Fire-fighting fireproof door alarm information transmission device and method based on Internet of Things

Abstract

The invention discloses a fire-fighting fireproof door alarm information transmission device and method based on the Internet of Things, and relates to the technical field of fire-fighting alarm, and the method comprises the following steps: carrying out the multi-band continuous scanning of a wireless communication environment of a fireproof door alarm device deployment region after a fire emergency mode is started, identifying a target communication protocol frequency band used by the fireproof door alarm device, and obtaining spectrum parameter information of each frequency band to form a to-be-analyzed communication frequency band set; and extracting a working frequency band used by the peripheral wireless equipment based on a pre-established environment wireless equipment frequency band database. Frequency spectrum mapping is constructed through frequency spectrum scanning and an environment database, intelligent interference prediction is realized in combination with a support vector machine, coding parameters are dynamically adjusted based on interference intensity, the transmission reliability of key alarm information in a high-interference environment is improved, feedforward recognition and closed-loop regulation and control capabilities are achieved, and the method is suitable for popularization and application. And the problems of slow response and unstable transmission of a traditional system are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of fire alarm technology, and in particular to a fire door alarm information transmission device and method based on the Internet of Things. Background Art

[0002] The fire door alarm information transmission device based on the Internet of Things is a fire emergency response system that integrates sensor monitoring, intelligent identification and remote communication functions. The device collects surrounding environmental data in real time and determines the fire risk level by deploying sensing units such as temperature sensors, smoke detectors, and door status monitors (such as switch sensors) on fire doors; once the set fire alarm threshold is triggered, the system will automatically close the fire door, and upload the alarm information to the cloud platform or fire management center through the built-in wireless communication module (such as NB-IoT, LoRa or 4G) to achieve remote alarm and situation monitoring. This device can further integrate intelligent algorithms to achieve prediction and early warning of fire spread trends, significantly improving the fire prevention and control capabilities and response efficiency of the internal areas of the building.

[0003] The prior art has the following deficiencies:

[0004] Existing IoT fire door alarm devices usually use low-power wide-area communication protocols such as NB-IoT and LoRa for remote alarm. However, when the device is deployed in a building environment with dense wireless devices, such as elevator control systems, industrial WLAN, wireless access control and other areas with highly overlapping frequency bands, it is very easy to cause cross-band electromagnetic coupling interference. During the peak interference period, the system communication link may experience instantaneous packet loss or transmission delay, resulting in the failure to upload alarm information in time. If such interference coincides with the rapid spread of fire, the system will not be able to quickly issue an alarm and close the fire door in a coordinated manner.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a fire door alarm information transmission device and method based on the Internet of Things. By introducing an interference frequency band identification mechanism combining spectrum scanning with an environmental database, a spectrum mapping relationship between the communication frequency band and the interference source frequency band is constructed, and a support vector machine model is combined to perform high-precision interference level prediction on the characteristic indicator vector group. When the electromagnetic coupling risk is detected, the coding parameter adaptive adjustment mechanism is automatically triggered, and the error correction capability is dynamically improved according to the interference intensity, ensuring that key alarm information can still be stably and reliably uploaded and drive linkage control operations in a high interference environment, so as to solve the problems in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: A method for transmitting fire door alarm information based on the Internet of Things, comprising the following steps:

[0008] After the fire emergency mode is started, continuously scan the wireless communication environment in the deployment area of the fire door alarm device in multiple frequency bands, identify the target communication protocol frequency band used by the fire door alarm device, and obtain the spectrum parameter information of each frequency band to form a set of communication frequency bands to be analyzed;

[0009] Based on the pre-established environmental wireless device frequency band database, extract the working frequency bands used by surrounding wireless devices, identify the interference frequency bands that have a frequency adjacent relationship with the target communication frequency band, and construct a spectrum mapping matrix between the communication frequency band and the interference source frequency band;

[0010] Based on the spectrum mapping matrix, use the feature engineering method to extract the feature indicators reflecting the coupling characteristics between the communication frequency band and the interference frequency band from it, and construct a feature indicator vector group for electromagnetic coupling interference determination through the analyzed feature indicators;

[0011] Input the feature indicator vector group into the support vector machine model that has been trained in advance with historical interference data, and through the model output the current coupling interference level identifier, intelligently predict the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band, and be used to determine whether the communication link is currently in a high electromagnetic coupling interference state;

[0012] When the prediction result of the support vector machine model indicates the existence of high electromagnetic coupling interference, trigger the encoding parameter adaptive regulation mechanism, dynamically adjust the redundancy encoding intensity according to the electromagnetic coupling interference intensity, and adopt a transmission format with a higher error correction coding level for communication during the peak period of the coupling interference intensity.

[0013] Preferably, the specific steps of continuously scanning the wireless communication environment in the deployment area of the fire door alarm device to identify the target communication protocol frequency band used are as follows:

[0014] In the initial stage after the fire door alarm device is started, set the scanning range through the spectrum analyzer connected to the device to cover the frequency band range used by the communication protocol;

[0015] Configure the scanning parameters including the scanning step size, dwell time, and power threshold, start the continuous spectrum scanning function and record the real-time power spectral density map;

[0016] Analyze the collected spectrum data, and identify the current active frequency band based on the power peak, modulation characteristics, or spectrum stability;

[0017] Match the frequency band attribution in combination with the communication protocol characteristics to determine the target communication protocol frequency band used by the fire door alarm device for subsequent interference analysis and frequency band mapping.

[0018] Preferably, identify the interference frequency bands that have a frequency adjacent relationship with the target communication frequency band. The specific steps are as follows:

[0019] Based on the pre-established environmental wireless device frequency band database, extract the operating frequency band parameters of known wireless devices in the deployment area of the fire door alarm device, including the center frequency, bandwidth range, and operating mode.

[0020] Compare the identified target communication protocol frequency band. Taking its center frequency as the benchmark, set the frequency adjacent judgment threshold as the interference judgment window.

[0021] Calculate the center frequency difference and analyze the bandwidth overlap ratio for all peripheral device frequency bands, and screen out the wireless device frequency bands whose center frequency has partial or complete bandwidth overlap with the target frequency band.

[0022] Record the screened frequency bands as potential interference source frequency bands, mark their corresponding device types, interference weights, and overlap attributes, and input them into the interference frequency band set for constructing the spectrum mapping matrix for subsequent electromagnetic coupling analysis and interference intensity modeling.

[0023] Preferably, use the feature engineering method to extract the feature indicators reflecting the coupling characteristics between the communication frequency band and the interference frequency band. Among them, the extracted indicators include the noise distribution asymmetry of the target frequency band in the equidistant frequency domains on the left and right sides and the dynamic overlap ratio between the interference frequency band and the target frequency band in multiple time periods. After analyzing the extracted feature indicators under the analysis window, generate the carrier-to-noise asymmetry factor and the dynamic overlap factor respectively, and construct a feature indicator vector group for electromagnetic coupling interference judgment through the carrier-to-noise asymmetry factor and the dynamic overlap factor.

