A monitoring point and data classification management method based on an intelligent monitoring platform

Abstract

The present invention provides a method for monitoring points and data classification management based on an intelligent monitoring platform, belonging to the technical field of data processing, including: Step 1: Import a preset BIM model into a preset intelligent monitoring platform and analyze the preset BIM model; Step 2: Create a monitoring project in the preset intelligent monitoring platform based on the model analysis result and configure the monitoring project information; Step 3: Obtain the monitoring requirements matching the monitoring project, thereby determining the type of monitoring points. At the same time, analyze the monitoring project information and create several monitoring points; Step 4: Real-time collect monitoring data based on the monitoring devices of the monitoring points and upload it to the preset intelligent monitoring platform through the network, and automatically associate the uploaded real-time data with the corresponding monitoring points; Step 5: Analyze the real-time data and optimize the deployment of monitoring points and the data of monitoring points. It provides an efficient and intelligent solution for application scenarios such as engineering monitoring and environmental monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a monitoring point and data classification management method based on an intelligent monitoring platform. Background Art

[0002] With the rapid development of intelligent monitoring technology, monitoring management based on BIM and Internet of Things technologies has been widely applied in fields such as engineering monitoring and environmental monitoring. Traditional monitoring solutions usually rely on single monitoring devices or simple data collection methods, making it difficult to meet diverse monitoring requirements.

[0003] In the prior art, some monitoring systems have achieved the integration of data collection and analysis by combining BIM models with monitoring devices. For example, by presetting the positions of monitoring points in the BIM model and associating device data with the model, the management of monitoring points based on the model has been initially realized. However, such solutions have insufficient flexibility in monitoring point deployment, low efficiency in data classification management, and limited real-time association performance: in dynamic monitoring scenarios, after real-time data is uploaded, it is necessary to manually associate monitoring points, which is time-consuming and error-prone, and difficult to meet the efficient management requirements of complex monitoring scenarios.

[0004] Therefore, the present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform. Summary of the Invention

[0005] The present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, which realizes the intelligent creation of monitoring points, the automatic classification management and real-time association of monitoring data by combining the BIM model with the intelligent monitoring platform, significantly improves the flexibility and accuracy of monitoring point deployment, effectively improves the management efficiency and data utilization rate of monitoring projects based on the analysis and optimization functions of real-time data, reduces the risk of errors caused by human intervention, meets the efficient management requirements of complex monitoring scenarios, and improves the full life cycle management ability of monitoring projects through the integrated operation of the intelligent monitoring platform, providing an efficient and intelligent solution for application scenarios such as engineering monitoring and environmental monitoring.

[0006] The present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, including:

[0007] Step 1: Import a preset BIM model into a preset intelligent monitoring platform and analyze the preset BIM model;

[0008] Step 2: Create a monitoring project in the preset intelligent monitoring platform based on the model analysis result and configure the monitoring project information;

[0009] Step 3: Obtain the monitoring requirements matching the monitoring items, and then determine the types of monitoring points. At the same time, analyze the monitoring item information, and then create several monitoring points based on the information analysis results and the types of monitoring points;

[0010] Step 4: Based on the monitoring devices at the monitoring points, collect monitoring data in real time and upload it to a preset intelligent monitoring platform through the network, and automatically associate the uploaded real-time data with the corresponding monitoring points;

[0011] Step 5: Analyze the real-time data and optimize the deployment of monitoring points and the data of monitoring points;

[0012] Among them, analyzing the real-time data and then optimizing the deployment of monitoring points and the data of monitoring points based on the real-time data includes:

[0013] Preprocess the collected real-time data and then analyze the real-time data;

[0014] Based on the analysis results, determine the preset monitoring level areas with anomalies and the types of anomalies;

[0015] When the number of anomalies in the preset monitoring level area exceeds the first preset threshold, then determine the optimization method for the deployment of monitoring points in the preset monitoring level area based on the preset anomaly type - method database and the type of anomaly, and then optimize the deployment of monitoring points;

[0016] If anomalies still occur in the preset monitoring level area where deployment optimization has been carried out, and the number of anomalies exceeds the second preset threshold, then determine the configuration optimization method for the monitoring points in the preset monitoring level area based on the preset anomaly type - configuration method database and the type of anomaly, and then optimize the configuration of monitoring points.

[0017] The present invention provides a method for classifying and managing monitoring points and data based on an intelligent monitoring platform. Import a preset BIM model into the preset intelligent monitoring platform and analyze the preset BIM model, including:

[0018] Conduct an initial analysis of the preset BIM model, determine the import format based on the initial analysis results, and then import the preset BIM model into the preset intelligent monitoring platform to generate a BIM file;

[0019] Determine the corresponding parsing method based on the preset BIM model import format and the preset format - method database, and parse the BIM file to determine the basic information of the preset BIM model;

[0020] Analyze the basic information of the preset BIM model, and then obtain the model analysis results.

[0021] The present invention provides a method for monitoring point and data classification management based on an intelligent monitoring platform, which creates monitoring items and configures monitoring item information in a preset intelligent monitoring platform based on the model analysis results, including:

[0022] Extract structure information based on the model analysis results, determine several monitoring targets for several preset monitoring level areas based on the structure information, and determine the monitoring item types corresponding to each monitoring target in combination with a preset target - type data table;

[0023] Obtain the first configuration information of the monitoring items corresponding to each monitoring item type in a preset item database;

[0024] Analyze all monitoring targets, and then determine the second configuration information of each monitoring item;

[0025] Create monitoring items corresponding to each monitoring target based on a preset item creation method, and configure monitoring item information based on the first configuration information and the second configuration information of the monitoring items corresponding to each monitoring target.

[0026] The present invention provides a method for monitoring point and data classification management based on an intelligent monitoring platform, which analyzes monitoring items to obtain monitoring requirements, and then determines the types of monitoring points. At the same time, it analyzes monitoring item information, and then creates several monitoring points based on the analysis results and the types of monitoring points, including:

[0027] Determine the key parameters to be monitored according to the monitoring targets corresponding to the objectives of the monitoring items;

[0028] Determine the types of monitoring points corresponding to each monitoring target based on the types of the key parameters to be monitored, and determine the monitoring devices corresponding to each monitoring point type based on a preset type - device type database;

[0029] Conduct a first analysis on the configuration information of the monitoring items, and then determine the corresponding preset monitoring level areas to be deployed for each monitoring point;

[0030] Conduct a second analysis on the configuration information of the monitoring items, and then determine the priority of each monitoring item;

[0031] Determine the priority of the corresponding preset monitoring level areas based on the priority of each monitoring item;

[0032] Determine the number of monitoring points to be deployed in each preset monitoring level area based on the priority of each preset monitoring level area and a preset priority - quantity data table;

[0033] Create several monitoring points based on the corresponding preset monitoring level areas to be deployed for each monitoring point, the number of monitoring points to be deployed in each preset monitoring level area, and a preset point creation method.

