Monitoring point and data classification management method based on intelligent monitoring platform

Abstract

The invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, and belongs to the technical field of data processing, and the method comprises the steps: 1, importing a preset BIM model into a preset intelligent monitoring platform, and carrying out the analysis of the preset BIM model; 2, creating a monitoring project in a preset intelligent monitoring platform based on a model analysis result, and configuring monitoring project information; step 3, acquiring a monitoring demand matched with the monitoring project, determining the type of a monitoring point, analyzing the information of the monitoring project, and creating a plurality of monitoring points; 4, the monitoring equipment based on the monitoring points collects monitoring data in real time, uploads the monitoring data to a preset intelligent monitoring platform through a network, and automatically associates the uploaded real-time data with the corresponding monitoring points; and 5, analyzing the real-time data, and optimizing the deployment of the monitoring points and the data of the monitoring points. And an efficient and intelligent solution is provided for application scenes such as engineering monitoring and environment 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 IoT technology has been widely used in engineering monitoring, environmental monitoring and other fields. Traditional monitoring solutions usually rely on a single monitoring device or a simple data collection method, which is difficult to meet diverse monitoring needs.

[0003] In the prior art, some monitoring systems have achieved the integration of data collection and analysis by combining BIM models with monitoring equipment. For example, by presetting the monitoring point locations in the BIM model and associating the equipment data with the model, model-based monitoring point management has been preliminarily achieved. However, this type of solution lacks flexibility in monitoring point deployment, has low efficiency in data classification management, and has limited real-time association performance: in dynamic monitoring scenarios, monitoring points need to be manually associated after real-time data is uploaded, which is time-consuming and error-prone, and is difficult to adapt to the efficient management needs 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 is used to realize the intelligent creation of monitoring points, automatic classification management and real-time association of monitoring data by combining the BIM model with the intelligent monitoring platform, significantly improving the flexibility and accuracy of monitoring point deployment, and based on the analysis and optimization function of real-time data, effectively improving the management efficiency and data utilization rate of monitoring projects, while reducing the risk of errors caused by human intervention, meeting the efficient management needs of complex monitoring scenarios, and improving the full life cycle management capabilities of monitoring projects through the integrated operation of the intelligent monitoring platform, providing efficient and intelligent solutions 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, comprising: 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, and then determine the type of monitoring points. At the same time, analyze the monitoring project information, and then 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; Among them, the real-time data is analyzed, and the monitoring point deployment and monitoring point data are optimized based on the real-time data, including: Preprocess the collected real-time data and then 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 then the monitoring point deployment optimization is performed; If anomalies still occur in the preset monitoring level area that has been deployed and optimized, 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 then the monitoring point configuration is optimized.

[0007] The present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, importing a preset BIM model into a preset intelligent monitoring platform, and analyzing 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 then 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 to obtain the model analysis results.

[0008] The present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, which creates a monitoring project and configures monitoring project information in a preset intelligent monitoring platform based on a model analysis result, 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.

[0009] The present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, which analyzes the monitoring project to obtain the monitoring requirements, and then determines the type of the monitoring point. At the same time, the monitoring project information is analyzed, and then a number of monitoring points are created based on the analysis results and the types of the monitoring points, including: Determine the key parameters that need to be monitored based on the monitoring objectives corresponding to the objectives of 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, thereby determining 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, thereby determining 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.

[0010] 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.

[0011] 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: Perform a second analysis on the configuration information of the monitoring items, and then 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 ith monitoring item, is the adjustment coefficient of the timeliness of the ith 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.

[0012] The present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, wherein monitoring equipment based on the monitoring point collects monitoring data in real time, and uploads the data to a preset intelligent monitoring platform through a network, and automatically associates 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, thereby generating first monitoring data corresponding to each monitoring point; Then, the first monitoring data corresponding to each monitoring point is uploaded to the preset intelligent monitoring platform based on the preset uploading method.

