Carbon dioxide monitoring stationing optimization method and device based on optical fiber sensing technology

Abstract

The invention provides a carbon dioxide monitoring stationing optimization method and device based on an optical fiber sensing technology, and relates to the field of carbon dioxide monitoring, and the method comprises the steps: obtaining carbon dioxide monitoring data in a monitoring range; performing statistical analysis on the monitoring data, and determining an optimal point distribution position for carbon dioxide monitoring; and performing point distribution simulation calculation according to the optimal point distribution position to obtain a point distribution optimization scheme, and performing monitoring point distribution optimization. According to the invention, current monitoring data and historical monitoring data of carbon dioxide can be analyzed, and the optimal distribution position of the optical fiber sensor is determined and fed back to a terminal of monitoring personnel.

Description

Technical Field

[0001] This application relates to the field of carbon dioxide monitoring, specifically a method and device for optimizing the deployment of carbon dioxide monitoring points based on fiber optic sensing technology. Background Technology

[0002] The application of fiber optic sensing technology in air monitoring involves setting up monitoring points. By utilizing fiber optic sensors to sense and transmit environmental parameter data in real time, it is possible to achieve real-time online monitoring of air quality and tracking and early warning of potential hazards. The application of fiber optic sensing technology not only improves the accuracy and efficiency of environmental monitoring but also helps to take timely measures to address air pollution problems. However, current methods for monitoring carbon dioxide in the air using fiber optic sensing technology still face the following problems: existing fiber optic sensors suffer from unreasonable placement and uneven distribution. These problems easily lead to inaccurate monitoring data, thus failing to accurately monitor air quality and consequently failing to provide accurate and scientific basis for air pollution control.

[0003] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section. Summary of the Invention

[0004] To address the problems in the existing technology, this application provides a method and device for optimizing the deployment of carbon dioxide monitoring points based on fiber optic sensing technology. This method can analyze current and historical monitoring data of carbon dioxide to determine the optimal deployment location of fiber optic sensors and provide feedback to the monitoring personnel's terminal.

[0005] To solve the above-mentioned technical problems, this application provides the following technical solution:

[0006] In a first aspect, this application provides a method for optimizing the deployment of carbon dioxide monitoring points based on fiber optic sensing technology, including:

[0007] Obtain carbon dioxide monitoring data within the monitoring area;

[0008] Statistical analysis of the monitoring data was performed to determine the optimal locations for carbon dioxide monitoring.

[0009] Based on the optimal placement location, a placement simulation calculation is performed to obtain a placement optimization scheme, and the placement optimization is monitored.

[0010] Furthermore, the carbon dioxide monitoring data includes: current monitoring data; acquiring carbon dioxide monitoring data within the monitoring range includes:

[0011] The monitoring range is determined based on the initial placement of the fiber optic sensors;

[0012] The air environment within the monitoring range is sensed in real time by the fiber optic sensor to obtain the current monitoring data.

[0013] Furthermore, the carbon dioxide monitoring data includes: current monitoring data and historical monitoring data; after acquiring the carbon dioxide monitoring data within the monitoring range, it includes:

[0014] Preprocessing operations are performed on the current monitoring data and historical monitoring data; wherein, the preprocessing operations include data cleaning, outlier handling, missing value handling, and data transformation;

[0015] The preprocessed current monitoring data and historical monitoring data are stored.

[0016] Furthermore, the statistical analysis of the monitoring data to determine the optimal location for carbon dioxide monitoring includes:

[0017] Perform cluster analysis on the current monitoring data to obtain the optimal monitoring point locations; or

[0018] The optimal location for the monitoring points is determined by performing characteristic analysis on the historical monitoring data.

[0019] Furthermore, the step of performing cluster analysis on the current monitoring data to obtain the optimal monitoring location includes:

[0020] Perform cluster analysis on the current monitoring data to obtain the maximum and minimum values ​​in the current monitoring data; wherein, the maximum value corresponds to the optimal point and the minimum value corresponds to the worst point; the average of the maximum value and the minimum value corresponds to the desired point;

[0021] Construct a standard element matrix based on the optimal point, the worst point, and the desired point, and draw a point cluster diagram, then determine the optimal point placement position in the point cluster diagram.

[0022] Furthermore, the step of performing characteristic analysis on the historical monitoring data to obtain the optimal deployment location includes:

[0023] Analyze the correlations, patterns, and trends among the historical monitoring data;

[0024] The optimal placement location is determined based on the correlation, regularity, and trend.

[0025] Furthermore, the step of performing placement simulation calculations based on the optimal placement locations to obtain a placement optimization scheme, and then monitoring the placement optimization, includes:

[0026] The optimal placement locations are simulated, and the monitoring effect of the carbon dioxide monitoring points after implementing the current placement optimization scheme is analyzed, and the placement optimization scheme is determined.

[0027] The monitoring results and the optimized deployment scheme are fed back to the monitoring terminal to adjust the initial deployment positions of the fiber optic sensors.

[0028] Secondly, this application provides a carbon dioxide monitoring site optimization device based on fiber optic sensing technology, comprising:

[0029] The monitoring data acquisition unit is used to acquire carbon dioxide monitoring data within the monitoring range;

[0030] The monitoring point analysis unit is used to statistically analyze the monitoring data and determine the optimal location for carbon dioxide monitoring.

[0031] The point optimization unit is used to perform point simulation calculations based on the optimal point locations, obtain a point optimization scheme, and monitor the point optimization.

[0032] Furthermore, the carbon dioxide monitoring data includes: current monitoring data; the monitoring data acquisition unit includes:

[0033] The monitoring range determination module is used to determine the monitoring range based on the initial deployment location of the fiber optic sensors;

[0034] The monitoring data acquisition module is used to sense the air environment within the monitoring range in real time through the fiber optic sensor and obtain the current monitoring data.