[0024] Preferably, input the feature indicator vector group composed of the carrier-to-noise asymmetry factor and the dynamic overlap factor into the support vector machine model that has been trained in advance with historical interference data. Output the electromagnetic coupling coefficient through the model, and based on the electromagnetic coupling coefficient, conduct an intelligent prediction of the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band, and use it to determine whether the communication link is currently in a high electromagnetic coupling interference state.

[0025] Preferably, compare and analyze the electromagnetic coupling coefficient generated when the support vector machine model conducts an intelligent prediction of the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band with the pre-set electromagnetic coupling risk threshold to determine whether the communication link is currently in a high electromagnetic coupling interference state. The specific determination steps are as follows:

[0026] If the electromagnetic coupling coefficient is greater than the electromagnetic coupling risk threshold, a risk signal is generated, and it is determined that the current communication link is in a high electromagnetic coupling interference state; if the electromagnetic coupling coefficient is less than or equal to the electromagnetic coupling risk threshold, a normal signal is generated, and it is determined that the current communication link is in a low-risk interference state and is communicating efficiently.

[0027] Preferably, under the analysis window, the specific steps for generating the carrier-to-noise asymmetry factor after analyzing the noise distribution asymmetry in the equidistant frequency domains on the left and right sides of the target frequency band are as follows:

[0028] Taking the center frequency of the target communication frequency band as a reference, set a frequency offset distance on each of its left and right sides to determine symmetric sampling points. Near each symmetric point, randomly select a sub-band region with a bandwidth, and calculate the integral energy of the noise power spectral density. The calculation expression is: , where: represents the noise power spectral density at frequency f; is the sub-band width; and are the left and right symmetric frequency points defined relative to the center frequency of the target communication frequency band, , , is the center frequency of the target communication frequency band; and respectively represent the noise energies of the left and right sub-bands, reflecting the interference intensity;

[0029] After obtaining the left and right side noise energies and , use the hyperbolic function to calculate the carrier-to-noise asymmetry factor. The calculation expression is: , where: is the carrier-to-noise asymmetry factor, characterizing the noise power asymmetry of the target communication frequency band at the equidistant frequency domain positions on its left and right sides; is the trend amplification factor, used to regulate the response sensitivity of the carrier-to-noise asymmetry factor to the degree of asymmetry.

[0030] Preferably, under the analysis window, the specific steps for generating the dynamic overlap factor after analyzing the dynamic overlap ratio between the interference frequency band and the target frequency band in multiple time periods are as follows:

[0031] Divide the analysis window into N equally spaced sub-windows. For the i-th sub-window, define the interference frequency band as , and the target communication frequency band as a fixed value , and calculate the actual occupancy ratio of the interference frequency band to the target frequency band in each sub-window. The calculation expression is: , where: represents the actual occupancy ratio of the interference frequency band to the target frequency band in the i-th sub-window; and are the upper and lower boundaries of the target communication frequency band and remain fixed; and are the upper and lower boundary frequencies of the interference frequency band in the i-th sub-window; represents the length of the frequency overlap interval between the interference frequency band and the target communication frequency band in the i-th sub-window;

[0032] Define a trend offset function to characterize the enhanced value of the perturbation difference between the current sub-window and its adjacent sub-window, and calculate the dynamic overlap factor through the trend offset function. The calculation expression is: , where: is the dynamic overlap factor, which is used to measure the frequency overlap intensity between the interference frequency band and the target communication frequency band under multiple sub-windows; is the trend offset enhancement function of the i-th sub-window, which is used to asymmetrically amplify the mid-segment offset; is the trend sensitivity index, which controls the response degree to mutations; and represent the actual occupancy ratios of the interference frequency band on both adjacent sides of the i-th sub-window to the target frequency band, which are used to construct the local trend response.

[0033] Preferably, when the communication link is in a high electromagnetic coupling interference state, the redundant coding intensity is dynamically adjusted according to the electromagnetic coupling interference intensity. The specific steps are as follows:

[0034] When it is detected that the electromagnetic coupling coefficient is greater than the electromagnetic coupling risk threshold, it enters the activation state, and a coding gain factor for regulating the coding intensity is calculated. The specific calculation expression is: , where: is the current electromagnetic coupling coefficient predicted by the support vector machine model, which represents the coupling strength between the target communication frequency band and the interference frequency band; is the electromagnetic coupling risk threshold. When it is determined to be in a high electromagnetic coupling interference state; is the coding gain factor, which is used to adjust the redundancy ratio and coding level subsequently; is the basic coupling amplitude response factor, which determines the influence degree of the amplitude exceeding the electromagnetic coupling risk threshold on the redundancy intensity; is the steepness adjustment coefficient of the non-linear activation curve, which is used to amplify the response of the electromagnetic coupling degree in the high-value interval; is the time derivative adjustment coefficient, which controls the weighted proportion of the electromagnetic coupling change trend; is the time derivative of the electromagnetic coupling coefficient, which reflects the rising or falling speed of the interference;

[0035] When the coding gain factor After that, the error correction coding configuration is dynamically adjusted according to the coding gain factor, including the redundancy ratio and the coding level, to form a new set of coding strategy parameters. The adjustment formula is as follows: , where: represents the redundancy ratio; is the system default base redundancy ratio; is the error correction coding level; is the default coding level; and are the gain mapping coefficients, used to control the sensitivity of redundancy enhancement and the amplitude of coding order improvement; is the updated set of coding parameter configurations, applied to the communication unit encoder layer.

[0036] A fire prevention door alarm information transmission device based on the Internet of Things, including a communication frequency band sensing module, an interference frequency band identification module, a feature index construction module, an interference risk intelligent discrimination module, and a coding parameter adaptive regulation module;

[0037] The communication frequency band sensing module, after the fire emergency mode is started, performs multi-band continuous scanning on the wireless communication environment in the deployment area of the fire prevention door alarm device, identifies the target communication protocol frequency band used by the fire prevention door alarm device, and obtains the spectrum parameter information of each frequency band to form a set of communication frequency bands to be analyzed;

[0038] The interference frequency band identification module, based on the pre-established environmental wireless device frequency band database, extracts the working frequency bands used by surrounding wireless devices, identifies the interference frequency bands with frequency adjacent relationships to the target communication frequency band, and constructs a spectrum mapping matrix between the communication frequency band and the interference source frequency band;

[0039] The feature index construction module, based on the spectrum mapping matrix, uses feature engineering methods to extract feature indexes reflecting the coupling characteristics between the communication frequency band and the interference frequency band, and constructs a feature index vector group for electromagnetic coupling interference determination through the analyzed feature indexes;

[0040] The interference risk intelligent discrimination module inputs the feature index vector group into the support vector machine model pre-trained with historical interference data, outputs the current coupling interference level identifier through the model, makes an intelligent prediction of the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band, and is used to determine whether the communication link is currently in a high electromagnetic coupling interference state;

[0041] The coding parameter adaptive regulation module, when the prediction result of the support vector machine model indicates the existence of high electromagnetic coupling interference, triggers the coding parameter adaptive regulation mechanism, dynamically adjusts the redundancy coding intensity according to the electromagnetic coupling interference intensity, and uses a higher error correction coding level transmission format for communication during the peak period of the coupling interference intensity.

