Digital monitoring and early warning method and system for historical mountainous cities and towns

Abstract

The invention relates to the field of monitoring and early warning of historical mountainous cities and towns, and discloses a digital monitoring and early warning method and system for historical mountainous cities and towns, which comprises the steps of collecting data and converging into a mountainous city and town database, and constructing a three-dimensional digital base, specifically, converting a data format by using ArcMap, generating a DEM digital elevation model by using a CAD topographic map, and constructing a three-dimensional model by using ArcScene, extracting elevation data of a protection range by a mask, covering a remote sensing image, and placing a monitoring element layer; acquiring data, and analyzing, evaluating and judging an early warning level; negative change degree values are calculated and arranged in a descending order; a report is output regularly or immediately according to a one-graph one-table feedback result; the method aims at solving the technical problem that in the prior art, full-process, visual and easy-to-operate mountain historical urban heritage protection is lacked.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of mountainous historical town monitoring and early warning, and particularly relates to a digital monitoring and early warning method and system for mountainous historical towns. BACKGROUND

[0002] In the protection of cultural heritage, historical and cultural heritage monitoring and early warning is a key link in the protection and management of heritage in China. Mountainous historical towns, as an important part of historical and cultural heritage, have distinctive characteristics and rich and diverse types of heritage. These towns carry rich historical and cultural information and are valuable carriers of regional traditional civilization and architectural wisdom.

[0003] However, the protection of mountainous historical towns faces many severe challenges. On the one hand, natural disasters such as landslides, mudslides, floods, etc. frequently occur in mountainous areas, which directly threaten the buildings, infrastructure and historical and cultural heritage of the towns. On the other hand, the economy in mountainous areas is generally underdeveloped, and the resources invested in heritage protection are limited, while the shortage of talents is prominent, further increasing the difficulty of heritage protection.

[0004] With the development of the times, the use of digital means to strengthen the protection and comprehensive utilization of historical and cultural heritage has become a new trend in modern heritage protection. Digital technology can provide more accurate and efficient protection means, which helps to achieve comprehensive monitoring and scientific management of heritage. However, the current digital methods still have a series of problems in the application of mountainous historical town protection.

[0005] Most of the current digital protection systems only stay at the basic function level of digital asset archiving, and there is insufficient research on how to further realize monitoring functions and directly express visualized early warning results to users. This makes the system unable to provide timely and effective early warning information for heritage protection in actual application, and it is difficult to meet the needs of dynamic protection of mountainous historical towns.

[0006] Some digital monitoring and early warning technologies have high technical thresholds and complex operation processes. The economy in mountainous areas is underdeveloped, and there is a lack of professional technical personnel. High-cost and complex operation monitoring and early warning models face great difficulties in popularization and application, and are difficult to be widely used in the protection of mountainous historical towns.

[0007] Current researches mostly use static monitoring methods that evaluate data at a single time point, lack of dynamic management mode, and ignore the continuity of the protection process. This makes the heritage protection work lack of systematicness and forward-looking, and unable to discover and solve potential problems in time. SUMMARY

[0008] The present application intends to provide a mountainous historical town digital monitoring and early warning method and system to solve the technical problem of lack of full-process, visual, easy-to-operate mountainous historical town heritage protection in the prior art.

[0009] To achieve the above-mentioned purpose, the present application adopts the following technical scheme: a mountainous historical town digital monitoring and early warning method, comprising: S1, collecting and processing monitoring data to form a mountainous town database, and constructing a three-dimensional digital base of the mountainous historical town from the mountainous town database; S2, obtaining monitoring data of the mountainous town database, analyzing and evaluating according to corresponding monitoring indicators, and judging the early warning level of each monitoring indicator; S3, calculating the negative change degree value of each mountainous historical town from the early warning level of the monitoring indicator, and arranging the negative change degree values of multiple mountainous historical towns in descending order; S4, feeding back the analysis result of the monitoring and early warning in the form of a Figure One table, and outputting a report regularly or immediately according to the early warning situation of the monitoring indicators; In S1, the three-dimensional digital base of the mountainous historical town is specifically constructed as follows: The data format is converted by using the extraction analysis module of ArcMap, the topographic map in CAD format is used as the original data, the elevation points in the Annotation layer of the CAD file are screened out, the 3D Analysis analysis module is used to create an irregular triangular mesh dataset and interpolate to generate a DEM digital elevation model; ArcScene is used to build a three-dimensional digital model; the DEM elevation data within the protection range of the mountainous historical town is extracted by mask, the elevation data is used as the basic height of three-dimensional visualization, and high-precision remote sensing images are overlaid on the DEM elevation data; the monitoring element layer related to the mountainous historical town is inserted to construct a three-dimensional visualization monitoring platform digital base.

[0010] The principle and advantages of the present application are as follows: the traditional two-dimensional monitoring method cannot intuitively present the spatial form and three-dimensional structure of the mountainous historical town. The three-dimensional digital base constructed by the present application enables users to accurately locate the spatial position of the alarm occurrence by rotating and zooming the three-dimensional model, and realize three-dimensional effect and hierarchical display. For example, users can more clearly observe the change of buildings, streets and other elements in the mountainous town located at different elevations and terrain parts, and create a more intuitive and effective monitoring and early warning result scene expression for managers, researchers and residents.

[0011] The scheme uses common ArcGIS and Python tools, does not need to customize the sensor network, and is suitable for popularization in economically underdeveloped mountainous areas. A user only needs to open the ArcScene three-dimensional geographic visualization platform, updates the data, and the data is automatically associated with the Python environment. The monitoring and evaluation model is selected in the Python environment for automatic operation, and the monitoring and early warning results can be obtained. The overall operation is simple and convenient, so that non-professional users can quickly get started, and the labor cost and technical threshold are reduced.

