Distributed fire emergency management system and method based on Internet of Things

Abstract

The invention discloses a distributed fire-fighting emergency management system and method based on the Internet of Things, and relates to the technical field of fire-fighting emergencies, and the management method comprises the following steps: carrying out the response efficiency evaluation of any fire-fighting log, obtaining the comprehensive response efficiency, and obtaining the normal response range of a fire-fighting monitoring platform; extracting all the fire protection logs which do not meet the normal response range, and judging whether an exception handling link exists or not; capturing a feature event in the fire protection log without the exception processing link; analyzing the influence degree of each feature event, and calibrating the influence degree of each feature event; comprehensive response efficiency evaluation is carried out on the newly generated fire protection log on the fire protection monitoring platform; and if the comprehensive response efficiency is lower than a normal response range, correcting the comprehensive response efficiency of the fire protection log based on the influence degree of each feature event, and if the corrected comprehensive response efficiency is still lower than the normal response range, carrying out risk early warning.

Description

Technical Field

[0001] The present invention relates to the field of fire emergency technology, and in particular to a distributed fire emergency management system and method based on the Internet of Things. Background Art

[0002] Distributed emergency firefighting refers to a firefighting system that manages and dispatches fire emergencies in a decentralized manner by introducing modern information technology, especially the Internet of Things technology. Unlike traditional centralized firefighting management, the distributed emergency firefighting system has the characteristics of real-time, flexibility and high efficiency, and can better adapt to various complex and dynamic firefighting scenarios.

[0003] Distributed fire emergency response uses a variety of sensors, monitoring equipment, communication networks and other technical means to achieve a series of automated management of fire source detection, data collection, information transmission, resource scheduling, and fire source processing; each processing link works independently and coordinates with each other, and each processing link plays a key role in the fire protection system. As long as there is a problem in one processing link, it is very likely to affect the subsequent fire protection processing and have a serious negative impact on the safety of people's lives and property. Summary of the invention

[0004] The purpose of the present invention is to provide a distributed fire emergency management system and method based on the Internet of Things to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a distributed fire emergency management method based on the Internet of Things, the management method comprising the following steps: Step S100: Generate a corresponding fire log for each fire handling process of the fire monitoring platform, and the fire log records the timestamps of each processing link in a fire handling process; the processing link is each processing step of the fire monitoring platform from monitoring the fire to completing the fire extinguishing, including fire monitoring, data analysis, resource scheduling and fire extinguishing, etc.; evaluate the response efficiency of each processing link in any fire log, obtain the comprehensive response efficiency of any fire log, and obtain the normal response range of the fire monitoring platform according to the distribution of each comprehensive response efficiency; Step S200: extracting all fire logs that do not meet the normal response range, analyzing the response efficiency of each processing link in each extracted fire log, and determining whether there is an abnormal processing link in each fire log; capturing characteristic events that affect the comprehensive response efficiency in the fire logs that do not have an abnormal processing link; Step S300: randomly selecting a fire log containing a characteristic event, analyzing the difference between the fire log and any other fire logs, and obtaining the influence degree of each characteristic event contained in the fire log; based on the influence of each characteristic event in the other fire logs, calibrating the influence degree of each characteristic event; Step S400: Evaluate the comprehensive response efficiency of the latest fire log generated on the fire monitoring platform; if the comprehensive response efficiency is lower than the normal response range, extract the characteristic events contained in the fire log, and correct the comprehensive response efficiency of the fire log based on the impact of each characteristic event. If the corrected comprehensive response efficiency is still lower than the normal response range, issue a risk warning.

[0006] Furthermore, step S100 includes the following steps: Step S101: Set corresponding evaluation rules for each processing link in any fire log to obtain the response efficiency of each processing link; set the response efficiency of the i-th processing link in any fire log as E i ; According to the formula: ; Wherein, j is a positive integer and j∈(1,i), n is the number of processing links in any fire log; the comprehensive response efficiency R of any fire log is calculated; although each processing link operates independently, information transmission is required between each processing link, so the response efficiency between each processing link will be affected. If the response efficiency of the previous processing link becomes low, the response efficiency of the subsequent processing link may be affected and reduced; Step S102: Obtain the comprehensive response efficiency of each fire log, and calculate the average value to obtain the average response efficiency R ave , calculate the fluctuation range of each comprehensive response efficiency and the average response efficiency; sort the fluctuation ranges from small to large according to the value, and select the largest fluctuation range f among the first x fluctuation ranges max , the minimum comprehensive response efficiency is calculated to be R min =R ave ×(1-f max ), the normal response range of the fire monitoring platform is generated as (R min ,100%).

