Communication system and method for respiratory protection device, and respiratory protection system

Abstract

The present invention belongs to the technical field of respiratory protection, and particularly relates to a communication system and method for respiratory protection devices, and a respiratory protection system. The communication system comprises: a pairing transmission module, which comprises a first pairing unit and a second pairing unit; and a master control module, which comprises a communication unit, a control unit and a storage unit, wherein the communication unit is in communication connection with the pairing transmission module, so as to perform information transmission, and the control unit collects and processes identity information of the first pairing unit and / or the second pairing unit and pairing information. Effective communication between respiratory protection devices and a master control module can be realized by means of the communication system; a database for the plurality of respiratory protection devices is established by means of the master control module, such that data can be analyzed, mined and visualized to discover the correlation and regularity between the data, thereby providing support for decision making; and a unified platform or system can also be integrated with other systems, so as to realize automated decision-making control and monitoring management, thereby improving the intelligence level of the system.

Description

Communication system, method and respiratory protection system applied to respiratory protection equipment Technical Field

[0001] The present invention belongs to the technical field of respiratory protection, and in particular relates to a communication system and method applied to respiratory protection equipment and a respiratory protection system. Background Art

[0002] Electric breathing equipment generally includes a main unit that can be worn around the human body's waist, a filter component detachably mounted on the main unit, a mask, a connecting tube between the main unit and the mask, a battery pack and other components.

[0003] In order to ensure that users get the best use effect, first of all, it is necessary to ensure the installation stability of the above components. For example, it is necessary to ensure that the filter components such as filter elements and gas filter canisters are installed in place to achieve effective filtration, and it is necessary to ensure the installation stability of the connecting pipe to ensure smooth flow of air between the host and the mask, etc.; secondly, it is also necessary to ensure that some components are within the effective use time, which is especially critical for the filter components. For example, for users who use respiratory protective equipment in toxic situations, the gas filter canister can only provide respiratory protection for a limited time. Once used for more than the set time, it is very likely to be suddenly exposed to toxic gas. Breakage may cause harm to the personal safety of the user. Therefore, it is necessary to record the usage time of each gas filter canister to remind the user that the gas filter canister is likely to reach the breakthrough time and needs to be replaced in time to avoid damage to personal safety. In addition, according to the noise limit requirements, different air volume controls are required for different filter components. For example, the resistance of the gas filter canister is greater than the resistance of the filter element. Only when working in the first gear can the gas filter canister meet the relevant quiet requirements. Therefore, the air volume gear needs to be lowered for the gas filter canister, while the air volume supply of the ordinary filter element can be achieved in the second or third gear.

[0004] To meet the above needs, effective technical means are currently available, such as using positioning detection devices to detect whether the filter component is installed in place, and accurately identifying the type and usage duration of the filter component; however, the current detection and identification data results only correspond to the use of independent respiratory protection equipment, and users can only manually judge and operate the usage status of the respiratory protection equipment based on the corresponding data.

[0005] With the development of product technology, effective analysis and decision-making control at the big data level has become one of the directions of product design and control. This requires integrating the above data into a unified platform or system. Therefore, a dedicated system that can realize data communication of respiratory protective equipment has become a technical requirement in this field. Summary of the Invention

[0006] The present invention provides a communication system, method and respiratory protection system applied to respiratory protection equipment, which can effectively solve the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] Communication systems for respiratory protective equipment, including:

[0009] A pairing transmission module, comprising a first pairing unit and a second pairing unit which are paired and connected within a set position range and are respectively mounted on different components of the respiratory protective device;

[0010] The main control module is connected to the pairing transmission modules and includes a communication unit, a control unit and a storage unit:

[0011] The communication unit is in communication with the pairing transmission module to transmit information;

[0012] The control unit collects and processes identity information of the first pairing unit and / or the second pairing unit, and pairing information of the first pairing unit and the second pairing unit, wherein the identity information at least includes component information of the corresponding respiratory protective device;

[0013] The storage unit stores the collection and processing results of the identity information and pairing information.

[0014] Furthermore, it also includes a positioning module installed at a set position of the respiratory protection device;

[0015] The communication unit is communicatively connected to the positioning module;

[0016] The control unit collects and processes the positioning information of the positioning module;

[0017] The storage unit stores the collection and / or processing results of the positioning information.

[0018] Furthermore, the control unit collects and processes the identity information, including:

[0019] Continuously determine whether a new pairing transmission module is successfully paired, until the determination result is yes, and then collect the identity information of the first pairing unit and / or the second pairing unit in the pairing transmission module;

[0020] Assign the identity information:

[0021] A unique identification part, recording a unique identification of a setting component corresponding to the first pairing unit and / or the second pairing unit installed on the respiratory protective device;

[0022] The dynamic identification part records the real-time setting parameters of the setting component.

