Fault detection and fault handling method and apparatus, computer device, and medium

Abstract

The present application relates to the technical field of data processing, and particularly relates to a fault detection and fault processing method and device, computer equipment and medium. The state information number of the device to be analyzed is analyzed to obtain a device signal fault level, the data transmission integrity rate is analyzed to determine a data transmission fault level, and the data packet loss rate is analyzed to obtain a network fault level. According to the device signal fault level, the data transmission fault level and the network fault level, the device fault level of the device is determined. Thus, the fault information of the detection device is automatically detected in multiple dimensions, so as to efficiently determine the device fault level.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a fault detection and fault handling method, apparatus, computer equipment, and medium. Background Technology

[0002] The core objective of data acquisition and equipment fault detection methods is to quickly identify abnormal equipment operation and accurately locate the cause of the fault through multi-dimensional data analysis. With the rapid development of Internet of Things (IoT) technology, equipment condition monitoring systems are increasingly widely used in industrial manufacturing, energy management and other fields.

[0003] Currently, most equipment fault detection methods rely on single-dimensional index analysis, such as judging online status through equipment heartbeat signals or evaluating communication quality based on network layer parameters. However, these methods lack quantitative evaluation of signal quality continuity, fail to establish a multi-dimensional evaluation system for the transmission process, and have insufficient fault location accuracy.

[0004] Therefore, how to automatically perform multi-dimensional detection of fault information of testing equipment in order to efficiently determine the fault level of the equipment has become an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a fault detection and fault handling method, apparatus, computer equipment, and medium to address the problem of how to automatically perform multi-dimensional detection of fault information of detection equipment in order to efficiently determine the fault level of the equipment.

[0006] In a first aspect, embodiments of the present invention provide a method for detecting faults in a data acquisition device, comprising:

[0007] The detection information of the device to be analyzed is obtained from the real-time detection information of the detection equipment, and the status signal of the device to be analyzed, the total amount of data transmitted by the device to be analyzed, and the interaction information between the device to be analyzed and the detection equipment are extracted from the detection information.

[0008] Based on the status signal, determine the device offline duration of the device to be analyzed, determine the device signal fault level based on the device offline duration, calculate the ratio of the total data volume to the preset data volume to obtain the data transmission integrity rate, and determine the data transmission fault level based on the data transmission integrity rate.

[0009] The interactive information is input into a preset extraction model, and the round-trip time of data packet transmission between the detection device and the device to be analyzed, the total amount of data received by the detection device, and the total amount of data packets sent by the device to be analyzed are output.

[0010] Based on the round-trip time, the information delay time is determined, and the ratio of the total received amount to the total sent amount is calculated to obtain the data packet loss rate of the device to be analyzed. Based on the information delay time and the data packet loss rate, the network fault level of the device to be analyzed is determined.

[0011] The equipment fault level of the device to be analyzed is determined based on the device signal fault level, the data transmission fault level, and the network fault level.

[0012] Secondly, embodiments of the present invention provide a method for handling faults in data acquisition devices, including:

[0013] After generating the equipment fault level using the aforementioned method for generating equipment fault detection, a historical fault database is obtained, and historical processing operations for signal faults corresponding to the equipment signal fault level, historical processing operations for data transmission faults corresponding to the data transmission fault level, and historical processing operations for network faults corresponding to the network fault level are extracted from the historical fault database.

[0014] The fault processing is performed on the device to be analyzed based on the signal fault history processing operation, the data transmission fault history processing operation, and the network fault history processing operation.

[0015] Thirdly, embodiments of the present invention provide a fault detection device for data acquisition equipment, comprising:

[0016] The information extraction module is used to acquire the detection information of the device to be analyzed in real time by the detection equipment, and extract the status signal of the device to be analyzed, the total amount of data transmitted by the device to be analyzed, and the interaction information between the device to be analyzed and the detection equipment from the detection information.

[0017] The fault level determination module is used to determine the device offline time of the device to be analyzed based on the status signal, determine the device signal fault level based on the device offline time, calculate the ratio of the total data volume to the preset data volume to obtain the data transmission integrity rate, and determine the data transmission fault level based on the data transmission integrity rate.

[0018] The interactive information extraction module is used to input the interactive information into a preset extraction model and output the round-trip time of data packet transmission between the detection device and the device to be analyzed, the total amount of data received by the detection device, and the total amount of data packets sent by the device to be analyzed.

[0019] The network fault determination module is used to determine the information delay time based on the round-trip time, calculate the ratio of the total received amount to the total sent amount to obtain the data packet loss rate of the device to be analyzed, and determine the network fault level of the device to be analyzed based on the information delay time and the data packet loss rate.

[0020] The equipment fault determination module is used to determine the equipment fault level of the equipment to be analyzed based on the equipment signal fault level, data transmission fault level, and network fault level.

[0021] Fourthly, an embodiment of the present invention provides a fault detection device for data acquisition equipment, comprising:

[0022] The historical data processing module is used to obtain a historical fault database after the equipment fault level is generated by the generation method of the acquisition equipment fault detection, and to extract the historical processing operations of signal faults corresponding to the equipment signal fault level, the historical processing operations of data transmission faults corresponding to the data transmission fault level, and the historical processing operations of network faults corresponding to the network fault level from the historical fault database.

[0023] The fault handling module is used to perform fault handling on the device to be analyzed based on the signal fault history handling operation, the data transmission fault history handling operation, and the network fault history handling operation.

[0024] Fifthly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for detecting or handling faults in the acquisition device.

[0025] Sixthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for detecting or handling faults in the acquisition device.

