A control method and system of an intelligent airbag

Abstract

The application discloses a kind of control method and system of intelligent air mould, it is related to air-filled structure intelligent control technical field, wherein method includes: according to preset time interval, periodically obtain the environment data and air mould state data around target air mould;Environment data and air mould state data are preprocessed, and time series data are generated;Time series data are input into trained LSTM neural network model, and the joint life attenuation coefficient of target air mould joint is output;Whether the joint life attenuation coefficient exceeds preset threshold value is judged;If exceed, then generate fault early warning signal;According to fault early warning signal, dynamically calculate air pressure adjustment amount by PID control algorithm, and generate control instruction to drive air pump to adjust the internal air pressure of target air mould.The safety, reliability and service life of air mould can be effectively improved by controlling air mould through the control method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control of inflatable structures, and more particularly to a control method and system for an intelligent airform. BACKGROUND

[0002] Airform structures, particularly those used in large-scale temporary constructions and advertising displays, are widely used due to their ease of installation, disassembly, and transportation. These structures are typically made of high-strength materials and rely on internal inflation to maintain their shape and stability. However, as the application scenarios continue to expand, higher requirements are placed on the safety and durability of airforms.

[0003] In the prior art, the monitoring and maintenance of airforms mainly rely on regular manual inspections and automatic alarm systems based on fixed thresholds. Although this method can ensure the safe operation of airforms to some extent, it still has the following shortcomings:

[0004] 1. Poor environmental adaptability: Traditional control systems often lack real-time response capabilities to external environmental changes (such as wind speed, temperature, humidity, etc.). For example, in extreme weather conditions, if the internal pressure of the airform cannot be adjusted in time, it may cause excessive stress on the joints, thereby accelerating aging or directly causing damage.

[0005] 2. Low data processing efficiency: Most existing monitoring systems use simple data analysis methods and cannot effectively utilize historical data for predictive maintenance. This means that potential problems may not be discovered until they occur, increasing maintenance costs and time.

[0006] 3. Lack of adaptive control mechanism: Although traditional PID control algorithms can adjust air pressure based on current conditions, their parameters are usually fixed and cannot adapt to different working conditions. Especially in complex and variable environments, this static adjustment method is particularly inadequate.

[0007] 4. Limitations of early warning systems: Current fault warning systems usually only provide basic alarm functions and lack specific guidance information, such as specific measures to adjust air pressure to avoid further damage. This makes it difficult for operators to take effective measures even if they receive warning signals due to a lack of professional knowledge.

[0008] Therefore, how to improve the safety, reliability, and service life of intelligent airforms through effective control is a problem that needs to be solved by those skilled in the art. SUMMARY

[0009] In view of the above problems, the present application provides a control method and system for an intelligent airform to at least solve some of the technical problems mentioned in the background.

[0010] To achieve the above object, the present application adopts the following technical solutions:

[0011] In one aspect, the present application provides a control method of an intelligent airbag, comprising the following steps:

[0012] Periodically acquiring environmental data and airbag state data around a target airbag at preset time intervals;

[0013] Preprocessing the environmental data and airbag state data to generate time series data;

[0014] Inputting the time series data into a trained LSTM neural network model to output a joint life attenuation coefficient at a joint of the target airbag;

[0015] Judging whether the joint life attenuation coefficient exceeds a preset threshold; if yes, generating a fault warning signal;

[0016] According to the fault warning signal, dynamically calculating a pressure adjustment amount by a PID control algorithm, and generating a control instruction to drive a gas pump to adjust the internal pressure of the target airbag.

[0017] Further, the environmental data includes wind speed, temperature, humidity, and airbag external air pressure;

[0018] The airbag state data includes airbag internal air pressure, airbag support point displacement, joint strain, and joint tension.

[0019] Further, the preset time interval is dynamically adjusted according to the current wind speed; and the preset time interval is inversely proportional to the wind speed.

