Intelligent emergency equipment alarm system based on wireless networking

Abstract

This invention relates to the field of wireless networking technology, specifically disclosing an intelligent emergency equipment alarm system based on wireless networking. The system includes a risk level classification module: retrieving disaster databases from recent years, calculating the total number of disasters per geographical unit, and determining the average disaster interval, combining these two indicators to complete the risk level assessment; an equipment information aggregation module: deployed at regional edge nodes, collecting the operating voltage of emergency equipment, setting the collection cycle according to risk level differences, marking abnormal states based on voltage deviation, and dynamically adjusting the aggregation frequency based on the number of abnormalities; and a communication module: constructing a "5G main link + LoRa, NB-IoT backup link" architecture, monitoring the main link status according to the aggregation frequency, and switching to the backup link based on scenario adaptability in case of failure. This multi-technology integrated wireless redundancy network achieves automatic fault switching through different wireless technologies, overcoming the communication limitations of single wireless networking and improving the system's disaster resistance redundancy and emergency adaptability.

Description

Technical Field

[0001] This invention relates to the field of wireless networking technology, and more specifically to an intelligent emergency equipment alarm system based on wireless networking. Background Technology

[0002] In the application scenarios of intelligent emergency equipment alarm systems, sudden disasters such as fires, earthquakes, and floods are often accompanied by extremely destructive environments, placing stringent requirements on the continuity of system communication. The core prerequisite for emergency response is the real-time transmission of equipment status data and alarm information. Once communication is interrupted, the command center will be unable to grasp the situation on the ground, and emergency equipment will be difficult to dispatch accurately, directly affecting rescue efficiency and personnel safety. Therefore, the disaster resistance redundancy capability of the communication link has become a core technical requirement of the system.

[0003] Traditional single-network wireless architectures have significant limitations: while LoRa and NB-IoT offer advantages such as wide coverage and low power consumption, their bandwidth is insufficient to support high-definition data transmission; although 5G provides high bandwidth and low latency communication, its signal is easily attenuated in complex terrain or disaster-prone scenarios. If a single network is interrupted due to line damage, signal blockage, or other issues, it will directly lead to system paralysis, failing to meet the rigid requirement of "uninterrupted communication" in emergency scenarios.

[0004] The demand for comprehensive coverage of emergency equipment is constantly increasing, and complex scenarios such as underground utility tunnels and mountain rescue bases place even higher demands on communication stability. Existing single wireless technologies are insufficient to simultaneously achieve wide coverage, high bandwidth, and disaster resilience, and cannot guarantee uninterrupted communication throughout the entire disaster lifecycle. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent emergency equipment alarm system based on wireless networking, thereby solving the above-mentioned technical problems.

[0006] The objective of this invention can be achieved through the following technical solutions: The intelligent emergency equipment alarm system based on wireless networking includes: Risk level classification module: Obtains disaster database for the past N years, counts the total number of disasters I within a geographic unit, and calculates the average interval between disasters. T i This represents the time of the i-th disaster in the current geographical unit, and the risk level of the current geographical unit is assessed based on the total number of disasters and the average interval between disasters. Equipment Information Aggregation Module: This module is deployed at the edge nodes of each region and is responsible for collecting the operating voltage u of all emergency equipment in the corresponding region; A preset collection period Q is set. Operational status information is collected every Q interval in low-risk areas and every Q / 2 interval in high-risk areas. The collected operational status information includes: Obtain the rated voltage U of the emergency equipment. When the operating voltage u deviates from the rated voltage U by more than ±λ during the operation of the equipment, mark the corresponding emergency equipment as an abnormal state. λ represents the preset deviation threshold. The aggregation frequency P of edge nodes for emergency equipment is set based on the number of times abnormal states occur. Communication module: It is pre-configured with a main communication link, a backup communication link 1 and a backup communication link 2. The main communication link uses 5G transmission and is responsible for transmitting on-site images collected by emergency equipment. The backup communication link 1 uses LoRa technology and is deployed in an underground utility tunnel. The backup communication link 2 uses NB-IoT technology as a global backup link. The transmission status of the main communication link is monitored using the summarizing frequency P. When the 5G signal strength is less than or equal to the preset standard strength and the packet loss rate is greater than 5%, the main communication link is determined to be faulty. When the main communication link fails, the switching mechanism is triggered. The optimal backup link is selected based on the scenario adaptability, including: underground utility tunnels are given priority to switch to backup communication link 1, and plain areas are given priority to switch to backup communication link 2.