[0026] The beneficial effects of this invention compared to existing technologies are as follows: By acquiring the detection information of the device under analysis from the real-time detection of the detection equipment, the status signal of the device under analysis, the total amount of data transmitted by the device under analysis, and the interaction information between the device under analysis and the detection equipment are extracted from the detection information. Based on the status signal, the offline time of the device under analysis is determined. Based on the offline time, the signal fault level of the device is determined. The ratio of the total data volume to the preset data volume is calculated to obtain the data transmission integrity rate. Based on the data transmission integrity rate, the data transmission fault level is determined. The interaction information is input into a preset extraction model, which outputs the round-trip time of data packet transmission between the detection equipment and the device under analysis, the total amount of data received by the detection equipment, and the total amount of data packets sent by the device under analysis. Based on the round-trip time, the information delay time is determined. The ratio of the total amount received to the total amount sent is calculated to obtain the data packet loss rate of the device under analysis. Based on the information delay time and the data packet loss rate, the network fault level of the device under analysis is determined. Based on the device signal fault level, the data transmission fault level, and the network fault level, the device fault level of the device under analysis is determined. The system analyzes the status information of the device under analysis to obtain the device signal fault level, analyzes the data transmission integrity rate to determine the data transmission fault level, and analyzes the data packet loss rate to obtain the network fault level. Based on the device signal fault level, data transmission fault level, and network fault level, the device fault level is determined. This automated, multi-dimensional detection of device fault information efficiently determines the device fault level. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention 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.

[0028] Figure 1 This is a schematic diagram of the application environment of a fault detection method for data acquisition equipment provided in Embodiment 1 of the present invention;

[0029] Figure 2 This is a flowchart illustrating a fault detection method for a data acquisition device provided in Embodiment 2 of the present invention;

[0030] Figure 3 This is a flowchart illustrating a fault detection method for a data acquisition device provided in Embodiment 3 of the present invention;

[0031] Figure 4 This is a flowchart illustrating a fault handling method for a data acquisition device provided in Embodiment 4 of the present invention;

[0032] Figure 5This is a flowchart illustrating a fault handling method for a data acquisition device provided in Embodiment 5 of the present invention;

[0033] Figure 6 This is a flowchart illustrating a fault handling method for a data acquisition device provided in Embodiment Six of the present invention;

[0034] Figure 7 This is a schematic diagram of the structure of a fault detection device for data acquisition equipment provided in Embodiment 7 of the present invention;

[0035] Figure 8 This is a schematic diagram of the structure of a fault handling device for data acquisition equipment provided in Embodiment 8 of the present invention;

[0036] Figure 9 This is a schematic diagram of the structure of a computer device provided in Embodiment 9 of the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0038] like Figure 1 The diagram illustrates an application environment for a data acquisition device fault detection method according to Embodiment 1 of the present invention. The client and server communicate with each other. Users can provide the server with conditions, requirements, and operational instructions for data acquisition device fault detection through the client. The server executes the data acquisition device fault detection method of the present invention based on the relevant content sent by the client. The client includes, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a dedicated server or a server cluster consisting of multiple servers.

[0039] like Figure 2 The diagram shown is a flowchart illustrating a fault detection method for a data acquisition device according to Embodiment 2 of the present invention. This fault detection method is applied in... Figure 1 The server-side of the data acquisition device. The fault detection method for this data acquisition device may include the following steps:

[0040] Step S201: Obtain the detection information of the device to be analyzed in real time by the detection device, and extract the status signal of the device to be analyzed, the total amount of data transmitted by the device to be analyzed, and the interaction information between the device to be analyzed and the detection device from the detection information.

[0041] The detection equipment continuously monitors the specific equipment to be analyzed. Equipped with various sensors and data acquisition capabilities, it can obtain diverse operational information about the equipment from different perspectives. For example, if the equipment to be analyzed is a machine on an industrial production line, the detection equipment might monitor the machine's current consumption using a current sensor and its vibration amplitude using a vibration sensor.

[0042] Real-time monitoring ensures that the acquired information accurately reflects the current operating status of the device being analyzed. Because the device's status can change at any time, real-time monitoring allows the server to stay informed about the device's latest condition, enabling a rapid response to any anomalies.

[0043] The testing equipment transmits the collected testing information to the server via a specific communication method (such as wired or wireless network). Once the server receives this information, it has the raw data to analyze the faults in the device being analyzed.

[0044] Status signals are information describing the current operating state of a device being analyzed. They can be a simple binary signal (such as device on or off) or a complex signal containing more status information. For example, for a smart meter, status signals might include whether the meter is powered on normally, whether it is in data acquisition mode, and whether there are any fault alarms.

[0045] Status signals help the server quickly determine whether the device under analysis is in a normal working state. If the status signals indicate that the device is in an abnormal state, then further in-depth analysis of potential problems with the device can be conducted. Subsequent steps, such as determining the device's offline duration based on status signals, are also based on an accurate assessment of the device's status.

[0046] Total data volume refers to the total amount of data transmitted by the device being analyzed within a certain period of time. This data volume can be in bytes, data packets, etc. For example, in a network camera fault detection scenario, the total data volume is the amount of video data transmitted by the camera within a certain time period.

[0047] Interactive information primarily records the data communication process between the device being analyzed and the detection device. It includes the sending and receiving times of data packets, the size of the data packets, and the sequence number of the data packets. For example, in a scenario involving the detection of an IoT device, interactive information could display how often the device sends data to the detection device and the amount of data sent each time.

[0048] Step S202: Based on the status signal, determine the device offline time of the device to be analyzed, determine the device signal fault level based on the device offline time, calculate the ratio of the total data volume to the preset data volume to obtain the data transmission integrity rate, and determine the data transmission fault level based on the data transmission integrity rate.

