Environmental air quality monitoring station house operation state construction method based on digital twinning

Abstract

The invention relates to a digital twinning-based environment air quality monitoring station building operation state construction method, which comprises the steps of obtaining multi-source operation data of an environment air quality monitoring station building, constructing a digital twinning object and carrying out domain processing, mapping the operation data into a digital twinning state variable according to an abstract rule, and carrying out normalization on the digital twinning state variable according to a normalization rule. And performing normalization processing on the digital twinning state variables, and combining the normalized digital twinning state variables to form a digital twinning state vector which is used for representing the operation state of the ambient air quality monitoring station house. The method has the advantages that the digital twinborn object corresponding to the monitoring station building is constructed, the multi-source operation data is mapped into the unified digital twinborn state, the digital twinborn state vector is continuously updated, the operation state can be slowly evolved along with time, the method is suitable for long-term operation management of the monitoring station building, good expansibility is achieved, and the method is suitable for large-scale popularization and application. And engineering interpretable operation and maintenance support is realized, and operation and maintenance personnel can directly position problems conveniently.

Description

Technical Field

[0001] This invention relates to a method for constructing the operational status of an ambient air quality monitoring station based on digital twins. Background Technology

[0002] Currently, automatic air quality monitoring stations are a crucial component of the ambient air quality monitoring system, and their operational status directly impacts the continuity, accuracy, and reliability of monitoring data. Existing ambient air quality monitoring stations typically monitor their operation by collecting environmental parameters, equipment operating parameters, and monitoring data quality parameters. However, current technologies often focus on judging single data points or thresholds, lacking a unified way to express the overall operational status of the monitoring station. Because the operation of monitoring stations involves multi-source heterogeneous data with inconsistent dimensions and fragmented semantics, it is difficult to directly determine the station's true operational status. This can lead to delays in identifying abnormal operating conditions, affecting the quality of monitoring data, failing to meet the needs of refined operational management, resulting in poor interpretability of operational assessment results, and hindering the continuous analysis and management of long-term operational status. Therefore, there is an urgent need for a technical solution that can transform multi-source operational data into a unified, evolving operational status. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for constructing the operational status of an ambient air quality monitoring station based on digital twins. By constructing a digital twin object corresponding to the monitoring station, multi-source operational data is mapped into a unified digital twin status, thereby realizing a structured and continuous expression of the operational status of the monitoring station.

[0004] To achieve the above objectives, the technical solution of the present invention is as follows: it is a method for constructing the operational status of an ambient air quality monitoring station based on digital twins, characterized by including the following steps: Step 1 Acquire multi-source operational data from the ambient air quality monitoring station, including at least: environmental parameter data reflecting the internal environmental conditions of the station, equipment operation data reflecting the working status of the monitoring equipment, and quality-related data reflecting the reliability of the monitoring data; Step Two Based on multi-source operational data from air quality monitoring stations, corresponding digital twin objects are constructed. Then, based on the physical attributes reflected by these digital twin objects, they are divided into domains to form... This forms an environmental state domain, an equipment operation state domain, and a data quality state domain. Step 3 Based on the preset state abstraction rules, the running data in each state domain in step two are mapped to digital twin state variables with engineering semantics; Step Four Based on the preset normalization rules, the digital twin state variables in step three are normalized so that their values ​​are mapped to a unified state space. Step 5 The normalized digital twin state variables from step four are combined in a preset order to form a digital twin state vector, which is used to characterize the operating status of the ambient air quality monitoring station. Step Six Repeat steps one through five to continuously update the twin state vector, reflecting the dynamic evolution of the monitoring station's operational status.

[0005] In this technical solution, the state abstraction rules in step three include at least one or more of the following principles: state variables have physical interpretability and can be mapped to specific operating states; state variables are not sensitive to instantaneous fluctuations and are used to reflect phased or long-term operating states; multiple homogeneous or related operating data are abstracted into a single state variable to achieve dimensionality compression; state variables change continuously over time and are used to describe the evolution process of operating states.

[0006] In this technical solution, the normalization rule in step three maps digital twin state variables of different dimensions to a unified state range based on the corresponding benchmark data, so as to achieve comparability between different state variables.

[0007] In this technical solution, the digital twin object is used to provide a structured representation of the operating status of the ambient air quality monitoring station in the digital space, and can reflect the status changes of the monitoring station at different operating times.

[0008] In this technical solution, the domain-based processing method is as follows: when the digital twin object reflects the internal environmental conditions of the monitoring station, the digital twin object is classified into the environmental state domain; when the digital twin object reflects the working status or health status of the monitoring equipment, the digital twin object is classified into the equipment operation status domain; when the digital twin object reflects the integrity, stability, or reliability of the monitoring data, the digital twin object is classified into the data quality status domain.

