A device broadcast message distribution method and system based on dynamic polling grouping

Abstract

The present invention discloses a device broadcast message distribution method and system based on dynamic polling grouping, which belongs to the field of device broadcast technology, and specifically comprises: setting an initial query interval, a central node sends a status query request to all devices every query interval, and after any device receives the query request, it sends its own monitoring data to the central instruction; obtaining quantitative indicators in the monitoring data, generating a quantitative indicator sequence of any device every n query intervals, calculating an initial coefficient according to the quantitative indicator sequence, and converting it into an adjustment coefficient of the query interval; calculating the duration of the next n query intervals according to the adjustment coefficient; setting a number of intervals, dividing devices with adjustment coefficients in the same interval into the same group, sorting the query priority of each group of devices from large to small according to the value of the adjustment coefficient, and performing each polling in sequence according to the priority.

Description

Technical Field

[0001] The present invention relates to the technical field of device broadcasting, and in particular to a method and system for distributing device broadcasting messages based on dynamic polling grouping. Background Art

[0002] In today's digital age, IoT technology is booming at an astonishing rate, and its application scenarios are becoming increasingly extensive, covering many fields such as smart homes, smart factories, and smart transportation. This has led to an explosive growth in the number of devices connected to the network, and the types of devices have also become diverse, ranging from simple sensors to complex industrial robots, from household appliances to medical monitoring equipment. In this context, how to efficiently manage and monitor these massive and diverse devices, ensure that they can operate stably and reliably, and obtain device status information in a timely manner for effective maintenance and optimization has become a key issue that needs to be solved in the field of IoT.

[0003] The traditional static polling method, which queries the status of each device in turn at fixed time intervals, can barely cope with the situation when the number of devices is small and the network environment is simple, but its drawbacks are exposed when facing a large number of devices and a complex and changeable network environment. When there are a large number of devices, the polling process takes a lot of time and network bandwidth, resulting in extremely low efficiency. Moreover, once the network is congested or overloaded, the transmission of polling messages will be seriously affected, the response time will be greatly extended, and even messages may be lost, making it impossible to obtain the status information of the device in a timely and accurate manner, thereby affecting the normal operation of the entire IoT system and the scientific nature of decision-making.

[0004] Although some existing dynamic polling methods can solve the above problems to a certain extent, they often lack the ability to adapt to changes in device status and cannot dynamically adjust the query interval according to the actual operating conditions of the device. Summary of the invention

[0005] The purpose of the present invention is to provide a device broadcast message distribution method and system based on dynamic polling grouping to solve the following technical problems:

[0006] The existing technology lacks the ability to adapt to changes in device status and cannot dynamically adjust the query interval according to the actual operating conditions of the device.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A device broadcast message distribution method based on dynamic polling grouping comprises the following steps:

[0009] Set the initial query interval T. The central node sends status query requests to all devices every query interval. After receiving the query request, any device sends its own monitoring data to the central command.

[0010] Obtain the quantitative index A in the monitoring data, and generate a quantitative index sequence A1, A2, ..., A of any device every n query intervals. n , n is the preset value, the proportion K of the number of values ​​in the quantitative index sequence that are not in the set interval [L, H] to the total number n is counted, and the weighted index A0 of the first n query intervals is calculated based on the weighted moving average method, A0 is added to the quantitative index sequence, a line graph of the quantitative index sequence is drawn, and the slope k between each two adjacent points and the minimum value k of the slopes of all line segments in the line graph are obtained. min , the maximum value k among all the line slopes in the line graph max , then the initial coefficient β is calculated according to the following formula:

[0011] ;

[0012] In the formula, k i represents the slope of the i-th line segment, μ represents the compensation coefficient, which is used to prevent the initial coefficient from being 0. The initial coefficient β is converted into the adjustment coefficient γ of the query interval. Then the duration T of the next n query intervals is i+1 =T / γ;

[0013] Set several intervals, divide the devices with the same adjustment coefficient into the same group, sort the query priority of each group of devices from large to small according to the value of the adjustment coefficient γ, and perform each polling in sequence according to the priority.

[0014] As a further solution of the present invention: the process of obtaining the interval value of the setting interval [L, H] is:

[0015] Get the error value t of the quantitative index, t=A a P, A a represents the average value of the quantitative indicator sequence, P represents the set percentage, then the interval value L=A a -t, interval value H=A a +t.

