An enterprise management information sharing system based on the Internet and data analysis

Abstract

The present invention discloses an enterprise management information sharing system based on the Internet and data analysis, which relates to the field of management consulting information sharing technology. The system realizes real-time monitoring of data transmission status through a first monitoring module, a data stream sharing framework and a busy detection unit, ensures timely detection of data transmission anomalies, and prevents problems caused by excessive load or network congestion. Transmission Condition Index Cs The calculation of the busy coefficient Fmx indicator provides a quantitative evaluation method for the system, which facilitates quick action to optimize the transmission path, adjust the bandwidth, or limit the flow of people to ensure the stable operation of the system. The second monitoring module detects the possibility of data damage or tampering by comparing the number of bytes and issues the second early warning instruction in time. The third monitoring module converts the first byte number into a hash value before sending the enterprise metadata, and checks the compression action of the shared positioning point in the middle to ensure that the data has not been tampered with.

Description

Technical Field

[0001] The present invention relates to the technical field of management consulting information sharing, and in particular to an enterprise management information sharing system based on the Internet and data analysis. Background Art

[0002] In today's ever-changing business environment, the success of an enterprise is closely related to the effective sharing of information. The enterprise management information sharing mechanism has become an important cornerstone for maintaining enterprise competitiveness and business flexibility. Enterprise management information sharing not only involves communication and collaboration between departments within the company, but also includes interactions with partners, suppliers and customers. In this complex environment, traditional sharing methods often cannot meet the growing needs of enterprises, so more innovative and flexible solutions are needed.

[0003] In the Chinese invention patent application published as CN111708996B, an Internet-based enterprise internal management consulting information sharing system is provided, which relates to the technical field of management consulting information sharing. The verification unit authenticates the received identity information according to the identity information in the identity database, and after the identity authentication is passed, the identity information, consulting information and the corresponding sharing level Q are shared. k Transmitted to the processor; the processor obtains the number of visits F by the user c , and perform the level correction step to obtain the shared correction level Q kx , and finally according to the shared revised level Q kx Select and share the modified level Q by node selection kx The corresponding information sharing channel G k , and obtain the information sharing channel G in the information sharing library k Corresponding consultation information level A k Shared information details corresponding to the consultation information in the

[0004] The platform does not do a good enough job in preventing leaks, which can easily lead to information leakage.

[0005] However, based on the above applications and existing technologies, when intelligently detecting identity authentication information, if the traffic of people sharing nodes, such as viewing, downloading and forwarding traffic, is large, the transmission load will increase. Excessive transmission load may cause incomplete or even damaged enterprise metadata, making the data unusable. Traditional sharing technology can automatically identify these problems, but cannot immediately handle them, and its auxiliary role is limited. To this end, an enterprise management information sharing system based on the Internet and data analysis is proposed. Summary of the invention

[0006] 1. Technical issues to be solved

[0007] In view of the deficiencies of the prior art, the present invention provides an enterprise management information sharing system based on the Internet and data analysis to solve the problems mentioned in the background technology.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: an enterprise management information sharing system based on the Internet and data analysis, comprising:

[0010] The data collection module collects and obtains a number of enterprise metadata from various sources through API and file upload to establish an enterprise management information database;

[0011] The first monitoring module is used to build a data flow sharing framework, which includes data flow nodes and data pipelines. The nodes in the data flow sharing framework are used to represent various shared positioning points on the path, and the target location of the transmission of several enterprise text information sources is located through the path to obtain the first transmission target path point; and in the process of collecting the path transmission, the timestamp, the stay time Tv and the transmission load Tc of each shared positioning point in the middle are recorded to build a transmission condition index Cs. When the transmission condition index Cs is higher than the first threshold, it indicates that the transmission is abnormal, and a first warning instruction is issued in the system;

[0012] After receiving the first warning instruction, the second monitoring module issues a collection task to collect and obtain the first byte of the enterprise metadata of the current shared positioning point and the bytes of several intermediate shared positioning points, as Q1, Q2, ..., Q n Indicates, where n represents the number of shared positioning points passed through, and compares whether the number of bytes of each shared positioning point is the same as the first number of bytes. If they are not the same, it means that there is a risk of abnormal damage or tampering of the data, and a second warning instruction is sent, and a second strategy is generated according to the second instruction;

[0013] The third monitoring module converts the first byte number Q1 into the first hash value before each enterprise metadata is sent, and identifies whether there is a compression action at the shared positioning point in the middle. When a compression action is identified, a first verification command is issued to identify the second hash value after the compression action and compare it with the first hash value. If they are different, a third early warning instruction is generated.

[0014] Preferably, the data acquisition module includes a data acquisition unit and a classification unit;

[0015] The data collection unit is used to obtain enterprise structured data using internal or external APIs of the enterprise, including JSON, XML, Excel, CSV and PDF formats, and after data collection, pre-process several enterprise metadata, including removing blanks, duplicate data and normalizing, and converting them into a unified text format. The normalization includes removing stop words and unifying capitalization.