[0013] Compared with the prior art, the present invention has the following beneficial effects: By combining the BIM model with the intelligent monitoring platform, the intelligent creation of monitoring points, automatic classification management and real-time association of monitoring data are realized, which significantly improves the flexibility and accuracy of monitoring point deployment. The analysis and optimization functions based on real-time data effectively improve the management efficiency and data utilization of monitoring projects, while reducing the risk of errors caused by human intervention, meeting the efficient management needs of complex monitoring scenarios. Through the integrated operation of the intelligent monitoring platform, the full life cycle management capabilities of the monitoring project are improved, providing efficient and intelligent solutions for application scenarios such as engineering monitoring and environmental monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is a flow chart of a monitoring point and data classification management method based on an intelligent monitoring platform provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1: The embodiment of the present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, such as Figure 1 As shown, including: 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, and then determine the type of monitoring points. At the same time, analyze the monitoring project information, and then 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; Among them, the real-time data is analyzed, and the monitoring point deployment and monitoring point data are optimized based on the real-time data, including: Preprocess the collected real-time data and then 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 then the monitoring point deployment optimization is performed; If anomalies still occur in the preset monitoring level area that has been deployed and optimized, 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 then the monitoring point configuration is optimized.

[0018] In this embodiment, the preset BIM model refers to a standardized model that has been created and stored, which serves as the basis for subsequent monitoring work. For example, a BIM model of a high-rise building includes detailed information such as the building's floor structure, load-bearing wall locations, and key equipment (such as HVAC, elevators, and fire-fighting facilities); In this embodiment, the intelligent monitoring platform is a digital management platform based on information technology, which has 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 construction projects supports the collection of data from sensors (such as temperature, stress, vibration) and presents it through a data visualization interface to facilitate the monitoring of the status of the building structure.

[0019] In this embodiment, the model analysis result refers to the information obtained by analyzing the BIM model, including the structural layout, material type, key parts, etc. 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 bridge, it is concluded that the key load-bearing parts of the bridge need to be equipped with displacement sensors, while the non-load-bearing areas do not need to be monitored; In this embodiment, monitoring project information refers to information related to a specific monitoring project, including monitoring purpose, scope, monitoring cycle, monitoring indicators, etc. For example, in a construction monitoring project, 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 cycle is once a day.

[0020] In this embodiment, monitoring requirements refer to determining the type, scope, and accuracy of the data to be monitored based on the specific objectives and requirements of the monitoring project. For example, the monitoring requirements may include settlement monitoring of a high-rise building, and the requirement is settlement data accurate to 0.1 mm, covering the positions of all main support columns; In this embodiment, the types of monitoring points are divided into different types according to monitoring requirements and BIM analysis results, such as structural monitoring points, environmental monitoring points, etc., to facilitate the targeted arrangement of monitoring equipment. For example: in subway tunnel construction, the types of monitoring points may include surface settlement monitoring points, tunnel lining stress monitoring points, etc.

[0021] In this embodiment, creating several monitoring points based on the information analysis results and the type of monitoring points includes: first, automatically generating a unique number for each monitoring point to ensure that the identifier of each monitoring point is not repeated, creating monitoring points in the project: creating different types of monitoring points and associating them with the project according to the needs of 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 name of the project associated with it, monitoring targets, monitoring parameters, monitoring equipment, etc., selecting the location of the monitoring point: when creating a monitoring point, the location of the monitoring point is selected to associate it with a specific area or component of the project. 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, and configure the monitoring frequency: set the frequency of data collection for each monitoring point (such as hourly, daily or 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 can have a lower sampling frequency. Set alarm thresholds and response mechanisms: Set alarm thresholds 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 monitoring equipment: Select appropriate monitoring equipment (such as displacement sensors, strain gauges, thermometers and hygrometers, etc.) for each monitoring point. The selection of equipment is usually based 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 at the monitoring point can be collected through the correct sensor.

[0022] In this embodiment, the monitoring device refers to a hardware device arranged at a monitoring point for collecting monitoring data in real time. The type of device varies depending on the monitoring requirements and the type of monitoring point. For example, in vibration monitoring of a building, an acceleration sensor is used as a monitoring device; in temperature monitoring, a temperature sensor is used.

[0023] In this embodiment, the type of anomaly refers to the specific problem category that deviates from the normal state found in the monitoring data analysis. These types are usually associated with monitoring objectives and key parameters and are used to guide optimization strategies. For example: Bridge monitoring: Type of anomaly: abnormal displacement, stress exceeding the limit, abnormal vibration frequency; Industrial equipment monitoring: Type of anomaly: excessive temperature, excessive vibration, abnormal noise; Environmental monitoring: Type of anomaly: sudden drop in temperature, too low humidity, PM2.5 exceeding the standard.

