Internet of Things intelligent analysis and early warning system and method based on intelligent gateway

Abstract

The present invention relates to the field of data processing, and more particularly to an intelligent gateway-based Internet of Things (IoT) intelligent analysis and early warning system and method. The method comprises: obtaining the coordinates of multiple IoT gateways and the coordinates of monitored objects connected to the gateways within a preset monitoring area; calculating the error index of the coordinates of all monitored objects connected to the gateways, classifying all the error indices, and screening out valid gateways; calculating the weights of the valid gateways, and taking the weighted sum of the weights of all valid gateways and the coordinates of the monitored objects connected to the valid gateways to obtain the fitted coordinates of the monitored objects; calculating the straight-line distance between the fitted coordinates of the monitored objects at adjacent moments, and sending an alarm signal to an IoT terminal when the straight-line distance of the monitored objects exceeds a preset movement response distance. The present invention improves the accuracy of positioning the monitored objects, thereby enabling more accurate judgment of the movement of the monitored objects.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more specifically, to an intelligent analysis and early warning system and method for the Internet of Things based on an intelligent gateway. Background Art

[0002] IoT positioning technology refers to the use of specific technical means to determine the spatial location of IoT devices or objects. It involves a variety of technologies, including radio frequency identification (RFID), Bluetooth, sensor technology, cloud computing, big data, and artificial intelligence. The IoT gateway is a crucial component of the IoT system, connecting IoT devices and cloud servers. IoT devices communicate and exchange data with cloud servers through the IoT gateway, enabling remote monitoring, control, and management. Therefore, the IoT gateway serves as a bridge and link between IoT devices, enabling more efficient, stable, and secure operation of the IoT system.

[0003] Currently, existing technologies, such as patent application publication number CN108076435A, disclose an IoT-based reverse personnel positioning system. This IoT-based reverse personnel positioning system includes an iBeacon Bluetooth positioning base station, a positioning terminal, an IoT routing gateway, and a server. The iBeacon base station emits identification information, which the positioning terminal receives and measures signal strength. This information is then transmitted to the IoT routing gateway via an IoT transmission module. The gateway then forwards the data to the server, which processes the information and determines the location of the positioning terminal. The positioning system in this patent application addresses the issues of low positioning accuracy and low terminal power consumption.

[0004] However, if there are influencing factors such as signal interference and obstacles, in these complex environments, the positioning accuracy may decrease, and it may be impossible to accurately and promptly respond to the movement of the monitored object. Summary of the Invention

[0005] In order to solve the above-mentioned technical problem of decreased positioning accuracy and inability to accurately and promptly respond to the movement of the monitored object, the present invention provides solutions in the following aspects.

[0006] In a first aspect, an IoT intelligent analysis and early warning method based on an intelligent gateway includes:

[0007] Obtain the coordinates of multiple IoT gateways and the coordinates of the monitoring objects connected to the gateways within the preset monitoring area;

[0008] Calculating the error index of the coordinates of all monitoring objects connected by the gateways, classifying all the error indexes, and screening out valid gateways;

[0009] Calculating the weight of the effective gateway, and obtaining the fitted coordinates of the monitored object by weighted summing the weights of all effective gateways and the coordinates of the monitored objects connected to the effective gateway; the weight of the effective gateway is positively correlated with the number of monitored objects connected to the effective gateway;

[0010] The straight-line distance between the fitted coordinates of the monitored objects at adjacent moments is calculated. When the straight-line distance of the monitored objects is greater than the preset mobile response distance, an abnormal alarm signal is sent to the IoT terminal.

[0011] The present invention calculates the coordinate error index of the monitored objects connected by all gateways and classifies these error indices. It can screen out valid gateways that provide more accurate location information, calculate the weights of the valid gateways, and obtain the fitted coordinates of the monitored objects through weighted summation, thereby improving the accuracy of positioning the monitored objects and accurately and promptly responding to the movement of the monitored objects.

