Data information management method and system

Abstract

The invention belongs to the technical field of data processing, and discloses a data information management method and system, and the method comprises the steps: collecting the data information of each department of an enterprise through a department cooperation platform; constructing a data information model; extracting structured data from the unstructured text data in the data information model in combination with a natural language processing technology, and constructing a data information base by using the structured data; obtaining an access request of the data information base, determining a data transmission demand according to the access request, and adjusting a data transmission mechanism through an active queue management algorithm; the performance index in the data transmission process is monitored in real time, and the parameters of the active queue management algorithm are dynamically adjusted according to the real-time performance index, so that the stability of data information transmission is realized. According to the method, the stability and reliability of data transmission are ensured, enterprises can better manage and utilize data information resources of the enterprises, and the service level and competitiveness are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data information management method and system. Background Art

[0002] Internal control and management information of enterprises are important components of enterprise development, and have a profound impact on the economic benefits and operational efficiency of enterprises. With the rapid development of computer and network technology, the demand for information management and control is becoming more urgent. Enterprise information management system integrates computer hardware, software, network communication equipment and other technologies to achieve efficient management and transmission of information, improve the management level and decision-making efficiency of enterprises. This system can not only optimize internal resource allocation, but also enhance information sharing and collaboration between departments, providing solid support for the stable development and competitiveness improvement of enterprises.

[0003] In an enterprise information management system, the data access requirements of various departments vary significantly. The data volume, priority, transmission frequency, etc. are different, and business requirements change frequently. The existing technology lacks a flexible and efficient queue management mechanism, and is unable to dynamically adjust the data transmission strategy according to real-time needs. As a result, under high load conditions, the system is difficult to adapt to various data transmission needs, resulting in problems such as low transmission efficiency and increased data latency, which affects the response speed and real-time nature of the business, and may further affect the overall operational efficiency and customer service quality of the enterprise.

[0004] Therefore, how to provide a data information management method and system is a problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present invention provide a data information management method and system to solve the problems in the prior art.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a data information management method is provided.

[0008] In one embodiment, a data information management method includes:

[0009] According to the enterprise functional structure, a department collaboration platform is established through cloud collaboration technology; and the department collaboration platform is used to collect data information from various departments of the enterprise;

[0010] Use BIM technology to model and differentiate the collected data information and build a data information model; combine natural language processing technology to extract structured data from the unstructured text data in the data information model, and use the structured data to build a data information database;

[0011] Obtain access requests from the data repository, determine data transmission requirements based on the access requests, and adjust the data transmission mechanism through active queue management algorithms;

[0012] Monitor the performance indicators of the data transmission process in real time, and dynamically adjust the parameters of the active queue management algorithm according to the real-time performance indicators to achieve stable data information transmission.

[0013] In one embodiment, the collected data information is modeled and differentiated using BIM technology to construct a data information model; natural language processing technology is combined to extract structured data from unstructured text data in the data information model, and the structured data is used to construct a data information library, including:

[0014] Preprocess the collected data information, including data cleaning and data deduplication;

[0015] Integrate the pre-processed data information into the BIM model, use the hierarchical structure and component relationship of the BIM model to organize and classify the data in an orderly manner, and obtain a data information model;

[0016] Use natural language processing technology to perform text analysis on unstructured text data in the data information model, extract entities and relationships between entities, and generate structured data based on the relationships between entities;

[0017] Integrate the extracted structured data into pre-configured data repositories.

[0018] In one embodiment, using natural language processing technology to perform text analysis on unstructured text data in a data information model, extracting entities and relationships between entities, and generating structured data based on the entities and relationships between entities includes:

[0019] Obtain unstructured text data in the data information model, use word embedding technology to process the unstructured text data, and convert the text into vectorized text through a pre-configured language model;

[0020] The vectorized text is input into the bidirectional long short-term memory network and the dilated convolutional layer to identify the feature information and long-distance dependencies in the text and generate a fused feature vector.

[0021] The fused feature vector is decoded using the conditional random field model, and the entities in the text are identified through the label prediction model to extract the named entities and label sequences.

[0022] The relationships between entities are analyzed based on the extracted entity label sequence, and relationships are extracted through preset rules to convert entities and relationships between entities into structured data.

