Methods and devices for fault diagnosis of LoRa+ transmission network equipment

Abstract

This invention relates to the field of network fault analysis technology, specifically a method and apparatus for fault diagnosis of LoRa+ transmission network equipment. The former includes: collecting diagnostic parameters from a data acquisition server and an A2 database; and, when the instrument is offline, analyzing the diagnostic parameters according to preset fault diagnosis rules to output equipment fault diagnosis results. Based on 15 types of diagnostic parameters and pre-set fault diagnosis rules, this invention can quickly and accurately diagnose specific equipment faults, achieving efficient monitoring of LoRa+ transmission network equipment in oil fields. It simplifies the fault analysis process and allows for the search of corresponding handling suggestions based on the output equipment fault diagnosis results. Maintenance personnel can then accurately and efficiently handle faults according to these suggestions, shortening fault handling time and improving instrument online rates.

Description

Technical Field

[0001] This invention relates to the field of network fault analysis technology, specifically a method and apparatus for diagnosing faults in LoRa+ transmission network equipment. Background Technology

[0002] The existing LoRa+ wireless network, including both hardware and software, comes from Aisen Intelligent. The LoRa+ network monitoring platform consists of two parts: SenzFlow, which provides three status parameters for the instrument: signal strength, signal-to-noise ratio, and online status; and loraFlow.io, which provides more low-level and complex data. A deep understanding of the LoRa+ network's operating mechanism is needed to extract the effective frequency points, spreading factor, signal strength, and signal-to-noise ratio from this complex data. The typical steps for troubleshooting instrument malfunctions include: 1. Checking if the device is online; 2. If offline, resetting the device; 3. If resetting does not resolve the issue, replacing the device. However, if the problem is a gateway failure or network coverage issue, replacing the device may not resolve the problem.

[0003] Existing LoRa+ transmission network monitoring platforms suffer from fragmented device status data, limited management tools, and the acquired data is often highly specialized and lacks centralized display. Fault analysis requires multiple uses of the tools to obtain relevant data, placing high demands on maintenance personnel's technical skills. Furthermore, the LoRa+ monitoring platforms provide limited device status parameters, failing to meet fault diagnosis needs. When a device malfunctions, it is only observed that the device is offline, without identifying the specific cause of the offline status. Without knowing the cause, it is impossible to quickly and effectively address the fault, forcing users to rely on experience and follow common troubleshooting methods sequentially, significantly prolonging troubleshooting time and sometimes rendering the fault unmanageable. Summary of the Invention

[0004] This invention provides a method and apparatus for diagnosing faults in LoRa+ transmission network devices, overcoming the shortcomings of the prior art. It can effectively solve the problem that existing LoRa+ transmission network device fault diagnosis methods can only determine whether the device is offline due to the limited number of parameters used in the diagnosis, and cannot accurately diagnose the specific fault content.

[0005] One of the technical solutions of this invention is achieved through the following measures: a method for diagnosing faults in LoRa+ transmission network devices, comprising:

[0006] Diagnostic parameters are collected in the data acquisition server and A2 database. These parameters include the best gateway, number of retransmissions, number of resets, whether a single well has a gateway, the gateway status of a single well, production status, the strongest signal of a single well, online rate, number of disconnections, frequency point, spreading factor, signal strength, signal-to-noise ratio, gateway status, and instrument status.

[0007] When the instrument is offline, the diagnostic parameters are analyzed according to the preset fault diagnosis rules, and the equipment fault diagnosis results are output.

[0008] The following are further optimizations and / or improvements to the above-mentioned technical solution:

[0009] The above fault diagnosis rules include:

[0010] If the instrument is offline and the single well has a gateway and the single well gateway is offline, it indicates that a single well gateway failure has occurred.

[0011] If the instrument is offline, the strongest signal in a single well is less than -120dB, and there is no gateway in the single well, it indicates a network coverage problem.

[0012] If the instrument is offline and the strongest signal in a single well is >-110dB, it indicates that there is a fault in the equipment antenna or battery.

[0013] If the instrument is offline and the online rate is >95%, it indicates that an intermittent fault has occurred.

[0014] If the instrument is offline and the production status is shut-in, it means that an offline event has occurred but no action is required.