[0023] Furthermore, the storage unit stores the unique identification part only once in the first storage area, and stores the dynamic identification part corresponding to the unique identification part in the second storage area.

[0024] Furthermore, establishing the correspondence between the first storage area and the second storage area includes:

[0025] Establishing a virtual file system in the first storage area to map each unique identifier to a unique file path or file name;

[0026] The dynamic data of the real-time setting parameters are stored in the second storage area in the form of files, and the path or file name of each file corresponds to the unique identifier.

[0027] Furthermore, the main control module also includes a data processing unit for processing and analyzing the data stored in the storage unit, and predicting the usage status of the respiratory protective equipment based on the analysis results.

[0028] Furthermore, the data processing unit includes:

[0029] A data processor, configured to process the data obtained from the storage unit, wherein the processing includes at least data cleaning, conversion, normalization, and feature engineering;

[0030] Data analysis algorithms conduct in-depth analysis and mining of processed data to discover trends and correlations in the data and obtain analytical results;

[0031] A prediction model is established based on the analysis results and is used to predict the future usage status of respiratory protective equipment.

[0032] Furthermore, the prediction model is an LSTM model, including:

[0033] An input layer, receiving the analysis result;

[0034] The LSTM layer learns the long-term dependencies of the time series data in the analysis results and generates an internal representation;

[0035] An output layer, receiving the internal representation from the LSMT layer and generating a final prediction result;

[0036] The sliding window divides the time series data from the analysis result into different windows, and moves on the time axis by sliding the window to generate a series of subsequence data, and inputs the subsequence data into the LSTM layer.

[0037] A communication method for respiratory protection equipment, using the communication system for the respiratory protection equipment described above, comprises:

[0038] Collecting pairing information of two components installed on the respiratory protective equipment and need to be installed relative to each other, and collecting component information of at least one of the components;

[0039] transmitting the pairing information and component information via a communication connection;

[0040] Processing the transmitted pairing information and component information;

[0041] The processing results of the different pairing information and component information are centrally stored.

[0042] Respiratory protection systems comprising a number of respiratory protective devices and a communication system as described above for use with the respiratory protective devices;

[0043] The communication system includes a plurality of paired transmission modules, each of which is mounted on two different components of the respiratory protective equipment that need to be mounted opposite to each other;

[0044] The communication system centrally collects, processes and stores the identity information and pairing information from different respiratory protective devices through the main control module and the plurality of pairing transmission modules;

[0045] The main control module is connected to the fan of the respiratory protection equipment, and controls at least the air supply volume of the fan according to the processing result, and monitors the use status of the setting component.

[0046] The technical solution of the present invention can achieve the following technical effects:

[0047] The communication system can realize effective communication between respiratory protective equipment and the main control module. By establishing a database of multiple respiratory protective equipment through the main control module, the data can be analyzed, mined and visualized to discover the correlation and regularity between the data, thereby providing support for decision-making; based on the results of data analysis, corresponding decision-making and control strategies can be formulated, such as formulating equipment maintenance plans, optimizing equipment design, improving production efficiency, etc. At the same time, the unified platform or system can also be integrated with other systems to realize automated decision-making control and monitoring management, thereby improving the intelligence level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] FIG1 is a framework diagram of a communication system applied to respiratory protective equipment;

[0050] FIG2 is a flow chart of the control unit's collection and processing of identity information;

[0051] FIG3 is a framework diagram of a data processing unit. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0054] Example 1

[0055] As shown in Figure 1, the communication system used in respiratory protective equipment includes:

[0056] A pairing transmission module, comprising a first pairing unit and a second pairing unit which are paired and connected within a set position range and are respectively mounted on different components of the respiratory protective device;

[0057] The main control module is connected to several paired transmission modules, including a communication unit, a control unit, and a storage unit:

[0058] The communication unit is in communication connection with the paired transmission module to transmit information;

[0059] The control unit collects and processes the identity information of the first pairing unit and / or the second pairing unit, and the pairing information of the first pairing unit and the second pairing unit, wherein the identity information at least includes component information of the corresponding respiratory protective device;

[0060] The storage unit stores the collection and processing results of the identity information and pairing information.