[0020] Further, the preprocessing of the environmental data and airbag state data to generate time series data specifically includes:

[0021] Respectively cleaning the environmental data and airbag state data;

[0022] Performing timestamp alignment processing on the cleaned environmental data and airbag state data;

[0023] Respectively normalizing the aligned environmental data and airbag state data;

[0024] Taking the current time as the end time of the time window, and according to the preset time window length, a continuous data segment is cut from the preprocessed environmental data and airbag state data to generate time series data.

[0025] Further, the training steps of the LSTM neural network model include:

[0026] Obtain historical environment data and historical airbag state data corresponding to a large number of airbags;

[0027] Preprocess the historical environment data and historical airbag state data to generate a plurality of historical time series data;

[0028] Add a corresponding joint life attenuation coefficient label to each historical time series data;

[0029] The historical time series data is used as input, and the corresponding joint life attenuation coefficient label is used as output to train the LSTM neural network model.

[0030] Further, the weighted combination loss function of the LSTM neural network model is represented as:

[0031]

[0032] Wherein, L represents the weighted combination loss function; α represents the weight coefficient, and α∈[0,1]; represents the mean square error term; represents the smooth L1 loss term; y represents the true joint life attenuation coefficient label; represents the joint life attenuation coefficient predicted by the LSTM neural network model; N represents the total number of historical time series data in a training batch; i represents the i-th historical time series data; y i represents the true joint life attenuation coefficient label corresponding to the i-th historical time series data; represents the joint life attenuation coefficient predicted by the LSTM neural network model based on the i-th historical time series data.

[0033] Further, the LSTM neural network model includes an input layer, a first LSTM layer, a second LSTM layer, a Dropout layer, a fully connected layer and an output layer;

[0034] The time series data is received through the input layer;

[0035] The primary features are extracted from the time series data through the first LSTM layer;

[0036] High-order features are extracted from the primary features through the second LSTM layer;

[0037] The regularization features are obtained by regularizing the high-order features through the Dropout layer;

[0038] The abstract representation of the joint life is generated by spatial mapping the regularization features through the fully connected layer;

[0039] The output layer generates and outputs a seam life attenuation coefficient at the airbag joint.

[0040] Further, it also includes generating maintenance instructions according to the fault warning signal and sending to the user terminal.

[0041] Further, according to the fault warning signal, the fault warning signal is dynamically calculated by a PID control algorithm, and a control instruction is generated to drive the air pump to adjust the internal air pressure of the target airbag; specifically including:

[0042] (1) Obtain the current deviation of the internal air pressure of the airbag from the preset upper limit of the safety air pressure at the current time, and obtain the historical deviation of the internal air pressure of the airbag from the preset upper limit of the safety air pressure at the discrete time points in the current control period; denoted as:

[0043] e(t) = P max -P current

[0044] e(k) = P max -P current (k)

[0045] Where e(t) represents the current deviation; P max represents the upper limit of the preset safety air pressure; P current represents the internal air pressure of the airbag at the current time; e(k) represents the historical deviation at time k; P current (k) represents the internal air pressure of the airbag at time k.

[0046] (2) Calculate the proportional term, integral term and differential term according to the current deviation and the historical deviation sequence; denoted as:

[0047] P out = K p ·e(t)

[0048]

[0049]

[0050] Where P out represents the proportional term; I out represents the integral term; D out represents the differential term; K p represents the PID control parameter preset for the proportional term; K i represents the PID control parameter preset for the integral term; K d represents the PID control parameter preset for the differential term; k=0 represents the starting time of the current control period; t represents the current time; Δt represents the time difference between adjacent control times.

[0051] (3) Superimposing the proportional term, the integral term and the differential term to obtain a gas pressure adjustment amount; denoted as:

[0052] ΔP = P out + I out + D out

[0053] Wherein, ΔP represents the gas pressure adjustment amount;

[0054] (4) According to the fault early warning signal, triggering a compensation factor, correcting the gas pressure adjustment amount, and taking the corrected gas pressure adjustment amount as a control instruction; denoted as:

[0055] ΔP' = β · ΔP

[0056] Wherein, ΔP' represents the control instruction, used to drive the gas pump to adjust the internal gas pressure of the target air mold; β represents the compensation factor.

