Construction and operation and maintenance management system and method based on big data information system

Abstract

The invention discloses a construction and operation and maintenance management system and method based on a big data information system, and relates to the technical field of big data information system operation and maintenance. Comprising a monitoring data collection and integration module, an abnormal label construction module, a security identification module, an abnormal data collection module, an operation and maintenance management data collection module, an operation and maintenance data analysis and prediction module and an automatic and intelligent operation and maintenance module. According to the invention, the real-time monitoring content data of the monitoring equipment corresponding to the campus safety accident and the network platform monitoring data in the designated area are quickly searched through the fragment monitoring content data and the fragment network platform monitoring data which are collected in the abnormal data collection module and have the abnormal label identifier; therefore, the process of quickly restoring the event is realized, the manual workload is reduced, the campus safety accident event can be quickly and efficiently solved, and the intelligent campus can be maintained and built.

Description

Technical Field

[0001] The present invention relates to the field of big data information system operation and maintenance technology, and in particular to a management system and method based on big data information system construction and operation and maintenance. Background Art

[0002] Campus safety is an important part of ensuring the safety of life and property of teachers and students and maintaining the normal educational and teaching order of the school. Campus safety is not only related to the physical and mental health of teachers and students, but also to the stability and development of the school. A safe and harmonious campus environment is the basis for the smooth progress of educational and teaching activities, and is also a necessary condition for cultivating the healthy growth of students.

[0003] Campus safety includes a lot of content, among which the personal safety of teachers and students on campus is the most important. In order to avoid the occurrence of safety accidents, modern campuses are equipped with monitoring systems to protect the safety of teachers and students on campus. However, once a safety accident occurs, it is a cumbersome and labor-intensive task to retrieve the monitoring content, and the accuracy of manually viewing the monitoring content for event backtracking is low, and it is easy to make mistakes or miss things. Therefore, the present invention proposes a management system and method based on the construction and operation and maintenance of a big data information system to solve the shortcomings of the prior art. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide a management system and method based on the construction and operation and maintenance of a big data information system, which can realize the rapid search of real-time monitoring content data of monitoring equipment corresponding to campus safety accident events and network platform monitoring data in a specified area through the fragment monitoring content data and fragment network platform monitoring data with abnormal label identification collected in the abnormal data collection module, thereby realizing the rapid restoration of the event process, reducing manual workload, facilitating the rapid and efficient resolution of campus safety accident events, and facilitating the maintenance and construction of a smart campus.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] Based on the construction of big data information system and operation and maintenance management system, it includes monitoring data collection and integration module, abnormal label construction module, safety identification module, abnormal data collection module, operation and maintenance management data collection module, operation and maintenance data analysis and prediction module and automatic and intelligent operation and maintenance module;

[0007] The monitoring data collection and integration module is used to collect the real-time monitoring content data of all monitoring devices in the specified area and the network platform monitoring data in the specified area in real time, and integrate the real-time monitoring content data and the network platform monitoring data to obtain standardized monitoring data;

[0008] The abnormal label construction module is used for developers to create a series of abnormal labels and store the created abnormal labels to form an abnormal label library. The abnormal labels in the abnormal label library are used to feedback various dangerous or abnormal behaviors;

[0009] The safety identification module is used to automatically identify and label abnormal data in the normalized monitoring data according to a series of abnormal labels in the abnormal label library;

[0010] The abnormal data collection module is used to intercept and separately collect the fragment monitoring content data and fragment network platform monitoring data under the abnormal label in the normalized monitoring data;

[0011] The operation and maintenance management data collection module is used to collect the operation logs of other modules in the system and perform network monitoring on other modules in the system;

[0012] The operation and maintenance data analysis and prediction module is used to analyze and mine the operation logs and network monitoring content collected by the operation and maintenance management data collection module to discover system operation and maintenance problems and potential risks;

[0013] The automatic and intelligent operation and maintenance module is used to use automated tools and technologies to automatically monitor, adjust and repair the system based on the feedback information obtained from the operation and maintenance data analysis and prediction module, and use artificial intelligence and machine learning to perform intelligent analysis and decision-making on operation and maintenance data.