[0012] The scheme can realize real-time updating and dynamic feedback. From data collection, analysis and evaluation to result feedback, a complete closed loop is formed. By associating the monitoring index database in the Python environment with the ArcScene three-dimensional geographic visualization data, using the warning priority sorting algorithm model, associating the ArcScene geographic data display environment, and placing the three-dimensional platform according to the warning gradient color, dynamic updating and feedback are realized. The user can quickly perceive the overall situation of the warning, and click to understand the specific warning information, so as to discover and solve potential problems in time.

[0013] Preferably, as an improvement, in S2, the monitoring index database in the format of GDB and CSV is constructed, the monitoring element layer in the three-dimensional digital model is formed into the monitoring database in the format of GDB, and the monitoring index data that cannot be visualized is stored in the format of CSV Relying on the Python environment and the data analysis module contained therein, a monitoring and early warning program is written, the monitoring index data is read in the Python environment in the DataFrame data structure, and the warning level of each monitoring index is judged by using the index classification type corresponding to the index.

[0014] The improvement has the beneficial effects that the monitoring index database in the format of GDB and CSV is constructed, the advantages of different formats are fully utilized, the GDB format is suitable for storing spatial data and element layer data related to the three-dimensional model, the spatial properties and visualization effect of the data are guaranteed, and the CSV format has the characteristics of simplicity and universality, so that the non-spatial data is stored and processed, the data storage is more flexible, and different types of data are managed and accessed.

[0015] The monitoring index data is read in the Python environment in the DataFrame data structure, and the DataFrame provides efficient data operation and analysis functions. It can conveniently perform data filtering, sorting, aggregation and other operations, so that the monitoring and early warning program can quickly process a large amount of monitoring index data, accurately judge the warning level of each monitoring index, greatly improve the efficiency of data processing and analysis, and ensure that the monitoring and early warning work can be carried out in time.

[0016] Preferably, as an improvement, the monitoring indicators include data contents of five dimensions, including landscape, landscape settlements, landscape architecture, landscape human settlements and landscape culture; The landscape includes the ratio of reduced forest land area to original forest land area, the ratio of reduced water area to original water area, and the growth of ancient trees; The landscape settlements include the ratio of newly added construction land area to original construction land area, the ratio of street and alley area change to original area, and the change of street and alley small terrain; The landscape architecture includes the ratio of building change layers to original layers, the ratio of key building offset angle to original angle, and the damage degree of traditional architecture, including doors and windows, walls, roofs and beams and columns; The landscape human settlements include rainfall, floods and sudden disasters, including fires, mudslides, landslides and rock collapses; The landscape culture includes the ratio of reduced number of intangible cultural heritage, the ratio of reduced number of rituals and customs or traditional festivals, and the ratio of reduced number of indigenous people.

[0017] The improvement has the beneficial effects that: mountainous historical towns have unique geographical environment and cultural heritage characteristics. The monitoring indicators in the improved scheme cover five dimensions of landscape, landscape settlements, landscape architecture, landscape human settlements and landscape culture, and comprehensively consider various aspects of mountainous historical towns. For example, the traditional street and alley small terrain change monitoring ignored by the prior art may trigger a series of chain reactions and increase the risk of disasters due to the complex terrain of mountainous areas.

[0018] By including both material heritage and intangible culture in the same monitoring indicator system, comprehensive monitoring of "material heritage + intangible culture" is achieved. This comprehensive monitoring method can more comprehensively reflect the overall situation of mountainous historical towns and avoid one-sidedness in heritage protection. For example, by focusing on the damage degree of buildings and considering changes in the number of indigenous people and traditional festival activities and other cultural dimension indicators, the dynamic changes of cultural heritage can be better understood, providing a more scientific basis for protection and inheritance.

[0019] Preferably, as an improvement, the indicator classification type includes a change measurement type indicator, a standard established type indicator and a trigger evaluation type indicator; The change measurement type indicator quantifies the change degree of the actual monitoring indicator data and the standard or original indicator data, makes a corresponding stage warning, and is divided into four warning gradients; The standard established type indicator divides the warning levels according to the existing national grading standards; The trigger evaluation type indicator is directly divided into four levels of warning once a change occurs.

[0020] The improved beneficial effects are: different types of monitoring indicators have different characteristics and change rules, and different early warning judgment methods are adopted for each type, which can better adapt to the characteristics of the indicators. The change type index can more accurately reflect the change of the index, and is divided into four early warning gradients according to the change degree, so that the early warning is more detailed and accurate, which helps the managers to take corresponding measures according to different early warning levels. For the standard established type index, the early warning level is determined according to the existing national grading standard. The existing national grading standard is summarized through a large amount of practice and research, and has authority and scientificity. The use of these standards can make the early warning level more standardized and unified, and avoid the confusion caused by inconsistent standards. Once the trigger evaluation type index changes, it is directly divided into four levels of early warning. This design can realize the timely response to sudden situations. In mountainous historical towns, sudden disasters such as fire, mudslide, landslide and dangerous rock collapse may cause serious damage to the town once they occur. By setting such indicators as trigger evaluation type, the managers can know the situation in the first time and take emergency measures to minimize the loss caused by disasters and ensure the safety of mountainous historical towns.

[0021] Preferably, as an improvement, in S3, the calculation of the negative change degree value is obtained by weighting and summing the early warning levels of the change type monitoring index, the standard established type monitoring index and the trigger evaluation type monitoring index; the calculation formula of the negative change degree value is as follows: ; Among them, represents the negative change degree value of the i th mountainous historical town, represents the number of j level early warning of the i th mountainous historical town, represents the weight of j level early warning; wherein the first level early warning is 0.1, the second level early warning is 0.2, the third level early warning is 0.3, and the fourth level early warning is 0.4.

