A security risk real-time monitoring data flow management and control host based on Internet of Things

Abstract

The application discloses a kind of based on safety risk real-time monitoring data stream control host computer of Internet of Things, can be in accordance with the need of destination port, and in accordance with data flow congestion index carries out data scheduling, it is convenient for safety risk real-time monitoring collaborative control;Based on Internet of Things technology, utilize the wireless real-time transmission of wireless radio frequency, the identity enablement of electronic tag, edge computing auxiliary real-time data stream state monitoring, realize the efficient, accurate scheduling control of safety risk factor real-time monitoring data stream;According to data flow congestion index, parameter monitoring device state, open and close data from machine to carry out data scheduling, realize the high-quality transmission of safety risk real-time monitoring data;Adopt master-slave machine integration manufacturing, host computer controls slave machine to open and close, realize the energy saving of data transmission;It can be in accordance with scene identification, parameter identification and carry out scheduling, improve the effect of target scene safety risk control.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of digitalization of safety production, and more particularly relates to a safety risk real-time monitoring data flow management and control master based on Internet of Things. BACKGROUND

[0002] There are numerous safety production risk factors, including human, material (machine) and environment factors of essential safety state at the level of safety production operation site, and management system, institution and safety awareness factors of essential safety guarantee at the level of safety production management. These safety risk factors are in the same space-time or different space-time, and it is necessary to collect and analyze these factors monitored in real time, evaluate the safety production state, intervene in the adverse state and prevent and control the occurrence of disaster risks. Meanwhile, safety risk prevention and control involves operation layer, management layer at all levels and enterprise headquarters, and is end-edge-cloud collaborative management and control.

[0003] Traditional safety production detection and monitoring data need to be analyzed and sorted by professional personnel and delivered to management personnel at all levels, which is low in efficiency and lacks real-time performance. SUMMARY

[0004] In view of the above defects or improvement needs of the prior art, the present application provides a safety risk real-time monitoring data flow management and control master based on Internet of Things, which realizes intelligent management and control of safety production big data.

[0005] To achieve the above purpose, according to the first aspect of the present application, a safety risk real-time monitoring data flow management and control master based on Internet of Things is provided, comprising: a scene selection module, a parameter selection module, a data transceiving module, a control module and a plurality of slave machines;

[0006] The scene selection module is used for user to select a monitoring scene;

[0007] The parameter selection module is used for user to select monitoring parameters to be transmitted under each monitoring scene;

[0008] The data transceiving module is used for receiving monitoring data from a sensing device and sending it to a target device;

[0009] The control module:

[0010] determines a data flow congestion index according to the data flow of the monitoring data received by the data transceiving module, and starts slave machine data transceiving when the data flow congestion index is greater than 1;

[0011] determines a data packet frequency anomaly index of the monitoring parameter according to the data packet frequency of the monitoring parameter received by the data transceiving module, and starts slave machine data transceiving when the data packet frequency anomaly index is greater than 1;

[0012] The application is used for determining the threshold range of each monitoring parameter according to the type of monitoring parameter selected by the user, and alarming when the monitoring value of any monitoring parameter is not within its threshold range.

[0013] According to the second aspect of the application, a safety risk real-time monitoring data flow management and control master machine based on Internet of Things is provided, which is applied to the management and control master machine of the first aspect, and comprises:

[0014] receiving monitoring data from the sensing device; starting the slave data transceiver when the data flow congestion index is greater than 1 or the data packet frequency anomaly index of any monitoring parameter is greater than 1; and alarming when the monitoring value of any monitoring parameter is not within its threshold range.

[0015] The data flow congestion index is calculated according to the data flow of the received monitoring data; the data packet frequency anomaly index of any monitoring parameter is calculated according to the data packet frequency of the received any monitoring parameter; and the threshold range of any monitoring parameter is obtained according to the type of monitoring parameter selected by the user.