[0014] A further improvement is that when the monitoring data aggregation integrates the real-time monitoring content data and the network platform monitoring data, it includes data preprocessing operations such as data cleaning, preprocessing and formatting of the real-time monitoring content data and the network platform monitoring data to ensure the accuracy and consistency of the real-time monitoring content data and the network platform monitoring data.

[0015] A further improvement is that the monitoring data collection and integration module also includes a method for separately constructing a database for storing the real-time monitoring content data and the network platform monitoring data after data preprocessing.

[0016] A further improvement is that when the safety identification module automatically identifies and labels abnormal data in the normalized monitoring data according to a series of abnormal labels in the abnormal label library, a classification algorithm is used to classify the normalized monitoring data into various abnormal labels.

[0017] A further improvement is that when the security identification module automatically identifies and labels abnormal data, it includes capturing voice content in the audio content of the normalized monitoring data that is semantically related to the abnormal label content, and includes analyzing action content in the video content of the normalized monitoring data that is semantically related to the abnormal label content.

[0018] A further improvement is that the abnormal data collection module includes an automatic summarization module, which is used to automatically summarize the fragment monitoring content data and fragment network platform monitoring data under the separately collected abnormal tags into the corresponding abnormal tags for classified storage.

[0019] A further improvement is that the abnormal data collection module is also used to analyze the segment monitoring content data and segment network platform monitoring data under the abnormal label to discover potential security issues other than the abnormal issues identified by the abnormal label.

[0020] A further improvement is that the big data information system construction and operation and maintenance management system also includes a new label automatic extraction and establishment module, which is used to search for content in the standardized monitoring data that cannot be associated with abnormal labels but can feedback dangerous or abnormal behaviors, and after analyzing the content, the keywords expressing the content are obtained as new abnormal labels and updated to the abnormal label library for storage.

[0021] A further improvement is that the operation log network monitoring content collected by the operation and maintenance management data collection module includes data noise removal, correction, filling of missing values, data standardization and normalization processing within the operation and maintenance management data collection module, and finally data dimensionality reduction processing.

[0022] The construction and operation and maintenance management method based on big data information system includes the following steps:

[0023] Step 1: Collect the real-time monitoring content data of all monitoring devices in the designated area and the network platform monitoring data in the designated area to form standardized monitoring data;

[0024] Step 2: Create a series of abnormal labels, and store the created abnormal labels to form an abnormal label library;

[0025] Step 3: Automatically identify and label abnormal data in the normalized monitoring data according to abnormal labels;

[0026] Step 4: intercept the segment monitoring content data and segment network platform monitoring data under the abnormal label and collect them separately;

[0027] Step 5: Collect the operation logs of other modules in the system and perform network monitoring on other modules in the system;

[0028] Step 6: Analyze and mine the operation logs and network monitoring content to find problems and potential risks in system operation and maintenance;

[0029] Step 7: Use automation tools and technologies to automatically monitor, adjust, and repair the system, and use artificial intelligence and machine learning to perform intelligent analysis and decision-making on operation and maintenance data.

[0030] The beneficial effects of the present invention are as follows: the present invention utilizes an abnormal label construction module to allow developers to establish a series of abnormal labels to feedback various dangerous behaviors or abnormal behaviors, and then utilizes a safety identification module to automatically identify and label abnormal data in the standardized monitoring data, and finally utilizes an abnormal data collection module to intercept and separately collect the fragment monitoring content data and fragment network platform monitoring data under the abnormal label. After a campus safety accident occurs, the fragment monitoring content data and fragment network platform monitoring data with abnormal label identification collected in the abnormal data collection module can be used to quickly find the real-time monitoring content data of the monitoring equipment corresponding to the campus safety accident and the network platform monitoring data in the specified area, thereby realizing rapid restoration of the event process, reducing manual workload, facilitating rapid and efficient resolution of campus safety accidents, and facilitating the maintenance and construction of a smart campus; the system of the present invention can also realize automatic and intelligent operation and maintenance to ensure the stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the system framework structure of the present invention;

[0032] Figure 2 It is a schematic diagram of the process of the present invention.