[0015] If the instrument is offline and the single well has a gateway and the single well gateway is online, it means that there is no connection to the best gateway;

[0016] If the meter is offline and the online rate is <50% and the number of disconnections is >10, it indicates that frequent disconnections have occurred.

[0017] The above also includes finding corresponding handling suggestions based on the output device fault diagnosis results. If the search for corresponding handling suggestions fails, the staff will handle the problem independently and then manually enter the corresponding handling suggestions.

[0018] The above also includes diversified data display, which includes dynamically displaying the collected diagnostic parameters, viewing the fault diagnosis results and corresponding handling suggestions.

[0019] The diagnostic parameters collected above from the data acquisition server and A2 database include:

[0020] Collect real-time data in the data acquisition server;

[0021] Retrieve log comments from database A2;

[0022] Directly extract frequency points, spreading factors, signal strength, signal-to-noise ratio, gateway status, and instrument status from real-time data and log notes. Utilize analysis and calculation to indirectly extract the best gateway, number of retransmissions, number of resets, presence or absence of a gateway in a single well, gateway status in a single well, production status, strongest signal in a single well, online rate, and number of disconnections from real-time data and log notes.

[0023] The second technical solution of the present invention is achieved through the following measures: a LoRa+ transmission network equipment fault diagnosis device, comprising:

[0024] The parameter acquisition unit collects diagnostic parameters from the data acquisition server and the A2 database. These diagnostic parameters include the optimal gateway, number of retransmissions, number of resets, presence or absence of a gateway in a single well, gateway status in a single well, production status, strongest signal in a single well, online rate, number of disconnections, frequency point, spreading factor, signal strength, signal-to-noise ratio, gateway status, and instrument status.

[0025] The fault analysis unit analyzes the diagnostic parameters according to preset fault diagnosis rules and outputs the equipment fault diagnosis results when the instrument is offline.

[0026] The fault handling knowledge base stores fault diagnosis rules, equipment fault diagnosis results, and collected diagnostic parameters.

[0027] The following are further optimizations and / or improvements to the above-mentioned technical solution:

[0028] The aforementioned fault analysis unit includes:

[0029] The fault diagnosis module analyzes the diagnostic parameters according to preset fault diagnosis rules and outputs the equipment fault diagnosis results when the instrument is offline.

[0030] The processing suggestion matching module searches for corresponding processing suggestions based on the output device fault diagnosis results. If the search for corresponding processing suggestions fails, the staff will handle the problem independently and then manually enter the corresponding processing suggestions.

[0031] The above also includes a human-computer interaction unit for diversified data display and manual data entry by staff. The diversified data display includes dynamically displaying the collected diagnostic parameters and viewing the fault diagnosis results and corresponding handling suggestions.

[0032] This invention collects diagnostic parameters from a data acquisition server and the A2 database. In addition to the original six categories of diagnostic parameters—frequency point, spreading factor, signal strength, signal-to-noise ratio, gateway status, and instrument status—nine new categories are added: optimal gateway, number of retransmissions, number of resets, presence or absence of a gateway in a single well, gateway status in a single well, production status, strongest signal in a single well, online rate, and number of disconnections. Based on these 15 categories of diagnostic parameters and pre-defined fault diagnosis rules, specific equipment faults can be quickly and accurately diagnosed, enabling efficient monitoring of LoRa+ transmission network equipment in oilfields. This simplifies the fault analysis process and solves the problem of existing methods that rely only on six categories of diagnostic parameters for LoRa+ transmission network equipment fault diagnosis, which, due to the limited number of parameters used, can only determine whether the equipment is offline but cannot accurately diagnose specific faults. Furthermore, this invention can also find corresponding handling suggestions based on the output equipment fault diagnosis results. Maintenance personnel can then accurately and efficiently handle faults according to these suggestions, shortening fault handling time and improving instrument online rate. Attached Figure Description

[0033] Appendix Figure 1 This is a schematic diagram of a diagnostic method according to the present invention.

[0034] Appendix Figure 2 This is a schematic diagram of another diagnostic method of the present invention.

[0035] Appendix Figure 3 This is a schematic diagram of the structure of a diagnostic device according to the present invention.

