A disaster warning data communication management system and method based on the Internet of Things

Abstract

The present invention relates to the field of communication management, and specifically to a disaster warning data communication management system and method based on the Internet of Things, comprising a homologous warning event classification module, an identifiable feature analysis module, a first push sequence generation module, an effective warning index analysis module and an optimal communication link judgment module; the homologous warning event classification module classifies a target warning event into a type of warning event and marks it as a homologous warning event; the identifiable feature analysis module identifies the identifiable features corresponding to each communication link in each type of homologous warning event; the first push sequence generation module is used to optimize and sort the communication links recorded for different disaster types under each homologous warning event to generate a first push sequence; the effective warning index analysis module analyzes the inspection indicators before and after the broadcast of each communication link to calculate the corresponding effective warning index; the optimal communication link judgment module is used to analyze and output the optimal communication link corresponding to each disaster type in each homologous warning event.

Description

Technical Field

[0001] The present invention relates to the technical field of communication management, and in particular to a disaster warning data communication management system and method based on the Internet of Things. Background Art

[0002] Wireless emergency broadcasting has the advantages of rapid transmission, wide coverage, convenient reception, and no restrictions on power and other conditions. In emergencies such as natural disasters, accidents, public health and social security, especially in the case of power and communication interruptions, emergency mobilization and work deployment can still be carried out through the emergency broadcasting system to convey disaster news or possible hazards caused by disasters to the people; the existing technology provides various communication links for disaster warning data transmission to ensure the effective transmission of disaster data; however, based on some special circumstances and energy shortages, how to effectively and efficiently select communication links to ensure the maximum conservation of resources and to achieve reasonable transmission of disaster warning data requires further analysis; and in the existing situation, disaster warnings are often urgent and sudden, and it is impossible to detect the communication links that can be allowed to be transmitted in a timely manner, which is easy to cause abnormalities in the communication links and cause the disaster warning data to be unable to be transmitted in a timely and effective manner, thereby causing immeasurable losses. Summary of the invention

[0003] The purpose of the present invention is to provide a disaster warning data communication management system and method based on the Internet of Things to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a disaster warning data communication management method based on the Internet of Things, comprising the following analysis steps:

[0005] Step S100: extracting disaster warning events broadcasted by the emergency broadcast system as target warning events, classifying target warning events recorded at the same broadcast location and with different numbers of corresponding communication links in one event as one type of warning events and marking them as homologous warning events; analyzing the identifiable features and feature indexes corresponding to each communication link in each type of homologous warning events based on the broadcast content data recorded by the homologous warning events;

[0006] Step S200: sorting out abnormal warning events of the same-source warning events corresponding to the disaster type with identifiable features, and optimizing and sorting the communication links of different disaster type records under each same-source warning event based on the abnormal warning events to generate a first push sequence;

[0007] Step S300: extracting result data of the communication links in the first push sequence in all corresponding homologous warning events, and analyzing the effective warning index of each communication link;

[0008] Step S400: Based on the effective warning index and the first push sequence, the optimal communication link corresponding to each disaster type in each homologous warning event is analyzed and output.

[0009] Further, the identifiable features and feature indexes corresponding to each communication link in each type of homologous warning event are analyzed, including the following steps:

[0010] Step S110: the broadcast content data refers to the content required to be broadcasted corresponding to the disaster type existing at the broadcast location; taking the disaster type recorded in the broadcast content data as an indicator, the disaster type contained in each type of homologous warning event and the corresponding broadcast communication link constitute a data pair A, A=(R1→P1), where R1 represents the disaster type, and P1 represents the communication link corresponding to the disaster type R1, and the data pairs corresponding to the same indicator are stored in the same indicator set;

[0011] Step S120: Obtain the number M of all types of communication links recorded in the indicator set, and calculate the characteristic index L of the i-th communication link in the j-th indicator set. i j , L i j =(1 / d i j ) (1 / D i ), d i j represents the number of all data pairs recorded in the jth indicator set belonging to the i-th communication link, D i Indicates the number of indicator sets that include the i-th communication link in the homologous warning event to which the i-th communication link belongs;