[0042] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0043] By introducing an interference frequency band identification mechanism that combines spectrum scanning and an environmental database, the present invention constructs a spectrum mapping relationship between the communication frequency band and the interference source frequency band, and combines a support vector machine model to perform high-precision interference level prediction on the feature index vector group. Thus, when an electromagnetic coupling risk is detected, an automatic trigger for the adaptive adjustment mechanism of the coding parameters is activated, and the error correction ability is dynamically enhanced according to the interference intensity to ensure that key alarm information can still be stably and reliably uploaded and drive linkage control operations in a high-interference environment. Compared with the existing static coding configuration or single anti-interference strategy, this method has the overall ability of feedforward identification and closed-loop regulation, and effectively solves the core problems of "inaccurate identification, imprecise response, and unstable transmission" of traditional fire communication in interference-intensive scenarios. Brief Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0045] Figure 1 It is a method flow chart of a method for transmitting fire door alarm information based on the Internet of Things according to the present invention.

[0046] Figure 2 It is a module schematic diagram of a device for transmitting fire door alarm information based on the Internet of Things according to the present invention. Detailed Embodiments

[0047] Now, the exemplary embodiments will be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these exemplary embodiments are provided so that the present disclosure will be more complete and comprehensive, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0048] The present invention provides a method for transmitting fire door alarm information based on the Internet of Things as shown in Figure 1 and includes the following steps:

[0049] After the fire emergency mode is started, a spectrum analyzer is used to perform multi-band continuous scanning on the wireless communication environment in the deployment area of the fire door alarm device, identify the target communication protocol frequency band used by the fire door alarm device, and obtain spectrum parameter information such as the center frequency, bandwidth, and power spectral density of each frequency band to form a set of communication frequency bands to be analyzed;

[0050] The specific steps of using a spectrum analyzer to perform multi-band continuous scanning of the wireless communication environment in the area where the fire door alarm device is deployed to identify the target communication protocol frequency band used by it are as follows: First, in the initial stage after the fire door alarm device is started, the scanning range is set through the spectrum analyzer connected to the device to cover the frequency band range used by typical communication protocols (such as NB-IoT, LoRa, etc.) (such as 430 MHz–470 MHz, 780 MHz–960 MHz, etc.); secondly, the scanning parameters including scanning step size, dwell time and power threshold are configured, the spectrum continuous scanning function is started and the real-time power spectrum density diagram is recorded; then, the collected spectrum data is analyzed, and the current active frequency band is identified based on the power peak, modulation characteristics or spectrum stability; finally, the frequency band attribution is matched in combination with the communication protocol characteristics (such as the spreading factor bandwidth of LoRa and the subcarrier division of NB-IoT) to determine the target communication protocol frequency band used by the fire door alarm device for subsequent interference analysis and frequency band mapping.

[0051] The role of this step is to provide spectrum basic data support and interference risk modeling basis for subsequent electromagnetic coupling interference identification and adaptive communication control, which is the perception entrance and data source in the entire interference prediction and control mechanism. In the fire emergency mode, due to the rapid increase in the on-site ambient temperature, the abnormal and frequent behavior of personnel, and the simultaneous activation of the alarm communication function of multiple devices, the wireless channel environment becomes instantly complex and highly dynamic, and it is very easy to have communication frequency band conflicts, signal blocking or interference superposition. Through the spectrum analyzer, the multi-band continuous scanning of the on-site wireless communication environment can fully and real-time perceive the wireless energy distribution state in the current area, and accurately identify the target communication protocol frequency band currently used by the fire door alarm device. Further extraction of key spectrum parameters such as center frequency, bandwidth, and power spectrum density not only helps to characterize the use characteristics of the communication link, but also provides the necessary original input for subsequent frequency adjacency evaluation with the interference source frequency band, bandwidth overlap rate calculation, and power interference trend analysis. By forming a set of communication frequency bands to be analyzed, the interference identification accuracy and response speed can be effectively improved, thereby ensuring that the system still has the ability to accurately judge and quickly control in a high-interference environment, and ensuring that the alarm information during the fire is uploaded to the background system in a timely and reliable manner and linked to the control equipment.

[0052] Based on the pre-established environmental wireless device frequency band database, the working frequency bands used by surrounding wireless devices such as elevator control systems, industrial WLAN, and security wireless units are extracted, the interference frequency bands that are adjacent to the target communication frequency bands are identified, and the spectrum mapping matrix between the communication frequency bands and the interference source frequency bands is constructed;

[0053] The specific steps for identifying interference frequency bands that are adjacent to the target communication frequency band are as follows: First, based on the pre-established environmental wireless device frequency band database, the working frequency band parameters of known wireless devices (such as elevator systems, industrial WLAN, wireless access control, security cameras, etc.) in the deployment area of ​​the fire door alarm device are extracted, including center frequency, bandwidth range and working mode; second, the identified target communication protocol frequency band (such as NB-IoT or LoRa) is compared, and the frequency adjacent judgment threshold (such as ±20 MHz or ±10% bandwidth) is set as the interference judgment window based on its center frequency; then, the center frequency difference calculation and bandwidth overlap ratio analysis are performed on all peripheral device frequency bands to screen out wireless device frequency bands whose center frequency has partial or complete bandwidth overlap with the target frequency band (if the bandwidth overlap exceeds 20%, it should be considered that there is partial bandwidth overlap); finally, these frequency bands are recorded as potential interference source frequency bands, and their corresponding device types, interference weights and overlapping properties are marked as interference frequency band set inputs for constructing the spectrum mapping matrix for subsequent electromagnetic coupling analysis and interference intensity modeling.

[0054] This process ensures that the potential coupling interference risks caused by spectrum proximity can be fully captured, improving the refinement and completeness of spectrum resource perception.

[0055] The main function of this step is to build an interference correlation model in the IoT communication environment, and provide accurate and structured spectrum data support for the subsequent identification, prediction and communication control of electromagnetic coupling interference. In the actual building environment, the fire door alarm device is not the only wireless device in operation. The communication protocols such as NB-IoT and LoRa used by it often work on adjacent or partially overlapping frequency bands with devices such as elevator control systems, industrial WLAN, security cameras, and wireless access control systems. Since these devices may be started centrally during a fire, causing multi-band high-power signals in the same physical space to be activated simultaneously, it is very easy to cause coupling effects such as intermodulation interference, bandwidth leakage, and frequency suppression between adjacent frequency bands, affecting the real-time transmission of key alarm data. Therefore, this step systematically extracts the communication frequency bands of all active devices in the area by calling the pre-established environmental wireless device frequency band database, and performs frequency spacing analysis and bandwidth overlap judgment with the target communication frequency band to identify the frequency band set with potential interference risks. Then, a spectrum mapping matrix with a "communication frequency band → interference source frequency band" relationship mapping is constructed to accurately characterize the influence range and interference path of the interference source on the target frequency band. This matrix serves as the core input for subsequent electromagnetic coupling modeling, interference intensity prediction, and redundant coding strategy adjustment. It plays a key role in bridging the spectrum structure and regulation mechanism, and significantly improves the system's perception and response accuracy to communication risks in multi-source interference scenarios.

