Space abnormity monitoring and early warning method and system under influence of anesthetic gas

Abstract

The invention relates to a space anomaly monitoring and early warning method and system under the influence of anesthetic gas, and relates to the field of data processing, a sensor array is arranged in a target space to monitor the concentration of anesthetic gas in real time, and when the concentration does not exceed the standard, operation characteristic parameters are further collected to carry out low-height operation prediction and anesthetic gas distribution prediction; finally, the anesthetic gas inhalation influence is analyzed in combination with low-height operation parameters and prediction distribution, the technical problems that accuracy is insufficient and the exposure risk of medical staff cannot be comprehensively evaluated when anesthetic gas monitoring depends on simple threshold judgment are solved, and accurate judgment and early warning are achieved. The accuracy and timeliness of anesthetic gas leakage monitoring are remarkably improved, meanwhile, the exposure risk of medical staff is comprehensively evaluated, and a powerful guarantee is provided for safety management of target spaces such as an operating room.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for monitoring and warning of spatial anomalies under the influence of anesthetic gas. Background Art

[0002] In the medical field, the operating room is a vital location for various surgical procedures, and the use of anesthetic gases is crucial for ensuring smooth surgical procedures and a painless patient experience. However, anesthetic gases are volatile and diffuse rapidly. Leaks not only pose a direct health threat to medical staff within the operating room, such as causing headaches and nausea, with the potential for more serious health problems with long-term exposure, but can also pollute the surgical environment, impacting the smooth progress of surgery and the patient's postoperative recovery.

[0003] Anesthetic gas diffusion scenarios differ from general gas leak warnings in that they exhibit localized accumulation, behavioral correlation, and latency. In enclosed environments, such as operating rooms, laminar flow ventilation is limited by equipment distribution and ventilation setup, resulting in inefficient gas replacement in blind spots. Conventional gas monitoring, however, focuses on single-point monitoring and early warning, lacking a deep understanding of the specific needs of anesthetic gas use scenarios, such as the correlation between exposure risk, behavioral characteristics, and latency.

[0004] Existing anesthetic gas monitoring methods mostly rely on simple threshold judgments, that is, when the detected anesthetic gas concentration exceeds the preset safety threshold, the system triggers an alarm. However, this method has obvious limitations. On the one hand, a single concentration threshold judgment cannot fully reflect the actual environmental conditions in the operating room. Factors such as airflow dynamics and temperature changes may affect the diffusion and distribution of anesthetic gases, thereby affecting the accuracy of concentration monitoring. On the other hand, existing methods often ignore the exposure risks of medical staff during actual operations, especially when working at low altitudes. Due to their proximity to the ground, medical staff may be more likely to inhale leaked anesthetic gases, and this is often overlooked in existing monitoring methods. In addition, with the continuous advancement of medical technology and the increasing complexity of the operating room environment, higher requirements are placed on the accuracy and real-time performance of anesthetic gas monitoring. Simple threshold judgments can no longer meet the safety management needs of modern operating rooms. Summary of the invention

[0005] The present invention addresses the technical problem that the existing technology relies on simple threshold judgment to monitor anesthetic gas, resulting in insufficient accuracy and inability to comprehensively assess the exposure risk of medical staff. It provides a spatial anomaly monitoring and early warning method and system under the influence of anesthetic gas to solve the problem.

[0006] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides a method for monitoring and warning of spatial anomalies under the influence of anesthetic gas. The method includes: monitoring and collecting the anesthetic gas concentrations at multiple positions through a sensor array deployed in a target space, performing discrimination and warning, and giving a warning when the warning conditions are met; when the warning conditions are not met, collecting the operation characteristic parameters in the target space, performing low-altitude operation prediction, and obtaining low-altitude operation parameters; predicting the distribution of anesthetic gas in the target space according to multiple anesthetic gas concentrations, and obtaining a predicted anesthetic gas distribution; analyzing the influence of anesthetic gas inhalation according to the low-altitude operation parameters and the predicted anesthetic gas distribution, obtaining anesthetic gas influence parameters, performing discrimination and warning, and giving a warning when the inhalation warning conditions are met.

[0007] In a second aspect, the present invention provides a system for monitoring and warning of spatial anomalies under the influence of anesthetic gas. The system includes: a sensing and monitoring module for monitoring and collecting the anesthetic gas concentrations at multiple positions through a sensor array deployed in a target space, performing discrimination and warning, and giving a warning when the warning conditions are met; a parameter collection module for collecting the operation characteristic parameters in the target space when the warning conditions are not met, performing low-altitude operation prediction, and obtaining low-altitude operation parameters; a distribution prediction module for predicting the distribution of anesthetic gas in the target space according to multiple anesthetic gas concentrations, and obtaining a predicted anesthetic gas distribution; an analysis and warning module for analyzing the influence of anesthetic gas inhalation according to the low-altitude operation parameters and the predicted anesthetic gas distribution, obtaining anesthetic gas influence parameters, performing discrimination and warning, and giving a warning when the inhalation warning conditions are met.