[0012] Preferably, multiple IoT gateways are deployed in a distributed manner.

[0013] By distributing IoT gateways across different geographical locations, you can ensure that even if some IoT gateways fail or experience interference, other IoT gateways can continue to operate.

[0014] Preferably, the standard deviation of the distance between the coordinates of the connected monitoring objects corresponding to each gateway and all other gateways is calculated. For each gateway, the corresponding standard deviation is negative as an exponent, and the final error index is obtained by natural exponential function operation.

[0015] By calculating the error index, the relative position stability of each gateway in the network and the degree of its impact on other gateways can be evaluated.

[0016] Preferably, before calculating the standard deviation of the distances between the coordinates of the monitoring objects connected to each gateway and all other gateways, a pre-check of the number of monitoring objects connected to the gateway is performed. The pre-check operation process is as follows:

[0017] The number of monitoring objects connected to each gateway is counted. If the number of monitoring objects connected to a gateway is 0, it is directly marked as an invalid gateway, and the distance between the coordinates of the monitoring objects connected to it and other gateways is not calculated.

[0018] Preferably, screening out valid gateways includes:

[0019] All the error indexes are classified by threshold value. When the error index is less than or equal to a preset error threshold, the gateway corresponding to the error index is classified as a valid gateway; otherwise, it is classified as an invalid gateway.

[0020] A gateway with a smaller error index (i.e., a valid gateway) typically indicates that the coordinate distribution of its monitored objects is relatively concentrated, and the positional relationship between them and the objects monitored by other gateways is relatively stable. The data provided by such gateways is likely to be more accurate because they are less susceptible to abnormal locations or noise.

[0021] Preferably, the weight acquisition process includes:

[0022] First, count the number of monitoring objects connected to each valid gateway and record it as the monitoring value of the gateway; then find the maximum monitoring value among all valid gateways; finally, divide the monitoring value of each gateway by the maximum value to obtain the corresponding weight.

[0023] The more monitoring objects a gateway is connected to, the greater its weight is, which means that its role in the network is more important.

[0024] Preferably, all the error indices are subjected to binary classification, wherein a classification index is calculated, and the classification index is:

[0025] Where, is the classification index, For the first classification group, The second classification group, The first classification group Elements and second classification group The sum of the elements of is the maximum value of the elements in the first classification group, is the minimum value of the elements in the first classification group, is the maximum value of the elements in the second classification group, is the minimum value of the elements in the second classification group; when the classification index reaches the maximum value, the best first classification group and second classification group are output, and the classification group with the smallest range value of the elements in the group is regarded as the valid classification group, and the gateway corresponding to the elements in the group is the valid gateway.

[0026] Preferably, the weight acquisition process includes:

[0027] The number of monitoring objects connected to each valid gateway is counted, and the weight corresponding to each valid gateway is obtained by dividing the number of monitoring objects connected to each valid gateway by the sum of the number of monitoring objects connected to all valid gateways.

[0028] Preferably, the preset error index threshold is selected as the average value or percentile of the error indexes of all gateways.

[0029] In the second aspect, the IoT intelligent analysis and early warning system based on the smart gateway includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned IoT intelligent analysis and early warning method based on the smart gateway is implemented.

[0030] The beneficial effects of the present invention are:

[0031] The present invention effectively classifies gateways, assigns weights to them according to the number of monitored objects connected to them, and takes a weighted sum of the weights of all valid gateways and the coordinates of the monitored objects connected to them to obtain the fitted coordinates of the monitored objects. This further improves the reliability and accuracy of the positioning results, thereby accurately and promptly responding to the movement of the monitored objects. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a method flow chart of steps S1 to S4 in the lubrication system monitoring method based on machine vision in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0034] The present invention analyzes Internet of Things devices and the monitoring objects connected thereto within a monitoring area.

[0035] The embodiment of the present invention discloses an intelligent analysis and early warning method for the Internet of Things based on an intelligent gateway, referring to Figure 1 , including steps S1 to S4, specifically as follows:

[0036] S1: Obtain the coordinates of multiple IoT gateways and the coordinates of the monitoring objects connected to the gateways within the preset monitoring area.

