Internet of things data transmission system based on lora technology

Abstract

The application belongs to the field of Internet of Things, relates to data analysis technology, and is used for solving the problem that the existing Internet of Things data transmission system cannot perform deep analysis on influencing factors of data anomaly, specifically an Internet of Things data transmission system based on LORA technology, comprising a transmission management platform, wherein the transmission management platform is communicatively connected with a data transmission module, a transmission detection module, an anomaly analysis module and a storage module; the data transmission module is used for transmitting Internet of Things data through LORA technology, and an Internet of Things data transmission network is constructed through multiple LORA devices; the application detects and analyzes the transmission rate of the Internet of Things data transmission network, comprehensively analyzes the minimum value of the transmission rate in each channel, combines the data transmission rate deviation degree in each channel to perform numerical calculation to obtain a transmission coefficient, and thus feeds back the data transmission rate through the transmission coefficient.

Description

Technical Field

[0001] This invention belongs to the field of the Internet of Things (IoT) and relates to data analysis technology, specifically an IoT data transmission system based on LoRa technology. Background Technology

[0002] LoRa is a radio frequency IC suitable for use in the Internet of Things (IoT). Its design philosophy is low power consumption, long distance, simple network, and easy expansion. In general communication, the communication distance is directly proportional to the power consumption. The longer the transmission distance, the higher the power consumption. However, LoRa can achieve long-distance low-power communication, i.e., high penetration.

[0003] Existing IoT data transmission systems generally lack the function of detecting and analyzing the data transmission rate of wireless transmission. Therefore, they cannot provide early warning and feedback in the first instance when data transmission anomalies occur. At the same time, existing IoT data transmission systems cannot perform in-depth analysis of the factors affecting data anomalies, resulting in the inability to directly handle the abnormal factors when data transmission anomalies occur, leading to low efficiency in handling data transmission anomalies.

[0004] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide an IoT data transmission system based on LoRa technology to solve the problem that existing IoT data transmission systems cannot perform in-depth analysis of the factors affecting data anomalies.

[0006] The technical problem that this invention aims to solve is: how to provide an IoT data transmission system that can perform in-depth analysis of the influencing factors of data anomalies.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] An IoT data transmission system based on LoRa technology includes a transmission management platform, which is communicatively connected to a data transmission module, a transmission detection module, an anomaly analysis module, and a storage module.

[0009] The data transmission module is used to transmit IoT data via LoRa technology and to build an IoT data transmission network through multiple LoRa devices.

[0010] The transmission detection module is used to detect and analyze the transmission rate of IoT data: the data transmission cycle of the IoT is divided into several detection periods, and the upper low value SD, upper bias value SP, lower low value XD, and lower bias value XP of the IoT data transmission network are obtained within each detection period; the transmission coefficient CS of the IoT data transmission network within the detection period is obtained by numerically calculating the upper low value SD, upper bias value SP, lower low value XD, and lower bias value XP; the value of the transmission coefficient CS is used to determine whether the data transmission rate within the detection period meets the requirements.

[0011] The anomaly analysis module is used to detect and analyze the cause of transmission anomalies in the IoT data transmission network after receiving a transmission anomaly signal: it obtains the data sending end and data receiving end in the IoT data transmission network, marks the total amount of data processed by the data sending end and data receiving end in the IoT data transmission network during the detection period as the sending amount and receiving amount, respectively, marks the difference between the sending amount and the receiving amount as the sending-receiving difference, marks the ratio of the sending-receiving difference to the sending amount as the loss ratio, and determines the cause of the data transmission anomaly by the magnitude of the loss ratio.

[0012] As a preferred embodiment of the present invention, the process of obtaining the upper low value SD and the upper bias value SP includes: obtaining the minimum uplink rate of each uplink transmission channel in the Internet of Things data transmission network during the detection period and marking it as the uplink value of the uplink transmission channel; summing up the uplink values ​​of all uplink transmission channels and taking the average value to obtain the upper low value SD of the Internet of Things data transmission network during the detection period; establishing an uplink set of all uplink values ​​of all uplink channels; and calculating the variance of the uplink set to obtain the upper bias value SP.