[0008] The beneficial effects of the present invention are as follows: By deploying a sensor array in the target space to monitor the anesthetic gas concentration in real time, and further collecting operation characteristic parameters for low-altitude operation prediction and anesthetic gas distribution prediction when the concentration does not exceed the standard, finally analyzing the influence of anesthetic gas inhalation by combining the low-altitude operation parameters and the predicted distribution, accurate discrimination and warning are achieved, significantly improving the accuracy and timeliness of anesthetic gas leakage monitoring, and comprehensively evaluating the exposure risk of medical staff, providing a strong guarantee for the safety management of target spaces such as operating rooms. Description of the Drawings

[0009] Figure 1 It is a flowchart of a method for monitoring and warning of spatial anomalies under the influence of anesthetic gas provided by the present invention.

[0010] Figure 2 It is a structural diagram of a system for monitoring and warning of spatial anomalies under the influence of anesthetic gas provided by the present invention.

[0011] Description of the reference numerals: sensing and monitoring module 11, parameter collection module 12, distribution prediction module 13, analysis and warning module 14. Detailed implementation manners

[0012] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0013] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0014] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. In order for any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for the purpose of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid unnecessary details from obscuring the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein. Embodiment 1

[0015] As Figure 1 shown, the embodiment of the present invention provides a method for monitoring and warning of spatial anomalies under the influence of anesthetic gas. The method includes: S10: Monitoring and collecting the anesthetic gas concentrations at multiple positions through a sensor array arranged in a target space, performing discrimination and warning, and giving a warning when the warning conditions are met.

[0016] For example, in the target space, the concentration of anesthetic gas at multiple key locations is monitored and data collected in real time and continuously by using a uniformly distributed sensor array. The target space is a place related to anesthetic gas monitoring, such as an operating room, and is a limited area where the subsequent sensor array monitors and collects data on the concentration of anesthetic gas. The sensor array refers to a collection of multiple sensors uniformly distributed in the target space. These sensors work together to monitor and collect data on the concentration of anesthetic gas at multiple key locations in real time and continuously. They are distributed throughout the target space, such as the operating room. Each sensor is responsible for detecting the concentration of anesthetic gas in its vicinity. The combination of numerous sensors forms a system that can comprehensively monitor the concentration of anesthetic gas at key locations in the space. These sensors are highly sensitive and accurate, and can accurately capture subtle changes in the concentration of anesthetic gas.

[0017] The collected anesthetic gas concentration data is then transmitted to the data processing center and compared with the preset anesthetic gas concentration threshold. The preset anesthetic gas concentration threshold is set based on the prescribed range of tolerance concentration and is not specifically limited here. If the anesthetic gas concentration at any location reaches or exceeds the preset threshold, the system will immediately trigger the early warning mechanism and notify relevant personnel through sound and light alarms, so that timely measures can be taken to prevent the accident from escalating. For example, in an operating room environment, if the anesthetic gas concentration suddenly rises due to equipment failure, any position in the sensor array will quickly capture this anomaly and trigger an early warning to ensure that medical staff can respond quickly and protect the safety of patients and themselves. Threshold judgment can achieve rapid and accurate early warning, effectively reducing the safety risks caused by anesthetic gas leakage.

[0018] If the current concentration data does not meet the warning conditions, the system will continue to execute subsequent steps, including collecting operation characteristic parameters, making low-altitude operation predictions and anesthetic gas distribution predictions, to further comprehensively assess the safety conditions in the space.

[0019] S20: When the warning conditions are not met, the operation characteristic parameters in the target space are collected, low-altitude operation prediction is performed, and low-altitude operation parameters are obtained.

[0020] Specifically, when the anesthetic gas concentration within the target space does not reach the preset warning threshold, the system further collects characteristic parameters of the operation within the target space. These parameters include, but are not limited to, the type of surgery, expected surgery time, the number of instrument uses, and the amount of instrument used. These parameters are directly related to the potential demand and pattern of low-altitude operations within the operating room.

[0021] Given that the density of anesthetic gas is greater than that of air, it usually tends to accumulate within the height range of 0 - 50 cm above the ground after leakage. This height range is exactly the height range where the heads of medical staff are located during low-height operations, such as when squatting to organize instruments or adjust equipment, thus increasing the risk of inhaling anesthetic gas. Based on this background, using the collected operation characteristic parameters, the possible locations, time series, and frequencies of low-height operations are predicted through a preset low-height operation prediction model. For example, in a complex operation expected to last for a long time, according to the operation type and instrument usage plan, it is predicted that a large number of instruments will need to be organized in the middle stage of the operation, and this operation is mostly carried out under the operating table, that is, the low-height operation area, and then the specific time period and frequency are planned.

[0022] Through the above steps, not only are potential anesthetic gas inhalation risk areas and time periods identified in advance, but also a scientific basis is provided for subsequent safety protection measures, effectively reducing the risk of medical staff inhaling anesthetic gas due to low-height operations and ensuring occupational health and safety in the operating room.

[0023] S30: Based on multiple anesthetic gas concentrations, predict the anesthetic gas distribution in the target space to obtain the predicted anesthetic gas distribution.