[0056] Based on the spectrum mapping matrix, the feature engineering method is used to extract the feature indicators reflecting the coupling characteristics between the communication frequency band and the interference frequency band, and the feature indicator vector group for electromagnetic coupling interference determination is constructed through the analyzed feature indicators.

[0057] The feature engineering method is used to extract the feature indicators reflecting the coupling characteristics between the communication frequency band and the interference frequency band. Among them, the extracted indicators include the noise distribution asymmetry on the equidistant frequency domains on both the left and right sides of the target frequency band and the dynamic overlap ratio between the interference frequency band and the target frequency band in multiple time periods. Under the analysis window, after analyzing the extracted feature indicators, the carrier-to-noise asymmetry factor and the dynamic overlap factor are generated respectively, and the feature indicator vector group for electromagnetic coupling interference determination is constructed through the carrier-to-noise asymmetry factor and the dynamic overlap factor.

[0058] The higher the noise distribution asymmetry on the equidistant frequency domains on both the left and right sides of the target frequency band, the more it usually indicates that the communication frequency band is being subjected to electromagnetic coupling interference from an interference frequency band on one side, and the higher the risk. Under ideal conditions, the noise floor distribution of a non-interfered spectrum environment should present an approximately symmetric distribution, that is, the background noise power at equal distances on both sides of the target frequency band should be approximately the same. However, when there is a high-power emission source (such as an industrial WLAN or an elevator radio unit) in an adjacent frequency band, the spectrum sideband leakage or modulated out-of-band energy will penetrate towards one side of the target frequency band, causing a significant increase in the noise power on that side and forming a carrier-to-noise asymmetry phenomenon (Carrier-to-Noise Asymmetry). This asymmetry not only reflects the energy shift in the frequency domain but also implies that the modulation crosstalk or frequency modulation drift in the physical layer may be acting across frequencies on the target communication channel, thereby leading to an increase in the bit error rate, packet loss, or transmission delay. Therefore, the noise distribution asymmetry is a sensitive feature indicator with direction discrimination significance and can be used as an important signal feature for evaluating the intensity of electromagnetic coupling interference.

[0059] Under the analysis window, the specific steps for generating the carrier-to-noise asymmetry factor by analyzing the noise distribution asymmetry on the equidistant frequency domains on both the left and right sides of the target frequency band are as follows:

[0060] Taking the center frequency of the target communication frequency band as a reference, a frequency offset distance is set on both its left and right sides to determine the symmetric sampling points. Near each symmetric point, a sub-frequency band region with a bandwidth is randomly selected, and the integral energy of the noise power spectral density is calculated. The calculation expression is: , where: represents the noise power spectral density at frequency f; is the sub-frequency band width, which determines the size of the integral region; and are the left and right symmetric frequency points defined relative to the center frequency of the target communication frequency band, , , is the center frequency of the target communication frequency band; and Respectively represent the noise energy of the left and right sub-bands, reflecting the interference intensity;

[0061] The purpose of this step is to establish a preliminary directional perception basis for spectrum interference through the integrated energy difference in the left and right symmetrical regions, and to provide energy input characteristics for asymmetric modeling.

[0062] Obtaining the noise energy on the left and right sides and Finally, the carrier-noise asymmetry factor is calculated using a hyperbolic function to enhance the ability to perceive small differences while ensuring the continuity and boundedness of the carrier-noise asymmetry factor. The calculation expression of the carrier-noise asymmetry factor is: ,in: The carrier-noise asymmetry factor characterizes the asymmetry of the noise power at equidistant frequency domain positions on the left and right sides of the target communication frequency band. is the trend amplification factor, which is used to adjust the response sensitivity of the carrier-noise asymmetry factor to the asymmetry degree; Hyperbolic tangent function, compressing the output value to , the closer the output value is to 1, the higher the degree of asymmetry;

[0063] The purpose of this step is to convert the left and right noise energy difference into a quantifiable, directional carrier-noise asymmetry factor, providing a quantitative basis for the intelligent model to judge the communication interference trend and adjust the transmission strategy.

[0064] It can be seen from the carrier-to-noise asymmetry factor that, under the analysis window, the carrier-to-noise asymmetry factor generated after analyzing the asymmetry of the noise distribution on the equidistant frequency domains on the left and right sides of the target frequency band is larger, indicating that the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band is higher, and vice versa, indicating that the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band is lower. The reason is that electromagnetic coupling interference usually propagates to adjacent frequency bands in the form of energy leakage or modulation crosstalk, and manifests as an abnormal increase in the local frequency domain noise level, especially when there is a high-power interference source on only one side of the frequency band, this impact shows obvious spectral directionality. The carrier-to-noise asymmetry factor quantifies this directional shift by comparing the nonlinear normalized integrated energy of the noise on the left and right sides. Therefore, the larger its value, the more concentrated and intrusive the interference is, and the more significant the impact on the communication link is.

[0065] A high dynamic overlap ratio between the target frequency band and the interference frequency band in multiple time periods usually indicates a stronger risk of electromagnetic coupling interference between the communication frequency band and the interference frequency band. This is because in a complex wireless environment, interference sources often exhibit non-persistent emission behaviors, such as burst reporting, periodic beam switching, or frequency offset caused by temperature drift. If its spectral energy repeatedly enters the target communication frequency band or its sideband region within multiple time segments, it means that this interference has the superposition effect of stability and persistence. This "high-frequency dynamic overlap" feature will significantly increase the modulation crosstalk, frame error rate, and packet retransmission probability between signals. Especially for low-power protocols such as LoRa or NB-IoT, the link reliability is highly sensitive to the spectral cleanliness. Therefore, the higher the overlap ratio, the more significant the coupling interference risk. The dynamic overlap ratio not only captures the frequency domain intersection but also incorporates the change trend in the time dimension. It is an important precursor parameter for identifying the evolution of weak coupling into strong coupling and has extremely high discriminant value in the spectral mapping matrix.

[0066] After analyzing the dynamic overlap ratio between the interference frequency band and the target frequency band in multiple time periods under the analysis window, the specific steps to generate the dynamic overlap factor are as follows:

[0067] Divide the analysis window into N equally spaced sub-windows (for example, one spectral snapshot per second). For the i-th sub-window, define the interference frequency band as , and the target communication frequency band as a fixed value . Calculate the actual occupancy ratio of the interference frequency band to the target frequency band in each sub-window. The calculation expression is: , where: represents the actual occupancy ratio of the interference frequency band to the target frequency band in the i-th sub-window; and are the upper and lower boundaries of the target communication frequency band, which are fixed and unchanged; and are the upper and lower boundary frequencies of the interference frequency band in the i-th sub-window, which will change dynamically due to frequency modulation or drift; represents the length of the frequency overlap interval used to calculate the interference frequency band and the target communication frequency band in the i-th sub-window;

[0068] The function of this step is to construct a multi-time-period overlap rate sequence , forming a perception of the time distribution of the coupling strength for subsequent construction of the dynamic overlap factor.

