Network traffic information management platform based on internet of things

Abstract

The application discloses a network flow information management platform based on an internet of things, and belongs to the technical field of network information management; and aims to solve the technical problem that the existing scheme cannot perform abnormal influence evaluation in different aspects when network flow information supervision and analysis is abnormal, and dynamically implement network flow control according to the evaluation result; the application performs data analysis on the overall bandwidth usage and the local bandwidth usage obtained through monitoring, traces and marks the single abnormal type corresponding to the overall instantaneous bandwidth usage state abnormality or the local instantaneous bandwidth usage state abnormality, processes the necessary state of the overall bandwidth usage control corresponding to the analysis platform server, and the necessary state of the local bandwidth usage control caused by different hotspot information items to the platform server, and dynamically implements the overall bandwidth usage control scheme and the local bandwidth usage control scheme caused by different hotspot information items to the platform server.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of network information management, in particular to a network traffic information management platform based on the Internet of Things. BACKGROUND

[0002] The network traffic information management platform is a system specially used for monitoring, analyzing and optimizing network traffic, which provides a comprehensive perspective for enterprises or organizations to understand their network usage, helps to identify potential problems, and takes measures to improve network performance and security.

[0003] After searching, the Chinese invention with the application number 2022108682041 and the name of a network traffic control method based on an information management platform discloses a method for controlling data traffic according to the fixed set traffic threshold of the existing server, which cannot maximize the use of network resources. It includes: monitoring the overall bandwidth utilization of the platform server and monitoring the local bandwidth utilization of the platform server caused by the hotspot information project; if the overall bandwidth utilization exceeds the overall bandwidth threshold, but the local bandwidth utilization does not exceed the local bandwidth threshold, the overall traffic control method is started; if the overall bandwidth utilization does not exceed the overall bandwidth threshold, but the local bandwidth utilization exceeds the local bandwidth threshold, the local traffic control method is started; if the overall bandwidth utilization exceeds the overall bandwidth threshold, and the local bandwidth utilization exceeds the local bandwidth threshold, the overall traffic control method and the local traffic control method are started at the same time. The invention is used for network traffic control.

[0004] However, the existing network traffic information management platform still has some defects in implementation. For network traffic control, it only stays in the overall traffic usage and local traffic usage supervision, evaluation and control, and does not perform multi-dimensional supervision and analysis on the influence of different abnormal traffic usage, and does not evaluate whether to regulate the overall traffic usage and local traffic usage according to the analysis results, so as to avoid the poor use effect of the overall traffic usage and local traffic usage after regulation, and cannot perform different aspect abnormal influence evaluation when the network traffic information supervision and analysis is abnormal, and dynamically implement network traffic control according to the evaluation results. SUMMARY

[0005] The purpose of the present application is to provide a network traffic information management platform based on the Internet of Things, which solves the technical problem that the network traffic information supervision and analysis cannot be performed in different aspects when the network traffic information supervision and analysis is abnormal, and dynamically implements network traffic control according to the evaluation results.

[0006] The purpose of the present application can be realized by the following technical solutions:

[0007] The network traffic information management platform based on the Internet of Things comprises:

[0008] a network traffic monitoring processing analysis module for monitoring the overall bandwidth usage of the platform server and monitoring the local bandwidth usage caused by the hotspot information item of the platform server, and performing data analysis on the overall bandwidth usage and the local bandwidth usage obtained by monitoring, determining the corresponding overall instantaneous bandwidth usage state and local instantaneous bandwidth usage state, and performing trace analysis and marking on the single abnormal type corresponding to the abnormal overall instantaneous bandwidth usage state or the abnormal local instantaneous bandwidth usage state;

[0009] a network traffic monitoring integrated management module for performing integrated processing and analysis of single abnormal influence on the overall bandwidth usage monitoring data of the platform server and the local bandwidth usage monitoring data caused by the hotspot information item of the platform server, respectively, and determining the corresponding overall bandwidth usage control necessary state of the platform server and the local bandwidth usage control necessary state caused by different hotspot information items of the platform server according to the analysis result, to dynamically implement the overall bandwidth usage control scheme and the local bandwidth usage control scheme caused by different hotspot information items of the platform server.