[0016] Preferably, the classification unit is used to establish a classification model, and the classification model is used to establish a classification model through a machine learning algorithm, including a decision tree, a random forest, a support vector machine or a deep learning model. After training, the preprocessed enterprise metadata is used to identify entities and keywords in the text through natural language processing technology NLP, and the entities and keywords that appear the most times are extracted, and the first participle entity and the second participle keyword are obtained respectively, and the first participle entity and the second participle keyword are sequentially combined to generate a first label, which is attached to the enterprise metadata file name as a suffix, and the first label is automatically matched and classified through the classification model and included in each classification group. The classification groups include finance, technology, human resources, sales or production management groups, and an enterprise management information database is established.

[0017] Preferably, the first monitoring module includes a data encryption unit, a data stream sharing framework building unit and a first monitoring unit;

[0018] The data encryption unit is used to set corresponding encryption information according to the classification of a number of enterprise text information;

[0019] The data flow sharing framework construction unit is used to create nodes in the data flow sharing framework, representing each shared positioning point on the path; each node is a key point in data transmission, including a server, a database and an application; data transmission between nodes is carried out through a data pipeline; the nodes in the data flow sharing framework are used to locate the source and target location of a number of enterprise text information;

[0020] The first monitoring unit is used to collect the timestamp, the dwell time Tv and the transmission load Tc of each shared positioning point during the data transmission process, and generate the transmission condition index Cs through the following formula after dimensionless processing:

[0021]

[0022] Where i = 1, 2, ..., m, where m is the number of times each metadata stays at each shared positioning point; Tv i Indicates the actual duration of each shared positioning point metadata. Represented as the preset stay time threshold, Tc iis the transmission load of the shared positioning point during the i-th collection, is the transmission load threshold of enterprise metadata; F1 and F2 are weight coefficients: 0≤F1≤1, 0≤F2≤1 and F2+F1=1;

[0023] The residence time Tv and the transmission load Tc are obtained through network browsing monitoring tools such as Wireshark, NetFlow or Nagios.

[0024] Preferably, the first monitoring module further includes a first screening unit and a first trigger instruction unit;

[0025] The first screening unit is used to compare the transmission condition index Cs with a first threshold value. When the transmission condition index Cs is higher than the first threshold value, it indicates that a data transmission abnormality occurs on the path. The first trigger instruction unit generates a first warning instruction, indicating that the data stays on the path for longer than expected or network congestion causes a load abnormality. A first strategy is generated in response to the first warning command, and the first strategy is used to optimize the bandwidth of the transmission path.

[0026] Preferably, the first monitoring module further includes a tracking unit and a buffer strategy unit;

[0027] The tracking unit reacquires the transmission condition index Cs after the first strategy optimizes the bandwidth of the transmission path. If the transmission condition index Cs is still higher than the first threshold, the enterprise metadata of each transmission node is cached through the buffering strategy unit to optimize the transmission load Tc, and the transmission node with a transmission condition index Cs higher than the first threshold * 120 is defined as a peak load node, a cache layer is introduced and configured to increase CPU and memory, and the number of containers is dynamically adjusted according to the system load through the container orchestration tool Kubernetes to support horizontal and vertical scaling.

[0028] Preferably, the first monitoring module further includes a busy detection unit;

[0029] The busy detection unit is used to collect the real-time shared viewing flow Gk, real-time downloading flow Xz and real-time forwarding flow Zf in the same time period during the process of sharing enterprise metadata at each shared positioning point, perform linear normalization processing, and map the corresponding data values ​​in the interval [0,1], and then generate the busy coefficient Fmx according to the following formula:

[0030]

[0031] in, To measure the average of real-time shared viewing traffic in each time period, To download the average value of the flow of people in real time in each same time period; is the mean of the real-time forwarding flow in each same time period; α, β and γ are weight coefficients: 0≤β≤1, 0≤α≤1, 0≤γ≤1, and α+β+γ=1, where i=1, 2, …k, k is the number in the monitoring period, which is a positive integer greater than 1.

[0032] Preferably, the first monitoring module further includes an overload strategy unit;

[0033] The overload strategy unit is used to compare the busy coefficient Fmx with the second threshold to obtain an overload evaluation result:

[0034] When the busy coefficient Fmx>120*the second threshold, a first overload level is obtained, and a first overload strategy is obtained according to the first overload level, including: limiting the number of visitors, prohibiting downloading and forwarding actions in the same time period, so as to optimize the speed limit;

[0035] When the second threshold value is less than the busy coefficient Fmx≤120*the second threshold value, a second overload level is obtained, and a second overload strategy is obtained according to the second overload level, including: limiting the concurrent amount of metadata transmission, or limiting the number of downloads and forwardings according to priority;

[0036] When the busy index Fmx ≤ the second threshold, the third qualified level is obtained, indicating that there is no abnormality, access / download and abnormal acquisition are not restricted, and the busy status continues to be monitored.