[0035] Furthermore, the carbon dioxide monitoring data includes: current monitoring data and historical monitoring data; the monitoring data acquisition unit further includes:

[0036] The preprocessing module is used to perform preprocessing operations on the current monitoring data and historical monitoring data; wherein, the preprocessing operations include data cleaning, outlier handling, missing value handling, and data transformation;

[0037] The monitoring data storage module is used to store the preprocessed current monitoring data and historical monitoring data.

[0038] Furthermore, the placement analysis unit includes:

[0039] The cluster analysis module is used to perform cluster analysis on the current monitoring data to obtain the optimal distribution points; or

[0040] The correlation analysis module is used to perform characteristic analysis on the historical monitoring data to obtain the optimal deployment location.

[0041] Furthermore, the point clustering analysis module includes:

[0042] The extreme value analysis module is used to perform point cluster analysis on the current monitoring data to obtain the maximum and minimum values ​​in the current monitoring data; wherein, the maximum value corresponds to the optimal point, and the minimum value corresponds to the worst point; the average value of the maximum value and the minimum value corresponds to the expected point;

[0043] The first optimal point generation module is used to construct a standard element matrix and draw a point cluster map based on the optimal point, the worst point and the desired point, and determine the optimal point location in the point cluster map.

[0044] Furthermore, the correlation analysis module includes:

[0045] The property analysis module is used to analyze the correlation, regularity, and trend among the historical monitoring data;

[0046] The second optimal placement generation module is used to determine the optimal placement location based on the correlation, regularity, and trend.

[0047] Furthermore, the placement optimization unit includes:

[0048] The monitoring effect analysis module is used to simulate the optimal location of the monitoring points, analyze the monitoring effect of the carbon dioxide monitoring points after implementing the current optimized location plan, and determine the optimized location plan.

[0049] The monitoring deployment optimization module is used to feed back the monitoring results and the deployment optimization scheme to the monitoring terminal so as to adjust the initial deployment positions of the fiber optic sensors.

[0050] Thirdly, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the carbon dioxide monitoring site optimization method based on fiber optic sensing technology.

[0051] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the carbon dioxide monitoring site optimization method based on fiber optic sensing technology.

[0052] Fifthly, this application provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the carbon dioxide monitoring site optimization method based on fiber optic sensing technology.

[0053] To address the problems in existing technologies, this application provides a method and apparatus for optimizing the deployment of carbon dioxide monitoring points based on fiber optic sensing technology. This method can acquire current and historical monitoring data of carbon dioxide within the monitoring range, and then analyze the current and historical data to determine the optimal deployment locations of the fiber optic sensors. Based on the optimal deployment locations, an optimized deployment scheme for the fiber optic sensors is generated. Before actual implementation, the effectiveness of the optimized deployment scheme can be simulated and calculated during the effect evaluation phase. The evaluation results are fed back to the monitoring terminal, allowing monitoring personnel to decide whether to implement the optimized deployment scheme based on the evaluation results. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a schematic diagram of the carbon dioxide monitoring site optimization system in the embodiments of this application;

[0056] Figure 2 This is a schematic diagram of the workflow of the carbon dioxide monitoring site optimization system in the embodiments of this application;

[0057] Figure 3 This is a flowchart of the carbon dioxide monitoring site optimization method based on fiber optic sensing technology in the embodiments of this application;

[0058] Figure 4 This is a flowchart of the carbon dioxide monitoring site optimization method based on fiber optic sensing technology in the embodiments of this application;

[0059] Figure 5 This is one of the flowcharts for obtaining monitoring data in the embodiments of this application;

[0060] Figure 6 This is the second flowchart of the process for obtaining monitoring data in the embodiments of this application;

[0061] Figure 7 This is one of the flowcharts for obtaining the optimal placement positions in the embodiments of this application;

[0062] Figure 8 This is the second flowchart illustrating the process of obtaining the optimal placement of points in the embodiments of this application;

[0063] Figure 9 This is a flowchart of the optimized placement scheme obtained in the embodiments of this application;

[0064] Figure 10 This is a structural diagram of the carbon dioxide monitoring deployment optimization device based on fiber optic sensing technology in the embodiments of this application;

[0065] Figure 11 This is one of the structural diagrams of the monitoring data acquisition unit in the embodiments of this application;

[0066] Figure 12 This is the second structural diagram of the monitoring data acquisition unit in the embodiments of this application;

[0067] Figure 13 This is a structural diagram of the point analysis unit in the embodiments of this application;

[0068] Figure 14 This is a structural diagram of the point aggregation analysis module in an embodiment of this application;

[0069] Figure 15 This is a structural diagram of the association analysis module in an embodiment of this application;

[0070] Figure 16 This is a structural diagram of the placement optimization unit in the embodiments of this application;

[0071] Figure 17 This is a schematic diagram of the structure of the electronic device in the embodiments of this application. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0073] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation portals are provided for users to choose to authorize or refuse.

[0074] Provide users with corresponding operation entry points, allowing them to choose to agree to or reject the automated decision results; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0075] In one embodiment, see Figure 3 In order to analyze current and historical carbon dioxide monitoring data, determine the optimal placement of fiber optic sensors, and provide feedback to monitoring personnel's terminals, this application provides a carbon dioxide monitoring placement optimization method based on fiber optic sensing technology, including:

[0076] S101: Acquire carbon dioxide monitoring data within the monitoring range;

[0077] S102: Perform statistical analysis on the monitoring data to determine the optimal location for carbon dioxide monitoring points;

[0078] S103: Perform point placement simulation calculations based on the optimal point placement locations to obtain a point placement optimization scheme, and monitor the point placement optimization.

[0079] Understandably, see Figure 1 In this embodiment, a data acquisition unit obtains current and historical monitoring data of carbon dioxide within the monitoring range. Then, an optimized placement unit analyzes the current and historical monitoring data to determine the optimal placement locations of the fiber optic sensors. Based on these optimal locations, an optimized placement scheme for the fiber optic sensors is generated. Optimizing the monitoring placement improves the accuracy of the monitoring data, thereby accurately monitoring air quality and providing accurate and scientific evidence for air pollution control.