[0069] To identify whether the interference has a trend of "stable suppression" or "periodic intrusion" in multiple sub-windows, define a trend offset function to characterize the enhanced value of the perturbation difference between the current sub-window and its adjacent sub-window, and calculate the dynamic overlap factor through the trend offset function. The calculation expression is: , where: is the dynamic overlap factor, which is used to measure the frequency overlap intensity between the interference frequency band and the target communication frequency band under multiple sub-windows; is the trend offset enhancement function of the i-th sub-window, which is used to asymmetrically amplify the mid-segment offset; is the trend sensitivity index, which controls the response degree to mutations (for example, taking values of 1.5 or 2); and represent the actual occupancy ratio of the interference frequency bands on both adjacent sides of the i-th sub-window to the target frequency band, which is used to construct the local trend response; this construction uses the product form to perform internal suppression on the "stationary sequence" (if , the dynamic overlap factor is close to zero), and exponentially enhances the "local mutations" (such as burst suppression, short-term drift).

[0070] The function of this step is to comprehensively evaluate the non-linear coupling trend of the interference frequency band on the time axis, strengthen the sensitivity to irregular intrusion behaviors through the asymmetric structure. The larger the dynamic overlap factor, the more frequent and discontinuous the suppression behaviors of the interference frequency band are, posing a substantial threat to the stability of the communication link.

[0071] It can be seen from the dynamic overlap factor that under the analysis window, the larger the performance value of the dynamic overlap factor generated after analyzing the dynamic overlap ratio between the interference frequency band and the target frequency band in multiple time periods, the higher the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band. Conversely, it indicates a lower electromagnetic coupling interference risk between the communication frequency band and the interference frequency band. The reason is that the dynamic overlap factor quantifies the dynamic coupling behavior between the two by performing trend, concentration, and perturbation enhancement analysis on the frequency overlap ratio sequence between the interference frequency band and the target communication frequency band in multiple time periods within the analysis window. When the dynamic overlap factor is high, it means that the interference frequency band frequently and violently intrudes or approaches the target frequency band in the time dimension, and its overlap behavior is no longer random or occasional, but has a certain stability or periodic intrusion trend. Such coupling often leads to serious consequences such as modulation crosstalk, frame loss, and unstable communication links, belonging to a high-risk interference state. On the contrary, if the dynamic overlap factor is small, it means that the interference frequency band only approaches the target frequency band occasionally or briefly, the overlap behavior is sparse, and the perturbation intensity is low. The system can maintain normal communication through the conventional error correction mechanism, so the corresponding coupling interference risk is low. The size of the dynamic overlap factor essentially reflects the comprehensive influence of spatial frequency domain overlap + time variation trend, so it is an effective indicator for judging the strength of coupling interference risk.

[0072] Input the feature index vector group into the support vector machine model that has been pre-trained with historical interference data, and output the current coupling interference level identifier through the model to intelligently predict the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band, and is used to determine whether the communication link is currently in a high electromagnetic coupling interference state;

[0073] Input the feature index vector group composed of the noise-to-noise asymmetry factor and the dynamic overlap factor into the support vector machine model that has been pre-trained with historical interference data. Output the electromagnetic coupling coefficient through the model, and based on the electromagnetic coupling coefficient, intelligently predict the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band, and use it to determine whether the communication link is currently in a high electromagnetic coupling interference state.

[0074] The support vector machine model pre-trained with historical interference data refers to the process of using a large number of labeled wireless communication interference sample data to perform supervised learning training on the support vector machine (SVM, Support Vector Machine) before system deployment or during the initialization stage of communication security policies, enabling it to automatically determine the electromagnetic coupling interference risk state between the communication frequency band and the interference frequency band. In this context, the training input of the support vector machine model is the feature index vector group composed of the "noise-to-noise asymmetry factor" and the "dynamic overlap factor", and the training output is the interference level label corresponding to each group of vectors (such as "no interference", "mild coupling interference", "moderate coupling interference", "severe coupling interference", etc.). As a classic two-class or multi-class classification algorithm, the basic idea of the support vector machine is to find the optimal hyperplane in the feature space to achieve the optimal interval division of various samples on both sides of this plane. Since electromagnetic interference data often exhibits non-linear distribution characteristics, the support vector machine model maps the original feature space to a high-dimensional space through a kernel function (such as the RBF kernel or polynomial kernel), so as to achieve linear separability in the high-dimensional feature space, and then output an electromagnetic coupling coefficient that accurately reflects the degree of coupling risk as the discrimination basis.

[0075] During the training process, it is first necessary to construct a historical sample library that includes various scenarios, multiple communication protocol frequency bands, and various interference sources. Each sample contains two core feature parameters: the noise-to-noise asymmetry factor and the dynamic overlap factor. The noise-to-noise asymmetry factor index is used to measure the noise power difference in the left and right adjacent frequency regions of the target communication frequency band, that is, the noise asymmetry degree of the left and right frequency bands. If the communication frequency band is continuously interfered by a certain frequency band on one side, its noise environment will show significant asymmetry, and the noise-to-noise asymmetry factor will deviate significantly from zero. This characteristic can reflect the existence of directional coupling interference, such as the unilateral leakage suppression effect of the elevator wireless module or access control system on the communication frequency band in a specific direction. The dynamic overlap factor is used to describe the dynamic change rate of the spectrum overlap between the interference frequency band and the communication frequency band in the time dimension, that is, the overlap probability of the interference frequency band repeatedly invading the communication frequency band in multiple time periods. A high dynamic overlap factor indicates that the interference source does not appear accidentally in the communication frequency band, but has a periodic occupation characteristic, representing the coupling risk of time stability. These two parameters jointly construct a two-dimensional feature space, where each sample point represents the coupling state of a certain frequency band pair at a certain moment in a specific environment.

[0076] After inputting these feature samples into a support vector machine for training, the support vector machine will learn how to divide the boundaries of different electromagnetic coupling levels in the two-dimensional noise-carrier asymmetry factor - dynamic overlap factor space. After the training is completed, the model can be used in the real-time prediction stage: The system takes the real-time calculated values of the current noise-carrier asymmetry factor and dynamic overlap factor as input vectors and inputs them into the trained model. The model outputs a normalized electromagnetic coupling coefficient, and the numerical range can be set between 0 and 1, which is used to represent the coupling strength between the current communication frequency band and the interference frequency band.