[0037] Preferably, after receiving the first warning instruction, the second monitoring module issues a collection task to collect the byte counts of the current shared positioning point and several midway shared positioning points, and obtain the byte counts of the enterprise metadata of each shared positioning point, including marking the starting byte count as the first byte count Q1, marking the byte counts of each midway shared positioning point as Q2, ..., Q n Indicates that the number of bytes of each shared positioning point in the middle is compared with the first number of bytes to check whether it is consistent with the first number of bytes Q1. If the number of bytes of the shared positioning point in the middle is inconsistent with the first number of bytes, it indicates that the data is damaged or tampered with; a second warning instruction is generated;

[0038] A second strategy is generated according to the second warning instruction to confirm that the data is damaged or tampered with, and the first data recovery strategy is started to restore the original data from the backup through the first verification code or use redundant data to rebuild the damaged part for repair, and then retry the transmission.

[0039] Preferably, a second data recovery strategy is generated based on the third warning instruction, including: reviewing the access rights of users and systems to ensure that only authorized entities can perform data compression and decompression, and in the event of inconsistency in the data compression package due to erroneous modification, rolling back to the enterprise management information database for re-backup through the second verification code.

[0040] (III) Beneficial effects

[0041] The present invention provides an enterprise management information sharing system based on the Internet and data analysis. It has the following beneficial effects:

[0042] (1) The first monitoring module can monitor the status of data on the shared path in real time by building a data flow sharing framework and collecting various indicators during the transmission process. By calculating the transmission condition index Cs, the system can quickly detect anomalies in the data transmission process and issue warning instructions in a timely manner. This real-time monitoring and warning mechanism helps to avoid potential risks in the data sharing process. When the transmission condition index Cs is higher than the predetermined threshold, the first monitoring module will issue a first warning instruction to prompt the data on the path. According to the first warning instruction, the system can take corresponding strategies to optimize the transmission path, such as adjusting the bandwidth, redistributing the load, etc. The application of this strategy can effectively improve the efficiency of data transmission and reduce the problems caused by excessive load during the transmission process. In addition, through the deep detection of the busy detection unit, the shared traffic of enterprise metadata can be monitored in real time at each shared positioning point in the middle, including viewing, downloading and forwarding behaviors. This real-time monitoring capability can capture the peak period of the data sharing process in a timely manner, which helps to give early warning and take appropriate measures. By generating the busy coefficient Fmx through the formula, the busy detection unit can accurately reflect the busyness of the shared positioning point. If the busy coefficient Fmx exceeds the preset second threshold, the system can take measures according to the overload strategy, such as limiting the number of visitors, reducing downloads and forwarding, or adjusting system resources. This mechanism effectively prevents system overload and helps to reasonably allocate and optimize resources. This provides the system with a quantitative indicator to measure the busyness of the shared path.

[0043] (2) After receiving the first warning instruction, the second monitoring module can issue a collection task to obtain the number of bytes of each shared positioning point and compare these byte numbers with the initial byte number. If the byte numbers are inconsistent, the system can identify that the data may be damaged or tampered with, and then generate a second warning instruction. This detection mechanism helps to detect data anomalies in a timely manner and prevent enterprise metadata from losing integrity due to tampering or damage.

[0044] (3) Before the enterprise metadata is sent, the third monitoring module converts the first byte number into a hash value and detects whether compression occurs at the shared positioning point in the middle. When compression is detected, the module identifies the compressed hash value and compares it with the previous hash value. If the hash values ​​are inconsistent, the system will issue a third warning instruction to indicate that the data may have been tampered with. This mechanism can effectively ensure the integrity and security of data during sharing and transmission, and reduce potential data risks. The third monitoring module and the second monitoring module have a two-pronged monitoring and verification function, reducing the probability of abnormal data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The present invention is a block diagram of an enterprise management information sharing system based on the Internet and data analysis. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Example 1

[0048] See also Figure 1 The present invention provides an enterprise management information sharing system based on the Internet and data analysis, comprising:

[0049] The data collection module collects and obtains a number of enterprise metadata from various sources through API and file upload to establish an enterprise management information database;

[0050] The first monitoring module is used to build a data flow sharing framework, which includes data flow nodes and data pipelines. The nodes in the data flow sharing framework are used to represent various shared positioning points on the path, and the target location of the transmission of several enterprise text information sources is located through the path to obtain the first transmission target path point; and in the process of collecting the path transmission, the timestamp, the stay time Tv and the transmission load Tc of each shared positioning point in the middle are recorded to build a transmission condition index Cs. When the transmission condition index Cs is higher than the first threshold, it indicates that the transmission is abnormal, and a first warning instruction is issued in the system;

[0051] Through the data flow sharing framework, the system can clearly display the flow of data on each node. This makes it easy to track the source, path and transmission target location of the data, ensuring that the flow of data is transparent and traceable. The module can monitor the timestamp, dwell time Tv and transmission load Tc of each shared positioning point during the path transmission process, and use this information to calculate the transmission condition index Cs. When Cs exceeds the set threshold, it indicates that there may be a transmission anomaly, and the system will issue a first warning instruction to remind the user or system administrator to take action. The first monitoring module can detect factors that may cause data loss, damage or delay during the transmission process in advance, thereby reducing these risks. By issuing warnings in time, users can immediately deal with potential problems and ensure the reliability of data transmission. By recording the dwell time and transmission load of shared positioning points, the module can help identify bottlenecks on the transmission path. Based on this information, the system can adjust the allocation of network resources, optimize the transmission path, and avoid network congestion.

