidth of the channel The ratio evaluation avoids delays in high-time data transmission due to insufficient channel bandwidth; Step 4: Dynamic Weight Adjustment Mechanism: A dynamic weight adjustment mechanism is introduced to adjust the weights in the intelligent routing algorithm to adapt to changes in the enterprise's business environment and network conditions. Specifically, the weights in the path evaluation function are dynamically adjusted using the following formula: ,in Indicates the current time step. This indicates the weight value of the previous time step, i.e., the previous shift or time period, which serves as the basis for the current adjustment; For the first The actual transmission success rate of the data is within the range of [value missing]. ; For time window The average success rate within; To control the learning rate, adjust the step size; Display window The number of data entries processed within; The difference determines the direction of weight adjustment; for example, in large automobile manufacturing companies, the amount of order data transmission surges during peak production periods. If a certain channel is transmitting urgent order data, the actual transmission success rate will be affected. Consistently below average success rate ,Right now The algorithm will automatically reduce the weight of this channel when processing high-urgency data (e.g., The weight of a channel is reduced in subsequent path selection if it is associated with high urgency; conversely, if a channel performs well when transmitting data under real-time monitoring by the transmission equipment, its weight is increased accordingly. This dynamic adjustment mechanism enables the algorithm to continuously learn and adapt to changes in enterprise business and network environment, continuously optimize path selection strategies, and improve the reliability and efficiency of data transmission. Step 5, Optimal Path Selection and Execution: After completing the comprehensive evaluation of all available channels... After calculation, the channel with the highest score is selected as the optimal transmission path using the following formula. To ensure that data can be transmitted in the best way under current conditions; where when When this occurs, it indicates that a single channel may not be able to meet the data transmission requirements, triggering a multi-channel parallel transmission strategy. For example, in large electronics manufacturing companies, when production data is summarized and transmitted at the end of each month, the data volume is huge and has a certain timeliness requirement, which may not be met by single-channel transmission. In this case, the algorithm will divide the data into multiple parts and transmit them through multiple channels at the same time. The data transmitted through each channel is processed with redundant encoding. Even if some data is lost or erroneous during transmission, the complete information can be recovered from the data transmitted through other channels, which greatly improves the transmission speed and avoids the impact of slow single-channel transmission on the company's production data analysis and decision-making. Step 6, Real-time Monitoring and Adaptive Adjustment: Continuously monitor the actual transmission delay during the data transmission process along the optimal path. Packet loss rate ,when or ,in If a problem is detected in the transmission path based on a preset transmission delay value, a path switching mechanism is immediately triggered, meaning steps 1-5 are re-executed to select a new path. In large machinery manufacturing enterprises, when a transmission channel experiences network instability due to a surge in power consumption by equipment within the factory, causing the transmission delay to suddenly increase by more than 1.2 times the expected value, the algorithm quickly detects the anomaly, immediately stops transmission along the current path, re-analyzes data characteristics and the status of each channel, and selects a new, stable channel to continue transmitting production data, ensuring the stability and continuity of data transmission and guaranteeing the normal operation of the enterprise's business.

[0025] Through the above series of closely coordinated processing steps, combined with specific algorithm formulas, this improved intelligent routing algorithm can accurately and efficiently select the optimal transmission path for enterprise data, effectively solving the problems of low data transmission efficiency and error susceptibility in traditional systems, and significantly improving the data transmission performance of enterprise computer information systems. The specific execution steps of the multi-channel parallel transmission strategy are as follows: Data partitioning: Dividing the processed data into portions according to the channel bandwidth ratio. Sub-block ,satisfy ; Redundant coding: Parity check is used to add check information to each sub-block, where the check block is... ; Parallel transmission: via One available channel can transmit both sub-blocks and parity blocks simultaneously, utilizing the remaining bandwidth of the channel. Improve transmission efficiency.

[0026] The processed data is divided based on the channel bandwidth ratio. Sub-block The specific implementation steps are as follows: The intelligent routing transmission module first filters out the set of available channels that meet the criteria. And obtain the remaining bandwidth ratio of each channel, where ,in For channel bandwidth utilization Indicates the remaining available bandwidth of the channel; Calculate the data allocation for each channel: based on the total amount of data processed. Calculate the size of each sub-block based on the proportion of remaining bandwidth in each channel: ; Perform data splitting: from raw data Starting from the initial position, according to the calculated Divide the data into sub-blocks in sequence: Sub-block Includes data processing before Bytes, sub-blocks The following contains data Byte, ..., Sub-block Includes remaining data byte; Multi-channel parallel transmission: dividing the sub-blocks Through their respective channels Transmission efficiency is improved by making full use of the available bandwidth of each channel.

[0027] Data storage module: It adopts a distributed storage system to receive and store data processed by the distributed data processing module, and also has data backup and recovery functions to ensure data security and integrity; The implementation steps of the data storage module are briefly illustrated below: First, it receives various types of data processed by the distributed data processing module, such as production data and inventory data. This data is then sharded and distributed across a cluster of multiple storage nodes to avoid single-point storage pressure and improve concurrent processing capabilities. During storage, multiple replicas are generated for each data shard and stored on different physical nodes to ensure data redundancy. For example, production data shards are stored simultaneously on nodes A, B, and C to prevent data loss due to the failure of a single node. When writing data, the sharding rules are first determined, such as sharding by data type or timestamp. After receiving the data, the master node synchronizes it to the replica nodes to ensure data consistency across replicas. Simultaneously, the metadata server records the data's storage location, sharding information, etc., for easy subsequent retrieval and reading.