[0034] The present invention provides a method for monitoring point and data classification management based on an intelligent monitoring platform. The types of monitoring points include: displacement monitoring points, stress monitoring points, temperature and humidity monitoring points, and vibration monitoring points.

[0035] The present invention provides a method for monitoring point and data classification management based on an intelligent monitoring platform. It performs a second analysis on the configuration information of monitoring items, and then determines the priority of each monitoring item, including:

[0036] Performing a second analysis on the configuration information of monitoring items, and then determining the risk-related parameters, resource allocation-related parameters, and monitoring requirement-related parameters of each monitoring item;

[0037] Determining the priority of each monitoring item based on the risk-related parameters, resource allocation-related parameters, and monitoring requirement-related parameters of each monitoring item:

[0038] Wherein, is the priority of the i-th monitoring item, is the risk level of the i-th monitoring item, is the importance coefficient of the i-th monitoring item, is the adjustment coefficient of risk and importance, is the interaction weight coefficient of risk and importance, is the monitoring accuracy coefficient of the i-th monitoring item, is the sensitivity coefficient of the i-th monitoring item, is the project complexity coefficient of the i-th monitoring item, is the resource availability coefficient of the i-th monitoring item, is the timeliness coefficient of the i-th monitoring item, and is the adjustment coefficient of the timeliness of the i-th monitoring item, is the interaction weight of the monitoring accuracy and sensitivity of the i-th monitoring item, is the adjustment coefficient of monitoring accuracy and sensitivity, is the weight coefficient of the project complexity of the i-th monitoring item, is the adjustment coefficient of the timeliness of the monitoring item.

[0039] The present invention provides a method for monitoring point and data classification management based on an intelligent monitoring platform. The monitoring equipment based on the monitoring point collects monitoring data in real time and uploads it to a preset intelligent monitoring platform through a network, and automatically associates the uploaded real-time data with the corresponding monitoring point, including:

[0040] Extract the acquisition accuracy of each monitoring point from the preset priority - type - accuracy database based on the priority of the corresponding preset monitoring level area to be deployed for each monitoring point and the type of each monitoring point;

[0041] Determine the acquisition method of each monitoring point based on the acquisition accuracy of each monitoring point and the preset accuracy - method database;

[0042] Collect monitoring data based on the acquisition method of each monitoring point and the monitoring device of each monitoring point;

[0043] Assign a unique identifier to each monitoring point based on the preset method;

[0044] Add a timestamp and the identifier of the monitoring point to the monitoring data of each monitoring point, and then generate the first monitoring data corresponding to each monitoring point;

[0045] Then upload the first monitoring data corresponding to each monitoring point to the preset intelligent monitoring platform based on the preset upload method.

[0046] Compared with the prior art, the beneficial effects of this application are as follows:

[0047] By combining the BIM model with the intelligent monitoring platform, the intelligent creation of monitoring points, the automatic classification management and real - time association of monitoring data are realized, significantly improving the flexibility and accuracy of monitoring point deployment. Based on the analysis and optimization function of real - time data, the management efficiency and data utilization rate of monitoring projects are effectively improved. At the same time, the risk of errors caused by human intervention is reduced, meeting the high - efficiency management requirements of complex monitoring scenarios. Through the integrated operation of the intelligent monitoring platform, the full - life - cycle management ability of monitoring projects is improved, providing an efficient and intelligent solution for application scenarios such as engineering monitoring and environmental monitoring. Brief Description of the Drawings

[0048] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following - described drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a flowchart of a method for monitoring point and data classification management based on an intelligent monitoring platform provided by an embodiment of the present invention. Detailed Embodiments

[0050] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts fall within the scope of protection of the present invention.

[0051] Embodiment 1:

[0052] An embodiment of the present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, as Figure 1 shown, including:

[0053] Step 1: Import a preset BIM model into a preset intelligent monitoring platform and analyze the preset BIM model;

[0054] Step 2: Create a monitoring project in the preset intelligent monitoring platform based on the model analysis result and configure the monitoring project information;

[0055] Step 3: Obtain the monitoring requirements matching the monitoring project, and then determine the type of monitoring points. At the same time, analyze the monitoring project information, and then create a number of monitoring points based on the information analysis result and the type of monitoring points;

[0056] Step 4: Real-time collect monitoring data based on the monitoring devices of the monitoring points and upload it to the preset intelligent monitoring platform through the network, and automatically associate the uploaded real-time data with the corresponding monitoring points;

[0057] Step 5: Analyze the real-time data and optimize the monitoring point deployment and the monitoring point data;

[0058] Among them, analyzing the real-time data, and then optimizing the monitoring point deployment and the monitoring point data based on the real-time data, including:

[0059] Preprocess the collected real-time data, and then analyze the real-time data;

[0060] Determine the preset monitoring level area where an abnormality occurs and the type of abnormality based on the analysis result;

[0061] When the number of times an abnormality occurs in the preset monitoring level area exceeds the first preset threshold, then determine the monitoring point deployment optimization method for the preset monitoring level area based on the preset abnormality type - method database and the type of abnormality, and then perform the monitoring point deployment optimization;

[0062] If abnormalities still occur in the preset monitoring level area where deployment optimization has been carried out, and the number of occurrences of abnormalities exceeds the second preset threshold, then based on the preset abnormality type - configuration method database and the abnormality type, determine the configuration optimization method of the monitoring points in the preset monitoring level area, and then carry out the configuration optimization of the monitoring points.

[0063] In this embodiment, the preset BIM model refers to a standardized model that has been created and stored, serving as the basis for subsequent monitoring work. For example, a BIM model of a high-rise building, which contains detailed information such as the building's floor structure, the location of load-bearing walls, and key equipment (such as HVAC, elevators, and fire-fighting facilities);

[0064] In this embodiment, the intelligent monitoring platform is a digital management platform based on information technology, with data collection, analysis, management, and visualization functions. The preset intelligent monitoring platform refers to a platform that has been built and has standardized functional modules. For example, a real-time monitoring platform for a construction project that supports data collection from sensors (such as temperature, stress, and vibration) and presents it through a data visualization interface for easy monitoring of the building structure status.