[0022] The improved beneficial effects are: this quantitative result provides an intuitive and unified basis for evaluating the conditions of each mountainous historical town, avoids the one-sidedness and inaccuracy of subjective judgment or single index evaluation, and enables the managers to clearly understand the severity of the problems faced by each town. According to the descending list generated by the negative change degree value, the seriousness of the hidden dangers or damage of each mountainous historical town can be clearly reflected. In the case of limited resources, this sorting method provides a clear decision reference for the managers, so that they can pay more attention to the mountainous historical towns with high negative change degree value, major hidden dangers or serious damage, and reasonably allocate resources such as manpower, material resources and financial resources, improve the efficiency and pertinence of protection work, and ensure that the towns most in need of protection and repair are timely and effectively treated.

[0023] Preferably, as an improvement, in S1, the basic spatial data of the monitoring early warning elements is built into the three-dimensional digital model base built in ArcScene; For building contour type monitoring surface elements, based on spatial position coordinates, the three-dimensional digital model is overlaid, and according to the height of each building, a cube is formed floating on the underlying terrain; For traditional streets and alleys, which do not have height information, the three-dimensional digital model is overlaid based on their spatial position coordinates, making them float above the underlying terrain; For small and micro terrain and ancient tree monitoring point elements, based on their spatial position coordinates, they float above the underlying terrain as three-dimensional small nails, and all monitoring elements are temporarily white when there is no early warning.

[0024] The beneficial effects of this improvement are: this three-dimensional and intuitive display method allows managers to clearly see the position and distribution of various monitoring elements in three-dimensional space, greatly improving the understanding and grasp of the spatial structure of mountainous historical towns, and helping to more accurately judge the relationship and potential impact between monitoring elements. Managers can quickly find monitoring elements that have occurred early warning in the three-dimensional digital model, quickly locate the specific location, provide convenience for timely response measures, and improve the efficiency of emergency response.

[0025] Preferably, as an improvement, in S4, Figure One The table is an early warning chart and an early warning table; The setting method of the early warning chart is to set the three-dimensional digital base early warning visualization standard, according to the early warning index type and early warning level, set different early warning item color and symbol standard for each monitoring data layer in the three-dimensional digital base in ArcScene environment; update the three-dimensional digital base early warning situation, rewrite the original GDB database into a GDB database that has updated the early warning field, so that the monitoring index analysis and early warning result is associated with the three-dimensional visualization platform, and the early warning chart is automatically updated; The setting method of the early warning table is to count the monitoring situation of each monitoring index in the Python environment, put the early warning items on top and write them into the table.

[0026] The improved benefits are: the early warning information is presented in an intuitive graphical manner on the three-dimensional digital base, and the manager can quickly understand the early warning situation of each monitoring element through the difference in color and symbol, without complex analysis and interpretation, greatly improving the efficiency of information acquisition. By updating the three-dimensional digital base early warning situation, the original GDB database is rewritten as a GDB database with updated early warning fields, so that the monitoring index analysis and early warning results can be automatically associated with the three-dimensional visualization platform and automatically updated to form an early warning map. This dynamic updating mechanism ensures that the early warning map can reflect the latest monitoring situation of the mountain historical town in real time, providing timely and accurate decision-making basis for managers.

[0027] In the Python environment, the monitoring situation of each monitoring index is counted and written into a table, which can record the specific data and state of each monitoring index in detail. The early warning table can classify, summarize and analyze the monitoring data, providing comprehensive monitoring information for managers and helping them to deeply understand the changes of various indexes of mountain historical towns.

[0028] Preferably, as an improvement, the ranking of mountain historical towns that trigger early warning of the index is directly displayed at the top; when the early warning of the trigger index is monitored, the monitoring network generates a monitoring report and feeds back immediately; when the trigger index does not issue an early warning, the monitoring network generates a monitoring report according to a fixed time period.

[0029] The improved benefits are: the ranking of mountain historical towns that trigger early warning of the index is directly displayed at the top, which can quickly focus the relevant personnel on the towns with urgent risks or problems, improve the information acquisition efficiency, and facilitate timely response measures.

[0030] A digital monitoring and early warning system for mountain historical towns, for implementing a digital monitoring and early warning method for mountain historical towns, comprising: A data acquisition and aggregation module acquires multi-source data, uses Python data analysis package and ArcGIS platform to develop an automatic preprocessing module, standardizes the data, removes outliers and missing values, and constructs a real-time updated mountain town database containing point, line, surface and table data; A three-dimensional digital base construction module uses ArcMap extraction analysis module to convert data format, creates a DEM digital elevation model, realizes CAD data to elevation raster data conversion; uses ArcScene to build a three-dimensional digital model, extracts DEM elevation data within the protection range and covers high-precision remote sensing images; organizes monitoring and early warning element basic information, embeds it in the three-dimensional model base, differentially displays different types of monitoring surface elements, and constructs a three-dimensional visualization digital base; The monitoring and early warning analysis module relies on the Python environment and the data analysis module to write a monitoring and early warning program, reads monitoring index data, judges the early warning level of each monitoring element according to the index classification type and early warning standard; the early warning levels of the monitoring indexes of the change measurement type and the standard established type are weighted and summed to obtain the negative change degree value of each mountain historical town; The early warning result feedback module feeds back the monitoring and early warning result to the three-dimensional digital base, sets the color matching and symbol standard according to the early warning index type and level, forms an early warning graph, generates an early warning table according to the monitoring results of the space and non-space indexes, and outputs a report according to the early warning situation regularly or immediately, tops the mountain historical town triggering the index early warning and immediately generates an early warning report as the most priority content.