[0057] On the other hand, the present application provides a control system for intelligent air mold, applying the above method, the system comprises: data acquisition module, pretreatment module, attenuation coefficient prediction module, judgment module and control module;

[0058] The data acquisition module is used to periodically acquire the environmental data and air mold state data around the target air mold according to a preset time interval;

[0059] The pretreatment module is used to pretreat the environmental data and air mold state data to generate time series data;

[0060] The attenuation coefficient prediction module is used to input the time series data into the trained LSTM neural network model to output the joint life attenuation coefficient of the target air mold joint;

[0061] The judgment module is used to judge whether the joint life attenuation coefficient exceeds a preset threshold; if so, a fault early warning signal is generated;

[0062] The control module is used to dynamically calculate a gas pressure adjustment amount according to the fault early warning signal through a PID control algorithm, and generate a control instruction to drive the gas pump to adjust the internal gas pressure of the target air mold.

[0063] According to the above technical solution, compared with the prior art, the present application provides a control method and system for intelligent air mold, which has the following beneficial effects:

[0064] The application can identify potential failure risks in advance by periodically acquiring environmental data and air mold state data and using an LSTM neural network model to predict the joint life attenuation coefficient, thereby generating a failure warning signal; this enables users to take action before the problem becomes serious, greatly reducing the possibility of air mold sudden failure.

[0065] According to the failure warning signal, the internal air pressure of the air mold is adjusted by real-time calculation of the air pressure adjustment amount through a PID control algorithm; this flexibility ensures that the air mold can maintain the best state under different environments, improves the ability to respond to emergencies, and enhances the safety of use; this is particularly important in application scenarios such as emergency rescue tents.

[0066] The technical solutions of the embodiments of the application will be described in further detail below with reference to the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the application, and those skilled in the art can obtain other drawings according to the provided drawings without creative labor.

[0068] Figure 1 The control method flowchart of the intelligent air mold provided for the embodiments of the application.

[0069] Figure 2 The control system framework schematic diagram of the intelligent air mold provided for the embodiments of the application. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the application will be described in detail below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only some embodiments of the application, not all embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0071] Embodiment 1

[0072] Embodiment 1 of the application discloses a control method of an intelligent air mold, as shown in Figure 1 The method comprises the following steps:

[0073] S1, periodically acquiring environmental data and air mold state data around the target air mold according to a preset time interval;

[0074] S2, preprocessing the environmental data and air mold state data to generate time series data;

[0075] S3. Input the time series data into the trained LSTM neural network model and output the seam life decay coefficient at the seam of the target inflatable model.

[0076] S4. Determine whether the seam life decay coefficient exceeds the preset threshold; if it does, generate a fault warning signal.

[0077] S5. Based on the fault warning signal, dynamically calculate the air pressure adjustment amount through the PID control algorithm, and generate control commands to drive the air pump to adjust the internal air pressure of the target air model.

[0078] The intelligent air model control method provided in this invention can achieve real-time monitoring of the health status of the joints by periodically monitoring environmental data and air model status data, and using an LSTM neural network model to predict the seam life decay coefficient. When an abnormality is detected, a fault warning signal can be issued in a timely manner, and the internal air pressure can be dynamically adjusted according to the PID control algorithm, thereby effectively preventing safety accidents caused by seam damage.

[0079] The labels S1-S5 above are only for the convenience of subsequent explanation and do not limit the execution order of each step; next, each of the above steps will be explained in detail.

[0080] In step S1 above, environmental data and inflatable model status data around the target inflatable model are periodically acquired at preset time intervals.

[0081] The environmental data mentioned above include wind speed, temperature, humidity, and external air pressure of the inflatable model, which are obtained by ultrasonic anemometers, temperature sensors, humidity sensors, and barometers arranged on the outer surface of the inflatable model, respectively.