[0065] In this embodiment, the model analysis result refers to the information obtained by analyzing the BIM model, including the structural layout, material type, and key parts of the building, which is used to guide the deployment of monitoring points and the selection of monitoring equipment. For example, by analyzing the BIM model of a certain bridge, it is concluded that displacement sensors need to be installed at the key load-bearing parts of the bridge, while non-load-bearing areas do not need to be monitored;

[0066] In this embodiment, the monitoring project information refers to the information related to a specific monitoring project, including the monitoring purpose, scope, monitoring period, monitoring indicators, etc. For example, in a construction monitoring project of a building, the monitoring project information includes that the monitoring object is the foundation pit retaining structure, the monitoring indicators are soil displacement, foundation pit deformation, etc., and the monitoring period is once a day.

[0067] In this embodiment, the monitoring requirement refers to determining the data type, range, and accuracy that need to be monitored according to the specific objectives and requirements of the monitoring project. For example, the monitoring requirement may include the settlement monitoring of a certain high-rise building, and the requirement is settlement data accurate to 0.1 mm, covering the positions of all main support columns;

[0068] In this embodiment, the types of monitoring points are classified into different types, such as structural monitoring points and environmental monitoring points, according to the monitoring requirements and BIM analysis results, in order to facilitate the targeted arrangement of monitoring equipment. For example, in the construction of a subway tunnel, the types of monitoring points may include surface settlement monitoring points and tunnel lining stress monitoring points.

[0069] In this embodiment, creating several monitoring points based on the information analysis results and the types of monitoring points includes: First, a unique number is automatically generated for each monitoring point to ensure that the identifiers of each monitoring point are not repeated. Create monitoring points in the project: According to the requirements of the project, create different types of monitoring points and associate them with the project, which is usually completed in the "Monitoring Point Management" module of the monitoring platform. Each monitoring point will contain information such as the associated project name, monitoring objective, monitoring parameters, monitoring equipment, etc. Select the location of the monitoring point: When creating a monitoring point, associate it with the specific area or component of the project by selecting the location of the monitoring point. For example, select a specific structural component in the BIM model as the installation location of the monitoring point, or select a geographical location in the GIS map as the monitoring point. Configure the monitoring frequency: Set the data collection frequency for each monitoring point (such as every hour, daily, or in real-time). Different types of monitoring points may have different sampling frequency requirements. For example, displacement monitoring points may require a higher sampling frequency, while environmental monitoring points may have a lower sampling frequency. Set the alarm threshold and response mechanism: Set the alarm threshold for each monitoring point. When the monitoring data exceeds the preset safety value, the system will automatically trigger an alarm and notify the project leader or relevant personnel. Select the monitoring equipment: Select appropriate monitoring equipment for each monitoring point (such as displacement sensors, strain gauges, temperature and humidity meters, etc.). The selection of equipment usually depends on the type of monitoring point and the measurement requirements. Equipment configuration: Enter the detailed parameters of the equipment (such as model, range, accuracy, installation location, etc.) and associate the equipment with the monitoring point. This step ensures that the data of the monitoring point can be collected through the correct sensor.

[0070] In this embodiment, the monitoring equipment refers to the hardware equipment arranged at the monitoring point for real-time collection of monitoring data. The equipment type varies according to the monitoring requirements and the type of monitoring point. For example, in the vibration monitoring of a building, an acceleration sensor is used as the monitoring equipment; in temperature monitoring, a temperature sensor is used.

[0071] In this embodiment, the types of anomalies refer to the specific problem categories found to deviate from the normal state in the monitoring data analysis. These types are usually associated with the monitoring objectives and key parameters and are used to guide optimization strategies. Examples: Bridge monitoring: Types of anomalies: Displacement anomaly, stress overrun, vibration frequency anomaly. Industrial equipment monitoring: Types of anomalies: Over-high temperature, excessive vibration, abnormal noise. Environmental monitoring: Types of anomalies: Sudden temperature drop, too low humidity, PM2.5 exceeding the standard.

[0072] In this embodiment, the first preset threshold is a set limit value for the number of anomalies occurring in a certain monitoring area. When the number of anomalies exceeds this threshold, the system triggers the optimization of the monitoring point deployment to address the problem. For example, in bridge monitoring: the first preset threshold: if the number of stress anomalies in the main girder area of a certain bridge reaches 5 times, the optimization of the monitoring point deployment is triggered; in greenhouse environment monitoring: the first preset threshold: if the number of humidity anomalies in a certain greenhouse area reaches 3 times, the strategy of redeploying the monitoring points is triggered.

[0073] In this embodiment, the optimization of the monitoring point deployment is to re-layout the existing monitoring points according to the abnormal conditions in the monitoring area, mainly by increasing or deleting monitoring points to improve the accuracy and coverage of monitoring. Increasing monitoring points: for areas with frequent anomalies, increase the density of monitoring points to capture abnormal data in more detail; deleting monitoring points: for areas with low anomaly frequency or reduced monitoring requirements, reduce the number of monitoring points to optimize resource utilization. For example, increasing monitoring points: when frequent displacement anomalies occur in the main girder of a bridge, additional displacement sensors are added to cover the abnormal area; deleting monitoring points: when there are no anomalies on the bridge deck area for a long time, some temperature monitoring points are deleted.

[0074] In this embodiment, the second preset threshold is a higher limit value for the anomaly frequency. If the anomaly remains unresolved and the number exceeds this threshold after the optimization of the monitoring point deployment, then the configuration of the monitoring points (such as device type, acquisition accuracy, etc.) needs to be optimized. For example, in bridge monitoring: the second preset threshold: if the stress anomaly still reaches 10 times after the deployment optimization in the main girder area, the configuration optimization is triggered; in environmental monitoring: the second preset threshold: if the greenhouse humidity anomaly exceeds 6 times, the device accuracy optimization is carried out.

[0075] In this embodiment, the preset anomaly type - configuration method database is a mapping table that records the configuration optimization methods corresponding to different anomaly types. It provides reference methods for anomaly resolution, including device replacement, parameter adjustment, or sensor upgrade, etc. For example, example of database content: displacement anomaly → add high-precision laser displacement sensors to improve the acquisition accuracy; temperature anomaly → replace the temperature and humidity sensor to reduce the device error; vibration anomaly → upgrade the vibration sensor to support higher-frequency acquisition. Application scenario: if a displacement anomaly occurs in the main girder area of a bridge, the configuration optimization method is to add laser displacement sensors and increase the acquisition frequency at the same time.

[0076] In this embodiment, the anomaly type is the specific abnormal phenomenon detected during the monitoring process, such as the displacement anomaly, stress overlimit, etc. mentioned above. The anomaly type here is associated with the anomaly type - configuration method database and directly determines the configuration optimization method. For example, in bridge monitoring: displacement anomaly, crack expansion anomaly, vibration frequency anomaly; industrial equipment monitoring: bearing temperature too high, rotation speed anomaly, equipment noise anomaly; environmental monitoring: PM2.5 exceeding the standard, soil humidity insufficient, light anomaly.