[0007] As a further aspect of the present invention: in the risk level classification module, a minimum data sample size I is preset. min , let I≤I min The corresponding geographic unit is marked as an invalid unit, and the invalid unit is removed without further processing.

[0008] As a further aspect of the present invention: in the equipment information aggregation module, the method for setting the aggregation frequency P of the edge nodes for emergency equipment based on the number of occurrences of abnormal states includes: Obtain the number of times the fault state occurs, G, and calculate the total frequency. ,in, This represents rounding up G / δ, where δ represents the preset gradient value, and P... s This represents the preset original frequency.

[0009] As a further aspect of the present invention: when the summarizing frequency P = P s At that time, calculate the corresponding summary period T. p =1 / P, ensuring the aggregation period T p ≤Q / 2; If the aggregation period T p >Q / 2, summarizing period T p The value is corrected to Q / 2, and the summarizing frequency P is synchronously corrected to 2 / Q.

[0010] As a further aspect of the present invention: in the communication module, when the emergency equipment detects a disaster, all communication links are activated, allowing the three communication links to transmit in parallel.

[0011] As a further aspect of the present invention: in the risk level classification module, a rolling update cycle X is preset, and every interval X, the total number of disasters occurring in each geographical unit and the average interval between disasters are automatically updated, the risk level is reassessed, and the collection cycle is adjusted accordingly.

[0012] As a further aspect of the present invention: in the risk level classification module, the risk level of the current geographical unit is assessed based on the total number of disasters and the average interval between disasters, including the following steps: Using the current time point as the origin, obtain the number of disasters J and the average disaster interval Δt within the past year, which will satisfy the constraints. The corresponding geographical unit is designated as a high-risk area; otherwise, it is designated as a low-risk area.

[0013] As a further aspect of the present invention: in the communication module, when a fault occurs in the main communication link and a switching mechanism is triggered, the switching time is ensured to be less than or equal to (W×8) / B, where W represents the storage space corresponding to the data packet to be sent, and B represents the bandwidth of the communication link.

[0014] The beneficial effects of this invention are: It constructs a multi-technology integrated wireless redundancy network, achieving automatic fault switching through the complementary advantages of different wireless technologies. This effectively solves the communication shortcomings of single wireless networking in the background technology, significantly improving the system's disaster resistance redundancy and emergency adaptability, including: (1) Accurately classify and adapt to risk scenarios. By dynamically assessing the disaster risk level of geographical units and setting different collection cycles, it can ensure the real-time monitoring of equipment status in high-risk areas and reduce energy consumption in low-risk areas, thus meeting the needs of full coverage.

[0015] (2) Multi-mode converged communication breaks through technical limitations. The main and backup link collaborative architecture takes into account the high bandwidth advantage of 5G and the wide coverage characteristics of LoRa and NB-IoT. It can accurately switch between different scenarios such as underground pipe corridors and plains, avoid system paralysis caused by a single network interruption, and ensure smooth communication throughout the disaster cycle.

[0016] (3) Dynamically optimize communication and data acquisition strategies, adjust the aggregation frequency and update the risk level by associating abnormal states, and combine with the parallel transmission mechanism during disasters to ensure that equipment data and alarm information are transmitted in real time and reliably, providing support for the command center's situational awareness and precise dispatch, improving rescue efficiency and ensuring personnel safety. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a schematic diagram of the structure of the intelligent emergency equipment alarm system based on wireless networking according to the present invention.

[0019] Figure 2 This is a flowchart illustrating the emergency equipment aggregation frequency of the intelligent emergency equipment alarm system based on wireless networking according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 As shown, this invention is an intelligent emergency equipment alarm system based on wireless networking, comprising: Risk level classification module: Obtains disaster database for the past N years, counts the total number of disasters I within a geographic unit, and calculates the average interval between disasters. T i This represents the time of the i-th disaster in the current geographical unit, and the risk level of the current geographical unit is assessed based on the total number of disasters and the average interval between disasters. Equipment Information Aggregation Module: This module is deployed at the edge nodes of each region and is responsible for collecting the operating voltage u of all emergency equipment in the corresponding region; A preset collection period Q is set. Operational status information is collected every Q interval in low-risk areas and every Q / 2 interval in high-risk areas. The collected operational status information includes: Obtain the rated voltage U of the emergency equipment. When the operating voltage u deviates from the rated voltage U by more than ±λ during the operation of the equipment, mark the corresponding emergency equipment as an abnormal state. λ represents the preset deviation threshold. The aggregation frequency P of edge nodes for emergency equipment is set based on the number of times abnormal states occur. Communication module: It is pre-configured with a main communication link, a backup communication link 1 and a backup communication link 2. The main communication link uses 5G transmission and is responsible for transmitting on-site images collected by emergency equipment. The backup communication link 1 uses LoRa technology and is deployed in an underground utility tunnel. The backup communication link 2 uses NB-IoT technology as a global backup link. The transmission status of the main communication link is monitored using the summarizing frequency P. When the 5G signal strength is less than or equal to the preset standard strength and the packet loss rate is greater than 5%, the main communication link is determined to be faulty. When the main communication link fails, the switching mechanism is triggered. The optimal backup link is selected based on the scenario adaptability, including: underground utility tunnels are given priority to switch to backup communication link 1, and plain areas are given priority to switch to backup communication link 2.