[0024] In this embodiment, the first preset threshold is a set limit value for the number of abnormalities in a certain monitoring area. When the number of abnormalities exceeds the threshold, the system triggers the optimization of monitoring point deployment to deal with the problem. For example: Bridge monitoring: The first preset threshold: If the number of stress abnormalities in the main beam area of ​​a bridge reaches 5 times, the optimization of monitoring point deployment is triggered. Greenhouse environment monitoring: The first preset threshold: If the number of humidity abnormalities in a greenhouse area reaches 3 times, the redeployment strategy of the monitoring point is triggered.

[0025] In this embodiment, the monitoring point deployment optimization is to rearrange the existing monitoring points according to the abnormal conditions in the monitoring area, mainly by adding or deleting monitoring points to improve the accuracy and coverage of monitoring. Adding monitoring points: For areas with frequent abnormalities, increase the density of monitoring points to capture abnormal data in more detail. Deleting monitoring points: For areas with low abnormal frequency or reduced monitoring needs, reduce monitoring points to optimize resource usage. For example: Adding monitoring points: When frequent displacement abnormalities occur in the main beam of the bridge, additional displacement sensors are added to cover the abnormal area. Deleting monitoring points: When there is no abnormality in the bridge deck area for a long time, delete some temperature monitoring points.

[0026] In this embodiment, the second preset threshold is a higher abnormal frequency limit value. If the abnormality is still not resolved after the monitoring point deployment optimization and the number of times exceeds the threshold, the configuration of the monitoring point (such as equipment type, collection accuracy, etc.) needs to be optimized. For example: Bridge monitoring: The second preset threshold: If the stress abnormality still reaches 10 times after the deployment optimization of the main beam area, the configuration optimization is triggered. Environmental monitoring: The second preset threshold: If the greenhouse humidity abnormality exceeds 6 times, the equipment accuracy optimization is performed.

[0027] In this embodiment, the preset abnormality type-configuration method database is a mapping relationship table that records the configuration optimization methods corresponding to different abnormality types. It provides a reference method for abnormality resolution, including equipment replacement, parameter adjustment or sensor upgrade, etc. For example: Database content example: Displacement abnormality → Add a high-precision laser displacement sensor to improve acquisition accuracy. Temperature abnormality → Replace the temperature and humidity sensor to reduce equipment error. Vibration abnormality → Upgrade the vibration sensor to support higher frequency acquisition. Application scenario: If the main beam area of ​​the bridge has a displacement abnormality, the configuration optimization method is to add a laser displacement sensor and increase the acquisition frequency at the same time.

[0028] In this embodiment, the abnormality type is a specific abnormal phenomenon detected during the monitoring process, such as the aforementioned displacement abnormality, stress exceeding the limit, etc. The abnormality type here is associated with the abnormality type-configuration mode database, which directly determines the configuration optimization mode. For example: bridge monitoring: displacement abnormality, crack expansion abnormality, vibration frequency abnormality, industrial equipment monitoring: bearing temperature is too high, speed abnormality, equipment noise abnormality, environmental monitoring: PM2.5 exceeds the standard, soil moisture is insufficient, and light is abnormal.

[0029] In this embodiment, the configuration optimization method of the monitoring points in the preset monitoring level area refers to a method of further adjusting and optimizing the equipment, acquisition accuracy, acquisition method, etc. of the existing monitoring points when the anomaly cannot be resolved after the deployment optimization of the monitoring points. Example: Bridge monitoring: Current situation: After deployment optimization, the stress anomaly in the main beam area of ​​the bridge still exists. Configuration optimization method: Replace with a higher-precision fiber optic stress sensor. Adjust the acquisition frequency of the sensor from once per second to five times per second. Use redundant sensor technology to cross-validate data at multiple points.

[0030] The beneficial effects of the above technical solution are: by combining the BIM model with the intelligent monitoring platform, the intelligent creation of monitoring points, automatic classification management and real-time association of monitoring data are realized, which significantly improves the flexibility and accuracy of monitoring point deployment. The analysis and optimization functions based on real-time data effectively improve the management efficiency and data utilization of monitoring projects, while reducing the risk of errors caused by human intervention, meeting the efficient management needs of complex monitoring scenarios, and improving the full life cycle management capabilities of monitoring projects through the integrated operation of the intelligent monitoring platform, providing efficient and intelligent solutions for application scenarios such as engineering monitoring and environmental monitoring.