[0016] Overall, compared with the prior art, the above technical solutions conceived by the application can achieve the following beneficial effects:

[0017] 1. The safety risk real-time monitoring data flow management and control master machine based on Internet of Things can schedule data according to the needs of the destination port and the data flow congestion index, facilitating the safety risk real-time monitoring collaborative management and control. Based on Internet of Things technology, wireless real-time transmission of wireless radio frequency, identity enablement of electronic tags, edge computing assisted real-time data flow state monitoring, efficient and accurate scheduling and control of safety risk factor real-time monitoring data flow are realized.

[0018] 2. The safety risk real-time monitoring data flow management and control master machine based on Internet of Things schedules data according to the data flow congestion index and the state of the parameter monitoring device, and opens and closes the data transceiver slave machine to realize high-quality transmission of safety risk real-time monitoring data.

[0019] 3. The safety risk real-time monitoring data flow management and control master machine based on Internet of Things adopts integrated manufacturing of master and slave machines, the slave machine adopts the plug-and-play mode, and the master machine controls the opening and closing of the slave machine to realize energy saving of data transmission.

[0020] 4. The safety risk real-time monitoring data flow management and control master machine based on Internet of Things can be scheduled according to the scene identifier and the parameter identifier to improve the effect of target scene safety risk management and control. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1A schematic diagram of a master machine for monitoring and controlling data flow based on real-time monitoring of security risks of Internet of Things provided by the present application;

[0022] Figure 2 A schematic diagram of a master machine for monitoring and controlling data flow based on real-time monitoring of security risks of Internet of Things provided by the present application;

[0023] Figure 3 A schematic diagram of data flow scheduling and classification provided by the present application;

[0024] Figure 4 A schematic diagram of slave machine scheduling provided by the present application;

[0025] Figure 5 A schematic diagram of a method for monitoring and controlling data flow based on real-time monitoring of security risks of Internet of Things provided by the present application. DETAILED DESCRIPTION

[0026] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0027] The embodiment of the present application provides a master machine for monitoring and controlling data flow based on real-time monitoring of security risks of Internet of Things, as shown in the figure, comprising a scene selection module, a parameter selection module, a data transceiver module and a control module. Figure 1

[0028] The scene selection module is used for a user to select a monitoring scene.

[0029] The parameter selection module is used for a user to select a monitoring parameter type to be transmitted under each monitoring scene.

[0030] The data transceiver module is used for receiving monitoring data from a sensing device and sending it to a target device.

[0031] Specifically, the scene selection module and the parameter selection module can be set in the form of a key. The selection of the monitoring scene includes the selection of the number and type of the monitoring scene, and the selection of the monitoring parameter includes the selection of the number and type of the monitoring parameter.

[0032] The definition of the monitoring scene is that in the same monitoring space, different processes, procedures or devices can be used as a monitoring scene, corresponding to different monitoring scene types. For example, process A, process B, device C, device D, procedure E and procedure F.

[0033] The sensing device is an intelligent sensor. ​

[0034] the control module:

[0035] determining a data flow congestion index according to the data flow of the monitoring data received by the data transceiving module, and initiating slave data transceiving when the data flow congestion index is greater than 1;

[0036] determining a data packet frequency abnormality index of the monitoring parameter according to the data packet frequency of the monitoring parameter received by the data transceiving module, and initiating slave data transceiving when the data packet frequency abnormality index is greater than 1;

[0037] determining the threshold range of each monitoring parameter according to the type of the monitoring scene and the monitoring parameter selected by the user, and alarming when the monitoring value of any monitoring parameter is not within the threshold range thereof;

[0038] wherein, when initiating the slave, part of the monitoring parameters are allocated to the slave for transceiving according to the type of the monitoring scene or the monitoring parameter.

[0039] Further, the control module comprises a CPU, a data receiving control module, a data sending control module, a parameter data abnormality analysis and identification and warning module, and a data flow congestion analysis and identification and warning module.

[0040] The data flow congestion analysis and identification and warning module is configured to calculate a data flow congestion index according to the data flow of the monitoring data received by the data transceiving module, and initiate slave data transceiving when the data flow congestion index is greater than 1.