[0024] Furthermore, considering the limited number of sensors and the inability to directly cover and monitor the anesthetic gas concentration at every location, interpolation prediction technology is adopted to enhance the comprehensiveness of monitoring. Specifically, the anesthetic gas concentration data at multiple monitoring points are collected through a sensor array deployed at key positions. Subsequently, based on these discrete concentration data points and combined with the three-dimensional space coordinate system of the target space, mathematical interpolation algorithms (such as Kriging interpolation, inverse distance weighting interpolation, etc.) are used to estimate and predict the concentration values at positions not directly monitored in the space.

[0025] The specific interpolation estimation depends on multiple anesthetic gas concentration values monitored by sensors as input, and a continuous anesthetic gas concentration distribution field is constructed through interpolation algorithms to obtain the predicted anesthetic gas distribution. For example, in a rectangular operating room, if sensors are only arranged at the four corners, the anesthetic gas concentrations in the center, edges, and other areas not directly monitored in the operating room can be predicted based on the concentration data at these four points.

[0026] Predicting the concentration diffusion through interpolation can not only fill the blank areas monitored by sensors, provide a comprehensive anesthetic gas concentration distribution map, but also provide data support for subsequent risk assessment, ventilation control, and personnel protection measures, effectively improving the scientificity and safety of anesthetic gas management in target spaces such as operating rooms.

[0027] S40: Based on the low-height operation parameters and the predicted distribution of anesthetic gases, conduct an analysis of the impact of anesthetic gas inhalation to obtain anesthetic gas impact parameters, and perform discrimination and warning. When the inhalation warning conditions are met, issue a warning.

[0028] Specifically, after obtaining the low-height operation parameters (including the number of low-height operations, specific locations, and the duration of each squat) and the predicted distribution of anesthetic gases, execute the anesthetic gas inhalation impact analysis process. The anesthetic gas inhalation impact analysis process uses the predicted anesthetic gas distribution data and combines it with the low-height operation parameters to calculate the cumulative inhaled anesthetic gas concentration of medical staff at different operation positions and different operation time periods. For example, if the prediction shows that the anesthetic gas concentration is relatively high in a certain low-height operation area of the operating room, and the medical staff performs multiple and long-duration squatting operations in this area, the system will comprehensively consider these factors and use a specific algorithm model (possibly involving concentration-time integral calculation) to quantify the cumulative inhalation concentration.

[0029] Furthermore, combine the cumulative inhalation concentration with the number of low-height operations to analyze and obtain the impact level of anesthetic gases on medical staff. This impact level serves as an anesthetic gas impact parameter, which is used to evaluate the health risks that medical staff may suffer from inhaling anesthetic gases, such as dizziness and affecting the normal functioning of blood vessels. In particular, consider the dizziness situation that may be aggravated due to changes in blood circulation when medical staff stand up from a squatting position.

[0030] After completing the above analysis, compare and discriminate the anesthetic gas impact parameter with the preset inhalation warning conditions. Once it is found that the impact parameter reaches or exceeds the warning threshold, immediately trigger the warning mechanism to remind relevant personnel to take necessary protective measures, such as adjusting the operation plan, strengthening ventilation, or providing personal protective equipment.

[0031] By comprehensively considering the anesthetic gas concentration distribution and the characteristics of low-height operations, accurate assessment and timely warning of the anesthetic gas inhalation risk of medical staff are achieved, effectively ensuring the occupational health and safety of medical staff in target spaces such as operating rooms.

[0032] In a preferred embodiment, through a sensor array deployed in the target space, monitor and collect the anesthetic gas concentrations at multiple positions, conduct discrimination and warning, and issue a warning when the warning conditions are met, including: Through a sensor array deployed in the target space, monitor and collect the anesthetic gas concentrations at multiple positions.

[0033] Judge whether there is any anesthetic gas concentration greater than or equal to the anesthetic gas concentration threshold. If so, the warning conditions are met and a warning is issued. If not, the warning conditions are not met and no warning is issued.

[0034] Optionally, a sensor array deployed within the target space serves as the core component of the monitoring system, responsible for collecting real-time and accurate anesthetic gas concentration data at multiple key locations. These sensors are carefully arranged to ensure full coverage of the target space without wasting resources to capture any possible changes in anesthetic gas concentration.

[0035] The collected concentration data is then transmitted to the data processing center for rapid comparison and analysis using a pre-set discrimination algorithm. The system then determines, one by one, whether the anesthetic gas concentration at each monitoring point has reached or exceeded the pre-set anesthetic gas concentration threshold. For example, in an operating room environment, if a sensor detects that the anesthetic gas concentration has suddenly risen to or exceeded a safety threshold (e.g., 50 ppm), the system will immediately determine that a warning condition has been met and trigger the corresponding warning mechanism.

[0036] The early warning mechanism may include sound and light alarms, automatic notifications to relevant personnel, etc., to ensure a quick response and take necessary measures, such as starting the ventilation system, evacuating personnel, etc., so as to effectively prevent safety accidents caused by anesthetic gas leakage.

[0037] If the anesthetic gas concentration at all monitoring points is lower than the threshold, the system determines that the warning conditions are not met, continues to maintain the monitoring status, and does not trigger the warning.

[0038] The above steps significantly improve the response speed and accuracy to anesthetic gas leaks through real-time monitoring and rapid identification, providing strong guarantees for the safe management of target spaces such as operating rooms.