[0031] The improved beneficial effects are: real-time data updating, timely reflection of monitoring index changes in the system, let managers grasp the latest status of the town at any time, realize dynamic monitoring and early warning. From data collection and aggregation, real-time updating database is constructed by using multi-source data; to three-dimensional digital base construction, providing realistic space background for monitoring; to monitoring and early warning analysis, accurately judging early warning level and negative change degree value; finally, the early warning result feedback generates early warning graph, table and report, forming a complete closed loop. After data collection, automatic preprocessing removes abnormal missing values; monitoring and early warning analysis relies on program to automatically read data, judge level and calculate negative change degree value; early warning result feedback can also automatically update early warning graph and generate report, reducing manual intervention, improving efficiency and accuracy. On the three-dimensional digital base, different types of monitoring elements are displayed differently, and the early warning graph displays early warning information with different color matching and symbols, so that managers can understand at a glance. Even non-professionals can quickly get started, making it convenient to carry out monitoring and early warning work, providing strong and convenient technical support for the protection and management of mountain historical towns. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The technical roadmap of the embodiments of the present application.

[0033] Figure 2 The three-dimensional visualization monitoring platform base data format conversion technical flowchart.

[0034] Figure 3 The data format conversion flowchart.

[0035] Figure 4 The three-dimensional visualization monitoring platform digital stereoscopic flowchart.

[0036] Figure 5 The monitoring space element example graph.

[0037] Figure 6 The three-dimensional visualization monitoring platform digital base of mountain historical towns.

[0038] Figure 7 The flow chart of police case processing for mountainous historical town.

[0039] Figure 8 Local details after the warning of the three-dimensional visualization scene of the digital monitoring and early warning of the mountainous historical town.

[0040] Figure 9 The table of digital monitoring and early warning of the mountainous historical town. DETAILED DESCRIPTION

[0041] The following will be further described in detail through specific embodiments: EMBODIMENT Basically as shown in the accompanying drawings, a digital monitoring and early warning method and system for a mountainous historical town, including a digital monitoring and early warning method for a mountainous historical town and a digital monitoring and early warning system for a mountainous historical town for implementing the method. Figure 1 For monitoring a mountainous historical town, a mountainous town is a city located and constructed in a mountainous region, and the mountainous topography and geomorphology have an impact on the structure, layout mode, spatial expansion direction and ecological environment of the town space; It presents a spatial form and environmental characteristics that are completely different from plain cities. A historical town is a city or town formed in a certain historical period, which can reflect the social and historical style and traditional cultural value of a specific period, and its natural and artificial environment. It has significant "genetic" in both spatial form and value concept, and is a gene information bank of region, traditional civilization construction method and cultural wisdom.

[0042] Therefore, a mountainous historical town refers to a traditional town developed in this special geographical environment condition of mountains. Such towns are located on the banks of rivers or river junctions near mountains and water, and people have built many traditional towns in the process of adapting to nature, forming a relatively unique way of production and life, reflecting unique natural landscape, spatial three-dimensional form, local architectural features and historical humanistic value.

[0043] This embodiment takes the digital monitoring and early warning of a certain town as an example for specific description; a digital monitoring and early warning method for a mountainous historical town, comprising:

[0044] S1, collecting and processing monitoring data to form a mountainous town database, and constructing a three-dimensional digital base of a mountainous historical town from the mountainous town database.

[0045] ​The way of collecting monitoring data needs to be matched with the characteristics of each monitoring index object in the mountain historical town monitoring and early warning index system. Among them, the monitoring data collection methods include: obtaining land use data through annual land change survey data; obtaining spatial feature data such as streets and alleys, traditional buildings, etc. through artificial patrol and street view image recognition methods; obtaining key building protection data through tilt angle instruments and monitoring devices such as cameras; collecting population and weather data from government departments; obtaining fire hazard data through artificial patrol, resident self-checking and other methods.

[0046] Data aggregation processing includes developing an automatic preprocessing module for multi-source monitoring index data cleaning and standardization using Python data analysis packages such as Numpy, Pandas, GeoPandas, and ArcGIS operation platform, standardizing the collected data to ensure consistency in data format, unit, and range, and removing outliers and missing values to ensure data quality and improve the reliability of analysis results, thereby building a real-time updated, spatio-temporally unified, format-standardized, and rapidly responsive digital monitoring platform, mountain town database.

[0047] The mountain historical town database includes point data, line data, surface data, and table data. Point data includes historical resource points other than buildings, such as ancient trees, ancient wells and bridges, small and micro topography, etc.; line data includes road traffic, traditional streets and alleys, etc.; surface data includes building monomers, land use coverage, water area conditions, etc.; table data includes weather temperature, flood water level, natural disaster occurrence, non-material cultural heritage change, etc.

[0048] The construction of the three-dimensional digital base of the mountain historical town uses spatial data processing technology of ArcMap and three-dimensional visualization technology of ArcScene. As shown in the accompanying Figure 2 , the specific construction steps are as follows: First, as shown in the accompanying Figure 3 , the data format is converted using the extraction analysis module of ArcMap. Taking the CAD format 1:500 topographic map as the original data, the elevation points in the Annotation layer of the CAD file are selected, and the 3D Analysis analysis module is used to create an irregular triangle mesh dataset and interpolate to generate a DEM digital elevation model, realizing the conversion of CAD data to elevation raster data, which is convenient for subsequent three-dimensional operation.

[0049] Then, as shown in the accompanying Figure 4 , a three-dimensional digital model is built using ArcScene. The DEM elevation data within the protection range of the mountain historical town is extracted by mask, and the elevation data is used as the basic height of three-dimensional visualization. High-precision remote sensing images are overlaid on the DEM elevation data to make it present a more realistic three-dimensional digital model.

