Enterprise computer informatization construction system and method

Abstract

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

Description

Technical Field

[0001] The present invention relates to the field of enterprise computer informatization construction, and in particular to an enterprise computer informatization construction system and method. Background Art

[0002] As enterprises continue to expand and their operations become increasingly complex, the development of enterprise computer information technology has become crucial. Currently, most enterprises utilize traditional systems based on centralized databases and linear data transmission architectures when implementing computer information technology. This system often suffers from inefficiencies and error-prone data transmission. For example, in large manufacturing companies, production, warehousing, sales, and other departments frequently need to transfer production data, inventory data, and sales data. Traditional systems use a sequential data transmission method. Data transmission delays or errors in any link disrupt the entire data transmission link, impacting data acquisition and business processing in subsequent departments and reducing overall operational efficiency. Furthermore, large data volumes significantly increase the processing pressure on centralized databases, resulting in longer data transmission response times and making it difficult to meet the real-time and efficient business needs of enterprises. Summary of the Invention

[0003] The purpose of the present invention is to provide an enterprise computer information construction system and method to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned object, the present invention provides the following technical solutions: a data acquisition module, a distributed data processing module, an intelligent routing transmission module, a data storage module and a data application module;

[0005] Data collection module: used to collect business data generated by various departments of the enterprise, and 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;

[0006] Distributed data processing module: This module uses a distributed computing architecture to receive the collected data processed by the data acquisition module, segment the collected data according to preset rules, where the preset rules include segmentation dimensions related to data transmission requirements, and simultaneously processes the segmented data in parallel, specifically performing data cleaning and data analysis operations on the segmented data to obtain processed data;

[0007] 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. It also monitors the data transmission status in real time during the transmission process. When a transmission path fails or is delayed, it automatically switches to an alternative path to ensure data transmission continuity.

[0008] Data storage module: uses a distributed storage system to receive and store data processed by the distributed data processing module. It also has data backup and recovery functions to ensure data security and integrity.

[0009] Data application module: connects to the data storage module to provide data query, report generation, and data analysis application services for various departments of the enterprise, enabling each department to obtain the required data in a timely manner and assist in business decision-making.

[0010] Preferably, the segmented data carries clear business attributes or transmission requirement tags, providing a basis for path selection in the intelligent routing transmission module.

[0011] Preferably, the specific implementation logic of the intelligent routing transmission module is as follows:

[0012] Step 1: Data feature extraction and quantification: Obtain processing data from the distributed data processing module, perform multi-dimensional feature extraction and quantification based on the different business attributes and transmission requirements of the processed data, and generate a multi-dimensional feature vector: D i =[d1,d2,…,d i ], where d i Represents the extracted features. For example, d1 represents the data type code, d2 represents the urgency, which is quantified by preset business rules, d3 is the data volume, which is used by the improved intelligent routing algorithm to evaluate whether different channels can carry the data transmission, d4 represents the timeliness requirement, and d5 represents the data value density;

[0013] Step 2: Transmission channel status perception: Construct channel status matrix: Where n is the number of available channels, c i1 Indicates the bandwidth utilization of the i-th channel, which is used to indicate the current busyness of the channel; c i2 is the average delay of the ith channel; c i3 represents the packet loss rate of the i-th channel; c i4 The reliability score of the i-th channel is obtained based on the historical transmission success rate of the channel;

[0014] Step 3: Improved path evaluation function: Combine the above data features with the channel status and calculate the comprehensive score of each channel for specific data using the following formula: Score(D i ,C i )=α·F data (D i )+β·F channel (C i )+γ·F inter (D,C i ),in:

[0015] Data fitness score F data (D i )=ω1d i2 +ω2d i4 +ω3d i5 , ω1+ω2+ω3=1, and ω1, ω2, ω3 are weight coefficients that are dynamically adjusted according to the enterprise business priority;

[0016] Channel performance score: Among them, θ1+θ2+θ3+θ4=1, θ1, θ2, θ3, θ4 are weight coefficients obtained through historical data training to ensure the scientificity and rationality of the scoring;

[0017] Interaction optimization items: When the data timeliness requirement is high, that is, d4 ≥ 0.7, the amount of data d3 and the channel remaining bandwidth (1-c i1 ) ratio evaluation to avoid delays in high-timeliness data transmission due to insufficient channel bandwidth;