[0061] In this embodiment, by setting the first pairing unit and the second pairing unit in the pairing transmission module, the assembly judgment of each component in the respiratory protection equipment can be effectively solved. During use, the first pairing unit and the second pairing unit are respectively installed on the two components waiting to be installed relative to each other, so that whether the two components are installed in place can be judged by establishing or releasing the pairing connection; in the application of the communication system, for example, the first pairing unit in a pairing transmission module is installed on the filter component, and the second pairing unit is installed on the shell. The result of successful pairing of the first pairing unit and the second pairing unit can be used to determine whether the filter component is installed in place relative to the shell; for another example, the first pairing unit in a group of pairing transmission modules is installed at one end of the connecting tube, and the second pairing unit is installed on the host or the mask. The result of successful pairing of the first pairing unit and the second pairing unit can be used to determine whether the connecting tube is installed in place relative to the host or the mask.

[0062] The main control module is connected to several pairing transmission modules, and can simultaneously realize the installation accuracy judgment of multiple groups of components on multiple respiratory protective equipment, and serve as a platform for data storage and processing; the work performed by the main control module includes the collection and processing of the identity information of the first pairing unit and / or the second pairing unit, including the collection and processing of the identity information of only the first pairing unit, the collection and processing of the identity information of only the second pairing unit, and the collection and processing of the identity information of the first pairing unit and the second pairing unit at the same time; during the implementation process, the purpose of identifying the identity information at least includes judging the component information of the first pairing unit and the second pairing unit through the identity information, for example, when the first pairing unit is installed on the filter component, the model, usage time, working status, etc. of the filter component are reflected through the identity information.

[0063] The storage unit stores the acquisition time, original data, processing results, etc. of the above information for subsequent use.

[0064] This communication system enables effective communication between respiratory protective equipment and the main control module. The main control module then establishes a database of multiple respiratory protective equipment devices, enabling data analysis, mining, and visualization to uncover correlations and patterns, thereby supporting decision-making. For example, this system can analyze the service life of different filter components and the stability of equipment under different pairing conditions. Based on the results of this data analysis, appropriate decision-making and control strategies can be formulated, such as developing equipment maintenance plans, optimizing equipment design, and improving production efficiency. Furthermore, this unified platform or system can be integrated with other systems to achieve automated decision-making, control, and monitoring management, further enhancing the system's intelligence.

[0065] During implementation, pairing signals can be transmitted via one or more of Bluetooth, Zigbee, Wi-Fi, Ultra-Wideband (UWB), NFC (13.56MHz, 433MHz, 860MHz-960MHz), and 2.4G devices. The communication connection between the communication unit and the pairing transmission module can be achieved via one or more of GPRS / CDMA wireless communication technology, digital radio communication, spread spectrum microwave communication, wireless bridge, satellite communication, and shortwave communication. When selecting a communication technology, factors such as communication security, cost, energy consumption, and compatibility with device hardware and software must also be considered. After comprehensively considering these factors, the most appropriate communication technology can be selected based on actual needs to ensure communication stability and reliability.

[0066] As a preferred embodiment of the above, the communication system applied to the respiratory protection device further includes a positioning module installed at a set position of the respiratory protection device;

[0067] The communication unit is communicatively connected with the positioning module;

[0068] The control unit collects and processes the positioning information of the positioning module;

[0069] The storage unit stores the collection and / or processing results of the positioning information.

[0070] The location information collected by the positioning module can achieve effective management and maintenance of respiratory protective equipment. Users can easily track the location history of the equipment, understand the usage and location changes of the equipment, and help formulate more effective maintenance plans and management strategies; the positioning module provides accurate positioning information, which is very useful for scenarios that require operation in complex environments or require rapid positioning. For example, in an emergency, the location of the equipment and the user can be accurately located to speed up rescue; location data can be used to analyze the usage patterns and behavior of the equipment. By analyzing the usage of the equipment in different locations, potential improvement points and optimization solutions can be identified, thereby improving the performance and usage efficiency of the equipment.

[0071] For communication system products, the positioning module can be integrated with the first pairing unit and / or the second pairing unit in the pairing transmission module and installed as a whole. This reduces the difficulty of installation and is beneficial to the application of the communication system.

[0072] As a preferred embodiment of the above, as shown in FIG2 , the control unit collects and processes the identity information, including:

[0073] Continue to determine whether a new pairing transmission module is successfully paired, until the determination result is yes, then collect the identity information of the first pairing unit and / or the second pairing unit in the pairing transmission module;

[0074] Give identity information:

[0075] The unique identification part records the unique identification of the setting component corresponding to the first pairing unit and / or the second pairing unit installed on the respiratory protective device;

[0076] The dynamic identification part records the real-time setting parameters of the setting component.