[0077] In this embodiment, the configuration optimization method for the preset monitoring level area monitoring points refers to the method of further adjusting and optimizing the equipment, acquisition accuracy, acquisition method, etc. of the existing monitoring points when the anomalies cannot be resolved even after the deployment optimization of the monitoring points. Example: Bridge monitoring: Current situation: After the deployment optimization, the stress anomaly in the main girder area of the bridge still exists. Configuration optimization method: Replace the optical fiber stress sensor with higher accuracy. Adjust the acquisition frequency of the sensor from once per second to five times per second. Use redundant sensor technology to cross-verify data at multiple points.

[0078] The beneficial effects of the above technical solution are as follows: By combining the BIM model with the intelligent monitoring platform, the intelligent creation of monitoring points, the automatic classification management and real-time association of monitoring data are realized, significantly improving the flexibility and accuracy of the deployment of monitoring points. Based on the analysis and optimization functions of real-time data, the management efficiency and data utilization rate of the monitoring project are effectively improved. At the same time, the risk of errors caused by human intervention is reduced, meeting the high-efficiency management requirements of complex monitoring scenarios. Through the integrated operation of the intelligent monitoring platform, the full-life cycle management ability of the monitoring project is improved, providing an efficient and intelligent solution for application scenarios such as engineering monitoring and environmental monitoring.

[0079] Embodiment 2:

[0080] The embodiment of the present invention provides a method for monitoring point and data classification management based on an intelligent monitoring platform. Import a preset BIM model into a preset intelligent monitoring platform and analyze the preset BIM model, including:

[0081] Perform an initial analysis on the preset BIM model, determine the import format based on the initial analysis result, and then import the preset BIM model into the preset intelligent monitoring platform to generate a BIM file;

[0082] Determine the corresponding parsing method based on the preset BIM model import format and the preset format-method database, and parse the BIM file to determine the basic information of the preset BIM model;

[0083] Analyze the basic information of the preset BIM model to obtain the model analysis result.

[0084] In this embodiment, the initial analysis is the first step of analyzing the preset BIM model, aiming to check the integrity, compatibility, and adaptability of the model content, so as to determine how the model is docked with the intelligent monitoring platform. Example: When performing an initial analysis on a BIM model of a bridge, check whether the model contains data on key bridge nodes, such as the position of bearings and span information, and at the same time judge whether the file format (such as IFC, Revit) meets the platform requirements;

[0085] In this embodiment, the import format refers to the file format or data specification that the BIM model needs to conform to when importing into the intelligent monitoring platform, ensuring that the model can be correctly parsed and used. For example, the import formats supported by the intelligent monitoring platform include the IFC format (Industry Foundation Classes) or Revit files (.rvt). The BIM model of a bridge project needs to be converted to the IFC format before it can be imported into the platform;

[0086] In this embodiment, the BIM file is a digital file after format conversion or direct import, containing detailed information about a building or engineering project, and can be managed and parsed on the intelligent monitoring platform. For example, in the BIM file of a high-rise building, the structure, materials, equipment locations, etc. of each floor are described in detail, which is used for the layout planning of subsequent monitoring points.

[0087] In this embodiment, the preset format-method database is a database pre-established in the intelligent monitoring platform, used to store BIM files in different formats and their corresponding parsing methods, facilitating the platform to automatically select parsing strategies according to the file format. For example, the format-method database stores the parsing method for IFC format files (parsing building geometry and attribute information) and the parsing method for Revit files (parsing specific design levels and construction details).

[0088] In this embodiment, the basic information of the BIM model refers to the core information extracted from the BIM model, usually including structural information (such as the positions and dimensions of beams, columns, and floors), material information (such as the types of reinforced concrete), and equipment information (such as sensor layout points). For example, in the BIM model of a certain tunnel, the basic information includes the total length of the tunnel, the radius of the arch ring, the positions of key nodes (such as excavation sections and construction sections), and the strength grades of the component materials.

[0089] The beneficial effects of the above technical solution are: through the intelligent matching of the import format and parsing method of the BIM model, the efficient parsing of BIM model data and the extraction of basic information are realized, and the model analysis results are automatically generated, improving the adaptability of the BIM model in the monitoring platform and the intelligent level of data processing, and being applicable to complex building information management scenarios.

[0090] Embodiment 3:

[0091] The embodiment of the present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, creating a monitoring project and configuring monitoring project information in a preset intelligent monitoring platform based on the model analysis results, including:

[0092] Extract structural information based on the model analysis results, determine several monitoring objectives for several preset monitoring level regions based on the structural information, and determine the monitoring item types corresponding to each monitoring objective in combination with a preset objective-type data table;

[0093] Obtain the first configuration information of the monitoring items corresponding to each monitoring item type in a preset item database;

[0094] Analyze all monitoring objectives, and then determine the second configuration information of each monitoring item;

[0095] Create monitoring items corresponding to each monitoring objective based on a preset item creation method, and configure the monitoring item information based on the first configuration information and the second configuration information of the monitoring items corresponding to each monitoring objective.

[0096] In this embodiment, the structural information is the core building or engineering structure feature data extracted from the BIM model, including component types, dimensions, positions, and mutual relationships, which is used to guide the layout of monitoring points and the division of monitoring levels. For example, in the BIM model of a bridge, the structural information includes the span of the main girder, the position of the bearings, the arch radius of the arch bridge, and the depth and diameter of the pile foundation;

[0097] In this embodiment, the preset monitoring level regions are regions divided according to monitoring requirements and structural information, usually classified according to risk levels, importance, or monitoring requirements, which are used to determine the monitoring focus and resource allocation. For example, in a certain high-rise building, three-level monitoring regions are divided: Level 1 region: building foundation (key bearing capacity region), Level 2 region: main load-bearing components (beam-column joints), Level 3 region: ordinary walls and secondary components;

[0098] In this embodiment, the monitoring objective refers to the specific object that needs to be monitored, usually determined based on the division of the monitoring region and structural characteristics, and may include components, environmental conditions, or specific parameters. For example, in a foundation pit project, the monitoring objectives include: surface settlement around the foundation pit, horizontal displacement of the retaining structure, and change in the groundwater level;

[0099] In this embodiment, the preset objective-type data table is a pre-defined association table that stores the mapping relationship between monitoring objectives and monitoring item types, which helps the intelligent monitoring platform quickly match the objectives and the corresponding monitoring types. For example, some content in the objective-type data table: Monitoring objective: support beam → Monitoring type: stress monitoring item, Monitoring objective: ground around the foundation pit → Monitoring type: settlement monitoring item, Monitoring objective: tunnel lining → Monitoring type: displacement monitoring item;

[0100] In this embodiment, the monitoring project type is a specific monitoring task category divided according to different monitoring objectives, which defines the data types to be collected and the analysis methods. For example, in a railway project, the monitoring project types include: vibration monitoring project (detecting the dynamic impact of trains on bridges), stress monitoring project (monitoring the bearing capacity of the main girder), and temperature monitoring project (monitoring the impact of ambient temperature on steel structures).