[0012] Step S130: When there is L i j =1 / d i j And L i j ≥L0 j When the mark satisfies L i j =1 / d i j And L i j ≥L0 j The communication link is an effective and identifiable feature of the corresponding indicator set to record the disaster type; where L0 j Indicates the feature index threshold set in the jth indicator set; marks the indicator set to which the effective identifiable features belong, which does not meet L i j =1 / d i j or L ij ≥L0 j The communication link is the corresponding indicator set recording the identifiable features of the disaster type to be investigated.

[0013] The purpose of analyzing identifiable features is to conduct a digital analysis of the communication links that record different types of disasters at the same broadcast location, so as to preliminarily clarify the selectivity of communication links in specific situations, provide a data basis for quickly making the best choice when disaster warning is needed, ensure the efficiency of disaster warning, and the effectiveness of communication links in extreme situations such as power outages.

[0014] Further, step S200 includes the following steps:

[0015] Step S210: obtaining an abnormal warning event to which the communication link corresponding to the identification feature to be investigated belongs, wherein the abnormal warning event refers to an event in which the communication transmission of disaster warning data is interrupted at the corresponding broadcast location, obtaining an indicator set corresponding to the identification feature to be investigated as an indicator set to be analyzed, and the indicator set to be analyzed records at least two identification features to be investigated;

[0016] Step S220: extract the occurrence frequency T of the abnormal warning event corresponding to the kth identification feature to be examined in the set of indicators to be analyzed k , and the duration of sustainable communication in the event of power outage H k ; Normalize the occurrence frequency and sustainable communication duration corresponding to each identification feature to be investigated to obtain T k0 and H k0 ; Using the formula:

[0017] Y k =L k [(1 / T k0 )+H k0 ]

[0018] Calculate the optimization index Y of the kth identification feature to be examined in the set of indicators to be analyzed k ; L k Represents the characteristic index of the kth identification feature to be examined;

[0019] Step S230: Acquire all the identification features to be examined in each set of indicators to be analyzed, and sort the identification features to be examined from large to small according to the values ​​of the corresponding optimization indexes to generate a first push sequence.

[0020] The calculation of the optimization index can effectively analyze the weight of each communication link under the corresponding disaster type selectable communication, and measure the communication security and effectiveness of the corresponding communication link from the perspective of abnormal occurrence, and the communication efficiency of the communication link adapted from extreme situations.

[0021] Further, step S300 includes the following process:

[0022] Extract the warning purpose of the same-source warning event in the first push sequence at the broadcast location. The result data refers to the assessment index corresponding to the disaster warning data when the warning purpose is achieved after communication transmission. The assessment index Q1 of the disaster warning data before each communication link broadcast and the assessment index Q2 after each communication link broadcast in the first push sequence are obtained;

[0023] Using the formula: V=Q2 / Q1;

[0024] Calculate the effective warning index V of each communication link in the first push sequence.

[0025] The smaller the effective warning index is, the better the transmission warning effect of the communication link is;

[0026] Furthermore, step S400 includes:

[0027] When there are valid identifiable features in the same-source warning events, the communication link corresponding to the output valid identifiable features is the optimal communication link for the corresponding disaster type;

[0028] Extract the position sequence of the communication links corresponding to the first push sequence; compare the effective warning index V of each communication link based on the position sequence, and sort the communication links in the first push sequence from small to large according to the effective warning index V to generate a second sequence;

[0029] When the first push sequence is exactly the same as the second sequence, the first communication link outputting the first push sequence is the optimal communication link for the disaster type corresponding to the first push sequence in the homologous warning event;

[0030] When the first communication link of the first push sequence is different from that of the second sequence, the average effective warning index V0 and average optimization index Y0 of the communication link corresponding to the first push sequence and other homologous warning events containing the communication link are obtained. The average effective warning index and the average optimization index reflect the stability of the communication link under different disaster warning data broadcasts at different broadcast locations; and the difference degree Z of the first communication link in the first push sequence is calculated, Z=|V-V0| / |Y-Y0|;

[0031] When Z=1, any communication link in the first push sequence is output as the optimal communication link for the corresponding disaster type in the corresponding homologous warning event;

[0032] When Z>1, the first communication link in the first push sequence is output as the optimal communication link for the corresponding disaster type in the homologous warning event;

[0033] When Z<1, the first communication link in the second sequence is output as the optimal communication link corresponding to the disaster type in the homologous warning event.