[0077] In the above embodiment, the system can promptly identify and record new pairing situations. In addition to the initial installation, this process is particularly critical for identifying new filter components during subsequent replacement of filter components. This helps ensure that the real-time status of each component in the device is accurately monitored and recorded, thereby improving the real-time performance and reliability of the system.

[0078] During the implementation process, it is not necessary to collect identity information for some components of the respiratory protection equipment. For example, when installing the filter component relative to the shell of the respiratory protection equipment, it is necessary to identify the relative installation positions of the two through the first pairing unit connected to the shell and the second pairing unit connected to the filter component. As for the identity information, it is only necessary to identify the component information of the filter component through the second pairing unit, and there is no need to identify the component information of the shell through the first pairing unit. Specifically, it is necessary to collect component information for components with more critical service life, such as battery packs, main units, filter components, etc., while the mask, connecting tube, shell, etc. can be selected according to actual needs.

[0079] A unique identification part is given to the identity information, which can record the unique identification of the setting component corresponding to the first pairing unit and / or the second pairing unit in the pairing transmission module. This design can ensure that each component has unique identity information, avoid information confusion and misidentification, and improve the accuracy and credibility of the data; the dynamic identification part records the real-time setting parameters of the setting component, which means that the system can record the current status and parameter information of each component of the equipment in real time, which helps users to understand the working status and performance parameters of the equipment in a timely manner, and facilitates timely adjustment and optimization of the equipment's working efficiency and performance; by recording the unique identification and dynamic parameters of each setting component, the system can achieve personalized management and optimization of each component. For example, personalized maintenance plans and optimization strategies can be formulated for different components, thereby improving the service life and performance of the equipment.

[0080] By continuously collecting and processing identity information, the system can accumulate a large amount of data for data analysis and decision support. By analyzing the pairing status of each component of the device and the dynamic parameter changes, potential problems and improvement points can be discovered, thereby guiding future equipment design and management strategies.

[0081] From the perspective of data storage optimization, as a preferred embodiment of the above embodiment, the storage unit stores the unique identification part only once in the first storage area, and stores the dynamic identification part corresponding to the unique identification part in the second storage area.

[0082] In the above optimization scheme, by storing the unique identification part only once, repeated storage of the same identity information is avoided, effectively saving storage space. This is particularly important for the storage and management of large amounts of data, helping to reduce system costs and improve storage efficiency. Establishing a dedicated storage area for the dynamic identification part and the unique identification part ensures consistency and relevance between the data. This design enables related data to be stored and managed in groups, improving data reliability and management efficiency. By establishing a dedicated storage area for the dynamic identification part, related data can be organized and stored together, facilitating subsequent data retrieval and processing, which helps to improve data access speed and processing efficiency, and enhances system performance and responsiveness. By optimizing the design of the storage unit, redundant storage of the same data is avoided, reducing the risk of data redundancy. This helps maintain data consistency and integrity, reducing the complexity and risk of data management.

[0083] As a preference of the above embodiment, establishing the correspondence between the first storage area and the second storage area includes:

[0084] Establishing a virtual file system in the first storage area, mapping each unique identifier to a unique file path or file name;

[0085] The dynamic data of the real-time setting parameters are stored in the second storage area in the form of files, and the path or file name of each file corresponds to the unique identifier.

[0086] During the implementation process, it is necessary to ensure that each unique identifier in the first storage area has its corresponding dynamic data file, and the naming rules of the path or file name can be clearly matched with the unique identifier. In this way, when it is necessary to obtain the dynamic data corresponding to a unique identifier, it is only necessary to use the unique identifier to construct the file path or file name to accurately find the corresponding dynamic data file.

[0087] In the first storage area, a virtual file system is established, in which each unique identifier is mapped to a unique file path or file name. For example, assuming the unique identifier is ID_001, it can be mapped to the path / unique_id / ID_001 or the file name ID_001.txt; in the second storage area, the corresponding dynamic data file can be stored in the path / dynamic_data / ID_001_data.txt or the file name ID_001_data.txt.

[0088] The control unit's collection and processing of pairing information generally includes the following aspects:

[0089] When pairing is successful, the control unit will record the time of successful pairing, the unique identifier of the device, and the structural information of the successful pairing, such as the pairing of the filter component and the housing. This information can help with subsequent data analysis and device management.