[0101] In this embodiment, the first configuration information is the general configuration content for a certain type of monitoring project, which does not involve specific monitoring objectives and usually includes basic information such as monitoring frequency and monitoring accuracy requirements. For example, for a settlement monitoring project, the first configuration information may include: data collection frequency: once per hour, data transmission method: wireless network, data analysis method: time series analysis.

[0102] In this embodiment, the second configuration information is the parameters and deployment details further defined based on specific monitoring objectives, including the type of monitoring equipment, installation location, and monitoring index range. For example, for the monitoring project of beam-column joints of a certain building, the second configuration information includes: equipment type: displacement sensor and stress gauge, deployment location: core area of the joint and midpoint of the supporting beam, monitoring index: displacement (accuracy 0.01 mm), stress (accuracy 0.1 MPa).

[0103] In this embodiment, the preset project creation method is a defined process for quickly creating a monitoring project and generating relevant configuration files according to the monitoring objective, configuration information, and monitoring project type. For example, the steps of the preset project creation method are: matching the monitoring objective and monitoring project type from the target-type data table, obtaining the first configuration information and the second configuration information, generating the monitoring project configuration file, and automatically deploying the monitoring equipment. For example, when creating a displacement monitoring project for the main girder of a bridge, the method automatically retrieves the monitoring type (displacement monitoring) corresponding to the target (main girder), loads the equipment deployment location (center of the beam), and the monitoring frequency (once every 10 minutes).

[0104] The beneficial effects of the above technical solution are: by extracting structural information based on the model analysis results, accurately determining the monitoring objective and project type in combination with the target-type data table and the preset database, and automatically configuring the basic and specific configuration information, the creation and configuration of the monitoring project are realized to be efficient and intelligent, and the deployment accuracy and management efficiency in complex monitoring scenarios are improved.

[0105] Example 4:

[0106] An embodiment of the present invention provides a method for managing monitoring points and data classification based on an intelligent monitoring platform, analyzing the monitoring project to obtain monitoring requirements, and then determining the type of monitoring points. At the same time, analyzing the monitoring project information, and then creating a number of monitoring points based on the analysis results and the type of monitoring points, including:

[0107] Determine the key parameters to be monitored according to the monitoring objectives corresponding to the monitoring items;

[0108] Determine the type of monitoring points corresponding to each monitoring objective based on the type of key parameters to be monitored, and determine the monitoring devices corresponding to each monitoring point type based on the preset type-device type database;

[0109] Conduct a first analysis of the configuration information of the monitoring item, and then determine the corresponding preset monitoring level areas to be deployed for each monitoring point;

[0110] Conduct a second analysis of the configuration information of the monitoring item, and then determine the priority of each monitoring item;

[0111] Determine the priority of the corresponding preset monitoring level areas based on the priority of each monitoring item;

[0112] Determine the number of monitoring points to be deployed in each preset monitoring level area based on the priority of each preset monitoring level area and the preset priority-number data table;

[0113] Create a number of monitoring points based on the corresponding preset monitoring level areas to be deployed for each monitoring point, the number of monitoring points to be deployed in each preset monitoring level area, and the preset point creation method.

[0114] In this embodiment, the key parameters refer to the physical quantities or indicators that must be focused on and collected in order to achieve the monitoring objectives in the monitoring item. These parameters directly reflect the state or change trend of the monitoring object and guide the subsequent monitoring point type, device selection and monitoring strategy. Examples: Bridge monitoring: Key parameters: Displacement, stress, vibration frequency of the bridge main girder. Dam monitoring: Key parameters: Water pressure, leakage amount, concrete stress change. Greenhouse environment monitoring: Key parameters: Temperature, humidity, light intensity, CO2 concentration.

[0115] In this embodiment, the type of monitoring points is a specific classification of monitoring points determined based on the key parameters of the monitoring item. This classification is usually associated with the characteristics, objectives and key parameters of the monitoring object and determines the types of monitoring devices required. Examples: Bridge monitoring: Monitoring point types: Stress monitoring points, displacement monitoring points, vibration monitoring points. Greenhouse environment monitoring: Monitoring point types: Temperature and humidity monitoring points, CO2 concentration monitoring points. Industrial equipment monitoring: Monitoring point types: Vibration monitoring points, temperature monitoring points, noise monitoring points;

[0116] In this embodiment, the preset type-device type database is a pre-set comparison table that records the monitoring device types corresponding to each monitoring point type. The role of this database is to quickly map the monitoring point type to the required device, facilitating the selection and deployment of monitoring devices.

[0117] Example: Database content example: Displacement monitoring point → Laser displacement sensor, temperature and humidity monitoring point → Temperature and humidity sensor, vibration monitoring point → Acceleration sensor, stress monitoring point → Fiber optic stress sensor. Application scenario: When vibration monitoring points need to be arranged for bridge monitoring, the acceleration sensor can be quickly determined as the monitoring device for vibration monitoring points through the database.

[0118] In this embodiment, the first analysis refers to the preliminary processing of the configuration information of the monitoring items to determine the monitoring density or monitoring priority to be deployed in different regions. By analyzing information such as monitoring requirements and environmental conditions within the region, different monitoring level regions are divided;

[0119] In this embodiment, the preset monitoring level region refers to classifying the region based on the requirements of the monitoring items and dividing it into different levels according to the importance of the monitoring requirements, such as high-priority, medium-priority, and low-priority regions. Example: Bridge monitoring: First analysis: Analyze the stress conditions, traffic flow, etc. of the main beam and support piers of the bridge. The main beam is a high-priority region, and the support pier is a medium-priority region. Preset monitoring level region: High-priority region: Key regions with large stress on the main beam of the bridge, Medium-priority region: Stress-bearing areas of the bridge piers, Low-priority region: Road area of the bridge deck.

[0120] In this embodiment, the second analysis is to conduct an in-depth analysis of the monitoring items, comprehensively evaluate their risk levels, the importance of monitoring requirements, and resource allocation conditions, so as to determine the priority of each monitoring item;

[0121] In this embodiment, the priority of the monitoring items refers to sorting different monitoring items according to their importance and urgency based on the results of the second analysis. Items with higher priority will be processed and monitored points will be deployed preferentially. Example: Bridge monitoring: Second analysis: Due to long-term overloading, cracks have appeared in the main beam of a certain bridge, the traffic volume is large, and the risk is relatively high. It is evaluated as a high-priority project. Priority of monitoring items: High-priority project: Focus on monitoring the stress and crack propagation of the main beam of the bridge, Medium-priority project: Monitor the inclination and settlement of the support piers, Low-priority project: Deformation monitoring of the road surface of the bridge deck.