[0034] A disaster warning data communication management system based on the Internet of Things, comprising a homologous warning event classification module, an identifiable feature analysis module, a first push sequence generation module, an effective warning index analysis module and an optimal communication link judgment module;

[0035] The homologous warning event classification module is used to classify target warning events that record the same broadcast location and the number of communication links corresponding to the broadcast in one event is not one into one type of warning events and mark them as homologous warning events;

[0036] The identifiable feature analysis module is used to identify the identifiable features corresponding to each communication link in each type of homologous warning event;

[0037] The first push sequence generation module is used to optimize and sort the communication links of different disaster type records under each homologous warning event based on the abnormal warning event to generate a first push sequence;

[0038] The effective warning index analysis module is used to analyze the inspection indicators before and after the broadcast of each communication link to calculate the corresponding effective warning index;

[0039] The optimal communication link judgment module is used to analyze and output the optimal communication link corresponding to each disaster type in each homologous warning event.

[0040] Further, the identifiable feature analysis module includes an index set generation unit, a feature index calculation unit and an identifiable feature output unit;

[0041] The indicator set generation unit is used to form data pairs from the disaster type contained in each type of homologous warning event and the corresponding broadcast communication link, and store the data pairs corresponding to the same indicator in the same indicator set;

[0042] The characteristic index calculation unit is used to obtain the number of all types of communication links recorded in the indicator set, and calculate the characteristic index of the communication link in the indicator set;

[0043] The identifiable feature output unit is used to distinguish between effective identifiable features and identifiable features to be examined.

[0044] Furthermore, the first push sequence generation module includes a to-be-analyzed indicator set construction unit, an optimization index calculation unit, and a first push sequence output unit;

[0045] The to-be-analyzed indicator set construction unit is used to obtain an indicator set corresponding to the to-be-investigated identification feature as the to-be-analyzed indicator set;

[0046] The optimization index calculation unit is used to extract the occurrence frequency of abnormal warning events corresponding to the identification features to be examined in the set of indicators to be analyzed, as well as the duration of sustainable communication in the case of power energy interruption, and calculate the optimization index of the identification features to be examined in the set of indicators to be analyzed;

[0047] The first push sequence output unit is used to obtain all the identification features to be examined in each set of indicators to be analyzed, and to sort the identification features to be examined from large to small according to the values ​​of the corresponding optimization indexes to generate a first push sequence.

[0048] Furthermore, the optimal communication link determination module includes an effective identifiable feature retrieval unit, a sequence comparison unit, and an optimal communication link output unit;

[0049] The effective identifiable feature retrieval unit is used to identify whether there is an effective identifiable feature in the homologous warning event;

[0050] The sequence comparison unit is used for comparing the first push sequence and the second sequence;

[0051] The optimal communication link output unit is used to output the optimal communication link under different comparison results.

[0052] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention analyzes the warning events reported by the emergency broadcast system, preliminarily divides the communication links with identifiable characteristics, and provides fast and convenient communication link selection for subsequent identification of disaster types; and based on the transmission of disaster warning data including multiple communication links, analyzes the optimal communication link from the perspectives of warning effect, proportion of sudden abnormal events and warning stability; realizes the data selection basis for the optimal communication link when multiple communication links cannot exist under extreme circumstances; and also provides the reliability basis of link layout for supervisors, thereby realizing the high efficiency, effectiveness and convenience of communication transmission of disaster warning data. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0054] Figure 1 It is a structural schematic diagram of a disaster warning data communication management system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0055] 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.