[0077] The greatest advantage of this mechanism lies in:

[0078] It can structure the dynamic and time-varying interference behavior data into a learnable pattern;

[0079] Quickly and accurately judge the multi-dimensional interference situation through the classification ability of the support vector machine;

[0080] Use physical layer features (such as noise-carrier asymmetry factor, dynamic overlap factor) to establish an early prediction model for the degradation of communication link quality;

[0081] Implement a closed-loop control logic from interference detection to anti-interference regulation, and enhance the communication robustness of the system in high-risk situations such as fires. Therefore, the "support vector machine model pre-trained with historical interference data" is not only the implementation form of the core engine of the system's intelligent prediction, but also the key center for realizing the full-process intelligent perception and control of "electromagnetic coupling risk → regulation response". This model has extremely strong adaptability and generalization performance in engineering practice and is applicable to the Internet of Things security communication guarantee system under different frequency band protocols and different deployment scenarios.

[0082] Compare and analyze the electromagnetic coupling coefficient generated when the support vector machine model makes an intelligent prediction of the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band with a pre-set electromagnetic coupling risk threshold (such as 0.7 or 0.8) to determine whether the current communication link is in a high electromagnetic coupling interference state. The specific determination steps are as follows:

[0083] If the electromagnetic coupling coefficient is greater than the electromagnetic coupling risk threshold, a risk signal is generated, and it is determined that the current communication link is in a high electromagnetic coupling interference state; if the electromagnetic coupling coefficient is less than or equal to the electromagnetic coupling risk threshold, a normal signal is generated, and it is determined that the current communication link is in a low-risk interference state and is communicating efficiently.

[0084] When the prediction result of the support vector machine model indicates the existence of high electromagnetic coupling interference, trigger the adaptive regulation mechanism of coding parameters, dynamically adjust the redundancy coding strength according to the electromagnetic coupling interference intensity. During the peak period of the coupling interference intensity, adopt a transmission format with a higher error correction coding level to improve the reliability of data transmission;

[0085] When the communication link is in a state of high electromagnetic coupling interference, dynamically adjust the redundancy coding strength according to the electromagnetic coupling interference intensity. The specific steps are as follows:

[0086] When it is detected that the electromagnetic coupling coefficient is greater than the electromagnetic coupling risk threshold, enter the activation state, and calculate a coding gain factor for regulating the coding strength. This coding gain factor takes the amplitude by which the coupling strength exceeds the threshold as the core driving factor, and combines the time variation trend, and introduces a non-linear activation modulation function for calculation. The specific calculation expression is: , where: is the electromagnetic coupling coefficient currently predicted by the support vector machine model, representing the coupling strength between the target communication frequency band and the interference frequency band; is the electromagnetic coupling risk threshold, when is determined to be in a state of high electromagnetic coupling interference; is the coding gain factor, which is used to adjust the redundancy ratio and coding level subsequently; is the basic coupling amplitude response factor, which determines the influence degree of the amplitude exceeding the electromagnetic coupling risk threshold on the redundancy strength; is the non-linear activation curve steepness adjustment coefficient, which is used to amplify the response of the electromagnetic coupling degree in the high value range; is the time derivative adjustment coefficient, which controls the weighted proportion of the electromagnetic coupling change trend (such as sudden increase in interference); is the time derivative of the electromagnetic coupling coefficient, which reflects the rising or falling speed of the interference;

[0087] This step fuses the static coupling strength and the interference change trend to generate a quantifiable and continuously adjustable coding gain factor , which serves as the core driving quantity for subsequent coding parameter updates. By introducing a non-linear activation function (in the form of Softplus), it is ensured that the regulation mechanism is insensitive to small fluctuations and responds quickly to large disturbances, enhancing the system robustness.

[0088] When the coding gain factor is obtained, dynamically adjust the error correction coding configuration according to this coding gain factor, including the redundancy ratio and the coding level, to form a new coding strategy parameter group. The adjustment formula is as follows: , where: represents the redundancy ratio (the ratio of redundant bits to effective bits), and increasing this value can improve the fault tolerance ability; is the system default basic redundancy ratio, usually 0.1 - 0.3; is the error correction coding level, which is usually mapped to the LDPC code depth, convolutional code constraint length or Turbo code structure complexity; It is the default encoding level, which indicates the lowest fault-tolerant configuration of the system in a non-interference state; and is the gain mapping coefficient, which is used to control the sensitivity of redundancy enhancement and the amplitude of coding order increase; is an updated encoding parameter configuration set, applied to the communication unit encoder layer;

[0089] This step converts the coding gain factor into actual communication parameter adjustment actions to form a nonlinear adaptive coding control mechanism. This ensures that when the interference intensity is higher and the changes are more drastic, a stronger error correction strategy (such as a higher LDPC level and a larger convolution kernel) is adopted, thereby improving the data integrity and transmission reliability of the system under high-coupling interference conditions.

[0090] The core function of this step is to build an intelligent adaptive anti-interference mechanism based on dynamic identification of interference intensity, which is used to ensure the transmission reliability and system stability of the IoT communication link in a high electromagnetic coupling interference environment. When the prediction results of the support vector machine model show that there is a high electromagnetic coupling risk between the current communication frequency band and the interference frequency band, it means that the communication link is facing strong interference caused by factors such as adjacent frequencies, bandwidth overlap or energy leakage. At this time, if the original low redundancy and low error correction level configuration is maintained, it will easily cause serious problems such as communication data packet loss, increased bit reversal rate, and control command lag, which will lead to the failure of fire warning information to be uploaded in time and the failure of fire door linkage response. Therefore, by triggering the adaptive control mechanism of coding parameters, real-time perception and response to changes in electromagnetic interference intensity, and dynamically adjusting the redundant coding strength and error correction coding level, it can not only effectively improve the error recovery capability and reduce the probability of retransmission, but also extend the effective communication window and improve the channel utilization efficiency. When the interference intensity reaches its peak, the system automatically switches to a higher level of error correction format (such as enhanced LDPC code, deep convolutional code, etc.) to further enhance the fault tolerance to instantaneous signal pollution and data distortion, ensuring that key alarm data can still be transmitted to the target node stably, accurately and safely in extreme electromagnetic environments. Realizing closed-loop robust communication control from "interference identification" to "regulation response" is a key step to ensure the reliable operation of the intelligent fire protection system.

[0091] Through the above-mentioned method for transmitting fire door alarm information based on the Internet of Things, it is possible to achieve real-time perception, intelligent discrimination, and dynamic anti-interference regulation of the interference states of multi-source wireless devices in complex building environments, significantly improving the communication robustness and response reliability of the system under extreme conditions such as fires. This method introduces an interference frequency band identification mechanism that combines spectrum scanning and an environmental database to construct a spectrum mapping relationship between the communication frequency band and the interference source frequency band, and combines a support vector machine model to predict the interference level with high precision for the feature index vector group. Thus, when an electromagnetic coupling risk is detected, it can automatically trigger an adaptive adjustment mechanism for coding parameters, dynamically improving the error correction ability according to the interference intensity, ensuring that key alarm information can still be stably and reliably uploaded in a high-interference environment and driving linkage control operations. Compared with existing static coding configurations or single anti-interference strategies, this method has the overall ability of feedforward identification and closed-loop regulation, effectively solving the core problems of "in timely identification, inaccurate response, and unstable transmission" of traditional fire communication in interference-intensive scenarios, and having strong engineering practical value and promotion significance.