[0037] In this embodiment of the present invention, a geographic information system (GIS) or mapping service is used to determine the scope and boundaries of the monitoring area. IoT gateways are deployed in a distributed manner based on the area and topographical characteristics of the monitoring area. This ensures that IoT gateways are evenly distributed throughout the monitoring area, avoiding blind spots or overlapping areas. The geographic coordinates (longitude and latitude) of each IoT gateway can be recorded using GPS positioning equipment, on-site measurements combined with mapping applications, or by a professional installation team.

[0038] In an embodiment of the present invention, the monitored object is set as a person, and the monitored object is set to wear a positioning device such as a positioning bracelet, watch and card. The positioning device communicates with each gateway through a WiFi signal. The gateway can receive the WiFi signal from the positioning device and determine the coordinates of the device through calculation by the server, and then determine the coordinates of the monitored object.

[0039] The coordinates of each gateway and the monitored object are converted into a unified format, and the real-time locations of the IoT gateway and the monitored object are displayed on a visualization platform, such as a map.

[0040] S2: Calculate the error indexes of the coordinates of all monitoring objects connected to the gateways, classify all the error indexes, and screen out valid gateways.

[0041] When a person is moving around while wearing a positioning device (such as a wristband or watch), their location constantly changes, and the primary gateway (the gateway that monitors their surroundings) they receive information from also changes. This change can cause signal interference or connection loss due to exceeding the effective range. These factors can lead to deviations in positioning results. Furthermore, different gateways may provide inconsistent positioning results, resulting in positioning errors. To address positioning errors, it is necessary to calculate and comprehensively analyze the positioning results from different gateways to obtain an overall error index. This error index can be used to assess the accuracy and synchronization of positioning results.

[0042] By calculating the error index, we can determine whether the positioning of each gateway is synchronized, that is, whether they provide consistent location information. If the error index is within an acceptable range, it means that the positioning of the gateways is synchronized and the positioning results can be trusted. If the error index is too large, it indicates that there is a synchronization problem, and further adjustment or optimization of the gateway layout and configuration is needed to reduce positioning errors and ensure the accuracy and reliability of the positioning system.

[0043] Specifically, taking a monitored object as an example and the positioning results of any two gateways as an example, the error index of each gateway is calculated.

[0044] In an IoT monitoring system, multiple gateways are deployed within a monitoring area, each connected to a certain number of monitored objects. To assess the accuracy and reliability of each gateway, the standard deviation of the distances between the coordinates of each gateway and all other connected objects is calculated, and the error index is calculated based on this standard deviation.

[0045] However, it should be noted that before calculating the error index, if the number of monitoring objects connected to a gateway is 0, the distance between the coordinates of the monitoring objects of other gateways may not be calculated because the gateway has no data points. Therefore, the number of monitoring objects connected to each gateway is counted. If the number of monitoring objects connected to a gateway is 0, it is directly marked as an invalid gateway, and the distance between the coordinates of the monitoring objects connected to it and other gateways is not calculated;

[0046] Alternatively, the error exponent may be directly assigned a default value, such as infinity or a sufficiently large number, so that the gateway is classified as an invalid gateway during classification because the error is too high.

[0047] Exemplarily, the above error index satisfies the relationship:

[0048] ;

[0049] Where, For the The error index corresponding to the gateway, is the base of natural logarithms, For the The standard deviation of the distances between the coordinates of the connected monitoring objects corresponding to a gateway and all other gateways.

[0050] The smaller the standard deviation, the larger the error index, indicating that the gateway has higher accuracy and reliability; the larger the standard deviation, the smaller the error index, indicating that the gateway has lower accuracy and reliability.