[0031] Embodiment 2: The embodiment of the present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, importing a preset BIM model into a preset intelligent monitoring platform, and analyzing 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 then 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 to obtain the model analysis results.

[0032] In this embodiment, the initial analysis is the first step of analyzing the preset BIM model, the purpose of which is to check the integrity, compatibility and adaptability of the model content, so as to determine how the model is connected to the intelligent monitoring platform. For example, when performing an initial analysis on a BIM model of a bridge, check whether the model contains data on key nodes of the bridge, such as support positions and span information, and determine whether the file format (such as IFC, Revit) is adapted to the platform requirements; In this embodiment, the import format refers to the file format or data specification that the BIM model needs to comply with when it is imported into the intelligent monitoring platform to ensure that the model can be correctly parsed and used. For example, the import formats supported by the intelligent monitoring platform include IFC format (Industry Foundation Classes) or Revit file (.rvt). The BIM model of the bridge project needs to be converted to IFC format before it can be imported into the platform; In this embodiment, the BIM file is a digital file that has been converted or directly imported, and contains detailed information about a building or engineering project, which can be managed and analyzed on the intelligent monitoring platform. For example, the BIM file of a high-rise building describes in detail the structure, materials, equipment location, etc. of each floor, which is used for the layout planning of subsequent monitoring points.

[0033] In this embodiment, the preset format-method database is a database pre-established in the intelligent monitoring platform, which is used to store BIM files of different formats and their corresponding parsing methods, so that the platform can automatically select the parsing strategy according to the file format. For example: the format-method database stores the parsing method of IFC format files (parsing building geometry and attribute information) and the parsing method of Revit files (parsing specific design levels and construction details).

[0034] In this embodiment, the basic information of the BIM model refers to the core information extracted from the BIM model, which generally includes structural information (such as the location and size of beams, columns, and floor slabs), material information (such as the type of reinforced concrete), and equipment information (such as sensor layout points). For example, in a tunnel BIM model, the basic information includes the total length of the tunnel, the arch radius, the location of key nodes (such as the excavation section and the construction section), and the strength grade of the component material.

[0035] The beneficial effects of the above technical solution are: through the intelligent matching of the import format and parsing method of the BIM model, efficient parsing of BIM model data and extraction of basic information are achieved, and model analysis results are automatically generated, which improves the adaptability of the BIM model in the monitoring platform and the intelligent level of data processing, and is suitable for complex building information management scenarios.

[0036] Embodiment 3: The embodiment of the present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, which creates a monitoring project and configures monitoring project information in a preset intelligent monitoring platform based on a model analysis result, 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.

[0037] In this embodiment, the structural information is the core building or engineering structural feature data extracted from the BIM model, including the component type, size, location and relationship, which is used to guide the arrangement of monitoring points and the division of monitoring levels. For example, in a BIM model of a bridge, the structural information includes the span of the main beam, the support position, the arch radius of the arch bridge, and the depth and diameter of the pile foundation; In this embodiment, the preset monitoring level area is an area divided according to monitoring requirements and structural information, usually classified according to risk level, importance or monitoring requirements, and used to determine monitoring priorities and resource allocation. For example, in a high-rise building, three levels of monitoring areas are divided: the first level area: building foundation (critical bearing capacity area), the second level area: main load-bearing components (beam-column nodes), and the third level area: ordinary walls and secondary components; In this embodiment, the monitoring target refers to the specific object to be monitored, which is usually determined based on the division of the monitoring area and the structural characteristics, and may include components, environmental conditions or specific parameters. For example, in a foundation pit project, the monitoring targets include: surface settlement around the foundation pit, horizontal displacement of the supporting structure, and changes in the groundwater level; In this embodiment, the preset target-type data table is a predefined association table that stores the mapping relationship between the monitoring target and the monitoring project type, helping the intelligent monitoring platform to quickly match the target and the corresponding monitoring type. For example: some contents in the target-type data table: Monitoring target: support beam → Monitoring type: stress monitoring project, Monitoring target: ground around foundation pit → Monitoring type: settlement monitoring project, Monitoring target: tunnel lining → Monitoring type: displacement monitoring project; In this embodiment, the monitoring project type is a specific monitoring task category divided according to different monitoring targets, which defines the type of data that needs to be collected and the analysis method. 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 main beams), and temperature monitoring project (monitoring the impact of ambient temperature on steel structures).