[0016] As a further solution of the present invention, the specific process of converting the initial coefficient β into the adjustment coefficient γ of the query interval is:

[0017] Generate a numerical sequence of adjustment coefficients γ, γ∈1, 2, ..., m, where m represents a natural number, obtain the adjustment coefficient γ closest to the initial coefficient β in the sequence, and then convert the initial coefficient β into the corresponding adjustment coefficient γ.

[0018] As a further solution of the present invention: the specific process of calculating the weighted index A0 of the first n query intervals based on the weighted moving average method is:

[0019] Get the quantitative index sequence collected in the first n query intervals, EA1, EA2, ..., EAn, and assign a corresponding weight to each first quantitative index. The weight increases gradually according to the time sequence. The calculation formula of the weighted index A0 is:

[0020] ;

[0021] Among them, EA i represents the i-th pre-quantization index, w i Represents the weight of the i-th pre-quantization indicator.

[0022] As a further solution of the present invention: for any device, when the central node sends a status query instruction to the device and does not receive any feedback from the device, the central node sends a query instruction to the device again after a time interval of T / m.

[0023] As a further solution of the present invention: for a newly added device, when the central node performs a status query, the duration of the initial n query intervals is set to T / m.

[0024] As a further solution of the present invention: when the central node detects network congestion or excessive load, the adjustment coefficient corresponding to each group of equipment is reduced to the corresponding adjustment coefficient in the previous interval until the network or load returns to normal. The intervals are sorted from small to large according to the values, and the number of adjustment coefficients in each interval is the same.

[0025] The present invention also includes a device broadcast message distribution system based on dynamic polling grouping, which is used to implement the above-mentioned device broadcast message distribution method based on dynamic polling grouping, including:

[0026] The central node is used to send status query requests to all devices at every query interval;

[0027] The device side is used to send its own monitoring data to the central command after receiving the query request;

[0028] The interval adjustment module is used to set the initial query interval T; obtain the quantitative index A in the monitoring data, and generate a quantitative index sequence A1, A2, ..., A of any device after n query intervals. n , n is a preset value, the initial coefficient β is calculated according to the quantitative index sequence, and β is converted into the adjustment coefficient γ of the query interval; then the duration T of the next n query intervals i+1 =T / γ;

[0029] The polling grouping module is used to set several intervals, divide the devices with the same adjustment coefficient into the same group, sort the query priority of each group of devices from large to small according to the value of the adjustment coefficient γ, and perform each polling in sequence according to the priority.

[0030] Beneficial effects of the present invention:

[0031] (1) The present invention obtains the quantitative indicator sequence of the device and calculates the initial coefficient β, which is then converted into the adjustment coefficient of the query interval, thereby dynamically adjusting the query interval according to the device status. For important devices with frequent status changes, the query interval can be shortened to obtain the latest status information in a timely manner, thereby improving the real-time monitoring; while for devices with stable status, the query interval is extended to reduce unnecessary queries and avoid resource waste, thereby improving the overall polling efficiency. By dynamically adjusting the query interval and grouping polling, a large amount of network resource waste caused by frequent, fixed-interval queries on all devices in traditional static polling methods is avoided. Under the premise of ensuring the device monitoring effect, unnecessary network data transmission is reduced, so that network resources can be more reasonably allocated to devices that really need to be focused on, thereby improving the utilization efficiency of network resources. It is particularly suitable for IoT scenarios with a large number of devices and limited network resources.

[0032] (2) The present invention divides the devices into different groups according to the adjustment coefficients, and sorts the query priorities of each group of devices according to the values ​​of the adjustment coefficients. Each polling is performed in sequence according to the priority. In this way, priority can be given to those device groups with large status changes and requiring special attention, further improving the pertinence and efficiency of polling and ensuring that the status of key devices can be grasped in a timely and accurate manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention will be further described below in conjunction with the accompanying drawings.

[0034] Figure 1 The present invention is a flow chart of a device broadcast message distribution method based on dynamic polling grouping;

[0035] Figure 2 The present invention is a module schematic diagram of a device broadcast message distribution system based on dynamic polling grouping. DETAILED DESCRIPTION

[0036] 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.