[0090] The control unit verifies and parses the pairing information to ensure its accuracy and completeness, which at least involves verifying the structural information of the successful pairing, such as checking whether the structure of the successful pairing meets the expected conditions of the device assembly;

[0091] The control unit often further processes the pairing information to meet specific needs. For example, it can analyze the assembly status of the device based on the structural information of successful pairing to monitor the assembly accuracy and stability of the device. If the control unit detects that the pairing information is abnormal or inconsistent, it may trigger an alarm or an exception handling mechanism. For example, if the pairing information shows that the pairing structure is mismatched or unreasonable, it may be necessary to trigger an alarm and perform further inspection and processing.

[0092] As a preferred embodiment of the above embodiment, the main control module further includes a data processing unit for processing and analyzing the data stored in the storage unit, and predicting the usage status of the respiratory protective equipment according to the analysis results.

[0093] In this preferred solution, since the data processing unit obtains data directly from the storage unit, it can ensure that the processed data is consistent with the stored data, avoiding data inconsistency or conflict; the data stored in the storage unit is updated in real time, so the data processing unit can instantly obtain the latest data, thereby performing real-time processing and analysis; the data in the storage unit is stored after verification and confirmation, so the accuracy and reliability of the data can be guaranteed. The data processing unit processes and analyzes based on these accurate data to obtain accurate prediction results.

[0094] During the implementation process, by predicting the usage status of respiratory protective equipment, potential problems or signs of failure can be discovered in a timely manner, and preventive maintenance measures can be taken to avoid equipment shutdown or damage due to unexpected failures, thereby improving the reliability and sustainability of the equipment; the prediction results can be used to rationally plan and optimize the utilization of resources, including manpower, materials and equipment, thereby improving resource utilization efficiency and reducing production costs; in addition, according to the prediction results, corresponding measures can be taken to reduce excessive use or improper operation of equipment, extend the service life of equipment, and reduce equipment maintenance and replacement costs. Safety hazards or risk factors in equipment can be discovered in a timely manner, and corresponding safety measures can be taken to ensure the safety of operators and the production environment.

[0095] As a preferred embodiment of the above, as shown in FIG3 , the data processing unit includes:

[0096] A data processor processes the data obtained from the storage unit, and the processing includes at least data cleaning, conversion, normalization and feature engineering;

[0097] Data cleaning is used to detect and correct errors, missing values, or outliers in the data to ensure data quality and consistency; data conversion involves converting data from its original format to a format suitable for analysis and modeling, such as encoding data into numeric types and converting categorical variables into numeric types; data normalization is used to adjust the scale and range of the data to ensure that the weights of different features are relatively consistent, avoiding excessive influence of certain features on the model; feature engineering refers to improving data representation capabilities by creating new features or combining existing features, making the data more interpretable and predictive;

[0098] Data analysis algorithms conduct in-depth analysis and mining of processed data to discover trends and correlations in the data and obtain analytical results;

[0099] Data analysis algorithms include statistical analysis, machine learning algorithms, deep learning algorithms, etc. Specifically, data analysis algorithms usually include the following aspects:

[0100] Statistical analysis is the basis of data analysis. It describes the basic characteristics and distribution of data by calculating various statistical quantities of data, such as mean, variance, correlation coefficient, etc. Statistical analysis can help understand the central tendency, degree of dispersion and correlation between different variables of the data, thereby revealing the overall characteristics of the data.

[0101] Machine learning algorithms are a type of algorithm that can learn and extract patterns and regularities from data. By learning and training on large amounts of historical data, machine learning algorithms can discover hidden patterns in the data and establish predictive models to predict future trends and states. Machine learning algorithms that can be applied in this embodiment include linear regression, decision trees, support vector machines, random forests, etc.

[0102] Deep learning algorithm is a machine learning method based on artificial neural networks. It learns the complex features and representations of data through a multi-level neural network structure. Deep learning algorithm performs well in processing large-scale data and complex pattern recognition tasks. It can automatically learn abstract representations in data and apply them to tasks such as prediction, classification, and clustering.

[0103] A prediction model is established based on the analysis results to predict the future usage status of respiratory protective equipment.

[0104] Predictive models can be established using various machine learning algorithms or statistical models, such as linear regression, decision trees, random forests, and neural networks. These models convert the features and correlations obtained from data analysis into patterns and rules that can be used for prediction, thereby enabling accurate predictions of future equipment status. The establishment of predictive models requires training and verification to ensure that they have good generalization capabilities and predictive accuracy to meet the needs of practical applications.

[0105] Data analysis algorithms provide the foundation and support for predictive models, helping to identify patterns and correlations in the data; predictive models are built using these analysis results to predict future trends and conditions. By combining these two processes, we can more comprehensively understand and utilize data, thereby improving the accuracy and reliability of predictions.