[0018] Step 4: Dynamic weight adjustment mechanism: Introduce a dynamic weight adjustment mechanism 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: Where t represents the current time step, t-1 represents the previous time step, that is, the weight value of the previous shift or time period, which serves as the basis for the current adjustment; r i is the actual transmission success rate of the i-th data, and its value range is [0,1]; is the average success rate within the time window T; η is the learning rate, which controls the adjustment step size; ω represents the number of processed data in the window T; The difference determines the direction of weight adjustment;

[0019] Step 5, optimal path selection and execution: After completing the comprehensive score of all available channels (Score i ,C i ) is calculated, 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; When , it means that a single channel may not be able to meet the data transmission requirements, and the multi-channel parallel transmission strategy is triggered. is the preset threshold;

[0020] Step 6: Real-time monitoring and adaptive adjustment: Continuously monitor the actual transmission delay L while processing data and transmitting it along the optimal path. actual, packet loss rate P actual , when L actual >1.2L expected or P actual >0.05, where L expected The transmission delay value is preset, and it is determined that there is a problem with the transmission path, and the path switching mechanism is immediately triggered, that is, steps 1-5 are executed again to select a new path.

[0021] Preferably, the multi-channel parallel transmission strategy is specifically implemented in the following steps:

[0022] Data segmentation: Divide the processed data into k sub-blocks D1, D2, ..., D according to the channel bandwidth ratio k ,satisfy

[0023] Redundant coding: Parity check is used to add check information to each sub-block, where the check block is P = D1⊕D2⊕…⊕D k ;

[0024] Parallel transmission: Sub-blocks and check blocks are transmitted simultaneously through k available channels, using the remaining bandwidth of the channel (1-c i1 ) Improve transmission efficiency.

[0025] Preferably, the processed data is divided into k sub-blocks D1, D2, ..., D based on the channel bandwidth ratio. k The specific implementation steps are as follows:

[0026] The intelligent routing transmission module first selects the available channel set {C1, C2, ..., C k}, and obtain the remaining bandwidth ratio of each channel, where where c i1 For channel C i Bandwidth utilization, 1-c i1 Indicates the remaining available bandwidth of the channel;

[0027] Calculate the data allocation for each channel: Calculate the size of each sub-block based on the total amount of processed data |D| and the remaining bandwidth ratio of each channel:

[0028] Perform data segmentation: Starting from the starting position of the original data D, press the calculated |D i |Divide the data into sub-blocks in sequence: sub-block D1 contains the first |D1| bytes of the processed data, sub-block D2 contains the next |D2| bytes of the data, ..., sub-block D k Contains the rest of the data |D k |byte;

[0029] Multi-channel parallel transmission: the divided sub-blocks D1, D2, ..., D kThrough the corresponding channels C1, C2, ..., C k transmission, making full use of the available bandwidth of each channel and improving the overall transmission efficiency.

[0030] Preferably, a method for building enterprise computer informationization includes the following steps:

[0031] Data collection: The data collection module collects business data from the business systems of various departments of the enterprise according to the preset collection cycle and collection rules, and performs format conversion and verification on the data;

[0032] 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.

[0033] Intelligent routing transmission: The distributed data processing module transmits the processed data to the intelligent routing transmission module. The intelligent routing transmission module selects the optimal transmission path for the data based on the improved intelligent routing algorithm and transmits the data to the data storage module.

[0034] Data storage and application: The data storage module receives and stores data, and the data application module obtains data from the data storage module to provide corresponding data application services for various departments of the enterprise.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] Traditional systems use fixed-path sequential transmission, and single-channel failures can easily lead to link interruption (such as the production progress of a manufacturing company affected by transmission delays in the background technology). The present invention uses an intelligent routing algorithm to analyze data characteristics (such as urgency, data volume) and channel status (bandwidth utilization, delay, packet loss rate) in real time, dynamically select the optimal path, and improve transmission efficiency. When the single-channel score is insufficient, a multi-channel parallel transmission strategy is triggered, and the data is divided according to the channel bandwidth ratio and transmitted synchronously through redundant coding (such as parity check). This can also improve transmission efficiency and solve the problem of "single-channel failure causing overall interruption."