[0056] See also Figure 1 The present invention provides a technical solution: a disaster warning data communication management method based on the Internet of Things, comprising the following analysis steps:

[0057] Step S100: extracting disaster warning events broadcasted by the emergency broadcast system as target warning events, classifying target warning events recorded at the same broadcast location and with different numbers of corresponding communication links in one event as one type of warning events and marking them as homologous warning events; analyzing the identifiable features and feature indexes corresponding to each communication link in each type of homologous warning events based on the broadcast content data recorded by the homologous warning events;

[0058] Step S200: sorting out abnormal warning events of the same-source warning events corresponding to the disaster type with identifiable features, and optimizing and sorting the communication links of different disaster type records under each same-source warning event based on the abnormal warning events to generate a first push sequence;

[0059] Step S300: extracting result data of the communication links in the first push sequence in all corresponding homologous warning events, and analyzing the effective warning index of each communication link;

[0060] Step S400: Based on the effective warning index and the first push sequence, the optimal communication link corresponding to each disaster type in each homologous warning event is analyzed and output.

[0061] Identifying the identifiable features and feature indexes corresponding to each communication link in each type of homologous warning event includes the following steps:

[0062] Step S110: the broadcast content data refers to the content required to be broadcasted corresponding to the disaster type existing at the broadcast location; taking the disaster type recorded in the broadcast content data as an indicator, the disaster type contained in each type of homologous warning event and the corresponding broadcast communication link constitute a data pair A, A=(R1→P1), where R1 represents the disaster type, and P1 represents the communication link corresponding to the disaster type R1, and the data pairs corresponding to the same indicator are stored in the same indicator set;

[0063] Step S120: Obtain the number M of all types of communication links recorded in the indicator set, and calculate the characteristic index L of the i-th communication link in the j-th indicator set. i j , Li j =(1 / d i j ) (1 / D i ), d i j represents the number of all data pairs recorded in the jth indicator set belonging to the i-th communication link, D i Indicates the number of indicator sets that include the i-th communication link in the homologous warning event to which the i-th communication link belongs;

[0064] Step S130: When there is L i j =1 / d i j And L i j ≥L0 j When the mark satisfies L i j =1 / d i j And L i j ≥L0 j The communication link is an effective and identifiable feature of the corresponding indicator set to record the disaster type; where L0 j Indicates the feature index threshold set in the jth indicator set; generally set to 1 / 2; marks the indicator set to which the effective identifiable features belong, which does not meet L i j =1 / d i j or L i j ≥L0 j The communication link is the corresponding indicator set recording the identifiable features of the disaster type to be investigated.

[0065] As shown in the examples:

[0066] The broadcast location of the same source warning event record is the expressway; there are the following indicator sets:

[0067] Indicator set 1: Disaster type is foggy weather, and communication links are radio broadcast and voice broadcast;

[0068] Indicator set 2: The disaster type is rainstorm weather, and the communication link is radio broadcast and light broadcast;

[0069] M=3, the characteristic index of radio broadcasting in indicator set 1 is L1 1 =1 / 2 1 / 2=1 / 4;

[0070] The characteristic index of the sound broadcast in indicator set 1 is L21 =1 / 2 1=1 / 2;

[0071] The characteristic index of radio broadcasting in indicator set 2 is L2 1 =L1 1 =1 / 4;

[0072] The characteristic index of the light report in indicator set 2 is L3 2 =1 / 2 1=1 / 2;

[0073] There exists a satisfying L i j =1 / d i j And L i j ≥L0 j The communication link includes sound broadcast and light broadcast; therefore, the sound broadcast is an effective and identifiable feature of the disaster type of foggy weather, and the light broadcast is an effective and identifiable feature of the disaster type of rainstorm weather; at this time, all indicator sets recorded by the same source warning event output corresponding identifiable features, so there is no need to analyze the radio broadcast communication link; if there is a new indicator set, and there is a radio broadcast in the corresponding communication link in the new indicator set, and the corresponding feature indexes do not meet L i j =1 / d i j And L i j ≥L0 j , it is necessary to analyze the data corresponding to radio broadcasting without indicator set 1 and indicator set 2.