[0122] In this embodiment, the priority of the preset monitoring level region is to further sort the divided monitoring regions based on the priority of each monitoring item. The region priority is used to guide the deployment order and quantity allocation of monitoring points. Example: Bridge monitoring: Main beam (high-priority region): 10 monitoring points need to be arranged, Support pier (medium-priority region): 5 monitoring points need to be arranged, Road surface of the bridge deck (low-priority region): 2 monitoring points need to be arranged.

[0123] In this embodiment, the preset point creation method refers to a method of generating the positions and quantities of monitoring points in the intelligent monitoring platform according to the monitoring items and regional requirements. Common methods include uniform distribution, density distribution, and key-point distribution, etc. Examples: Uniform distribution: Displacement monitoring points are evenly arranged at a spacing of 10 meters in the main beam area of a bridge; Density distribution: More stress monitoring points are arranged in the bridge area with more cracks; Key-point distribution: Displacement monitoring points are arranged in the foundation area of the bridge support pier to focus on monitoring settlement and inclination.

[0124] The beneficial effects of the above technical solution are as follows: Through the multi-dimensional analysis of monitoring items and configuration information, the monitoring requirements, types of monitoring points, and deployment areas are accurately determined. Combining the priority and the equipment database, the creation and distribution of monitoring points are optimized, improving the scientificity of monitoring point configuration, resource utilization rate, and monitoring efficiency, and being applicable to the dynamic management requirements of complex monitoring scenarios.

[0125] Embodiment 5:

[0126] The embodiment of the present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform. The types of monitoring points include: displacement monitoring points, stress monitoring points, temperature and humidity monitoring points, and vibration monitoring points.

[0127] In this embodiment, the displacement monitoring points are used to detect the displacement changes of structures or components under the action of external forces. The monitoring objects usually include the deformation or deflection of structures such as bridges, tunnels, and foundation pits. The displacement monitoring data is used to evaluate the overall stability or local deformation of the structure. Examples: Bridge monitoring: Displacement monitoring points are set in the middle of the main beam of a certain bridge to monitor the deflection changes under the action of vehicle loads; Foundation pit monitoring: Displacement monitoring points are set on the retaining piles of the foundation pit to detect the horizontal displacement of the retaining structure; Tunnel monitoring: Displacement monitoring points are arranged at the crown of the tunnel to monitor the convergence displacement of the surrounding rock.

[0128] In this embodiment, the stress monitoring points are used to monitor the stress distribution and changes generated inside or on the surface of the structure, mainly for load-bearing or stressed structures such as steel structures and concrete components. The stress monitoring data can reflect the stress state of the structure and its safety margin. Examples: Bridge monitoring: Stress monitoring points are set at the nodes of the main beam of a steel bridge to monitor the stress concentration at the nodes in real time and prevent fatigue failure; Dam monitoring: Stress monitoring points are set inside the concrete dam body of the dam to monitor the stress changes of the dam body to ensure the structural safety of the dam body; Building monitoring: Stress monitoring points are set at the intersections of shear walls and columns of high-rise buildings to detect the stress changes under seismic loads.

[0129] In this embodiment, the temperature and humidity monitoring points are used to detect the changes in temperature and humidity inside the environment or structure. These factors may directly or indirectly affect the durability and performance of the structure, such as material expansion, crack propagation, and environmental corrosion, etc. For example: Tunnel monitoring: Temperature and humidity monitoring points are arranged in the tunnel to monitor the changes in the internal humid and hot environment to prevent the lining structure from getting damp or condensing. Bridge monitoring: Temperature monitoring points are arranged near the expansion joints of the bridge to detect the influence of temperature changes on the structure expansion. Storage facility monitoring: Temperature and humidity monitoring points are set in the granary or oil tank to ensure that the environmental conditions are suitable for material storage;

[0130] In this embodiment, the vibration monitoring points are used to monitor the vibration response of the structure under dynamic loads (such as wind, earthquake, vehicle, mechanical vibration, etc.) to evaluate the dynamic performance and safety status of the structure. Equipment types: accelerometers, velocity sensors, vibration sensors, etc. For example: Bridge monitoring: Vibration monitoring points are arranged near the main cable of the suspension bridge to detect the bridge vibration response under strong wind and prevent resonance. Earthquake monitoring: Vibration monitoring points are set in the core tube of high-rise buildings to detect the acceleration response under earthquake loads. Industrial equipment monitoring: Vibration monitoring points are arranged on the generator set or large-scale mechanical equipment to monitor the vibration frequency and amplitude during equipment operation and prevent faults.

[0131] The beneficial effects of the above technical solutions are as follows: Classify and manage according to the monitoring point types (displacement, stress, temperature and humidity, vibration), realize the refined processing and targeted analysis of monitoring data, improve the adaptability and management efficiency of the intelligent monitoring platform to various monitoring requirements, and effectively meet the monitoring requirements of complex scenarios.

[0132] Embodiment 6:

[0133] The embodiment of the present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, which performs a second analysis on the configuration information of the monitoring items, and then determines the priority of each monitoring item, including:

[0134] Perform a second analysis on the configuration information of the monitoring items, and then determine the risk-related parameters, resource configuration-related parameters, and monitoring requirement-related parameters of each monitoring item;

[0135] Determine the priority of each monitoring item based on the risk-related parameters, resource configuration-related parameters, and monitoring requirement-related parameters of each monitoring item:

[0136]

[0137] Wherein, is the priority of the i-th monitoring item, is the risk level of the i-th monitoring item, is the importance coefficient of the i-th monitoring item, is the adjustment coefficient for risk and importance. is the interaction weight coefficient for risk and importance. is the monitoring accuracy coefficient for the i-th monitoring item. is the sensitivity coefficient for the i-th monitoring item. is the project complexity coefficient for the i-th monitoring item. is the resource availability coefficient for the i-th monitoring item. is the timeliness coefficient for the i-th monitoring item, and is the adjustment coefficient for the timeliness of the i-th monitoring item. is the interaction weight between the monitoring accuracy and sensitivity of the i-th monitoring item. is the adjustment coefficient for monitoring accuracy and sensitivity. is the weight coefficient for the project complexity of the i-th monitoring item. is the adjustment coefficient for the timeliness of the monitoring item.