[0022] It should be noted that the core function of the risk level classification module is to achieve accurate quantitative assessment of disaster risk in geographical units, providing data support for subsequent equipment monitoring strategy optimization. First, the system calls a standardized disaster database covering the entire region over the past N years via an interface, where N can be flexibly configured according to the historical disaster cycle characteristics of different regions, generally ranging from 5 to 10 years. Then, based on grid-like geographical units divided by latitude and longitude, the system counts the total number of various sudden disasters I within each unit, and extracts the specific time Ti of the i-th disaster occurrence within that unit. The average disaster interval is obtained by calculating the arithmetic mean of the differences between the occurrence times of two adjacent disasters, thus reflecting the frequency of disaster occurrence. Finally, a dual assessment index system is constructed by combining the total number of occurrences I and the average disaster interval. When the total number of occurrences I exceeds a preset threshold and the average disaster interval is less than a set critical value, the geographical unit is determined to be a high-risk area; otherwise, it is a low-risk area. It should be noted that to ensure the reliability of the assessment results, a minimum data sample size needs to be preset. When the total number of occurrences does not meet the conditions, the corresponding geographical unit is marked as an invalid unit, and the risk assessment results of that unit are removed and not included in the subsequent monitoring strategy planning scope. The equipment information aggregation module adopts a distributed edge node deployment architecture. Based on the geographical unit division results, edge computing nodes are deployed in core locations within each region to achieve localized collection and preliminary processing of operational data for all emergency equipment within the corresponding region. The primary core parameter collected by this module is the real-time operating voltage *u* of the emergency equipment, while simultaneously collecting auxiliary parameters such as equipment operating temperature and operating time. To adapt to the monitoring needs of areas with different risk levels, the system pre-sets a basic collection cycle *Q* (default value is 30 minutes, adjustable according to equipment type). Low-risk areas strictly adhere to a cycle of *Q* for collecting operational status information, while high-risk areas shorten the collection cycle to *Q / 2*, thereby improving the efficiency of detecting equipment anomalies in high-risk areas. During the collection of operational status information, the rated voltage *U* of each type of emergency equipment must be pre-entered. By calculating the difference between the operating voltage u and the rated voltage U in real time, when the absolute value of the difference exceeds the preset deviation threshold λ (λ is set according to the equipment voltage stability requirements, generally ±5%-±10% of the rated voltage), the corresponding emergency equipment is immediately marked as abnormal, and information such as the time of occurrence and duration of the abnormality is recorded. To ensure timely reporting of abnormal information, the module dynamically sets the aggregation frequency P of the edge nodes for emergency equipment based on the number of times the abnormal status occurs. Specifically, by obtaining the number of times the abnormal status of the equipment occurs G per unit time, a preset gradient value δ is introduced (δ is 2-5, representing that the aggregation frequency increases by one level for every δ increase in the number of abnormalities), and the aggregation frequency P is calculated as (rounded up (G / δ) × Ps), where Ps is the preset original aggregation frequency, thereby achieving dynamic adaptation where the more frequent the abnormalities, the more timely the aggregation and reporting.The communication module adopts a multi-mode fusion architecture of "primary and backup coordination and scenario adaptation," with three pre-set functionally complementary communication links. The primary communication link uses 5G technology, leveraging its high bandwidth and low latency advantages to transmit high-bandwidth data such as high-definition images and real-time video streams collected by emergency equipment, ensuring the command center has a clear understanding of the situation on site. Backup communication link 1 uses LoRa technology, specifically deployed in special scenarios with severe signal shielding and complex terrain, such as underground utility tunnels, utilizing its strong penetration and anti-interference capabilities to ensure basic data transmission in such scenarios. Backup communication link 2 uses NB-IoT technology as a globally universal backup link, leveraging its wide coverage and low power consumption to cover areas with weak 5G signals, such as mountain rescue bases and remote rural areas. To ensure the continuous smooth operation of the communication links, the system synchronously monitors the primary communication link at the equipment information aggregation frequency P. The transmission status of the link is monitored in real time by collecting two core indicators: 5G signal strength and data packet loss rate. When the 5G signal strength is less than or equal to the preset standard strength (set according to emergency communication requirements, generally -100dBm) and the data packet loss rate is greater than 5%, the main communication link is immediately determined to be faulty and a link switching mechanism is triggered. During the switching process, an optimal selection strategy adapted to the scenario is adopted. In underground utility tunnel scenarios, the switch is prioritized to backup communication link 1, utilizing the penetration advantage of LoRa technology to ensure communication continuity. In conventional scenarios such as plains and open areas, the switch is prioritized to backup communication link 2, relying on the wide coverage characteristics of NB-IoT to achieve rapid connection. It is also important to note that seamless data transmission must be ensured during the link switching process. Data caching and breakpoint resume technology are used to avoid data loss during the switching process, ensuring the integrity and continuity of information obtained by the command center.

