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

Abstract

The invention relates to the field of data processing, in particular to an Internet of Things intelligent analysis and early warning system and method based on an intelligent gateway, and the method comprises the steps: obtaining the coordinates of a plurality of Internet of Things gateways and the coordinates of monitoring objects connected with the gateways in a preset monitoring region; calculating error indexes of the coordinates of the monitored objects connected with all gateways, classifying all the error indexes, and screening out effective gateways; the weights of the effective gateways are calculated, and the weights of all the effective gateways and the coordinates of the monitoring objects connected with the effective gateways are weighted and summed to obtain fitting coordinates of the monitoring objects; and calculating a linear distance between the fitting coordinates of the monitored object at adjacent moments, and when the linear distance of the monitored object is greater than a preset movement response distance, sending an alarm signal to the Internet of Things terminal. According to the invention, the positioning accuracy of the monitored object is improved, so that the movement condition of the monitored object can be judged more accurately.

Description

Technical Field

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

[0002] Internet of Things positioning technology refers to determining the spatial position of Internet of Things devices or objects through specific technical means. It involves a variety of technologies including radio frequency identification (RFID), Bluetooth, sensor technology, cloud computing, big data, and artificial intelligence. Among them, the Internet of Things gateway is an important part of the Internet of Things system, which connects Internet of Things devices and cloud servers. Internet of Things devices communicate and exchange data with the cloud server through the Internet of Things gateway to achieve remote monitoring, control, and management. Therefore, the Internet of Things gateway is the bridge and link between Internet of Things devices, enabling the Internet of Things system to operate more efficiently, stably, and securely.

[0003] Currently, the prior art such as the patent application document with the publication number CN108076435A discloses an Internet of Things-based reverse personnel positioning system. The Internet of Things-based reverse personnel positioning system includes: iBeacon Bluetooth positioning base stations, positioning terminals, Internet of Things routing gateways, and servers. The iBeacon base stations emit identification information, the positioning terminals receive this information and measure the signal strength, and then send this information to the Internet of Things routing gateway through the Internet of Things transmission module. The gateway then forwards the data to the server, and finally the server processes this information and determines the position of the positioning terminal. The positioning system of this patent application document solves the problems of low positioning accuracy and low power consumption of the terminal.

[0004] However, in the case of influencing factors such as signal interference and obstruction by obstacles, in these complex environments, it is easy to cause a decrease in positioning accuracy and inability to accurately and timely respond to the movement of the monitoring object. Summary of the Invention

[0005] To solve the above technical problems of the decrease in positioning accuracy and inability to accurately and timely respond to the movement of the monitoring object, the present invention provides solutions in the following aspects.

[0006] In the first aspect, an Internet of Things intelligent analysis and early warning method based on an intelligent gateway includes: Obtaining the coordinates of multiple Internet of Things gateways and the coordinates of the monitoring objects connected to the gateways within a preset monitoring area; Calculating the error index of the coordinates of the monitoring objects connected to all the gateways, classifying all the error indexes, and screening out valid gateways; Calculate the weights of the effective gateways, and perform a weighted summation of the weights of all effective gateways and the coordinates of the monitored objects connected to the effective gateways to obtain the fitted coordinates of the monitored objects; the weight of the effective gateway is positively correlated with the number of monitored objects connected to the effective gateway; Calculate the straight-line distance between the fitted coordinates of the monitored objects at adjacent moments. When the straight-line distance of the monitored object is greater than the preset movement response distance, an abnormal alarm signal is sent to the Internet of Things terminal.

[0007] In the present invention, by calculating the coordinate error indices of the monitored objects connected to all gateways and classifying these error indices, effective gateways that provide more accurate position information can be screened out, and the weights of the effective gateways are calculated. The fitted coordinates of the monitored objects are obtained by means of weighted summation, thereby improving the accuracy of positioning the monitored objects and accurately and timely responding to the movement of the monitored objects.

[0008] Preferably, multiple Internet of Things gateways are distributed in a distributed manner.

[0009] By distributing Internet of Things gateways at different geographical locations, it can be ensured that even if some Internet of Things gateways fail or are interfered, other Internet of Things gateways can still continue to work.