[0037] Real-time monitoring and path self-healing: Traditional systems cannot detect transmission anomalies in real time, and fault recovery relies on manual intervention. This solution continuously monitors transmission delay and packet loss rate. When an anomaly occurs (such as delay exceeding 1.2 times the expected value or packet loss rate exceeding 5%), it automatically switches to an alternative path within 100ms without manual intervention, ensuring data transmission continuity. This solution is particularly suitable for production monitoring scenarios with strict real-time requirements.

[0038] Breaking through the performance bottleneck of centralized processing: Distributed computing architecture: Traditional centralized databases are slow to respond when processing massive amounts of data (for example, large enterprises can spend hours processing monthly data). This invention uses a distributed data processing module to segment data into multiple sub-blocks based on business dimensions (such as production stage and timeliness), leveraging multiple nodes to parallelize data cleaning and analysis tasks.

[0039] Business-Oriented Intelligent Segmentation: Traditional systems lack the ability to perceive data transmission needs (e.g., the mixed transmission of data of different priorities in the background art leads to delays in critical data). This invention segments data using pre-set rules (e.g., timeliness level and business priority), allowing for 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, fully utilizing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Schematic diagram of the system structure of the present invention;

[0041] Figure 2 This is a working logic diagram of the intelligent routing module of the present invention;

[0042] Figure 3 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0044] Example 1

[0045] See also 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;

[0046] Data collection module: used to collect business data generated by various departments of the enterprise, and 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;

[0047] Distributed data processing module: adopts a distributed computing architecture to receive the collected data processed by the data acquisition module, and segments the collected data according to preset rules, where the preset rules include segmentation dimensions related to data transmission requirements. At the same time, the segmented data is processed in parallel, specifically performing 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.

[0048] Here we take an example to illustrate the implementation steps of segmenting the collected data according to preset rules:

[0049] 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 to avoid single channel congestion (such as dividing 1GB of log data into 10 100MB sub-blocks).

[0050] Segmentation by timeliness: Separate real-time data (such as equipment monitoring) from batch data (such as monthly reports), prioritize real-time data, and select low-latency channels.

[0051] Segmentation by business priority: Separate high-priority data (such as order data) from low-priority data (such as employee attendance data) to ensure that critical data is transmitted first;

[0052] Perform data splitting operations

[0053] Taking "split by timeliness level" as an example, the steps are as follows: Parse the data timeliness field: extract the timeliness level d4 from the collected data (real-time data d4=1 batch data d4=0.3); Grouping rule: real-time data subset D real ={d|d4=1}, batch data subset: D batch ={d|d4=0.3}; Data balance adjustment: If the real-time data volume is too large, it is further divided into D according to the data volume real1 ,D real2 ,…, ensure that each sub-block is suitable for single-channel transmission;

[0054] Parallel processing configuration: Assign processing nodes: Assign independent processing nodes to each data subset, for example: D real Assigned to node A to perform real-time data cleaning (such as filtering invalid device signals); D batch Assigned to Node B to perform batch data analysis (such as sales trend statistics); Processing task priority: Adjust node resource allocation based on data priority. For example, Node A is given priority in obtaining CPU computing power to ensure that the real-time data processing delay is less than 50ms;

[0055] Transmission requirements are bound to segmentation results: Real-time data subset: The sub-data blocks generated after processing need to be marked with a "high timeliness" tag 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 subset: Marked with a "low timeliness" tag, it allows the use of channels with sufficient bandwidth but low costs (such as Wi-Fi) and can be transmitted during network idle periods;

[0056] Parallel processing and transmission coordination: Pipeline operation: After completing data cleaning, the processing node directly pushes the sub-data block to the intelligent routing module without waiting for all data processing to be completed, shortening the end-to-end time; Load balancing: Monitors the load of each processing node. If the backlog of data on a node exceeds the threshold, the subsequent sub-blocks will be automatically reallocated to an idle node (such as node C).