[0023] In another preferred embodiment of the present invention, a minimum data sample size I is preset. min , let I≤I min The corresponding geographic unit is marked as an invalid unit, and the invalid unit is removed without further processing.

[0024] It is worth noting that, to ensure the reliability of risk level classification and avoid assessment bias caused by small sample data, the system presets a minimum data sample size, which is generally no less than 3 times. During the geographical unit disaster risk assessment process, the system counts the total number of disasters I within the unit over the past N years. When I ≤ I0.05 minIf the system determines that the disaster data sample size for a given unit is insufficient to support risk assessment, it automatically marks the corresponding geographic unit as invalid and removes it from all subsequent operational processes, including risk level assessment, monitoring cycle configuration, and edge node deployment. This approach avoids erroneous risk level determinations due to insufficient data samples, prevents resource waste caused by over-monitoring in high-risk areas or over-monitoring in low-risk areas, improves overall system efficiency, and ensures that risk assessment results accurately reflect the disaster occurrence patterns of geographic units.

[0025] In another preferred embodiment of the present invention, the method for setting the aggregation frequency P of edge nodes for emergency equipment based on the occurrence frequency of abnormal states includes: Obtain the number of times the fault state occurs, G, and calculate the total frequency. ,in, This represents rounding up G / δ, where δ represents the preset gradient value, and P... s This represents the preset original frequency.

[0026] Understandably, firstly, edge nodes need to record the abnormal status triggering of each emergency equipment in the corresponding area in real time, and count the number of times G of abnormal status of a single or similar emergency equipment occurs per unit time. This statistical process needs to cover all preset abnormal types such as voltage deviation and operational failure to ensure data integrity. Subsequently, a preset gradient value δ is introduced to quantify the correlation between the number of abnormalities and the summarization frequency. Finally, the summarization frequency P is calculated using a formula.

[0027] On the one hand, this method links the aggregation frequency with the degree of equipment anomaly. The more anomalies there are, the higher the aggregation frequency, ensuring that the status information of equipment with high-frequency anomalies is reported first, allowing the command center to grasp the equipment failure status in a timely manner and avoid the accumulation of risks and hidden dangers. On the other hand, it avoids the waste of resources caused by fixed-frequency aggregation. When the equipment is operating stably, it maintains a lower aggregation frequency, reducing the energy consumption of edge nodes and the transmission pressure of communication links. When equipment anomalies occur frequently, it automatically increases the frequency to ensure the timeliness of information. It takes into account both system operating efficiency and emergency response needs, and provides efficient data support for the precise scheduling of emergency equipment.

[0028] In a preferred embodiment, when the summarizing frequency P = P s At that time, calculate the corresponding summary period T. p =1 / P, ensuring the aggregation period T p ≤Q / 2; If the aggregation period T p >Q / 2, summarizing period T p The value is corrected to Q / 2, and the summarizing frequency P is synchronously corrected to 2 / Q.