[0041] When the slave needs to be initiated, the data transceived by the master and the slave can be allocated according to the scene type or the parameter type. For example, if the user selects multiple monitoring scenes, when the data flow congestion index is greater than 1, the transceiving task of the monitoring parameters under part of the monitoring scenes can be allocated to the slave to execute; if the user only selects a single monitoring scene, when the data flow congestion index is greater than 1, the transceiving task of the monitoring parameters corresponding to part of the monitoring parameter types can be allocated to the slave to execute.

[0042] When allocating the transceiving task, the master and the slave are preferentially allocated averagely according to the scene type or the parameter type; if the master and the slave cannot be allocated averagely, the master executes more transceiving tasks than the slave. For example, if the user selects three monitoring scenes A, B and C, when the data flow congestion index is greater than 1, the transceiving task of the monitoring parameters of scene C can be allocated to the slave to execute, and the master continues to execute the transceiving task of the monitoring parameters of monitoring scenes A and B.

[0043] Further, the data flow congestion analysis and identification and warning module is further configured to alarm (for example, alarm by controlling the sound and light alarm module) when the data flow congestion index is greater than 1.

[0044] Furthermore, the data flow Q of the monitoring data received by the data transceiver module i The Data Flow Performance Index (DFPI) satisfies the following relationship:

[0045]

[0046] When the sensing frequency, data packet byte count, and data packet frequency of each monitoring parameter are the same, Q0 is the baseline flow rate of the monitoring data, Q0 = N. para ×B DB ×f sensor Q i =f receive ×B DB ×N para f sensor To determine the sensing frequency of the monitoring parameters by the sensing device, B DB f is the number of bytes in the data packets containing the monitoring parameters sensed by the sensing device. receive The frequency of data packets received by the data transceiver module to receive monitoring data; when the sensing frequency, data packet byte count, and data packet frequency of each monitoring parameter are different, n represents the number of monitoring parameters.

[0047] When DFPI > 1, the received data volume is less than the baseline data flow, indicating that real-time data packets for security risk parameters are lost, and there is a conflict in the transmission of multiple parameters and multiple wireless information channels. In this case, a new data receiving slave device needs to be started to receive data. To achieve data management and control, the allocation method of master and slave device transmit and receive parameter data should be based on the needs of the management department, either by scenario type or by parameter type.

[0048] The parameter data anomaly analysis, identification, and warning module is also used to calculate the data packet frequency anomaly index of any monitoring parameter based on the data packet frequency received by the data transceiver module, and to start the slave device to transmit and receive data when the index is greater than 1.

[0049] Furthermore, the parameter data anomaly analysis, identification, and warning module is also used to issue an alarm when the data flow congestion index is greater than 1 (for example, by controlling the audible and visual alarm module).

[0050] Furthermore, the data packet frequency f of the monitoring parameter j received by the data transceiver module receivej The packet frequency anomaly index RPPAI with monitoring parameter j j The following relationship must be satisfied:

[0051]

[0052] Among them, f` sensorj For To count the perception frequency of the monitoring parameter j of the perception device, f receivej To count the perception frequency of the monitoring parameter j of the perception device, f To count the perception frequency of the monitoring parameter j of the perception device, f

[0053] It can be understood that, in the calculation of the data packet frequency anomaly index, in order to ensure the smoothness of the data and the accuracy of the calculation result, the statistical time length of the data packet frequency and the perception frequency is respectively 10 times the statistical time length of the data packet frequency f receivej and the data packet frequency f receivej received by the data transceiver module.

[0054] When the data packet frequency of any monitoring parameter j received by the data transceiver module is significantly lower than the perception frequency of j of the perception device, that is, the received parameter data packet frequency anomaly index RPPAI j of j is greater than 1, a new data receiving slave should be started to receive data. The new slave transceiver data can be allocated according to the parameter type or the scene type, and the original master should be removed accordingly.

[0055] Further, when DFPI>1 and RPPAI j ≤1, the number of started slaves is

[0056] When RPPAI j >1 and DFPI≤1, the number of started slaves is

[0057] Wherein, is rounded down.