[0038] 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 targets 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.

[0039] In this embodiment, the second configuration information is parameters and deployment details further defined based on the specific monitoring target, including the type of monitoring equipment, installation location and monitoring indicator range. For example, for a beam-column node monitoring project of a certain building, the second configuration information includes: equipment type: displacement sensor and strain gauge, deployment location: node core area and midpoint of support beam, monitoring indicators: displacement (accuracy 0.01 mm), stress (accuracy 0.1 MPa).

[0040] In this embodiment, the preset project creation method is a defined process for quickly creating a monitoring project and generating a related configuration file based on the monitoring target, configuration information and monitoring project type. For example, the steps of the preset project creation method are: matching the monitoring target and the monitoring project type from the target-type data table, obtaining the first configuration information and the second configuration information, generating a monitoring project configuration file and automatically deploying the monitoring equipment. For example, when creating a displacement monitoring project for a bridge main beam, the method automatically retrieves the monitoring type (displacement monitoring) corresponding to the target (main beam), loads the equipment deployment position (beam center) and the monitoring frequency (once every 10 minutes).

[0041] The beneficial effects of the above technical solution are: by extracting structural information based on model analysis results, accurately determining monitoring targets and project types in combination with target-type data tables and preset databases, and automatically configuring basic and specific configuration information, efficient and intelligent creation and configuration of monitoring projects are achieved, improving deployment accuracy and management efficiency in complex monitoring scenarios.

[0042] Embodiment 4: The embodiment of the present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, analyzes the monitoring project to obtain the monitoring requirements, and then determines the type of the monitoring point, and analyzes the monitoring project information at the same time, and then creates a plurality of monitoring points based on the analysis results and the type of the monitoring point, including: Determine the key parameters that need to be monitored based on the monitoring objectives corresponding to the objectives of 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, thereby determining 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, thereby determining 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.

[0043] In this embodiment, 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 project. These parameters directly reflect the state or change trend of the monitored object, and guide the subsequent monitoring point type, equipment selection and monitoring strategy. For example: bridge monitoring: key parameters: displacement, stress, vibration frequency of the bridge main beam; dam monitoring: key parameters: water pressure, leakage, concrete stress change; greenhouse environment monitoring: key parameters: temperature, humidity, light intensity, CO2 concentration.

[0044] In this embodiment, the type of monitoring point is a specific monitoring point classification determined based on the key parameters of the monitoring project. This classification is usually associated with the characteristics, objectives and key parameters of the monitored object, and determines the type of monitoring equipment required. For example: Bridge monitoring: Monitoring point type: stress monitoring point, displacement monitoring point, vibration monitoring point. Greenhouse environment monitoring: Monitoring point type: temperature and humidity monitoring point, CO2 concentration monitoring point. Industrial equipment monitoring: Monitoring point type: vibration monitoring point, temperature monitoring point, noise monitoring point; In this embodiment, the preset type-device type database is a pre-set comparison table that records the monitoring device type corresponding to each monitoring point type. The function of this database is to quickly map the monitoring point type to the required device, facilitating the selection and deployment of monitoring devices. For example: Database content examples: displacement monitoring point → laser displacement sensor, temperature and humidity monitoring point → temperature and humidity sensor, vibration monitoring point → acceleration sensor, stress monitoring point → optical fiber stress sensor, application scenario: when vibration monitoring points need to be deployed for bridge monitoring, the database can be used to quickly determine that the vibration monitoring points require acceleration sensors as monitoring equipment.

[0045] In this embodiment, the first analysis refers to the preliminary processing of the configuration information of the monitoring project to determine the monitoring density or monitoring priority that needs to be deployed in different areas. Different monitoring level areas are divided by analyzing the monitoring requirements, environmental conditions and other information in the area; In this embodiment, the preset monitoring level area refers to the classification of areas based on the requirements of the monitoring project, and is divided into different levels according to the importance of the monitoring needs, such as high priority, medium priority and low priority areas. For example: Bridge monitoring: First analysis: Analyze the stress conditions, traffic flow, etc. of the bridge main beam and supporting piers. The main beam is a high priority area, and the supporting pier is a medium priority area. Preset monitoring level areas: High priority area: key area where the bridge main beam is subjected to greater stress, medium priority area: bridge pier stress area, low priority area: bridge deck road area.