[0007] Further, step S200 includes the following steps: Step S201: randomly select a fire log and obtain the comprehensive impact efficiency R of the fire log. If R<R min , then the fire log is set as a characteristic fire log; Step S202: Obtain the response efficiency of the ith processing link in each fire log, and calculate the average value to obtain the average response efficiency of the ith processing link (E i ) ave ; Set the response efficiency of the i-th processing link in the characteristic fire log to E i , if E i <(E i ) ave , then the i-th processing link is set as the characteristic processing link of the characteristic fire log; Step S203: Obtain all feature processing links in the feature fire log to obtain a feature processing link set; arbitrarily select the jth feature processing link in the feature processing link set, obtain the processing information corresponding to the jth feature processing link and extract a number of feature information, establish a feature event database, and perform similarity comparison between the several feature information and the feature events stored in the feature event database; feature events include various objective factors that are uncontrollable by humans, such as road congestion, complex environment, and bad weather. The impact of these feature events on response efficiency has nothing to do with the system itself. Therefore, when judging whether there are abnormalities in each processing link of the system, the interference of these feature events should be excluded as much as possible; Step S204: Set a similarity threshold. If the similarity between a certain feature information and a certain feature event exceeds the similarity threshold, the certain feature event is set as the feature event of the feature fire log. If the similarities between the several feature information and each feature event are less than the similarity threshold, the j-th feature processing link is set as the exception processing link; if there is an exception processing link in the feature fire log, the feature fire log is set as an abnormal fire log.

[0008] Further, step S300 includes the following steps: Step S301: randomly select a characteristic fire log and extract each characteristic event of the characteristic fire log; randomly select the kth characteristic event from each characteristic event, and obtain the number of times that the kth characteristic event is a characteristic event in each fire log as M k , assuming that the number of fire logs stored on the fire monitoring platform is M, the occurrence ratio of the kth characteristic event is H k =M k / M; Step S302: Set the processing link where the kth characteristic event is located as the i-th processing link, and obtain the number of characteristic events contained in the i-th processing link in the characteristic fire log as A i ; Set the response efficiency of the i-th processing link in the characteristic fire log to E i, and the average response efficiency of the i-th processing link is (E i ) ave , the influence ratio of the kth characteristic event in the characteristic fire log on the response efficiency of the i-th processing link is G k =[(E i ) ave -E i ] / A i The probability of a characteristic event will directly reflect the impact of this characteristic event, because the probability of occurrence can explain how the response efficiency of the corresponding processing link is affected by the difficulty of this characteristic event; Step S303: Obtain all characteristic fire logs containing the k-th characteristic event, obtain the impact ratio of the k-th characteristic event on the response efficiency of the i-th processing link in each characteristic fire log, and calculate the average value to obtain the average impact ratio of the k-th characteristic event on the i-th processing link (G k ) ave ; According to the formula: ; Where s is the total number of characteristic events contained in all characteristic fire logs; the influence degree Y of the kth characteristic event on the comprehensive response efficiency of the fire log is calculated. k .

[0009] Further, step S400 includes the following steps: Step S401: Obtain a fire log on the fire monitoring platform with the shortest time interval between the log generation time and the current time, evaluate the response efficiency of each processing link in the fire log according to the set evaluation rules, and obtain the comprehensive response efficiency E of the fire log new ; Step S402: Set the normal response range of the fire monitoring platform to (R min ,100%), if E new <R min , then obtain each feature processing link in the fire log; extract the processing information corresponding to any feature processing link, obtain several feature information of the feature processing link, and compare the similarity with the feature events stored in the feature event database; if the similarity between the several feature information of the feature processing link and each feature event is less than the set similarity threshold, then issue a risk warning for the fire log; Step S403: When a plurality of characteristic events are extracted from the plurality of characteristic information, the influence of each characteristic event on the comprehensive response efficiency of the fire log is obtained; according to the formula: ; Where p is a positive integer and p∈(1,q), q is the number of characteristic events in the fire log, Y p is the influence of the pth characteristic event on the comprehensive response efficiency of the fire log; the corrected comprehensive response efficiency E of the fire log is calculated ac If E ac <R min , a risk warning is issued for the fire log.