[0009] In this technical solution, the digital twin state vector serves as the input for monitoring station operation assessment, anomaly identification, or maintenance decision-making, supporting the operation and management of the monitoring station.

[0010] The advantages of this invention compared to existing technologies are as follows: By constructing a digital twin object corresponding to the monitoring station, multi-source operational data is mapped to a unified digital twin state. Through continuous updates to the digital twin state vector, the operational state can evolve slowly over time, making it suitable for long-term operation and management of the monitoring station. The constructed digital twin state can serve as the basic input for subsequent operational assessments, anomaly identification, or maintenance decisions, exhibiting good scalability. By uniformly defining the state, cross-site migration and cross-cycle reuse are achieved. The state allows for direct domain location, providing engineering-interpretable maintenance support and facilitating direct problem identification by maintenance personnel. Detailed Implementation

[0011] The specific embodiments of the present invention will be further described below. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0012] It is a method for constructing the operational status of an ambient air quality monitoring station based on digital twins, characterized by the following steps: Step 1 Acquire multi-source operational data from the ambient air quality monitoring station, including at least: environmental parameter data reflecting the internal environmental conditions of the station, equipment operation data reflecting the working status of the monitoring equipment, and quality-related data reflecting the reliability of the monitoring data; Step Two Based on multi-source operational data from air quality monitoring stations, corresponding digital twin objects are constructed. Then, based on the physical attributes reflected by these digital twin objects, they are divided into domains to form... This forms an environmental state domain, an equipment operation state domain, and a data quality state domain. Step 3 Based on the preset state abstraction rules, the running data in each state domain in step two are mapped to digital twin state variables with engineering semantics; Step Four Based on the preset normalization rules, the digital twin state variables in step three are normalized so that their values ​​are mapped to a unified state space. Step 5 The normalized digital twin state variables from step four are combined in a preset order to form a digital twin state vector, which is used to characterize the operating status of the ambient air quality monitoring station. Step Six Repeat steps one through five to continuously update the twin state vector, reflecting the dynamic evolution of the monitoring station's operational status.

[0013] In this embodiment, the state abstraction rules in step three include at least one or more of the following principles: state variables have physical interpretability and can be mapped to specific operating states; state variables are not sensitive to instantaneous fluctuations and are used to reflect phased or long-term operating states; multiple homogeneous or related operating data are abstracted into a single state variable to achieve dimensionality compression; state variables change continuously over time and are used to describe the evolution process of operating states.

[0014] State abstraction rules follow these principles: Physical interpretability: Every state variable must be able to be mapped back to a specific physical operating state.

[0015] Engineering stability: State variables are not sensitive to instantaneous fluctuations and reflect long-term or phased operating states.

[0016] Dimensionality compressibility: Multiple data sources that are from the same source or are related should be abstracted into a single state variable.

[0017] State evolvability: State variables should change continuously over time to describe the evolution of operating states.

[0018] (a) Abstract rules of the environmental state domain By comparing environmentally relevant operational data with the baseline of the equipment's permissible operating environment, state variables reflecting environmental suitability are abstracted. For example: temperature → suitable temperature state; humidity → stable humidity state, with the key point being whether it affects the normal operation of the equipment.

[0019] (ii) Abstract rules for the equipment operating state domain Based on whether the equipment is in a preset operating state and its load condition, the equipment operating state variables are abstracted. Current → Load health status; Pump status → Sample availability status. The key is whether the equipment is "healthy".

[0020] (III) Abstract rules for the data quality status domain Based on the completeness, stability, and consistency of monitoring data within a preset time window, data quality state variables are abstracted. Efficiency → Data usability; Drift → Data stability. The key is whether the data is "reliable." The station temperature data is compared with the equipment's allowable temperature range to abstract the temperature state variable; the analyzer's operating current is compared with its rated current range to abstract the operating current state variable; and the data efficiency within the preset time window is statistically analyzed to abstract the data efficiency state variable.

[0021] In this embodiment, the normalization rule in step three maps digital twin state variables of different dimensions to a unified state range based on the corresponding benchmark data, so as to achieve comparability between different state variables.

[0022] Examples of the normalization rule are illustrated below: State range: 0, 10, 10, 1 or 0, 1000, 1000, 100 (0, 10, 10, 1 is used as an example below) By using the above normalization rules, operational data of different dimensions and from different sources are uniformly mapped into digital twin state variables, realizing a unified expression of the multidimensional operational status of the monitoring station.

[0023] In this embodiment, the digital twin object is used to provide a structured representation of the operating status of the ambient air quality monitoring station in the digital space, and can reflect the status changes of the monitoring station at different operating times.

