Construction method and application of cultural heritage digital monitoring system platform

Abstract

The invention relates to the technical field of cultural heritage digital monitoring system platforms, in particular to a cultural heritage digital monitoring system platform construction method, which comprises the following steps: S1, designing a database architecture; s2, data acquisition; s3, data storage and analysis; s4, displaying the data; s5, generating an alarm mechanism; the cultural heritage is monitored in different aspects, the monitoring effect is more comprehensive, and environment quality monitoring, termite monitoring, lightning protection monitoring, temperature and humidity monitoring, audience dynamic detection, electric power monitoring, fare collection system monitoring, thermal power monitoring, cultural relic building monitoring and outdoor display monitoring jointly form a complete digital monitoring system for the cultural heritage. According to the cultural heritage monitoring system, comprehensive and scientific management of the cultural heritage is realized, data can be rapidly processed and analyzed in cooperation with a linear regression model, data evaluation can be rapidly performed, and an alarm mechanism is generated in time when an abnormal condition occurs, so that the monitoring effect is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of a cultural heritage digital monitoring system platform, and in particular to a method for constructing a cultural heritage digital monitoring system platform and its application. Background Art

[0002] In the digital age, one of the basic trends in the protection and inheritance of cultural heritage is digitization. The digitization of cultural heritage refers to the use of digital technologies such as digital acquisition, digital storage, digital processing, digital display, and digital communication to convert, reproduce, and restore cultural heritage into a shareable and renewable digital form, and interpret it from a new perspective, preserve it in new ways, and utilize it according to new needs. With the continuous development of digital technology, digitization plays an increasingly important role in our lives, especially in the protection and inheritance of cultural heritage. Digital technology has become an indispensable means.

[0003] The existing cultural heritage digital monitoring system platform only monitors the cultural heritage itself, but cannot monitor the cultural heritage in combination with the surrounding environment. The monitoring of cultural heritage is not comprehensive, does not have an early warning function, and cannot predict results based on current data.

[0004] Therefore, in order to address the above-mentioned problems of incomplete monitoring of cultural heritage and the inability to predict results based on current data, a method for constructing a digital monitoring system platform for cultural heritage with comprehensive monitoring and early warning can be designed. Summary of the invention

[0005] In order to overcome the problems of incomplete monitoring of cultural heritage and the inability to predict results based on current data.

[0006] The technical solution of the present invention is: a method for constructing a cultural heritage digital monitoring system platform, comprising the following steps:

[0007] S1: Design database architecture

[0008] S11: Database type selection

[0009] S12: Design database architecture

[0010] S121: Entity-relationship model: describes the organizational structure of data through the relationship between entities, including the attributes of entities and the connections between entities;

[0011] S122: Determine the definitions of various entities and attributes and define the relationships between them according to specific business requirements;

[0012] S13: Database table design

[0013] S131: Design the database table structure based on the entity-relationship model;

[0014] S132: Optimize table structure, avoid redundant fields and tables, make rational use of associated tables, and improve data access efficiency;

[0015] S14: Database index design, select fields as index columns;

[0016] S15: Database paradigm design, designing tables into standard forms based on the functional dependencies of database tables;

[0017] S16: Database partition design, distributing data to multiple physical storage devices according to rules;

[0018] S17: Database backup and recovery strategy: Design a reasonable backup and recovery strategy to ensure data security and reliability, perform regular database backup, and conduct data integrity checks and recovery tests;

[0019] S18: Database performance monitoring and optimization. We monitor the performance of the database system using tools such as Prometheus, Grafana, and Nagios. We collect key indicators such as query time, CPU, and memory usage, and set threshold alarm mechanisms. The alarm thresholds for query time, CPU, and memory usage are expressed as T max 、CPU max and M max Indicates that the real-time query time, CPU and memory usage are compared with the alarm threshold. If T>T max or CPU>CPU max or M>M max , then perform performance optimization, such as adjusting indexes, optimizing query statements, and improving database performance;

[0020] S19: Database security design, design reasonable user permissions and access control strategies, limit user access rights to the database, encrypt and store important data to ensure data confidentiality and integrity, design reasonable data backup and disaster recovery solutions to prevent data loss and system failures;

[0021] S2: Data collection, including but not limited to environmental quality monitoring, termite monitoring, lightning protection monitoring, temperature and humidity monitoring, audience dynamics detection, power monitoring, ticket sales and inspection system monitoring, thermal monitoring, cultural relics and building monitoring, and outdoor display monitoring;

[0022] S21: Determine reasonable monitoring points, monitoring cycle and sampling time;

[0023] S22: Use appropriate instruments and equipment to take samples;

[0024] S3: Data storage and analysis

[0025] S31: Data storage

[0026] S311: Data cleaning, including removing duplicate data, correcting erroneous data, and handling missing values;

[0027] S312: Data conversion, converting data into the format and structure required by the database;

[0028] S313: Data standardization to ensure data consistency;

[0029] S32: Data Analysis

[0030] S321: Building a model

[0031] S3211: Determine independent and dependent variables;

[0032] S3212: Data cleaning, including processing missing values, handling outliers, and adding categorical variables;

[0033] S3213: Split the test set and the training set. First, randomly extract 20% of the data and keep it as test data to verify the model. The other 80% of the data is used as the training set to build the model.