[0080] The method provided in this application can be implemented using a carbon dioxide monitoring site optimization system based on fiber optic sensing technology. See [link to relevant documentation]. Figure 1 The system specifically includes:

[0081] The data acquisition unit is used to define the monitoring range based on the initial deployment location of the fiber optic sensors, and then use the fiber optic sensors to sense the air environment within the monitoring range in real time, obtain the current monitoring data of carbon dioxide, and can also obtain historical monitoring data within the monitoring range.

[0082] The optimized deployment unit is used to analyze the acquired current and historical carbon dioxide monitoring data, determine the optimal deployment location of the fiber optic sensors through analysis, and generate a deployment optimization scheme based on the optimal deployment location.

[0083] The effect evaluation unit is used to simulate and calculate the effect of the site optimization plan and feed the evaluation results back to the monitoring personnel's terminal (hereinafter referred to as the monitoring terminal). The monitoring personnel decide whether to implement the site optimization plan based on the feedback results.

[0084] Before executing the generated site optimization plan, through Figure 1The scheme evaluation module simulates and calculates the optimized deployment scheme. By simulating the generated optimized deployment scheme, the monitoring effect of the carbon dioxide monitoring points after implementation can be analyzed, thus evaluating the monitoring effect. Specifically, it assesses whether the optimized deployment scheme passes the simulation calculation of the scheme evaluation module. The evaluation result is fed back to the monitoring personnel's terminal through the result feedback module. Based on the feedback evaluation result, the monitoring personnel decide whether to implement the optimized deployment scheme. If the optimized deployment scheme passes the evaluation, the optimized deployment scheme and its evaluation result are output, allowing frontline staff to redeploy the fiber optic sensors to their optimal locations. If the optimized deployment scheme fails the evaluation, the initial positions of the fiber optic sensors need to be adjusted to obtain monitoring data from the newly adjusted positions and historical monitoring data. The optimized deployment scheme and scheme evaluation are then re-analyzed and regenerated until the generated optimized deployment scheme passes the evaluation of the scheme evaluation module. By optimizing the deployment positions of fiber optic sensors, the accuracy of monitoring data can be improved, thereby accurately monitoring air quality within the monitoring range and providing accurate and scientific basis for air pollution control.

[0085] The specific implementation method for evaluating whether the site selection optimization plan passes the simulation calculation of the plan evaluation module is as follows:

[0086] ① Data collection: Collect relevant data on site selection and the environment, including but not limited to real-time data on carbon dioxide concentration, site location, and environmental parameters.

[0087] ② Model Construction: Based on the established site optimization plan, a carbon dioxide concentration prediction model is built using machine learning algorithms, including but not limited to regression models and neural networks. The model's input is the data collected in the previous step, and its output is the corresponding carbon dioxide concentration prediction result.

[0088] ③ Simulation and Calculation: The established site selection optimization plan is combined with the prediction model to conduct simulation experiments. During the experiment, monitoring data is collected in real time to observe and evaluate the real-time effect of the site selection optimization plan. Specifically, the effectiveness of the site selection optimization plan is determined by comparing the changes in carbon dioxide concentration before and after the experiment.

[0089] ④ Results generation: Visualize the experimental results, such as by drawing charts or generating reports, so that the effect of the site optimization plan can be understood more intuitively.

[0090] For details, see Figure 2 This embodiment also proposes an optimization method for a carbon dioxide monitoring site optimization system based on fiber optic sensing technology, including the following steps:

[0091] (1) Determine the monitoring range based on the initial placement of the fiber optic sensors, use the fiber optic sensors to obtain the current monitoring data of carbon dioxide in the monitoring range, and at the same time obtain the historical monitoring data in the monitoring range.

[0092] (2) By analyzing the current monitoring data of carbon dioxide using the matter-element analysis method, the best, worst and expected points of the current monitoring data of carbon dioxide are identified, and the optimal location of the monitoring points is determined.

[0093] (3) Use big data mining technology to find out the distribution pattern and trend of carbon dioxide in historical monitoring data, and then determine the best location for monitoring.

[0094] (4) Based on the analysis results of the current and historical monitoring data of carbon dioxide, an optimized site selection plan is generated.

[0095] (5) Simulate and calculate the effect of the site optimization plan, and feed the evaluation results back to the monitoring personnel's terminal. The monitoring personnel decide whether to implement the site optimization plan based on the feedback evaluation results.

[0096] Its working principle is as follows: Based on the initial deployment location of the fiber optic sensors, the range division module can define the monitoring range of the fiber optic sensors. Then, the data acquisition module uses the fiber optic sensors to sense the air environment within the monitoring range in real time, thereby acquiring the current monitoring data of carbon dioxide within the monitoring range. Simultaneously, it acquires a large amount of historical monitoring data for the monitoring range. The data analysis module can analyze the current and historical monitoring data of carbon dioxide separately. For the current monitoring data of carbon dioxide, matter-element analysis can be used to determine the optimal deployment location of the fiber optic sensors. Furthermore, big data mining technology can be used to identify the distribution patterns and trends of carbon dioxide in historical monitoring data, thereby determining the optimal deployment location of the fiber optic sensors. Furthermore, based on the analysis results of current and historical carbon dioxide monitoring data, a site optimization plan is generated. Before actual implementation, the optimized plan is simulated and calculated by the plan evaluation module. This allows for analysis of the monitoring effect of the carbon dioxide monitoring sites after the optimization plan is implemented. The evaluation results are then fed back to the monitoring personnel's terminal via the result feedback module. The monitoring personnel decide whether to implement the optimized plan based on the feedback evaluation results. By optimizing the placement of fiber optic sensors, the accuracy of monitoring data can be improved, thereby accurately monitoring air quality within the monitoring range and providing accurate and scientific basis for air pollution control.