[0051] In an IoT monitoring system, if deviations in the positioning data of multiple gateways are detected, this indicates a potential problem with the current positioning process. To ensure accuracy and reliability, the positioning results of these gateways must be screened. This screening process can determine which gateways' data are reliable, allowing the precise location of the monitored object to be determined.

[0052] Valid gateways are screened based on their error index.

[0053] In the embodiment of the present invention, the general steps of screening are as follows:

[0054] The error index of all gateways is classified by threshold. If the error index is less than or equal to the preset error threshold, the gateway corresponding to the error index is classified as a valid gateway; otherwise, it is classified as an invalid gateway. The threshold can be selected based on the average, percentile, or median of the error index, or the threshold value can be adjusted through experimental verification.

[0055] In other embodiments, the general steps of screening are as follows:

[0056] The error index of all gateways is classified into two categories, wherein the classification index is calculated, and the classification index is:

[0057] ;

[0058] Where, is the classification index, For the first classification group, The second classification group, The first classification group Elements and second classification group The sum of the elements of is the maximum value of the elements in the first classification group, is the minimum value of the elements in the first classification group, is the maximum value of the elements in the second classification group, is the minimum value of the elements in the second classification group; when the classification index reaches the maximum value, the best first classification group and second classification group are output, and the classification group with the smallest range value of the elements in the group is regarded as the valid classification group, and the gateway corresponding to the elements in the group is the valid gateway.

[0059] When the classification index reaches the maximum value, the best first classification group and second classification group are output, and the classification group with the smallest range value of the elements in the group is regarded as the valid classification group, and the gateway corresponding to the elements in the group is the valid gateway.

[0060] S3: Calculate the weight of the effective gateway, and obtain the fitting coordinates of the monitoring object by weighted summing the weights of all effective gateways and the coordinates of the monitoring objects connected to the effective gateway; the weight of the effective gateway is positively correlated with the number of monitoring objects connected to the effective gateway.

[0061] Different gateways may have similar error indices, but the number of connected objects may vary significantly. By factoring the number of connected objects into the weights, we can more accurately assess each gateway's positioning capabilities. This allows the system to more accurately determine the location of an object, especially when multiple gateways provide similar but slightly different data.

[0062] For example, first count the number of monitoring objects connected to each valid gateway and record it as the monitoring value of the gateway; then find the maximum monitoring value among all valid gateways; finally divide the monitoring value of each gateway by the maximum value to obtain the corresponding weight. The above weights satisfy the relationship:

[0063] ;

[0064] Where, For the The weight of a valid gateway, For the The number of monitoring objects connected by valid gateways (corresponding to the above monitoring values), The total number of valid gateways.

[0065] In other embodiments, the weights satisfy the relationship:

[0066] ;

[0067] Where, For the The weight of a valid gateway, For the The number of monitoring objects connected by valid gateways, The total number of valid gateways.

[0068] When calculating the true location of the monitored object, more reliance will be placed on the data provided by gateways with higher weights.

[0069] S4: Calculate the straight-line distance between the fitted coordinates of the monitored objects at adjacent moments. When the straight-line distance between the monitored objects is greater than the preset mobile response distance, send an abnormal alarm signal to the IoT terminal.

[0070] Given gateways, each of which provides a coordinate of the location of the monitored object , and each gateway has a weight .

[0071] Then, the horizontal coordinate of the fitted position of the monitoring object is The relationship is satisfied:

[0072] ;

[0073] The vertical coordinate of the fitted position of the monitoring object The relationship is satisfied:

[0074] ;

[0075] At this point, the fitting position of the monitored object is obtained .

[0076] Furthermore, the adjacent moments are calculated as and The fitting coordinates of the corresponding monitoring object and , use the Euclidean distance formula to calculate the distance between the two fitting coordinates, preset a movement response distance (for example, in an indoor security monitoring system, the monitoring objects are mainly people. According to historical data analysis, the common movement distance of people is within 1 meter. In order to reduce false alarms, the movement response distance can be set to 1.5 meters. This can capture most normal movements and avoid false alarms caused by small vibrations or slight movements). When the straight-line distance of the monitoring object is greater than the preset movement response distance, an alarm signal is sent to the IoT terminal.