[0034] S3214: Model tuning includes: testing whether the residuals are normally distributed, testing whether the residuals are homoscedastic, testing whether the residuals are endogeneous, testing whether the residuals are time series correlated, and testing whether there is collinearity between independent variables;

[0035] S322: Data evaluation: Use new data sets to test the model to check the accuracy and predictive ability of the model. Use appropriate evaluation indicators, such as accuracy, recall, and F1 score, to evaluate the model. If the model performs well, it can continue to be used. If not, readjust the model or re-examine the data and goals.

[0036] S323: Use the trained model for evaluation;

[0037] S4: Data presentation;

[0038] S5: Generate an alarm mechanism based on the results of data analysis.

[0039] Preferably, the database type includes relational databases such as MySQL and Oracle or non-relational databases such as MongoDB and Redis.

[0040] Preferably, the database table structure includes but is not limited to table name, field name, field type, constraints and indexes.

[0041] Preferably, the distribution rule adopts any one of continuous allocation, link allocation, index allocation and distributed storage.

[0042] Preferably, environmental quality monitoring includes air quality, humidity and temperature at the cultural heritage site; temperature and humidity monitoring includes real-time monitoring of temperature and humidity in the cultural heritage preservation environment; audience dynamics monitoring includes monitoring of audience flow and activities; and cultural relic building monitoring includes regular inspection of the structure and materials of cultural relic buildings.

[0043] Preferably, the specific steps of data standardization are as follows: (1) clarify the purpose and requirements of data standardization; (2) pre-process the data; (3) select an appropriate standardization method based on the characteristics of the data and the goals of standardization; (4) standardize the data according to the selected standardization method, and use programming languages ​​or data analysis tools to implement the standardization algorithm; (5) verify the standardized data to check whether the data meets the requirements of standardization, and use statistical methods or visualization tools to evaluate the effectiveness of standardization; (6) document the standardization methods and steps to facilitate subsequent data processing and analysis, and regularly evaluate and update the standardization methods and standardization indicators to adapt to changes in data and changes in demand.

[0044] Preferably, the data is presented in the form of data reports, data tables and visual charts.

[0045] The application of the construction method of the cultural heritage digital monitoring system platform in the protection, inheritance and research of cultural heritage.

[0046] The beneficial effects of the present invention are as follows: by monitoring cultural heritage from different aspects, the monitoring effect is more comprehensive; environmental quality monitoring, termite monitoring, lightning protection monitoring, temperature and humidity monitoring, audience dynamic detection, power monitoring, ticket sales and inspection system monitoring, thermal monitoring, cultural relics and building monitoring, and outdoor display monitoring together constitute a complete system for digital monitoring of cultural heritage, thereby achieving comprehensive and scientific management of cultural heritage; in conjunction with a linear regression model, data can be quickly processed and analyzed, data can be quickly evaluated, and in the event of an abnormal situation, an alarm mechanism can be generated in time to ensure the monitoring effect. DETAILED DESCRIPTION

[0047] The present invention will be further described below in conjunction with the embodiments.

[0048] The present invention provides an embodiment: a method for constructing a cultural heritage digital monitoring system platform, comprising the following steps:

[0049] S1: Design database architecture

[0050] S11: Database type selection

[0051] S12: Design database architecture

[0052] S121: Entity-relationship model: describes the organizational structure of data through the relationship between entities, including the attributes of entities and the connections between entities;

[0053] S122: Determine the definitions of various entities and attributes and define the relationships between them according to specific business requirements;

[0054] S13: Database table design

[0055] S131: Design the database table structure based on the entity-relationship model;

[0056] S132: Optimize table structure, avoid redundant fields and tables, make rational use of associated tables, and improve data access efficiency;

[0057] S14: Database index design, select fields as index columns;

[0058] S15: Database paradigm design, designing tables into standard forms based on the functional dependencies of database tables;

[0059] S16: Database partition design, distributing data to multiple physical storage devices according to rules;

[0060] S17: Database backup and recovery strategy: Design a reasonable backup and recovery strategy to ensure data security and reliability, perform regular database backup, and conduct data integrity checks and recovery tests;