[0010] In order to better implement the above method, a distributed fire emergency management system based on the Internet of Things is also proposed. The management system includes a response efficiency evaluation module, a characteristic event analysis module, an event impact analysis module and a response abnormality judgment module; The response efficiency evaluation module is used to generate a corresponding fire log for each fire handling process of the fire monitoring platform. The fire log records the timestamps of each processing link in a fire handling process; the response efficiency of each processing link in any fire log is evaluated to obtain the comprehensive response efficiency of any fire log, and the normal response range of the fire monitoring platform is obtained according to the distribution of each comprehensive response efficiency; The characteristic event analysis module is used to extract all fire logs that do not meet the normal response range, analyze the response efficiency of each processing link in each extracted fire log, and determine whether there is an abnormal processing link in each fire log; and capture the characteristic events that affect the comprehensive response efficiency in the fire logs without abnormal processing links; An event impact analysis module is used to arbitrarily select a fire log containing a characteristic event, analyze the difference between the fire log and any other fire logs, and obtain the impact degree of each characteristic event contained in the fire log; based on the impact of each characteristic event in the other fire logs, calibrate the impact degree of each characteristic event; The response anomaly judgment module is used to evaluate the comprehensive response efficiency of the latest fire log generated on the fire monitoring platform; if the comprehensive response efficiency is lower than the normal response range, the characteristic events contained in the fire log are extracted, and the comprehensive response efficiency of the fire log is corrected based on the impact of each characteristic event. If the corrected comprehensive response efficiency is still lower than the normal response range, a risk warning is issued.

[0011] Further, the response efficiency evaluation module includes a comprehensive response efficiency analysis unit and a response efficiency range division unit; The comprehensive response efficiency analysis unit is used to generate a corresponding fire log for each fire handling process of the fire monitoring platform. The fire log records the timestamps of each processing link in a fire handling process; the response efficiency of each processing link in any fire log is evaluated to obtain the comprehensive response efficiency of any fire log; the response efficiency range division unit is used to obtain the normal response range of the fire monitoring platform according to the distribution of each comprehensive response efficiency.

[0012] Furthermore, the characteristic event analysis module includes an abnormality handling link analysis unit and a characteristic event capture unit; The abnormal processing link analysis unit is used to extract all fire logs that do not meet the normal response range, analyze the response efficiency of each processing link in each extracted fire log, and determine whether there is an abnormal processing link in each fire log; the characteristic event capture unit is used to capture characteristic events that affect the comprehensive response efficiency in the fire logs where there is no abnormal processing link.

[0013] Further, the event impact analysis module includes an impact degree analysis unit and an impact degree calibration unit; The impact degree analysis unit is used to arbitrarily select a fire log containing characteristic events, analyze the differences between the fire log and any other fire logs, and obtain the impact degree of each characteristic event contained in the fire log; the impact degree calibration unit is used to calibrate the impact degree of each characteristic event based on the impact of each characteristic event in the other fire logs.