[0106] The prediction model is an LSTM model, including:

[0107] Input layer, receiving analysis results;

[0108] LSTM layer, which learns the long-term dependencies of time series data in the analysis results and generates internal representations;

[0109] The output layer receives the internal representation from the LSMT layer and generates the final prediction result;

[0110] Sliding window, divides the time series data from the analysis results into different windows, and moves on the time axis by sliding the window to generate subsequence data of the series and input it into the LSTM layer.

[0111] Using deep learning models such as LSTM for prediction can better capture long-term dependencies and complex patterns in time series data, thereby improving the accuracy and stability of predictions. Combined with sliding window technology, it can increase the diversity and richness of data and improve the generalization ability and prediction effect of the model.

[0112] As an example, let's assume that you need to predict the life of a filter component in a respiratory protective device:

[0113] First, a series of data on the usage status of the filter components in the respiratory protective equipment is collected, including information such as operating time, operating temperature, and operating humidity. Then, this data is processed and analyzed, including operations such as data cleaning, conversion, and normalization, to obtain a set of analytical results for prediction.

[0114] Next, these analysis results are input into the prediction model: at the input layer, these analysis results are passed to the LSTM layer, which will learn the time series patterns and long-term dependencies in the data, such as how the usage patterns of filter components change over time. By learning these patterns, the LSTM layer will generate an internal representation to capture the underlying laws and trends in the data; at the output layer, the internal representation from the LSTM layer is received and converted into the final prediction result, namely the life prediction of the filter component. This prediction result can indicate when the filter component may need to be replaced in the future, so that corresponding maintenance measures can be taken in advance to avoid equipment downtime or impact on usage due to filter component failure.

[0115] In addition, by introducing the sliding window method, the original time series data can be divided into different windows and slid on the time axis to generate a series of subsequence data. The advantage of doing so is that it can increase the diversity and richness of the data, thereby improving the generalization ability and prediction accuracy of the model.

[0116] The above model structure is suitable for processing various types of component data, including but not limited to filter components, battery components, sensor components, etc. Regardless of the type of component data, it can be analyzed and predicted through the predictive model to achieve effective management and maintenance of the usage status of each component of respiratory protective equipment. For the specific application scenarios of respiratory protective equipment, some component data has obvious time characteristics. For example, the service life of the filter component changes over time, the battery capacity and performance may gradually decrease as the number of charge and discharge cycles of the battery increases, and the sensor component may be affected by environmental factors during long-term use, resulting in performance degradation or failure, etc. The use of LSTM model can well capture the long-term dependencies and patterns in this time series data, thereby achieving accurate prediction of the component usage status. Sliding window technology can increase the diversity and richness of data, better reflect the temporal characteristics of the data, and improve the generalization ability and prediction effect of the predictive model.

[0117] During implementation, the sliding mode of the sliding window can be achieved by setting two key parameters:

[0118] Window size: Indicates the amount of time series data contained in each window. The window size setting usually needs to be determined based on the characteristics of the data and the prediction task, and can be tuned based on experience or experiments.

[0119] Sliding step size: Indicates the distance each sliding window moves on the time axis, also known as the sliding interval. It is used to control the overlap and continuity between windows. A smaller sliding step size can increase the overlap between windows, thereby improving data utilization and model stability, but it will increase the amount of computation. A larger sliding step size can reduce the overlap and reduce the amount of computation, but may cause some loss of information.

[0120] As a preferred approach, the relationship between the window size and the sliding step size can be optimized as follows:

[0121] S=W×(α+β) / 2

[0122] Where S represents the sliding step size, W represents the window size, α and β represent the periodicity factor and trend factor, respectively, which are adjusted based on the trends in the analysis results of the data analysis algorithm. S represents the sliding step size, and its unit depends on the unit of the window size W. W represents the window size, and its unit is usually time units such as days or hours. α and β represent the periodicity factor and trend factor, respectively, and are unitless pure numerical values. Their values ​​are usually between 0 and 1, indicating the degree to which the corresponding factor affects the sliding step size.

[0123] Specifically, the cyclical factor α is used to consider the cyclical changes in the data, such as the impact of seasonal changes or periodic events on the use status of respiratory protective equipment. A common method for calculating the cyclical factor is to use Fourier transform or autocorrelation function to analyze the cyclical characteristics of the data and extract the value of the cyclical factor from it. The specific calculation method may involve complex frequency domain analysis technology to determine the cyclical components in the data and quantify their impact. The trend factor β is used to consider the trend changes in the data, such as the continuous growth or decline trend of the data over time. The method for calculating the trend factor can be based on linear regression, moving average or other time series analysis techniques to determine the overall trend direction and change speed of the data. The specific calculation method may involve fitting the data or estimating the trend line to obtain the value of the trend factor.