[0061] S18: Database performance monitoring and optimization. We monitor the performance of the database system using tools such as Prometheus, Grafana, and Nagios. We collect key indicators such as query time, CPU, and memory usage, and set threshold alarm mechanisms. The alarm thresholds for query time, CPU, and memory usage are expressed as T max 、CPU max and M max Indicates that the real-time query time, CPU and memory usage are compared with the alarm threshold. If T>T max or CPU>CPU max or M>M max , then perform performance optimization, such as adjusting indexes, optimizing query statements, and improving database performance;

[0062] S19: Database security design, design reasonable user permissions and access control strategies, limit user access rights to the database, encrypt and store important data to ensure data confidentiality and integrity, design reasonable data backup and disaster recovery solutions to prevent data loss and system failures;

[0063] S2: Data collection, including but not limited to environmental quality monitoring, termite monitoring, lightning protection monitoring, temperature and humidity monitoring, audience dynamics detection, power monitoring, ticket sales and inspection system monitoring, thermal monitoring, cultural relics and building monitoring, and outdoor display monitoring;

[0064] S21: Determine reasonable monitoring points, monitoring cycle and sampling time;

[0065] S22: Use appropriate instruments and equipment to take samples;

[0066] S3: Data storage and analysis

[0067] S31: Data storage

[0068] S311: Data cleaning, including removing duplicate data, correcting erroneous data, and handling missing values;

[0069] S312: Data conversion, converting data into the format and structure required by the database;

[0070] S313: Data standardization to ensure data consistency;

[0071] S32: Data Analysis

[0072] S321: Building a model

[0073] S3211: Determine independent and dependent variables;

[0074] S3212: Data cleaning, including processing missing values, handling outliers, and adding categorical variables;

[0075] S3213: Split the test set and the training set. First, randomly extract 20% of the data and keep it as test data to verify the model. The other 80% of the data is used as the training set to build the model.

[0076] S3214: Model tuning includes: testing whether the residuals are normally distributed, testing whether the residuals are homoscedastic, testing whether the residuals are endogeneous, testing whether the residuals are time series correlated, and testing whether there is collinearity between independent variables;

[0077] S322: Data evaluation: Use new data sets to test the model to check the accuracy and predictive ability of the model. Use appropriate evaluation indicators, such as accuracy, recall, and F1 score, to evaluate the model. If the model performs well, it can continue to be used. If not, readjust the model or re-examine the data and goals.

[0078] S323: Use the trained model for evaluation;

[0079] S4: Data presentation;

[0080] S5: Generate an alarm mechanism based on the results of data analysis.

[0081] Preferably, the database type includes relational databases such as MySQL and Oracle or non-relational databases such as MongoDB and Redis.

[0082] Preferably, the database table structure includes but is not limited to table name, field name, field type, constraints and indexes.

[0083] Preferably, the distribution rule adopts any one of continuous allocation, link allocation, index allocation and distributed storage.

[0084] Preferably, environmental quality monitoring includes air quality, humidity and temperature at the cultural heritage site; temperature and humidity monitoring includes real-time monitoring of temperature and humidity in the cultural heritage preservation environment; audience dynamics monitoring includes monitoring of audience flow and activities; and cultural relic building monitoring includes regular inspection of the structure and materials of cultural relic buildings.

[0085] Preferably, the specific steps of data standardization are as follows: (1) clarify the purpose and requirements of data standardization; (2) pre-process the data; (3) select an appropriate standardization method based on the characteristics of the data and the goals of standardization; (4) standardize the data according to the selected standardization method, and use programming languages ​​or data analysis tools to implement the standardization algorithm; (5) verify the standardized data to check whether the data meets the requirements of standardization, and use statistical methods or visualization tools to evaluate the effectiveness of standardization; (6) document the standardization methods and steps to facilitate subsequent data processing and analysis, and regularly evaluate and update the standardization methods and standardization indicators to adapt to changes in data and changes in demand.

[0086] Preferably, the data is presented in the form of data reports, data tables and visual charts.

[0087] The application of the construction method of the cultural heritage digital monitoring system platform in the protection, inheritance and research of cultural heritage.

[0088] Through the above steps, cultural heritage is monitored from different aspects, and the monitoring effect is more comprehensive, forming a complete system for digital monitoring of cultural heritage. Data can be quickly processed and analyzed, and data can be quickly evaluated. When encountering abnormal situations, an alarm mechanism can be generated in time to ensure the monitoring effect and solve the problems of incomplete monitoring of cultural heritage and the inability to predict results based on current data.