[0097] As described above, the carbon dioxide monitoring site optimization method based on fiber optic sensing technology provided in this application can acquire current and historical monitoring data of carbon dioxide within the monitoring range. Then, it analyzes the current and historical monitoring data to determine the optimal site location of the fiber optic sensors. Based on the optimal site location, it generates an optimized site location scheme for the fiber optic sensors. Before the optimized site location scheme is put into actual implementation, the implementation effect of the optimized site location scheme can be simulated and calculated in the effect evaluation stage. The evaluation results will be fed back to the monitoring terminal, allowing monitoring personnel to decide whether to implement the optimized site location scheme based on the evaluation results.

[0098] In one embodiment, see Figure 4 The carbon dioxide monitoring data includes: current monitoring data; acquiring carbon dioxide monitoring data within the monitoring range includes:

[0099] S201: Determine the monitoring range based on the initial placement of the fiber optic sensors;

[0100] S202: The air environment within the monitoring range is sensed in real time through the fiber optic sensor to obtain the current monitoring data.

[0101] Understandable Figure 1 The data acquisition unit in the system includes: a range division module, a data acquisition module, and a data transmission module.

[0102] The monitoring range is divided into several modules: the range division module is used to obtain the initial deployment location of the fiber optic sensors and define the monitoring range based on the initial deployment location; the data acquisition module is used to perceive the air environment within the monitoring range in real time through the fiber optic sensors and finally obtain the current monitoring data of carbon dioxide within the monitoring range, as well as the historical monitoring data of the monitoring range; and the data transmission module is used to transmit the current and historical monitoring data of carbon dioxide to the optimized deployment unit for analysis in real time.

[0103] In addition, deploying fiber optic sensors refers to using the location of the fiber optic sensors as the initial deployment points, and defining the monitoring range based on these initial deployment points. The fiber optic sensors can monitor carbon dioxide levels within the monitoring range, ultimately obtaining current carbon dioxide monitoring data.

[0104] Historical monitoring data can be data that has been stored in a database in the past. It can be retrieved from that database when necessary.

[0105] In practical implementation, the monitoring range is determined through a range delineation module. First, fiber optic sensors are deployed, and the deployment location is used as the initial placement location for the fiber optic sensors. Based on the initial placement location, the monitoring range of the fiber optic sensors can be defined. Then, the data acquisition module uses the fiber optic sensors to sense the air environment within the monitoring range in real time. The fiber optic sensors utilize photonic crystal fiber gas sensing technology, guiding light through the photonic bandgap principle to achieve laser propagation in the air fiber core region, confining most of the light energy in this area, thereby measuring the carbon dioxide concentration and obtaining the current monitoring data of carbon dioxide within the monitoring range. Simultaneously, a large amount of historical monitoring data for the monitoring range is acquired. The acquired current and historical monitoring data of carbon dioxide can be transmitted to the optimization placement unit through the data transmission module, allowing the optimization placement unit to analyze the current and historical monitoring data of carbon dioxide.

[0106] As can be seen from the above description, the carbon dioxide monitoring site optimization method based on fiber optic sensing technology provided in this application can use fiber optic sensors to acquire current and historical monitoring data of carbon dioxide within the monitoring range.

[0107] In one embodiment, see Figure 5 After acquiring carbon dioxide monitoring data within the monitoring range, the carbon dioxide monitoring site optimization method based on fiber optic sensing technology provided in this application further includes:

[0108] S301: Perform preprocessing operations on the current monitoring data and historical monitoring data; wherein, the preprocessing operations include data cleaning, outlier handling, missing value handling, and data transformation;

[0109] S302: Store the preprocessed current monitoring data and historical monitoring data.

[0110] Understandable Figure 1 The data acquisition module includes a data processing module and a data backup module.

[0111] The data processing module is used to preprocess the acquired current and historical monitoring data of carbon dioxide, including data cleaning, outlier handling, missing value handling, and data transformation. The preprocessed current and historical monitoring data of carbon dioxide are transmitted by the data transmission module. The data backup module is used to back up the preprocessed current and historical monitoring data of carbon dioxide and store the backed-up current and historical monitoring data of carbon dioxide.

[0112] In practice, before transmitting current and historical carbon dioxide monitoring data, a data processing module is required to preprocess the data. This preprocessing includes: data cleaning to remove incomplete, erroneous, duplicate, or inconsistent portions; outlier handling to detect and correct any missing values ​​using imputation or interpolation; missing value handling to identify and correct any missing values ​​by imputation or deletion; and data transformation to convert the raw data into a more suitable format for analysis, typically employing methods such as standardization, normalization, and discretization.

[0113] After undergoing data cleaning, outlier handling, missing value handling, and data transformation, the quality of current and historical carbon dioxide monitoring data can be improved, thereby enhancing the accuracy of subsequent analysis. The pre-processed current and historical carbon dioxide monitoring data are then transmitted via the data transmission module. Simultaneously, a data backup module backs up the pre-processed current and historical carbon dioxide monitoring data, preventing data loss.

[0114] As can be seen from the above description, the carbon dioxide monitoring site optimization method based on fiber optic sensing technology provided in this application can use fiber optic sensors to acquire current and historical monitoring data of carbon dioxide within the monitoring range.

[0115] In one embodiment, see Figure 6 The step of statistically analyzing the current or historical monitoring data and determining the optimal locations for carbon dioxide monitoring includes:

[0116] S401: Perform cluster analysis on the current monitoring data to obtain the optimal monitoring point location; or

[0117] S402: Perform characteristic analysis on historical monitoring data to obtain the optimal location for the monitoring points.