[0049] Optionally, the status signal can be detected and recorded when the device is offline.

[0050] Extract the device's offline duration from the detection information corresponding to the offline status signal.

[0051] Determine the level of device signal failure based on the duration of device offline.

[0052] Among them, status signals reflect the current working status of the device under analysis and are the key basis for determining whether the device is offline. Typically, status signals use some kind of code or specific identifier to represent different states of the device, such as "0" indicating that the device is offline and "1" indicating that the device is online.

[0053] The server continuously monitors changes in the status signal. A timer starts when the status signal changes from "online" to "offline" and stops when it changes back to "online." This time interval is the device's offline duration. If the device remains offline, the offline duration accumulates. Device offline duration is a crucial indicator of the severity of a signal failure. Generally, the longer the offline duration, the greater the likelihood of a signal failure and the higher the failure level. Different offline duration ranges can be pre-defined to correspond to different failure levels. For example, an offline duration of 0-1 minute is considered a minor failure, possibly due to temporary network fluctuations or a brief device restart. An offline duration of 1-2 minutes is considered a moderate failure, possibly indicating a minor hardware malfunction or unstable network connection. An offline duration exceeding 2 minutes is considered a severe failure, potentially indicating serious issues such as hardware damage or network interruption.

[0054] Total data volume refers to the actual amount of data transmitted by the device under analysis within a certain period of time, while preset data volume is a standard value pre-set based on the amount of data that should be transmitted under normal operating conditions. The setting of preset data volume usually takes into account factors such as device functions, operating modes, and business requirements.

[0055] The formula for calculating data transmission integrity rate is: Data transmission integrity rate = (Total data volume / Preset data volume) × 100%. For example, if the preset data volume is 1000MB and the actual total data volume is 800MB, then the data transmission integrity rate = (800 / 1000) × 100% = 80%.

[0056] Data transmission integrity rate reflects the completeness of data during transmission. A lower integrity rate indicates more data loss or untransmitted portions, signifying a more severe data transmission failure. Fault levels can be categorized based on different data transmission integrity rate ranges. For example, a data transmission integrity rate of 90%-100% is considered a no-fault or minor fault, indicating possible minor data loss that does not affect normal device operation. A data transmission integrity rate of 70%-90% is considered a moderate fault, indicating significant data loss that may affect some device functions. A data transmission integrity rate below 70% is considered a severe fault, indicating substantial data loss and potential device malfunction.

[0057] Step S203: Input the interactive information into the preset extraction model and output the round-trip time of data packet transmission between the detection device and the device to be analyzed, the total amount of data received by the detection device, and the total amount of data packets sent by the device to be analyzed.

[0058] The preset extraction model is a specially trained algorithm model, including but not limited to deep learning models, rule-based models, and machine learning models. Its main task is to accurately identify and extract useful key indicators from complex interaction information. This model acts like an intelligent data filter, capable of finding the round-trip time, total received amount, and total sent amount from massive amounts of raw interaction information.

[0059] During model training, a large amount of historical interaction data is used as training samples. This data includes interaction information under different network environments and device states, and is labeled with corresponding real values ​​such as round-trip time, total received data, and total sent data. Through machine learning or deep learning algorithms, the model learns the mapping relationship between interaction information and these key metrics, thus enabling it to accurately output the required metrics when faced with new interaction information.

[0060] Round-trip time (RTT) refers to the total time it takes for a data packet to travel from the device being analyzed to the detection device and back. It reflects the latency in data transmission between devices and is an important indicator of network communication efficiency. A longer RTT indicates greater latency during data transmission, potentially indicating network congestion or slow device processing speeds. For example, in real-time video call scenarios, excessively long RTTs can lead to video stuttering and audio delays.

[0061] Total received data refers to the number of data packets or bytes actually received by the detection device from the device under analysis within a certain time period. It reflects how much of the data sent by the device under analysis successfully reached the detection device. By comparing this total received data with the total sent data packets of the device under analysis, it can be determined whether data loss has occurred. If the total received data is significantly less than the total sent data, it means that packet loss occurred during data transmission, requiring further investigation of network faults. Total sent data refers to the number of data packets or bytes sent by the device under analysis within the same time period. It reflects the data sending capacity and workload of the device under analysis. Combining the total received data and round-trip time allows for a comprehensive evaluation of the device's network performance. For example, if the total sent data is large, but the total received data is small and the round-trip time is long, it may indicate insufficient network bandwidth or network interference.

[0062] For example, if the device to be analyzed is a smart camera, and the detection device is a regional gateway, the camera continuously sends video data packets to the gateway, and the gateway records the interaction information between itself and the camera. After inputting this interaction information into a preset extraction model, the model outputs a round-trip time of 50 milliseconds, indicating that it takes approximately 50 milliseconds for a data packet to travel from the camera to the gateway and back to the camera. The detection device receives a total of 1000 data packets, and the device to be analyzed sends a total of 1020 data packets. Therefore, it can be inferred that approximately 20 data packets were lost during transmission, possibly indicating a minor network fault.

[0063] Step S204: Determine the information delay time based on the round-trip time, calculate the ratio of the total received amount to the total sent amount to obtain the data packet loss rate of the device to be analyzed, and determine the network fault level of the device to be analyzed based on the information delay time and the data packet loss rate.

[0064] In network communication, Round-Trip Time (RTT) refers to the time it takes for a data packet to travel from the sender to the receiver. Generally, RTT can be approximated as a measure of the delay in information transmission within the network. The time consumed in the process of a data packet being sent from the device to be analyzed to the detection device and back is mainly due to delays caused by network transmission and device processing. Therefore, in this step, the RTT obtained in step S203 can be directly determined as the information delay time. For example, if the RTT output in step S203 for data packet transmission between the detection device and the device to be analyzed is 80 milliseconds, then the information delay time is 80 milliseconds.