[0037] See also Figure 1 As shown, the present invention is a device broadcast message distribution method based on dynamic polling grouping, comprising the following steps:

[0038] 1. When building a device broadcast message distribution system based on dynamic polling grouping, you first need to set an initial query interval T. This T value is pre-calculated and determined based on comprehensive factors such as the average response speed of the device, network bandwidth, and expected device status change frequency. It provides a basic time benchmark for the entire polling process. As the core control unit of the entire system, the central node will strictly follow this initial query interval T and send a status query request to all devices under its jurisdiction every T time. These requests are sent in the form of broadcasts to ensure that all devices connected to the central node can receive them.

[0039] When any device successfully receives the status query request sent by the central node, it will immediately start its own data collection and processing mechanism. The device will obtain the current monitoring data from its built-in sensor or monitoring module. This data may include the device's operating parameters, environmental information, fault codes and other information related to the device status. After preliminary data processing and formatting, the device will encapsulate these monitoring data into a response message, and then send its own monitoring data back to the central node through a pre-established communication link, so that the central node can timely understand the real-time status of each device, providing a basis for subsequent polling interval adjustments and device management decisions.

[0040] 2. In the process of dynamic polling management of equipment, in order to more accurately grasp the changing rules of equipment status, it is necessary to extract key quantitative indicators A from the monitoring data sent back by the equipment. This quantitative indicator A is an important parameter that can intuitively reflect the operating status of the equipment. For example, for a temperature sensor device, quantitative indicator A may be the temperature value it detects; for an industrial motor, quantitative indicator A may be the motor speed or current, etc.

[0041] As polling continues, after every n query intervals (n is a fixed value preset according to actual needs, such as n can be 10, 20, etc.), a series of quantitative index values ​​of the device within these n query intervals will be collected, thereby generating a quantitative index sequence A1, A2, ..., A n This sequence contains the quantitative indicator data of the device when it is queried n times in succession, providing a basis for subsequent analysis and calculation.

[0042] Next, we need to calculate the initial coefficient β based on this quantitative indicator sequence. The specific process is relatively complex and logical. First, we need to obtain the error value t of the quantitative indicator. The calculation formula is t=A aP, where A a is the average value of the quantitative index sequence, which represents the average level of the device quantitative index in these n query intervals; P is a pre-set percentage used to determine a reasonable error range. a The error value t calculated by and P can help us define a normal fluctuation range of quantitative indicators [A a -t,A a +t].

[0043] Then, we need to count the number of values ​​in the quantitative index sequence that are not within this normal fluctuation range, and calculate the proportion K of these abnormal values ​​to the total number n. This proportion K can reflect the frequency of abnormal fluctuations in the quantitative index of the equipment, and provide an important reference for the subsequent coefficient calculation.

[0044] At the same time, it is also necessary to calculate the weighted index A0 of the first n query intervals. The specific operation is to obtain the quantitative index sequence EA1, EA2, ..., EA n , which represent the actual quantitative indicator values ​​of the device at each query interval. In order to more accurately reflect the importance of recent data, EA i Assign the corresponding weight w i , and the weight gradually increases according to the time sequence, that is, the closer the quantitative index value is to the current moment, the greater its weight is, because recent data can better reflect the latest status of the device. According to this weight distribution principle, the calculation formula of the weighted index A0 is:

[0045] ;

[0046] The weighted index A0 calculated by this formula takes the historical data into account and can more comprehensively represent the overall level of the quantitative indicators of the equipment in the first n query intervals.

[0047] After obtaining the weighted index A0, add it to the original quantitative index sequence A1, A2, ..., A n In this example, an extended quantitative index sequence is formed. Subsequently, a line graph of the quantitative index sequence is drawn based on this extended sequence. Through the line graph, the changing trend of the device quantitative index over time can be intuitively observed. In the line graph, the slope k between each two adjacent points is obtained. These slopes k reflect the rate of change of the quantitative index between adjacent query intervals. Furthermore, the minimum value k among all the line segment slopes in the line graph is found. min and the maximum value k max , these two extreme values ​​can reflect the extreme changes in quantitative indicators.

[0048] Finally, according to the above calculation and analysis results, the calculation formula of the initial coefficient β is:

[0049] ;

[0050] In the formula, k i represents the slope of the ith line segment, and μ represents the compensation coefficient, which is used to prevent the value of the initial coefficient from being 0. Through this calculation formula, an initial coefficient β can be obtained that can comprehensively reflect the characteristics and trends of changes in the quantitative indicators of the equipment.