[0014] Further, the response abnormality judgment module includes an abnormality primary judgment unit and an abnormality secondary judgment unit; The abnormal primary judgment unit is used to evaluate the comprehensive response efficiency of the fire log newly generated on the fire monitoring platform; if the comprehensive response efficiency is lower than the normal response range, the characteristic events contained in the fire log are extracted; the abnormal secondary judgment unit is used to correct the comprehensive response efficiency of the fire log based on the influence degree of each characteristic event, and if the corrected comprehensive response efficiency is still lower than the normal response range, a risk warning is issued.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention analyzes the response status of each processing link in the distributed fire emergency system, helps the staff to promptly discover possible anomalies in the system, avoid problems in subsequent fire treatment, and protect the lives and property of the people; 2. The present invention analyzes the fire logs with low response efficiency, takes into account the influence of various objective factors, such as underdeveloped transportation at the destination, poor signal, etc., and investigates these objective factors to obtain the accurate response efficiency of the entire system; improves the abnormal investigation efficiency and the work efficiency of the staff; 3. The present invention corrects the impact results of various characteristic events through the latest fire log, obtains a more accurate response efficiency of the system, helps staff to effectively discover and troubleshoot abnormal situations, and reduces the probability of problems occurring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the steps of a distributed fire emergency management method based on the Internet of Things; Figure 2 This is a structural diagram of a distributed fire emergency management system based on the Internet of Things. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example: Figure 1 to Figure 2 As shown, the present invention provides a distributed fire emergency management method based on the Internet of Things, and the management method includes the following steps: Step S100: Generate a corresponding fire log for each fire handling process of the fire monitoring platform, and the fire log records the timestamps of each processing link in a fire handling process; evaluate the response efficiency of each processing link in any fire log, obtain the comprehensive response efficiency of any fire log, and obtain the normal response range of the fire monitoring platform according to the distribution of each comprehensive response efficiency; Example 1: The fire monitoring platform monitors fire conditions in various places through sensors and monitoring installed in various places. When the sensor in one place detects that the concentration of harmful gases exceeds the standard, the relevant information is transmitted to the fire monitoring platform for fire judgment, and the fire fighting resources around the fire location are dispatched to extinguish the fire at the fire location; therefore, the processing links recorded in the fire log include fire monitoring, data analysis, resource dispatch and fire extinguishing; Wherein, step S100 includes the following steps: Step S101: Set corresponding evaluation rules for each processing link in any fire log to obtain the response efficiency of each processing link; set the response efficiency of the i-th processing link in any fire log as Ei ; According to the formula: ; Where j is a positive integer and j∈(1,i), n is the number of processing links in any fire log; the comprehensive response efficiency R of any fire log is calculated; Example 2: Assume that there are three processing links in a fire log, and the response efficiencies are 90%, 95%, and 85% respectively. Therefore, the comprehensive response efficiency of the fire log is R = (90% + 90% × 95% + 90% × 95% × 85%) / 3 = (0.9 + 0.855 + 0.72675) / 3 = 82.725%; Step S102: Obtain the comprehensive response efficiency of each fire log, and calculate the average value to obtain the average response efficiency R ave , calculate the fluctuation range of each comprehensive response efficiency and the average response efficiency; sort the fluctuation ranges from small to large according to the value, and select the largest fluctuation range f among the first x fluctuation ranges max , the minimum comprehensive response efficiency is calculated to be R min =R ave ×(1-f max ), the normal response range of the fire monitoring platform is generated as (R min ,100%).

[0019] Step S200: extracting all fire logs that do not meet the normal response range, analyzing the response efficiency of each processing link in each extracted fire log, and determining whether there is an abnormal processing link in each fire log; capturing characteristic events that affect the comprehensive response efficiency in the fire logs that do not have an abnormal processing link; Wherein, step S200 includes the following steps: Step S201: randomly select a fire log and obtain the comprehensive impact efficiency R of the fire log. If R<R min , then the fire log is set as a characteristic fire log; Step S202: Obtain the response efficiency of the ith processing link in each fire log, and calculate the average value to obtain the average response efficiency of the ith processing link (E i ) ave ; Set the response efficiency of the i-th processing link in the characteristic fire log to E i , if E i <(E i ) ave , then the i-th processing link is set as the characteristic processing link of the characteristic fire log; Step S203: Obtain all feature processing links in the feature fire log to obtain a feature processing link set; arbitrarily select the jth feature processing link in the feature processing link set, obtain processing information corresponding to the jth feature processing link and extract a number of feature information, establish a feature event database, and perform similarity comparison between the several feature information and the feature events stored in the feature event database; Step S204: Set a similarity threshold. If the similarity between a certain feature information and a certain feature event exceeds the similarity threshold, the certain feature event is set as the feature event of the feature fire log. If the similarities between the several feature information and each feature event are less than the similarity threshold, the j-th feature processing link is set as the exception processing link; if there is an exception processing link in the feature fire log, the feature fire log is set as an abnormal fire log.