[0023] In one embodiment, the vectorized text is input into a bidirectional long short-term memory network and a dilated convolutional layer, feature information and long-distance dependencies in the text are identified, and a fused feature vector is generated, including:

[0024] The vectorized text is input into the bidirectional long short-term memory network, and the context information in the text sequence is identified through the forward and backward long short-term memory layers in the bidirectional long short-term memory network to obtain the front and back context features of the text;

[0025] The output of the bidirectional long short-term memory network is used as input and passed to the dilated convolution layer. Through the convolution operation, the feature information and long-distance dependencies in the text are extracted to generate a fused feature vector.

[0026] In one embodiment, the fused feature vector is decoded using a conditional random field model, and entities in the text are identified using a label prediction model. Extracting named entities and label sequences includes:

[0027] Based on the pre-configured label sequence, the conditional random field model calculates the conditional probability of each label sequence according to the fused feature vector;

[0028] The conditional probability is decoded using a dynamic programming algorithm to obtain an initial label sequence, which is then mapped to the original text to identify named entities in the text.

[0029] For each identified named entity, a label sequence corresponding to each named entity is extracted, where the label sequence includes a start label, an end label, and an intermediate label.

[0030] In one embodiment, obtaining an access request to a data information repository, determining a data transmission requirement according to the access request, and adjusting a data transmission mechanism through an active queue management algorithm includes:

[0031] Determine data transmission requirements by analyzing access requests to the data information repository, including data volume, transmission priority and frequency;

[0032] According to the data transmission requirements, an active queue management algorithm is used to establish a data transmission queue for the data information that needs to be transmitted; and the performance indicators of the data transmission system are calculated, and the transmission mechanism of the data information is dynamically adjusted according to the performance indicators of the data transmission system.

[0033] In one embodiment, according to the data transmission requirements, an active queue management algorithm is used to establish a data transmission queue for the data information to be transmitted; and the performance index of the data transmission system is calculated. The transmission mechanism of the data information is dynamically adjusted according to the performance index of the data transmission system, including:

[0034] Initialize the data transmission queue according to the transmission requirements and set the basic parameters of the queue, including the queue length, buffer size and initial value of the transmission rate;

[0035] Acquire transmission parameters of the data transmission system, and calculate the performance index of the data transmission system using a performance index calculation formula;

[0036] According to the calculated performance indicators and preset thresholds, the data transmission queue is dynamically managed using the active queue management algorithm.

[0037] In one embodiment, dynamically managing the data transmission queue using an active queue management algorithm according to the calculated performance indicator and the preset threshold includes:

[0038] Compare and analyze the obtained performance indicators with the preset thresholds to evaluate whether the data transmission system performance meets the preset performance requirements;

[0039] According to the comparative analysis results, the active queue management algorithm is used to determine the corresponding dynamic adjustment strategy, and the dynamic adjustment strategy is executed to adjust the data transmission queue in real time.

[0040] According to a second aspect of an embodiment of the present invention, a data information management system is provided.

[0041] In one embodiment, a data information management system includes:

[0042] The data acquisition module is used to establish a department collaboration platform based on the enterprise functional structure through cloud collaboration technology; and use the department collaboration platform to collect data information from various departments of the enterprise;

[0043] The information database establishment module is used to model and differentiate the collected data information using BIM technology and build a data information model; it combines natural language processing technology to extract structured data from the unstructured text data in the data information model and uses the structured data to build a data information database;

[0044] A transmission mechanism adjustment module is used to obtain access requests from the data information library, determine data transmission requirements based on the access requests, and adjust the data transmission mechanism through an active queue management algorithm;

[0045] The algorithm parameter adjustment module is used to monitor the performance indicators of the data transmission process in real time, and dynamically adjust the parameters of the active queue management algorithm according to the real-time performance indicators to achieve stable data information transmission.

[0046] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0047] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0048] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0049] In one embodiment, a computer program is stored on a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0050] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0051] The present invention improves the efficiency and accuracy of data processing by utilizing advanced technologies such as cloud collaboration technology, BIM technology, natural language processing technology, and active queue management algorithm, while optimizing the data transmission mechanism to ensure efficient data transmission. By combining natural language processing and machine learning technology (such as bidirectional long short-term memory network, dilated convolutional layer, conditional random field model, etc.), intelligent analysis of unstructured text data and extraction of structured data are realized, and the intelligent level of data processing is improved. By real-time monitoring of performance indicators in the data transmission process and dynamically adjusting the parameters of the active queue management algorithm according to the real-time performance indicators, the stability and reliability of data transmission are ensured, which helps enterprises to better manage and utilize their data information resources and improve their business level and competitiveness.