[0036] Appendix Figure 4 This is a schematic diagram of another diagnostic device according to the present invention. Detailed Implementation

[0037] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0038] The present invention will be further described below with reference to embodiments and accompanying drawings:

[0039] Example 1: As shown in the attached document Figure 1 As shown, this embodiment of the invention discloses a method for fault diagnosis of LoRa+ transmission network devices, including:

[0040] Step S110: Collect diagnostic parameters in the data acquisition server and A2 database. The diagnostic parameters include the best gateway, number of retransmissions, number of resets, whether a single well has a gateway, the gateway status of a single well, production status, the strongest signal of a single well, online rate, number of disconnections, frequency point, spreading factor, signal strength, signal-to-noise ratio, gateway status, and instrument status.

[0041] Step S120: When the instrument is offline, analyze the diagnostic parameters according to the preset fault diagnosis rules and output the equipment fault diagnosis results.

[0042] This invention collects diagnostic parameters from a data acquisition server and the A2 database. In addition to the original six categories of diagnostic parameters—frequency point, spreading factor, signal strength, signal-to-noise ratio, gateway status, and instrument status—nine new categories are added: optimal gateway, number of retransmissions, number of resets, presence or absence of a gateway in a single well, gateway status in a single well, production status, strongest signal in a single well, online rate, and number of disconnections. Based on these 15 categories of diagnostic parameters and pre-defined fault diagnosis rules, specific equipment faults can be quickly and accurately diagnosed. This enables efficient monitoring of LoRa+ transmission network equipment in oilfields, simplifies the fault analysis process, shortens fault handling time, and improves instrument online rate. It also solves the problem of existing methods that rely solely on six categories of diagnostic parameters for LoRa+ transmission network equipment fault diagnosis, which, due to the limited number of parameters used, can only determine whether the equipment is offline but cannot accurately diagnose specific faults.

[0043] Example 2: This embodiment of the invention discloses a method for fault diagnosis of LoRa+ transmission network equipment, including:

[0044] Step S210: Collect diagnostic parameters in the data acquisition server and A2 database. The diagnostic parameters include the best gateway, number of retransmissions, number of resets, whether a single well has a gateway, the gateway status of a single well, production status, the strongest signal of a single well, online rate, number of disconnections, frequency point, spreading factor, signal strength, signal-to-noise ratio, gateway status, and instrument status.

[0045] The diagnostic parameters collected above from the data acquisition server and A2 database specifically include:

[0046] (1) Collect real-time data in the data acquisition server;

[0047] (2) Retrieve log comments from database A2;

[0048] (3) Directly capture frequency points, spreading factors, signal strength, signal-to-noise ratio, gateway status, and instrument status from real-time data and log notes. Use analysis and calculation to indirectly capture the best gateway, number of retransmissions, number of resets, whether a single well has a gateway, the gateway status of a single well, production status, the strongest signal of a single well, online rate, and number of disconnections from real-time data and log notes.

[0049] Step S220: When the instrument is offline, analyze the diagnostic parameters according to the preset fault diagnosis rules and output the equipment fault diagnosis results.

[0050] The fault diagnosis rules in the above steps include:

[0051] If the instrument is offline and the single well has a gateway and the single well gateway is offline, it indicates that a single well gateway failure has occurred.

[0052] If the instrument is offline and (the strongest signal in a single well is <-120dB) and there is no gateway in the single well, it indicates a network coverage problem.

[0053] If the instrument is offline and (strongest signal in a single well > -110dB), it indicates that there is a problem with the equipment antenna or battery.

[0054] If the instrument is offline (online rate > 95%), it indicates an intermittent fault.

[0055] If the instrument is offline and the production status is shut-in, it means that an offline event has occurred but no action is required.

[0056] If the instrument is offline and the single well has a gateway and the single well gateway is online, it means that there is no connection to the best gateway;

[0057] If the meter is offline (online rate < 50%) and has more than 10 disconnections, it indicates that frequent disconnections have occurred.

[0058] The content of the fault diagnosis rules here can be adjusted according to the actual situation.