[0020] Step S300: randomly selecting a fire log containing a characteristic event, analyzing the difference between the fire log and any other fire logs, and obtaining the influence degree of each characteristic event contained in the fire log; based on the influence of each characteristic event in the other fire logs, calibrating the influence degree of each characteristic event; Wherein, step S300 includes the following steps: Step S301: randomly select a characteristic fire log and extract each characteristic event of the characteristic fire log; randomly select the kth characteristic event from each characteristic event, and obtain the number of times that the kth characteristic event is a characteristic event in each fire log as M k , assuming that the number of fire logs stored on the fire monitoring platform is M, the occurrence ratio of the kth characteristic event is H k =M k / M; Example 3: Select characteristic event A, set the number of times characteristic event A is a characteristic event in each fire log to 10 times, set the number of fire logs stored on the fire monitoring platform to 20, and obtain the occurrence ratio of characteristic event A to 50%; Step S302: Set the processing link where the kth characteristic event is located as the i-th processing link, and obtain the number of characteristic events contained in the i-th processing link in the characteristic fire log as A i ; Set the response efficiency of the i-th processing link in the characteristic fire log to E i , and the average response efficiency of the i-th processing link is (E i ) ave , the influence ratio of the kth characteristic event in the characteristic fire log on the response efficiency of the i-th processing link is G k =[(Ei ) ave -E i ] / A i ; Example 4: Select characteristic event A, set the processing link where characteristic event A is located as the first processing link, wherein the number of characteristic events included in the first processing link is 2; set the response efficiency of characteristic event A in the current fire log to 80%, set the average response efficiency of the first processing link to 90%, and obtain the influence of characteristic event A in the fire log on the response efficiency of the first processing link as 5%; Step S303: Obtain all characteristic fire logs containing the k-th characteristic event, obtain the impact ratio of the k-th characteristic event on the response efficiency of the i-th processing link in each characteristic fire log, and calculate the average value to obtain the average impact ratio of the k-th characteristic event on the i-th processing link (G k ) ave ; According to the formula: ; Where s is the total number of characteristic events contained in all characteristic fire logs; the influence degree Y of the kth characteristic event on the comprehensive response efficiency of the fire log is calculated. k ; Example 5: Based on Example 2 and Example 3, the occurrence ratio of characteristic event A is 50%, and the impact ratio of characteristic event A on the response efficiency of the first processing link in the above fire log is 5%. The average impact ratio of characteristic event A on the response efficiency of the first processing link is set to 4%; the occurrence ratios of the remaining characteristic events are obtained to be 40% and 60% respectively, and the impact degree of characteristic event A on the comprehensive response efficiency of the fire log is calculated to be Y=4%×(50% / 150%)=1.33%; Step S400: Evaluate the comprehensive response efficiency of the fire log newly generated on the fire monitoring platform; if the comprehensive response efficiency is lower than the normal response range, extract the characteristic events contained in the fire log, and correct the comprehensive response efficiency of the fire log based on the influence of each characteristic event; if the corrected comprehensive response efficiency is still lower than the normal response range, issue a risk warning; Wherein, step S400 includes the following steps: Step S401: Obtain a fire log on the fire monitoring platform with the shortest time interval between the log generation time and the current time, evaluate the response efficiency of each processing link in the fire log according to the set evaluation rules, and obtain the comprehensive response efficiency E of the fire log new ; Step S402: Set the normal response range of the fire monitoring platform to (R min,100%), if E new <R min , then obtain each feature processing link in the fire log; extract the processing information corresponding to any feature processing link, obtain several feature information of the feature processing link, and compare the similarity with the feature events stored in the feature event database; if the similarity between the several feature information of the feature processing link and each feature event is less than the set similarity threshold, then issue a risk warning for the fire log; Step S403: When a plurality of characteristic events are extracted from the plurality of characteristic information, the influence of each characteristic event on the comprehensive response efficiency of the fire log is obtained; according to the formula: ; Where p is a positive integer and p∈(1,q), q is the number of characteristic events in the fire log, Y p is the influence of the pth characteristic event on the comprehensive response efficiency of the fire log; the corrected comprehensive response efficiency E of the fire log is calculated ac If E ac <R min , then a risk warning is issued for the fire log; Example 6: The comprehensive impact efficiency of the fire log before correction is set to 80%, and there are two characteristic events with impact levels of 1.3% and 2% respectively. The comprehensive impact efficiency of the fire log after correction is calculated to be 82.66%. The normal response range of the fire monitoring platform is set to (82%, 100%). Because 82.66%>82%, the corrected comprehensive impact efficiency is within the normal response range, and there is no abnormality in each processing link of the fire monitoring platform.