[0052] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0054] Figure 1 is a flow chart of a data information management method according to an exemplary embodiment;

[0055] Figure 2 is a principle block diagram of a data information management system according to an exemplary embodiment;

[0056] Figure 3The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0057] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0058] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0059] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0060] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0061] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0062] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0063] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0064] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0065] Figure 1 An embodiment of the data information management method of the present invention is shown.

[0066] In this optional embodiment, the data information management method includes:

[0067] Step S101, establishing a department collaboration platform through cloud collaboration technology according to the enterprise functional structure; and using the department collaboration platform to collect data information of various departments of the enterprise;

[0068] Step S103, using BIM technology to model and differentiate the collected data information, and construct a data information model; combining natural language processing technology, extracting structured data from the unstructured text data in the data information model, and using the structured data to construct a data information library;

[0069] Step S105, obtaining an access request from the data information repository, determining data transmission requirements according to the access request, and adjusting the data transmission mechanism through an active queue management algorithm;

[0070] Step S107, real-time monitoring of performance indicators during data transmission, and dynamic adjustment of parameters of the active queue management algorithm according to the real-time performance indicators to achieve stable data information transmission.

[0071] In this optional embodiment, the collected data information can be preprocessed by using BIM technology to model and differentiate, and construct a data information model; natural language processing technology is combined to extract structured data from the unstructured text data in the data information model, and the structured data is used to construct a data information library; the preprocessing includes data cleaning and data deduplication; the preprocessed data information is integrated into the BIM model, and the hierarchical structure and component relationships of the BIM model are used to organize and classify the data in an orderly manner to obtain a data information model; natural language processing technology is used to perform text analysis on the unstructured text data in the data information model, entities and relationships between entities are extracted, and structured data is generated based on the relationships between entities; the extracted structured data is integrated into a pre-configured data information library.

[0072] Specifically, the purpose of data cleaning is to identify and correct errors, incomplete or inconsistent information in the data, including deleting invalid or redundant data, filling missing values, correcting incorrect data formats, etc.

[0073] Data deduplication refers to the removal of duplicate data records to avoid duplicate calculations or misleading results during data analysis and model building.

[0074] It should be noted that the BIM model is a three-dimensional digital model that integrates all relevant information of a construction project. It contains the building's geometric information, spatial relationships, attribute information, construction and management data, etc. The hierarchical structure and component relationships of the BIM model are the basis for orderly organization and classification of data.

[0075] Integrate the pre-processed data information into the BIM model, and use the hierarchical structure and component relationship of the BIM model to organize and classify the data in an orderly manner. The specific process is as follows:

[0076] Map the pre-processed data information with the components in the BIM model. This involves matching and converting data fields to ensure that the data can be accurately associated with the corresponding components in the model.

[0077] After data mapping is completed, the pre-processed data information is filled into the corresponding components of the BIM model. This includes component attribute information (such as material, size, performance, etc.), spatial relationship information (such as location, orientation, etc.) and other relevant information (such as construction records, maintenance records, etc.).

[0078] After the data is filled, the data is checked and verified to ensure the accuracy and completeness of the data. This includes checking the format, scope, logical relationship, etc. of the data, as well as comparing and verifying with the actual project situation.

[0079] In this optional embodiment, when using natural language processing technology to perform text analysis on unstructured text data in a data information model, extracting entities and relationships between entities, and generating structured data based on entities and relationships between entities, the unstructured text data in the data information model can be obtained, and word embedding technology can be used to perform text processing on the unstructured text data, and the text can be converted into vectorized text through a pre-configured language model; the vectorized text is input into a bidirectional long short-term memory network and an expanded convolutional layer to identify feature information and long-distance dependencies in the text, and generate a fused feature vector; the fused feature vector is decoded using a conditional random field model, and entities in the text are identified through a label prediction model to extract named entities and label sequences; the relationship between entities is analyzed based on the extracted entity label sequence, and relationship extraction is performed through preset rules to convert entities and relationships between entities into structured data.