[0077] An embodiment of the present invention also discloses an Internet of Things intelligent analysis and early warning system based on an intelligent gateway, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, the Internet of Things intelligent analysis and early warning method based on the intelligent gateway according to the present invention is implemented.

[0078] The system includes a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, the lubrication system monitoring method based on machine vision according to the first aspect of the present invention is implemented.

[0079] The system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and therefore will not be described in detail here.

[0080] It should be noted that those skilled in the art may make various modifications and improvements without departing from the scope of the present invention, and these modifications and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be based on the appended claims.

Claims

1. An IoT intelligent analysis and early warning method based on an intelligent gateway is characterized in that: include: Obtain the coordinates of multiple IoT gateways and the coordinates of the monitored objects connected to the gateways within the preset monitoring area. Each gateway provides a coordinate about the location of the monitored object; Calculating the error index of the coordinates of the monitoring objects connected to all gateways, including: calculating the standard deviation of the distance between the coordinates of the monitoring objects connected to each gateway and all other gateways, for each gateway, taking the negative of the corresponding standard deviation as the exponent, and obtaining the final error index through the natural exponential function operation; Classifying all the error indices to select valid gateways includes: performing binary classification on all the error indices; wherein, calculating a classification index, wherein the classification index is: , is the classification index, For the first classification group, The second classification group, The first classification group Elements and second classification group The sum of the elements of is the maximum value of the elements in the first classification group, is the minimum value of the elements in the first classification group, is the maximum value of the elements in the second classification group, is the minimum value of the elements in the second classification group; when the classification index reaches the maximum value, the best first classification group and second classification group are output, and the classification group with the smallest range value of the elements in the group is regarded as the valid classification group, and the gateway corresponding to the elements in the group is the valid gateway; Calculating the weight of the valid gateway includes: first counting the number of monitoring objects connected to each valid gateway, recording it as the monitoring value of the gateway; then finding the maximum monitoring value among all valid gateways; finally dividing the monitoring value of each gateway by the maximum value to obtain the corresponding weight; The weights of all valid gateways and the coordinates of the monitoring objects connected to the valid gateways are weighted and summed to obtain the fitted coordinates of the monitoring objects; the weight of the valid gateway is positively correlated with the number of monitoring objects connected to the valid gateway; The straight-line distance between the fitted coordinates of the monitored objects at adjacent moments is calculated. When the straight-line distance of the monitored objects is greater than the preset mobile response distance, an abnormal alarm signal is sent to the IoT terminal.

2. The method for intelligent analysis and early warning of the Internet of Things based on an intelligent gateway according to claim 1 is characterized in that: Multiple IoT gateways are deployed in a distributed manner.

3. The method for intelligent analysis and early warning of the Internet of Things based on an intelligent gateway according to claim 1 is characterized in that: Before calculating the standard deviation of the distances between the coordinates of the connected monitoring objects corresponding to each gateway and all other gateways, a pre-check of the number of monitoring objects connected to the gateway is also included. The pre-check operation process is as follows: The number of monitoring objects connected to each gateway is counted. If the number of monitoring objects connected to a gateway is 0, it is directly marked as an invalid gateway, and the distance between the coordinates of the monitoring objects connected to it and other gateways is not calculated.

4. The method for intelligent analysis and early warning of the Internet of Things based on an intelligent gateway according to claim 1 is characterized in that: The weight acquisition process can also be replaced by: The number of monitoring objects connected to each valid gateway is counted, and the weight corresponding to each valid gateway is obtained by dividing the number of monitoring objects connected to each valid gateway by the sum of the number of monitoring objects connected to all valid gateways.

5. The IoT intelligent analysis and early warning system based on intelligent gateway is characterized by: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method for intelligent analysis and early warning of the Internet of Things based on an intelligent gateway according to any one of claims 1 to 4 is implemented.

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