[0052] After receiving the first warning instruction, the second monitoring module issues a collection task to collect and obtain the first byte of the enterprise metadata of the current shared positioning point and the bytes of several intermediate shared positioning points, as Q1, Q2, ..., Q n Indicates, where n represents the number of shared positioning points passed through, and compares whether the number of bytes of each shared positioning point is the same as the first number of bytes. If they are not the same, it means that there is a risk of abnormal damage or tampering of the data, and a second warning instruction is sent, and a second strategy is generated according to the second instruction;

[0053] The third monitoring module converts the first byte number Q1 into the first hash value before each enterprise metadata is sent, and identifies whether there is a compression action at the shared positioning point in the middle. When a compression action is identified, a first verification command is issued to identify the second hash value after the compression action and compare it with the first hash value. If they are different, a third early warning instruction is generated.

[0054] In this embodiment, a data stream sharing framework is constructed through the first monitoring module, and the system can monitor the shared positioning points in the transmission path in real time, detect abnormal situations, and issue a first warning instruction. This helps to quickly discover and deal with problems in the transmission process to prevent data damage or incompleteness. The second monitoring module ensures data consistency by collecting the number of bytes of enterprise metadata and comparing the number of bytes of different shared positioning points. If the number of bytes does not match, the system will automatically issue a warning and initiate the corresponding strategy to prevent data tampering and damage. The third monitoring module converts the first number of bytes into a hash value before each enterprise metadata is sent, and identifies the compression action of the shared positioning point in the middle. By verifying the compressed hash value, the system can ensure the security and integrity of the compression process. If the hash value does not match, the system will generate a warning and take corresponding measures.

[0055] The system can detect the transmission load through the transmission condition index Cs. When the transmission condition index CsCs exceeds the threshold, the system can automatically adjust the bandwidth of the transmission path and even use a cache strategy to alleviate the load. This dynamic expansion mechanism can improve the stability of the system and prevent data loss or damage caused by excessive traffic.

[0056] Example 2: This example is an explanation of Example 1. Figure 1 ,Specifically, the data acquisition module includes a data acquisition unit and a classification unit;

[0057] The data collection unit is used to obtain enterprise structured data using internal or external APIs of the enterprise, including JSON, XML, Excel, CSV and PDF formats, and after data collection, pre-process several enterprise metadata, including removing blanks, duplicate data and normalizing, and converting them into a unified text format. The normalization includes removing stop words and unifying capitalization.

[0058] In the present embodiment, this flexibility helps to obtain data from different sources through Internet technology, and improves the compatibility and expansibility of the system. The data acquisition unit performs pre-processing after data acquisition, including removing blanks, duplicate data and normalization. This helps to improve the quality of data, reduce data noise and redundancy, thereby improving the accuracy and efficiency of subsequent data analysis. Through normalization (including removing stop words and unifying capitalization), the data acquisition unit can convert data into a unified text format to ensure the consistency of data in the entire system. This uniformity helps to simplify data analysis and processing procedures, and reduces the complexity caused by inconsistent data formats.

[0059] Example 3: This example is an explanation of Example 2. Figure 1 Specifically, the classification unit is used to establish a classification model, and the classification model is used to establish a classification model through a machine learning algorithm, including a decision tree, a random forest, a support vector machine or a deep learning model. After training, the preprocessed enterprise metadata is used to identify entities and keywords in the text through natural language processing technology NLP, and the entities and keywords that appear the most times are extracted, and the first participle entity and the second participle keyword are obtained respectively, and the first participle entity and the second participle keyword are sequentially combined to generate a first label, which is attached to the enterprise metadata file name as a suffix, and the first label is automatically matched and classified through the classification model and included in each classification group. The classification group includes finance, technology, human resources, sales or production management group, and an enterprise management information library is established.

[0060] In this embodiment, the classification unit establishes a classification model through a machine learning algorithm to achieve automatic classification of enterprise metadata. A variety of machine learning methods such as decision trees, random forests, support vector machines or deep learning models are used to ensure the accuracy and robustness of the classification process and reduce manual intervention. By using natural language processing (NLP) technology to identify entities and keywords in text, the most commonly used entities and keywords can be quickly extracted to obtain the first segmentation entity and the second segmentation keyword. This efficient data processing capability can greatly improve the analysis efficiency of the system. The first segmentation entity and the second segmentation keyword are sequentially combined to generate a unique label and attach it to the enterprise metadata file name. This dynamic classification method can adapt to changes in different data sources and different business scenarios, making the classification model more flexible and adaptable. The generated label is used as a suffix of the file name, providing an accurate data indexing function. Through the label, users can quickly find the required data, improve the efficiency of data query, and reduce the time and cost of data retrieval. By establishing a classification model and automatically matching the classification, the classification unit can ensure that the data in the enterprise management information library is organized in a consistent manner and ensure consistency in data sharing and analysis. This helps to avoid data confusion and misleading interpretation. The classification unit automatically divides the data into different classification groups and establishes an enterprise management information database, providing rich basic data support for enterprise management and decision analysis. Enterprises can conduct in-depth analysis based on these classified data to make more accurate and efficient business decisions.