[0010] Preferably, the overall bandwidth usage of the monitoring platform server and the local bandwidth usage caused by the hotspot information item of the platform server are analyzed by a bandwidth usage identification model, and the corresponding bandwidth usage state value KSk is output; k is 1, 2; respectively representing the overall bandwidth usage of the platform server, the local bandwidth usage caused by the hotspot information item of the platform server;

[0011] wherein the expression of the bandwidth usage identification model is In the formula, LSk is LS1, LS2, respectively, the overall bandwidth usage of the platform server, and the local bandwidth usage caused by the hotspot information item of the platform server; LS0k is LS01, LS02, respectively, the overall bandwidth standard usage of the platform server, and the local bandwidth standard usage caused by the hotspot information item of the platform server.

[0012] Preferably, the corresponding overall instantaneous broadband usage trace instruction or local instantaneous broadband usage trace instruction is generated according to the bandwidth usage state value with a value of 1, and the first abnormal duration and the first abnormal broadband usage total amount corresponding to the abnormal overall instantaneous broadband usage state are monitored and counted according to the overall instantaneous broadband usage trace instruction, and the second abnormal duration and the second abnormal broadband usage total amount corresponding to the abnormal local instantaneous broadband usage state are monitored and counted according to the local instantaneous broadband usage trace instruction, through the formula a corresponding first wideband abnormal state value YZ1k is calculated; in the formula, YZ1k is YZ11 and YZ12, which are respectively a first wideband abnormal state value corresponding to an overall instantaneous wideband use state abnormality and a first wideband abnormal state value corresponding to a local instantaneous wideband use state abnormality; TYk is TY1 and TY2, which are respectively a first abnormal duration and a second abnormal duration; and TY0k is TY01 and TY02, which are respectively a first abnormal standard duration and a second abnormal standard duration.

[0013] Preferably, a corresponding second wideband abnormal state value YZ2k is calculated by the formula

[0014] Preferably, data analysis is performed on the first wideband abnormal state value and the second wideband abnormal state value calculated to determine a single abnormal type corresponding to the overall instantaneous wideband use state abnormality or the local instantaneous wideband use state abnormality.

[0015] If YZ1k>0 and YZ2k>0 do not exist at the same time, it is prompted that the single abnormal type corresponding to the overall instantaneous wideband use state abnormality or the local instantaneous wideband use state abnormality is a mild abnormal type.

[0016] If YZ1k>0 and YZ2k>0 exist at the same time, it is prompted that the single abnormal type corresponding to the overall instantaneous wideband use state abnormality or the local instantaneous wideband use state abnormality is a severe abnormal type.

[0017] Preferably, a plurality of first abnormal durations and first wideband use amounts obtained through overall wideband use rate processing analysis of the monitoring platform server are sorted and combined respectively, and a plurality of second abnormal durations and second wideband use amounts obtained through local wideband use rate processing analysis of the hotspot information item causing the platform server are sorted and combined respectively to obtain an abnormal duration sorting sequence and a wideband use amount sorting sequence, and the element with the largest value in the abnormal duration sorting sequence and the wideband use amount sorting sequence is marked as a first abnormal selected value and a second abnormal selected value respectively.

[0018] Preferably, the median values of all elements in the abnormal duration sorting sequence and the wideband use amount sorting sequence are obtained respectively and marked as a third abnormal selected value and a fourth abnormal selected value respectively. ​

[0019] Preferably, the different bandwidth usage corresponding processing of the different abnormal selected values obtained is analyzed by the traffic usage identification model, and the corresponding control necessity identification is output;

[0020] The control necessity identification contains a numerical value of 0, 1 or 2;

[0021] According to the control necessity identification with the numerical value of 0, the existing overall traffic control scheme and the local traffic control scheme caused by the platform server corresponding to the hotspot information item are maintained.