[0013] As a preferred embodiment of the present invention, the process of obtaining the lower low value XD and the lower bias value XP includes: obtaining the minimum downlink transmission rate of each downlink transmission channel in the Internet of Things data transmission network during the detection period and marking it as a downlink value; summing all downlink values ​​and taking the average value to obtain the lower low value XD of the Internet of Things data transmission network during the detection period; establishing a downlink set for the downlink values ​​of all downlink transmission channels; and calculating the variance of the downlink set to obtain the lower bias value XP.

[0014] In a preferred embodiment of the present invention, the specific process for determining whether the data transmission rate during the detection period meets the requirements includes: obtaining the transmission threshold CSmin through the storage module, comparing the transmission coefficient CS of the IoT data transmission network with the transmission threshold CSmin; if the transmission coefficient CS is less than the transmission threshold CSmin, it is determined that the data transmission rate of the IoT data transmission network during the detection period does not meet the requirements, and the transmission detection module sends a transmission abnormality signal to the transmission management platform; after receiving the transmission abnormality signal, the transmission management platform sends the transmission abnormality signal to the abnormality analysis module; if the transmission coefficient CS is greater than or equal to the transmission threshold CSmin, it is determined that the data transmission rate of the IoT data transmission network during the detection period meets the requirements, and the transmission detection module sends a transmission normal signal to the transmission management platform.

[0015] In a preferred embodiment of the present invention, the specific process for determining the cause of data transmission anomalies includes: obtaining a loss threshold through a storage module, comparing the loss ratio of the IoT data transmission network during the detection period with the loss threshold; if the loss ratio is greater than or equal to the loss threshold, the cause of the IoT data transmission network anomaly is determined to be a server anomaly, and the anomaly analysis module sends a server maintenance signal to the transmission management platform; after receiving the server maintenance signal, the transmission management platform sends the server maintenance signal to the mobile terminal of the management personnel; if the loss ratio is less than the loss threshold, interference analysis is performed on the IoT data transmission network.

[0016] As a preferred embodiment of the present invention, the specific process of interference analysis of the Internet of Things data transmission network includes: marking the data transmitting end and the data receiving end with the longest straight-line distance as the remote transmitting end and the remote receiving end, respectively; drawing a circle with the midpoint of the line connecting the physical locations of the remote transmitting end and the remote receiving end as the center and r1 as the radius; marking the obtained circular area as the interference area; obtaining the number of processing plants, communication base stations and power plants in the interference area and marking them as JG, JZ and FD, respectively; obtaining the interference coefficient GR of the interference area by numerical calculation of JG, JZ and FD; obtaining the interference threshold GRmax through the storage module; comparing the interference coefficient GR of the interference area with the interference threshold GRmax; and determining the cause of the data transmission anomaly based on the comparison result.

[0017] In a preferred embodiment of the present invention, the specific process of comparing the interference coefficient GR of the interference area with the interference threshold GRmax includes: if the interference coefficient GR is less than the interference threshold GRmax, the cause of the abnormal transmission of the IoT data transmission network is determined to be a hardware failure, and the anomaly analysis module sends a hardware maintenance signal to the transmission management platform. After receiving the hardware maintenance signal, the transmission management platform sends the hardware maintenance signal to the mobile terminal of the management personnel; if the interference coefficient GR is greater than or equal to the interference threshold GRmax, the cause of the abnormal transmission of the IoT data transmission network is determined to be transmission interference, and the anomaly analysis module sends a transmission interference signal to the transmission management platform. After receiving the transmission interference signal, the transmission management platform sends the transmission interference signal to the mobile terminal of the management personnel.

[0018] The working method of this IoT data transmission system based on LoRa technology includes the following steps:

[0019] Step 1: Detect and analyze the transmission rate of IoT data: Divide the data transmission cycle of IoT into several detection periods. During the detection period, obtain the upper low value, upper bias value, lower low value, and lower bias value of the IoT data transmission network and perform numerical calculations to obtain the transmission coefficient. Determine whether the data transmission rate meets the requirements based on the magnitude of the transmission coefficient.

[0020] Step 2: When the data transmission rate does not meet the requirements, detect and analyze the reasons for the abnormal transmission of IoT data transmission network and obtain the loss ratio. The magnitude of the loss ratio is used to determine whether the cause of the data transmission abnormality is a server abnormality.