[0028] During data reading, the module parses the query request from the data application module, finds the storage location of the target data through the metadata server, and reads the data in parallel from multiple replica nodes to improve reading efficiency. For requests with high real-time requirements, the module prioritizes low-latency nodes to ensure that the data is returned quickly.

[0029] Regarding data backup and recovery, the module performs full data backups regularly, such as taking a snapshot of all data every Sunday and storing it on offline media or in an off-site disaster recovery center; it also performs incremental backups daily, backing up only the data that has changed since the last backup, reducing storage resource consumption. When a storage node failure or data corruption is detected, the module automatically triggers a recovery process, which involves finding the most recent backup data, rebuilding data shards and copies on healthy nodes, and restoring the system to normal operation.

[0030] To ensure data security and integrity, the module encrypts transmitted and stored data. SSL / TLS is used during transmission, and sensitive data is encrypted using AES-256 during storage. Regular hash verification is performed, comparing the hash values ​​of each replica node to detect data tampering or corruption. Role-based access control is implemented, allowing different department users to read and write data according to their permissions, and all access operations are recorded in an audit log to ensure traceability of data access. Through these steps, the data storage module achieves efficient data storage, secure backup, and rapid recovery, ensuring enterprise data security and business continuity.

[0031] Data Application Module: Connects to the data storage module, providing data query, report generation, and data analysis application services for various departments within the enterprise, enabling each department to obtain the data it needs in a timely manner and assist in business decision-making.

[0032] Example 2 Please see Figure 3 A method for building an enterprise's computer information system includes the following steps: Data Acquisition: The data acquisition module collects business data from the business systems of various departments of the enterprise according to the preset acquisition cycle and acquisition rules, and performs format conversion and verification processing on the data; Data segmentation and parallel processing: The data acquisition module transmits the processed data to the distributed data processing module, which segments the data and starts multiple processing nodes to process the segmented data in parallel. Intelligent routing transmission: The distributed data processing module transmits the processed data to the intelligent routing transmission module, which selects the optimal transmission path for the data based on the improved intelligent routing algorithm and transmits the data to the data storage module. Data storage and application: The data storage module receives and stores data, while the data application module retrieves data from the data storage module and provides corresponding data application services to various departments of the enterprise.

[0033] Compared with existing enterprise computer information technology, this invention has significant advantages. In data processing, the distributed data processing module, through data segmentation and parallel processing technology, distributes the large amount of data processing tasks originally concentrated on a single node to multiple nodes simultaneously, effectively reducing the processing pressure on individual nodes and significantly improving data processing efficiency. It can quickly process massive amounts of data generated by enterprises, meeting their timeliness requirements. In data transmission, the intelligent routing transmission module's intelligent routing algorithm dynamically selects the optimal transmission path based on data characteristics and the real-time status of the transmission channel, and promptly switches paths when transmission problems occur. This effectively solves the problems of low data transmission efficiency and error susceptibility in traditional systems, ensuring accurate and rapid data transmission to its destination. Furthermore, the data acquisition module's data format conversion and verification functions guarantee data quality, the data storage module's distributed storage and data backup and recovery functions ensure data security, and the data application module's rich data application services enhance the value of data utilization for enterprises. Through the collaborative work of these modules, this invention comprehensively improves the data transmission reliability and overall operational efficiency of enterprise computer information systems, providing strong technical support for enterprises' digital operations and development, powerfully promoting the enterprise's informatization process, and enhancing the enterprise's competitiveness in the market.