[0074] The purpose of analyzing identifiable features is to conduct a digital analysis of the communication links that record different types of disasters at the same broadcast location, so as to preliminarily clarify the selectivity of communication links in specific situations, provide a data basis for quickly making the best choice when disaster warning is needed, ensure the efficiency of disaster warning, and the effectiveness of communication links in extreme situations such as power outages.

[0075] Step S200 includes the following steps:

[0076] Step S210: obtaining an abnormal warning event to which the communication link corresponding to the identification feature to be investigated belongs, wherein the abnormal warning event refers to an event in which the communication transmission of disaster warning data is interrupted at the corresponding broadcast location, obtaining an indicator set corresponding to the identification feature to be investigated as an indicator set to be analyzed, and the indicator set to be analyzed records at least two identification features to be investigated;

[0077] Step S220: extract the occurrence frequency T of the abnormal warning event corresponding to the kth identification feature to be examined in the set of indicators to be analyzed k , and the duration of sustainable communication in the event of power outage H k ; Normalize the occurrence frequency and sustainable communication duration corresponding to each identification feature to be investigated to obtain T k0 and H k0 ; Using the formula:

[0078] Y k =L k [(1 / T k0 )+H k0 ]

[0079] Calculate the optimization index Y of the kth identification feature to be examined in the set of indicators to be analyzed k ; L k Represents the characteristic index of the kth identification feature to be examined;

[0080] Step S230: Acquire all the identification features to be examined in each set of indicators to be analyzed, and sort the identification features to be examined from large to small according to the values ​​of the corresponding optimization indexes to generate a first push sequence.

[0081] The first number in the first push sequence indicates the communication link that is currently most suitable for transmission for the disaster type corresponding to the set of indicators to be analyzed. The calculation of the optimization index can effectively analyze the weight of each communication link under the optional communication of the corresponding disaster type, and measure the communication security and effectiveness of the corresponding communication link from the perspective of abnormal occurrence, and the communication efficiency of the communication link adapted from extreme situations.

[0082] Step S300 includes the following process:

[0083] Extract the warning purpose of the same-source warning event in the first push sequence at the broadcast location. The result data refers to the assessment index corresponding to the disaster warning data when the warning purpose is achieved after communication transmission. The assessment index Q1 of the disaster warning data before each communication link broadcast and the assessment index Q2 after each communication link broadcast in the first push sequence are obtained;

[0084] Using the formula: V=Q2 / Q1;

[0085] Calculate the effective warning index V of each communication link in the first push sequence.

[0086] For example, if the warning data for foggy weather disasters is to reduce vehicle speed, the inspection index Q1 is the proportion of vehicles exceeding the preset speed per unit time before the warning; the inspection index Q2 is the proportion of vehicles exceeding the preset speed per unit time after the warning; the smaller the effective warning index, the better the transmission warning effect of the communication link;

[0087] Step S400 includes:

[0088] When there are valid identifiable features in the same-source warning events, the communication link corresponding to the output valid identifiable features is the optimal communication link for the corresponding disaster type;

[0089] Extract the position sequence of the communication links corresponding to the first push sequence; compare the effective warning index V of each communication link based on the position sequence, and sort the communication links in the first push sequence from small to large according to the effective warning index V to generate a second sequence;

[0090] When the first push sequence is exactly the same as the second sequence, the first communication link outputting the first push sequence is the optimal communication link for the disaster type corresponding to the first push sequence in the homologous warning event;

[0091] When the first communication link of the first push sequence is different from that of the second sequence, the average effective warning index V0 and average optimization index Y0 of the communication link corresponding to the first push sequence and other homologous warning events containing the communication link are obtained. The average effective warning index and the average optimization index reflect the stability of the communication link under different disaster warning data broadcasts at different broadcast locations; and the difference degree Z of the first communication link in the first push sequence is calculated, Z=|V-V0| / |Y-Y0|;

[0092] When Z=1, any communication link in the first push sequence is output as the optimal communication link for the corresponding disaster type in the corresponding homologous warning event;

[0093] When Z>1, the first communication link in the first push sequence is output as the optimal communication link for the corresponding disaster type in the homologous warning event;

[0094] When Z<1, the first communication link in the second sequence is output as the optimal communication link corresponding to the disaster type in the homologous warning event.