[0057] 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. It also monitors the data transmission status in real time during the transmission process. When a transmission path fails or is delayed, it automatically switches to an alternative path to ensure data transmission continuity. The specific implementation logic is as follows:

[0058] Step 1: Data feature extraction and quantification: Obtain processing data from the distributed data processing module, perform multi-dimensional feature extraction and quantification based on the different business attributes and transmission requirements of the processed data, and generate a multi-dimensional feature vector: D i =[d1,d2,…,d i ], where d i Represents the extracted features. For example, d1 represents the data type encoding, such as production data encoding 1, equipment data encoding 2, quality data encoding 3, etc. 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; d2 represents the urgency, which is quantified by preset business rules. Taking the order data of a large manufacturing company as an example, assuming that the remaining time of the order delivery is t and the total production cycle is T, the urgency quantification value is The higher the value, the more urgent the order; this type of data should be transmitted first to ensure timely production and delivery. d3 is the data size (in MB), which directly affects the bandwidth resources required for transmission. Quantifying this value facilitates the algorithm's evaluation of whether different channels can carry the data. d4 represents timeliness requirements, with a value of 1 for real-time data, 0.7 for quasi-real-time data, and 0.3 for batch data. Real-time data, such as the real-time operating parameters of equipment, has extremely high transmission latency requirements and should be prioritized over low-latency channels. Batch data can be transmitted when the channel load is relatively low. d5 represents data value density, with a value of 1 for critical business data and 0.5 for auxiliary data. Data value density reflects the importance of data to business decisions. The loss or delayed transmission of critical business data can affect normal business operations and is therefore a key consideration during route selection. Through these feature extraction and quantification, the data is converted into a multidimensional feature vector that can be processed by the algorithm, providing a data foundation for subsequent path selection.

[0059] Step 2: Transmission channel status perception: Construct channel status matrix: Where n is the number of available channels; c i1 represents the bandwidth utilization of the i-th channel. This value 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 cause congestion and delay; c i2 is the average delay of the i-th channel (unit: ms), which directly affects the timeliness of data transmission. For data that is sensitive to delay, such as real-time monitoring data, a channel with low average delay must be selected; c i3 represents the packet loss rate of the i-th channel, which is related to the integrity of data transmission. A high packet loss rate will lead to data errors or loss, affecting subsequent business processing; c i4 The reliability score for the i-th channel is calculated based on the channel's historical transmission success rate, reflecting the channel's long-term transmission stability. By continuously and in real time collecting these indicators to form a channel status matrix, the algorithm can accurately grasp the real-time operation status of each channel, providing an objective basis for path evaluation.

[0060] Step 3: Improved path evaluation function: Combine the above data features with the channel status and calculate the comprehensive score of each channel for specific data using the following formula: Sdcore(D i ,C i )=α·F data (D i )+β·F channel (C i )+γ·F inter (D,C i ),in:

[0061] Data fitness score Fdata (D i )=ω1d i2 +ω2d i4 +ω3d i5 , ω1+ω2+ω3=1, and ω1, ω2, ω3 are weight coefficients that are dynamically adjusted according to the enterprise's business priorities. For example, during the peak production season, the urgency weight ω1 of order data will increase accordingly to ensure that order data can be transmitted through fast and stable channels first. This formula evaluates the adaptability of data and channels at the business demand level through weighted calculation of data urgency, timeliness requirements, and value density.

[0062] Channel performance score: Among them, θ1+θ2+θ3+θ4=1, θ1, θ2, θ3, θ4 are weight coefficients obtained through historical data training to ensure the scientificity and rationality of the scoring;

[0063] It should be noted that θ1, θ2, θ3, and θ4 are weight coefficients obtained through historical data training. This step is explained here. The specific implementation logic is as follows:

[0064] Historical data collection and organization: In the actual operation process of large-scale manufacturing enterprises, relevant data of each transmission channel is continuously collected over a period of time (such as the past 3 months), including bandwidth utilization, average delay, packet loss rate and reliability score, and the characteristics of the data transmitted through these channels and the transmission results (such as transmission success or failure, if failed, record the specific reason). These data are organized to form a historical data set. Each data record contains information such as the channel number, the value of each performance indicator, the type of transmitted data, the urgency, timeliness requirements, data volume, and transmission results. For example, when recording a piece of equipment monitoring data transmitted through channel 1, the bandwidth utilization of channel 1 at that time was 60%, the average delay was 30ms, the packet loss rate was 1%, and the reliability score was 0.9. The type of data is equipment data, the urgency quantitative value is 0.8, the timeliness requirement is real-time (value 1), the data volume is 5MB, and the transmission result is successful;