[0092] The present invention provides a Figure 2 fire door alarm information transmission device based on the Internet of Things as shown, including a communication frequency band perception module, an interference frequency band identification module, a feature index construction module, an interference risk intelligent discrimination module, and an adaptive regulation module for coding parameters;

[0093] The communication frequency band perception module, after the fire emergency mode is started, performs multi-band continuous scanning on the wireless communication environment in the deployment area of the fire door alarm device, identifies the target communication protocol frequency band used by the fire door alarm device, and obtains the spectrum parameter information of each frequency band to form a set of communication frequency bands to be analyzed;

[0094] The interference frequency band identification module, based on a pre-established environmental wireless device frequency band database, extracts the working frequency bands used by surrounding wireless devices, identifies the interference frequency bands having a frequency adjacent relationship with the target communication frequency band, and constructs a spectrum mapping matrix between the communication frequency band and the interference source frequency band;

[0095] The feature index construction module, based on the spectrum mapping matrix, uses feature engineering methods to extract feature indexes reflecting the coupling characteristics between the communication frequency band and the interference frequency band from it, and constructs a feature index vector group for electromagnetic coupling interference determination through the analyzed feature indexes;

[0096] The interference risk intelligent discrimination module inputs the feature index vector group into a support vector machine model that has been trained in advance with historical interference data, outputs the current coupling interference level identifier through the model, makes an intelligent prediction of the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band, and is used to determine whether the communication link is currently in a high electromagnetic coupling interference state;

[0097] The encoding parameter adaptive regulation module triggers the encoding parameter adaptive regulation mechanism when the prediction result of the support vector machine model indicates the existence of high electromagnetic coupling interference, dynamically adjusts the redundant encoding intensity according to the electromagnetic coupling interference intensity, and adopts a transmission format with a higher error correction encoding level for communication during the peak period of the coupling interference intensity.

[0098] The method for transmitting fire door alarm information based on the Internet of Things provided by the embodiments of the present invention is implemented through the above-mentioned device for transmitting fire door alarm information based on the Internet of Things. The specific method and process of the device for transmitting fire door alarm information based on the Internet of Things can be found in the embodiments of the above-mentioned method for transmitting fire door alarm information based on the Internet of Things, and will not be elaborated here.

[0099] The above formulas are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0100] Only some exemplary embodiments of the present invention have been described by way of illustration above. Undoubtedly, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0101] As described above, this is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for transmitting fire door alarm information based on the Internet of Things, characterized in that, It includes the following steps: After the fire emergency mode is started, continuously scan the wireless communication environment in the deployment area of the fire door alarm device in multiple frequency bands, identify the target communication protocol frequency band used by the fire door alarm device, and obtain the spectrum parameter information of each frequency band to form a set of communication frequency bands to be analyzed; Based on the pre-established environmental wireless device frequency band database, extract the working frequency bands used by the surrounding wireless devices, identify the interference frequency bands that have a frequency adjacent relationship with the target communication frequency band, and construct a spectrum mapping matrix between the communication frequency band and the interference source frequency band; Based on the spectrum mapping matrix, use the feature engineering method to extract the feature indicators reflecting the coupling characteristics between the communication frequency band and the interference frequency band from it, and construct a feature indicator vector group for electromagnetic coupling interference determination through the analyzed feature indicators; Input the feature indicator vector group into the support vector machine model that has been trained in advance with historical interference data, and output the current coupling interference level identifier through the model to intelligently predict the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band, and to determine whether the communication link is currently in a high electromagnetic coupling interference state; When the prediction result of the support vector machine model indicates the existence of high electromagnetic coupling interference, trigger the adaptive regulation mechanism of the coding parameters, dynamically adjust the redundancy coding intensity according to the electromagnetic coupling interference intensity, and use a transmission format with a higher error correction coding level for communication during the peak period of the coupling interference intensity.

2. The method for transmitting fire door alarm information based on the Internet of Things according to claim 1, wherein The specific steps for continuously scanning the wireless communication environment in the deployment area of the fire door alarm device in multiple frequency bands to identify the target communication protocol frequency band used are as follows: In the initial stage after the fire door alarm device is started, set the scanning range through the spectrum analyzer connected to the device to cover the frequency band range used by the communication protocol; Configure the scanning parameters including the scanning step size, dwell time, and power threshold, start the continuous spectrum scanning function, and record the real-time power spectral density map; Analyze the collected spectrum data, and identify the currently active frequency band based on the power peak, modulation characteristics, or spectrum stability; Combine the communication protocol characteristics for frequency band attribution matching to determine the target communication protocol frequency band used by the fire door alarm device for subsequent interference analysis and frequency band mapping.

3. A method for transmitting fire door alarm information based on the Internet of Things according to claim 1, characterized in that The specific steps for identifying the interference frequency bands that have a frequency adjacent relationship with the target communication frequency band are as follows: Based on the pre-established environmental wireless device frequency band database, extract the working frequency band parameters of the known wireless devices in the deployment area of the fire door alarm device, including the center frequency, bandwidth range, and working mode; Compare the identified target communication protocol frequency band, and set the frequency adjacent judgment threshold as the interference judgment window based on its center frequency; Calculate the difference in the center frequency and analyze the bandwidth overlap ratio for all the surrounding device frequency bands, and screen out the wireless device frequency bands whose center frequency has partial or complete bandwidth overlap with the target frequency band; Record the screened frequency bands as potential interference source frequency bands, mark their corresponding device types, interference weights, and overlap attributes, and input them as the interference frequency band set for constructing the spectrum mapping matrix for subsequent electromagnetic coupling analysis and interference intensity modeling.

4. A method for transmitting fire door alarm information based on the Internet of Things according to claim 1, characterized in that, Feature engineering methods are used to extract feature indicators that reflect the coupling characteristics between the communication frequency band and the interference frequency band. Among them, the extracted indicators include the noise distribution asymmetry in the equidistant frequency domains on the left and right sides of the target frequency band and the dynamic overlap ratio between the interference frequency band and the target frequency band in multiple time periods. After analyzing the extracted feature indicators under the analysis window, a noise-to-noise asymmetry factor and a dynamic overlap factor are respectively generated, and a feature indicator vector group for electromagnetic coupling interference determination is constructed through the noise-to-noise asymmetry factor and the dynamic overlap factor.

5. A method for transmitting fire door alarm information based on the Internet of Things according to claim 4, characterized in that, The feature indicator vector group composed of the noise-to-noise asymmetry factor and the dynamic overlap factor is input into a support vector machine model that has been pre-trained with historical interference data. The electromagnetic coupling coefficient is output through the model, and the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band is intelligently predicted based on the electromagnetic coupling coefficient, and it is used to determine whether the communication link is currently in a high electromagnetic coupling interference state.