[0039] In a preferred embodiment, when the warning conditions are not met, the operation characteristic parameters in the target space are collected to perform low-altitude operation prediction and obtain low-altitude operation parameters, including: When the warning conditions are not met, before the operation is performed, the operation characteristic parameters in the target space are collected, where the operation characteristic parameters include the operation type and equipment usage parameters.

[0040] The operation characteristic parameters are input into a low-altitude operation prediction network for low-altitude operation prediction, and the prediction output obtains low-altitude operation parameters, wherein the low-altitude operation parameters include the number of low-altitude operations, the low-altitude operation time series and the low-altitude operation position series.

[0041] Specifically, when the anesthetic gas concentration monitoring results in the target space show that the warning conditions are not met, the system will further execute the low-altitude operation prediction process to identify potential risks.

[0042] Before the operation begins, the operation characteristic parameters in the target space are actively collected. These parameters are key factors affecting whether low-altitude operations occur and the form of low-altitude operations, including but not limited to the type of operation (such as cardiac surgery, neurosurgery, etc., different types of operations have different requirements for low-altitude operations) and instrument usage parameters (such as the frequency and duration of use of specific instruments, etc., instrument operation may require medical staff to be in a low-altitude working state).

[0043] Subsequently, the collected operation characteristic parameters are input into the pre-trained low-altitude operation prediction network. The low-altitude operation prediction network is a model built based on machine learning, which can predict and output low-altitude operation parameters based on the input operation characteristic parameters. The low-altitude operation parameters specifically include the number of low-altitude operations (i.e., the frequency at which low-altitude operations are expected to be performed during the operation), the low-altitude operation time series (i.e., the time point or time period at which each low-altitude operation is expected to occur), and the low-altitude operation position sequence (i.e., the location or area at which each low-altitude operation is expected to occur). For example, if the prediction network determines based on the input operation type and instrument usage parameters that in an operation that is expected to last a long time, medical staff will perform low-altitude operations multiple times in the middle of the operation to organize or adjust the instruments, the network will output the corresponding number of low-altitude operations, time series, and position series.

[0044] By predicting low-altitude operation parameters in advance, key data support can be provided for subsequent analysis of the impact of anesthetic gas inhalation, thereby more accurately assessing the exposure risk of medical staff and formulating corresponding protective measures, effectively improving the safety management level and occupational health protection capabilities in target spaces such as operating rooms.

[0045] In a preferred embodiment, the training step of the low-altitude operation prediction network includes: Based on machine learning, a low-altitude operation prediction network is constructed, wherein the input features of the low-altitude operation prediction network are operation feature parameters, and the output features are low-altitude operation parameters.

[0046] According to the historical operation data in the target space, a set of sample operation feature parameters is collected as input feature training data.

[0047] The number of low-altitude operations, low-altitude operation time series and low-altitude operation position series performed under different sample operation characteristic parameters are collected, and the sample low-altitude operation parameter set is annotated to serve as the output feature training data.

[0048] The input feature training data and the output feature training data are used to perform supervised training on the low-altitude operation prediction network, and the training is completed after the test accuracy converges.

[0049] Furthermore, in the process of constructing a low-altitude operation prediction network, a neural network model with a specific input-output structure was designed and built based on machine learning technology. The network's input features were set as operation characteristic parameters. These parameters include key information such as operation type and equipment usage parameters, which are important factors affecting the occurrence and form of low-altitude operations. The output features were set as low-altitude operation parameters, specifically including the number of low-altitude operations, low-altitude operation time series, and low-altitude operation location series. These parameters can comprehensively reflect the specific circumstances of low-altitude operations.

[0050] In order to train the low-altitude work prediction network, it is necessary to collect a large number of sample work feature parameter sets from the historical work data of the target space. These sets will serve as input feature training data to provide a basis for network learning. At the same time, it is also necessary to collect and annotate the number of low-altitude work operations, time series, and position series corresponding to each sample work feature parameter in the actual operation process, thereby forming a sample low-altitude work parameter set as output feature training data. For example, when analyzing the historical data of a heart surgery, if it is found that medical staff in this type of surgery often perform multiple low-altitude operations to organize equipment within a specific time period, this information will be annotated and included in the training data.

[0051] Finally, supervised training of the low-altitude operation prediction network is performed using the prepared input and output feature training data. During training, the network continuously adjusts its internal parameters to minimize the error between the predicted output and the actual annotations. When the test accuracy converges to a stable and high level, the network is considered to have fully learned the mapping relationship between the operation feature parameters and the low-altitude operation parameters, thus completing training.

[0052] The low-altitude operation prediction network obtained through training can accurately and efficiently predict low-altitude operation situations in future operations, providing strong support for safety management in target spaces such as operating rooms, helping to formulate protective measures in advance and reduce the exposure risk of medical staff.

[0053] In a preferred embodiment, predicting the anesthetic gas distribution in the target space based on multiple anesthetic gas concentrations to obtain the predicted anesthetic gas distribution includes: Obtain the spatial coordinate system in the target space.

[0054] Anesthetic gas concentration interpolation processing is performed in the spatial coordinate system according to the multiple anesthetic gas concentrations to predict and obtain a predicted anesthetic gas distribution.