[0046] In this embodiment, the second analysis is to conduct an in-depth analysis of the monitoring items, comprehensively evaluate their risk level, the importance of monitoring needs and resource allocation conditions, so as to determine the priority of each monitoring item; In this embodiment, the priority of the monitoring project refers to the ranking of different monitoring projects according to their importance and urgency based on the results of the second analysis. Projects with high priority will be processed and deployed with monitoring points first. For example: Bridge monitoring: Second analysis: A bridge is evaluated as a high priority project due to long-term overloading, cracks in the main beam, heavy traffic volume, and high risk. The priority of the monitoring project is: High priority project: focus on monitoring the stress and crack extension of the bridge main beam, medium priority project: monitor the inclination and settlement of the supporting pier, and low priority project: deformation monitoring of the bridge deck road.

[0047] In this embodiment, the priority of the preset monitoring level area is to further sort the divided monitoring areas based on the priority of each monitoring project. The regional priority is used to guide the deployment order and quantity allocation of monitoring points. For example: bridge monitoring: main beam (high priority area): 10 monitoring points need to be deployed, supporting pier (medium priority area): 5 monitoring points need to be deployed, bridge deck road (low priority area): 2 monitoring points need to be deployed.

[0048] In this embodiment, the preset point creation method refers to a method for generating the location and number of monitoring points in the intelligent monitoring platform according to the monitoring project and regional requirements. Common methods include uniform distribution, density distribution, and key point distribution, for example: uniform distribution: evenly arrange displacement monitoring points at intervals of 10 meters in the main beam area of ​​the bridge, density distribution: arrange more stress monitoring points in the bridge area with more cracks, and key point distribution: arrange displacement monitoring points in the foundation area of ​​the bridge support pier, focusing on monitoring settlement and tilt.

[0049] The beneficial effects of the above technical solution are: through multi-dimensional analysis of monitoring items and configuration information, the monitoring needs, monitoring point types and deployment areas are accurately determined, and the creation and distribution of monitoring points are optimized in combination with priorities and equipment databases, thereby improving the scientific nature of monitoring point configuration, resource utilization and monitoring efficiency, and is suitable for the dynamic management needs of complex monitoring scenarios.

[0050] Embodiment 5: The embodiment of the present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, and the types of monitoring points include: displacement monitoring points, stress monitoring points, temperature and humidity monitoring points, and vibration monitoring points.

[0051] In this embodiment, the displacement monitoring point is used to detect the displacement change of the structure or component under the action of external force. The monitoring object usually includes 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. For example: bridge monitoring: a displacement monitoring point is set in the middle of the main beam of a bridge to monitor the deflection change under the action of vehicle loads; foundation pit monitoring: a displacement monitoring point is set on the foundation pit support piles to detect the horizontal displacement of the support structure; tunnel monitoring: a displacement monitoring point is arranged on the tunnel vault to monitor the convergence displacement of the surrounding rock; In this embodiment, the stress monitoring point is used to monitor the stress distribution and changes generated inside or on the surface of the structure, mainly for load-bearing or force-bearing structures such as steel structures and concrete components. The stress monitoring data can reflect the stress state of the structure and its safety margin. For example: bridge monitoring: stress monitoring points are set at the nodes of the main beam of the steel bridge to monitor the stress concentration of the nodes in real time to prevent fatigue failure. Dam monitoring: stress monitoring points are set inside the concrete dam body to monitor the stress changes of the dam body and ensure the structural safety of the dam body. Building monitoring: stress monitoring points are set at the junction of the shear wall and the column of the high-rise building to detect stress changes under seismic loads. In this embodiment, the temperature and humidity monitoring points are used to detect changes in temperature and humidity in the environment or inside the structure. These factors may directly or indirectly affect the durability and performance of the structure, such as material expansion, crack expansion, and environmental corrosion. For example: Tunnel monitoring: Arrange temperature and humidity monitoring points in the tunnel to monitor changes in the internal hot and humid environment to prevent the lining structure from getting damp or condensing. Bridge monitoring: Arrange temperature monitoring points near the expansion joints of the bridge to detect the impact of temperature changes on structural expansion. Storage facility monitoring: Set temperature and humidity monitoring points in granaries or oil tanks to ensure that environmental conditions are suitable for material storage. 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: accelerometer, velocity sensor, vibration sensor, etc. For example: Bridge monitoring: vibration monitoring points are arranged near the main cable of the suspension bridge to detect the vibration response of the bridge under strong wind and prevent resonance. Earthquake monitoring: vibration monitoring points are set in the core tube of a high-rise building to detect the acceleration response under earthquake loads. Industrial equipment monitoring: vibration monitoring points are arranged on generator sets or large mechanical equipment to monitor the vibration frequency and amplitude of the equipment during operation and prevent failures.