[0089] The embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for constructing a cultural heritage digital monitoring system platform, characterized by: The steps include: S1: Design database architecture S11: Database type selection S12: Design database architecture S121: Entity-relationship model: describes the organizational structure of data through the relationship between entities, including the attributes of entities and the connections between entities; S122: Determine the definitions of various entities and attributes and define the relationships between them according to specific business requirements; S13: Database table design S131: Design the database table structure based on the entity-relationship model; S132: Optimize table structure, avoid redundant fields and tables, make rational use of associated tables, and improve data access efficiency; S14: Database index design, select fields as index columns; S15: Database paradigm design, designing tables into standard forms based on the functional dependencies of database tables; S16: Database partition design, distributing data to multiple physical storage devices according to rules; S17: Database backup and recovery strategy: Design a reasonable backup and recovery strategy to ensure data security and reliability, perform regular database backup, and conduct data integrity checks and recovery tests; S18: Database performance monitoring and optimization. We monitor the performance of the database system using tools such as Prometheus, Grafana, and Nagios. We collect key indicators such as query time, CPU, and memory usage, and set threshold alarm mechanisms. The alarm thresholds for query time, CPU, and memory usage are expressed as T max 、CPU max and M max Indicates that the real-time query time, CPU and memory usage are compared with the alarm threshold. If T>T max or CPU>CPU max or M>M max , then perform performance optimization, such as adjusting indexes, optimizing query statements, and improving database performance; S19: Database security design, design reasonable user permissions and access control strategies, limit user access rights to the database, encrypt and store important data to ensure data confidentiality and integrity, design reasonable data backup and disaster recovery solutions to prevent data loss and system failures; S2: Data collection, including but not limited to environmental quality monitoring, termite monitoring, lightning protection monitoring, temperature and humidity monitoring, audience dynamics detection, power monitoring, ticket sales and inspection system monitoring, thermal monitoring, cultural relics and building monitoring, and outdoor display monitoring; S21: Determine reasonable monitoring points, monitoring cycle and sampling time; S22: Use appropriate instruments and equipment to take samples; S3: Data storage and analysis S31: Data storage S311: Data cleaning, including removing duplicate data, correcting erroneous data, and handling missing values; S312: Data conversion, converting data into the format and structure required by the database; S313: Data standardization to ensure data consistency; S32: Data Analysis S321: Building a model S3211: Determine independent and dependent variables; S3212: Data cleaning, including processing missing values, handling outliers, and adding categorical variables; S3213: Split the test set and the training set. First, randomly extract 20% of the data and keep it as test data to verify the model. The other 80% of the data is used as the training set to build the model. S3214: Model tuning includes: testing whether the residuals are normally distributed, testing whether the residuals are homoscedastic, testing whether the residuals are endogeneous, testing whether the residuals are time series correlated, and testing whether there is collinearity between independent variables; S322: Data evaluation: Use new data sets to test the model to check the accuracy and predictive ability of the model. Use appropriate evaluation indicators, such as accuracy, recall, and F1 score, to evaluate the model. If the model performs well, it can continue to be used. If not, readjust the model or re-examine the data and goals. S323: Use the trained model for evaluation; S4: Data presentation; S5: Generate an alarm mechanism based on the results of data analysis.

2. The method for constructing a cultural heritage digital monitoring system platform according to claim 1 is characterized by: Database types include relational databases such as MySQL and Oracle or non-relational databases such as MongoDB and Redis.

3. The method for constructing a cultural heritage digital monitoring system platform according to claim 1 is characterized by: The database table structure includes but is not limited to table name, field name, field type, constraints and indexes.

4. The method for constructing a cultural heritage digital monitoring system platform according to claim 1, characterized in that: The distribution rule adopts any one of continuous allocation, linked allocation, index allocation and distributed storage.

5. The method for constructing a cultural heritage digital monitoring system platform according to claim 1, characterized in that: Environmental quality monitoring includes the air quality, humidity and temperature of the cultural heritage site; temperature and humidity monitoring includes real-time monitoring of the temperature and humidity of the cultural heritage preservation environment; audience dynamics monitoring includes monitoring of audience flow and activities; and cultural relic building monitoring includes regular inspections of the structure and materials of cultural relic buildings.

6. The method for constructing a cultural heritage digital monitoring system platform according to claim 1, characterized in that: The specific steps of data standardization are: (1) clarify the purpose and requirements of data standardization; (2) pre-process the data; (3) select an appropriate standardization method based on the characteristics of the data and the goals of standardization; (4) standardize the data based on the selected standardization method, and use programming languages ​​or data analysis tools to implement the standardization algorithm; (5) verify the standardized data to check whether the data meets the requirements of standardization, and use statistical methods or visualization tools to evaluate the effectiveness of standardization; (6) document the standardization methods and steps to facilitate subsequent data processing and analysis, and regularly evaluate and update the standardization methods and standardization indicators to adapt to changes in data and changes in demand.

7. The method for constructing a cultural heritage digital monitoring system platform according to claim 1, characterized in that: The data is presented in the form of data reports, data tables and visual charts.

8. Application of the method for constructing a cultural heritage digital monitoring system platform according to claim 1 in the protection, inheritance and research of cultural heritage.