[0033] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0034] Embodiment 1

[0035] according to Figure 1 As shown, this embodiment proposes a management system and method based on the construction and operation and maintenance of a big data information system, which includes a monitoring data collection and integration module, an abnormal label construction module, a safety identification module, an abnormal data collection module, an operation and maintenance management data collection module, an operation and maintenance data analysis and prediction module, and an automatic and intelligent operation and maintenance module;

[0036] The monitoring data collection and integration module is used to collect the real-time monitoring content data of all monitoring devices in the specified area and the network platform monitoring data in the specified area in real time, and integrate the real-time monitoring content data and the network platform monitoring data to obtain standardized monitoring data. When the monitoring data collection integrates the real-time monitoring content data and the network platform monitoring data, it includes data cleaning, preprocessing and formatting of the real-time monitoring content data and the network platform monitoring data to ensure the accuracy and consistency of the real-time monitoring content data and the network platform monitoring data, and is used to construct a database storage for the real-time monitoring content data and the network platform monitoring data after data preprocessing; the network platform monitoring data in the present invention refers to various public videos uploaded by teachers and students on campus public network platforms such as campus networks and campus forums;

[0037] The abnormal label construction module is used for developers to establish a series of abnormal labels and store the established abnormal labels to form an abnormal label library. The abnormal labels in the abnormal label library are used to feedback various dangerous or abnormal behaviors. The abnormal labels are set by campus safety managers. For example, collision, crowding, pushing and other entries that indicate the possibility of safety accidents are set as abnormal labels.

[0038] The safety identification module is used to automatically identify and label abnormal data in the normalized monitoring data according to a series of abnormal labels in the abnormal label library; the safety identification module automatically searches for monitoring data that matches the content of "collision", "crowding" and "pushing" in the normalized monitoring data according to the set abnormal labels (such as the set "collision", "crowding" and "pushing"). This process includes capturing the voice content in the audio content of the normalized monitoring data that is semantically related to the abnormal label content, and analyzing the action content in the video content of the normalized monitoring data that is semantically related to the abnormal label content. Analyzing the action content and the voice content separately can reduce the loss of key information, which is conducive to improving the quality of safety identification. Then, a classification algorithm is used to classify the normalized monitoring data into various abnormal labels, and then the corresponding labels are marked;

[0039] The abnormal data collection module is used to intercept and separately collect the fragment monitoring content data and fragment network platform monitoring data under the abnormal label in the normalized monitoring data. The abnormal data collection module includes an automatic summarization module, which is used to automatically summarize the fragment monitoring content data and fragment network platform monitoring data under the abnormal label that are separately collected into the corresponding abnormal label for classified storage; the video content used to prompt abnormalities can be easily found quickly when used, which reduces the search workload and helps to reduce the pressure of the system search function; the abnormal data collection module is also used to analyze the fragment monitoring content data and fragment network platform monitoring data under the abnormal label to find potential security problems other than the abnormal problems identified by the abnormal label. The above modules can realize comprehensive identification of abnormalities, which is conducive to improving the construction of a safe campus;

[0040] The big data information system construction and operation and maintenance management system also includes a new label automatic extraction and establishment module, which is used to search for content that cannot be associated with abnormal labels but can feedback dangerous or abnormal behaviors in the standardized monitoring data, and analyze the content to obtain keywords expressing the content as new abnormal labels to update to the abnormal label library for storage. The above operation is conducive to improving the richness of the abnormal label library and can achieve more comprehensive identification of abnormal content;