[0050] Finally, the monitoring element layer related to the mountainous historical town is placed, and the digital base of the three-dimensional visualization monitoring platform is constructed.

[0051] The basic coordinate points and height information of the elements that need to be monitored, warned and visualized are sorted out, the basic spatial data of the monitoring and warning elements are built into the three-dimensional digital model base built in ArcScene, and the different types of monitoring surface elements are differentially displayed on the three-dimensional data model.

[0052] As shown in the accompanying Figure 5 For building contour type monitoring surface elements, based on the spatial position coordinates, the three-dimensional digital model is overlaid, and a cube is formed according to the height of each building, which is suspended on the basic terrain.

[0053] For traditional streets and alleys, which do not have height information, the three-dimensional digital model is overlaid based on their spatial position coordinates, and they can float above the basic terrain.

[0054] For small and micro terrain, ancient trees and famous trees, etc. monitoring point elements, based on their spatial position coordinates, they can float above the basic terrain as a three-dimensional small nail. When there is no warning, all monitoring elements are temporarily presented in white.

[0055] Through the above-mentioned manner, as shown in the accompanying Figure 6 The monitoring and warning digital base is presented in front of the user in a three-dimensional and real scene manner. The user can use the mouse to rotate and drag the observation angle, or use the scroll wheel to adjust the observation distance, so that he can clearly and intuitively understand the topography and street pattern of the mountainous historical town. When the warning item appears, the user can also clearly and intuitively observe it, so as to better make emergency actions and protection measures based on the mountainous terrain.

[0056] S2, acquire the monitoring data of the mountainous town database, analyze and evaluate according to the corresponding monitoring indicators, and judge the warning level of each monitoring indicator.

[0057] Relying on Python environment and its included data analysis modules such as os, Numpy, Pandas, Geopandas, etc., write monitoring and warning program, so that it can read monitoring indicator data, use the corresponding indicator classification type of the indicator, and realize warning analysis.

[0058] The monitoring indicator database in GDB and CSV format is constructed. The monitoring element layer in the three-dimensional digital model constitutes the monitoring database in GDB format, and some monitoring indicator data that cannot be visualized, such as the number of indigenous people and rainfall, is stored in CSV format.

[0059] The monitoring index data is read in a Python environment in a DataFrame data structure, and the warning levels of the monitoring indexes are determined according to the warning standards or thresholds of different monitoring elements of the mountainous historical town.

[0060] The monitoring indexes include data contents of five dimensions, including landscape, landscape settlement, landscape architecture, landscape habitat and landscape culture.

[0061] The landscape includes the ratio of reduced forest area to original forest area, the ratio of reduced water area to original water area, and the growth of ancient trees. The landscape settlement includes the ratio of newly added construction land area to original construction land area, the ratio of street and alley area change to original area, and the change of street and alley micro-topography. The landscape architecture includes the ratio of building change to original number of floors, the ratio of key building offset angle to original angle, and the damage degree of traditional architecture. The monitoring range of traditional architecture damage includes doors and windows, walls, roofs and beams. The landscape habitat includes rainfall, floods, and disasters such as fire, mudslide, landslide and rock collapse. The landscape culture includes the ratio of reduced number of intangible cultural heritage, the ratio of reduced number of rituals and customs or traditional festivals, and the ratio of reduced number of indigenous people.

[0062] The monitoring index system comprehensively considers the protection planning of historical and cultural towns, relevant literature on historical town protection, field research, expert analysis, department discussion, and resident interview, so as to determine the core content of mountainous historical town monitoring. Considering the systematization, representativeness and availability of monitoring data, the changed and observable micro-objects such as building monomer, geological disaster and street pattern are determined as the monitoring focus, and the experiential indexes in the literature review and the characteristic indexes of mountainous town and historical town are also considered. The relevant departments, experts and residents of the historical town are invited to score and grade the indexes, and finally the three items with the highest scores in each dimension are determined to constitute the monitoring index system.

[0063] Among them, according to the warning standards or thresholds of different monitoring elements of the mountainous historical town, the warning levels of each monitoring element are determined. Specifically, according to the index grading type of different monitoring indexes, the warning levels are determined.

[0064] The index grading type includes change measurement type index, standard setting type index and trigger evaluation type index. According to the characteristics of each monitoring index and the threshold setting principle, the corresponding index grading type is reasonably selected. In order to make the results comparable, the results of different index grading types adopt unified warning grading standards.

[0065] The measuring change type index quantifies the change degree of the actual monitoring index data and the standard or original index data, and makes a corresponding stage warning. According to the relevant experience in the industry, four warning gradients of 0%-10%, 10%-20%, 20%-30%, 30% and above are divided, which correspond to the first, second, third, fourth warning respectively.

[0066] The standard established type index divides the warning level according to the existing national grading standard. For example, the flood warning is divided into five-year, ten-year, twenty-year, fifty-year and above, which correspond to the first, second, third, fourth warning respectively.

[0067] The trigger evaluation type index directly makes the fourth warning once the change occurs, which is most likely to cause a disastrous event.