[0022] Preferably, according to the control necessity identification with the numerical value of 1, the overall traffic local optimization control scheme and the local traffic local optimization control scheme caused by the platform server corresponding to the hotspot information item are implemented;

[0023] According to the control necessity identification with the numerical value of 2, the target associated traffic usage severity abnormality label is associated and the overall traffic overall optimization control scheme and the local traffic overall optimization control scheme caused by the platform server corresponding to the hotspot information item are implemented.

[0024] Preferably, the expression of the traffic usage identification model is In the formula, YX1k, YX2k, YX3k and YX4k are respectively the first abnormal selected value, the second abnormal selected value, the third abnormal selected value and the fourth abnormal selected value obtained by corresponding processing of different bandwidth usage; ak, bk, ck and dk are respectively the first abnormal standard value, the second abnormal standard value, the third abnormal standard value and the fourth abnormal standard value corresponding to different bandwidth usage.

[0025] Compared with the prior art, the present application has the following beneficial effects:

[0026] The present application realizes the digital processing and screening classification of the overall instantaneous bandwidth usage state abnormality or the local instantaneous bandwidth usage state abnormality by analyzing the overall bandwidth usage rate and the local bandwidth usage rate obtained by monitoring, determining the corresponding overall instantaneous bandwidth usage state and local instantaneous bandwidth usage state, and performing trace analysis and marking on the single abnormal type corresponding to the overall instantaneous bandwidth usage state abnormality or the local instantaneous bandwidth usage state abnormality. It can also provide reliable local abnormal processing analysis data support for the dynamic implementation analysis of subsequent different bandwidth usage control schemes.

[0027] The application realizes autonomous abnormal influence evaluation in different aspects when network flow information supervision and analysis is abnormal, and dynamically implements network flow management and control according to the evaluation results, improves the diversity and reliability of network flow abnormal information management, by processing the overall bandwidth usage control necessary state of the analysis platform server and the local bandwidth usage control necessary state of the platform server caused by different hot information items, and dynamically implementing the overall bandwidth usage control scheme and the local bandwidth usage control scheme of the platform server caused by different hot information items. BRIEF DESCRIPTION OF DRAWINGS

[0028] The application will be further described below in combination with the drawings.

[0029] Figure 1 The step block diagram of the network flow information management platform based on the Internet of Things is shown. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the application will be clearly and completely described below in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0031] As shown in Figure 1 The application is a network flow information management platform based on the Internet of Things, comprising a network flow supervision processing and analysis module and a network flow supervision integration management module.

[0032] The network flow supervision processing and analysis module is used for monitoring the overall bandwidth usage rate of the platform server and the local bandwidth usage rate of the platform server caused by hot information items, and performing data analysis on the monitored overall bandwidth usage rate and local bandwidth usage rate to determine the corresponding overall instantaneous bandwidth usage state and local instantaneous bandwidth usage state, and performing trace analysis and marking on the single abnormal type corresponding to the overall instantaneous bandwidth usage state abnormality or local instantaneous bandwidth usage state abnormality; comprising:

[0033] The monitored overall bandwidth usage rate of the platform server and the local bandwidth usage rate of the platform server caused by hot information items are analyzed by a bandwidth usage identification model, and the corresponding bandwidth usage state value KSk is output; k is 1 or 2, representing the overall bandwidth usage of the platform server and the local bandwidth usage of the platform server caused by hot information items, respectively.

[0034] The expression of the bandwidth usage identification model is In the formula, LSk is LS1 and LS2, which are the overall bandwidth usage rate of the platform server and the local bandwidth usage rate caused by the hot information item of the platform server, respectively; LS0k is LS01 and LS02, which are the overall bandwidth standard usage rate of the platform server and the local bandwidth standard usage rate caused by the hot information item of the platform server, respectively, and can be determined according to existing bandwidth usage design requirement data;

[0035] The bandwidth usage state value contains a value of 0 or 1.

[0036] The bandwidth usage state value with a value of 0 indicates that the corresponding overall instantaneous bandwidth usage state or local instantaneous bandwidth usage state is normal.