[0138] In this embodiment, the risk-related parameters are factors used to quantify the potential safety risks or losses of the monitoring item. These parameters evaluate the degree of danger that the monitored object may face and its impact on society, economy, and environment. Typical parameters: Risk level: Evaluate the possibility of failure or malfunction according to the current state of the monitored object (such as aging, stress, etc.). Sensitivity coefficient: Measure the response degree of the monitored object to external environmental changes (such as wind, earthquake, temperature). Example: Bridge monitoring: Risk level: There are obvious cracks in the main girder of an old bridge, and it is evaluated as a high risk level. Sensitivity coefficient: The bridge located in an earthquake-prone area is highly sensitive to earthquake vibrations, and the sensitivity coefficient is high.

[0139] In this embodiment, the resource allocation-related parameters are factors used to evaluate the availability and adaptability of the resources (such as equipment, manpower, funds, etc.) required to complete the monitoring task. These parameters determine the feasibility and priority of project implementation. Typical parameters: Resource availability coefficient: The current available degree of resources such as monitoring equipment and technical teams. Project complexity coefficient: Measure the complexity of the project in terms of technology, space, implementation difficulty, etc. Example: High-rise building monitoring: Resource availability coefficient: If the project needs to use drones for facade crack detection, but the number of drones is insufficient, the resource availability is low. Project complexity coefficient: The building is tall and the facade is complex, and special equipment is required for assistance, so the complexity coefficient is high.

[0140] In this embodiment, the parameters related to monitoring requirements are factors used to evaluate the requirements of a project for accuracy, timeliness, and monitoring scope. These parameters reflect the importance and urgency of the monitoring task. Typical parameters: Monitoring accuracy coefficient: The requirement of the project for the accuracy of monitoring data. Timeliness coefficient: The time sensitivity of the delivery of monitoring results. For example, in the monitoring of buildings in the typhoon-affected area: Monitoring accuracy coefficient: High-precision real-time data is required for the monitoring of building deformation under wind load, so the monitoring accuracy requirement is high. Timeliness coefficient: As the typhoon is approaching, the monitoring needs to be completed quickly, so the timeliness coefficient is high. In the monitoring of tunnel construction: Monitoring accuracy coefficient: The displacement of the surrounding rock during construction needs to be accurately monitored to ensure construction safety, so the accuracy requirement is high. Timeliness coefficient: The monitoring data can be updated daily, and the timeliness requirement is low.

[0141] The beneficial effects of the above technical solution are as follows: Through in-depth analysis of the configuration information of the monitoring project, combined with the dynamic balance of multiple parameters and adjustment coefficients, the priority of the monitoring project is accurately evaluated and determined, ensuring the rationality of resource allocation and the pertinence of monitoring. Considering multi-dimensional factors such as risk, importance, complexity, and timeliness, the scientificity and accuracy of monitoring decisions are improved, and it is applicable to complex monitoring requirement scenarios.

[0142] Embodiment 7:

[0143] The embodiment of the present invention provides a method for classifying and managing monitoring points and data based on an intelligent monitoring platform. The monitoring equipment based on the monitoring points collects monitoring data in real time and uploads it to a preset intelligent monitoring platform through the network, and automatically associates the uploaded real-time data with the corresponding monitoring points, including:

[0144] Based on the priority of the corresponding preset monitoring level area to be deployed for each monitoring point and the type of each monitoring point, extract the collection accuracy of each monitoring point from the preset priority-type-accuracy database;

[0145] Based on the collection accuracy of each monitoring point and the preset accuracy-method database, determine the collection method of each monitoring point;

[0146] Based on the collection method of each monitoring point and the monitoring equipment of each monitoring point, collect monitoring data;

[0147] Based on the preset method, assign a unique identifier to each monitoring point;

[0148] Add a timestamp and the identifier of the monitoring point to the monitoring data of each monitoring point, and then generate the first monitoring data corresponding to each monitoring point;

[0149] Furthermore, upload the first monitoring data corresponding to each monitoring point to the preset intelligent monitoring platform based on the preset upload method.

[0150] In this embodiment, the acquisition accuracy refers to the measurement error range of the monitoring device for the target physical quantities (such as displacement, stress, temperature, humidity, etc.) during the acquisition of monitoring data. The higher the acquisition accuracy, the stronger the reliability and accuracy of the monitoring data. Generally, the acquisition accuracy is closely related to the type of monitoring point, monitoring device, environmental factors, etc. Example: Displacement monitoring point: For the displacement monitoring of a bridge, using a laser rangefinder, the accuracy may reach 0.1 mm, indicating that the error of each measurement does not exceed 0.1 mm. Temperature and humidity monitoring point: For the temperature and humidity monitoring in a warehouse, the accuracy requirements are ±0.5 °C and ±2% relative humidity to ensure that the monitoring results of temperature and humidity are accurate enough to prevent damage to materials.

[0151] In this embodiment, the acquisition method refers to the method or means of collecting monitoring data. According to the different types of monitoring points, acquisition accuracy requirements, and environmental conditions, the monitoring device may adopt different acquisition methods. These methods can be real-time acquisition, timed acquisition, trigger acquisition, etc. The selection of the acquisition method directly affects the quality of the data, the processing method, and the upload frequency. Example: Displacement monitoring point, in the displacement monitoring of a bridge, the real-time continuous acquisition method is adopted, and the deformation of the bridge is monitored at any time through a laser displacement sensor. Temperature and humidity monitoring point, in the warehouse monitoring, the timed acquisition method is adopted, and the temperature and humidity data are acquired every 30 minutes.

[0152] In this embodiment, the first monitoring data refers to the data set that is collected by the monitoring device and preliminarily processed and is associated with a specific monitoring point. These data usually include content such as timestamps, monitoring point identifiers, actual monitoring values, etc., and serve as the basic data for further analysis and processing. Example: Displacement monitoring point: For the displacement monitoring point of a certain bridge, the first monitoring data may include data such as "timestamp: 2024-12-13 10:00:00", "monitoring point identifier: A01", "displacement value: 3.5 mm", etc. Temperature and humidity monitoring point: The first monitoring data of the temperature and humidity monitoring point in the warehouse may include information such as "timestamp: 2024-12-13 10:00:00", "monitoring point identifier: B02", "temperature: 22 °C", "humidity: 60%".

[0153] In this embodiment, the preset upload method refers to the method and frequency of uploading data set according to requirements for different types of monitoring points and data in the intelligent monitoring platform. The upload method can include forms such as scheduled upload, event-triggered upload, real-time upload, etc. This method ensures that data can be transmitted to the intelligent monitoring platform in a timely and accurate manner for further analysis and storage. For example, for the scheduled upload method: for some environmental monitoring points (such as temperature and humidity monitoring), the system may be preset to upload data once an hour. For the event-triggered upload method: for the stress monitoring of a bridge, the system is set to automatically upload real-time data when the monitored stress exceeds a certain threshold. For the real-time upload method: for the displacement monitoring of a bridge or tunnel, the system is set to upload data in real time to ensure that data can be transmitted immediately when major changes occur.