6. The method for transmitting fire door alarm information based on the Internet of Things according to claim 5, wherein, The electromagnetic coupling coefficient generated when the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band is intelligently predicted through the support vector machine model is compared and analyzed with a preset electromagnetic coupling risk threshold to determine whether the communication link is currently in a high electromagnetic coupling interference state. The specific determination steps are as follows: If the electromagnetic coupling coefficient is greater than the electromagnetic coupling risk threshold, a risk signal is generated, and it is determined that the current communication link is in a high electromagnetic coupling interference state; if the electromagnetic coupling coefficient is less than or equal to the electromagnetic coupling risk threshold, a normal signal is generated, and it is determined that the current communication link is in a low-risk interference state and is communicating efficiently.

7. A method for transmitting fire door alarm information based on the Internet of Things according to claim 4, characterized in that, Under the analysis window, the specific steps for generating the noise-to-noise asymmetry factor after analyzing the noise distribution asymmetry in the equidistant frequency domains on the left and right sides of the target frequency band are as follows: Taking the center frequency of the target communication frequency band as a reference, set a frequency offset distance on each of its left and right sides to determine symmetric sampling points. Near each symmetric point, randomly select a sub-band region with a bandwidth and calculate the integral energy of the noise power spectral density. The calculation expression is: , where: represents the noise power spectral density at frequency f; is the sub-band width; and are the left and right symmetric frequency points defined relative to the center frequency of the target communication frequency band, 、 , is the center frequency of the target communication frequency band; and respectively represent the noise energies of the left and right sub-bands, reflecting the interference intensity; After obtaining the left and right noise energies and the hyperbolic function is used to calculate the noise - to - carrier asymmetry factor, and the calculation expression is: where: is the noise - to - carrier asymmetry factor, which characterizes the noise power asymmetry of the target communication frequency band at the equidistant frequency domain positions on its left and right sides; is the trend amplification coefficient, which is used to regulate the response sensitivity of the noise - to - carrier asymmetry factor to the degree of asymmetry.

8. A method for transmitting fire door alarm information based on the Internet of Things according to claim 4, characterized in that, Under the analysis window, the specific steps for generating the dynamic overlap factor after analyzing the dynamic overlap ratio between the interference frequency band and the target frequency band in multiple time periods are as follows: Divide the analysis window into N equally spaced sub - windows. For the i - th sub - window, define the interference frequency band as , and the target communication frequency band as a fixed value . Calculate the actual occupancy ratio of the interference frequency band to the target frequency band in each sub - window. The calculation expression is: , where: represents the actual occupancy ratio of the interference frequency band to the target frequency band in the i - th sub - window; and are the upper and lower boundaries of the target communication frequency band, which are fixed; and are the upper and lower boundary frequencies of the interference frequency band in the i - th sub - window; represents the length of the frequency overlap interval used to calculate the interference frequency band and the target communication frequency band in the i - th sub - window; Define a trend offset function to characterize the enhanced value of the perturbation difference between the current sub-window and its adjacent sub-windows, and calculate the dynamic overlap factor through the trend offset function. The calculation expression is: , where: is the dynamic overlap factor, which is used to measure the frequency overlap intensity between the interference frequency band and the target communication frequency band under multiple sub-windows; is the trend offset enhancement function of the i-th sub-window, which is used to asymmetrically amplify the mid-segment offset; is the trend sensitivity index, which controls the response degree to mutations; and represent the actual occupancy ratios of the interference frequency bands on both adjacent sides of the i-th sub-window to the target frequency band, which are used to construct the local trend response.

9. A method for transmitting fire door alarm information based on the Internet of Things according to claim 5, characterized in that, When the communication link is in a high electromagnetic coupling interference state, the redundancy coding strength is dynamically adjusted according to the electromagnetic coupling interference intensity. The specific steps are as follows: When the detected electromagnetic coupling coefficient is greater than the electromagnetic coupling risk threshold, it enters the activation state, and a coding gain factor for regulating the coding strength is calculated. The specific calculation expression is as follows: , where: is the electromagnetic coupling coefficient currently predicted by the support vector machine model, representing the coupling strength between the target communication frequency band and the interference frequency band; is the electromagnetic coupling risk threshold. When is determined to be in a high electromagnetic coupling interference state; is the coding gain factor, which is used to adjust the redundancy ratio and coding level subsequently; is the basic coupling amplitude response factor, which determines the influence degree of the amplitude exceeding the electromagnetic coupling risk threshold on the redundancy strength; is the steepness adjustment coefficient of the non-linear activation curve, which is used to amplify the response of the electromagnetic coupling degree in the high value range; is the time derivative adjustment coefficient, which controls the weighted proportion of the change trend of the electromagnetic coupling; is the time derivative of the electromagnetic coupling coefficient, which reflects the rising or falling speed of the interference; When the coding gain factor is obtained After that, the error correction coding configuration is dynamically adjusted according to the coding gain factor, including the redundancy ratio and the coding level, so as to form a new set of coding strategy parameters. The adjustment formula is as follows: , where: represents the redundancy ratio; is the system default base redundancy ratio; is the error correction coding level; is the default coding level; and are the gain mapping coefficients, which are used to control the sensitivity of redundancy enhancement and the amplitude of coding order improvement; is the updated set of coding parameter configurations, which is applied to the communication unit encoder layer.

10. An information transmission device for fireproof door alarms based on the Internet of Things, which is used to implement the information transmission method for fireproof door alarms based on the Internet of Things described in any one of the above claims 1-9, and is characterized in that, It includes a communication frequency band sensing module, an interference frequency band identification module, a feature indicator construction module, an interference risk intelligent discrimination module, and a coding parameter adaptive regulation module; The communication frequency band sensing module, after the fire emergency mode is started, continuously scans the wireless communication environment in the deployment area of the fire door alarm device in multiple frequency bands, identifies the target communication protocol frequency band used by the fire door alarm device, and obtains the spectrum parameter information of each frequency band to form a set of communication frequency bands to be analyzed; The interference frequency band identification module, based on a pre-established environmental wireless device frequency band database, extracts the working frequency bands used by surrounding wireless devices, identifies the interference frequency bands having a frequency adjacent relationship with the target communication frequency band, and constructs a spectrum mapping matrix between the communication frequency band and the interference source frequency band; The feature indicator construction module, based on the spectrum mapping matrix, uses feature engineering methods to extract feature indicators that reflect the coupling characteristics between the communication frequency band and the interference frequency band, and constructs a feature indicator vector group for electromagnetic coupling interference determination through the analyzed feature indicators; The intelligent discrimination module for interference risk inputs the feature index vector group into the support vector machine model that has been pre-trained with historical interference data, outputs the current coupling interference level identifier through the model, makes an intelligent prediction of the electromagnetic coupling interference risk between the communication frequency band and the interference frequency band, and is used to determine whether the communication link is currently in a high electromagnetic coupling interference state; The encoding parameter adaptive regulation module, when the prediction result of the support vector machine model indicates the existence of high electromagnetic coupling interference, triggers the encoding parameter adaptive regulation mechanism, dynamically adjusts the redundancy encoding intensity according to the electromagnetic coupling interference intensity, and adopts a transmission format with a higher error correction coding level for communication during the peak period of the coupling interference intensity.

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