[0061] Example 4: This example is an explanation of Example 1. Figure 1 ,Specifically, the first monitoring module includes a data encryption unit, a data stream sharing framework building unit and a first monitoring unit;

[0062] The data encryption unit is used to set corresponding encryption information according to the classification of a number of enterprise text information;

[0063] The data flow sharing framework construction unit is used to create nodes in the data flow sharing framework, representing each shared positioning point on the path; each node is a key point in data transmission, including a server, a database and an application; data transmission between nodes is carried out through a data pipeline; the nodes in the data flow sharing framework are used to locate the source and target location of a number of enterprise text information;

[0064] The first monitoring unit is used to collect the timestamp, the dwell time Tv and the transmission load Tc of each shared positioning point during the data transmission process, and generate the transmission condition index Cs through the following formula after dimensionless processing:

[0065]

[0066] Where i = 1, 2, ..., m, where m is the number of times each metadata stays at each shared positioning point; Tv i Indicates the actual duration of each shared positioning point metadata. Represented as the preset stay time threshold, Tc i is the transmission load of the shared positioning point during the i-th collection, is the transmission load threshold of enterprise metadata; F1 and F2 are weight coefficients: 0≤F1≤1, 0≤F2≤1 and F2+F1=1;

[0067] The residence time Tv and the transmission load Tc are obtained through network browsing monitoring tools such as Wireshark, NetFlow or Nagios.

[0068] In this embodiment, by calculating the transmission condition index Cs, the first monitoring module can monitor abnormal conditions in the data transmission process in real time. When Cs is higher than the first threshold, the system will immediately issue an early warning to help the enterprise take countermeasures before the problem occurs to avoid data transmission interruption or damage. The Cs formula combines the dwell time, transmission load, preset threshold and weight coefficient together to provide an indicator for comprehensively measuring the data transmission status. The calculation method of this index takes into account a variety of factors, making the monitoring results more accurate and comprehensive. Based on the calculation results of Cs, the enterprise can dynamically adjust the data transmission strategy. For example, when Cs is too high, it can trigger the optimization of the transmission path, the redistribution of the load, and even the adoption of strategies such as caching or delayed transmission to maintain the stability of the transmission. The transmission condition index Cs can reflect the load and efficiency of each node in the data transmission process. By optimizing the transmission path and load balancing, enterprises can improve data transmission efficiency, avoid excessive congestion, and ensure smooth data transmission.

[0069] Example 5: This example is an explanation of Example 4. Please refer to Figure 1 , specifically, the first monitoring module also includes a first screening unit and a first trigger instruction unit;

[0070] The first screening unit is used to compare the transmission condition index Cs with the first threshold. When the transmission condition index Cs is higher than the first threshold, it means that data transmission abnormality occurs on the path. When Cs exceeds the threshold, it means that there may be problems such as too long residence time or network congestion during the transmission process. Such a real-time detection mechanism helps enterprises take action before problems occur to avoid data loss or transmission interruption.

[0071] The first trigger instruction unit generates a first warning instruction, indicating that the data stays on the path for longer than expected or network congestion causes abnormal load; in response to the first warning command, a first strategy is generated, and the first strategy is used to optimize the bandwidth of the transmission path.

[0072] In this embodiment, after the first warning instruction is issued, the enterprise can generate a first strategy according to the instruction to optimize the bandwidth of the transmission path. This strategy may include reallocating network traffic, increasing bandwidth, adjusting the transmission path, etc. This optimization strategy helps the enterprise maintain the stability of data transmission and avoid data transmission anomalies caused by network congestion.

[0073] Example 6: This example is an explanation of Example 5. Please refer to Figure 1 ,Specifically, the first monitoring module also includes a tracking unit and a buffer strategy unit;

[0074] The tracking unit reacquires the transmission condition index Cs after the first strategy optimizes the bandwidth of the transmission path. If the transmission condition index Cs is still higher than the first threshold, the enterprise metadata of each transmission node is cached through the buffering strategy unit to optimize the transmission load Tc, and the transmission node with a transmission condition index Cs higher than the first threshold * 120 is defined as a peak load node, a cache layer is introduced and configured to increase CPU and memory, and the number of containers is dynamically adjusted according to the system load through the container orchestration tool Kubernetes to support horizontal and vertical scaling.