[0082] The aforementioned inflatable model status data includes: internal air pressure, displacement of inflatable model support points, joint strain, and joint tension, which are obtained through pressure sensors arranged inside the inflatable model, displacement sensors arranged on the inflatable model support points, and strain gauges and tension sensors arranged at the joints of the inflatable model, respectively.

[0083] The preset time interval can be dynamically adjusted according to the current wind speed, and the adjustment is based on the fact that the preset time interval is inversely proportional to the wind speed; that is, the higher the wind speed, the shorter the preset time interval. By dynamically adjusting the preset time interval according to the current wind speed, the adaptability of the inflatable model control under different meteorological conditions is improved. In particular, under high wind speed conditions, the status of the inflatable model can be monitored more frequently, ensuring the safety of the inflatable model.

[0084] In step S2 above, environmental data and inflatable model status data are preprocessed to generate time series data; specifically including:

[0085] (1) Perform data cleaning on environmental data and air model status data respectively; Specifically: if environmental data or air model status data exceeds the range of the corresponding sensor, the data point is removed; for the missing data points after removal, linear interpolation of adjacent time point data is used to fill them; if the number of consecutive missing data points exceeds a preset value (e.g., 5), they are marked as invalid data segments and discarded.

[0086] (2) Timestamp alignment is performed on the cleaned environmental data and inflatable model status data;

[0087] (3) Normalize the aligned environmental data and the inflatable model status data respectively;

[0088] (4) Using the current time as the end time of the time window, extract continuous data segments from the preprocessed environmental data and air model status data according to the preset time window length to generate time series data;

[0089] By cleaning, aligning timestamps, and normalizing environmental and inflatable model status data, the quality of the data input into the LSTM neural network model is ensured, thus improving the accuracy of the prediction results.

[0090] In step S3 above, the time series data is input into the trained LSTM neural network model, and the joint life decay coefficient at the joint of the target air model is output; the joint life decay coefficient reflects the degree of degradation of joint strain and joint tension.

[0091] The following explains the relevant content of the above LSTM neural network model:

[0092] (1) The training steps of the above LSTM neural network model include:

[0093] 1) Obtain a large amount of historical environmental data and historical inflatable model status data;

[0094] 2) Preprocess historical environmental data and historical air model status data to generate multiple historical time series data; specifically:

[0095] For historical environmental data and historical inflatable model status data, data cleaning, timestamp alignment and normalization are performed in sequence. Then, according to the preset time window length, multiple continuous data segments are extracted from the normalized environmental data and inflatable model status data to generate multiple corresponding historical time series data.

[0096] 3) Add a corresponding joint life decay coefficient label to each historical time series data;

[0097] 4) Use historical time series data as input and the corresponding seam life decay coefficient label as output to train the LSTM neural network model.

[0098] (2) The weighted combination loss function of the above LSTM neural network model is expressed as:

[0099]

[0100] Where L represents the weighted combination loss function; α represents the weight coefficient, and α∈[0,1]; This represents the mean square error term; y represents the smoothed L1 loss term; y represents the actual seam life decay coefficient label. The y represents the seam life decay coefficient predicted by the LSTM neural network model; N represents the total number of historical time series data in a training batch; i represents the i-th historical time series data; y i The label represents the actual seam life decay coefficient corresponding to the i-th historical time series data. This represents the seam life decay coefficient predicted by the LSTM neural network model based on the i-th historical time series data. The weighted combined loss function combines mean squared error and smoothed L1 loss term, which can reduce the impact of outliers on the model and improve the model's generalization ability while ensuring the model's training stability.

[0101] (3) The structure of the above LSTM neural network model:

[0102] The aforementioned LSTM neural network model includes an input layer, a first LSTM layer, a second LSTM layer, a Dropout layer, a fully connected layer, and an output layer; specifically:

[0103] 1) The input layer serves as the data interface, receiving preprocessed time series data;

[0104] 2) The first LSTM layer contains 128 memory units and uses the tanh activation function. It extracts primary features from the original time series data through gating mechanisms (input gate, forget gate, and output gate) to capture short-period patterns (e.g., instantaneous stress changes caused by gusts).