[0037] The bandwidth usage state value with a value of 1 indicates that the corresponding overall instantaneous bandwidth usage state or local instantaneous bandwidth usage state is abnormal.

[0038] In the embodiment of the application, by monitoring and processing analysis of the overall bandwidth usage rate of the platform server and the local bandwidth usage rate caused by the hot information item of the platform server, both the corresponding overall instantaneous bandwidth usage state and the local instantaneous bandwidth usage state can be obtained, and reliable abnormal monitoring data support can be provided for subsequent trace evaluation of overall instantaneous bandwidth usage state abnormality and / or local instantaneous bandwidth usage state abnormality.

[0039] According to the bandwidth usage state value with a value of 1, corresponding overall instantaneous wideband usage trace instructions or local instantaneous wideband usage trace instructions are generated, and according to the overall instantaneous wideband usage trace instructions, the first abnormal duration and the first abnormal wideband usage total amount corresponding to the corresponding overall instantaneous wideband usage state abnormality are monitored and counted, and according to the local instantaneous wideband usage trace instructions, the second abnormal duration and the second abnormal wideband usage total amount corresponding to the corresponding local instantaneous wideband usage state abnormality are monitored and counted, the first abnormal wideband usage total amount and the second abnormal wideband usage total amount are obtained by calculating the difference between the overall bandwidth usage rate and the overall bandwidth standard usage rate, the difference between the local bandwidth usage rate and the local bandwidth standard usage rate, and summing them up, through the formula The corresponding first wideband abnormal state value YZ1k is calculated and obtained; in the formula, YZ1k is YZ11 and YZ12, which are the first wideband abnormal state value corresponding to the overall instantaneous wideband usage state abnormality and the first wideband abnormal state value corresponding to the local instantaneous wideband usage state abnormality, respectively; TYk is TY1 and TY2, which are the first abnormal duration and the second abnormal duration, respectively; TY0k is TY01 and TY02, which are the first abnormal standard duration and the second abnormal standard duration, respectively, and can be determined according to existing bandwidth usage design requirement data or determined according to previous trial operation test data.

[0040] and, by formula corresponding second wideband abnormal state value YZ2k is calculated; in the formula, YZ2k is YZ21 and YZ22, which are respectively a second wideband abnormal state value corresponding to an overall instantaneous wideband use state abnormality and a second wideband abnormal state value corresponding to a local instantaneous wideband use state abnormality; LYk is LY1 and LY2, which are respectively a first abnormal wideband use total amount and a second abnormal wideband use total amount; LY0k is LY01 and LY02, which are respectively a first abnormal standard wideband use total amount and a second abnormal standard wideband use total amount, and can be determined according to existing bandwidth use design requirement data or determined according to early trial operation test data;

[0041] It should be noted that the first wideband abnormal state value and the second wideband abnormal state value are used to calculate abnormal data corresponding to an overall instantaneous wideband use state abnormality or a local instantaneous wideband use state abnormality from different aspects, to digitally represent a single abnormal type corresponding thereto;

[0042] The first wideband abnormal state value and the second wideband abnormal state value calculated are subjected to data analysis to determine a single abnormal type corresponding to an overall instantaneous wideband use state abnormality or a local instantaneous wideband use state abnormality;

[0043] If YZ1k>0 and YZ2k>0 do not exist at the same time, it is prompted that the single abnormal type corresponding to the overall instantaneous wideband use state abnormality or the local instantaneous wideband use state abnormality is a mild abnormal type;

[0044] If YZ1k>0 and YZ2k>0 exist at the same time, it is prompted that the single abnormal type corresponding to the overall instantaneous wideband use state abnormality or the local instantaneous wideband use state abnormality is a severe abnormal type;

[0045] In the embodiment of the application, through data analysis on the monitored overall bandwidth use rate and local bandwidth use rate, corresponding overall instantaneous wideband use state and local instantaneous wideband use state are determined, and a single abnormal type corresponding to an overall instantaneous wideband use state abnormality or a local instantaneous wideband use state abnormality is traced back and marked, so that digital processing and screening and classification of the overall instantaneous wideband use state abnormality or the local instantaneous wideband use state abnormality are realized, and reliable local abnormal processing analysis data support can be provided for dynamic implementation analysis of a subsequent different bandwidth use control scheme.