[0021] Step 3: When the data transmission anomaly is unrelated to the server, perform interference analysis on the IoT data transmission network and obtain the interference coefficient. Based on the magnitude of the interference coefficient, mark the cause of the data transmission anomaly as hardware failure or transmission interference.

[0022] The present invention has the following beneficial effects:

[0023] 1. The transmission detection module can detect and analyze the transmission rate of the Internet of Things data transmission network. By comprehensively analyzing the minimum transmission rate in each channel and combining the deviation of the data transmission rate in each channel, the transmission coefficient is obtained by numerical calculation. The transmission coefficient is then used to provide feedback on the data transmission rate, and timely feedback is provided when abnormal data transmission occurs.

[0024] 2. The anomaly analysis module can detect and analyze the causes of transmission anomalies in the IoT data transmission network. By analyzing the amount of data processed at the data sending and receiving ends, the loss ratio can be obtained. The loss ratio can be used to provide feedback on the working status of the server. This allows for feedback when data transmission anomalies are caused by server malfunctions, enabling direct maintenance and optimization of the server and improving the efficiency of handling data transmission anomalies.

[0025] 3. By performing interference analysis on the Internet of Things (IoT) data transmission network, we can comprehensively analyze the data transmission interference elements existing in the interference area, thereby obtaining the interference coefficient of the interference area. The magnitude of the interference coefficient provides feedback on the degree of external interference encountered during wireless data transmission, and further marks the influencing factors of abnormal data transmission. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0028] Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0029] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Example 1

[0031] like Figure 1 As shown, the IoT data transmission system based on LoRa technology includes a transmission management platform, which is communicatively connected to a data transmission module, a transmission detection module, an anomaly analysis module, and a storage module.

[0032] The data transmission module is used to transmit IoT data via LoRa technology and to build an IoT data transmission network using multiple LoRa devices.

[0033] The transmission detection module is used to detect and analyze the transmission rate of IoT data. It divides the IoT data transmission cycle into several detection periods, and within each period, acquires the upper low value (SD), upper bias value (SP), lower low value (XD), and lower bias value (XP) of the IoT data transmission network. The acquisition process of the upper low value (SD) and upper bias value (SP) includes: obtaining the minimum uplink rate of each uplink transmission channel in the IoT data transmission network within the detection period and marking it as the uplink value of the uplink transmission channel; summing and averaging the uplink values ​​of all uplink transmission channels to obtain the upper low value (SD) of the IoT data transmission network within the detection period; and summing and averaging the uplink values ​​of all uplink transmission channels. An uplink set is established, and the variance of the uplink set is calculated to obtain the upper bias value SP. The process of obtaining the lower bias value XD and the lower bias value XP includes: obtaining the minimum downlink transmission rate of each downlink transmission channel in the IoT data transmission network during the detection period and marking it as the downlink value; summing all downlink values ​​and taking the average to obtain the lower bias value XD of the IoT data transmission network during the detection period; establishing a downlink set of downlink values ​​of all downlink transmission channels and calculating the variance of the downlink set to obtain the lower bias value XP; and obtaining the transmission coefficient C of the IoT data transmission network during the detection period using the formula CS=α1*(SD+XD)-α2*(SP+XP). S, the transmission coefficient, is a numerical value reflecting the overall data transmission rate of the IoT data transmission network. The larger the value of the transmission coefficient, the faster the overall data transmission rate of the IoT data transmission network. α1 and α2 are both proportionality coefficients, and α1 > α2 > 1. The transmission threshold CSmin is obtained through the storage module. The transmission coefficient CS of the IoT data transmission network is compared with the transmission threshold CSmin: If the transmission coefficient CS is less than the transmission threshold CSmin, it is determined that the data transmission rate of the IoT data transmission network during the detection period does not meet the requirements, and the transmission detection module sends a transmission anomaly signal to the transmission management platform. After receiving the transmission anomaly signal, the transmission management platform sends the transmission anomaly signal to the anomaly analysis module. If the transmission coefficient CS is greater than or equal to the transmission threshold CSmin, it is determined that the data transmission rate of the IoT data transmission network during the detection period meets the requirements, and the transmission detection module sends a normal transmission signal to the transmission management platform. The transmission rate of the IoT data transmission network is detected and analyzed. By comprehensively analyzing the minimum transmission rate in each channel and combining it with the degree of deviation in the data transmission rate in each channel, the transmission coefficient is obtained through numerical calculation. The transmission coefficient provides feedback on the data transmission rate, allowing for timely feedback when data transmission anomalies occur.