[0095] A disaster warning data communication management system based on the Internet of Things, comprising a homologous warning event classification module, an identifiable feature analysis module, a first push sequence generation module, an effective warning index analysis module and an optimal communication link judgment module;

[0096] The homologous warning event classification module is used to classify target warning events that record the same broadcast location and the number of communication links corresponding to the broadcast in one event is not one into one type of warning events and mark them as homologous warning events;

[0097] The identifiable feature analysis module is used to identify the identifiable features corresponding to each communication link in each type of homologous warning event;

[0098] The first push sequence generation module is used to optimize and sort the communication links of different disaster type records under each homologous warning event based on the abnormal warning event to generate a first push sequence;

[0099] The effective warning index analysis module is used to analyze the inspection indicators before and after the broadcast of each communication link to calculate the corresponding effective warning index;

[0100] The optimal communication link judgment module is used to analyze and output the optimal communication link corresponding to each disaster type in each homologous warning event.

[0101] The identifiable feature analysis module includes an index set generation unit, a feature index calculation unit and an identifiable feature output unit;

[0102] The indicator set generation unit is used to form data pairs from the disaster type contained in each type of homologous warning event and the corresponding broadcast communication link, and store the data pairs corresponding to the same indicator in the same indicator set;

[0103] The characteristic index calculation unit is used to obtain the number of all types of communication links recorded in the indicator set, and calculate the characteristic index of the communication link in the indicator set;

[0104] The identifiable feature output unit is used to distinguish between effective identifiable features and identifiable features to be examined.

[0105] The first push sequence generation module includes a to-be-analyzed indicator set construction unit, an optimization index calculation unit, and a first push sequence output unit;

[0106] The to-be-analyzed indicator set construction unit is used to obtain an indicator set corresponding to the to-be-investigated identification feature as the to-be-analyzed indicator set;

[0107] The optimization index calculation unit is used to extract the occurrence frequency of abnormal warning events corresponding to the identification features to be examined in the set of indicators to be analyzed, as well as the duration of sustainable communication in the case of power energy interruption, and calculate the optimization index of the identification features to be examined in the set of indicators to be analyzed;

[0108] The first push sequence output unit is used to obtain all the identification features to be examined in each set of indicators to be analyzed, and to sort the identification features to be examined from large to small according to the values ​​of the corresponding optimization indexes to generate a first push sequence.

[0109] The optimal communication link judgment module includes an effective identifiable feature retrieval unit, a sequence comparison unit, and an optimal communication link output unit;

[0110] The effective identifiable feature retrieval unit is used to identify whether there is an effective identifiable feature in the homologous warning event;

[0111] The sequence comparison unit is used for comparing the first push sequence and the second sequence;

[0112] The optimal communication link output unit is used to output the optimal communication link under different comparison results.