[0041] The operation and maintenance management data collection module is used to collect the operation logs of the remaining modules in the system and perform network monitoring on the remaining modules in the system. The network monitoring content of the operation logs collected by the operation and maintenance management data collection module includes data noise removal, correction, and filling of missing values, data standardization and normalization processing, and finally data dimension reduction processing. Dimension reduction processing is beneficial to reducing the transportation capacity of the system; the operation and maintenance data analysis and prediction module is used to analyze and mine the operation logs and network monitoring content collected by the operation and maintenance management data collection module to discover problems and potential risks in system operation and maintenance; the automatic and intelligent operation and maintenance module is used to use automation tools and technologies to realize automatic monitoring, automatic adjustment and automatic repair of the system according to the feedback information obtained by the operation and maintenance data analysis and prediction module, and use artificial intelligence and machine learning operation and maintenance data for intelligent analysis and decision-making.

[0042] Embodiment 2

[0043] according to Figure 2 As shown, this embodiment proposes a method for building and operating a big data information system, including the following steps:

[0044] Step 1: Collect the real-time monitoring content data of all monitoring devices in the designated area and the network platform monitoring data in the designated area to form standardized monitoring data;

[0045] Step 2: Create a series of abnormal labels, and store the created abnormal labels to form an abnormal label library;

[0046] Step 3: Automatically identify and label abnormal data in the normalized monitoring data according to abnormal labels;

[0047] Step 4: intercept the segment monitoring content data and segment network platform monitoring data under the abnormal label and collect them separately;

[0048] Step 5: Collect the operation logs of other modules in the system and perform network monitoring on other modules in the system;

[0049] Step 6: Analyze and mine the operation logs and network monitoring content to find problems and potential risks in system operation and maintenance;

[0050] Step 7: Use automation tools and technologies to automatically monitor, adjust, and repair the system, and use artificial intelligence and machine learning to perform intelligent analysis and decision-making on operation and maintenance data.

[0051] The present invention uses an abnormal label construction module to allow developers to establish a series of abnormal labels to feedback various dangerous behaviors or abnormal behaviors, and then uses a safety identification module to automatically identify and label abnormal data in the standardized monitoring data. Finally, the abnormal data collection module is used to intercept the fragment monitoring content data and the fragment network platform monitoring data under the abnormal label and collect them separately. After a campus safety accident occurs, the fragment monitoring content data and the fragment network platform monitoring data with abnormal label identification collected in the abnormal data collection module can be used to quickly find the real-time monitoring content data of the monitoring equipment corresponding to the campus safety accident event and the network platform monitoring data in the specified area, thereby realizing rapid restoration of the event process, reducing manual workload, facilitating rapid and efficient resolution of campus safety accidents, and facilitating the maintenance and construction of a smart campus; the system of the present invention can also realize automatic and intelligent operation and maintenance to ensure the stable operation of the system.

[0052] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. Based on the construction and operation and maintenance management system of big data information system, it is characterized by: It includes monitoring data collection and integration module, abnormal label construction module, safety identification module, abnormal data collection module, operation and maintenance management data collection module, operation and maintenance data analysis and prediction module and automatic and intelligent operation and maintenance module; The monitoring data collection and integration module is used to collect the real-time monitoring content data of all monitoring devices in the specified area and the network platform monitoring data in the specified area in real time, and integrate the real-time monitoring content data and the network platform monitoring data to obtain standardized monitoring data; The abnormal label construction module is used for developers to create a series of abnormal labels and store the created abnormal labels to form an abnormal label library. The abnormal labels in the abnormal label library are used to feedback various dangerous or abnormal behaviors; The safety identification module is used to automatically identify and label abnormal data in the normalized monitoring data according to a series of abnormal labels in the abnormal label library; The abnormal data collection module is used to intercept and separately collect the fragment monitoring content data and fragment network platform monitoring data under the abnormal label in the normalized monitoring data; The operation and maintenance management data collection module is used to collect the operation logs of other modules in the system and perform network monitoring on other modules in the system; The operation and maintenance data analysis and prediction module is used to analyze and mine the operation logs and network monitoring content collected by the operation and maintenance management data collection module to discover system operation and maintenance problems and potential risks; The automatic and intelligent operation and maintenance module is used to use automated tools and technologies to automatically monitor, adjust and repair the system based on the feedback information obtained from the operation and maintenance data analysis and prediction module, and use artificial intelligence and machine learning to perform intelligent analysis and decision-making on operation and maintenance data.