[0029] It is important to note that when the calculated aggregation frequency P equals the preset original frequency Ps, the system needs to further calculate the corresponding aggregation period. At this point, it is crucial to verify the numerical relationship between this aggregation period and half the collection period for high-risk areas, ensuring that the aggregation period does not exceed the collection interval for high-risk areas. If the calculation reveals that the aggregation period is greater than the collection interval for high-risk areas, it indicates that the current aggregation frequency cannot match the monitoring timeliness requirements of high-risk areas. The system will automatically activate the parameter correction mechanism, directly forcibly adjusting the aggregation period to the collection interval for high-risk areas, while simultaneously updating the aggregation frequency in reverse, thereby ensuring the consistency between parameters.

[0030] It can ensure the timeliness of reporting emergency equipment status information in high-risk areas, avoid delays in abnormal information due to excessively long aggregation intervals, and ensure that the aggregation frequency and collection cycle are accurately matched through standardized correction logic, preventing parameter conflicts from affecting the overall monitoring accuracy of the system, and providing reliable operational support for the rapid identification of abnormal status of emergency equipment.

[0031] In another preferred embodiment of the present invention, when the emergency equipment detects a disaster, it activates all communication links, enabling the three communication links to transmit in parallel.

[0032] It should be noted that when emergency equipment detects a disaster, it activates all communication links for parallel transmission to maximize the reliability and timeliness of disaster information transmission. Extreme disasters can easily cause partial link interruptions; parallel transmission of multiple links enables multi-channel data backup, avoiding information loss due to single-link failures and ensuring that the command center can obtain on-site data in a timely manner.

[0033] In another preferred embodiment of the present invention, a rolling update cycle X is preset, and the total number of disasters and the average interval between disasters in each geographical unit are automatically updated every X interval, the risk level is reassessed and the collection cycle is adjusted accordingly.

[0034] It is understandable that it is necessary to ensure the dynamism and timeliness of disaster risk levels for geographical units, adapting to the changing characteristics of disaster occurrence patterns. In emergency scenarios, the frequency and intervals of regional disasters may change due to factors such as environmental changes and the implementation of disaster prevention measures. Fixed assessment cycles can easily lead to a disconnect between risk levels and actual conditions.

[0035] The system automatically updates the total number of disasters and the average interval for each geographic unit according to a cycle X, reassesses the risk level and adjusts the data collection cycle. This not only allows for the timely identification of areas with rising risks and the strengthening of monitoring efforts by shortening the data collection cycle, but also dynamically reduces the monitoring frequency in areas with reduced risks, thereby reducing energy consumption and communication resource usage at edge nodes.

[0036] This approach ensures that system risk assessments are always aligned with reality, avoids oversights in monitoring high-risk areas or waste of resources in low-risk areas, and improves the overall operational efficiency and adaptability of the system to emergency responses.

[0037] In another preferred embodiment of the present invention, the risk level of the current geographical unit is assessed based on the total number of disasters and the average interval between disasters, including the following steps: Using the current time point as the origin, obtain the number of disasters J and the average disaster interval Δt within the past year, which will satisfy the constraints. The corresponding geographical unit is designated as a high-risk area; otherwise, it is designated as a low-risk area.

[0038] It is worth noting that by using the dual constraints of short-term disaster data from the past year and historical average intervals, the risk level of geographical units can be accurately and dynamically determined. Compared with assessment methods that rely solely on long-term historical data, focusing on the number of disasters J in the past year can keenly capture recent trends in the frequency of regional disasters and promptly identify areas where risk has suddenly increased; combining this with the average disaster interval Δt can take into account the long-term patterns of regional disasters and avoid misjudgments caused by short-term, sporadic disasters.

[0039] The synergistic evaluation of dual indicators ensures accurate identification of high-risk areas and effectively distinguishes low-risk areas, providing a scientific basis for differentiated equipment collection cycles. It strengthens monitoring of high-risk areas while avoiding resource waste in low-risk areas, ultimately improving the operational efficiency and emergency response capabilities of the entire emergency equipment alarm system.

[0040] In another preferred embodiment of the present invention, when a failure of the main communication link triggers the switching mechanism, the switching time is ensured to be less than or equal to (W×8) / B, where W represents the storage space corresponding to the data packet to be sent, and B represents the bandwidth of the communication link.

[0041] It is worth noting that quantitative indicators are used to ensure the timeliness of link switching and avoid interruption of emergency data transmission due to switching delays. In the formula, the storage space W of the data packets to be sent determines the total amount of data. Multiplying by 8 converts the storage unit from bytes to bits, and then dividing by the target backup link bandwidth B yields the theoretical transmission time, which serves as the upper limit of the switching time.