[0065] Then use the Calculate the channel transmission performance score for each data in the historical data set;

[0066] Set optimization goals and algorithms: Using the actual transmission results of data in historical data sets as a reference, set the optimization goal to make the model score highly correlated with the actual transmission quality. For example, for data that is successfully transmitted and of high quality (such as no delay or packet loss), the corresponding channel score should be high; for data that fails to transmit or has poor quality, the channel score should be low. Select an appropriate optimization algorithm, such as the gradient descent algorithm. The gradient descent algorithm gradually optimizes the model by continuously adjusting the weights and updating the weights in the direction that reduces the objective function (such as the sum of the squares of the difference between the predicted score and the actual transmission quality);

[0067] Iterative weight optimization: Divide the historical data set into a training set and a test set (e.g., a ratio of 7:3). Using the training set data, the weights θ1, θ2, θ3, and θ4 in the initial evaluation model are iteratively updated using the gradient descent algorithm. In each iteration, the difference between the model's predicted score and the actual transmission quality is calculated, the gradient of the weight is calculated based on the difference, and the weight is adjusted according to the set learning rate. After multiple rounds of iterations, the optimized model is evaluated using the test set data to check the generalization ability of the model, that is, to evaluate the performance of the model on data that has not participated in training. If the test results are not ideal, adjust parameters such as the learning rate and number of iterations, and continue iterative optimization until the model can accurately reflect the channel transmission performance on the test set and obtain the final weight value;

[0068] Weight Verification and Application: The final weight values ​​are applied to new, actual data transmission scenarios, continuously monitoring the model's evaluation results and data transmission status. If a significant deviation is found between the model evaluation results and the actual transmission performance, new historical data is collected and the above training steps are repeated to further optimize the weights. This ensures the scientific and reasonable evaluation of channel transmission performance and provides reliable support for the intelligent routing algorithm to select the optimal transmission path.

[0069] Interaction optimization items: When the data timeliness requirement is high, that is, d4 ≥ 0.7, the amount of data d3 and the channel remaining bandwidth (1-c i1 ) ratio evaluation to avoid delays in high-timeliness data transmission due to insufficient channel bandwidth;

[0070] Step 4: Dynamic weight adjustment mechanism: Introduce a dynamic weight adjustment mechanism 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: Where t represents the current time step, t-1 represents the previous time step, that is, the weight value of the previous shift or time period, which serves as the basis for the current adjustment; r i is the actual transmission success rate of the i-th data, and its value range is [0,1]; is the average success rate within the time window T; η is the learning rate, which controls the adjustment step size; m represents the number of processed data in the window T; The difference between the two determines the direction of weight adjustment; for example, in a large automobile manufacturing company, the amount of order data transmission increases dramatically during the peak production period. If a channel is transmitting urgent order data, the actual transmission success rate r i Consistently below-average success rate Right now The algorithm will automatically reduce the weight of the channel when processing high-urgency data (such as α t The algorithm then prioritizes the most urgent parts of a transmission (e.g., those associated with high urgency), reducing the use of that channel in subsequent path selection. Conversely, if a channel performs well when the transmission device monitors data in real time, its weight will be increased accordingly. This dynamic adjustment mechanism enables the algorithm to continuously learn and adapt to changes in the enterprise's business and network environment, continuously optimizing path selection strategies and improving data transmission reliability and efficiency.

[0071] Step 5, optimal path selection and execution: After completing the comprehensive score of all available channels (Score i ,C i ) is calculated, 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; When , it means that a single channel may not be able to meet the data transmission requirements, and the multi-channel parallel transmission strategy is triggered. For example, at the end of each month, large electronics manufacturers aggregate and transmit production data. The data volume is enormous and timeliness requirements are high, so single-channel transmission may not be sufficient. The algorithm then divides the data into multiple parts and transmits them simultaneously through multiple channels. 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 restored through data transmitted through other channels, significantly improving transmission speed and preventing the impact of slow single-channel transmission on the company's production data analysis and decision-making.