[0010] Preferably, before calculating the standard deviation of the distances between the coordinates of the monitored objects corresponding to each gateway and all other gateways, it further includes pre-checking the number of monitored objects connected to the gateway. The operation process of the pre-check is as follows:

[0011] By calculating the error indices, the relative position stability of each gateway in the network and the degree of influence on other gateways can be evaluated.

[0012] Preferably, before calculating the standard deviation of the distances between the coordinates of the monitored objects corresponding to each gateway and all other gateways, it further includes pre-checking the number of monitored objects connected to the gateway. The operation process of the pre-check is as follows: Count the number of monitored objects connected to each gateway. If the number of monitored objects connected to the gateway is 0, it is directly marked as an invalid gateway, and the calculation of the distance between the coordinates of the monitored objects corresponding to it and other gateways is not performed.

[0013] Preferably, the screening out of the effective gateways includes: Perform threshold classification on all the error indices. When the error index is less than or equal to the preset error threshold, the gateway corresponding to the error index is classified as an effective gateway; otherwise, it is classified as an invalid gateway.

[0014] Gateways with smaller error indices (i.e., effective gateways) usually mean that the coordinate distributions of their monitored objects are relatively concentrated, and the positional relationships with the monitored objects of other gateways are relatively stable. The data provided by such gateways may be more accurate because they are less susceptible to abnormal positions or noise.

[0015] Preferably, the process of obtaining the weights includes: First, count the number of monitored objects connected to each effective gateway, denoted as the monitoring value of the gateway; then find the maximum monitoring value among all effective gateways; finally, divide the monitoring value of each gateway by the maximum value to obtain the corresponding weight.

[0016] The gateway that connects more monitored objects has a greater weight, indicating that it plays a more important role in the network.

[0017] Preferably, perform binary classification on all the error indices. Among them, calculate the classification index, and the classification index is: ; In the formula, is the classification index, is the first classification group, is the second classification group, is the sum of the elements of the first classification group and the elements of the second classification group , 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, output the best first classification group and second classification group, and take the classification group with the smallest extreme difference of the elements within the group as the effective classification group, and the gateways corresponding to the elements within the group are effective gateways.

[0018] Preferably, the process of obtaining the weights includes: Count the number of monitored objects connected to each effective gateway, and divide the number of monitored objects connected to each effective gateway by the sum of the number of monitored objects connected to all effective gateways to obtain the weights corresponding to each effective gateway.

[0019] Preferably, the preset error index threshold selects the average value or percentile of the error indices of all gateways.

[0020] In a second aspect, an Internet of Things intelligent analysis and early warning system based on an intelligent gateway includes: a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned Internet of Things intelligent analysis and early warning method based on an intelligent gateway is implemented.

[0021] The beneficial effects of the present invention are as follows: By effectively classifying gateways, the present invention assigns weights to them according to the number of monitored objects connected to the effective gateways, and performs weighted summation on the weights of all effective gateways and the coordinates of the monitored objects they are connected to, obtaining the fitted coordinates of the monitored objects, further improving the reliability and accuracy of the positioning result, and thus accurately and timely responding to the movement of the monitored objects. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flowchart of the method from step S1 to step S4 in the monitoring method of the lubrication system based on machine vision according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention.

[0024] The present invention analyzes the Internet of Things devices in a monitoring area and the monitored objects connected to them.

[0025] An embodiment of the present invention discloses an Internet of Things intelligent analysis and early warning method based on an intelligent gateway. Referring to Figure 1 , it includes steps S1 to S4, which are specifically as follows: S1: Obtain the coordinates of multiple Internet of Things gateways and the coordinates of the monitored objects connected to the gateways in a preset monitoring area.

[0026] In an embodiment of the present invention, the scope and boundary of the monitoring area are determined through a geographic information system (GIS) or a map service. The Internet of Things gateways are deployed in a distributed manner according to the area and terrain characteristics of the monitoring area, so as to ensure that the Internet of Things gateways are evenly distributed throughout the monitoring area, avoiding blind spots or overlapping areas. The deployment position of each Internet of Things gateway can record its geographical coordinates (longitude, latitude) through a GPS positioning device, on-site measurement combined with a map application, or a professional installation team.