[0051] And convert β into the adjustment coefficient γ of the query interval. The specific process is:

[0052] First, it is necessary to generate a numerical sequence of the adjustment coefficient γ, which contains continuous natural numbers starting from 1, that is, γ∈1, 2, ..., m, where m is a natural number upper limit predetermined according to the system design and device characteristics. Each value in this sequence represents a possible query interval adjustment multiple. The larger the value, the shorter the query interval and the higher the polling frequency; conversely, the smaller the value, the longer the query interval and the lower the polling frequency. Moreover, since the adjustment coefficient is a natural number, the query interval of different devices is an integer fraction of the initial interval length, so that the query interval adjustment of the entire system presents a regular, easy to manage and control state. This design not only simplifies the calculation and adjustment process of the query interval and reduces the complexity of system implementation, but also facilitates unified monitoring and optimization of the query conditions of different devices.

[0053] For example, assuming that the initial query interval T is set to 30 minutes, and m is determined to be 5, then the generated numerical sequence of the adjustment coefficient γ is 1, 2, 3, 4, 5. When the adjustment coefficient γ of a device is 1, its query interval is 1 times the initial interval T, that is, 30 minutes; if the adjustment coefficient γ of another device is 3, its query interval becomes 1 / 3 of the initial interval T, that is, 10 minutes. In this way, the query interval of all devices is an integer multiple of 30 minutes. The system can easily plan and schedule the query time of each device based on these integer multiples, avoiding resource conflicts and management confusion caused by too dispersed query intervals.

[0054] Then, the adjustment coefficient γ closest to the initial coefficient β value is found from the generated adjustment coefficient γ value sequence. This process involves comparing the β value with each γ value in the sequence one by one, and by calculating the difference between them, determining which γ value has the smallest difference with the β value. The γ value closest to the β value is selected as the query interval adjustment coefficient of the current device.

[0055] After completing the above conversion, the duration T of the next n query intervals can be calculated based on the adjustment coefficient γ i+1 The specific calculation formula is T i+1=T / γ, where T is the query interval initially set. From this formula, we can see that the adjustment coefficient γ plays a role in scaling the original query interval T. When the γ value is large, T i+1 will decrease accordingly, which means that the device will be queried more frequently in the next n query cycles in order to capture its state changes more timely; and when the γ value is small, T i+1 The query frequency of the device will be reduced, thereby reducing unnecessary queries and resource consumption.

[0056] For example, assuming that the initial query interval T is set to 30 minutes, and the adjustment coefficient γ calculated through the above process is 2, then the duration of the next n query intervals T i+1 It becomes 30 minutes divided by 2, which is 15 minutes. This means that in the next n pollings, the central node will send a status query request to the device every 15 minutes, instead of once every 30 minutes, thus achieving the purpose of dynamically adjusting the query interval according to the device status, making device management more efficient, flexible and targeted.

[0057] 3. When implementing the device broadcast message distribution strategy based on dynamic polling grouping, in order to further optimize the device management efficiency and resource allocation, a refined grouping and priority sorting mechanism will be adopted. First of all, it is necessary to reasonably set several different intervals according to the value range of the adjustment coefficient γ. The division of these intervals is based on a comprehensive consideration of the frequency of device status changes, importance, and the balance of system resource allocation. For example, devices with a smaller adjustment coefficient γ can be grouped into one interval. Such devices are usually in a more stable state and have a longer query interval; while devices with a larger adjustment coefficient γ can be grouped into another interval. These devices may have faster status changes and need to be queried more frequently.

[0058] Next, all devices are divided into several different groups according to the interval of the device's adjustment coefficient γ. Devices in the same group have similar query interval adjustment multiples, that is, their query frequencies are similar. This grouping method enables the system to perform targeted management and monitoring on device groups with different status characteristics.

[0059] After completing the device grouping, in order to ensure the efficiency and focus of the polling process, it is necessary to sort the query priority of each group of devices. The basis for sorting is the value of the adjustment coefficient γ, which is in descending order. In other words, the larger the adjustment coefficient γ, the more frequent the device status changes may be, and the more important it may be to the system, so it is given a higher query priority. Each time a polling operation is performed, the system will first query the devices in the group with the highest priority, and then proceed downward until all groups are polled.