[0072] Step 6: Real-time monitoring and adaptive adjustment: Continuously monitor the actual transmission delay L while processing data and transmitting it along the optimal path. actual , packet loss rate P actual , when L actual >1.2L expected or P actual >0.05, where L expectedThe algorithm detects a transmission path problem based on a preset transmission delay value and immediately triggers a path switching mechanism, re-executing steps 1-5 to select a new path. In large-scale machinery manufacturing enterprises, when a transmission channel experiences network instability due to a surge in power consumption within the factory equipment, and transmission delay suddenly increases by more than 1.2 times the expected value, the algorithm quickly detects the anomaly, immediately halts transmission along the current path, reanalyzes data characteristics and channel status, and selects a new, stable channel to continue transmitting production data, ensuring stable and continuous data transmission and safeguarding the normal operation of the enterprise's business.

[0073] 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 solve the problems of low data transmission efficiency and prone to errors in traditional systems, and significantly improve the data transmission performance of enterprise computer information construction systems.

[0074] The specific implementation steps of the multi-channel parallel transmission strategy are as follows:

[0075] Data segmentation: Divide the processed data into k sub-blocks D1, D2, ..., D according to the channel bandwidth ratio k ,satisfy

[0076] Redundant coding: Parity check is used to add check information to each sub-block, where the check block is P = D1⊕D2⊕…⊕D k ;

[0077] Parallel transmission: Sub-blocks and check blocks are transmitted simultaneously through k available channels, using the remaining bandwidth of the channel (1-c i1 ) Improve transmission efficiency.

[0078] The processed data is divided into k sub-blocks D1, D2, ..., D based on the channel bandwidth ratio. k The specific implementation steps are as follows:

[0079] The intelligent routing transmission module first selects the available channel set {C1, C2, ..., C k}, and obtain the remaining bandwidth ratio of each channel, where where c i1 For channel C i Bandwidth utilization, 1-c i1 Indicates the remaining available bandwidth of the channel;

[0080] Calculate the data allocation for each channel: Calculate the size of each sub-block based on the total amount of processed data |D| and the remaining bandwidth ratio of each channel:

[0081] Perform data segmentation: Starting from the starting position of the original data D, press the calculated |Di |Divide the data into sub-blocks in sequence: sub-block D1 contains the first |D1| bytes of the processed data, sub-block D2 contains the next |D2| bytes of the data, ..., sub-block D k Contains the rest of the data |D k |byte;

[0082] Multi-channel parallel transmission: the divided sub-blocks D1, D2, ..., D k Through the corresponding channels C1, C2, ..., C k Transmission, fully utilize the available bandwidth of each channel to improve overall transmission efficiency

[0083] Data storage module: uses a distributed storage system to receive and store data processed by the distributed data processing module. It also has data backup and recovery functions to ensure data security and integrity.

[0084] For the data storage module, the implementation steps are briefly explained with examples: First, various types of data processed by the distributed data processing module, such as production data, inventory data, etc., are received, these data are sharded, and the data is dispersed and stored in a cluster composed of multiple storage nodes to avoid single-point storage pressure and improve concurrent processing capabilities. During the storage process, multiple copies are generated for each data shard and stored in different physical nodes to ensure data redundancy. For example, the production data shards are stored in nodes A, B, and C at the same time to prevent data loss due to a single node failure. When writing data, the data sharding rules are first determined, such as sharding by data type or timestamp. After the master node receives the data, it is synchronized to the replica node to ensure that the data of each replica is consistent. At the same time, the storage location, sharding information, etc. of the data are recorded in the metadata server to facilitate subsequent rapid retrieval and reading.

[0085] When reading data, the module parses the query request of the data application module, finds the storage location of the target data through the metadata server, and reads data from multiple replica nodes in parallel to improve reading efficiency. For requests with high real-time requirements, it gives priority to low-latency node responses to ensure that data is returned quickly.

[0086] Regarding data backup and recovery, the module regularly performs full data backups, such as a weekly snapshot backup of all data on Sundays, which is stored to offline media or a remote disaster recovery center. Daily incremental backups are performed, backing up only the data that has changed since the last backup, thus reducing storage resource usage. If a storage node failure or data corruption is detected, the module automatically triggers a recovery process, locating the most recent backup data, rebuilding the data shards and replicas on healthy nodes, and restoring normal system operation.