[0124] In general, both the cyclical factor α and the trend factor β are adjusted to better reflect the characteristics of the data, but they focus on the cyclical and trend changes in the data respectively, and therefore play different roles in adjusting the sliding window.

[0125] In the specific scenario of respiratory protective equipment, the technical advantages of adopting the above solution are reflected in the following aspects:

[0126] More accurate predictions: By adjusting the cyclical factor α and the trend factor β, the sliding window size and step size can better adapt to cyclical and trend changes in the use status of respiratory protective equipment. For example, if the equipment exhibits seasonal changes or long-term trends, adjusting these two factors can enable the sliding window to more effectively capture these characteristics, thereby improving prediction accuracy.

[0127] More efficient data utilization: By optimizing the sliding window approach, we can increase the continuity between windows while maintaining a certain degree of overlap, thereby improving data utilization. In the respiratory protection equipment scenario, if data within a certain time period has a significant impact on predicting the equipment status in the next time period, then adopting an appropriate sliding window design can better utilize this information, allowing the model to more accurately capture changes in equipment status.

[0128] Reducing information loss: By adjusting the sliding step size, you can reduce the amount of computation while maintaining forecast accuracy. A smaller sliding step size increases the overlap between windows, increasing data continuity and thus reducing information loss; while a larger sliding step size reduces the amount of computation and improves the efficiency of model training and forecasting. In the prediction task of respiratory protective equipment, this means that large amounts of time series data can be processed more efficiently while maintaining forecast accuracy.

[0129] Example 2

[0130] A communication method applied to respiratory protective equipment, using the communication system of the respiratory protective equipment as described in the first embodiment, includes:

[0131] Collecting pairing information of two components installed on the respiratory protective equipment and need to be installed relative to each other, and collecting component information of at least one of the components;

[0132] Transmitting pairing information and component information via a communication connection;

[0133] Process the transmitted pairing information and component information;

[0134] The processing results of different pairing information and component information are centrally stored.

[0135] The technical effects of this embodiment are as described in the first embodiment and will not be repeated here.

[0136] Example 3

[0137] A respiratory protection system comprising a plurality of respiratory protection devices and a communication system applied to the respiratory protection devices as described in the first embodiment;

[0138] The communication system includes a plurality of paired transmission modules, which are respectively installed on two different components on a plurality of respiratory protective devices that need to be installed relative to each other;

[0139] The communication system collects, processes and stores the identity information and pairing information from different respiratory protective equipment through the main control module and several pairing transmission modules;

[0140] The main control module is connected to the fan of the respiratory protection equipment, and controls at least the air supply volume of the fan according to the processing result, and monitors the use status of the setting component.

[0141] During implementation, compared with the technical effects that can be achieved in Example 1, in this embodiment, more precise control of the respiratory protection equipment can be further effectively achieved. For example, by controlling the air volume supplied by the host, the air volume of different filter components can be adjusted accordingly to meet the set noise requirements. Specifically, when the filter component adopts a canister, the air volume needs to be reduced due to its large resistance, and when the filter component adopts an ordinary filter element, the air volume can be appropriately increased due to its small resistance; by controlling the air volume supplied by the fan through the main control module, the air volume can be adjusted according to the characteristics of different filter components to meet the set noise requirements. This fine control can ensure the performance and comfort of the respiratory protection equipment under different working conditions.

[0142] Component information can be used to effectively monitor the operating status of each component, including production date, material, version, etc., as well as the usage time of the filter element and canister, the remaining battery charge, etc. This information helps to monitor the operating status of the equipment in real time, identify problems in a timely manner, and take appropriate measures, thereby improving the reliability and safety of the equipment. The main control module can achieve remote control of the respiratory protective equipment. This function allows operators to remotely adjust the equipment's operating parameters and perform remote maintenance and management, improving the equipment's intelligence level and ease of use.

[0143] The respiratory protection system in this embodiment, through further technical improvements and functional enhancements, improves the monitoring, control, and management capabilities of respiratory protection equipment, providing users with a safer, more convenient, and more comfortable user experience. During implementation, a display device may be provided, specifically through a display screen, including an LCD screen, a screen display, a touch screen, etc., and the displayed content may include one or more of the following functions: the connection status of components, including filter elements, canisters, hoses, etc., usage time, aging level, ventilator usage status, and various alarm reminders, including filter element not installed, blockage, canister not installed, or excessive usage time, fan abnormality, mainboard abnormality, battery pack abnormality, etc.