[0046] The network traffic supervision integration management module is used for integrated processing and analysis of single abnormal influence of overall bandwidth usage supervision data of the platform server and local bandwidth usage supervision data caused by different hotspot information items, and determines the necessary state of overall bandwidth usage control of the platform server and the necessary state of local bandwidth usage control caused by different hotspot information items, to dynamically implement the overall bandwidth usage control scheme and the local bandwidth usage control scheme caused by different hotspot information items; comprising:

[0047] The first abnormal duration and the first abnormal broadband usage total quantity obtained by processing and analyzing the overall bandwidth usage of the monitoring platform server are respectively sorted and combined, and the second abnormal duration and the second abnormal broadband usage total quantity caused by the local bandwidth usage of the hotspot information item are respectively sorted and combined, to obtain the abnormal duration sorting sequence and the abnormal broadband usage total quantity sorting sequence, and the elements with the maximum value in the abnormal duration sorting sequence and the abnormal broadband usage total quantity sorting sequence are respectively marked as the first abnormal selected value and the second abnormal selected value;

[0048] In addition, the median values of all elements in the abnormal duration sorting sequence and the abnormal broadband usage total quantity sorting sequence are respectively obtained and marked as the third abnormal selected value and the fourth abnormal selected value;

[0049] Unlike the prior art scheme which only analyzes the abnormality of a single monitoring item and then implements the corresponding traffic control scheme, the present application can effectively improve the diversity and reliability of the corresponding traffic usage abnormality analysis by obtaining the corresponding monitoring item data from different aspects and integrating processing and analysis.

[0050] The different abnormal selected values obtained by processing different bandwidth usage rates are analyzed by a traffic usage identification model, and the corresponding control necessary identifier CBk is outputted;

[0051] The expression of the traffic usage identification model is In the formula, YX1k, YX2k, YX3k and YX4k are respectively the first abnormal selected value, the second abnormal selected value, the third abnormal selected value and the fourth abnormal selected value obtained by processing different bandwidth usage rates; ak, bk, ck and dk are respectively the first abnormal standard value, the second abnormal standard value, the third abnormal standard value and the fourth abnormal standard value corresponding to different bandwidth usage rates, which can be determined according to the existing bandwidth usage design requirement data, or determined according to the test data of the trial operation in the early stage;

[0052] The control necessary identifier comprises a numerical value of 0, 1 or 2;

[0053] According to the control necessary identifier with the numerical value of 0, the target associated traffic is marked with a normal label, and the existing overall traffic control scheme and the local traffic control scheme corresponding to the hotspot information item are maintained;

[0054] According to the control necessary identifier with the numerical value of 1, the target associated traffic is marked with a mild abnormal label, and the overall traffic local optimization control scheme and the local traffic local optimization control scheme corresponding to the hotspot information item are implemented;

[0055] According to the control necessary identifier with the numerical value of 2, the target associated traffic is marked with a severe abnormal label, and the overall traffic overall optimization control scheme and the local traffic overall optimization control scheme corresponding to the hotspot information item are implemented;

[0056] The overall traffic local optimization control scheme and the local traffic local optimization control scheme can specifically adjust the corresponding broadband threshold by a first adjustment ratio based on the existing overall traffic control scheme and the local traffic control scheme.

[0057] The overall traffic overall optimization control scheme and the local traffic overall optimization control scheme can specifically adjust the corresponding broadband threshold by a second adjustment ratio based on the existing overall traffic control scheme and the local traffic control scheme. The first adjustment ratio and the second adjustment ratio are real numbers greater than 1, and the first adjustment ratio is less than the second adjustment ratio. The specific numerical values of the first adjustment ratio and the second adjustment ratio are not fixed, and can be dynamically adjusted according to actual conditions, or can be determined according to the test data of the trial operation in the early stage to meet the real-time needs of different network congestion states.