[0027] In an embodiment of the present invention, the monitored objects are set as people, and it is set that the monitored objects wear positioning devices such as positioning bracelets, watches, and cards. The positioning devices communicate with each gateway through WiFi signals. The gateway can receive the WiFi signals from the positioning devices and determine the coordinates of the devices through the calculation of the server, and then determine the coordinates of the monitored objects.

[0028] Convert the coordinates of each gateway and the coordinates of the monitored objects into a unified format, and display the real-time positions of the Internet of Things gateways and the monitored objects on a visualization platform, such as on a map.

[0029] S2: Calculate the error index of the coordinates of the monitored objects connected to all gateways, classify all the error indices, and filter out the valid gateways.

[0030] When a person is wearing a positioning device (such as a positioning bracelet, watch, etc.) and moving, since the person's position is constantly changing, the main gateways (i.e., the gateways that monitor the surrounding positions) received by these devices will also change accordingly. This change may cause signal interference or loss of connection due to the distance exceeding the effective range. These factors may all lead to deviations in the positioning results. In addition, since different gateways may give inconsistent positioning results, positioning errors occur. To solve the problem of 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 evaluate the accuracy and synchronization of the positioning results.

[0031] By calculating the error index, it can be determined whether the positioning of each gateway is synchronized, that is, whether it provides mutually consistent position information. If the error index is within an acceptable range, it indicates that the positioning of the gateway is synchronized and these positioning results can be trusted; if the error index is too large, it indicates that there is an out-of-sync problem, and it is necessary to further adjust or optimize the layout and configuration of the gateway to reduce the positioning error and ensure the accuracy and reliability of the positioning system.

[0032] Specifically, taking a monitored object as an example and taking the positioning results of any two gateways as an example, calculate the error index of each gateway.

[0033] In the Internet of Things monitoring system, multiple gateways are deployed in the monitoring area, and each gateway is connected to a certain number of monitored objects. To evaluate the accuracy and reliability of each gateway, it is necessary to calculate the standard deviation of the distances between the coordinates of the monitored objects corresponding to each gateway and all other gateways, and calculate the error index based on this.

[0034] However, it should be noted that before calculating the error index, considering that when the number of monitored objects connected to a certain gateway is 0, the distance between the coordinates of the monitored objects of this gateway and other gateways may not be calculable because there are no data points for this gateway. Therefore, count the number of monitored objects connected to each gateway. If the number of monitored objects connected to a gateway is 0, directly mark it as an invalid gateway and do not calculate the distance between the coordinates of the monitored objects corresponding to it and other gateways; Or directly assign its error index a default value, such as infinity or a sufficiently large number, so that it is classified as an invalid gateway during classification because the error is too high.

[0035] Exemplarily, the above error index satisfies the relationship: ; In the formula, is the error index corresponding to the th gateway, is the base of the natural logarithm, is the standard deviation of the distances between the coordinates of the monitored objects connected to the

[0036] wherein, the smaller the standard deviation, the larger the error index, indicating higher accuracy and reliability of the gateway; the larger the standard deviation, the smaller the error index, indicating lower accuracy and reliability of the gateway.

[0037] In the Internet of Things monitoring system, when it is found that the positioning data of multiple gateways is deviated, it indicates that there may be problems in the current positioning process. To ensure accuracy and reliability, it is necessary to screen the positioning results of these gateways. Through screening, it is possible to determine which gateway data is reliable, so as to accurately judge the position of the monitored object.

[0038] Screen the effective gateways based on the error index of the gateway.

[0039] In the embodiments of the present invention, the general steps of screening are as follows: Perform threshold classification on the error indexes of all gateways. When the error index is less than or equal to the preset error threshold, the gateway corresponding to the error index is classified as an effective gateway; otherwise, it is classified as an invalid gateway. Among them, the average value, percentile or median of the error index can be selected as the threshold, or the value of the threshold can be adjusted through experimental verification.

[0040] In other embodiments, the general steps of screening are as follows: Perform binary classification on the error indexes of all gateways. Among them, calculate the classification index, and the classification index is: ; In the formula, is the classification index, is the first classification group, is the second classification group, is the sum of the elements of the first classification group and the elements of the second classification group , 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, output the best first classification group and second classification group, and take the classification group with the smallest extreme difference of the elements in the group as the effective classification group, and the gateways corresponding to the elements in the group are effective gateways.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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: ; 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.