[0080] It should be noted that word embedding technology can convert words or phrases in a text into high-dimensional vectors, which can capture the semantic relationship between words. Through a pre-configured language model, the text is converted into vectorized text, providing a basis for subsequent feature extraction and entity recognition.

[0081] In this optional embodiment, when the vectorized text is input into the bidirectional long short-term memory network and the dilated convolution layer, the feature information and long-distance dependencies in the text are identified, and a fused feature vector is generated, the vectorized text can be input into the bidirectional long short-term memory network, and the context information in the text sequence is identified through the forward and backward long short-term memory layers in the bidirectional long short-term memory network to obtain the front and back context features of the text; the output of the bidirectional long short-term memory network is used as input and passed to the dilated convolution layer, and the feature information and long-distance dependencies in the text are extracted through convolution operations to generate a fused feature vector.

[0082] It should be noted that the vectorized text is input into the BiLSTM and Dilated Convolution layers to identify the feature information and long-distance dependencies in the text. BiLSTM can capture the bidirectional dependencies in the text, while the Dilated Convolution layer can expand the receptive field and capture longer context information.

[0083] In this optional embodiment, when the fused feature vector is decoded using the conditional random field model, and entities in the text are identified through the label prediction model to extract named entities and label sequences, the conditional random field model can calculate the conditional probability of each label sequence based on the fused feature vector based on the pre-configured label sequence; the conditional probability is decoded using a dynamic programming algorithm to obtain an initial label sequence, and the initial label sequence is mapped to the original text to identify the named entities in the text; for each identified named entity, the label sequence corresponding to each named entity is extracted, and the label sequence includes a start label, an end label, and an intermediate label.

[0084] It should be noted that the CRF model (conditional random field model) calculates the conditional probability of each label sequence based on the fused feature vector. This process involves linear transformation of the feature vector and then applying the softmax function to obtain the label probability distribution at each position. Since there may be dependencies between labels, the CRF model is able to take this dependency into account and give the joint probability of the entire label sequence. In order to find the most likely label sequence, the CRF model is usually decoded using a dynamic programming algorithm (such as the Viterbi algorithm). This algorithm iteratively calculates the maximum probability at each position and retains the best path to that position, ultimately obtaining the best label sequence for the entire sequence.

[0085] In this optional embodiment, when obtaining an access request to a data information repository, determining data transmission requirements based on the access request, and adjusting the data transmission mechanism through an active queue management algorithm, the data transmission requirements can be determined by analyzing the access request to the data information repository, and the transmission requirements include data volume, transmission priority rate, and frequency; based on the data transmission requirements, an active queue management algorithm is used to establish a data transmission queue for the data information that needs to be transmitted; and the performance indicators of the data transmission system are calculated, and the transmission mechanism of the data information is dynamically adjusted based on the performance indicators of the data transmission system.

[0086] In this optional embodiment, according to the data transmission requirements, an active queue management algorithm is used to establish a data transmission queue for the data information to be transmitted; and the performance indicators of the data transmission system are calculated. When the transmission mechanism of the data information is dynamically adjusted according to the performance indicators of the data transmission system, the data transmission queue can be initialized according to the transmission requirements, and the basic parameters of the queue can be set. The basic parameters include the initial values ​​of the queue length, buffer size and transmission rate; the transmission parameters of the data transmission system are obtained, and the performance indicators of the data transmission system are calculated using the performance indicator calculation formula; according to the calculated performance indicators and the preset threshold, the data transmission queue is dynamically managed using the active queue management algorithm.

[0087] What is needed is to initialize the data transmission queue according to the transmission requirements, including setting initial parameters such as queue length, buffer size, and transmission rate.

[0088] Queue length: Preset a reasonable queue length based on the data volume to accommodate data of different priorities.

[0089] Buffer size: Determine the buffer size to reduce data transmission jitter and ensure smooth data transmission.

[0090] Initial transmission rate: Set an initial transmission rate based on the transmission priority and network status to avoid congestion in the initial stage.