[0118] For step S401, in one embodiment, see [link to example]. Figure 7 The step of performing cluster analysis on the current monitoring data to obtain the optimal monitoring location includes:

[0119] S501: Perform cluster analysis on the current monitoring data to obtain the maximum and minimum values ​​in the current monitoring data; wherein, the maximum value corresponds to the best point, and the minimum value corresponds to the worst point; the average of the maximum value and the minimum value corresponds to the desired point;

[0120] S502: Construct a standard element matrix and draw a point cluster diagram based on the optimal point, the worst point, and the desired point, and determine the optimal point placement position in the point cluster diagram.

[0121] Understandably, current carbon dioxide monitoring data is analyzed using matter-element analysis, while historical monitoring data is mined and analyzed using big data mining techniques. Among these, Figure 1 The data analysis module can analyze both current and historical carbon dioxide monitoring data separately. Current carbon dioxide monitoring data can be analyzed using matter-element analysis, which identifies the maximum and minimum values ​​(optimal and worst points) within the current data. The average of these maximum and minimum values ​​is then calculated and used as the desired point. A standard element matrix can be constructed using these three values ​​(optimal, worst, and desired point), and a correlation function can be established to create a point cluster graph. This graph allows for the determination of optimal monitoring point locations. Historical monitoring data can be analyzed using big data mining techniques.

[0122] The specific implementation process of using matter-element analysis to analyze current monitoring data can be as follows:

[0123] ① Data preparation: Obtain carbon dioxide monitoring data for the current preset time period.

[0124] ② Construct a matter-element matrix: The acquired carbon dioxide monitoring data are mapped to different element positions in the matrix according to the location of the monitoring points. These data are then combined into a matter-element matrix, where each row of the matrix represents a monitoring point and each column represents a time period of a day. Then, the carbon dioxide concentration corresponding to each element position in the matrix is ​​calculated.

[0125] ③ Determine the evaluation indicators: In this embodiment, the maximum and minimum values ​​are selected as evaluation indicators respectively.

[0126] ④ Determine the weights: After determining the evaluation indicators, the weights can be set according to the actual working conditions. For example, the maximum and minimum values ​​can be multiplied by a weight coefficient to reflect the importance of these two indicators. The weights of the maximum and minimum values ​​can be adjusted using the following formula:

[0127] Standardized weight = (original weight - minimum weight) / (maximum weight - minimum weight).

[0128] Wherein, the original weight is the sum of the maximum value weight and the minimum value weight; the maximum weight is the maximum value among all evaluation indicators; and the minimum weight is the minimum value among all evaluation indicators.

[0129] ⑤ Extreme average: After determining the evaluation indicators and weights, the average of the maximum and minimum values ​​is obtained by adding the maximum and minimum values ​​and dividing by 2, and is used as the expected point.

[0130] ⑥ Draw a point cluster map: Visualize the data in the matter element matrix and draw a point cluster map composed of the maximum, minimum and average values.

[0131] ⑦ Determine the optimal placement location: By observing the point cluster map, find the locations where the carbon dioxide concentration fluctuates less, and thus determine the optimal placement location.

[0132] As can be seen from the above description, the carbon dioxide monitoring site optimization method based on fiber optic sensing technology provided in this application can perform point cluster analysis on the current monitoring data to obtain the optimal site location.

[0133] For step S402, in one embodiment, see [link to example]. Figure 8 The step of performing characteristic analysis on the historical monitoring data to obtain the optimal deployment location includes:

[0134] S601: Analyze the correlation, regularity, and trend among the historical monitoring data;

[0135] S602: Determine the optimal placement location based on the correlation, regularity, and trend.

[0136] Understandably, big data mining technology is used to mine and analyze historical monitoring data. Big data mining algorithms discover potential correlations, patterns, and trends in the data, thereby identifying the distribution patterns and trends of carbon dioxide in historical monitoring data. Based on these patterns and trends, the optimal placement locations for the sensors are determined. Finally, the solution generation module can determine the optimal placement locations for the fiber optic sensors based on the analysis results of current and historical carbon dioxide monitoring data, and generate an optimized placement plan based on these optimal locations.

[0137] The specific implementation process of mining and analyzing historical monitoring data using big data mining technology is as follows:

[0138] ① Obtain and organize historical monitoring data within the monitoring scope.

[0139] ② Use data mining algorithms to analyze historical monitoring data, such as time series analysis algorithms, clustering algorithms, and association rule mining algorithms, to find potential patterns and trends in historical monitoring data.

[0140] For example, time series analysis can reveal trends in carbon dioxide concentration, such as daily, weekly, or monthly variations. Digitally representing these changes provides a concrete digital representation of patterns and trends, which can then be used as input data for subsequent model training.

[0141] ③ After finding potential patterns and rules, further utilize machine learning algorithms to model historical monitoring data, such as using random forest algorithm, support vector machine algorithm, etc., to determine the optimal location of the monitoring points;

[0142] ④ Clustering algorithms can be used to identify areas with high carbon dioxide concentrations and areas with low carbon dioxide concentrations, so that the number of monitoring points can be increased or decreased in these areas.

[0143] ⑤ Association rule mining algorithms can be used to find associations between carbon dioxide concentration and other environmental variables. These associations can all be represented digitally, serving as objective evidence of the correlation.

[0144] Big data mining technology uses data mining algorithms to identify the distribution patterns and trends of carbon dioxide in historical monitoring data. Based on these patterns and trends, the optimal locations for monitoring sensors are determined. Analysis of the carbon dioxide monitoring data (primarily historical data, but also including current data) generates optimized sensor placement plans. These plans adjust the initial placement of fiber optic sensors to their optimal locations, thus addressing issues of unreasonable and uneven sensor placement. This improves the accuracy of monitoring data and provides a precise and scientific basis for air pollution control.

[0145] As can be seen from the above description, the carbon dioxide monitoring site optimization method based on fiber optic sensing technology provided in this application can perform characteristic analysis on historical monitoring data to obtain the optimal site location.