[0055] Specifically, when performing the prediction of anesthetic gas distribution in the target space, the three-dimensional spatial coordinate system of the target space needs to be clarified. This coordinate system provides a basic framework for the subsequent concentration interpolation processing and ensures the spatial accuracy and locatability of the prediction results.

[0056] Then, based on the anesthetic gas concentration data collected by sensors placed at multiple key locations within the target space, the system performs interpolation of the anesthetic gas concentration within the defined spatial coordinate system. This interpolation process uses mathematical algorithms (such as kriging and spline interpolation) to estimate the concentration values at unknown points based on the concentration data at known points, thereby constructing a map of the anesthetic gas concentration distribution within the entire target space. For example, in a rectangular operating room, if sensors are placed only at the four corners, the system can use interpolation to predict the anesthetic gas concentrations in the center, edges, and other areas not directly monitored, forming a complete predicted anesthetic gas distribution.

[0057] The predicted anesthetic gas distribution obtained through interpolation processing not only fills the blank areas monitored by sensors, but also provides continuous spatial distribution information of anesthetic gas, providing a scientific basis for subsequent risk assessment, ventilation control, and personnel protection measures, effectively improving the accuracy and safety of anesthetic gas management in target spaces such as operating rooms.

[0058] In a preferred embodiment, based on the low-altitude operation parameters and the predicted anesthetic gas distribution, an anesthetic gas inhalation impact analysis is performed to obtain anesthetic gas impact parameters, perform a judgment and early warning, and issue an early warning when the inhalation early warning conditions are met, including: A low-altitude operation position sequence within the low-altitude operation parameter is obtained, the predicted anesthetic gas distribution is input for indexing, and an inhaled anesthetic gas concentration sequence is obtained.

[0059] The low-altitude operation time sequence in the low-altitude operation parameter is obtained, and the cumulative inhaled anesthetic gas concentration is calculated in combination with the inhaled anesthetic gas concentration sequence.

[0060] Anesthetic gas inhalation effects are classified according to the cumulative inhaled anesthetic gas concentration and the number of low-altitude operations within the low-altitude operation parameter to obtain an inhalation effect grade as an anesthetic gas effect parameter.

[0061] Determine whether the anesthetic gas impact parameter is greater than or equal to a preset inhalation impact level threshold. If so, the inhalation warning condition is met and a warning is issued. If not, the inhalation warning condition is not met and no warning is issued.

[0062] For example, in the analysis process for the impact of anesthetic gas inhalation in the target space, a low-altitude work position sequence is extracted from the low-altitude work parameters, and these position information are input into the obtained predicted anesthetic gas distribution data for indexing. The low-altitude work position sequence refers to an ordered arrangement of a series of low-altitude work positions extracted from the low-altitude work parameters. These position information are used to input into the predicted anesthetic gas distribution data for indexing to obtain the anesthetic gas concentration corresponding to each position, and finally form an inhaled anesthetic gas concentration sequence. For example, in an operating room space, medical staff perform operations at different low-altitude positions, and these low-altitude positions are arranged in a certain order to form a low-altitude work position sequence. Through this step, the inhaled anesthetic gas concentration corresponding to each low-altitude work position can be obtained, thereby forming an inhaled anesthetic gas concentration sequence, which records in detail the concentration of anesthetic gas inhaled when performing low-altitude operations at different positions.

[0063] Subsequently, the low-altitude operation time series in the low-altitude operation parameters is further combined with the inhaled anesthetic gas concentration series for comprehensive calculation. Specifically, based on the duration of each low-altitude operation and the anesthetic gas concentration at the corresponding location, the inhaled volume of each operation is calculated and accumulated to obtain the cumulative inhaled anesthetic gas concentration. The cumulative inhaled anesthetic gas concentration can intuitively reflect the total amount of anesthetic gas that medical personnel may inhale during the completion of all low-altitude operations.

[0064] Furthermore, a categorized analysis of the impact of anesthetic gas inhalation will be conducted based on the cumulative inhaled anesthetic gas concentration and the number of low-altitude operations in the low-altitude operation parameter. A preset algorithm or model can assess the level of inhalation impact that medical personnel may experience under different operating conditions and output this as the anesthetic gas impact parameter. For example, if the cumulative inhaled concentration is high and the number of operations is frequent, the inhalation impact level may be high, indicating that the medical personnel face a greater health risk.

[0065] Finally, the calculated anesthetic gas impact parameter is compared with the preset inhalation impact level threshold. If the impact parameter is greater than or equal to the threshold, the inhalation warning condition is determined to be met, and the system immediately triggers the warning mechanism to remind relevant personnel to take necessary protective measures. Conversely, if the impact parameter is lower than the threshold, the warning condition is determined to be not met, and the system will continue monitoring without triggering the warning.

[0066] By comprehensively considering low-altitude working parameters and predicting the distribution of anesthetic gas, accurate assessment and timely warning of the impact of anesthetic gas inhalation can be achieved, providing strong occupational health protection for medical staff in target spaces such as operating rooms.