[0113] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0114] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A disaster warning data communication management method based on the Internet of Things, characterized in that: The analysis steps include: Step S100: extracting disaster warning events broadcasted by the emergency broadcast system as target warning events, classifying target warning events recorded at the same broadcast location and with different numbers of corresponding communication links in one event as one type of warning events and marking them as homologous warning events; Based on the broadcast content data of the same-source warning event records, the identifiable features and feature indexes corresponding to each communication link in each type of same-source warning event are analyzed; The step of analyzing the identifiable features and feature indexes corresponding to each communication link in each type of homologous warning event includes the following steps: Step S110: The broadcast content data refers to the content required to be broadcasted corresponding to the disaster type existing at the broadcast location; taking the disaster type recorded in the broadcast content data as an indicator, the disaster type contained in each type of homologous warning event and the corresponding broadcast communication link constitute a data pair A, A=(R1→P1), where R1 represents the disaster type, and P1 represents the communication link corresponding to the disaster type R1, and the data pairs corresponding to the same indicator are stored in the same indicator set; Step S120: Obtain the number M of all types of communication links recorded in the indicator set, and calculate the characteristic index L of the i-th communication link in the j-th indicator set. i j , L i j =(1 / d i j )*(1 / D i ), d i j represents the number of all data pairs recorded in the jth indicator set belonging to the i-th communication link, D i Indicates the number of indicator sets that include the i-th communication link in the homologous warning event to which the i-th communication link belongs; Step S130: When there is L i j =1 / d i j And L i j ≥L0 j When the mark satisfies L i j =1 / d i j And L i j ≥L0 j The communication link is an effective and identifiable feature of the corresponding indicator set to record the disaster type; where L0 j Indicates the feature index threshold set in the jth indicator set; marks the indicator set to which the effective identifiable features belong, which does not meet L i j =1 / d i j or L i j ≥L0 j The communication link is the identifiable feature to be investigated for the corresponding indicator set recording the disaster type; Step S200: sorting out abnormal warning events of the same-source warning events corresponding to the disaster type with identifiable features, and optimizing and sorting the communication links of different disaster type records under each same-source warning event based on the abnormal warning events to generate a first push sequence; Step S300: extracting result data of the communication links in the first push sequence in all corresponding homologous warning events, and analyzing the effective warning index of each communication link; Step S400: Based on the effective warning index and the first push sequence, the optimal communication link corresponding to each disaster type in each homologous warning event is analyzed and output.

2. The method for managing disaster warning data communication based on the Internet of Things according to claim 1, characterized in that: The step S200 includes the following steps: Step S210: obtaining an abnormal warning event to which the communication link corresponding to the identification feature to be investigated belongs, wherein the abnormal warning event refers to an event in which the communication transmission of disaster warning data is interrupted at the corresponding broadcast location, and obtaining an indicator set corresponding to the identification feature to be investigated as an indicator set to be analyzed, wherein the indicator set to be analyzed records at least two identification features to be investigated; Step S220: extract the occurrence frequency T of the abnormal warning event corresponding to the kth identification feature to be examined in the set of indicators to be analyzed k , and the duration of sustainable communication in the event of power outage H k ; Normalize the occurrence frequency and sustainable communication duration corresponding to each identification feature to be investigated to obtain T k0 and H k0 ; Using the formula: Y k =L k *[(1 / T k0 )+H k0 ] Calculate the optimization index Y of the kth identification feature to be examined in the set of indicators to be analyzed k ; L k Represents the characteristic index of the kth identification feature to be examined; Step S230: Acquire all the identification features to be examined in each set of indicators to be analyzed, and sort the identification features to be examined from large to small according to the values ​​of the corresponding optimization indexes to generate a first push sequence.

3. The method for managing disaster warning data communication based on the Internet of Things according to claim 2, characterized in that: The step S300 includes the following process: Extract the warning purpose of the same-source warning event belonging to the first push sequence at the broadcast location, the result data refers to the assessment index corresponding to the disaster warning data when the warning purpose is achieved after communication transmission, and obtain the assessment index Q1 of the disaster warning data before each communication link broadcast and the assessment index Q2 after each communication link broadcast in the first push sequence; Using the formula: V = Q2 / Q1; Calculate the effective warning index V of each communication link in the first push sequence.