[0045] In other embodiments, the weights satisfy the relationship: ; 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.

[0046] When calculating the true location of the monitored object, more reliance will be placed on the data provided by gateways with higher weights.

[0047] 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.

[0048] Given gateways, each of which provides a coordinate of the location of the monitored object , and each gateway has a weight .

[0049] Then, the horizontal coordinate of the fitted position of the monitoring object is The relationship is satisfied: ; The vertical coordinate of the fitted position of the monitoring object The relationship is satisfied: ; At this point, the fitting position of the monitored object is obtained .

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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 Internet of Things intelligent analysis and early warning method based on an intelligent gateway, characterized in that, Including: Obtain the coordinates of multiple IoT gateways and the coordinates of the monitored objects connected to the gateways within a preset monitoring area; Calculate the error index of the coordinates of the monitored objects connected to all gateways, classify all the error indexes, and screen out the effective gateways; Calculate the weights of the effective gateways, and perform weighted summation of the weights of all effective gateways and the coordinates of the monitored objects connected to the effective gateways to obtain the fitted coordinates of the monitored objects; the weight of the effective gateway is positively correlated with the number of monitored objects connected to the effective gateway; Calculate the straight-line distance between the fitted coordinates of the monitored objects at adjacent moments. When the straight-line distance of the monitored objects is greater than the preset movement response distance, send an abnormal alarm signal to the IoT terminal.

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

3. The Internet of Things intelligent analysis and early warning method based on an intelligent gateway according to claim 2, wherein, Calculate the standard deviation of the distances between the coordinates of the monitored objects corresponding to each gateway and all other gateways. For each gateway, take the negative of the corresponding standard deviation as the index, and obtain the final error index through the natural exponential function operation.

4. The Internet of Things intelligent analysis and early warning method based on an intelligent gateway according to claim 3, characterized in that, Before calculating the standard deviation of the distances between the coordinates of the monitored objects corresponding to each gateway and all other gateways, it also includes a pre-check on the number of monitored objects connected to the gateway. The operation process of the pre-check is as follows: Count the number of monitored objects connected to each gateway. If the number of monitored objects connected to the gateway is 0, directly mark it as an invalid gateway and do not calculate the distance between its coordinates and the coordinates of the monitored objects corresponding to other gateways.

5. The Internet of Things intelligent analysis and early warning method based on an intelligent gateway according to claim 4, wherein, The screening out of the effective gateways includes: Perform threshold classification on all the error indexes. When the error index is less than or equal to the preset error threshold, classify the gateway corresponding to the error index as an effective gateway; otherwise, classify it as an invalid gateway.

6. The Internet of Things intelligent analysis and early warning method based on an intelligent gateway according to claim 5, wherein, The process of obtaining the weight includes: First, count the number of monitored objects connected to each effective gateway, denoted as the monitoring value of the gateway; then find the maximum monitoring value among all effective gateways; finally, divide the monitoring value of each gateway by the maximum value to obtain the corresponding weight.

7. The Internet of Things intelligent analysis and early warning method based on an intelligent gateway according to claim 3, wherein, Perform binary classification on all the error indexes. Among them, calculate the classification index, and the classification index is: ; where, is the classification index, is the first classification group, is the second classification group, is the first classification group the sum of the elements of and the second classification group ; 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 of the elements within the group is used as the effective classification group, and the gateway corresponding to the elements within the group is the effective gateway.

8. The Internet of Things intelligent analysis and early warning method based on an intelligent gateway according to claim 1, characterized in that The process of obtaining the weight includes: Count the number of monitored objects connected to each effective gateway, and divide the number of monitored objects connected to each effective gateway by the sum of the number of monitored objects connected to all effective gateways to obtain the weights corresponding to each effective gateway.

9. The Internet of Things intelligent analysis and early warning method based on an intelligent gateway according to claim 5, characterized in that The preset error index threshold selects the average value or percentile of the error indexes of all gateways.

10. The Internet of Things intelligent analysis and early warning system based on an intelligent gateway is characterized in that Including: A processor and a memory, where the memory stores computer program instructions. When the computer program instructions are executed by the processor, the IoT intelligent analysis and early warning method based on intelligent gateways according to any one of claims 1-9 is implemented.

Images