[0058] Further, when DFPI>1 and RPPAI j >1, the number of started slaves is

[0059] The parameter data anomaly analysis and alarm module is also used to determine the threshold range of each monitoring parameter according to the user-selected monitoring scene and monitoring parameter type, and to alarm when the monitoring value of any monitoring parameter is not within its threshold range.

[0060] Specifically, the V i threshold range of the monitoring value of the monitoring parameter is: V i ∈[V min , V max ], when the measured value indicates that the safety risk parameter monitoring sensor is faulty. That is, when there is no data packet, or there is a data packet but the data is incorrect, it indicates that the parameter monitoring intelligent device (i.e. the perception device) is faulty.

[0061] The data receiving control module is configured to control the data transceiver module to receive the monitoring parameters in groups according to scene types, parameter types, transmission frequencies, data formats, or monitoring data frequency sizes.

[0062] The data sending control module is configured to control the data transceiver module to send the monitoring parameters to target devices in groups according to the needs of a control department, or according to scene types, parameter types, transmission frequencies, data formats, or monitoring data frequency sizes.

[0063] Further, the calculation of the data flow congestion index DFPI and the data packet frequency anomaly index RPPAI j may be real-time; in order to save resources for calculation, the data flow congestion index DFPI can also be calculated according to a preset period, and the data packet frequency anomaly index RPPAI j may be spot-checked, that is, the data packet frequency anomaly index of any monitoring parameter is spot-checked.

[0064] Further, the control host also includes a scene transmission selection module configured to allow a user to select whether to send the monitoring data to a target device outside the monitoring scene.

[0065] Further, when the data transceiver module sends the monitoring data to a target device inside the monitoring scene, the control module assigns a first identification code to the monitoring data; the first identification code includes a monitoring scene code and a parameter type code.

[0066] When the data transceiver module sends the monitoring data to a target device outside the monitoring scene, the control module assigns a second identification code to the monitoring data; the second identification code includes a specific identification code and the first identification code.

[0067] Specifically, the safety risk real-time monitoring data flow control host provided by the present application adopts a master-slave machine integrated control mode, the master controls the working state of the slave, and the master starts and stops the data transceiving of the slave.

[0068] Both the master and the slave can select a monitoring scene through a panel scene setting key, and set the type of a monitoring parameter through a parameter selection setting key, to form a scene parameter type set Ω = {P1, P2, …, P N}, a parameter type identification code set {Para1, Para2, …, Para N}, a parameter sensor number set P i = {P i1 , P i2 , …, P iM}, and a parameter real-time data identification set {ID+Para1, ID+Para2, …, ID+Para N}.

[0069] By the scene out transmission data key, the monitoring scene data out transmission is confirmed and the identification U is assigned. The out transmission data identification code set {U+ID+Para1, U+ID+Para2, …, U+ID+Para N} is assigned.

[0070] As Figure 2 shown, the host can independently transmit and receive data from the slave, but the slave must receive the host instruction to transmit and receive data. For a certain data flow destination (such as a data control department), the slave and its parameters can be manually set to transmit data to the destination by calling the slave.

[0071] The slave can be used in a plug-and-play manner, and the slave calls the larger one of the number of slaves determined according to the data flow congestion index and the parameter data packet reception anomaly index:

[0072] When the host control module monitors and finds that the data flow congestion index DFPI is in the interval (1, 2], start 1 slave to receive; when DFPI = (2, 3], start 2 slaves to receive, and so on.

[0073] When the host control module monitors and finds that the received parameter data packet anomaly index RPPAI is in the interval (1, 2), start 1 slave to receive; when RPPAI = [2, 3], start 2 slaves to receive, and so on.

[0074] The number of forwarding slaves is set according to the larger one of the number of data flow destinations and the number of receiving slaves.

[0075] Thus, energy-saving, efficient, and high-quality data flow direction scheduling is achieved.