[0021] A distributed fire emergency management system based on the Internet of Things, the management system includes a response efficiency evaluation module, a characteristic event analysis module, an event impact analysis module and a response abnormality judgment module; The response efficiency evaluation module is used to generate a corresponding fire log for each fire handling process of the fire monitoring platform. The fire log records the timestamps of each processing link in a fire handling process; the response efficiency of each processing link in any fire log is evaluated to obtain the comprehensive response efficiency of any fire log, and the normal response range of the fire monitoring platform is obtained according to the distribution of each comprehensive response efficiency; The characteristic event analysis module is used to extract all fire logs that do not meet the normal response range, analyze the response efficiency of each processing link in each extracted fire log, and determine whether there is an abnormal processing link in each fire log; and capture the characteristic events that affect the comprehensive response efficiency in the fire logs without abnormal processing links; An event impact analysis module is used to arbitrarily select a fire log containing a characteristic event, analyze the difference between the fire log and any other fire logs, and obtain the impact degree of each characteristic event contained in the fire log; based on the impact of each characteristic event in the other fire logs, calibrate the impact degree of each characteristic event; The response anomaly judgment module is used to evaluate the comprehensive response efficiency of the latest fire log generated on the fire monitoring platform; if the comprehensive response efficiency is lower than the normal response range, the characteristic events contained in the fire log are extracted, and the comprehensive response efficiency of the fire log is corrected based on the impact of each characteristic event. If the corrected comprehensive response efficiency is still lower than the normal response range, a risk warning is issued.

[0022] Among them, the response efficiency evaluation module includes a comprehensive response efficiency analysis unit and a response efficiency range division unit; The comprehensive response efficiency analysis unit is used to generate a corresponding fire log for each fire handling process of the fire monitoring platform. The fire log records the timestamps of each processing link in a fire handling process; the response efficiency of each processing link in any fire log is evaluated to obtain the comprehensive response efficiency of any fire log; the response efficiency range division unit is used to obtain the normal response range of the fire monitoring platform according to the distribution of each comprehensive response efficiency.

[0023] Among them, the characteristic event analysis module includes an abnormal processing link analysis unit and a characteristic event capture unit; The abnormal processing link analysis unit is used to extract all fire logs that do not meet the normal response range, analyze the response efficiency of each processing link in each extracted fire log, and determine whether there is an abnormal processing link in each fire log; the characteristic event capture unit is used to capture characteristic events that affect the comprehensive response efficiency in the fire logs where there is no abnormal processing link.

[0024] Among them, the event impact analysis module includes an impact degree analysis unit and an impact degree calibration unit; The impact degree analysis unit is used to arbitrarily select a fire log containing characteristic events, analyze the differences between the fire log and any other fire logs, and obtain the impact degree of each characteristic event contained in the fire log; the impact degree calibration unit is used to calibrate the impact degree of each characteristic event based on the impact of each characteristic event in the other fire logs.

[0025] Among them, the response abnormality judgment module includes an abnormality primary judgment unit and an abnormality secondary judgment unit; The abnormal primary judgment unit is used to evaluate the comprehensive response efficiency of the fire log newly generated on the fire monitoring platform; if the comprehensive response efficiency is lower than the normal response range, the characteristic events contained in the fire log are extracted; the abnormal secondary judgment unit is used to correct the comprehensive response efficiency of the fire log based on the influence degree of each characteristic event, and if the corrected comprehensive response efficiency is still lower than the normal response range, a risk warning is issued.

[0026] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A distributed fire emergency management method based on the Internet of Things, characterized by: The management method comprises the following steps: Step S100: Generate a corresponding fire log for each fire handling process of the fire monitoring platform, and the fire log records the timestamps of each processing link in a fire handling process; evaluate the response efficiency of each processing link in any fire log, obtain the comprehensive response efficiency of any fire log, and obtain the normal response range of the fire monitoring platform according to the distribution of each comprehensive response efficiency; Step S200: extracting all fire logs that do not meet the normal response range, analyzing the response efficiency of each processing link in each extracted fire log, and determining whether there is an abnormal processing link in each fire log; capturing characteristic events that affect the comprehensive response efficiency in the fire logs without abnormal processing links; Step S300: randomly selecting a fire log containing a characteristic event, analyzing the difference between the fire log and any other fire logs, and obtaining the influence degree of each characteristic event contained in the fire log; based on the influence of each characteristic event in the other fire logs, calibrating the influence degree of each characteristic event; Step S400: Evaluate the comprehensive response efficiency of the latest fire log generated on the fire monitoring platform; if the comprehensive response efficiency is lower than the normal response range, extract the characteristic events contained in the fire log, and correct the comprehensive response efficiency of the fire log based on the impact of each characteristic event. If the corrected comprehensive response efficiency is still lower than the normal response range, issue a risk warning.