[0052] The beneficial effects of the above technical solution are: classified management according to the type of monitoring point (displacement, stress, temperature and humidity, vibration), refined processing and targeted analysis of monitoring data, improved adaptability and management efficiency of the intelligent monitoring platform to various monitoring needs, and effectively met the monitoring requirements of complex scenarios.

[0053] Embodiment 6: 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: Perform a second analysis on the configuration information of the monitoring items, and then 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:

[0054] 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 ith monitoring item, is the adjustment coefficient of the timeliness of the ith 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.

[0055] In this embodiment, risk-related parameters are factors used to quantify the potential safety risks or losses of the monitored project. These parameters evaluate the degree of danger that the monitored object may face and the impact on society, economy, and environment. Typical parameters are: Risk level: evaluate the possibility of failure or failure based on the current state of the monitored object (such as aging, stress, etc.); Sensitivity coefficient: measure the response of the monitored object to changes in the external environment (such as wind, earthquake, temperature). For example: Bridge monitoring: Risk level: The main beam of an old bridge has obvious cracks and is assessed to be a high risk level; Sensitivity coefficient: The bridge is located in an earthquake-prone area and is highly sensitive to earthquake vibrations, so it has a high sensitivity coefficient.

[0056] In this embodiment, the resource configuration related parameters are factors for evaluating the availability and adaptability of 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 availability of resources such as monitoring equipment and technical teams, Project complexity coefficient: a measure of the complexity of the project in terms of technology, space, implementation difficulty, etc. For example: High-rise building monitoring: Resource availability coefficient: If the project requires drones for facade crack detection, but the number of drones is insufficient, the resource availability is low, and the project complexity coefficient: the building is high and the facade is complex, special equipment is required, and the complexity coefficient is high.

[0057] In this embodiment, the monitoring demand related parameters are factors used to evaluate the project's requirements for accuracy, timeliness, and monitoring scope. These parameters reflect the importance and urgency of the monitoring task. Typical parameters: Monitoring accuracy coefficient: The project's requirements for the accuracy of monitoring data, timeliness coefficient: The time sensitivity of the delivery of monitoring results, Example: Building monitoring in typhoon-affected areas: Monitoring accuracy coefficient: Building deformation monitoring under wind load requires high-precision real-time data, so the monitoring accuracy requirements are high, timeliness coefficient: The typhoon is approaching, and the monitoring needs to be completed quickly, so the timeliness coefficient is high, Tunnel construction monitoring: Monitoring accuracy coefficient: The displacement of the surrounding rock during construction needs to be accurately monitored to ensure construction safety, so the accuracy requirements are high, timeliness coefficient: The monitoring data can be updated every day, and the timeliness requirements are relatively low.

[0058] The beneficial effects of the above technical solution are: through in-depth analysis of the monitoring project configuration information, combined with the dynamic trade-off of multiple parameters and adjustment coefficients, accurate evaluation and determination of the priority of monitoring projects, ensuring the rationality of resource allocation and the targeted monitoring, taking into account multi-dimensional factors such as risk, importance, complexity and timeliness, the scientificity and accuracy of monitoring decisions are improved, and it is suitable for complex monitoring demand scenarios.

[0059] Embodiment 7: The embodiment of the present invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, wherein monitoring equipment based on the monitoring point collects monitoring data in real time, and uploads the data to a preset intelligent monitoring platform through a network, and automatically associates 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, thereby generating first monitoring data corresponding to each monitoring point; Then, the first monitoring data corresponding to each monitoring point is uploaded to the preset intelligent monitoring platform based on the preset uploading method.