[0076] To realize the real-time monitoring of the safety risk data flow scheduling of the application, the slave scheduling mode can be scheduled according to the data flow congestion index, the destination needs, as Figures 3-4 shown, according to the scene type, the parameter type, the transmission frequency, the data format, or the monitoring data frequency grouping, etc.

[0077] Further, the application is based on the multi-level cross-department safety risk control required by the patent with the application number CN2022111303888 and the invention name "A multi-level safety number intelligence monitoring system for process operation", and the data flow is efficiently and high-quality scheduled. To ensure the uniqueness of the real-time monitoring data out transmission, based on the patent with the application number CN2022114306089 and the invention name "A safety monitoring data identification lightweight system based on the Internet of Things", the intelligent sensors monitoring the safety risk factors in the received monitoring scene (the data sent based on the "A safety state real-time intelligent monitoring mother machine, method and system" (application number: 2022114489681) are assigned a unique identification code.

[0078] Specifically, since data transmission must ensure the uniqueness of the transmitted data, that is, data identification, it is necessary to identify the real-time data of safety monitoring. In order to save data transmission storage resources, the patent with the application number CN2022114306089 and the invention name "Safety monitoring data identification lightweight system based on Internet of Things" proposes a specific scheme for its identification lightweight. The intelligent monitoring sensor can be applied to multiple scenes at the same time. Based on the characteristics of small transmission distance in wireless network space, the same equipment in different space scenes and the real-time data of safety risk monitoring in the scene are realized. The data transmission module is used to assign a unique identifier to the transmission data, and the real-time data of the safety monitoring object is realized in the cloud space. The identification method based on the above patent makes the safety risk real-time monitoring data flow control mother machine assign a unique identifier to the transmission data when transmitting data to the outside.

[0079] In order to make the present application have universality, the device of the present application receives specific scene identification data, which must be the same as the data format of the identification data sent by the intelligent sensor in the scene. Therefore, the present application must have the function of assigning a unique identifier U outside the scene, adapt to the functions of multiple scenes ID(A, B, C, D, …), and set the function of selecting the type of parameters PARA in the scene to realize the target and uniqueness of data acquisition.

[0080] For example, a certain civil engineering construction site needs to monitor the safety of four tower cranes during lifting operation. The sensor types installed on the lifting frames of the four tower cranes are the same, but the scenes A, B, C, and D should be distinguished, respectively corresponding to the four tower crane lifting operations. In this way, when transmitting in the scene, it is ensured that the four tower crane data monitored is distinguishable and unique. When transmitting outside the scene, the same identifier U is used, such as u=0000000001, forming the scene data identifier u+A+Para+DATA, u+B+Para+DATA, u+C+Para+DATA, and u+D+Para+DATA for the four tower cranes. The parameter types of the four tower crane monitoring include 0101 environmental wind speed, 0301 tower frame inclination angle, 0601 equipment safety state, 0602 climbing claw state, 0603 brake and frame connection state on the tower body, 0604 lifting beam state, and 0801 tower crane lifting cylinder piston displacement and speed, a total of 7 parameters. The intelligent sensor uses the same parameter monitoring frequency of 5Hz to ensure real-time collaborative control of multiple source and risk parameter states. The length of the data packet transmitted in the scene is 20 bytes, and the length of the data packet transmitted outside the scene is 40 bytes. The climbing claw is the simplest process in the tower crane lifting operation process, and the monitoring time is less than 5S, which is more than 10 times the monitoring period.

[0081] 1. Data flow scheduling of the same control department

[0082] (1) Data flow congestion analysis when transmitting outside the scene (i.e. transmitting data to the monitoring scene outside scenes A, B, C, and D)

[0083] The data flow monitored simultaneously by each tower crane during the lifting operation is:

[0084] Q0=N para ×B DB ×f sensor =7×40×5=1400(byte / s)

[0085] That is, the total data flow of 4 tower cranes is 5600 byte / s

[0086] Monitoring statistics found that the data frequency received by the data flow control host was 110 Hz

[0087] Q i =f receive ×B DB =110×40=4400(byte / s)

[0088] Data congestion index:

[0089]

[0090] DFPI>1, indicating that the overall flow congestion of the transmission outside the scene, according to the flow statistics, one slave needs to be started, and the scene receiving C, D scene data is divided equally with the host.