[0065] Packet loss rate is an important indicator of network reliability, reflecting the proportion of data packets lost during data transmission. By comparing the total number of data packets sent by the device under analysis and the total number of data packets received by the detection device, we can calculate the number of lost data packets, and thus obtain the packet loss rate. The calculation formula is: Packet Loss Rate = (Total Sends - Total Receives) ÷ Total Sends × 100%. For example, assuming that step S203 shows that the total number of data packets sent by the device under analysis is 1000, and the total number of data packets received by the detection device is 950, then the packet loss rate = (1000 - 950) ÷ 1000 × 100% = 5%.

[0066] Information latency and packet loss rate are two key indicators for evaluating network performance. Generally, the longer the information latency and the higher the packet loss rate, the more severe the network failure. Therefore, the network failure level of the device under analysis can be determined based on pre-set threshold ranges for different levels of information latency and packet loss rate. For example, if the information latency is between 30-60 milliseconds or the packet loss rate is between 1%-3%, the network may have some minor problems, but the impact on general network applications is small, and this is classified as a Level 1 failure. If the information latency is between 60-100 milliseconds or the packet loss rate is between 3%-5%, network performance deteriorates significantly, which may affect applications with high real-time requirements, such as video calls and online games, and this is classified as a Level 2 failure. If the information latency is greater than 100 milliseconds or the packet loss rate is greater than 5%, the network failure is severe and may cause most network applications to be unusable, and this is classified as a Level 3 failure.

[0067] Step S205: Determine the equipment fault level of the device to be analyzed based on the equipment signal fault level, data transmission fault level, and network fault level.

[0068] In this system, different weights are assigned to the equipment signal fault level, data transmission fault level, and network fault level. A weighted average is then calculated, and the equipment fault level is determined based on the interval in which this average falls. The weighting of these factors needs to be tailored to the specific circumstances, considering the impact of each factor on the overall operation of the equipment.

[0069] For example, the weight of the equipment signal fault level is 0.3, the weight of the data transmission fault level is 0.3, and the weight of the network fault level is 0.4. The equipment signal fault level is 2 (moderate fault), the data transmission fault level is 3 (severe fault), and the network fault level is 3 (severe fault). The weighted average is calculated as (2×0.3+3×0.3+3×0.4)=2.7. A pre-defined fault level range is set: 1-1.5 is Level 1 (minor fault), 1.6-2.5 is Level 2 (moderate fault), and 2.6-3.5 is Level 3 (severe fault). Based on the calculated result of 2.7, the equipment fault level can be determined to be Level 3 (severe fault).

[0070] In this embodiment, by acquiring the detection information of the device under analysis from the real-time detection of the detection equipment, the status signal of the device under analysis, the total amount of data transmitted by the device under analysis, and the interaction information between the device under analysis and the detection equipment are extracted from the detection information. Based on the status signal, the offline time of the device under analysis is determined. Based on the offline time, the device signal fault level is determined. The ratio of the total data volume to a preset data volume is calculated to obtain the data transmission integrity rate. Based on the data transmission integrity rate, the data transmission fault level is determined. The interaction information is input into a preset extraction model, which outputs the round-trip time of data packet transmission between the detection equipment and the device under analysis, the total amount of data received by the detection equipment, and the total amount of data packets sent by the device under analysis. Based on the round-trip time, the information delay time is determined. The ratio of the total amount received to the total amount sent is calculated to obtain the data packet loss rate of the device under analysis. Based on the information delay time and the data packet loss rate, the network fault level of the device under analysis is determined. Based on the device signal fault level, data transmission fault level, and network fault level, the device fault level of the device under analysis is determined. The system analyzes the status information of the device under analysis to obtain the device signal fault level, analyzes the data transmission integrity rate to determine the data transmission fault level, and analyzes the data packet loss rate to obtain the network fault level. Based on the device signal fault level, data transmission fault level, and network fault level, the device fault level is determined. This automated, multi-dimensional detection of device fault information efficiently determines the device fault level.

[0071] like Figure 3 The diagram shown is a flowchart of a data acquisition device fault detection method provided in Embodiment 3 of the present invention. After determining the equipment fault level of the device to be analyzed in step S205, the data acquisition device fault detection method may further include the following steps:

[0072] Step S301: Obtain the online status signal of the device to be analyzed based on the status signal of the device to be analyzed;

[0073] Step S302: Repeatedly execute the step of obtaining the online status signal of the corresponding device based on the status signal of the device to be analyzed, until N online status signals of the devices to be analyzed are obtained, where N is a positive integer.

[0074] Step S303: Based on the N online status signals and the preset total number of devices, determine the offline devices, and based on the offline devices, obtain the offline gateway devices under the same gateway.

[0075] Step S304: Obtain the device location information of the offline gateway device, obtain the coverage area of ​​the offline gateway device based on the device location information, and determine the fault level of the gateway device based on the coverage area.

[0076] The monitoring system collects various status signals from the devices under analysis in real time, such as heartbeat packets sent by the devices, connection status of communication ports, and response information returned by the devices. These status signals are then analyzed according to pre-set rules. For example, if a device successfully sends a heartbeat packet and returns a normal response within a specified time, its online status signal is determined to be "online"; conversely, if no heartbeat packet is received or the response information is abnormal within a certain time, it is determined to be "offline".