[0091] In this optional embodiment, the performance indicator calculation formula is:

[0092]

[0093] Where G(k) represents the performance index of the data transmission system, H(k) represents the change trajectory of actual data transmission, F(k) represents the expected transmission demand, T(k) represents the optimization amount of the data transmission system, Q represents the deviation between the current state and the target state of the data transmission system, and S represents the change margin of the data transmission system.

[0094] In this optional embodiment, when the data transmission queue is dynamically managed using an active queue management algorithm based on the calculated performance indicators and preset thresholds, the obtained performance indicators can be compared and analyzed with the preset thresholds to evaluate whether the performance of the data transmission system meets the preset performance requirements; based on the comparative analysis results, the active queue management algorithm is used to determine the corresponding dynamic adjustment strategy, and the dynamic adjustment strategy is executed to make real-time adjustments to the data transmission queue.

[0095] It should be noted that according to the comparison results, the active queue management algorithm determines the appropriate adjustment strategy. The specific adjustment measures include:

[0096] Reduce the transmission rate: When the system load is too high, reduce the transmission rate to avoid queue backlog.

[0097] Increase the transmission rate: When the system load is low, increase the transmission rate appropriately to improve data transmission efficiency.

[0098] Discard low-priority data packets: In extreme cases, some low-priority data can be actively discarded to ensure the transmission of high-priority data.

[0099] Execution adjustment: According to the selected strategy, the parameters of the data transmission queue (such as rate, buffer size, etc.) are adjusted in real time so that the system maintains smooth transmission.

[0100] Figure 2 An embodiment of the data information management system of the present invention is shown.

[0101] In this optional embodiment, the data information management system includes:

[0102] The data acquisition module 201 is used to establish a department collaboration platform based on the enterprise functional structure through cloud collaboration technology; and use the department collaboration platform to collect data information of various departments of the enterprise;

[0103] The information base building module 203 is used to model and differentiate the collected data information using BIM technology to build a data information model; extract structured data from the unstructured text data in the data information model in combination with natural language processing technology, and build a data information base using the structured data;

[0104] The transmission mechanism adjustment module 205 is used to obtain access requests from the data information repository, determine data transmission requirements according to the access requests, and adjust the data transmission mechanism through an active queue management algorithm;

[0105] The algorithm parameter adjustment module 207 is used to monitor the performance indicators during data transmission in real time, and dynamically adjust the parameters of the active queue management algorithm according to the real-time performance indicators to achieve stable data information transmission.

[0106] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0107] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0108] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0109] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0110] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0111] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A data information management method, characterized in that: include: According to the enterprise functional structure, a departmental collaboration platform is established through cloud collaboration technology; and using the departmental collaboration platform to collect data information from various departments of the enterprise; Use BIM technology to model and differentiate the collected data information and build a data information model; combine natural language processing technology to extract structured data from the unstructured text data in the data information model, and use the structured data to build a data information database; Obtain access requests from the data repository, determine data transmission requirements based on the access requests, and adjust the data transmission mechanism through active queue management algorithms; Monitor the performance indicators of the data transmission process in real time, and dynamically adjust the parameters of the active queue management algorithm according to the real-time performance indicators.

2. The data information management method according to claim 1, characterized in that: The BIM technology is used to model and differentiate the collected data information to construct a data information model; Combined with natural language processing technology, structured data is extracted from unstructured text data in the data information model, and the structured data is used to build a data information library, including: Preprocessing the collected data information, including data cleaning and data deduplication; Integrate the pre-processed data information into the BIM model, use the hierarchical structure and component relationship of the BIM model to organize and classify the data in an orderly manner, and obtain a data information model; Use natural language processing technology to perform text analysis on unstructured text data in the data information model, extract entities and relationships between entities, and generate structured data based on the relationships between entities; Integrate the extracted structured data into pre-configured data repositories.

3. The data information management method according to claim 2, characterized in that: The method of using natural language processing technology to perform text analysis on unstructured text data in the data information model, extracting entities and relationships between entities, and generating structured data based on the entities and relationships between entities includes: Obtain unstructured text data in the data information model, use word embedding technology to process the unstructured text data, and convert the text into vectorized text through a pre-configured language model; The vectorized text is input into the bidirectional long short-term memory network and the dilated convolutional layer to identify the feature information and long-distance dependencies in the text and generate a fused feature vector. The fused feature vector is decoded using the conditional random field model, and the entities in the text are identified through the label prediction model to extract the named entities and label sequences. The relationships between entities are analyzed based on the extracted entity label sequence, and relationships are extracted through preset rules to convert entities and relationships between entities into structured data.