[0146] In one embodiment, see Figure 9 The step of performing placement simulation calculations based on the optimal placement locations to obtain a placement optimization scheme for monitoring placement optimization includes:

[0147] S701: Simulate the optimal placement locations, analyze the monitoring effect of carbon dioxide monitoring points after implementing the current placement optimization scheme, and determine the placement optimization scheme.

[0148] In order to analyze the monitoring effect of carbon dioxide monitoring points after implementing the current optimized deployment plan, the following analysis can be performed using the simulation evaluation results from the plan evaluation module:

[0149] ① Changes in carbon dioxide concentration: Compare the changes in carbon dioxide concentration before and after the implementation of the optimized monitoring station layout to evaluate the effectiveness of the optimized layout in reducing carbon dioxide concentration in the monitoring area. If the optimized layout significantly helps reduce carbon dioxide concentration in the monitoring area, it indicates that the optimized layout is effective; if there is no significant change, it may be necessary to re-examine the optimized layout.

[0150] ② Carbon dioxide concentration fluctuation: Assess the fluctuation of carbon dioxide concentration during the implementation of the optimized monitoring station layout. If the concentration fluctuation is small, it indicates that the monitoring station layout is relatively reasonable; if the fluctuation is large, further adjustments to the station locations may be necessary. In practice, a standard threshold for fluctuation can be set.

[0151] ③ Carbon dioxide concentration compliance rate: By comparing the carbon dioxide concentration compliance rate before and after the implementation of the site optimization plan, we can evaluate whether the air quality meets the standards, thereby understanding the impact of the site optimization plan on air quality. In practice, a compliance rate threshold can be set.

[0152] The above analysis confirms the effectiveness and feasibility of the site optimization scheme.

[0153] S702: Feedback the monitoring results and the optimized deployment scheme to the monitoring terminal to adjust the initial deployment positions of the fiber optic sensors.

[0154] Understandable Figure 1 The effectiveness evaluation unit includes: a scheme evaluation module and a results feedback module.

[0155] The scheme evaluation module simulates the generated optimized deployment scheme, analyzes the monitoring effect of carbon dioxide monitoring points after implementing the optimized scheme, and evaluates the monitoring effect. The result feedback module feeds back the evaluation results from the scheme evaluation module to the monitoring personnel's terminal. Based on the feedback evaluation results, the monitoring personnel decide whether to implement the optimized deployment scheme, and then adjust or redeploy the initial positions of the fiber optic sensors.

[0156] Whether monitoring personnel implement the site optimization plan is categorized into the following situations:

[0157] If the deployment optimization plan passes the evaluation, the deployment optimization plan will be implemented, and the deployment optimization plan and its evaluation results will be output so that front-line staff can redeploy the fiber optic sensors according to the deployment optimization plan.

[0158] If the deployment optimization plan fails the evaluation, the deployment optimization plan will not be implemented. Instead, the initial position of the fiber optic sensor will be adjusted, monitoring data and historical monitoring data at the newly adjusted position will be obtained, and the deployment optimization plan and plan evaluation will be re-analyzed and generated until the generated deployment optimization plan passes the evaluation of the plan evaluation module.

[0159] As can be seen from the above description, the carbon dioxide monitoring site optimization method based on fiber optic sensing technology provided in this application can perform site simulation calculations based on the optimal site locations to obtain a site optimization scheme for monitoring site optimization.

[0160] Based on the same inventive concept, this application also provides a carbon dioxide monitoring deployment optimization device based on fiber optic sensing technology, which can be used to implement the method described in the above embodiments, as shown in the following embodiments. Since the principle of solving the problem using the carbon dioxide monitoring deployment optimization device based on fiber optic sensing technology is similar to that of the carbon dioxide monitoring deployment optimization method based on fiber optic sensing technology, the implementation of the carbon dioxide monitoring deployment optimization device based on fiber optic sensing technology can refer to the implementation of the software performance benchmark determination method, and will not be repeated. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0161] In one embodiment, see Figure 10 In order to analyze current and historical carbon dioxide monitoring data, determine the optimal placement of fiber optic sensors, and provide feedback to monitoring personnel's terminals, this application provides a carbon dioxide monitoring placement optimization device based on fiber optic sensing technology, comprising:

[0162] The monitoring data acquisition unit 1001 is used to acquire carbon dioxide monitoring data within the monitoring range;

[0163] The site analysis unit 1002 is used to perform statistical analysis on the monitoring data and determine the optimal site locations for carbon dioxide monitoring.

[0164] The placement optimization unit 1003 is used to perform placement simulation calculations based on the optimal placement locations to obtain a placement optimization scheme and to monitor the placement optimization.

[0165] In one embodiment, see Figure 11 The monitoring data acquisition unit 1001 includes:

[0166] The monitoring range determination module 1101 is used to determine the monitoring range based on the initial deployment location of the fiber optic sensors;

[0167] The monitoring data acquisition module 1102 is used to perceive the air environment within the monitoring range in real time through the fiber optic sensor and obtain the current monitoring data.

[0168] In one embodiment, see Figure 12 The monitoring data acquisition unit 1001 further includes:

[0169] The preprocessing module 1201 is used to perform preprocessing operations on the current monitoring data and historical monitoring data; wherein, the preprocessing operations include data cleaning, outlier handling, missing value handling, and data transformation;

[0170] The monitoring data storage module 1202 is used to store the preprocessed current monitoring data and historical monitoring data.

[0171] In one embodiment, see Figure 13 The point analysis unit 1002 includes:

[0172] The cluster analysis module 1301 is used to perform cluster analysis on the current monitoring data to obtain the optimal distribution points; or

[0173] The correlation analysis module 1302 is used to perform characteristic analysis on historical monitoring data to obtain the optimal distribution location.