[0087] To ensure data security and integrity, the module encrypts data during transmission and storage, using the SSL / TLS protocol for transmission and AES-256 encryption for sensitive data during storage. It also regularly performs hash checks on data, comparing the hash values ​​of each replica node to detect data tampering or corruption. It also implements role-based access control, allowing users in different departments to read and write data based on their permissions. All access operations are recorded in an audit log to ensure data access traceability. Through these steps, the data storage module achieves efficient data storage, secure backup, and rapid recovery, ensuring enterprise data security and business continuity.

[0088] Data application module: connects to the data storage module to provide data query, report generation, and data analysis application services for various departments of the enterprise, enabling each department to obtain the required data in a timely manner and assist in business decision-making.

[0089] Example 2

[0090] See also Figure 3 , a method for building enterprise computer informationization, comprising the following steps:

[0091] Data collection: The data collection module collects business data from the business systems of various departments of the enterprise according to the preset collection cycle and collection rules, and performs format conversion and verification on the data;

[0092] 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.

[0093] Intelligent routing transmission: The distributed data processing module transmits the processed data to the intelligent routing transmission module. The intelligent routing transmission module selects the optimal transmission path for the data based on the improved intelligent routing algorithm and transmits the data to the data storage module.

[0094] Data storage and application: The data storage module receives and stores data, and the data application module obtains data from the data storage module to provide corresponding data application services for various departments of the enterprise.

[0095] Compared with existing enterprise computer information construction technologies, the present invention has significant advantages. In terms of data processing, the distributed data processing module uses data segmentation and parallel processing technology to disperse a large amount of data processing tasks originally concentrated on a single node to multiple nodes for simultaneous processing, effectively reducing the processing pressure of a single node, significantly improving data processing efficiency, and being able to quickly process the massive amount of data generated by the enterprise, meeting the enterprise's requirements for timeliness in data processing. In terms of data transmission, the intelligent routing algorithm of the intelligent routing transmission module can dynamically select the optimal transmission path based on the characteristics of the data and the real-time status of the transmission channel, and promptly switch paths when transmission problems occur, effectively solving the problems of low data transmission efficiency and proneness to errors in traditional systems, ensuring that data can be accurately and quickly transmitted to the destination. In addition, the data format conversion and verification functions of the data acquisition module ensure data quality, the distributed storage and data backup and recovery functions of the data storage module ensure data security, and the rich data application services of the data application module enhance the enterprise's use of data. Through the collaborative work of various modules, the present invention comprehensively improves the data transmission reliability and overall operating efficiency of the enterprise computer information construction system, provides strong technical support for the digital operation and development of enterprises, effectively promotes the enterprise's informationization process, and enhances the enterprise's competitiveness in the market.