[0034] The anomaly analysis module is used to detect and analyze the causes of transmission anomalies in the IoT data transmission network after receiving a transmission anomaly signal. It acquires the data sender and receiver data in the IoT data transmission network, labels the total data processing volume of the sender and receiver data in the detection period as the sending volume and receiving volume, respectively, labels the difference between the sending and receiving volumes as the transmission-reception difference, and labels the ratio of the transmission-reception difference to the sending volume as the loss ratio. This allows for the detection and analysis of the causes of transmission anomalies in the IoT data transmission network. By analyzing the data processing volume of the sender and receiver data, the loss ratio is obtained. The loss ratio is then used to provide feedback on the server's working status, thereby identifying data transmission anomalies caused by server malfunctions. Feedback is provided upon startup, allowing for direct server maintenance and optimization, thus improving the efficiency of handling data transmission anomalies. The storage module obtains a loss threshold, and the loss ratio of the IoT data transmission network during the detection period is compared with this threshold. If the loss ratio is greater than or equal to the threshold, the cause of the IoT data transmission network anomaly is determined to be a server malfunction. The anomaly analysis module sends a server maintenance signal to the transmission management platform, which then sends the signal to the administrator's mobile terminal. If the loss ratio is less than the threshold, interference analysis is performed on the IoT data transmission network: the data transmitter and receiver with the longest straight-line distance are marked as the remote transmitter and remote receiver, respectively. The center of a circle is the midpoint of the line connecting the physical locations of the remote transmitter and receiver, and the radius is r1. r1 is a constant value set by the administrator. The resulting circular area is marked as the interference area. The number of processing plants, communication base stations, and power plants within the interference area are obtained and marked as JG, JZ, and FD, respectively. The interference coefficient GR of the interference area is obtained using the formula GR = β1*JG + β2*JZ + β3*FD. The interference coefficient reflects the degree of external interference affecting data transmission within the interference area; the larger the interference coefficient, the higher the degree of external interference affecting data transmission within the interference area. β1, β2, and β3 are proportionality coefficients, and β1 > β2 > β3 > 1. The data is obtained through the storage module. Once the interference threshold GRmax is obtained, the interference coefficient GR of the interference area is compared with the interference threshold GRmax. If the interference coefficient GR is less than the interference threshold GRmax, the cause of the abnormal transmission of IoT data transmission network is determined to be a hardware failure. The anomaly analysis module sends a hardware maintenance signal to the transmission management platform. After receiving the hardware maintenance signal, the transmission management platform sends the hardware maintenance signal to the mobile terminal of the management personnel. If the interference coefficient GR is greater than or equal to the interference threshold GRmax, the cause of the abnormal transmission of IoT data transmission network is determined to be transmission interference. The anomaly analysis module sends a transmission interference signal to the transmission management platform. After receiving the transmission interference signal, the transmission management platform sends the transmission interference signal to the mobile terminal of the management personnel.Interference analysis of IoT data transmission networks allows for a comprehensive analysis of data transmission interference elements within the interference area, yielding an interference coefficient. The magnitude of this coefficient provides feedback on the degree of external interference experienced during wireless data transmission, further identifying factors influencing abnormal data transmission.

[0035] Example 2

[0036] like Figure 2 As shown, the IoT data transmission method based on LoRa technology includes the following steps:

[0037] Step 1: Detect and analyze the transmission rate of IoT data: Divide the IoT data transmission cycle into several detection periods. Within each detection period, obtain the upper low value, upper bias value, lower low value, and lower bias value of the IoT data transmission network and calculate the transmission coefficient. Use the magnitude of the transmission coefficient to determine whether the data transmission rate meets the requirements. Use the transmission coefficient to provide feedback on the data transmission rate and provide timely feedback when abnormal data transmission occurs.

[0038] Step 2: When the data transmission rate does not meet the requirements, detect and analyze the cause of the IoT data transmission network transmission anomaly and obtain the loss ratio. Determine whether the cause of the data transmission anomaly is a server anomaly by the magnitude of the loss ratio. When the data transmission anomaly is caused by a server anomaly, provide feedback and directly maintain and optimize the server.