[0174] In one embodiment, see Figure 14 The point clustering analysis module 1301 includes:

[0175] The extreme value analysis module 1401 is used to perform point cluster analysis on the current monitoring data to obtain the maximum and minimum values ​​in the current monitoring data; wherein, the maximum value corresponds to the optimal point, and the minimum value corresponds to the worst point; the average value of the maximum value and the minimum value corresponds to the expected point;

[0176] The first optimal point generation module 1402 is used to construct a standard element matrix and draw a point cluster map based on the optimal point, the worst point and the desired point, and determine the optimal point location in the point cluster map.

[0177] In one embodiment, see Figure 15 The correlation analysis module 1302 includes:

[0178] The property analysis module 1501 is used to analyze the correlation, regularity, and trend among the historical monitoring data;

[0179] The second optimal placement generation module 1502 is used to determine the optimal placement location based on the correlation, regularity and trend.

[0180] In one embodiment, see Figure 16The placement optimization unit 1003 includes:

[0181] The monitoring effect analysis module 1601 is used to simulate the optimal location of the monitoring points, analyze the monitoring effect of the carbon dioxide monitoring points after implementing the current optimized location scheme, and determine the optimized location scheme.

[0182] The monitoring deployment optimization module 1602 is used to feed back the monitoring results and the deployment optimization scheme to the monitoring terminal so as to adjust the initial deployment position of the fiber optic sensor.

[0183] From a hardware perspective, in order to analyze current and historical carbon dioxide monitoring data, determine the optimal placement of fiber optic sensors, and provide feedback to the monitoring personnel's terminal, this application provides an embodiment of an electronic device for implementing all or part of the aforementioned carbon dioxide monitoring placement optimization method based on fiber optic sensing technology. The electronic device specifically includes the following components:

[0184] The system comprises a processor, a memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between the carbon dioxide monitoring site optimization device based on fiber optic sensing technology and core business systems, user terminals, and related databases and other related equipment; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the carbon dioxide monitoring site optimization method based on fiber optic sensing technology and the embodiments of the carbon dioxide monitoring site optimization device based on fiber optic sensing technology, the contents of which are incorporated herein by reference, and repeated details will not be described again.

[0185] It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc.

[0186] In practical applications, the carbon dioxide monitoring deployment optimization method based on fiber optic sensing technology can be partially executed on the electronic device side as described above, or all operations can be completed in the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor.

[0187] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0188] Figure 17 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 17 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 17 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0189] In one embodiment, the carbon dioxide monitoring site optimization method based on fiber optic sensing technology can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0190] S101: Acquire carbon dioxide monitoring data within the monitoring range;

[0191] S102: Analyze the monitoring data to determine the optimal locations for carbon dioxide monitoring points;

[0192] S103: Perform point placement simulation calculations based on the optimal point placement locations to obtain a point placement optimization scheme for monitoring point placement optimization.

[0193] As described above, the carbon dioxide monitoring site optimization method based on fiber optic sensing technology provided in this application can acquire current and historical monitoring data of carbon dioxide within the monitoring range. Then, it analyzes the current and historical monitoring data to determine the optimal site location of the fiber optic sensors. Based on the optimal site location, it generates an optimized site location scheme for the fiber optic sensors. Before the optimized site location scheme is put into actual implementation, the implementation effect of the optimized site location scheme can be simulated and calculated in the effect evaluation stage. The evaluation results will be fed back to the monitoring terminal, allowing monitoring personnel to decide whether to implement the optimized site location scheme based on the evaluation results.

[0194] In another embodiment, the carbon dioxide monitoring site optimization device based on fiber optic sensing technology can be configured separately from the central processing unit 9100. For example, the data composite transmission device based on fiber optic sensing technology can be configured as a chip connected to the central processing unit 9100, and the function of the carbon dioxide monitoring site optimization method based on fiber optic sensing technology can be realized through the control of the central processing unit.

[0195] like Figure 17 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 17 All components shown; in addition, the electronic device 9600 may also include Figure 17 For components not shown, please refer to existing technology.

[0196] like Figure 17 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0197] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0198] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0199] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0200] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0201] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0202] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 9130 is also coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored sound via the speaker 9131.

[0203] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the carbon dioxide monitoring site optimization method based on fiber optic sensing technology, where the execution subject is a server or client, as described in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the carbon dioxide monitoring site optimization method based on fiber optic sensing technology, where the execution subject is a server or client, as described in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0204] S101: Acquire carbon dioxide monitoring data within the monitoring range;

[0205] S102: Analyze the monitoring data to determine the optimal locations for carbon dioxide monitoring points;

[0206] S103: Perform point placement simulation calculations based on the optimal point placement locations to obtain a point placement optimization scheme for monitoring point placement optimization.

[0207] As described above, the carbon dioxide monitoring site optimization method based on fiber optic sensing technology provided in this application can acquire current and historical monitoring data of carbon dioxide within the monitoring range. Then, it analyzes the current and historical monitoring data to determine the optimal site location of the fiber optic sensors. Based on the optimal site location, it generates an optimized site location scheme for the fiber optic sensors. Before the optimized site location scheme is put into actual implementation, the implementation effect of the optimized site location scheme can be simulated and calculated in the effect evaluation stage. The evaluation results will be fed back to the monitoring terminal, allowing monitoring personnel to decide whether to implement the optimized site location scheme based on the evaluation results.

[0208] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0209] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0210] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0211] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0212] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for optimizing the deployment of carbon dioxide monitoring points based on fiber optic sensing technology, characterized in that, include: Obtain carbon dioxide monitoring data within the monitoring area; Statistical analysis of the monitoring data was performed to determine the optimal locations for carbon dioxide monitoring. Based on the optimal placement location, a placement simulation calculation is performed to obtain a placement optimization scheme, and the placement optimization is monitored.

2. The carbon dioxide monitoring site optimization method based on fiber optic sensing technology according to claim 1, characterized in that, The carbon dioxide monitoring data includes: current monitoring data; the acquisition of carbon dioxide monitoring data within the monitoring range includes: The monitoring range is determined based on the initial placement of the fiber optic sensors; The air environment within the monitoring range is sensed in real time by the fiber optic sensor to obtain the current monitoring data.