[0077] The online status signals of the device under analysis may be random or inaccurate. Multiple acquisitions (until N online status signals are obtained) can improve data accuracy and reliability, providing a more comprehensive reflection of the device's actual operating status. After determining the fault level of the device under analysis, the acquisition device returns to the step of acquiring the real-time detection information of the device under analysis from the detection device, repeating this process continuously to extract the existence time of the online status signals of the device under analysis until N online status signals are collected. Here, N is a positive integer, and its specific value can be determined according to actual needs and data accuracy requirements; for example, N can be 10, 20, etc. The N collected online status signals are then comprehensively analyzed. If a device continuously lacks online status signals or the number of missing signals exceeds a certain threshold during these N acquisitions, the device can be determined as an offline device. After identifying all offline devices, based on the association between the devices and the gateway, offline devices under the same gateway are identified; these devices are the gateway offline devices.

[0078] The system uses the device's positioning system or pre-recorded device installation location information to obtain the location information of the offline gateway devices. Based on this location information, the geographical area covered by the offline gateway devices is determined. Geographic Information Systems (GIS) and other tools can be used for area division and calculation. Different gateway device fault levels are pre-defined for different coverage area sizes. For example, a coverage area smaller than a certain area threshold is classified as Level 1 (minor fault); a coverage area within a certain range is classified as Level 2 (moderate fault); and a coverage area exceeding a large area threshold is classified as Level 3 (severe fault).

[0079] In this embodiment, by extending the analysis from the individual fault conditions of the device to the overall operating status of devices under the same gateway, the fault level of the gateway device can be accurately assessed, which helps to promptly identify and resolve potential problems in the network and ensure the stable operation of the network.

[0080] like Figure 4 The diagram shown is a flowchart illustrating a data acquisition device fault handling method according to Embodiment 4 of the present invention. After the data acquisition device fault detection method generates the device fault level, the data acquisition device fault handling method may include the following steps:

[0081] Step S401: Obtain the historical fault database and extract the historical processing operations for signal faults of the corresponding device signal fault level, the historical processing operations for data transmission faults of the corresponding data transmission fault level, and the historical processing operations for network faults of the corresponding network fault level from the historical fault database.

[0082] Step S402: Based on the signal fault history processing operation, data transmission fault history processing operation, and network fault history processing operation, perform fault processing on the device to be analyzed.

[0083] First, a historical fault database needs to be retrieved from the storage device or system. This database may be a local database file or a database system stored on a server, such as MySQL or Oracle. Based on the device's signal fault level, data transmission fault level, and network fault level, the corresponding historical fault handling operations are searched in the historical fault database. For example, if the device's signal fault level is level two, all corresponding level two device signal fault level handling operation records are extracted from the database.

[0084] The historical processing operations for signal faults, data transmission faults, and network faults are integrated to formulate a specific fault handling plan for the device under analysis. For example, if the historical processing operation for signal faults suggests checking whether the signal source is normal, or the historical processing operation for data transmission faults suggests replacing the transmission line, then these operations are included in the handling plan. Following the formulated handling plan, the corresponding fault handling operations are performed on the device under analysis. During execution, it is necessary to closely monitor changes in the device's status and observe whether the fault has been resolved.

[0085] After completing the troubleshooting steps, evaluate the effectiveness. This can be done by retesting various equipment metrics, such as signal strength, data transmission rate, and network connectivity, to determine if the fault has been resolved. If the fault persists, it may be necessary to review past troubleshooting procedures or combine other methods for further analysis and resolution.

[0086] In this embodiment, the fault handling method using the historical fault database can improve the efficiency and accuracy of fault handling, reduce equipment downtime caused by improper fault handling, and also help to continuously accumulate and improve fault handling experience.

[0087] like Figure 5 The diagram shown is a flowchart illustrating a fault handling method for a data acquisition device according to Embodiment 5 of the present invention. Based on Embodiment 4 above, the fault handling of the device to be analyzed in step S402 may further include the following steps:

[0088] Step S501: Analyze whether the device offline time exceeds the preset offline time threshold. If it exceeds the preset offline time threshold, perform device maintenance fault handling operation. If the device offline time does not exceed the preset offline time threshold, perform remote fault handling operation.

[0089] Step S502: Analyze whether the data transmission integrity rate is lower than the preset data integrity rate threshold. If it is lower than the preset data integrity rate threshold, perform equipment maintenance fault handling operation. If the data transmission integrity rate is lower than the preset data integrity rate threshold, perform remote fault handling operation.

[0090] Step S503: Add the information delay time and data packet loss rate together to obtain the network composite value. Analyze whether the network composite value is higher than the preset composite threshold. If it is higher than the preset composite threshold, perform equipment maintenance fault processing operation. If the network composite value is not higher than the preset composite threshold, perform remote fault processing operation.

[0091] The process involves recording the time elapsed since the device went offline until the current moment using a detection device. This offline time is then compared to a preset offline time threshold. For example, if the preset threshold is 2 hours, and the offline time exceeds 3 hours, equipment maintenance procedures are initiated, such as dispatching maintenance personnel to the device's location for repair. If the offline time is 1 hour, within the threshold, remote troubleshooting procedures are performed, such as checking the device's configuration or restarting the device via remote control software.

[0092] The data transmission integrity rate is calculated by comparing the amount of data sent with the amount of data successfully received. For example, if 1000 data packets are sent and 950 are successfully received, the data transmission integrity rate is 95%.

[0093] The calculated data transmission integrity rate is compared with a preset data integrity rate threshold. Assuming the preset data integrity rate threshold is 98%, if the data transmission integrity rate is 95%, which is lower than the threshold, then equipment maintenance fault handling operations are performed; if the data transmission integrity rate is 99%, which is not lower than the threshold, then remote fault handling operations are performed.