2. The big data information system construction and operation and maintenance management system according to claim 1, characterized in that: When the monitoring data aggregation integrates the real-time monitoring content data and the network platform monitoring data, it includes data preprocessing operations such as data cleaning, preprocessing and formatting of the real-time monitoring content data and the network platform monitoring data to ensure the accuracy and consistency of the real-time monitoring content data and the network platform monitoring data.

3. The big data information system construction and operation and maintenance management system according to claim 2 is characterized by: The monitoring data collection and integration module also includes a module for separately constructing a database for storing the real-time monitoring content data and the network platform monitoring data after data preprocessing.

4. The big data information system construction and operation and maintenance management system according to claim 1, characterized in that: When the safety identification module automatically identifies and labels the abnormal data in the normalized monitoring data according to a series of abnormal labels in the abnormal label library, a classification algorithm is used to classify the normalized monitoring data into various abnormal labels.

5. The big data information system construction and operation and maintenance management system according to claim 4 is characterized by: When the security identification module automatically identifies and labels abnormal data, it includes capturing voice content in the audio content of the normalized monitoring data that is semantically related to the abnormal label content, and analyzing action content in the video content of the normalized monitoring data that is semantically related to the abnormal label content.

6. The big data information system construction and operation and maintenance management system according to claim 1 is characterized by: The abnormal data collection module includes an automatic summarization module, which is used to automatically summarize the fragment monitoring content data and fragment network platform monitoring data under the abnormal label that are separately collected into the corresponding abnormal label for classified storage.

7. The big data information system construction and operation and maintenance management system according to claim 6 is characterized by: The abnormal data collection module is also used to analyze the segment monitoring content data and segment network platform monitoring data under the abnormal label to discover potential security issues other than the abnormal issues identified by the abnormal label.

8. The big data information system construction and operation and maintenance management system according to claim 1 is characterized by: The big data information system construction and operation and maintenance management system also includes a new label automatic extraction and establishment module, which is used to search for content in the standardized monitoring data that cannot be associated with abnormal labels but can feedback dangerous or abnormal behaviors, and after analyzing the content, obtain keywords that express the content as new abnormal labels and update them to the abnormal label library for storage.

9. The big data information system construction and operation and maintenance management system according to claim 1, characterized in that: The operation log network monitoring content collected by the operation and maintenance management data collection module includes data noise removal, correction, filling of missing values, data standardization and normalization processing, and finally data dimensionality reduction processing within the operation and maintenance management data collection module.

10. A method for building and operating a big data information system, using the big data information system building and operating a big data information system management system described in any one of claims 1 to 9, comprising the following steps: Step 1: Collect the real-time monitoring content data of all monitoring devices in the designated area and the network platform monitoring data in the designated area to form standardized monitoring data; Step 2: Create a series of abnormal labels, and store the created abnormal labels to form an abnormal label library; Step 3: Automatically identify and label abnormal data in the normalized monitoring data according to abnormal labels; Step 4: intercept the segment monitoring content data and segment network platform monitoring data under the abnormal label and collect them separately; Step 5: Collect the operation logs of other modules in the system and perform network monitoring on other modules in the system; Step 6: Analyze and mine the operation logs and network monitoring content to find problems and potential risks in system operation and maintenance; Step 7: Use automation tools and technologies to automatically monitor, adjust, and repair the system, and use artificial intelligence and machine learning to perform intelligent analysis and decision-making on operation and maintenance data.

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