[0091] (2) Data flow congestion analysis when transmitting within the scene:

[0092] When transmitting within the scene, the data flow monitored simultaneously by each tower crane during the lifting operation is:

[0093] Q0=N para ×B DB ×f sensor =7×20×5=700(byte / s)

[0094] That is, the total data flow of 4 tower cranes is 2800 byte / s

[0095] Monitoring statistics found that the data frequency received by the data flow control host was 150 Hz

[0096] Q i =f receive ×B DB =150×20=3000(byte / s)

[0097] Data congestion index:

[0098]

[0099] DFPI<1, indicates that the total flow of the scene is not congested, and the slave machine does not need to be started.

[0100] (3) When the data packet frequency of the parameter PP of the tower crane 0801 in the monitoring scene B received by the data flow management master machine is 4Hz, the parameter data packet frequency anomaly index RPPAI of the monitoring scene B tower crane 0801 is:

[0101]

[0102] Because RPPAI>1, it indicates that there is a conflict in data transmission, a warning is given, and a slave machine needs to be started to receive scene B tower data.

[0103] All parameter monitoring values V i ∈[V min , V max ], indicating that the intelligent sensor is normal.

[0104] 2, Multi-department control data flow scheduling

[0105] The four tower cranes in the above scene are controlled by four contractors respectively, at this time, three sending slaves need to be started to send A, B, C, D tower crane safety monitoring real-time data to the target control department.

[0106] The safety risk factor state monitoring intelligent sensor (i.e. sensing device) sends real-time monitoring data to the upper computer. The real-time data frequency changes with the physical performance of the parameter intelligent sensor. A large amount of multi-source real-time data information wireless transceiver exists signal path conflict, interference, resulting in data loss, safety risk real-time collaborative control failure, therefore, it is necessary to monitor the state of the safety risk data flow that needs to be controlled in the scene in real time, and timely identify and exclude data transmission obstacles, and ensure high-quality, efficient and real-time data transmission. Therefore, the data flow management master machine, method and device based on the Internet of Things, calculate and analyze the data congestion index through data flow metering; count parameter data packets, analyze parameter data packet anomaly index, and then start and close data flow transceiver. When the control department is different, manually press the key to schedule the slave transceiver.

[0107] By monitoring the effective data interval of the parameter data packet, the sensor and intelligent module anomaly are identified.

[0108] In summary, the safety risk real-time monitoring data flow management and control master machine based on the Internet of Things provided by the embodiment of the application can select and identify a monitoring scene code (ID), a scene safety risk monitoring factor type identification code (ID-Para) and an off-site transmission identification code (U). The in-site transmission data is assigned a unique identification code (ID-Para-DATA) by a front-end intelligent sensor, and the off-site transmission data is assigned an in-site data unique identification code (U-ID-Para-DATA) by the application as needed. The safety risk real-time monitoring data flow management and control master machine based on the Internet of Things provided by the embodiment of the application can calculate real-time monitoring parameter data flow traffic and congestion indexes. Through the integrated setting mode of the master machine and the slave machine, the data flow can be scheduled, the target navigation of the real-time monitoring data flow can be realized, and abnormal warning can be realized. The data flow transmission channel and the destination port can be scheduled, and the parameter monitoring device state can be warned, thereby providing protection for the efficient and high-quality transmission of safety production monitoring big data.

[0109] The embodiment of the application provides a safety risk real-time monitoring data flow management and control method based on the Internet of Things, which is applied to the management and control master machine as described in any one of the above embodiments, as shown in the accompanying drawings, and comprises the following steps. Figure 5

[0110] receiving monitoring data from a sensing device; starting slave machine data transceiving when the data flow congestion index is greater than 1 or the data packet frequency abnormality index of any monitoring parameter is greater than 1; and alarming when the monitoring value of any monitoring parameter is not within the threshold range of the monitoring parameter.