[0058] In the embodiment of the application, the overall bandwidth usage control necessary state of the platform server and the local bandwidth usage control necessary state of the platform server caused by different hotspot information items are analyzed and processed, and the overall bandwidth usage control scheme and the local bandwidth usage control scheme of the platform server caused by different hotspot information items are dynamically implemented, so that the abnormal influence evaluation in different aspects is realized when the network traffic information is monitored and analyzed, and the network traffic control is adaptively and dynamically implemented according to the evaluation results, thereby improving the diversity and reliability of network traffic abnormal information management.

[0059] In addition, the formulas involved in the above are calculated by removing the dimension and taking the numerical value, and are obtained by collecting a large amount of data and simulating the software to obtain a formula closest to the real situation.

[0060] In several embodiments of the present application, it should be understood that the disclosed system can be implemented in other manners. For example, the division of the modules is merely logical function division, and there can be other division manners in actual implementation. For example, in a unit, a plurality of components or a plurality of modules can be combined or integrated into one component or module.

[0061] The modules described as separated components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, and can be located in one place or distributed on a plurality of network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0062] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of hardware plus software functional module.

[0063] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the essential characteristics of the present application.

[0064] Finally, it should be noted that the above embodiments are merely used to illustrate the technical solutions of the present application but not limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application.

Claims

1. A network traffic information management platform based on the Internet of Things, characterized in that, include: The network traffic monitoring, processing, and analysis module is used to monitor the overall bandwidth utilization of the platform server and the local bandwidth utilization of the platform server caused by monitoring hot information items. It also performs data analysis on the overall and local bandwidth utilization obtained from the monitoring to determine the corresponding overall instantaneous bandwidth utilization status and local instantaneous bandwidth utilization status. Furthermore, it performs traceability analysis and marking of the single abnormality type corresponding to the abnormal overall or local instantaneous bandwidth utilization status. The network traffic monitoring and management module is used to integrate and analyze the overall bandwidth usage monitoring data of the platform server and the local bandwidth usage monitoring data caused by monitoring hot information items, performing single-instance anomaly impact analysis. Based on the analysis results, it determines the necessary overall bandwidth usage control status for the platform server, as well as the necessary local bandwidth usage control status caused by different hot information items, to dynamically implement overall traffic control schemes and local traffic control schemes for different hot information items. This includes: sorting and combining several first anomaly durations and first anomaly bandwidth usage totals obtained from the overall bandwidth usage analysis of the monitoring platform server, with the first anomaly duration and first anomaly bandwidth usage totals corresponding to the overall instantaneous bandwidth usage anomaly status; and classifying the impact of hot information items on the platform server... The local bandwidth utilization analysis of the device obtains several second anomaly durations and second anomaly total bandwidth usage, which are then sorted and combined. These second anomaly durations and total anomaly total bandwidth usage correspond to the local instantaneous bandwidth utilization anomalies, resulting in corresponding anomaly duration sorting sequences and anomaly total bandwidth usage sorting sequences. The elements with the largest values ​​in these sequences are marked as the first and second anomaly selection values, respectively. The medians of all elements in the anomaly duration and total bandwidth usage sorting sequences are then obtained and marked as the third and fourth anomaly selection values, respectively. The different anomaly selection values ​​obtained for different bandwidth utilization rates are analyzed using a traffic usage identification model, and corresponding control necessary identifiers are output. The expression for the traffic usage identification model is: In the formula, YX1k, YX2k, YX3k, and YX4k represent the first, second, third, and fourth abnormal selection values ​​obtained for processing at different bandwidth utilization rates, respectively; ak, bk, ck, and dk represent the first, second, third, and fourth abnormal standard values ​​for different bandwidth utilization rates, respectively; the control necessity identifier contains values ​​of 0, 1, or 2; based on the control necessity identifier with a value of 0, the existing overall traffic control scheme or the corresponding hotspot information item causes the platform server to implement a local traffic optimization control scheme; based on the control necessity identifier with a value of 1, the overall traffic local optimization control scheme or the corresponding hotspot information item causes the platform server to implement a local traffic local optimization control scheme; based on the control necessity identifier with a value of 2, the traffic associated with the target is labeled with a severe anomaly, and the overall traffic optimization control scheme or the corresponding hotspot information item causes the platform server to implement an overall traffic optimization control scheme.