[0105] 3) The second LSTM layer is also configured with 128 units, which receive the output features of the first layer and learn long-term dependencies through the deep recurrent network structure to extract high-order features across time steps (e.g., material creep effect under continuous high temperature and high humidity).

[0106] 4) The Dropout layer randomly masks 20% of the neurons output by the second LSTM layer with a probability of 0.2, forcing the network to establish redundant feature expressions, suppressing overfitting, and outputting a regularized feature vector with strong generalization ability.

[0107] 5) The fully connected layer performs a nonlinear transformation (ReLU activation) on the 128-dimensional regularized features, maps them to a 32-dimensional feature space through the weight matrix, filters out noise and compresses key information, forming an abstract representation of the seam life.

[0108] 6) The output layer uses a linear activation function to convert the 32-dimensional feature vector into a scalar value. The scalar value is used to quantitatively characterize the degree of joint life decay, which serves as the core criterion for subsequent early warning and control.

[0109] In step S4 above, it is determined whether the seam life decay coefficient exceeds a preset threshold; if it does, a fault warning signal is generated; a maintenance instruction is generated based on the fault warning signal and sent to the user terminal simultaneously.

[0110] In step S5 above, based on the fault warning signal, the air pressure adjustment amount is dynamically calculated using a PID control algorithm, and a control command is generated to drive the air pump to adjust the internal air pressure of the target air model; specifically including:

[0111] (1) Obtain the current deviation between the internal air pressure of the inflatable model and the preset safe air pressure limit at the current moment; and obtain the historical deviation between the internal air pressure of the inflatable model and the preset safe air pressure limit at discrete time points within the current control cycle; expressed as:

[0112] e(t) = P max -P current

[0113] e(k)=P max -P current (k)

[0114] Where e(t) represents the current deviation; P max Indicates the preset safe air pressure limit; P current The current air pressure inside the inflatable model is represented by e(k); e(k) represents the historical deviation at time k; P current (k) represents the internal air pressure of the inflatable model at time k;

[0115] (2) Calculate the proportional term, integral term, and differential term based on the current deviation and the historical deviation sequence; expressed as:

[0116] P out =K p ·e(t)

[0117]

[0118] Among them, P out Indicates the proportion term; I out D represents the integral term; out K represents the differential term;p This represents the preset PID control parameters for the proportional term; K i This represents the preset PID control parameters for the integral term; K d This represents the preset PID control parameters for the derivative term; k = 0 indicates the start time of the current control cycle; t represents the current time; Δt represents the time difference between two adjacent control times.

[0119] (3) The proportional, integral, and differential terms are superimposed to obtain the pressure adjustment amount; expressed as:

[0120] ΔP=P out +I out +D out

[0121] Wherein, ΔP represents the air pressure adjustment amount;

[0122] (4) Based on the fault warning signal, trigger the compensation factor to correct the air pressure adjustment amount, and use the corrected air pressure adjustment amount as a control command; expressed as:

[0123] ΔP'=β·ΔP

[0124] Where ΔP' represents the control command used to drive the air pump to adjust the internal air pressure of the target air model; β represents the compensation factor.

[0125] For example, the intelligent air model control method provided in this embodiment of the invention can be applied to an emergency rescue tent. When encountering strong winds, the tent volume can be reduced by dynamically calculating and adjusting the internal air pressure control command, thereby reducing wind resistance and enhancing the safety and stability of the structure.

[0126] In summary, this invention provides a control method for an intelligent inflatable model. This method not only periodically acquires environmental and status data around the target inflatable model, but also generates time-series data through preprocessing and inputs this data into a trained LSTM neural network model to predict the lifespan decay coefficient at the seams. Once an anomaly is detected, a fault warning signal is automatically generated, and the air pressure adjustment is dynamically calculated using an improved PID control algorithm, thereby achieving precise control of the internal air pressure of the inflatable model. Furthermore, this invention considers the influence of external environmental factors, dynamically adjusting the data acquisition time interval according to the current wind speed, enhancing the adaptability and flexibility of the inflatable model control. Simultaneously, through in-depth analysis of the fault warning signal, specific maintenance instructions can be provided to users, helping them to more effectively manage and maintain the inflatable structure.