[0068] The corresponding index classification types of each monitoring index are shown in the following table: Table 1: Monitoring index corresponding index decomposition type

[0069] For example, the measuring change type index and its warning threshold in this case are as follows: The ratio of the reduced forest area to the original forest area: 0%-10%; 10%-20%; 20%-30%; 30%-100%; The ratio of the reduced water area to the original water area: 0%-10%; 10%-20%; 20%-30%; 30%-100%; The growth of ancient and famous trees: 0%-10% for dead branches, shoots; 10%-20% for tree body and large branches appearing to fall, split or break; 20%-30% for growth decline or damage; 30%-100% for lightning, cutting; The ratio of the new construction land area to the original construction land area: 0%-10%; 10%-20%; 20%-30%; 30%-100%; The ratio of the changed street area to the original area: 0%-10%; 10%-20%; 20%-30%; 30%-100%; The street and alley micro-topography change: 0%-10% for slight wear; 10%-20% for moderate wear; 20%-30% for severe wear; 30%-100% for rupture and flattening; The ratio of the changed building floor number to the original floor number: 0%-10%; 10%-20%; 20%-30%; 30%-100%; The ratio of the key building offset angle to the original angle: 0%-10%; 10%-20%; 20%-30%; 30%-100%; Traditional building damage degree: 0%-10% for door and window damage; 10%-20% for wall damage; 20%-30% for roof damage; 30%-100% for beam and column damage; Non-material cultural heritage reduction quantity proportion: 0%-10%; 10%-20%; 20%-30%; 30%-100%; Ritual customs or traditional festival reduction quantity proportion: 0%-10%; 10%-20%; 20%-30%; 30%-100%; Indigenous reduction quantity proportion: 0%-10%; 10%-20%; 20%-30%; 30%-100%.

[0070] The case standard established indicators and their early warning threshold values are as follows: Rainfall: 50mm or more in 12 hours, level 1 warning; 50mm or more in 6 hours, level 2 warning; 50mm or more in 3 hours, level 3 warning; 100mm or more in 3 hours, level 4 warning.

[0071] Flood: near 5 years (small flood) for flood element return period, level 1 warning; 5 years or more (medium flood) for flood element return period, level 2 warning; 20 years or more (large flood) for flood element return period, level 3 warning; 50 years or more (extra-large flood) for flood element return period, level 4 warning.

[0072] The case triggered evaluation indicators and their early warning threshold values are as follows: Fire, mudslides, landslides, rock collapse and other disasters: occur immediately, highest warning.

[0073] S3, calculate the negative change degree value of each mountainous historical town from the early warning level of the monitoring indicators, and arrange the negative change degree values of multiple mountainous historical towns in descending order, output the early warning situation of each mountainous historical town from the descending list, reflect the severity of the hidden dangers or damage of each mountainous historical town, and thus pay more attention to the mountainous historical towns with more severe hidden dangers or damage.

[0074] The calculation of the negative change degree value is obtained by weighting the early warning levels of the change measurement type monitoring indicators, the standard established type monitoring indicators and the triggered evaluation type monitoring indicators. The calculation formula of the negative change degree value is as follows: ; Wherein, represents the negative change degree value of the i-th mountainous historical town, represents the number of j-level early warning of the i-th mountainous historical town, The weight of the j-level warning; wherein, the first-level warning is 0.1, the second-level warning is 0.2, the third-level warning is 0.3, and the fourth-level warning is 0.4. The higher the value, the more serious the hidden danger of the mountain historical town or the higher the degree of destruction.

[0075] S4, the monitoring and early warning results obtained by analysis are fed back in the form of a Figure One table, and a report is output regularly or immediately according to the early warning situation of the monitoring indicators, as shown in the accompanying Figure 7 .

[0076] After the monitoring and early warning program is completed, i.e. after S3 is completed, the monitoring results that can be visually presented are fed back to the three-dimensional digital base to form a warning map, and the monitoring results of the spatial and non-spatial indicators are entered into a table to form a warning table.

[0077] As shown in Figure 8 , the three-dimensional digital base warning visualization standard is set, and different warning item color and symbol standards are set for the "warning" field of each monitoring data layer in the three-dimensional digital base under the ArcScene environment according to the warning indicator type and warning level.

[0078] Update the three-dimensional digital base warning situation. Rewrite the original GDB database to a GDB database that has updated the "warning" field. In this way, the analysis and early warning results of the monitoring indicators will be associated with the three-dimensional visualization platform, thereby realizing automatic updating of the warning results and forming a warning map.

[0079] As shown in the accompanying Figure 9 , the warning item statistical result table is output, the monitoring situation of each indicator is counted in the Python environment, the warning item is placed at the top and written into the table, and the table header includes "monitoring dimension", "monitoring indicator", "warning situation", etc. Export as "monitoring and early warning table" for users to view.

[0080] According to the early warning situation of the monitoring indicators, a report is output regularly or immediately. Specifically, the mountain historical town ranking that triggers the early warning of the trigger-type indicator is directly "queued" at the top as the most priority warning content. Once the early warning of the trigger-type indicator is monitored, the monitoring network needs to generate a monitoring report immediately and feed back. When the trigger-type indicator does not issue an early warning, the monitoring network can generate a monitoring report according to a fixed time period.

[0081] The change of the trigger-type indicator needs to be quickly notified and responded by the managers or decision-makers, otherwise it will cause irreversible serious damage and increase the repair cost.

[0082] The monitoring network can output feedback reports based on the overall score of the mountainous historical town monitoring results, according to the early warning levels of endangered, high risk, general risk, and no risk.

[0083] For a mountainous historical town evaluated as no risk, the protection level is very high, and there is no risk of destruction; for a mountainous historical town evaluated as general risk, the overall protection level is high, and there is no destructive event or significant destruction that requires immediate response, so the social resources for protection and repair can be tilted to other famous towns with more serious warning; for a mountainous historical town evaluated as high risk, it means that there has been a significant change, but it is still within the controllable and recoverable range, and appropriate human intervention and control protection efforts are needed; for a mountainous historical town evaluated as endangered, it means that there is a significant risk of destruction or has been severely damaged, and requires priority investment in resources and appropriate repair and protection actions.

[0084] A digital monitoring and early warning system for mountainous historical towns, comprising: A data collection and aggregation module collects land use data, spatial feature data, key building protection data, population and weather data, and fire hazard data through annual land change survey data, manual patrol, street view image recognition, monitoring sensors (such as inclinometers), camera monitoring, government department collection, and resident self-checking.