3. The carbon dioxide monitoring site optimization method based on fiber optic sensing technology according to claim 1, characterized in that, The carbon dioxide monitoring data includes: current monitoring data and historical monitoring data; after acquiring the carbon dioxide monitoring data within the monitoring range, it includes: Preprocessing operations are performed on the current monitoring data and historical monitoring data; wherein, the preprocessing operations include data cleaning, outlier handling, missing value handling, and data transformation; The preprocessed current monitoring data and historical monitoring data are stored.

4. The carbon dioxide monitoring site optimization method based on fiber optic sensing technology according to claim 3, characterized in that, The statistical analysis of the monitoring data to determine the optimal location for carbon dioxide monitoring includes: Perform cluster analysis on the current monitoring data to obtain the optimal monitoring point locations; or The optimal location for the monitoring points is determined by performing characteristic analysis on the historical monitoring data.

5. The carbon dioxide monitoring site optimization method based on fiber optic sensing technology according to claim 4, characterized in that, The step of performing cluster analysis on the current monitoring data to obtain the optimal monitoring location includes: Perform cluster analysis on the current monitoring data to obtain the maximum and minimum values ​​in the current monitoring data; wherein, the maximum value corresponds to the best point and the minimum value corresponds to the worst point; the average of the maximum value and the minimum value corresponds to the desired point; Construct a standard element matrix based on the optimal point, the worst point, and the desired point, and draw a point cluster diagram, then determine the optimal point placement position in the point cluster diagram.

6. The carbon dioxide monitoring site optimization method based on fiber optic sensing technology according to claim 4, characterized in that, The step of performing characteristic analysis on the historical monitoring data to obtain the optimal deployment location includes: Analyze the correlations, patterns, and trends among the historical monitoring data; The optimal placement location is determined based on the correlation, regularity, and trend.

7. The carbon dioxide monitoring site optimization method based on fiber optic sensing technology according to claim 1, characterized in that, The step of performing placement simulation calculations based on the optimal placement locations to obtain a placement optimization scheme, and monitoring the placement optimization, includes: The optimal placement locations are simulated, and the monitoring effect of the carbon dioxide monitoring points after implementing the current placement optimization scheme is analyzed, and the placement optimization scheme is determined. The monitoring results and the optimized deployment scheme are fed back to the monitoring terminal to adjust the initial deployment positions of the fiber optic sensors.

8. A carbon dioxide monitoring site optimization device based on fiber optic sensing technology, characterized in that, include: The monitoring data acquisition unit is used to acquire carbon dioxide monitoring data within the monitoring range; The monitoring point analysis unit is used to statistically analyze the monitoring data and determine the optimal monitoring point locations for carbon dioxide. The point optimization unit is used to perform point simulation calculations based on the optimal point locations, obtain a point optimization scheme, and monitor the point optimization.

9. The carbon dioxide monitoring site optimization device based on fiber optic sensing technology according to claim 8, characterized in that, The carbon dioxide monitoring data includes: current monitoring data; the monitoring data acquisition unit includes: The monitoring range determination module is used to determine the monitoring range based on the initial deployment location of the fiber optic sensors; The monitoring data acquisition module is used to sense the air environment within the monitoring range in real time through the fiber optic sensor and obtain the current monitoring data.

10. The carbon dioxide monitoring site optimization device based on fiber optic sensing technology according to claim 8, characterized in that, The carbon dioxide monitoring data includes: current monitoring data and historical monitoring data; the monitoring data acquisition unit further includes: The preprocessing module is used to perform preprocessing operations on the current monitoring data and historical monitoring data; wherein, the preprocessing operations include data cleaning, outlier handling, missing value handling, and data transformation; The monitoring data storage module is used to store the preprocessed current monitoring data and historical monitoring data.

11. The carbon dioxide monitoring site optimization device based on fiber optic sensing technology according to claim 10, characterized in that, The point analysis unit includes: The cluster analysis module is used to perform cluster analysis on the current monitoring data to obtain the optimal distribution points; or The correlation analysis module is used to perform characteristic analysis on the historical monitoring data to obtain the optimal deployment location.

12. The carbon dioxide monitoring site optimization device based on fiber optic sensing technology according to claim 11, characterized in that, The point clustering analysis module includes: The extreme value analysis module is used to perform point cluster analysis on the current monitoring data to obtain the maximum and minimum values ​​in the current monitoring data; wherein, the maximum value corresponds to the optimal point, and the minimum value corresponds to the worst point; the average value of the maximum value and the minimum value corresponds to the expected point; The first optimal point generation module is used to construct a standard element matrix and draw a point cluster map based on the optimal point, the worst point and the desired point, and determine the optimal point location in the point cluster map.

13. The carbon dioxide monitoring site optimization device based on fiber optic sensing technology according to claim 11, characterized in that, The correlation analysis module includes: The property analysis module is used to analyze the correlation, regularity, and trend among the historical monitoring data; The second optimal placement generation module is used to determine the optimal placement location based on the correlation, regularity, and trend.

14. The carbon dioxide monitoring site optimization device based on fiber optic sensing technology according to claim 8, characterized in that, The site optimization unit includes: The monitoring effect analysis module is used to simulate the optimal location of the monitoring points, analyze the monitoring effect of the carbon dioxide monitoring points after implementing the current optimized location plan, and determine the optimized location plan. The monitoring deployment optimization module is used to feed back the monitoring results and the deployment optimization scheme to the monitoring terminal so as to adjust the initial deployment positions of the fiber optic sensors.

15. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the carbon dioxide monitoring site optimization method based on fiber optic sensing technology as described in any one of claims 1 to 7.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the carbon dioxide monitoring site optimization method based on fiber optic sensing technology as described in any one of claims 1 to 7.

17. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the carbon dioxide monitoring site optimization method based on fiber optic sensing technology as described in any one of claims 1 to 7.

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