[0094] This calculation involves adding the information delay time and the data packet loss rate together. For example, if the information delay time is 50 milliseconds and the data packet loss rate is 3%, then the network composite value is 50 + 3 = 53 (this is just to illustrate the calculation method; in actual applications, unit conversions or other processing may be required).

[0095] The network composite value is compared with a preset composite threshold. If the preset composite threshold is 55 and the network composite value is 53, which is not higher than the threshold, then remote fault handling is performed; if the network composite value is 60, which is higher than the threshold, then equipment maintenance fault handling is performed.

[0096] In this embodiment, by analyzing different fault manifestations of the equipment, a more scientific approach can be taken to select the appropriate fault handling method, thereby improving the efficiency and accuracy of fault handling.

[0097] like Figure 6 The diagram shown is a flowchart illustrating a fault handling method for a data acquisition device according to Embodiment Six of the present invention. Step S501, which involves performing a remote fault handling operation, may include the following steps:

[0098] Step S601: Continuously detect the status signals of the device to be analyzed, and determine the abnormal signals by combining them with the historical fault database.

[0099] Step S602: Extract historical standard data corresponding to abnormal signals from the historical fault database, and determine the data processing scheme for the device to be analyzed based on the historical standard data.

[0100] Continuous monitoring of status signals involves using monitoring equipment or systems to continuously collect various status signals of the equipment being analyzed, such as voltage, current, temperature, and signal strength. These signals can be acquired through sensors, communication interfaces, etc. Abnormal signals are identified by comparing the collected status signals with data in a historical fault database. This database records the characteristics of various status signals during normal operation and when faults occur. By analyzing the differences between the current signal and historical data, it is determined whether any abnormal signals exist. For example, if historical data shows that the temperature during normal operation is between 20-30 degrees Celsius, while the currently detected temperature is 50 degrees Celsius, then this temperature signal is likely abnormal.

[0101] After identifying the abnormal signal, the corresponding historical standard data is retrieved from the historical fault database. This data may include the normal signal range, troubleshooting steps, and parameter adjustment suggestions. For example, if the abnormal signal is overheating, the historical standard data might show the normal temperature range and recommended cooling measures, such as adjusting the cooling fan speed or checking for blockages in the vents. Based on the extracted historical standard data and the actual condition of the equipment being analyzed, a specific data processing plan is developed. This plan might include adjusting equipment parameters, restarting the equipment, or updating software. For instance, if historical standard data indicates that reducing the equipment's operating frequency can resolve the overheating problem, the data processing plan could involve remotely adjusting the equipment's operating frequency.

[0102] In this embodiment, by utilizing the experience and data from the historical fault database, equipment faults can be identified and resolved remotely without on-site maintenance, thereby improving the efficiency and convenience of fault handling.

[0103] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0104] like Figure 7 The diagram shown is a schematic of a data acquisition device fault detection device provided in Embodiment 7 of the present invention. This data acquisition device fault detection device corresponds one-to-one with the data acquisition device fault detection methods in the above embodiments. The data acquisition device fault detection device includes an information extraction module 71, a fault level determination module 72, an interactive information extraction module 73, a network fault determination module 74, and a device fault determination module 75. Detailed descriptions of each functional module are as follows:

[0105] Information extraction module 71 is used to acquire the detection information of the device to be analyzed in real time by the detection equipment, and extract the status signal of the device to be analyzed, the total amount of data transmitted by the device to be analyzed, and the interaction information between the device to be analyzed and the detection equipment from the detection information;

[0106] The fault level determination module 72 is used to determine the offline duration of the device to be analyzed based on the status signal, determine the fault level of the device signal based on the offline duration, calculate the ratio of the total data volume to the preset data volume to obtain the data transmission integrity rate, and determine the data transmission fault level based on the data transmission integrity rate.

[0107] The interactive information extraction module 73 is used to determine the offline duration of the device to be analyzed based on the status signal, determine the fault level of the device signal based on the offline duration, calculate the ratio of the total data volume to the preset data volume to obtain the data transmission integrity rate, and determine the data transmission fault level based on the data transmission integrity rate.

[0108] The network fault determination module 74 is used to determine the information delay time based on the round-trip time, calculate the ratio of the total received amount to the total sent amount to obtain the data packet loss rate of the device under analysis, and determine the network fault level of the device under analysis based on the information delay time and the data packet loss rate.

[0109] The equipment fault determination module 75 is used to determine the equipment fault level of the equipment to be analyzed based on the equipment signal fault level, data transmission fault level, and network fault level.

[0110] Optionally, the fault detection device for the data acquisition equipment also includes:

[0111] The online signal extraction module is used to obtain the online status signal of the device to be analyzed based on the status signal of the device to be analyzed.

[0112] The return execution module is used to return to the execution after determining the equipment fault level of the equipment to be analyzed, and to perform the steps of obtaining the detection information of the equipment to be analyzed in real time by the detection equipment, extracting the existence time of the online status signal of the equipment to be analyzed in the detection information, until N online status signals of the equipment to be analyzed are obtained, where N is a positive integer.

[0113] The offline device determination module is used to determine offline devices based on N online status signals and a preset total number of devices, and to obtain the offline gateway devices under the same gateway based on the offline devices.

[0114] The coverage area determination module is used to obtain the device location information of the offline gateway device, obtain the coverage area of ​​the offline gateway device based on the device location information, and determine the fault level of the gateway device based on the coverage area.

[0115] Optionally, the aforementioned equipment fault determination module 75 further includes:

[0116] The offline signal recording unit is used to detect status signals and record the offline status signals when the device is offline.