[0085] Using Python data analysis packages (such as Numpy, Pandas, GeoPandas) and ArcGIS operation platform, an automatic preprocessing module for multi-source monitoring index data cleaning and standardization is developed. The collected data is standardized to ensure the consistency of data format, unit and range, and to remove outliers and missing values to ensure data quality.

[0086] A digital monitoring platform for mountainous historical towns is built, which is real-time updated, spatio-temporally unified, format-standardized, and fast-responding, including point data, line data, surface data, and table data.

[0087] A three-dimensional digital base construction module uses the extraction analysis module of ArcMap to convert data formats, create a DEM digital elevation model, and realize the conversion of CAD data to elevation raster data.

[0088] A three-dimensional digital model is built using ArcScene, DEM elevation data within the protection range of the mountainous historical town is extracted by masking, high-precision remote sensing images are covered, and a real three-dimensional digital model is presented.

[0089] The base coordinate points and height information of the elements to be monitored and warned are arranged, the base spatial data of the monitoring and warning elements are built into the three-dimensional digital model base, and different types of monitoring surface elements are displayed differently.

[0090] The monitoring and warning analysis module relies on the Python environment and its included data analysis module to write monitoring and warning programs, read monitoring index data, and use the index classification type corresponding to the index to perform warning analysis.

[0091] According to the index classification type and warning standard or threshold of different monitoring indicators, the warning level of each monitoring element is determined. By weighting the warning levels of the change measurement type monitoring indicators and the standard established type monitoring indicators, the negative change degree value of each mountain historical town is finally obtained by weighted summation.

[0092] The warning result feedback module feeds back the monitoring and warning results obtained by analysis to the three-dimensional digital base, sets different warning item color and symbol standards according to the warning index type and warning level, forms a warning map, and records the space and non-space index monitoring results in a table to form a warning table.

[0093] According to the warning situation of the monitoring indicators, the report is output regularly or immediately, the mountain historical town that triggers the warning of the index is ranked at the top, and the warning report is generated immediately as the most important warning content.

[0094] The warning decision support module formulates corresponding warning response measures according to the warning level and the negative change degree value, such as strengthening patrol and starting emergency plan, etc. It provides decision support for managers or decision makers based on the warning results, such as resource allocation and protection and repair plan formulation, etc.

[0095] The scheme builds a three-dimensional digital visualization scene for mountain historical town monitoring. By designing three monitoring and evaluation models, a four-level warning gradient of blue, yellow, orange and red is formed, which enables users to accurately locate the spatial position of the warning situation, realizes three-dimensional effect, hierarchical display, and creates a more intuitive and effective monitoring and warning result scene expression for managers, researchers and residents.

[0096] The scheme provides an easy-to-operate working environment for users. Users only need to open the ArcScene three-dimensional geographic visualization platform to update the data, and then the data will be automatically associated with the Python environment. In the Python environment, the monitoring and evaluation model is selected for automatic operation to obtain the monitoring and warning results. The overall operation is simple and convenient, and can meet the needs of users of different ages and knowledge levels.

[0097] The scheme can achieve real-time updating, dynamic feedback and whole-process monitoring and early warning. By associating the monitoring index database of the Python environment with the ArcScene three-dimensional geographic visualization data, using the alarm priority sorting algorithm model, relying on the DataFrame data structure in the python environment, the overall monitoring and early warning table can be output in real time, and the ArcScene geographic data display environment is associated, the alarm gradient color is placed on the three-dimensional platform to realize dynamic updating and feedback, so that the user can quickly perceive the overall situation of the alarm, and click to understand the specific early warning information.

[0098] The above is only an embodiment of the present application, and the specific technical solutions and / or common knowledge of the scheme are not described in detail. It should be noted that for those skilled in the art, without departing from the technical solutions of the present application, a number of modifications and improvements can be made, which should also be considered as the protection scope of the present application, which will not affect the effect and practicality of the present application. The protection scope claimed in this application should be subject to the content of its claims, and the specific implementation mode and the like recorded in the specification can be used to explain the content of the claims.

Claims

1. A digital monitoring and early warning method for mountainous historical towns, characterized in that, The method comprises the following steps: S1, collecting monitoring data and forming a mountain town database through aggregation and processing, and constructing a three-dimensional digital base of the mountain historical town from the mountain town database; S2, obtaining the monitoring data of the mountain town database, analyzing and evaluating according to the corresponding monitoring indicators, and judging the early warning level of each monitoring indicator; S3, calculating the negative change degree value of each mountain historical town from the early warning level of the monitoring indicator, and arranging the negative change degree values of multiple mountain historical towns in descending order; S4, feeding back the monitoring and early warning results obtained by analysis in the form of a chart and a table, and outputting a report regularly or immediately according to the early warning situation of the monitoring indicators; In S1, the specific steps of constructing the three-dimensional digital base of the mountain historical town comprise: Using the extraction analysis module of ArcMap to convert the data format, using the topographic map in CAD format as the original data, screening out the elevation points in the Annotation layer of the CAD file, using the 3D Analysis analysis module to create an irregular triangle mesh dataset and interpolate to generate a DEM digital elevation model; using ArcScene to build a three-dimensional digital model, mask extracting DEM elevation data within the protection range of the mountain historical town, using elevation data as the basic height of three-dimensional visualization, and covering high-precision remote sensing images on the DEM elevation data; inserting monitoring element layers related to the mountain historical town to construct a three-dimensional visualization monitoring platform digital base.