[0127] Example 2:

[0128] This invention provides a control system for an intelligent inflatable model, applying the control method provided in Embodiment 1 above; see also...Figure 2 As shown, the control system includes: a data acquisition module, a preprocessing module, an attenuation coefficient prediction module, a judgment module, and a control module; wherein:

[0129] The data acquisition module is used to periodically acquire environmental data and inflatable model status data around the target inflatable model at preset time intervals;

[0130] The preprocessing module is used to preprocess environmental data and inflatable model status data to generate time series data;

[0131] The decay coefficient prediction module is used to input time series data into a trained LSTM neural network model and output the decay coefficient of the seam life at the joint of the target inflatable model.

[0132] The judgment module is used to determine whether the seam life decay coefficient exceeds a preset threshold; if it does, a fault warning signal is generated.

[0133] The control module is used to dynamically calculate the air pressure adjustment amount based on the fault warning signal using a PID control algorithm, and generate control commands to drive the air pump to adjust the internal air pressure of the target air model.

[0134] For details of each of the above modules, please refer to the above embodiment 1.

[0135] Example 3:

[0136] Based on Embodiment 2 above, Embodiment 3 of the present invention further includes an LED array inside the target inflatable model, and the LED array dynamically changes the light pattern according to cloud instructions;

[0137] For example, in promotional events, an inflatable arch-shaped structure 15m high is deployed on-site, with an integrated LED array (e.g., 120 RGB LEDs per square meter) on the inner surface of the structure. When the cloud sends a command: lighting mode = pulse wave, the control module adjusts the duty cycle through PWM signals to achieve a dynamic flowing light effect (frequency 2Hz).

[0138] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0139] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control method for an intelligent inflatable model, characterized in that, Includes the following steps: The environmental data and status data of the target inflatable model are acquired periodically at preset time intervals. The environmental data and inflatable model status data are preprocessed to generate time series data; The time series data is input into the trained LSTM neural network model, and the joint life decay coefficient at the joint of the target inflatable model is output. Determine whether the seam life decay coefficient exceeds a preset threshold; If the value exceeds the limit, a fault warning signal will be generated; Based on the fault warning signal, the air pressure adjustment amount is dynamically calculated through the PID control algorithm, and a control command is generated to drive the air pump to adjust the internal air pressure of the target air model. The training steps of the LSTM neural network model include: Acquire a large amount of historical environmental data and historical inflatable model status data; The historical environmental data and historical air model status data are preprocessed to generate multiple historical time series data. Add a corresponding joint life decay coefficient label to each of the aforementioned historical time series data; The historical time series data is used as input, and the corresponding seam life decay coefficient label is used as output to train the LSTM neural network model. The weighted combination loss function of the LSTM neural network model is expressed as: Where L represents the weighted combination loss function; α represents the weight coefficient, and α∈[0,1]; This represents the mean square error term; y represents the smoothed L1 loss term; y represents the actual seam life decay coefficient label. The y represents the seam life decay coefficient predicted by the LSTM neural network model; N represents the total number of historical time series data in a training batch; i represents the i-th historical time series data; y i The label represents the actual seam life decay coefficient corresponding to the i-th historical time series data. This represents the seam life decay coefficient predicted by the LSTM neural network model based on the i-th historical time series data.

2. The control method for an intelligent air model according to claim 1, characterized in that: The environmental data includes: wind speed, temperature, humidity, and external air pressure of the inflatable model; The inflatable model status data includes: internal air pressure, displacement of inflatable model support points, joint strain, and joint tension.

3. The control method for an intelligent inflatable model according to claim 1, characterized in that, The preset time interval is dynamically adjusted according to the current wind speed; and the preset time interval is inversely proportional to the wind speed.