[0117] The offline duration determination unit is used to extract the device offline duration corresponding to the offline status signal in the detection information;

[0118] The signal level determination unit is used to determine the signal fault level of the device based on the device's offline duration.

[0119] like Figure 8 The diagram shown is a schematic of a data acquisition device fault handling device provided in Embodiment 8 of the present invention. This data acquisition device fault handling device corresponds one-to-one with the data acquisition device fault handling methods in the above embodiments. The data acquisition device fault handling device includes a historical data processing module 81 and a fault handling module 82. Detailed descriptions of each functional module are as follows:

[0120] The historical data processing module 81 is used to obtain the historical fault database after the equipment fault level is generated by the generation method of the equipment fault detection, and to extract the historical processing operations of the signal fault corresponding to the equipment signal fault level, the historical processing operations of the data transmission fault corresponding to the data transmission fault level, and the historical processing operations of the network fault corresponding to the network fault level from the historical fault database.

[0121] The fault handling module 82 is used to handle faults in the device to be analyzed based on the historical handling operations of signal faults, historical handling operations of data transmission faults, and historical handling operations of network faults.

[0122] Optionally, the above-mentioned data acquisition device fault handling module 82 also includes:

[0123] The offline threshold judgment unit is used to analyze whether the offline time of the device exceeds the preset offline time threshold. If it exceeds the preset offline time threshold, the device maintenance fault handling operation is performed. If the offline time of the device does not exceed the preset offline time threshold, the remote fault handling operation is performed.

[0124] The transmission threshold judgment unit is used to analyze whether the data transmission integrity rate is lower than the preset data integrity rate threshold. If it is lower than the preset data integrity rate threshold, the equipment maintenance fault handling operation is executed. If the data transmission integrity rate is lower than the preset data integrity rate threshold, the remote fault handling operation is executed.

[0125] The composite threshold judgment unit is used to add the information delay time and data packet loss rate to obtain the network composite value, analyze whether the network composite value is higher than the preset composite threshold. If it is higher than the preset composite threshold, the equipment maintenance fault handling operation is executed. If the network composite value is not higher than the preset composite threshold, the remote fault handling operation is executed.

[0126] Specific limitations regarding the fault detection and handling devices for data acquisition equipment can be found in the above description of the fault detection and handling methods for data acquisition equipment, and will not be repeated here. Each module in the aforementioned fault detection and handling devices can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0127] like Figure 9 The diagram shown is a schematic representation of a computer device structure according to Embodiment 9 of the present invention. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for detecting and handling faults in a data acquisition device.

[0128] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the fault detection method for the acquisition device described in the above embodiments, for example... Figures 2 to 3 As shown. When the processor executes the computer program, it implements the fault handling method for the acquisition device in the above embodiments, for example... Figures 4 to 6 As shown, to avoid repetition, it will not be described again here. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in this embodiment of the fault detection device for data acquisition equipment, for example... Figure 7 The functions of the information extraction module 71, fault level determination module 72, interactive information extraction module 73, network fault determination module 74, and equipment fault determination module 75 shown are, or, the functions of each module / unit in this embodiment of the data acquisition equipment fault handling device are implemented when the processor executes the computer program, for example... Figure 8The historical data processing module 81 and the fault processing module 82 shown are not described again here to avoid repetition.

[0129] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the fault detection method for the acquisition device described in the above embodiments, such as... Figures 2 to 5 As shown, to avoid repetition, it will not be described again here. When this computer program is executed by the processor, it implements the fault handling method for the acquisition device in the above embodiments, such as... Figure 6 As shown, to avoid repetition, it will not be described again here. Alternatively, when the computer program is executed by the processor, it implements the functions of each module / unit in this embodiment of the fault detection device for the acquisition equipment, for example... Figure 7 The functions of the information extraction module 71, fault level determination module 72, interactive information extraction module 73, network fault determination module 74, and equipment fault determination module 75 shown are not described again here to avoid repetition. Alternatively, when the computer program is executed by the processor, it implements the functions of each module / unit in this embodiment of the data acquisition device fault handling device, for example... Figure 8 The functions of the historical data processing module 81 and the fault processing module 82 shown are not described again here to avoid repetition. The computer-readable storage medium can be non-volatile or volatile.

[0130] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0131] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0132] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for detecting faults in data acquisition equipment, characterized in that, include: The detection information of the device to be analyzed is obtained in real time by the detection equipment. The status signal of the device to be analyzed, the total amount of data transmitted by the device to be analyzed, and the interaction information between the device to be analyzed and the detection equipment are extracted from the detection information. The status signal is information describing the current working status of the device to be analyzed. The total amount of data refers to the total amount of data transmitted. The interaction information records the data communication process between the device to be analyzed and the detection equipment. Based on the status signal, the offline duration of the device to be analyzed is determined. Based on the offline duration, the device signal fault level is determined. The data transmission integrity rate is calculated by comparing the total data volume with a preset data volume. Based on the data transmission integrity rate, the data transmission fault level is determined, including: determining the device signal fault level corresponding to different offline durations. The data transmission integrity rate reflects the integrity of the data during transmission. The lower the integrity rate, the more data is lost or not transmitted. The data transmission fault level is divided according to different data transmission integrity rate ranges. The interaction information is input into a preset extraction model, which outputs the round-trip time of data packet transmission between the detection device and the device to be analyzed, the total number of data packets received by the detection device, and the total number of data packets sent by the device to be analyzed. The extraction model extracts key indicators from the interaction information. Based on the round-trip time, the information delay time is determined, and the ratio of the total received amount to the total sent amount is calculated to obtain the data packet loss rate of the device to be analyzed. Based on the information delay time and the data packet loss rate, the network fault level of the device to be analyzed is determined. The equipment fault level of the device to be analyzed is determined based on the device signal fault level, the data transmission fault level, and the network fault level. After determining the equipment failure level of the device to be analyzed, the method further includes: Based on the status signal of the device to be analyzed, the online status signal of the corresponding device to be analyzed is obtained; The step of obtaining the online status signal of the device to be analyzed based on the status signal of the device to be analyzed is executed repeatedly until N online status signals of the device to be analyzed are obtained, where N is a positive integer. Based on the N online status signals and the preset total number of devices, offline devices are determined, and based on the offline devices, the offline gateway devices under the same gateway are obtained; Obtain the device location information of the offline gateway device, obtain the coverage area of ​​the offline gateway device based on the device location information, and determine the fault level of the gateway device based on the coverage area, including: using a geographic information system to divide and calculate the area, and pre-setting the fault level of the gateway device corresponding to different coverage area sizes.