[0039] Step 3: When the data transmission anomaly is unrelated to the server, perform interference analysis on the IoT data transmission network and obtain the interference coefficient. Based on the magnitude of the interference coefficient, mark the cause of the data transmission anomaly as hardware failure or transmission interference, and further mark the influencing factors of the data transmission anomaly.

[0040] An IoT data transmission system based on LoRa technology performs the following steps during operation: It divides the IoT data transmission cycle into several detection periods. Within each period, it acquires the upper low, upper skew, lower low, and lower skew values ​​of the IoT data transmission network and calculates the transmission coefficient. The magnitude of the transmission coefficient determines whether the data transmission rate meets requirements. Feedback on the data transmission rate is provided based on the transmission coefficient, and timely feedback is given when data transmission anomalies occur. When the data transmission rate does not meet requirements, the system analyzes the causes of the IoT data transmission network anomalies and obtains the loss ratio. The magnitude of the loss ratio determines whether the cause of the data transmission anomaly is a server malfunction. When the data transmission anomaly is caused by a server malfunction, feedback is given, and direct server maintenance and optimization are implemented. When the data transmission anomaly is unrelated to the server, interference analysis is performed on the IoT data transmission network, and the influencing factors of the data transmission anomaly are further identified and marked.

[0041] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0042] The above formulas are all derived from software simulation using a large amount of collected data, and are selected to be close to the true values. The coefficients in the formulas are set by those skilled in the art according to the actual situation; for example: the formula CS=α1*(SD+XD)-α2*(SP+XP); those skilled in the art collect multiple sets of sample data and set corresponding transmission coefficients for each set of sample data; substitute the set transmission coefficients and the collected sample data into the formulas, any two formulas form a system of two linear equations in two variables, filter the calculated coefficients and take the average value, and obtain the values ​​of α1 and α2 as 5.47 and 2.39 respectively;

[0043] The size of the coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial transmission coefficient set by those skilled in the art for each set of sample data. As long as it does not affect the proportional relationship between the parameter and the quantized value, such as the transmission coefficient being proportional to the value of the lower value.

[0044] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0045] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An IoT data transmission system based on LoRa technology, characterized in that, It includes a transmission management platform, which is communicatively connected to a data transmission module, a transmission detection module, an anomaly analysis module, and a storage module; The data transmission module is used to transmit IoT data via LoRa technology and to build an IoT data transmission network through multiple LoRa devices. The transmission detection module is used to detect and analyze the transmission rate of IoT data: the data transmission cycle of IoT is divided into several detection periods, and the upper low value SD, upper bias value SP, lower low value XD and lower bias value XP of IoT data transmission network are obtained within the detection period; the transmission coefficient CS of IoT data transmission network within the detection period is obtained by numerically calculating the upper low value SD, upper bias value SP, lower low value XD and lower bias value XP. The value of the transmission coefficient CS is used to determine whether the data transmission rate during the detection period meets the requirements. The anomaly analysis module is used to detect and analyze the cause of the transmission anomaly in the IoT data transmission network after receiving the transmission anomaly signal: obtain the data sending end and data receiving end in the IoT data transmission network, mark the total amount of data processed by the data sending end and data receiving end in the IoT data transmission network during the detection period as the sending amount and receiving amount respectively, mark the difference between the sending amount and the receiving amount as the sending-receiving difference, mark the ratio of the sending-receiving difference to the sending amount as the loss ratio, and determine the cause of the data transmission anomaly by the magnitude of the loss ratio; The process of obtaining the upper low value SD and upper bias value SP includes: obtaining the minimum uplink rate of each uplink transmission channel in the IoT data transmission network during the detection period and marking it as the uplink value of the uplink transmission channel; summing up the uplink values ​​of all uplink transmission channels and taking the average value to obtain the upper low value SD of the IoT data transmission network during the detection period; establishing an uplink set of all uplink transmission channels; and calculating the variance of the uplink set to obtain the upper bias value SP. The process of obtaining the lower minimum value XD and the lower bias value XP includes: obtaining the minimum downlink transmission rate of each downlink transmission channel in the IoT data transmission network during the detection period and marking it as the downlink value; summing all the downlink values ​​and taking the average value to obtain the lower minimum value XD of the IoT data transmission network during the detection period; establishing a downlink set for the downlink values ​​of all downlink transmission channels; and calculating the variance of the downlink set to obtain the lower bias value XP. The specific process for determining whether the data transmission rate during the detection period meets the requirements includes: obtaining the transmission threshold CSmin through the storage module, comparing the transmission coefficient CS of the IoT data transmission network with the transmission threshold CSmin; if the transmission coefficient CS is less than the transmission threshold CSmin, it is determined that the data transmission rate of the IoT data transmission network during the detection period does not meet the requirements, and the transmission detection module sends a transmission abnormality signal to the transmission management platform. After receiving the transmission abnormality signal, the transmission management platform sends the transmission abnormality signal to the abnormality analysis module; if the transmission coefficient CS is greater than or equal to the transmission threshold CSmin, it is determined that the data transmission rate of the IoT data transmission network during the detection period meets the requirements, and the transmission detection module sends a transmission normal signal to the transmission management platform. The specific process for determining the cause of data transmission anomalies includes: obtaining the loss threshold through the storage module, comparing the loss ratio of the IoT data transmission network during the detection period with the loss threshold; if the loss ratio is greater than or equal to the loss threshold, the cause of the IoT data transmission network anomaly is determined to be a server anomaly, and the anomaly analysis module sends a server maintenance signal to the transmission management platform, which then sends the server maintenance signal to the administrator's mobile terminal; if the loss ratio is less than the loss threshold, interference analysis is performed on the IoT data transmission network.