2. The method of claim 1, wherein the method comprises: In S2, the monitoring indicator database in GDB and CSV formats is constructed, the monitoring element layer in the three-dimensional digital model is formed into a monitoring database in GDB format, and the monitoring indicator data that cannot be visualized is stored in CSV format; Relying on the Python environment and the data analysis module contained therein, a monitoring and early warning program is written, the monitoring indicator data is read in the Python environment in the DataFrame data structure, the early warning level of each monitoring indicator is judged by using the index classification type corresponding to the index.

3. The digital monitoring and early warning method for a mountainous historical town according to claim 2, characterized in that: The monitoring indicators include five dimensions of data content, and the dimensions specifically include landscape, landscape settlement, landscape building, landscape residence and landscape culture; The landscape includes the ratio of reduced forest area to original forest area, the ratio of reduced water area to original water area, and the growth of ancient trees and famous trees; The landscape settlement includes the ratio of newly added construction land area to original construction land area, the ratio of street and alley area change to original area, and the change of street and alley small terrain; The landscape building includes the ratio of building change floor number to original floor number, the ratio of key building offset angle to original angle, and the damage degree of traditional building, and the monitoring range of traditional building damage includes doors and windows, walls, roofs and beams; The landscape residence includes rainfall, flood and sudden disaster, and the sudden disaster includes fire, debris flow, landslide and dangerous rock collapse; The landscape culture includes the ratio of the reduction of intangible cultural heritage, the ratio of the reduction of etiquette and custom or traditional festival, and the ratio of the reduction of aboriginal population.

4. The method of claim 3, wherein the method comprises the following steps: The index classification type includes a change measurement type index, a standard established type index and a trigger evaluation type index. The measurement change type index quantifies the change between the actual monitoring index data and the standard or original index data, and makes a corresponding stage warning, which is divided into four warning gradients; The standard established type index divides the warning level according to the existing national grading standard; The trigger evaluation type index is directly divided into four levels of warning as soon as it changes.

5. The digital monitoring and early warning method for a mountainous historical town according to claim 4, characterized in that: In S3, the calculation of the negative change degree value is obtained by weighting the warning levels of the measurement change type monitoring index, the standard established type monitoring index and the trigger evaluation type monitoring index, and finally weighted summation; The calculation formula of the negative change degree value is as follows: ； wherein, represents the negative change degree value of the i-th mountainous historical town, represents the number of j-level early warnings of the i-th mountainous historical town, represents the weight of the j-level early warning; wherein, the first-level early warning is 0.1, the second-level early warning is 0.2, the third-level early warning is 0.3, and the fourth-level early warning is 0.

4.

6. The digital monitoring and early warning method for a mountainous historical town according to claim 5, characterized in that: In S1, the basic spatial data of the monitoring and warning elements is embedded in the three-dimensional digital model base built in ArcScene; For building contour type monitoring surface elements, based on spatial position coordinates, a cube is formed floating on the underlying terrain according to the height of each building after being overlaid on the three-dimensional digital model; For traditional streets and alleys, which do not have height information, based on their spatial position coordinates, they are overlaid on the three-dimensional digital model and float above the underlying terrain; For small and micro terrain and ancient tree monitoring point elements, based on their spatial position coordinates, they float above the underlying terrain as three-dimensional small nails, and all monitoring elements are temporarily white when there is no warning.

7. A digital monitoring and early warning method for a mountainous historical town according to claim 6, characterized in that: In S4, one picture and one table are used for warning picture and warning table; The setting method of the warning picture is to set the three-dimensional digital base warning visualization standard, and according to the warning index type and warning level, the three-dimensional digital base in the ArcScene environment is set for the warning field of each monitoring data layer Different color and symbol standards of warning items are set; Update the three-dimensional digital base warning situation, rewrite the original GDB database to the GDB database updated with the warning field, associate the monitoring index analysis and warning result to the three-dimensional visualization platform, and automatically update to form a warning picture; The setting method of the warning table is to count the monitoring situation of each monitoring index in the Python environment, top the warning items and write them into the table.

8. The method of claim 7, wherein the method comprises: The ranking of the mountainous historical town where the trigger type index issues a warning is directly displayed on top; When the trigger type index is monitored, the monitoring network immediately generates a monitoring report and feedback; When the trigger type index does not issue a warning, the monitoring network generates a monitoring report according to a fixed time period.

9. A digital monitoring and early warning system for mountainous historic towns, using the early warning method according to any one of claims 1-8, characterized in that, It includes: Data acquisition and aggregation module, collect multi-source data, use Python data analysis package and ArcGIS platform to develop automatic preprocessing module, standardize data processing, remove outliers and missing values, and build real-time update mountain town database containing point, line, surface and table data; Three-dimensional digital base construction module, use ArcMap extraction analysis module to convert data format, create DEM digital elevation model, realize CAD data to elevation raster data conversion; With the help of ArcScene, build a three-dimensional digital model, mask extract DEM elevation data within the protection range and cover high-precision remote sensing image; Organize the basic information of monitoring and warning elements, embed them in the three-dimensional model base, and display different types of monitoring surface elements differently to build a three-dimensional visualization digital base; The monitoring and early warning analysis module relies on a Python environment and a data analysis module to write a monitoring and early warning program, reads monitoring index data, and determines the early warning level of each monitoring element according to the index classification type and the early warning standard. The early warning levels of the monitoring indexes of the change measurement type and the standard established type are weighted and summed to obtain the negative change degree value of each mountainous historical town. The early warning result feedback module feeds the monitoring and early warning result to the three-dimensional digital base, sets the color and symbol standard according to the early warning index type and level, forms an early warning map, and generates an early warning table according to the monitoring results of the spatial and non-spatial indexes. According to the early warning situation, a report is output regularly or immediately, the mountainous historical town triggering the index early warning is placed on top and an early warning report is immediately generated as the most priority content.

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