[0096] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention 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 collection module: used to collect business data generated by various departments of the enterprise, and 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: This module uses a distributed computing architecture to receive the collected data processed by the data acquisition module, segment the collected data according to preset rules, where the preset rules include segmentation dimensions related to data transmission requirements, and simultaneously processes the segmented data in parallel, specifically performing data cleaning and data analysis operations on the segmented 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. It also monitors the data transmission status in real time during the transmission process. When a transmission path fails or is delayed, it automatically switches to an alternative path to ensure data transmission continuity. Data storage module: uses a distributed storage system to receive and store data processed by the distributed data processing module. It also has data backup and recovery functions to ensure data security and integrity. Data application module: connects to the data storage module to provide data query, report generation, and data analysis application services for various departments of the enterprise, enabling each department to obtain the required data 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 implementation logic of the intelligent routing transmission module is as follows: Step 1: Data feature extraction and quantification: Obtain processing data from the distributed data processing module, perform multi-dimensional feature extraction and quantification based on the different business attributes and transmission requirements of the processed data, and generate a multi-dimensional feature vector: D i =[d1,d2,…,d i ], where d i represents the extracted features; Step 2: Transmission channel status perception: Construct channel status matrix: Where n is the number of available channels, c i1 Indicates the bandwidth utilization of the i-th channel, which is used to indicate the current busyness of the channel; c i2 is the average delay of the ith channel; c i3 represents the packet loss rate of the i-th channel; c i4 The reliability score of the i-th channel is obtained based on the historical transmission success rate of the channel; Step 3: Improved path evaluation function: Combine the above data features with the channel status and calculate the comprehensive score of each channel for specific data using the following formula: Score(D i ,C i )=α·F data (D i )+β·F channel (C i )+γ·F inter (D,C i ),in: Data fitness score F data (D i )=ω1d i2 +ω2d i4 +ω3d i5 , ω1+ω2+ω3=1, and ω1, ω2, ω3 are weight coefficients that are dynamically adjusted according to the enterprise business priority; Channel performance score: Among them, θ1+θ2+θ3+θ4=1, θ1, θ2, θ3, θ4 are weight coefficients obtained through historical data training to ensure the scientificity and rationality of the scoring; Interaction optimization items: When the data timeliness requirement is high, that is, d4 ≥ 0.7, the amount of data d3 and the channel remaining bandwidth (1-c i1 ) ratio evaluation to avoid delays in high-timeliness data transmission due to insufficient channel bandwidth; Step 4: Dynamic weight adjustment mechanism: Introduce a dynamic weight adjustment mechanism 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: Where t represents the current time step, t-1 represents the previous time step, that is, the weight value of the previous shift or time period, which serves as the basis for the current adjustment; r i is the actual transmission success rate of the i-th data, and its value range is [0,1]; is the average success rate within the time window T; η is the learning rate, which controls the adjustment step size; m represents the number of processed data in the window T; The difference determines the direction of weight adjustment; Step 5, optimal path selection and execution: After completing the comprehensive score of all available channels (Score i ,C i ) is calculated, 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; When , it means that a single channel may not be able to meet the data transmission requirements, and the multi-channel parallel transmission strategy is triggered. is the preset threshold; Step 6: Real-time monitoring and adaptive adjustment: Continuously monitor the actual transmission delay L while processing data and transmitting it along the optimal path. actual , packet loss rate P actual , when L actual >1.2L expected or P actual >0.05, where L expected The transmission delay value is preset, and it is determined that there is a problem with the transmission path, and the path switching mechanism is immediately triggered, that is, steps 1-5 are executed again to select a new path.

4. The enterprise computer information construction system according to claim 3, characterized in that: The specific execution steps of the multi-channel parallel transmission strategy are as follows: Data segmentation: Divide the processed data into k sub-blocks D1, D2, ..., D according to the channel bandwidth ratio k ,satisfy Redundant coding: Parity check is used to add check information to each sub-block, where the check block is Parallel transmission: Sub-blocks and check blocks are transmitted simultaneously through k available channels, using the remaining bandwidth of the channel (1-c i1 ) Improve transmission efficiency.

5. The enterprise computer information construction system according to claim 4, characterized in that: The processed data is divided into k sub-blocks D1, D2, ..., D based on the channel bandwidth ratio. k The specific implementation steps are as follows: The intelligent routing transmission module first selects the available channel set {C1, C2, ..., C k }, and obtain the remaining bandwidth ratio of each channel, where where c i1 For channel C i Bandwidth utilization, 1-c i1 Indicates the remaining available bandwidth of the channel; Calculate the data allocation for each channel: Calculate the size of each sub-block based on the total amount of processed data |D| and the remaining bandwidth ratio of each channel: Perform data segmentation: Starting from the starting position of the original data D, press the calculated |D i |Divide the data into sub-blocks in sequence: sub-block D1 contains the first |D1| bytes of the processed data, sub-block D2 contains the next |D2| bytes of the data, ..., sub-block D k Contains the rest of the data |D k |byte; Multi-channel parallel transmission: the divided sub-blocks D1, D2, ..., D k Through the corresponding channels C1, C2, ..., C k transmission, making full use of the available bandwidth of each channel and improving the overall transmission efficiency.

6. A method for building enterprise computer informationization according to any one of claims 1 to 5, characterized in that: The following steps are involved: Data collection: The data collection module collects business data from the business systems of various departments of the enterprise according to the preset collection cycle and collection rules, and performs format conversion and verification 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. The intelligent routing transmission module 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, and the data application module obtains data from the data storage module to provide corresponding data application services for various departments of the enterprise.

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