4. The method for managing disaster warning data communication based on the Internet of Things according to claim 3, characterized in that: The step S400 includes: When there are valid identifiable features in the same-source warning events, the communication link corresponding to the valid identifiable features is output as the optimal communication link for the corresponding disaster type; Extract the position sequence of the communication links corresponding to the first push sequence; compare the effective warning index V of each communication link based on the position sequence, and sort the communication links in the first push sequence from small to large according to the effective warning index V to generate a second sequence; When the first push sequence is exactly the same as the second sequence, the first communication link outputting the first push sequence is the optimal communication link for the disaster type corresponding to the first push sequence in the homologous warning event; When the first communication link of the first push sequence is different from the first communication link of the second sequence, the average effective warning index V0 and the average optimization index Y0 of the communication link corresponding to the first push sequence and other homologous warning events including the communication link are obtained, and the difference degree Z of the first communication link in the first push sequence is calculated, Z=|V-V0| / |Y-Y0|; When Z=1, any communication link in the first push sequence is output as the optimal communication link for the corresponding disaster type in the corresponding homologous warning event; When Z>1, the first communication link in the first push sequence is output as the optimal communication link for the corresponding disaster type in the homologous warning event; When Z<1, the first communication link in the second sequence is output as the optimal communication link corresponding to the disaster type in the homologous warning event.

5. A disaster warning data communication management system based on the Internet of Things, the system applying a disaster warning data communication management method based on the Internet of Things as claimed in any one of claims 1 to 4, characterized in that: It includes a homologous warning event classification module, an identifiable feature analysis module, a first push sequence generation module, an effective warning index analysis module and an optimal communication link judgment module; The homologous warning event classification module is used to classify target warning events that record the same broadcast location and the number of communication links corresponding to the broadcast in one event is not one as one type of warning event and mark them as homologous warning events; The identifiable feature analysis module is used to identify the identifiable features corresponding to each communication link in each type of homologous warning event; The first push sequence generation module is used to optimize and sort the communication links of different disaster type records under each homologous warning event based on the abnormal warning event to generate a first push sequence; The effective warning index analysis module is used to analyze the inspection indicators before and after each communication link broadcast to calculate the corresponding effective warning index; The optimal communication link judgment module is used to analyze and output the optimal communication link corresponding to each disaster type in each homologous warning event.

6. The disaster warning data communication management system based on the Internet of Things according to claim 5 is characterized in that: The identifiable feature analysis module includes an index set generation unit, a feature index calculation unit and an identifiable feature output unit; The indicator set generation unit is used to form data pairs from the disaster type included in each type of homologous warning event and the corresponding broadcast communication link, and store the data pairs corresponding to the same indicator in the same indicator set; The characteristic index calculation unit is used to obtain the number of all types of communication links recorded in the indicator set, and calculate the characteristic index of the communication link in the indicator set; The identifiable feature output unit is used to distinguish between valid identifiable features and identifiable features to be examined.

7. The disaster warning data communication management system based on the Internet of Things according to claim 6 is characterized in that: The first push sequence generation module includes a to-be-analyzed indicator set construction unit, an optimization index calculation unit, and a first push sequence output unit; The to-be-analyzed indicator set construction unit is used to obtain an indicator set corresponding to the to-be-investigated identification feature as the to-be-analyzed indicator set; The optimization index calculation unit is used to extract the occurrence frequency of abnormal warning events corresponding to the identification features to be examined in the set of indicators to be analyzed, as well as the duration of sustainable communication in the case of power energy interruption, and calculate the optimization index of the identification features to be examined in the set of indicators to be analyzed; The first push sequence output unit is used to obtain all identification features to be examined in each set of indicators to be analyzed, and to sort the identification features to be examined from large to small according to the values ​​of the corresponding optimization indexes to generate a first push sequence.

8. The disaster warning data communication management system based on the Internet of Things according to claim 7 is characterized in that: The optimal communication link judgment module includes an effective identifiable feature retrieval unit, a sequence comparison unit, and an optimal communication link output unit; The effective identifiable feature retrieval unit is used to identify whether there is an effective identifiable feature in the homologous warning event; The sequence comparison unit is used to compare the first push sequence and the second sequence; The optimal communication link output unit is used to output the optimal communication link under different comparison results.

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