[0059] Example 3: As shown in the attached document Figure 2 As shown, this embodiment of the invention discloses a method for fault diagnosis of LoRa+ transmission network devices, including:

[0060] Step S310: Collect diagnostic parameters in the data acquisition server and A2 database. The diagnostic parameters include the best gateway, number of retransmissions, number of resets, whether a single well has a gateway, the gateway status of a single well, production status, the strongest signal of a single well, online rate, number of disconnections, frequency point, spreading factor, signal strength, signal-to-noise ratio, gateway status, and instrument status.

[0061] Step S320: When the instrument is offline, analyze the diagnostic parameters according to the preset fault diagnosis rules and output the equipment fault diagnosis results.

[0062] Step S330: In the output device fault diagnosis results, find the corresponding handling suggestions based on the output device fault diagnosis results. If it is impossible to find the corresponding handling suggestions, the staff will handle it independently and then manually enter the corresponding handling suggestions.

[0063] Step S340: Perform diversified data display, which includes dynamically displaying the collected diagnostic parameters, viewing the fault diagnosis results and corresponding handling suggestions.

[0064] In the above steps, corresponding handling suggestions are found based on the fault diagnosis results of the output device. Here, handling suggestions for various equipment faults are pre-entered and stored manually (the handling suggestions can be given by experts or summarized by staff). They can be identified one by one using identification codes, or tabulated with equipment faults and handling suggestions, so that subsequent automatic and fast matching of corresponding handling suggestions based on equipment faults can be achieved. Maintenance personnel can handle faults accurately and efficiently based on the handling suggestions, shortening fault handling time and improving the online rate of instruments.

[0065] Example 4: As shown in the appendix Figure 3 As shown, this embodiment of the invention discloses a fault diagnosis device for LoRa+ transmission network equipment, comprising:

[0066] The parameter acquisition unit collects diagnostic parameters from the data acquisition server and the A2 database. These diagnostic parameters include the optimal gateway, number of retransmissions, number of resets, presence or absence of a gateway in a single well, gateway status in a single well, production status, strongest signal in a single well, online rate, number of disconnections, frequency point, spreading factor, signal strength, signal-to-noise ratio, gateway status, and instrument status.

[0067] The fault analysis unit analyzes the diagnostic parameters according to preset fault diagnosis rules and outputs the equipment fault diagnosis results when the instrument is offline.

[0068] The fault analysis unit includes:

[0069] The fault diagnosis module analyzes the diagnostic parameters according to preset fault diagnosis rules and outputs the equipment fault diagnosis results when the instrument is offline.

[0070] The processing suggestion matching module searches for corresponding processing suggestions based on the output device fault diagnosis results. If the search for corresponding processing suggestions fails, the staff will handle the problem independently and then manually enter the corresponding processing suggestions.

[0071] The fault handling knowledge base stores fault diagnosis rules, equipment fault diagnosis results, and collected diagnostic parameters.

[0072] Example 5: As shown in the attached document Figure 4 As shown, this embodiment of the invention discloses a fault diagnosis device for LoRa+ transmission network equipment, comprising:

[0073] The parameter acquisition unit collects diagnostic parameters from the data acquisition server and the A2 database. These diagnostic parameters include the optimal gateway, number of retransmissions, number of resets, presence or absence of a gateway in a single well, gateway status in a single well, production status, strongest signal in a single well, online rate, number of disconnections, frequency point, spreading factor, signal strength, signal-to-noise ratio, gateway status, and instrument status.

[0074] The fault analysis unit analyzes the diagnostic parameters according to preset fault diagnosis rules and outputs the equipment fault diagnosis results when the instrument is offline.

[0075] The fault handling knowledge base stores fault diagnosis rules, equipment fault diagnosis results, and collected diagnostic parameters.

[0076] The human-computer interaction unit provides diversified data display and allows staff to manually enter data. The diversified data display includes dynamically displaying the collected diagnostic parameters and viewing the fault diagnosis results and corresponding handling suggestions.

[0077] The aforementioned human-computer interaction unit can be a monitoring dashboard.

[0078] The above technical features constitute the preferred embodiment of the present invention, which has strong adaptability and optimal implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the requirements of different situations.