[0060] In this embodiment, the acquisition accuracy refers to the measurement error range of the target physical quantity (such as displacement, stress, temperature and humidity, etc.) of the monitoring equipment when collecting monitoring data. The higher the acquisition accuracy, the more reliable and accurate the monitoring data. Usually, the acquisition accuracy is closely related to the type of monitoring point, monitoring equipment, environmental factors, etc. For example: Displacement monitoring point: For the displacement monitoring of the bridge, a laser rangefinder is used, and the accuracy may reach 0.1mm, which means that the error of each measurement does not exceed 0.1mm. Temperature and humidity monitoring point: For temperature and humidity monitoring in the 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.

[0061] In this embodiment, the collection method refers to the method or means of collecting monitoring data. Depending on the type of monitoring point, collection accuracy requirements and environmental conditions, the monitoring equipment may adopt different collection methods. These methods can be real-time collection, timed collection, triggered collection, etc. The choice of collection method directly affects the quality of the data, the processing method and the upload frequency. For example: For displacement monitoring points, in the displacement monitoring of bridges, a real-time continuous collection method is adopted, and the deformation of the bridge is monitored at any time through a laser displacement sensor. For temperature and humidity monitoring points, in warehouse monitoring, a timed collection method is adopted to collect temperature and humidity data every 30 minutes.

[0062] In this embodiment, the first monitoring data refers to a data set associated with a specific monitoring point after being collected by the monitoring equipment and preliminarily processed. These data usually include timestamp, monitoring point identifier, actual monitoring value and other contents, which serve as basic data for further analysis and processing. For example: displacement monitoring point: For a displacement monitoring point of a bridge, the first monitoring data may include "timestamp: 2024-12-13 10:00:00", "monitoring point identifier: A01", "displacement value: 3.5mm" and other data; temperature and humidity monitoring point: The first monitoring data of the temperature and humidity monitoring point in the warehouse may include "timestamp: 2024-12-1310:00:00", "monitoring point identifier: B02", "temperature: 22°C", "humidity: 60%" and other information.

[0063] In this embodiment, the preset upload method refers to the method and frequency of uploading data set according to the needs for different types of monitoring points and data in the intelligent monitoring platform. The upload method may include scheduled upload, event-triggered upload, real-time upload and other forms. This method ensures that the data can be transmitted to the intelligent monitoring platform in a timely and accurate manner, and further analyzed and stored. For example: Scheduled upload method: For certain environmental monitoring points (such as temperature and humidity monitoring), the system may be preset to upload data once an hour, event-triggered upload method: For stress monitoring of bridges, the system is set to automatically upload real-time data when the stress exceeds a certain threshold, real-time upload method: For displacement monitoring of bridges or tunnels, the system is set to upload data in real time to ensure that data can be transmitted immediately when major changes occur.

[0064] The beneficial effects of the above technical solution are: dynamically matching the collection accuracy and collection method of the monitoring points through the priority-type-accuracy database and the accuracy-mode database, combining the unique identifier and timestamp to achieve automatic association and efficient upload of data, ensuring the accuracy, real-time and traceability of the monitoring data, improving the automation and management efficiency of the intelligent monitoring platform, and adapting to complex and diverse monitoring scenario requirements.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions 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, and then determine the type of monitoring points. At the same time, analyze the monitoring project information, and then 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; Among them, the real-time data is analyzed, and the monitoring point deployment and monitoring point data are optimized based on the real-time data, including: Preprocess the collected real-time data and then 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 then the monitoring point deployment optimization is performed; If anomalies still occur in the preset monitoring level area that has been deployed and optimized, 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 then the monitoring point configuration is optimized.

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 then 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 to 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 3, characterized in that: Analyze the monitoring project to obtain the monitoring requirements, and then determine the type of monitoring points. At the same time, analyze the monitoring project information, and then create several monitoring points based on the analysis results and the types of monitoring points, including: Determine the key parameters that need to be monitored based on the monitoring objectives corresponding to the objectives of 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, thereby determining 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, thereby determining 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.

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

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, and then 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 ith monitoring item, is the adjustment coefficient of the timeliness of the ith 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.

7. A monitoring point and data classification management method based on an intelligent monitoring platform according to claim 1, 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, thereby generating first monitoring data corresponding to each monitoring point; Then, the first monitoring data corresponding to each monitoring point is uploaded to the preset intelligent monitoring platform based on the preset uploading method.

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