[0075] In this embodiment, after the first strategy optimizes the bandwidth of the transmission path, the tracking unit reacquires the transmission condition index Cs to ensure continuous monitoring of the transmission status. This continuous monitoring mechanism helps to identify transmission anomalies that may reappear after the initial optimization, providing a continuous feedback loop for the system to ensure long-term stability. When the transmission condition index Cs is still higher than the first threshold, the buffering strategy unit can implement caching between transmission nodes. This caching mechanism can reduce data delay or loss caused by network congestion during data transmission and optimize the transmission load Tc. By introducing a cache layer, the system can more effectively handle peak loads and ensure the stability and reliability of data transmission. By monitoring the transmission condition index Cs, the buffering strategy unit can identify peak load nodes and take corresponding measures, such as increasing CPU and memory. This dynamic adjustment strategy ensures that the system can adapt to load fluctuations and avoid data transmission bottlenecks caused by insufficient resources. By introducing a cache layer and increasing computing resources at peak load nodes, the system can reduce the risk of transmission interruption and ensure the continuity and integrity of data transmission. The implementation of the buffering strategy can also alleviate transmission delays caused by network congestion and improve overall data transmission efficiency.

[0076] Example 7, this example is explained in Example 1, please refer to Figure 1 ,Specifically, the first monitoring module also includes a busy detection unit;

[0077] The busy detection unit is used to collect the real-time shared viewing flow Gk, real-time downloading flow Xz and real-time forwarding flow Zf in the same time period during the process of sharing enterprise metadata at each shared positioning point, perform linear normalization processing, and map the corresponding data values ​​in the interval [0,1], and then generate the busy coefficient Fmx according to the following formula:

[0078]

[0079] in, To measure the average of real-time shared viewing traffic in each time period, To download the average value of the flow of people in real time in each same time period; is the mean of the real-time forwarding flow in each same time period; α, β and γ are weight coefficients: 0≤β≤1, 0≤α≤1, 0≤γ≤1, and α+β+γ=1, where i=1, 2, …k, k is the number in the monitoring period, which is a positive integer greater than 1.

[0080] In this embodiment, the busy detection unit can monitor the shared traffic of enterprise metadata in real time at each midway shared positioning point, including viewing, downloading and forwarding behaviors. This real-time monitoring capability can timely capture the peak hours in the data sharing process, which helps to provide early warning and take appropriate measures. By generating the busy coefficient Fmx through the formula, the busy detection unit can accurately reflect the busyness of the shared positioning point. The calculation of the busy coefficient weights the viewing, downloading and forwarding traffic, thereby reflecting the overall busyness of the sharing activities in different time intervals. This provides the system with a quantitative indicator to measure the busyness of the shared path.

[0081] Example 8: This example is an explanation of Example 7. Please refer to Figure 1 , specifically, the first monitoring module also includes an overload strategy unit;

[0082] The overload strategy unit is used to compare the busy coefficient Fmx with the second threshold to obtain an overload evaluation result:

[0083] When the busy coefficient Fmx>120*the second threshold, a first overload level is obtained, and a first overload strategy is obtained according to the first overload level, including: limiting the number of visitors, prohibiting downloading and forwarding actions in the same time period, so as to optimize the speed limit;

[0084] When the second threshold value is less than the busy coefficient Fmx≤120*the second threshold value, a second overload level is obtained, and a second overload strategy is obtained according to the second overload level, including: limiting the concurrent amount of metadata transmission, or limiting the number of downloads and forwardings according to priority;

[0085] When the busy index Fmx ≤ the second threshold, the third qualified level is obtained, indicating that there is no abnormality, access / download and abnormal acquisition are not restricted, and the busy status continues to be monitored.

[0086] In this embodiment, the overload strategy unit can flexibly evaluate the overload condition of the system by monitoring the comparison between the busy coefficient Fmx and the second threshold value, and adopt different strategies according to different levels. This dynamic adjustment capability ensures that the system can respond in time to avoid transmission interruption in overload conditions. The multi-level evaluation and strategy application of the overload strategy unit ensure that corresponding measures are taken for different degrees of overload conditions. The setting of the first overload level, the second overload level and the third qualified level enables the system to flexibly adjust the strategy under different overload conditions to ensure stable operation of the system. At the first overload level, the load of the shared path can be quickly reduced by limiting the number of visitors, prohibiting downloading and forwarding, and other strategies. This effective overload risk mitigation strategy helps to prevent system performance degradation, transmission interruption or data corruption caused by overload. At the second overload level, the transmission pressure of the system can be further controlled by limiting the concurrent amount of metadata transmission, or limiting the number of downloads and forwarding according to priority. This strategy ensures that the transmission concurrency is controlled in overload conditions, thereby reducing the risk of system overload. The overload strategy unit can continue to monitor the busy state when the busy index reaches the third qualified level. This continuous monitoring ensures that the system is always under control and that if there are signs of overload, immediate action can be taken to prevent the problem from becoming a serious problem.