2. According to the distributed fire emergency management method based on the Internet of Things according to claim 1, it is characterized in that: The step S100 includes the following steps: Step S101: Set corresponding evaluation rules for each processing link in any fire log to obtain the response efficiency of each processing link; set the response efficiency of the i-th processing link in any fire log as E i ; According to the formula: ； Where j is a positive integer and j∈(1,i), n is the number of processing links in any fire log; the comprehensive response efficiency R of any fire log is calculated; Step S102: Obtain the comprehensive response efficiency of each fire log, and calculate the average value to obtain the average response efficiency R ave , calculate the fluctuation range of each comprehensive response efficiency and the average response efficiency; sort the fluctuation ranges from small to large according to the value, and select the largest fluctuation range f among the first x fluctuation ranges max , the minimum comprehensive response efficiency is calculated to be R min =R ave ×(1-f max ), the normal response range of the fire monitoring platform is generated as (R min ,100%).

3. According to the distributed fire emergency management method based on the Internet of Things according to claim 2, it is characterized in that: The step S200 includes the following steps: Step S201: randomly select a fire log and obtain the comprehensive impact efficiency R of the fire log. If R<R min , then the fire log is set as a characteristic fire log; Step S202: Obtain the response efficiency of the ith processing link in each fire log, and calculate the average value to obtain the average response efficiency of the ith processing link (E i ) ave ; Set the response efficiency of the i-th processing link in the characteristic fire log to E i , if E i <(E i ) ave , then the i-th processing link is set as the characteristic processing link of the characteristic fire log; Step S203: Obtain all feature processing links in the feature fire log to obtain a feature processing link set; arbitrarily select the jth feature processing link in the feature processing link set, obtain processing information corresponding to the jth feature processing link and extract a number of feature information, establish a feature event database, and perform similarity comparison between the several feature information and the feature events stored in the feature event database; Step S204: Set a similarity threshold. If the similarity between a certain feature information and a certain feature event exceeds the similarity threshold, the certain feature event is set as the feature event of the feature fire log. If the similarities between the several feature information and each feature event are less than the similarity threshold, the j-th feature processing link is set as the exception processing link; if there is an exception processing link in the feature fire log, the feature fire log is set as an abnormal fire log.

4. The distributed fire emergency management method based on the Internet of Things according to claim 3 is characterized in that: The step S300 includes the following steps: Step S301: randomly select a characteristic fire log and extract each characteristic event of the characteristic fire log; randomly select the kth characteristic event from each characteristic event, and obtain the number of times that the kth characteristic event is a characteristic event in each fire log as M k , assuming that the number of fire logs stored on the fire monitoring platform is M, the occurrence ratio of the kth characteristic event is H k =M k / M; Step S302: Set the processing link where the kth characteristic event is located as the i-th processing link, and obtain the number of characteristic events contained in the i-th processing link in the characteristic fire log as A i ; Set the response efficiency of the i-th processing link in the characteristic fire log to E i , and the average response efficiency of the i-th processing link is (E i ) ave , the influence ratio of the kth characteristic event in the characteristic fire log on the response efficiency of the i-th processing link is G k =[(E i ) ave -E i ] / A i ; Step S303: Obtain all characteristic fire logs containing the k-th characteristic event, obtain the impact ratio of the k-th characteristic event on the response efficiency of the i-th processing link in each characteristic fire log, and calculate the average value to obtain the average impact ratio of the k-th characteristic event on the i-th processing link (G k ) ave ; According to the formula: ； Where s is the total number of characteristic events contained in all characteristic fire logs; the influence degree Y of the kth characteristic event on the comprehensive response efficiency of the fire log is calculated. k .

5. The distributed fire emergency management method based on the Internet of Things according to claim 4 is characterized in that: The step S400 includes the following steps: Step S401: Obtain a fire log on the fire monitoring platform with the shortest time interval between the log generation time and the current time, evaluate the response efficiency of each processing link in the fire log according to the set evaluation rules, and obtain the comprehensive response efficiency E of the fire log new ; Step S402: Set the normal response range of the fire monitoring platform to (R min ,100%), if E new <R min , then obtain each feature processing link in the fire log; extract the processing information corresponding to any feature processing link, obtain several feature information of the feature processing link, and compare the similarity with the feature events stored in the feature event database; if the similarity between the several feature information of the feature processing link and each feature event is less than the set similarity threshold, then issue a risk warning for the fire log; Step S403: When a plurality of characteristic events are extracted from the plurality of characteristic information, the influence of each characteristic event on the comprehensive response efficiency of the fire log is obtained; according to the formula: ； Where p is a positive integer and p∈(1,q), q is the number of characteristic events in the fire log, Y p is the influence of the pth characteristic event on the comprehensive response efficiency of the fire log; the corrected comprehensive response efficiency E of the fire log is calculated ac If E ac <R min , a risk warning is issued for the fire log.