[0034] 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. An enterprise computer information construction system, comprising a data acquisition module, a distributed data processing module, an intelligent routing transmission module, a data storage module, and a data application module, characterized in that: Data acquisition module: Used to collect business data generated by various departments of the enterprise, and to perform preliminary format conversion and verification on the collected business data, converting it into a unified data format recognized by the system to obtain the collected data; Distributed data processing module: Adopting a distributed computing architecture, it receives the collected data after processing by the data acquisition module, divides the collected data according to preset rules, including the division dimensions related to data transmission requirements, and processes the divided data in parallel, specifically performing data cleaning and data analysis operations on the divided data to obtain processed data. Intelligent routing transmission module: Utilizing an improved intelligent routing algorithm, it selects the optimal transmission path for data processing based on the type and urgency of the data, as well as the real-time load of each transmission channel. Furthermore, it monitors the data transmission status in real time during transmission, automatically switching to a backup path when a transmission path fails or experiences a delay, ensuring continuous data transmission. Specific implementation steps are as follows: Step 1: Data Feature Extraction and Quantization: Obtain processed data from the distributed data processing module. Based on the different business attributes and transmission requirements of the processed data, perform multi-dimensional feature extraction and quantization to generate multi-dimensional feature vectors. ,in Indicates the extracted features; Step 2, Transmission Channel Status Awareness: Constructing the Channel Status Matrix: ,in Number of available channels Indicates the first The bandwidth utilization of a channel is used to indicate the current busy level of the channel; It is the first The average delay of each channel; Representing the Packet loss rate of each channel; For the first The reliability score of each channel is derived based on the channel's historical transmission success rate; Step 3, Improved Path Evaluation Function: This function organically combines the above data characteristics with channel status, calculating the comprehensive score for each channel based on specific data using the following formula: ,in: Data fit score , ,and Weighting coefficients are dynamically adjusted based on the company's business priorities; among which... , and They represent the first The urgency, timeliness requirements, and data value density of each channel; Channel performance rating: ,in , To ensure the scientific validity and rationality of the scoring, weight coefficients are derived by training with historical data. Interaction optimization items: When data timeliness is critical, i.e. At that time, the amount of data introduced With remaining bandwidth of the channel The ratio evaluation avoids delays in high-time data transmission due to insufficient channel bandwidth; Step 4: Dynamic Weight Adjustment Mechanism: A dynamic weight adjustment mechanism is introduced to adjust the weights in the intelligent routing algorithm to adapt to changes in the enterprise's business environment and network conditions. Specifically, the weights in the path evaluation function are dynamically adjusted using the following formula: ,in Indicates the current time step. This indicates the weight value of the previous time step, i.e., the previous shift or time period, which serves as the basis for the current adjustment; For the first The actual transmission success rate of the data is within the range of [value missing]. ; For time window The average success rate within; To control the learning rate, adjust the step size; Display window The number of data entries processed within; The difference determines the direction of weight adjustment; Step 5, Optimal Path Selection and Execution: After completing the comprehensive evaluation of all available channels... After calculation, the channel with the highest score is selected as the optimal transmission path using the following formula. To ensure that data can be transmitted in the best way under current conditions; where when When this occurs, it indicates that a single channel may not be able to meet the data transmission requirements, triggering a multi-channel parallel transmission strategy. The preset threshold; Step 6, Real-time Monitoring and Adaptive Adjustment: Continuously monitor the actual transmission delay during the data transmission process along the optimal path. Packet loss rate ,when or ,in If a problem is detected in the transmission path based on the preset transmission delay value, the path switching mechanism will be triggered immediately, that is, steps 1-5 will be executed again to select a new path. Data storage module: It adopts a distributed storage system to receive and store data processed by the distributed data processing module, and also has data backup and recovery functions to ensure data security and integrity; Data Application Module: Connects to the data storage module, providing data query, report generation, and data analysis application services for various departments within the enterprise, enabling each department to obtain the data it needs in a timely manner and assist in business decision-making.

2. The enterprise computer information construction system according to claim 1, characterized in that: The segmented data carries clear business attributes or transmission requirement tags, providing a basis for path selection in the intelligent routing transmission module.

3. The enterprise computer information construction system according to claim 1, characterized in that: The specific execution steps of the multi-channel parallel transmission strategy are as follows: Data partitioning: Dividing the processed data into portions according to the channel bandwidth ratio. Sub-block ,satisfy ; Redundant coding: Parity check is used to add check information to each sub-block, where the check block is... ; Parallel transmission: via One available channel can transmit both sub-blocks and parity blocks simultaneously, utilizing the remaining bandwidth of the channel. Improve transmission efficiency.

4. The enterprise computer information construction system according to claim 3, characterized in that: The processed data is divided based on the channel bandwidth ratio. Sub-block The specific implementation steps are as follows: The intelligent routing transmission module first filters out the set of available channels that meet the criteria. And obtain the remaining bandwidth ratio of each channel, where ,in For channel bandwidth utilization Indicates the remaining available bandwidth of the channel; Calculate the data allocation for each channel: based on the total amount of data processed. Calculate the size of each sub-block based on the proportion of remaining bandwidth in each channel: ; Perform data splitting: from raw data Starting from the initial position, according to the calculated Divide the data into sub-blocks in sequence: Sub-block Includes data processing before Bytes, sub-blocks The following contains data Byte, ..., Sub-block Includes remaining data byte; Multi-channel parallel transmission: dividing the sub-blocks Through their respective channels Transmission efficiency is improved by making full use of the available bandwidth of each channel.

5. A method for enterprise computer information construction according to any one of claims 1-4, characterized in that, Includes the following steps: Data Acquisition: The data acquisition module collects business data from the business systems of various departments of the enterprise according to the preset acquisition cycle and acquisition rules, and performs format conversion and verification processing on the data; Data segmentation and parallel processing: The data acquisition module transmits the processed data to the distributed data processing module, which segments the data and starts multiple processing nodes to process the segmented data in parallel. Intelligent routing transmission: The distributed data processing module transmits the processed data to the intelligent routing transmission module, which selects the optimal transmission path for the data based on the improved intelligent routing algorithm and transmits the data to the data storage module; Data storage and application: The data storage module receives and stores data, while the data application module retrieves data from the data storage module and provides corresponding data application services to various departments of the enterprise.

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