2. The network traffic information management platform based on the Internet of Things according to claim 1, characterized in that, The overall bandwidth utilization of the monitoring platform server and the local bandwidth utilization caused by monitoring hotspot information items are analyzed using a bandwidth utilization identification model, and the corresponding bandwidth utilization status value KSk is output; k is 1 or 2; representing the overall bandwidth utilization of the platform server and the local bandwidth utilization caused by hotspot information items, respectively. The expression for the bandwidth usage identification model is as follows: In the formula, LSk represents LS1 and LS2, which are the overall bandwidth utilization rate of the platform server and the local bandwidth utilization rate of the platform server caused by hot information items, respectively; LS0k represents LS01 and LS02, which are the overall standard bandwidth utilization rate of the platform server and the local standard bandwidth utilization rate of the platform server caused by hot information items, respectively.

3. The network traffic information management platform based on the Internet of Things according to claim 2, characterized in that, Based on the bandwidth usage status value of 1, a corresponding overall instantaneous bandwidth usage traceability command or a local instantaneous bandwidth usage traceability command is generated. Based on the overall instantaneous bandwidth usage traceability command, the duration and total bandwidth usage of the first abnormality corresponding to the overall instantaneous bandwidth usage status anomaly are monitored and statistically analyzed. Similarly, based on the local instantaneous bandwidth usage traceability command, the duration and total bandwidth usage of the second abnormality corresponding to the local instantaneous bandwidth usage status anomaly are monitored and statistically analyzed. This is then processed using the formula... Calculate and obtain the corresponding first bandwidth anomaly state value YZ1k; where YZ1k is YZ11 and YZ12, which are the first bandwidth anomaly state values ​​corresponding to the overall instantaneous bandwidth usage anomaly and the first bandwidth anomaly state values ​​corresponding to the local instantaneous bandwidth usage anomaly, respectively; TYk is TY1 and TY2, which are the duration of the first anomaly and the duration of the second anomaly, respectively. TY0k represents TY01 and TY02, which are the durations of the first and second abnormal criteria, respectively.

4. The network traffic information management platform based on the Internet of Things according to claim 3, characterized in that, Through formula Calculate and obtain the corresponding second bandwidth anomaly state value YZ2k; where YZ2k is YZ21 and YZ22, which are the second bandwidth anomaly state values ​​corresponding to the overall instantaneous bandwidth usage anomaly and the second bandwidth anomaly state values ​​corresponding to the local instantaneous bandwidth usage anomaly, respectively; LYk is LY1 and LY2, which are the first abnormal bandwidth usage total and the second abnormal bandwidth usage total, respectively; LY0k is LY01 and LY02, which are the first abnormal standard bandwidth usage total and the second abnormal standard bandwidth usage total, respectively.

5. The network traffic information management platform based on the Internet of Things according to claim 4, characterized in that, Data analysis is performed on the calculated first and second bandwidth anomaly values ​​to determine the single anomaly type corresponding to the overall instantaneous bandwidth usage anomaly or the local instantaneous bandwidth usage anomaly. If YZ1k>0 and YZ2k>0 do not exist simultaneously, it indicates that the single abnormality type corresponding to the overall instantaneous bandwidth usage status abnormality or the local instantaneous bandwidth usage status abnormality is a mild abnormality type. If YZ1k>0 and YZ2k>0 exist simultaneously, it indicates that the single anomaly type corresponding to the overall instantaneous bandwidth usage status anomaly or the local instantaneous bandwidth usage status anomaly is a severe anomaly type.

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