[0060] For example, suppose three intervals are set, corresponding to devices with adjustment coefficients γ of 1-2, 3-4, and 5-6. Then, the device group with adjustment coefficient γ of 5-6 will be given the highest query priority. The system will first query this group of devices to capture their status changes in a timely manner; then query the device group with adjustment coefficient γ of 3-4; and finally query the device group with adjustment coefficient γ of 1-2. Through this priority sorting and group polling method, the system can allocate query resources more reasonably under limited resource conditions, ensure that the status of key devices can be obtained and processed in a timely manner, and also take into account the basic monitoring needs of other devices, improve the equipment management efficiency and real-time performance of the entire IoT system, and lay a solid foundation for the realization of intelligent equipment monitoring and management.

[0061] 4. In the IoT system, the communication between the device and the central node may be affected by many factors, such as signal interference, device failure or network problems. In order to ensure that the status information of the device can be obtained in time, the system has designed an effective retry mechanism. When the central node sends a status query command to any device, if no feedback is received from the device within the expected time, the system will not immediately give up the query of the device, but will wait for a specific time before retrying. This specific time is set as the minimum query interval, that is, T / m. This design not only avoids missing the acquisition of device status due to short-term communication failures, but also prevents unnecessary waste of network resources due to too frequent retries. In this way, the system can try to establish a communication connection with the device multiple times within a reasonable time range, improve the success rate of obtaining device status information, and ensure the continuity and reliability of device monitoring.

[0062] 5. With the continuous expansion of the Internet of Things system, the addition of new devices is the norm. For newly added devices, the central node will adopt a special query interval setting strategy when performing the first status query. Specifically, the central node will set the duration of the initial n query intervals to the minimum query interval. The purpose of this setting is to quickly obtain the initial status information of the new device so that the system can understand the operating status of the new device as soon as possible and dynamically adjust the subsequent query intervals according to its actual performance. By shortening the initial query interval of the new device, the system can communicate with the new device more frequently, discover and deal with possible problems in a timely manner, and ensure that the new device can be smoothly integrated into the entire Internet of Things system. At the same time, it also helps the system to assign appropriate adjustment coefficients to the new device according to the state change characteristics, so as to achieve accurate dynamic polling management.

[0063] 6. During the operation of the IoT system, network congestion or excessive system load may occur, which will seriously affect the efficiency and accuracy of device status query. In order to cope with this situation, the system has designed an intelligent adjustment strategy. When the central node detects network congestion or excessive load, it will automatically adjust the adjustment coefficient corresponding to each group of devices. The specific adjustment method is to reduce the adjustment coefficient of each group of devices to the corresponding adjustment coefficient in the previous interval. The intervals here are sorted from small to large according to the value of the adjustment coefficient, and the number of adjustment coefficients in each interval is the same. By reducing the adjustment coefficient, the system can effectively extend the query interval of the device and reduce the number of queries to the device per unit time, thereby reducing network traffic and system load. This adjustment strategy can quickly relieve the pressure of the system and ensure the basic operation function of the system in the case of abnormal network or load. Once the network or load returns to normal, the system will gradually restore the adjustment coefficient to a normal level according to the actual status and needs of the device, re-optimize the query interval, and ensure the efficiency and real-time performance of device management.

[0064] Please refer to the attached Figure 2 As shown, the present invention also includes a device broadcast message distribution system based on dynamic polling grouping, which is used to implement the above-mentioned device broadcast message distribution method based on dynamic polling grouping, including:

[0065] The central node is used to send status query requests to all devices at every query interval;

[0066] The device side is used to send its own monitoring data to the central command after receiving the query request;

[0067] The interval adjustment module is used to set the initial query interval T; obtain the quantitative index A in the monitoring data, and generate a quantitative index sequence A1, A2, ..., A of any device after n query intervals. n , n is a preset value, the initial coefficient β is calculated according to the quantitative index sequence, and β is converted into the adjustment coefficient γ of the query interval; then the duration T of the next n query intervals i+1 =T / γ;

[0068] The polling grouping module is used to set several intervals, divide the devices with the same adjustment coefficient into the same group, sort the query priority of each group of devices from large to small according to the value of the adjustment coefficient γ, and perform each polling in sequence according to the priority.