[0154] The beneficial effects of the above technical solutions are as follows: By dynamically matching the acquisition accuracy and acquisition method of the monitoring points through the priority-type-accuracy database and the accuracy-method database, and combining the unique identifier and timestamp, the automatic association and efficient upload of data are realized, ensuring the accuracy, real-time performance, and traceability of the monitoring data, improving the automation and management efficiency of the intelligent monitoring platform, and meeting the requirements of complex and diverse monitoring scenarios.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A monitoring point and data classification management method based on an intelligent monitoring platform, characterized in that: include: Step 1: Import the preset BIM model into the preset intelligent monitoring platform and analyze the preset BIM model; Step 2: Create a monitoring project in the preset intelligent monitoring platform based on the model analysis results and configure the monitoring project information; Step 3: Obtain monitoring requirements that match the monitoring project, determine the type of monitoring points, and analyze the monitoring project information. Create several monitoring points based on the information analysis results and the types of monitoring points. Step 4: The monitoring equipment at the monitoring point collects monitoring data in real time and uploads it to the preset intelligent monitoring platform through the network, and automatically associates the uploaded real-time data with the corresponding monitoring point; Step 5: Analyze the real-time data and optimize the monitoring point deployment and monitoring point data, including: Preprocess the collected real-time data and analyze the real-time data; Determine the preset monitoring level area where the abnormality occurs and the type of the abnormality based on the analysis result; When the number of abnormalities in the preset monitoring level area exceeds the first preset threshold, the monitoring point deployment optimization mode of the preset monitoring level area is determined based on the preset abnormality type-mode database and the abnormality type, and the monitoring point deployment optimization is performed; If anomalies still occur in the preset monitoring level area after deployment optimization, and the number of anomalies exceeds the second preset threshold, the configuration optimization method of the monitoring points in the preset monitoring level area is determined based on the preset anomaly type-configuration method database and the anomaly type, and the monitoring point configuration is optimized; Wherein, step 3 includes: Determine the key parameters that need to be monitored based on the monitoring objectives corresponding to the monitoring project; Determine the type of monitoring point corresponding to each monitoring target based on the type of key parameters to be monitored, and determine the monitoring equipment corresponding to each monitoring point type based on a preset type-equipment type database; Performing a first analysis on the configuration information of the monitoring project to determine a corresponding preset monitoring level area to be deployed at each monitoring point; Performing a second analysis on the configuration information of the monitoring items to determine the priority of each monitoring item; Determine the priority of the corresponding preset monitoring level area based on the priority of each monitoring item; Determining the number of monitoring points to be deployed in each preset monitoring level area based on the priority of each preset monitoring level area and a preset priority-quantity data table; A number of monitoring points are created based on the corresponding preset monitoring level area to be deployed for each monitoring point, the number of monitoring points to be deployed in each preset monitoring level area, and the preset point creation method.

2. A monitoring point and data classification management method based on an intelligent monitoring platform according to claim 1, characterized in that: Import the preset BIM model into the preset intelligent monitoring platform and analyze the preset BIM model, including: Perform an initial analysis on the preset BIM model, determine the import format based on the initial analysis results, and import the preset BIM model into the preset intelligent monitoring platform to generate a BIM file; Determine a corresponding parsing method based on a preset BIM model import format and a preset format-method database, and parse the BIM file to determine basic information of the preset BIM model; Analyze the basic information of the preset BIM model and obtain the model analysis results.

3. A monitoring point and data classification management method based on an intelligent monitoring platform according to claim 2, characterized in that: Based on the model analysis results, create a monitoring project in the preset intelligent monitoring platform and configure the monitoring project information, including: Extracting structural information based on the model analysis results, and determining a number of monitoring targets in a number of preset monitoring level areas based on the structural information, and determining the type of monitoring project corresponding to each monitoring target in combination with a preset target-type data table; Acquire first configuration information of a monitoring item corresponding to each monitoring item type in a preset item database; Analyze all monitoring targets and determine the second configuration information of each monitoring item; A monitoring project corresponding to each monitoring target is created based on a preset project creation method, and monitoring project information configuration is performed based on first configuration information and second configuration information of the monitoring project corresponding to each monitoring target.

4. A monitoring point and data classification management method based on an intelligent monitoring platform according to claim 1, characterized in that: The types of monitoring points include: displacement monitoring points, stress monitoring points, temperature and humidity monitoring points, and vibration monitoring points.

5. The monitoring point and data classification management method based on the intelligent monitoring platform according to claim 1 is characterized in that: The monitoring equipment based on the monitoring point collects monitoring data in real time and uploads it to the preset intelligent monitoring platform through the network, automatically associating the uploaded real-time data with the corresponding monitoring point, including: Extracting the acquisition accuracy of each monitoring point from a preset priority-type-accuracy database based on the priority of the corresponding preset monitoring level area to be deployed for each monitoring point and the type of each monitoring point; Determine the collection mode of each monitoring point based on the collection accuracy of each monitoring point and a preset accuracy-mode database; Collect monitoring data based on the collection method of each monitoring point and the monitoring equipment at each monitoring point; Assigning a unique identifier to each monitoring point based on a preset method; Adding a timestamp and an identifier of the monitoring point to the monitoring data of each monitoring point, and generating first monitoring data corresponding to each monitoring point; The first monitoring data corresponding to each monitoring point is uploaded to the preset intelligent monitoring platform based on a preset uploading method.

6. A monitoring point and data classification management method based on an intelligent monitoring platform according to claim 4, characterized in that: Perform a second analysis on the configuration information of the monitoring items to determine the priority of each monitoring item, including: Perform a second analysis on the configuration information of the monitoring items to determine the risk-related parameters, resource allocation-related parameters, and monitoring demand-related parameters of each monitoring item; Determine the priority of each monitoring project based on the risk-related parameters, resource allocation-related parameters, and monitoring demand-related parameters of each monitoring project: in, is the priority of the ith monitoring item, is the risk level of the ith monitoring item, is the importance coefficient of the ith monitoring item, is the adjustment factor for risk and importance, is the interaction weight coefficient of risk and importance, is the monitoring accuracy coefficient of the i-th monitoring item, is the sensitivity coefficient of the ith monitoring item, is the project complexity coefficient of the i-th monitoring project, is the resource availability coefficient of the ith monitoring project, is the timeliness coefficient of the i-th monitoring item, is the interaction weight of the monitoring accuracy and sensitivity of the i-th monitoring item, To monitor the adjustment factor for accuracy and sensitivity, is the weight coefficient of the project complexity of the i-th monitoring project, It is the adjustment coefficient for the timeliness of the monitoring project.

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