[0087] Example 8: This example is explained in Example 1. Figure 1 Specifically, after receiving the first warning instruction, the second monitoring module issues a collection task to collect the byte counts of the current shared positioning point and several midway shared positioning points, and obtain the byte counts of the enterprise metadata of each shared positioning point, including marking the starting byte count as the first byte count Q1, marking the byte counts of each midway shared positioning point as Q2, ..., Q n Indicates that the number of bytes of each shared positioning point in the middle is compared with the first number of bytes to check whether it is consistent with the first number of bytes Q1. If the number of bytes of the shared positioning point in the middle is inconsistent with the first number of bytes, it indicates that the data is damaged or tampered with; a second warning instruction is generated;

[0088] A second strategy is generated according to the second warning instruction to confirm that the data is damaged or tampered with, and the first data recovery strategy is started to restore the original data from the backup through the first verification code or use redundant data to rebuild the damaged part for repair, and then retry the transmission.

[0089] In this embodiment, the second monitoring module can detect signs of data damage or tampering in a timely manner by comparing the number of bytes of each shared positioning point. Since the detection is performed at an early stage, this can prevent the damaged or tampered data from continuing to propagate in the shared system. By comparing the number of bytes at the starting point and the midway shared positioning points, the second monitoring module can ensure that the enterprise metadata maintains integrity during the transmission process. Any inconsistency in the number of bytes will trigger an early warning and reduce the risk of data integrity. Once the second early warning instruction is generated, the second monitoring module will start the first data recovery strategy to ensure the integrity of the data. This strategy can restore the original data from the backup through the first check code, or use redundant data to reconstruct the damaged part. Such a recovery mechanism can ensure data security and reduce losses caused by data damage or tampering.

[0090] Example 9: This example is an explanation of Example 1. Figure 1 Specifically, a second data recovery strategy is generated according to the third warning instruction, including: reviewing the access rights of users and systems to ensure that only authorized entities can perform data compression and decompression. In the event of inconsistency in the data compression package due to erroneous modification, the second verification code is used to roll back to the enterprise management information database for re-backup.

[0091] In this embodiment, before each enterprise metadata is sent, the first byte number is converted into a first hash value. This method can ensure that the data has not been tampered with during the transmission process. By comparing the hash values ​​before and after the compression action, the system can quickly identify whether there is data tampering, thereby improving data integrity and security. By identifying whether there is a compression action at the shared positioning point in the middle and comparing the hash values ​​before and after compression, the system can effectively prevent data tampering. This mechanism can reduce the risk of malicious actors modifying or destroying data and ensure the authenticity of data during the sharing process. When it is identified that the hash value after the compression action does not match the original hash value, the system will generate a third early warning instruction. This real-time anomaly detection mechanism can promptly discover problems in the data sharing process, facilitate quick action, and reduce the possibility of data loss. If the third early warning instruction indicates that the data is abnormal, the backup rollback is performed through the second check code to ensure data integrity. This can effectively prevent data damage caused by erroneous modification and provide a reliable recovery path. By identifying compression actions, comparing hash values, generating early warning instructions, and providing a backup rollback mechanism, the reliability of the entire system is greatly improved. When faced with data anomalies, enterprises can respond and recover quickly to ensure the stable operation of the system.