6. A distributed fire emergency management system, used to execute a distributed fire emergency management method based on the Internet of Things as claimed in any one of claims 1 to 5, characterized in that: The management system includes a response efficiency evaluation module, a characteristic event analysis module, an event impact analysis module and a response anomaly judgment module; The response efficiency evaluation module is used to generate a corresponding fire log for each fire handling process of the fire monitoring platform. The fire log records the timestamps of each processing link in a fire handling process; the response efficiency of each processing link in any fire log is evaluated to obtain the comprehensive response efficiency of any fire log, and the normal response range of the fire monitoring platform is obtained according to the distribution of each comprehensive response efficiency; The characteristic event analysis module is used to extract all fire logs that do not meet the normal response range, analyze the response efficiency of each processing link in each extracted fire log, and determine whether there is an abnormal processing link in each fire log; Capture characteristic events that affect the overall response efficiency in fire logs where there is no abnormal processing link; The event impact analysis module is used to arbitrarily select a fire log containing a characteristic event, analyze the difference between the fire log and any other fire logs, and obtain the impact degree of each characteristic event contained in the fire log; Based on the impact of each characteristic event in the rest of the fire logs, the impact degree of each characteristic event is calibrated; The response abnormality judgment module is used to evaluate the comprehensive response efficiency of the fire logs newly generated on the fire monitoring platform; If the comprehensive response efficiency is lower than the normal response range, the characteristic events contained in the fire log are extracted, and the comprehensive response efficiency of the fire log is corrected based on the impact degree of each characteristic event. If the corrected comprehensive response efficiency is still lower than the normal response range, a risk warning is issued.

7. A distributed fire emergency management system according to claim 6, characterized in that: The response efficiency evaluation module includes a comprehensive response efficiency analysis unit and a response efficiency range division unit; The comprehensive response efficiency analysis unit is used to generate a corresponding fire log for each fire handling process of the fire monitoring platform. The fire log records the timestamps of each processing link in a fire handling process; the response efficiency of each processing link in any fire log is evaluated to obtain the comprehensive response efficiency of any fire log; the response efficiency range division unit is used to obtain the normal response range of the fire monitoring platform according to the distribution of each comprehensive response efficiency.

8. A distributed fire emergency management system according to claim 6, characterized in that: The characteristic event analysis module includes an abnormality handling link analysis unit and a characteristic event capture unit; The abnormal processing link analysis unit is used to extract all fire logs that do not meet the normal response range, analyze the response efficiency of each processing link in each extracted fire log, and determine whether there is an abnormal processing link in each fire log; The characteristic event capturing unit is used to capture characteristic events that affect the comprehensive response efficiency in the fire logs where there is no abnormal processing link.

9. A distributed fire emergency management system according to claim 6, characterized in that: The event impact analysis module includes an impact degree analysis unit and an impact degree calibration unit; The impact degree analysis unit is used to arbitrarily select a fire log containing characteristic events, analyze the differences between the fire log and any other fire logs, and obtain the impact degree of each characteristic event contained in the fire log; the impact degree calibration unit is used to calibrate the impact degree of each characteristic event based on the impact of each characteristic event in the other fire logs.

10. A distributed fire emergency management system according to claim 6, characterized in that: The response abnormality judgment module includes an abnormality primary judgment unit and an abnormality secondary judgment unit; The abnormal initial judgment unit is used to evaluate the comprehensive response efficiency of the fire logs newly generated on the fire monitoring platform; If the comprehensive response efficiency is lower than the normal response range, the characteristic events contained in the fire log are extracted; the abnormal secondary judgment unit is used to correct the comprehensive response efficiency of the fire log based on the influence degree of each characteristic event. If the corrected comprehensive response efficiency is still lower than the normal response range, a risk warning is issued.

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