[0024] In this embodiment, the domain-based processing method is as follows: when the digital twin object reflects the internal environmental conditions of the monitoring station, the digital twin object is assigned to the environmental state domain; when the digital twin object reflects the working status or health status of the monitoring equipment, the digital twin object is assigned to the equipment operation status domain; when the digital twin object reflects the integrity, stability, or reliability of the monitoring data, the digital twin object is assigned to the data quality status domain.

[0025] In this embodiment, the digital twin state vector serves as input for monitoring station operation assessment, anomaly identification, or maintenance decisions, supporting the operation and management of the monitoring station.

[0026] In this embodiment, the environmental operation data within the monitoring station includes at least one or more of the following: station temperature data, station humidity data, sampling tube temperature data, sampling tube humidity data, sampling tube static pressure data, smoke status data, water intrusion status data, cleanliness data, air conditioning outlet temperature data, air conditioning inlet temperature data, standard gas cylinder pressure sensor data, standard gas leak-sensor value data, sampling tube flow rate data, electronic fence intrusion alarm status data, and particulate matter concentration data.

[0027] In this embodiment, the operational data of the monitoring equipment in the monitoring station include at least one or more of the following: analyzer operating current data, sampling pump status data, calibration valve status data, continuous operating time data, standard gas usage data, paper tape usage data, filter membrane usage data, air conditioning current data, sampling tube fan current data, station total current data, and station voltage data.

[0028] In this embodiment, the operational data for the integrity, accuracy, or reliability of the monitoring data includes at least one or more of the following: data efficiency data, data packet loss rate data, zero-point drift data, range drift data, zero-point noise data, range noise data, response time data, linear data, 7-day drift data, and conversion efficiency data.

[0029] The embodiments of the present invention have been described in detail above, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations of these embodiments without departing from the principles and spirit of the present invention still fall within the protection scope of the present invention.

Claims

1. A method for constructing the operational status of an ambient air quality monitoring station based on digital twins, characterized in that... Includes the following steps: Step 1 Acquire multi-source operational data from the ambient air quality monitoring station, including at least: environmental parameter data reflecting the internal environmental conditions of the station, equipment operation data reflecting the working status of the monitoring equipment, and quality-related data reflecting the reliability of the monitoring data; Step Two Based on multi-source operational data from air quality monitoring stations, corresponding digital twin objects are constructed. Then, based on the physical attributes reflected by these digital twin objects, they are divided into domains to form... This forms an environmental state domain, an equipment operation state domain, and a data quality state domain. Step 3 Based on the preset state abstraction rules, the running data in each state domain in step two are mapped to digital twin state variables with engineering semantics; Step Four Based on the preset normalization rules, the digital twin state variables in step three are normalized so that their values ​​are mapped to a unified state space. Step 5 The normalized digital twin state variables from step four are combined in a preset order to form a digital twin state vector, which is used to characterize the operating status of the ambient air quality monitoring station. Step Six Repeat steps one through five to continuously update the twin state vector, reflecting the dynamic evolution of the monitoring station's operational status.

2. The method for constructing the operational status of an ambient air quality monitoring station based on digital twins according to claim 1, characterized in that... The state abstraction rules in step three include at least one or more of the following principles: state variables are physically interpretable and can be mapped to specific operating states; state variables are not sensitive to instantaneous fluctuations and are used to reflect phased or long-term operating states; multiple homogeneous or related operating data are abstracted into a single state variable to achieve dimensionality compression; state variables change continuously over time and are used to describe the evolution process of operating states.

3. The method for constructing the operational status of an ambient air quality monitoring station based on digital twins according to claim 1, characterized in that... The normalization rule in step three maps digital twin state variables of different dimensions to a unified state range based on the corresponding benchmark data, so as to achieve comparability between different state variables.

4. The method for constructing the operational status of an ambient air quality monitoring station based on digital twins according to claim 1, characterized in that... The digital twin object is used to provide a structured representation of the operational status of the ambient air quality monitoring station in the digital space, and can reflect the status changes of the monitoring station at different operating times.

5. The method for constructing the operational status of an ambient air quality monitoring station based on digital twins according to claim 1, characterized in that... The domain-based processing method is as follows: when the digital twin object reflects the internal environmental conditions of the monitoring station, the digital twin object is classified into the environmental state domain; when the digital twin object reflects the working status or health status of the monitoring equipment, the digital twin object is classified into the equipment operation status domain; when the digital twin object reflects the integrity, stability, or reliability of the monitoring data, the digital twin object is classified into the data quality status domain.

6. The method for constructing the operational status of an ambient air quality monitoring station based on digital twins according to claim 1, characterized in that... The digital twin state vector serves as input for monitoring station operation assessment, anomaly identification, or maintenance decisions, supporting the operation and management of the monitoring station.