[0069] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A device broadcast message distribution method based on dynamic polling grouping, characterized in that: The following steps are involved: Set the initial query interval T. The central node sends status query requests to all devices every query interval. After receiving the query request, any device sends its own monitoring data to the central command. Obtain the quantitative index A in the monitoring data, and generate a quantitative index sequence A1, A2, ..., A of any device every n query intervals. n , n is the preset value, the proportion K of the number of values ​​in the quantitative index sequence that are not in the set interval [L, H] to the total number n is counted, and the weighted index A0 of the first n query intervals is calculated based on the weighted moving average method, A0 is added to the quantitative index sequence, a line graph of the quantitative index sequence is drawn, and the slope k between each two adjacent points and the minimum value k of the slopes of all line segments in the line graph are obtained. min , the maximum value k among all the line slopes in the line graph max , then the initial coefficient β is calculated according to the following formula: ； In the formula, k i represents the slope of the i-th line segment, μ represents the compensation coefficient, which is used to prevent the initial coefficient from being 0. The initial coefficient β is converted into the adjustment coefficient γ of the query interval. Then the duration T of the next n query intervals is i+1 =T / γ; Set several intervals, divide the devices with the same adjustment coefficient into the same group, sort the query priority of each group of devices from large to small according to the value of the adjustment coefficient γ, and perform each polling in sequence according to the priority.

2. A device broadcast message distribution method based on dynamic polling grouping according to claim 1, characterized in that: The process of obtaining the interval value of the setting interval [L, H] is as follows: Get the error value t of the quantitative index, t=A a P, A a represents the average value of the quantitative indicator sequence, P represents the set percentage, then the interval value L=A a -t, interval value H=A a +t.

3. A device broadcast message distribution method based on dynamic polling grouping according to claim 2, characterized in that: The specific process of converting the initial coefficient β into the adjustment coefficient γ of the query interval is: Generate a numerical sequence of adjustment coefficients γ, γ∈1, 2, ..., m, where m represents a natural number, obtain the adjustment coefficient γ closest to the initial coefficient β in the sequence, and then convert the initial coefficient β into the corresponding adjustment coefficient γ.

4. The method for distributing device broadcast messages based on dynamic polling grouping according to claim 2, characterized in that: The specific process of calculating the weighted index A0 of the first n query intervals based on the weighted moving average method is: Get the quantitative index sequence collected in the first n query intervals, EA1, EA2, ..., EAn, and assign a corresponding weight to each first quantitative index. The weight increases gradually according to the time sequence. The calculation formula of the weighted index A0 is: ； Among them, EA i represents the i-th pre-quantization index, w i Represents the weight of the i-th pre-quantization indicator.

5. The method for distributing device broadcast messages based on dynamic polling grouping according to claim 3, characterized in that: For any device, when the central node sends a status query command to the device and does not receive any feedback from the device, the central node will send a query command to the device again after a time interval of T / m.

6. The method for distributing device broadcast messages based on dynamic polling grouping according to claim 3, characterized in that: For a newly added device, when the central node performs a status query, the duration of the initial n query intervals is set to T / m.

7. The method for distributing device broadcast messages based on dynamic polling grouping according to claim 1, characterized in that: When the central node detects network congestion or excessive load, the adjustment coefficient corresponding to each group of equipment is reduced to the corresponding adjustment coefficient in the previous interval until the network or load returns to normal. The intervals are sorted from small to large according to the values, and the number of adjustment coefficients in each interval is the same.

8. A device broadcast message distribution system based on dynamic polling grouping, used to implement a device broadcast message distribution method based on dynamic polling grouping according to any one of claims 1 to 7, characterized in that: include: The central node is used to send status query requests to all devices at every query interval; The device side is used to send its own monitoring data to the central command after receiving the query request; The interval adjustment module is used to set the initial query interval T; obtain the quantitative index A in the monitoring data, and generate a quantitative index sequence A1, A2, ..., A of any device after n query intervals. n , n is a preset value, the initial coefficient β is calculated according to the quantitative index sequence, and β is converted into the adjustment coefficient γ of the query interval; then the duration T of the next n query intervals i+1 =T / γ; The polling grouping module is used to set several intervals, divide the devices with the same adjustment coefficient into the same group, sort the query priority of each group of devices from large to small according to the value of the adjustment coefficient γ, and perform each polling in sequence according to the priority.

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