[0067] In a preferred embodiment, according to the cumulative inhaled anesthetic gas concentration and the number of low-altitude operations within the low-altitude operation parameters, an anesthetic gas inhalation impact classification is performed to obtain an inhalation impact level, which is used as an anesthetic gas impact parameter, including: According to the historical record data of anesthetic gas inhalation impact, collect the sample cumulative inhaled anesthetic gas concentration set and the sample low-altitude operation number set of the sample users, and collect the sample inhalation impact level set of the sample users.

[0068] Use a decision tree to construct an anesthetic inhalation impact classifier based on the sample cumulative inhaled anesthetic gas concentration set, the sample low-altitude operation number set, and the sample inhalation impact level set.

[0069] Combine the cumulative inhaled anesthetic gas concentration and the number of low-altitude operations within the low-altitude operation parameters, input them into the anesthetic inhalation impact classifier, and classify and output to obtain the inhalation impact level, which is used as the anesthetic gas impact parameter.

[0070] Specifically, in the process of constructing the anesthetic gas inhalation impact classification system, rely on the historical record data of anesthetic gas inhalation impact to systematically collect multi-dimensional information of sample users, specifically covering the sample cumulative inhaled anesthetic gas concentration set, the sample low-altitude operation number set, and the corresponding sample inhalation impact level set. These set data serve as the basis for training the classifier, ensuring the accuracy and reliability of the classification model. For example, by collecting the inhalation concentration, the number of operations, and the final impact level of different medical staff in a similar surgical environment, a representative sample data set can be constructed.

[0071] Subsequently, use the decision tree algorithm to train the model based on the sample data set to construct an anesthetic inhalation impact classifier. The decision tree algorithm recursively divides the data set into several subsets, each subset corresponding to a decision node, until all subsets are correctly classified or reach a preset stop condition, thus forming a tree-shaped structure model that can automatically classify. This classifier can learn the complex relationship between the cumulative inhaled anesthetic gas concentration, the number of low-altitude operations, and the inhalation impact level, providing a basis for subsequent real-time classification.

[0072] In practical applications, combine the cumulative inhaled anesthetic gas concentration obtained by real-time monitoring with the number of low-altitude operations in the low-altitude operation parameters as input features and input them into the trained anesthetic inhalation impact classifier. The classifier automatically classifies the input features according to the learned classification rules and outputs the corresponding inhalation impact level, which is used as the anesthetic gas impact parameter to evaluate the inhalation risk of medical staff under the current operating conditions.

[0073] By constructing an anesthetic inhalation impact classifier based on a decision tree, the automated and precise classification of the impact of anesthetic gas inhalation is achieved. This not only improves the efficiency and accuracy of risk assessment but also provides a scientific basis for the early warning mechanism, helping to take protective measures in a timely manner and reducing the occupational exposure risk of medical staff.

[0074] A method for monitoring and early warning of spatial anomalies under the influence of anesthetic gas provided by an embodiment of the present invention has at least the following technical effects: 1. Through the sensor array deployed in the target space, the real-time monitoring and discriminative early warning of the anesthetic gas concentration at multiple positions are realized. Once the detected anesthetic gas concentration exceeds the threshold, the early warning will be immediately triggered, effectively preventing safety accidents caused by anesthetic gas leakage and improving the spatial safety.

[0075] 2. When the early warning conditions are not met, the operation characteristic parameters in the target space can be collected, and the low-height operation parameters can be predicted through the low-height operation prediction network. Combining the predicted anesthetic gas distribution, the impact of anesthetic gas inhalation can be further analyzed to obtain the inhalation impact level as the basis for early warning, enabling the assessment of potential risks before operation and providing a scientific basis for taking protective measures.

[0076] 3. An anesthetic inhalation impact classifier is constructed based on machine learning technology, which can automatically classify and output the inhalation impact level according to the cumulative anesthetic gas inhalation concentration and the number of low-height operations. This not only improves the accuracy and efficiency of risk assessment but also makes the early warning mechanism more scientific and reasonable. When the inhalation impact level exceeds the preset threshold, the early warning will be automatically triggered, effectively protecting the occupational health and safety of personnel. Embodiment Two

[0077] As Figure 2 shown, based on the same inventive concept as the method for monitoring and early warning of spatial anomalies under the influence of anesthetic gas provided in Embodiment One, an embodiment of the present invention further provides a system for monitoring and early warning of spatial anomalies under the influence of anesthetic gas, and the system includes: A sensing and monitoring module 11, configured to monitor and collect the anesthetic gas concentrations at multiple positions through the sensor array deployed in the target space, perform discriminative early warning, and give an early warning when the early warning conditions are met.

[0078] A parameter collection module 12, configured to collect the operation characteristic parameters in the target space when the early warning conditions are not met, perform low-height operation prediction, and obtain the low-height operation parameters.

[0079] A distribution prediction module 13, configured to predict the anesthetic gas distribution in the target space according to multiple anesthetic gas concentrations and obtain the predicted anesthetic gas distribution.

[0080] The analysis and warning module 14 is used to analyze the impact of inhaled anesthetic gas based on the low-altitude operation parameters and the predicted anesthetic gas distribution, obtain anesthetic gas impact parameters, conduct discrimination and warning, and issue a warning when the inhalation warning conditions are met.