2. The IoT data transmission system based on LoRa technology according to claim 1, characterized in that, The specific process of interference analysis for IoT data transmission networks includes: marking the data transmitter and receiver with the longest straight-line distance as the remote transmitter and remote receiver, respectively; drawing a circle with the midpoint of the line connecting the physical locations of the remote transmitter and remote receiver as the center and r1 as the radius; marking the resulting circular area as the interference area; obtaining the number of processing plants, communication base stations, and power plants within the interference area and marking them as JG, JZ, and FD, respectively; obtaining the interference coefficient GR of the interference area by numerically calculating JG, JZ, and FD; obtaining the interference threshold GRmax through the storage module; comparing the interference coefficient GR of the interference area with the interference threshold GRmax; and determining the cause of the data transmission anomaly based on the comparison result.

3. The IoT data transmission system based on LoRa technology according to claim 2, characterized in that, The specific process of comparing the interference coefficient GR of the interference area with the interference threshold GRmax includes: if the interference coefficient GR is less than the interference threshold GRmax, the cause of the abnormal transmission of the IoT data transmission network is determined to be a hardware failure. The anomaly analysis module sends a hardware maintenance signal to the transmission management platform. After receiving the hardware maintenance signal, the transmission management platform sends the hardware maintenance signal to the mobile terminal of the management personnel. If the interference coefficient GR is greater than or equal to the interference threshold GRmax, the cause of the abnormal transmission of the IoT data transmission network is determined to be transmission interference. The anomaly analysis module sends a transmission interference signal to the transmission management platform. After receiving the transmission interference signal, the transmission management platform sends the transmission interference signal to the mobile terminal of the management personnel.

4. The IoT data transmission system based on LoRa technology according to claim 3, characterized in that, The working method of this IoT data transmission system based on LoRa technology includes the following steps: Step 1: Detect and analyze the transmission rate of IoT data: Divide the data transmission cycle of IoT into several detection periods. During the detection period, obtain the upper low value, upper bias value, lower low value, and lower bias value of the IoT data transmission network and perform numerical calculations to obtain the transmission coefficient. Determine whether the data transmission rate meets the requirements based on the magnitude of the transmission coefficient. Step 2: When the data transmission rate does not meet the requirements, detect and analyze the reasons for the abnormal transmission of IoT data transmission network and obtain the loss ratio. The magnitude of the loss ratio is used to determine whether the cause of the data transmission abnormality is a server abnormality. Step 3: When the data transmission anomaly is unrelated to the server, perform interference analysis on the IoT data transmission network and obtain the interference coefficient. Based on the magnitude of the interference coefficient, mark the cause of the data transmission anomaly as hardware failure or transmission interference.

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