[0092] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An enterprise management information sharing system based on the Internet and data analysis, characterized by: include, The data collection module collects and obtains a number of enterprise metadata from various sources through API and file upload to establish an enterprise management information database; The first monitoring module is used to build a data flow sharing framework, which includes data flow nodes and data pipelines. The nodes in the data flow sharing framework are used to represent various shared positioning points on the path, and the target positions of the transmission of several enterprise text information sources are located through the path to obtain the first transmission target path point; During the data transmission process, the timestamp, the dwell time Tv and the transmission load Tc of each shared positioning point are recorded to construct a transmission condition index Cs. When the transmission condition index Cs is higher than a first threshold, it indicates that the transmission is abnormal, and a first warning instruction is issued in the system. After receiving the first warning instruction, the second monitoring module issues a collection task to collect and obtain the first byte of the enterprise metadata of the current shared positioning point and the bytes of several intermediate shared positioning points, as Q1, Q2, ..., Q n Indicates, where n represents the number of shared positioning points passed through, and compares whether the number of bytes of each shared positioning point is the same as the first number of bytes. If they are not the same, it means that the data is at risk of abnormal damage or tampering, and sends a second warning instruction, and generates a second strategy according to the second warning instruction, and generates a second strategy according to the second warning instruction to confirm that the data is damaged or tampered, and starts the first data recovery strategy, recovers the original data from the backup through the first check code or uses redundant data to rebuild the damaged part for repair, and then tries to transmit again; The third monitoring module converts the first byte number Q1 into a first hash value before each enterprise metadata is sent, and identifies whether there is a compression action at the shared positioning point in the middle. When a compression action is identified, a first verification command is issued to identify the second hash value after the compression action and compare it with the first hash value. If they are different, a third warning instruction is generated; The data acquisition module includes a data acquisition unit and a classification unit; The data collection unit is used to obtain enterprise structured data using an internal or external API of the enterprise, including JSON, XML, Excel, CSV and PDF formats, and after data collection, pre-process a number of enterprise metadata, including removing blanks, duplicate data and normalizing, and converting to a unified text format, wherein the normalization includes removing stop words and unifying capitalization; The classification unit is used to establish a classification model, and the classification model is used to establish a classification model through a machine learning algorithm, including a decision tree, a random forest, a support vector machine or a deep learning model. After training, the pre-processed enterprise metadata is used to identify entities and keywords in the text through the natural language processing technology NLP, and the entities and keywords that appear the most times are extracted, and the first segmentation entity and the second segmentation keyword are respectively obtained, and the first segmentation entity and the second segmentation keyword are sequentially combined to generate a first label, which is attached to the enterprise metadata file name as a suffix, and the first label is automatically matched and classified through the classification model and included in each classification group. The classification group includes a finance, technology, human resources, sales or production management group, and an enterprise management information library is established; The first monitoring module includes a data encryption unit, a data stream sharing framework construction unit and a first monitoring unit; The data encryption unit is used to set corresponding encryption information according to the classification of a number of enterprise text information; The data flow sharing framework construction unit is used to create nodes in the data flow sharing framework, representing each shared positioning point on the path; each node is a key point in data transmission, including a server, a database and an application; data transmission between nodes is carried out through a data pipeline; the nodes in the data flow sharing framework are used to locate the source and target location of a number of enterprise text information; The first monitoring unit is used to record the timestamp, the dwell time Tv and the transmission load Tc of each shared positioning point during the data transmission process, and after dimensionless processing, generate the transmission condition index Cs through the following formula: Where i = 1, 2, ..., m, where m is the number of times each metadata stays at each shared positioning point; Tv i Indicates the actual duration of each shared positioning point metadata. Represented as the preset stay time threshold, Tc i is the transmission load of the shared positioning point during the i-th collection, is the transmission load threshold of enterprise metadata; F1 and F2 are weight coefficients: 0≤F1≤1, 0≤F2≤1 and F2+F1=1; The residence time Tv and transmission load Tc are obtained through network browsing monitoring tools such as Wireshark, NetFlow or Nagios. The first monitoring module also includes a busy detection unit; The busy detection unit is used to collect the real-time shared viewing flow Gk, real-time downloading flow Xz and real-time forwarding flow Zf in the same time period during the process of sharing enterprise metadata at each shared positioning point, perform linear normalization processing, and map the corresponding data values ​​in the interval [0,1], and then generate the busy coefficient Fmx according to the following formula: in, To measure the average of real-time shared viewing traffic in each time period, To download the average value of the flow of people in real time in each same time period; is the mean of the real-time forwarding flow in each same time period; α, β and γ are weight coefficients: 0≤β≤1, 0≤α≤1, 0≤γ≤1, and α+β+γ=1, where i=1, 2, …k, k is the number in the monitoring period, which is a positive integer greater than 1; The first monitoring module also includes an overload strategy unit; The overload strategy unit is used to compare the busy coefficient Fmx with the second threshold to obtain an overload evaluation result: When the busy coefficient Fmx>120*the second threshold, a first overload level is obtained, and a first overload strategy is obtained according to the first overload level, including: limiting the number of visitors, prohibiting downloading and forwarding actions in the same time period, so as to optimize the speed limit; When the second threshold value is less than the busy coefficient Fmx≤120*the second threshold value, a second overload level is obtained, and a second overload strategy is obtained according to the second overload level, including: limiting the concurrent amount of metadata transmission, or limiting the number of downloads and forwardings according to priority; When the busy index Fmx ≤ the second threshold, the third qualified level is obtained, indicating that there is no abnormality, access / download and forwarding are not restricted, and the busy status continues to be monitored.

2. According to claim 1, the enterprise management information sharing system based on the Internet and data analysis is characterized by: The first monitoring module also includes a first screening unit and a first trigger instruction unit; The first screening unit is used to compare the transmission condition index Cs with a first threshold value. When the transmission condition index Cs is higher than the first threshold value, it indicates that a data transmission abnormality occurs on the path. The first trigger instruction unit generates a first warning instruction, indicating that the data stays on the path for longer than expected or network congestion causes a load abnormality. A first strategy is generated in response to the first warning instruction, and the first strategy is used to optimize the bandwidth of the transmission path.

3. The enterprise management information sharing system based on the Internet and data analysis according to claim 1 is characterized by: The first monitoring module also includes a tracking unit and a buffer strategy unit; The tracking unit reacquires the transmission condition index Cs after the first strategy optimizes the bandwidth of the transmission path. If the transmission condition index Cs is still higher than the first threshold, the enterprise metadata of each transmission node is cached through the buffering strategy unit to optimize the transmission load Tc, and the transmission node with a transmission condition index Cs higher than the first threshold * 120 is defined as a peak load node, a cache layer is introduced and configured to increase CPU and memory, and the number of containers is dynamically adjusted according to the system load through the container orchestration tool Kubernetes to support horizontal and vertical scaling.

4. The enterprise management information sharing system based on the Internet and data analysis according to claim 3 is characterized by: A second data recovery strategy is generated based on the third warning instruction, including: reviewing the access rights of users and systems to ensure that only authorized entities can perform data compression and decompression. In the event of inconsistency in the data compression package due to erroneous modification, the second verification code is used to roll back to the enterprise management information database for re-backup.

Images