[0081] Furthermore, the sensing and monitoring module 11 is also used to perform the following steps: Monitor and collect the anesthetic gas concentrations at multiple positions through a sensor array deployed in the target space; determine whether there is any anesthetic gas concentration greater than or equal to the anesthetic gas concentration threshold. If so, the warning conditions are met and a warning is issued. If not, the warning conditions are not met and no warning is issued.

[0082] Furthermore, the parameter acquisition module 12 is also used to perform the following steps: When the warning conditions are not met, before the operation, collect the operation characteristic parameters in the target space, where the operation characteristic parameters include the operation type and the instrument usage parameters; input the operation characteristic parameters into a low-altitude operation prediction network for low-altitude operation prediction, and predict and output to obtain low-altitude operation parameters, where the low-altitude operation parameters include the low-altitude operation times, the low-altitude operation time series, and the low-altitude operation position series.

[0083] Furthermore, the parameter acquisition module 12 is also used to perform the following steps: Based on machine learning, construct a low-altitude operation prediction network, where the input features of the low-altitude operation prediction network are operation characteristic parameters and the output features are low-altitude operation parameters; according to the historical operation data in the target space, collect a sample set of operation characteristic parameters as input feature training data; collect the low-altitude operation times, the low-altitude operation time series, and the low-altitude operation position series during the operation under different sample operation characteristic parameters, and label them to obtain a sample set of low-altitude operation parameters as output feature training data; use the input feature training data and the output feature training data to perform supervised training on the low-altitude operation prediction network, and complete the training after the test accuracy converges.

[0084] Furthermore, the distribution prediction module 13 is also used to perform the following steps: Obtain the spatial coordinate system in the target space; perform anesthetic gas concentration interpolation processing in the spatial coordinate system based on the multiple anesthetic gas concentrations, and predict to obtain the predicted anesthetic gas distribution.

[0085] Furthermore, the analysis and warning module 14 is also used to perform the following steps: Obtain the low-altitude operation position sequence within the low-altitude operation parameters, input the predicted anesthetic gas distribution for indexing, and obtain the inhaled anesthetic gas concentration sequence; obtain the low-altitude operation time sequence within the low-altitude operation parameters, combine it with the inhaled anesthetic gas concentration sequence, and calculate to obtain the cumulative inhaled anesthetic gas concentration; based on the cumulative inhaled anesthetic gas concentration and the number of low-altitude operations within the low-altitude operation parameters, conduct classification of the influence of anesthetic gas inhalation to obtain the inhalation influence level, which is used as the anesthetic gas influence parameter; determine whether the anesthetic gas influence parameter is greater than or equal to the preset inhalation influence level threshold. If so, the inhalation warning condition is met and a warning is issued. If not, the inhalation warning condition is not met and no warning is issued.

[0086] Furthermore, the analysis and warning module 14 is also used to perform the following steps: According to the historical record data of the influence of anesthetic gas inhalation, collect the set of sample cumulative inhaled anesthetic gas concentrations, the set of sample low-altitude operation times, and the set of sample inhalation influence levels of sample users; use a decision tree to construct an anesthetic inhalation influence classifier based on the set of sample cumulative inhaled anesthetic gas concentrations, the set of sample low-altitude operation times, and the set of sample inhalation influence levels; combine the cumulative inhaled anesthetic gas concentration and the number of low-altitude operations within the low-altitude operation parameters, input them into the anesthetic inhalation influence classifier, and classify and output to obtain the inhalation influence level, which is used as the anesthetic gas influence parameter.

[0087] Through the foregoing detailed description of a method for monitoring and warning spatial anomalies under the influence of anesthetic gas in this specification, those skilled in the art can clearly know a system for monitoring and warning spatial anomalies under the influence of anesthetic gas in this embodiment. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For related parts, refer to the description in the method section.

[0088] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring and warning of spatial anomalies under the influence of anesthetic gases, characterized in that, The method includes: Monitoring and collecting the anesthetic gas concentrations at multiple positions through a sensor array deployed in the target space, performing discrimination and early warning, and giving an early warning when the early warning conditions are met; When the early warning conditions are not met, collecting the operation characteristic parameters in the target space, performing low-altitude operation prediction, and obtaining low-altitude operation parameters; Predicting the anesthetic gas distribution in the target space according to the multiple anesthetic gas concentrations, and obtaining the predicted anesthetic gas distribution; Analyzing the influence of anesthetic gas inhalation according to the low-altitude operation parameters and the predicted anesthetic gas distribution, obtaining anesthetic gas influence parameters, performing discrimination and early warning, and giving an early warning when the inhalation early warning conditions are met.

2. The spatial anomaly monitoring and early warning method under the influence of anesthetic gas according to claim 1, characterized in that, Monitoring and collecting the anesthetic gas concentrations at multiple positions through a sensor array deployed in the target space, performing discrimination and early warning, and giving an early warning when the early warning conditions are met, including: Monitoring and collecting the anesthetic gas concentrations at multiple positions through a sensor array deployed in the target space; Judging whether there is any anesthetic gas concentration greater than or equal to the anesthetic gas concentration threshold. If so, the early warning conditions are met and an early warning is given. If not, the early warning conditions are not met and no early warning is given.