4. The data information management method according to claim 3, characterized in that: The vectorized text is input into the bidirectional long short-term memory network and the dilated convolutional layer, the feature information and long-distance dependencies in the text are identified, and the fused feature vector is generated, including: The vectorized text is input into the bidirectional long short-term memory network, and the context information in the text sequence is identified through the forward and backward long short-term memory layers in the bidirectional long short-term memory network to obtain the front and back context features of the text; The output of the bidirectional long short-term memory network is used as input and passed to the dilated convolution layer. Through the convolution operation, the feature information and long-distance dependencies in the text are extracted to generate a fused feature vector.

5. The data information management method according to claim 3, characterized in that: The method of decoding the fused feature vector using the conditional random field model and identifying entities in the text using the label prediction model to extract named entities and label sequences includes: Based on the pre-configured label sequence, the conditional random field model calculates the conditional probability of each label sequence according to the fused feature vector; The conditional probability is decoded using a dynamic programming algorithm to obtain an initial label sequence, which is then mapped to the original text to identify named entities in the text. For each identified named entity, a tag sequence corresponding to each named entity is extracted, where the tag sequence includes a start tag, an end tag, and a middle tag.

6. The data information management method according to claim 1, characterized in that: The obtaining of the access request to the data information repository, determining the data transmission requirement according to the access request, and adjusting the data transmission mechanism through the active queue management algorithm includes: Determine data transmission requirements by analyzing access requests to the data information repository, wherein the transmission requirements include data volume, transmission priority rate, and frequency; According to the data transmission requirements, an active queue management algorithm is used to establish a data transmission queue for the data information that needs to be transmitted; and the performance indicators of the data transmission system are calculated, and the transmission mechanism of the data information is dynamically adjusted according to the performance indicators of the data transmission system.

7. The data information management method according to claim 6, characterized in that: According to the data transmission requirements, an active queue management algorithm is used to establish a data transmission queue for the data information to be transmitted; And calculate the performance index of the data transmission system, and dynamically adjust the transmission mechanism of data information according to the performance index of the data transmission system, including: Initialize the data transmission queue according to the transmission requirements and set the basic parameters of the queue, including the initial values ​​of the queue length, buffer size and transmission rate; Acquire transmission parameters of the data transmission system, and calculate the performance index of the data transmission system using a performance index calculation formula; According to the calculated performance indicators and preset thresholds, the data transmission queue is dynamically managed using the active queue management algorithm.

8. The data information management method according to claim 7, characterized in that: The performance index calculation formula is: Where G(k) represents the performance index of the data transmission system, H(k) represents the change trajectory of actual data transmission, F(k) represents the expected transmission demand, T(k) represents the optimization amount of the data transmission system, Q represents the deviation between the current state and the target state of the data transmission system, and S represents the change margin of the data transmission system.

9. The data information management method according to claim 7, characterized in that: The dynamically managing the data transmission queue using the active queue management algorithm according to the calculated performance index and the preset threshold comprises: Compare and analyze the obtained performance indicators with the preset thresholds to evaluate whether the data transmission system performance meets the preset performance requirements; According to the comparative analysis results, the active queue management algorithm is used to determine the corresponding dynamic adjustment strategy, and the dynamic adjustment strategy is executed to adjust the data transmission queue in real time.

10. A data information management system, characterized in that: include: Data acquisition module, used to establish a departmental collaboration platform through cloud collaboration technology based on the enterprise functional structure; and using the departmental collaboration platform to collect data information from various departments of the enterprise; The information base establishment module is used to model and differentiate the collected data information using BIM technology and build a data information model; it combines natural language processing technology to extract structured data from the unstructured text data in the data information model and uses the structured data to build a data information base; A transmission mechanism adjustment module is used to obtain access requests from the data information library, determine data transmission requirements based on the access requests, and adjust the data transmission mechanism through an active queue management algorithm; The algorithm parameter adjustment module is used to monitor the performance indicators of the data transmission process in real time and dynamically adjust the parameters of the active queue management algorithm according to the real-time performance indicators.

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