[0111] The data flow congestion index is calculated according to the data flow of the received monitoring data; the data packet frequency abnormality index of any monitoring parameter is calculated according to the data packet frequency of the received any monitoring parameter; and the threshold range of any monitoring parameter is obtained according to the user-selected monitoring scene and monitoring parameter type.

[0112] Those skilled in the art can easily understand that the above description is only a preferred embodiment of the application and is not intended to limit the application. Any modification, equivalent replacement and improvement made within the spirit and principle of the application shall be included in the protection scope of the application.​

Claims

1. A data stream management and control machine for real-time security risk monitoring based on the Internet of Things, characterized in that, include: The system includes a scene selection module, a parameter selection module, a data transceiver module, a control module, and multiple slave devices. The scene selection module is used to allow users to select monitoring scenes; The parameter selection module is used to allow users to select the monitoring parameters to be transmitted in various monitoring scenarios; The data transceiver module is used to receive monitoring data from the sensing device and send it to the target device; The control module: The data congestion index is determined based on the data flow of the monitoring data received by the data transceiver module, and when the index is greater than 1, the slave device is activated to transmit and receive data. The data packet frequency anomaly index is used to determine the data packet frequency anomaly index of the monitoring parameter based on the data packet frequency received by the data transceiver module, and when the index is greater than 1, the slave device is activated to transmit and receive data. It is used to determine the threshold range of each monitoring parameter based on the monitoring scenario and the type of monitoring parameter selected by the user, and to issue an alarm when the monitoring value of any monitoring parameter is outside its threshold range; When the slave device is started, some monitoring parameters are allocated to the slave device for transmission and reception according to the monitoring scenario or the type of monitoring parameters. The data flow of the monitoring data received by the data transceiver module Data Stream Congestion Index The following relationship must be satisfied: ； in, As a baseline flow rate for monitoring data, , , , , The monitoring parameters of the sensing devices are respectively The sensing frequency and data transceiver module receive monitoring data. Data packet frequency, monitoring parameters sensed by sensing devices Number of bytes in data packets The number of monitored parameters; The monitoring parameters received by the data transceiver module Data packet frequency With monitoring parameters Data packet frequency anomaly index The following relationship must be satisfied: in, For The sensing devices obtain monitoring parameters based on the statistical duration. The perceived frequency, For This data was obtained from the statistical duration. when and At that time, the number of slave devices started is ; when and At that time, the number of slave devices started is ; in, This is for rounding down.

2. The control machine as described in claim 1, characterized in that, when and At that time, the number of slave devices started is .

3. The control machine as described in claim 1, characterized in that, It also includes an off-site transmission selection module, which allows users to choose whether to send monitoring data to target devices outside the monitoring scene.

4. The control machine as described in claim 1, characterized in that, When the data transceiver module sends the monitoring data to the target device within the monitoring scene, the control module assigns a first identification code to the monitoring data; the first identification code includes a monitoring scene code and a parameter type code; When the data transceiver module sends the monitoring data to a target device outside the monitoring scene, the control module assigns a second identification code to the monitoring data; the second identification code includes a specific identification code and a first identification code.

5. The control machine as described in claim 1, characterized in that, The control module is also used to issue an alarm when the data flow congestion index is greater than 1 or the abnormal data packet frequency index of any monitoring parameter is greater than 1.

6. A method for real-time monitoring and control of security risk data streams based on the Internet of Things, applied to the control machine as described in any one of claims 1-5, characterized in that, include: Receive monitoring data from self-sensing devices; When the data flow congestion index is greater than 1 or the data packet frequency anomaly index of any monitoring parameter is greater than 1, the slave device starts sending and receiving data; when the monitoring value of any monitoring parameter is outside its threshold range, an alarm is triggered. The data flow congestion index is calculated based on the data flow of the received monitoring data; the data packet frequency anomaly index of any monitoring parameter is calculated based on the data packet frequency of the received monitoring parameter; and the threshold range of any monitoring parameter is obtained based on the monitoring scenario and monitoring parameter type selected by the user.

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