[0042] The significance of this constraint lies in clarifying the time threshold for link switching, ensuring efficient connection between the switching process and data transmission, and preventing the retention of critical data such as emergency equipment status and on-site disaster conditions. Simultaneously, this quantitative standard can guide the bandwidth selection and data caching strategy design of backup links, ensuring seamless communication link continuity in disaster scenarios.

[0043] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. An intelligent emergency equipment alarm system based on wireless networking, characterized in that, include: Risk level classification module: Obtains disaster database for the past N years, counts the total number of disasters I within a geographic unit, and calculates the average interval between disasters. T i This represents the time of the i-th disaster in the current geographical unit, and the risk level of the current geographical unit is assessed based on the total number of disasters and the average interval between disasters. Equipment Information Aggregation Module: This module is deployed at the edge nodes of each region and is responsible for collecting the operating voltage u of all emergency equipment in the corresponding region; A preset collection period Q is set. Operational status information is collected every Q interval in low-risk areas and every Q / 2 interval in high-risk areas. The collected operational status information includes: Obtain the rated voltage U of the emergency equipment. When the operating voltage u deviates from the rated voltage U by more than ±λ during the operation of the equipment, mark the corresponding emergency equipment as an abnormal state. λ represents the preset deviation threshold. The aggregation frequency P of edge nodes for emergency equipment is set based on the number of times abnormal states occur. Communication module: It is pre-configured with a main communication link, a backup communication link 1 and a backup communication link 2. The main communication link uses 5G transmission and is responsible for transmitting on-site images collected by emergency equipment. The backup communication link 1 uses LoRa technology and is deployed in an underground utility tunnel. The backup communication link 2 uses NB-IoT technology as a global backup link. The transmission status of the main communication link is monitored using the summarizing frequency P. When the 5G signal strength is less than or equal to the preset standard strength and the packet loss rate is greater than 5%, the main communication link is determined to be faulty. When the main communication link fails, the switching mechanism is triggered. The optimal backup link is selected based on the scenario adaptability, including: underground utility tunnels are given priority to switch to backup communication link 1, and plain areas are given priority to switch to backup communication link 2.

2. The intelligent emergency equipment alarm system based on wireless networking according to claim 1, characterized in that, In the aforementioned risk level classification module, a minimum data sample size I is preset. min , let I≤I min The corresponding geographic unit is marked as an invalid unit, and the invalid unit is removed without further processing.

3. The intelligent emergency equipment alarm system based on wireless networking according to claim 1, characterized in that, In the equipment information aggregation module, the method for setting the aggregation frequency P of edge nodes for emergency equipment based on the occurrence frequency of abnormal states includes: Obtain the number of times the fault state occurs, G, and calculate the total frequency. ,in, This represents rounding up G / δ, where δ represents the preset gradient value, and P... s This represents the preset original frequency.

4. The intelligent emergency equipment alarm system based on wireless networking according to claim 3, characterized in that, When the summing frequency P=P s At that time, calculate the corresponding summary period T. p =1 / P, ensuring the aggregation period T p ≤Q / 2; If the aggregation period T p >Q / 2, summarizing period T p The value is corrected to Q / 2, and the summarizing frequency P is synchronously corrected to 2 / Q.

5. The intelligent emergency equipment alarm system based on wireless networking according to claim 1, characterized in that, In the communication module, when the emergency equipment detects a disaster, it activates all communication links, enabling the three communication links to transmit in parallel.

6. The intelligent emergency equipment alarm system based on wireless networking according to claim 1, characterized in that, In the risk level classification module, a pre-set rolling update cycle X is set. Every X interval, the total number of disasters and the average interval between disasters in each geographical unit are automatically updated, the risk level is reassessed, and the collection cycle is adjusted accordingly.

7. The intelligent emergency equipment alarm system based on wireless networking according to claim 1, characterized in that, In the risk level classification module, the risk level of the current geographic unit is assessed based on the total number of disasters and the average interval between disasters, including the following steps: Using the current time point as the origin, obtain the number of disasters J and the average disaster interval Δt within the past year, which will satisfy the constraints. The corresponding geographical unit is designated as a high-risk area; otherwise, it is designated as a low-risk area.

8. The intelligent emergency equipment alarm system based on wireless networking according to claim 1, characterized in that, In the communication module, when a fault occurs in the main communication link and a switching mechanism is triggered, the switching time is ensured to be less than or equal to (W×8) / B, where W represents the storage space corresponding to the data packet to be sent, and B represents the bandwidth of the communication link.

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