[0144] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A communication system for respiratory protective equipment, characterized in that: include: A pairing transmission module, comprising a first pairing unit and a second pairing unit which are paired and connected within a set position range and are respectively mounted on different components of the respiratory protective device; The main control module is connected to the pairing transmission modules and includes a communication unit, a control unit and a storage unit: The communication unit is in communication with the pairing transmission module to transmit information; The control unit collects and processes identity information of the first pairing unit and / or the second pairing unit, and pairing information of the first pairing unit and the second pairing unit, wherein the identity information at least includes component information of the corresponding respiratory protective device; The storage unit stores the collection and processing results of the identity information and pairing information; The main control module also includes a data processing unit that processes and analyzes the data stored in the storage unit and realizes the usage status prediction of the respiratory protective equipment according to the analysis results; The data processing unit includes: A data processor, configured to process the data obtained from the storage unit, wherein the processing includes at least data cleaning, conversion, normalization, and feature engineering; Data analysis algorithms conduct in-depth analysis and mining of processed data to discover trends and correlations in the data and obtain analytical results; A prediction model, established based on the analysis results, for predicting the future use status of respiratory protective equipment; The prediction model is an LSTM model, including: An input layer, receiving the analysis result; The LSTM layer learns the long-term dependencies of the time series data in the analysis results and generates an internal representation; An output layer, receiving the internal representation from the LSMT layer and generating a final prediction result; Sliding window, dividing the time series data from the analysis results into different windows, and moving the windows on the time axis in a sliding manner to generate a series of subsequence data, and inputting the subsequence data into the LSTM layer; The relationship between window size and sliding step size is set as: S=W×(α+β) / 2; Among them, S represents the sliding step, W represents the window size, α and β represent the periodic factor and trend factor, respectively, which are adjusted according to the trend in the analysis results of the data analysis algorithm; the unit of S depends on the unit of W; the unit of W is the time unit; α and β are unitless pure numerical values ​​with values ​​between 0 and 1, indicating the degree of influence of the corresponding factors on the sliding step; the periodic factor α is used to consider the periodic changes in the data, and the trend factor β is used to consider the trend changes in the data.

2. The communication system for respiratory protection equipment according to claim 1, wherein: Also included is a positioning module installed at a set position of the respiratory protection device; The communication unit is communicatively connected to the positioning module; The control unit collects and processes the positioning information of the positioning module; The storage unit stores the collection and / or processing results of the positioning information.

3. The communication system for respiratory protection equipment according to claim 1, wherein: The control unit collects and processes the identity information, including: Continuously determine whether a new pairing transmission module is successfully paired, until the determination result is yes, and then collect the identity information of the first pairing unit and / or the second pairing unit in the pairing transmission module; Assign the identity information: A unique identification part, recording a unique identification of a setting component corresponding to the first pairing unit and / or the second pairing unit installed on the respiratory protective device; The dynamic identification part records the real-time setting parameters of the setting component.

4. The communication system for respiratory protection equipment according to claim 3, wherein: The storage unit stores the unique identification part only once in the first storage area, and stores the dynamic identification part corresponding to the unique identification part in the second storage area.

5. The communication system for respiratory protection equipment according to claim 4, characterized in that: Establishing the correspondence between the first storage area and the second storage area includes: Establishing a virtual file system in the first storage area to map each unique identifier to a unique file path or file name; The dynamic data of the real-time setting parameters are stored in the second storage area in the form of files, and the path or file name of each file corresponds to the unique identifier.

6. A communication method for respiratory protection equipment, comprising: using the communication system for respiratory protection equipment according to claim 1, wherein: include: Collecting pairing information of two components installed on the respiratory protective equipment and need to be installed relative to each other, and collecting component information of at least one of the components; transmitting the pairing information and component information via a communication connection; Processing the transmitted pairing information and component information; The processing results of the different pairing information and component information are centrally stored.

7. A respiratory protection system, characterized in that comprising a plurality of respiratory protective devices, and a communication system for use in the respiratory protective devices as claimed in claim 1; The communication system includes a plurality of paired transmission modules, each of which is mounted on two different components of the respiratory protective equipment that need to be mounted opposite to each other; The communication system centrally collects, processes and stores the identity information and pairing information from different respiratory protective devices through the main control module and the plurality of pairing transmission modules; The main control module is connected to the fan of the respiratory protection equipment, and controls at least the air supply volume of the fan according to the processing result, and monitors the use status of the setting component.

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