3. The spatial anomaly monitoring and early warning method under the influence of anesthetic gas according to claim 1, characterized in that When the early warning conditions are not met, collecting the operation characteristic parameters in the target space, performing low-altitude operation prediction, and obtaining low-altitude operation parameters, including: When the early warning conditions are not met, before the operation, collecting the operation characteristic parameters in the target space, where the operation characteristic parameters include the operation type and the instrument use parameters; Inputting the operation characteristic parameters into a low-altitude operation prediction network for low-altitude operation prediction, and predicting and outputting to obtain low-altitude operation parameters, where the low-altitude operation parameters include the low-altitude operation times, the low-altitude operation time series, and the low-altitude operation position series.

4. The spatial anomaly monitoring and early warning method under the influence of anesthetic gas according to claim 3, characterized in that, The training steps of the low-altitude operation prediction network include: Based on machine learning, constructing a low-altitude operation prediction network, where the input feature of the low-altitude operation prediction network is the operation characteristic parameters, and the output feature is the low-altitude operation parameters; According to the historical operation data in the target space, collecting a set of sample operation characteristic parameters as input feature training data; Collecting the low-altitude operation times, the low-altitude operation time series, and the low-altitude operation position series during the operation under different sample operation characteristic parameters, and labeling to obtain a set of sample low-altitude operation parameters as output feature training data; Using the input feature training data and the output feature training data to perform supervised training on the low-altitude operation prediction network, and completing the training after the test accuracy converges.

5. The method for monitoring and warning of spatial anomalies under the influence of anesthetic gases according to claim 1, wherein, Predicting the anesthetic gas distribution in the target space according to the multiple anesthetic gas concentrations, and obtaining the predicted anesthetic gas distribution, including: Obtaining the space coordinate system in the target space; Performing anesthetic gas concentration interpolation processing in the space coordinate system according to the multiple anesthetic gas concentrations, and predicting to obtain the predicted anesthetic gas distribution.

6. The method for monitoring and warning of spatial anomalies under the influence of anesthetic gases according to claim 1, characterized in that, Analyzing the influence of anesthetic gas inhalation according to the low-altitude operation parameters and the predicted anesthetic gas distribution, obtaining anesthetic gas influence parameters, performing discrimination and early warning, and giving an early warning when the inhalation early warning conditions are met, including: Obtain the low-altitude operation position sequence within the low-altitude operation parameters, input the predicted anesthetic gas distribution for indexing, and obtain the inhaled anesthetic gas concentration sequence; Obtain the low-altitude operation time sequence within the low-altitude operation parameters, combine it with the inhaled anesthetic gas concentration sequence, and calculate to obtain the cumulative inhaled anesthetic gas concentration; Based on the cumulative inhaled anesthetic gas concentration and the number of low-altitude operations within the low-altitude operation parameters, conduct classification of the influence of anesthetic gas inhalation, obtain the inhalation influence level, and use it as the anesthetic gas influence parameter; Judge whether the anesthetic gas influence parameter is greater than or equal to the preset inhalation influence level threshold. If so, the inhalation warning condition is met and a warning is issued. If not, the inhalation warning condition is not met and no warning is issued.

7. The spatial anomaly monitoring and early warning method under the influence of anesthetic gas according to claim 6, characterized in that, Based on the cumulative inhaled anesthetic gas concentration and the number of low-altitude operations within the low-altitude operation parameters, conduct classification of the influence of anesthetic gas inhalation, obtain the inhalation influence level, and use it as the anesthetic gas influence parameter, including: According to the historical record data of the influence of anesthetic gas inhalation, collect the set of sample cumulative inhaled anesthetic gas concentrations, the set of sample low-altitude operation times, and the set of sample inhalation influence levels of sample users; Use a decision tree to construct an anesthetic inhalation influence classifier based on the set of sample cumulative inhaled anesthetic gas concentrations, the set of sample low-altitude operation times, and the set of sample inhalation influence levels; Combine the cumulative inhaled anesthetic gas concentration and the number of low-altitude operations within the low-altitude operation parameters, input them into the anesthetic inhalation influence classifier, and classify and output to obtain the inhalation influence level, which is used as the anesthetic gas influence parameter.

8. A spatial anomaly monitoring and early warning system under the influence of anesthetic gas, characterized in that, For implementing the method for monitoring and warning of spatial anomalies under the influence of anesthetic gas according to any one of claims 1-7, the system includes: A sensing and monitoring module for monitoring and collecting the anesthetic gas concentrations at multiple positions through a sensor array deployed in the target space, conducting discrimination and warning, and issuing a warning when the warning condition is met; A parameter acquisition module for collecting the operation characteristic parameters in the target space when the warning condition is not met, conducting low-altitude operation prediction, and obtaining low-altitude operation parameters; A distribution prediction module for predicting the anesthetic gas distribution in the target space based on multiple anesthetic gas concentrations, and obtaining the predicted anesthetic gas distribution; An analysis and warning module for conducting analysis of the influence of anesthetic gas inhalation based on the low-altitude operation parameters and the predicted anesthetic gas distribution, obtaining the anesthetic gas influence parameter, conducting discrimination and warning, and issuing a warning when the inhalation warning condition is met.

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