4. The control method for an intelligent inflatable model according to claim 1, characterized in that, The preprocessing of the environmental data and inflatable model status data to generate time-series data specifically includes: The environmental data and the inflatable model status data were cleaned separately. Timestamp alignment was performed on the cleaned environmental data and inflatable model status data. The aligned environmental data and the inflatable model status data were normalized respectively. Using the current time as the end time of the time window, and according to the preset time window length, continuous data segments are extracted from the preprocessed environmental data and air model status data to generate time series data.

5. The control method for an intelligent inflatable model according to claim 1, characterized in that, The LSTM neural network model includes an input layer, a first LSTM layer, a second LSTM layer, a Dropout layer, a fully connected layer, and an output layer. The time-series data is received through the input layer; Primary features are extracted from the time series data through the first LSTM layer; High-order features are extracted from the primary features using the second LSTM layer; The higher-order features are regularized using the Dropout layer to obtain regularized features. The regularized features are spatially mapped using the fully connected layer to generate an abstract representation of the seam lifetime. The output layer generates and outputs the seam life decay coefficient at the joint of the air mold.

6. The control method for an intelligent inflatable model according to claim 1, characterized in that, Also includes: Maintenance instructions are generated based on the fault warning signal and sent to the user terminal.

7. The control method for an intelligent air model according to claim 1, characterized in that, The step involves dynamically calculating the air pressure adjustment amount using a PID control algorithm based on the fault warning signal, and generating control commands to drive the air pump to adjust the internal air pressure of the target air model; specifically including: (1) Obtain the current deviation between the internal air pressure of the inflatable model and the preset safe air pressure limit at the current moment; and obtain the historical deviation between the internal air pressure of the inflatable model and the preset safe air pressure limit at discrete time points within the current control cycle; expressed as: e(t)=P max -P current e(k)=P max -P current (k) Where e(t) represents the current deviation; P max Indicates the preset safe air pressure limit; P current The current air pressure inside the inflatable model is represented by e(k); e(k) represents the historical deviation at time k; P current (k) represents the internal air pressure of the inflatable model at time k; (2) Calculate the proportional term, integral term, and differential term based on the current deviation and the historical deviation sequence; expressed as: P out =K p ·e(t) Among them, P out Indicates the proportion term; I out D represents the integral term; out K represents the differential term; p This represents the preset PID control parameters for the proportional term; K i This represents the preset PID control parameters for the integral term; K d This represents the preset PID control parameters for the derivative term; k = 0 indicates the start time of the current control cycle; t represents the current time; Δt represents the time difference between two adjacent control times. (3) The proportional term, integral term, and differential term are superimposed to obtain the air pressure adjustment amount; expressed as: ΔP=P out +I out +D out Wherein, ΔP represents the air pressure adjustment amount; (4) Based on the fault warning signal, a compensation factor is triggered to correct the air pressure adjustment amount, and the corrected air pressure adjustment amount is used as a control command; expressed as: ΔP'=β·ΔP Wherein, ΔP' represents a control command used to drive the air pump to adjust the internal air pressure of the target air model; β represents a compensation factor.

8. A control system for an intelligent inflatable model, characterized in that, The system using the method according to any one of claims 1-7 comprises: a data acquisition module, a preprocessing module, an attenuation coefficient prediction module, a judgment module, and a control module; The data acquisition module is used to periodically acquire environmental data and inflatable model status data around the target inflatable model at preset time intervals. The preprocessing module is used to preprocess the environmental data and the inflatable model status data to generate time series data; The attenuation coefficient prediction module is used to input the time series data into the trained LSTM neural network model and output the attenuation coefficient of the joint life at the joint of the target air model. The judgment module is used to determine whether the seam life decay coefficient exceeds a preset threshold; if it does, a fault warning signal is generated. The control module is used to dynamically calculate the air pressure adjustment amount according to the fault warning signal through a PID control algorithm, and generate control commands to drive the air pump to adjust the internal air pressure of the target air model.

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