Claims

1. A method for fault diagnosis of LoRa+ transmission network equipment, characterized in that, include: Diagnostic parameters are collected in the data acquisition server and A2 database. These parameters include the best gateway, number of retransmissions, number of resets, whether a single well has a gateway, the gateway status of a single well, production status, the strongest signal of a single well, online rate, number of disconnections, frequency point, spreading factor, signal strength, signal-to-noise ratio, gateway status, and instrument status. When the instrument is offline, the diagnostic parameters are analyzed according to the preset fault diagnosis rules, and the equipment fault diagnosis results are output.

2. The method for fault diagnosis of LoRa+ transmission network equipment according to claim 1, characterized in that, The fault diagnosis rules include: If the instrument is offline and the single well has a gateway and the single well gateway is offline, it indicates that a single well gateway failure has occurred. If the instrument is offline, the strongest signal in a single well is less than -120dB, and there is no gateway in the single well, it indicates a network coverage problem. If the instrument is offline and the strongest signal in a single well is >-110dB, it indicates that there is a fault in the equipment antenna or battery. If the instrument is offline and the online rate is >95%, it indicates that an intermittent fault has occurred. If the instrument is offline and the production status is shut-in, it means that an offline event has occurred but no action is required. If the instrument is offline and the single well has a gateway and the single well gateway is online, it means that there is no connection to the best gateway; If the meter is offline and the online rate is <50% and the number of disconnections is >10, it indicates that frequent disconnections have occurred.

3. The method for fault diagnosis of LoRa+ transmission network equipment according to claim 1 or 2, characterized in that, It also includes finding corresponding handling suggestions based on the output device fault diagnosis results. If the search for corresponding handling suggestions fails, the staff will handle the problem independently and then manually enter the corresponding handling suggestions.

4. The method for fault diagnosis of LoRa+ transmission network equipment according to claim 1 or 2, characterized in that, It also includes diversified data display, which includes dynamically displaying the collected diagnostic parameters, viewing the fault diagnosis results and corresponding handling suggestions.

5. The method for fault diagnosis of LoRa+ transmission network equipment according to claim 3, characterized in that, It also includes diversified data display, which includes dynamically displaying the collected diagnostic parameters, viewing the fault diagnosis results and corresponding handling suggestions.

6. The method for fault diagnosis of LoRa+ transmission network equipment according to any one of claims 1 to 5, characterized in that, The process of collecting diagnostic parameters from the data acquisition server and the A2 database includes: Collect real-time data in the data acquisition server; Retrieve log comments from database A2; Directly extract frequency points, spreading factors, signal strength, signal-to-noise ratio, gateway status, and instrument status from real-time data and log notes. Utilize analysis and calculation to indirectly extract the best gateway, number of retransmissions, number of resets, presence or absence of a gateway in a single well, gateway status in a single well, production status, strongest signal in a single well, online rate, and number of disconnections from real-time data and log notes.

7. A fault diagnosis device for LoRa+ transmission network equipment using the method described in any one of claims 1 to 6, characterized in that, include: The parameter acquisition unit collects diagnostic parameters from the data acquisition server and the A2 database. These diagnostic parameters include the optimal gateway, number of retransmissions, number of resets, presence or absence of a gateway in a single well, gateway status in a single well, production status, strongest signal in a single well, online rate, number of disconnections, frequency point, spreading factor, signal strength, signal-to-noise ratio, gateway status, and instrument status. The fault analysis unit analyzes the diagnostic parameters according to preset fault diagnosis rules and outputs the equipment fault diagnosis results when the instrument is offline. The fault handling knowledge base stores fault diagnosis rules, equipment fault diagnosis results, and collected diagnostic parameters.

8. The LoRa+ transmission network equipment fault diagnosis device according to claim 7, characterized in that, The fault analysis unit includes: The fault diagnosis module analyzes the diagnostic parameters according to preset fault diagnosis rules and outputs the equipment fault diagnosis results when the instrument is offline. The processing suggestion matching module searches for corresponding processing suggestions based on the output device fault diagnosis results. If the search for corresponding processing suggestions fails, the staff will handle the problem independently and then manually enter the corresponding processing suggestions.

9. The LoRa+ transmission network equipment fault diagnosis device according to claim 7 or 8, characterized in that, It also includes a human-computer interaction unit for diversified data display and manual data entry by staff. The diversified data display includes dynamically displaying the collected diagnostic parameters and viewing the fault diagnosis results and corresponding handling suggestions.

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