2. The fault detection method for data acquisition equipment according to claim 1, characterized in that, The step of determining the device offline duration of the device to be analyzed based on the status signal, and determining the device signal fault level based on the device offline duration, includes: Detect the status signal and record the offline status signal when the device is offline; Extract the device offline duration corresponding to the offline status signal from the detection information; The device signal fault level is determined based on the device's offline duration.

3. A method for handling faults in data acquisition equipment, characterized in that, include: After generating the equipment fault level based on the generation method for fault detection of acquisition equipment according to any one of claims 1 to 2, a historical fault database is obtained, and the historical processing operations of signal faults corresponding to the equipment signal fault level, the historical processing operations of data transmission faults corresponding to the data transmission fault level, and the historical processing operations of network faults corresponding to the network fault level are extracted from the historical fault database. The fault processing is performed on the device to be analyzed based on the signal fault history processing operation, the data transmission fault history processing operation, and the network fault history processing operation.

4. The fault handling method for the data acquisition device according to claim 3, characterized in that, The fault handling of the device to be analyzed includes: Analyze whether the device's offline time exceeds a preset offline time threshold. If it exceeds the preset offline time threshold, perform device maintenance fault handling operation. If the device's offline time does not exceed the preset offline time threshold, perform remote fault handling operation. Analyze whether the data transmission integrity rate is lower than a preset data integrity rate threshold. If it is lower than the preset data integrity rate threshold, then perform the equipment maintenance fault handling operation. If the data transmission integrity rate is lower than the preset data integrity rate threshold, then perform the remote fault handling operation. The information delay time and the data packet loss rate are added together to obtain a network composite value. It is then analyzed whether the network composite value is higher than a preset composite threshold. If it is higher than the preset composite threshold, the equipment maintenance fault handling operation is performed. If the network composite value is not higher than the preset composite threshold, the remote fault handling operation is performed.

5. The fault handling method for the data acquisition device according to claim 4, characterized in that, The process of performing the remote fault handling operation includes: The status signal of the device under analysis is continuously detected, and the abnormal signal is determined by combining it with the historical fault database; Extract historical standard data corresponding to the abnormal signal from the historical fault database, and determine the data processing scheme for the device to be analyzed based on the historical standard data.

6. A fault detection device for data acquisition equipment, characterized in that, include: The information extraction module is used to acquire the detection information of the device to be analyzed in real time by the detection equipment, and extract the status signal of the device to be analyzed, the total amount of data transmitted by the device to be analyzed, and the interaction information between the device to be analyzed and the detection equipment from the detection information. The status signal is information describing the current working status of the device to be analyzed, the total amount of data refers to the total amount of data transmitted, and the interaction information records the data communication process between the device to be analyzed and the detection equipment. The fault level determination module is used to determine the device offline duration of the device to be analyzed based on the status signal, determine the device signal fault level based on the device offline duration, calculate the ratio of the total data volume to the preset data volume to obtain the data transmission integrity rate, and determine the data transmission fault level based on the data transmission integrity rate. This includes: determining the device signal fault level corresponding to different device offline durations, where the data transmission integrity rate reflects the integrity of the data during transmission; a lower integrity rate indicates more data loss or untransmitted portions, and the data transmission fault level is divided according to different data transmission integrity rate ranges. The interaction information extraction module is used to input the interaction information into a preset extraction model and output the round-trip time of data packet transmission between the detection device and the device to be analyzed, the total amount of data received by the detection device, and the total amount of data packets sent by the device to be analyzed. The extraction model extracts key indicators from the interaction information. The network fault determination module is used to determine the information delay time based on the round-trip time, calculate the ratio of the total received amount to the total sent amount to obtain the data packet loss rate of the device to be analyzed, and determine the network fault level of the device to be analyzed based on the information delay time and the data packet loss rate. The equipment fault determination module is used to determine the equipment fault level of the equipment to be analyzed based on the equipment signal fault level, the data transmission fault level, and the network fault level. The fault detection device for the data acquisition equipment also includes: The online signal extraction module is used to obtain the online status signal of the device to be analyzed based on the status signal of the device to be analyzed. The return execution module is used to return to the execution after determining the equipment fault level of the equipment to be analyzed, and to perform the steps of obtaining the detection information of the equipment to be analyzed in real time by the detection equipment, extracting the existence time of the online status signal of the equipment to be analyzed in the detection information, until N online status signals of the equipment to be analyzed are obtained, where N is a positive integer. The offline device determination module is used to determine offline devices based on N online status signals and a preset total number of devices, and to obtain the offline gateway devices under the same gateway based on the offline devices. The coverage area determination module is used to obtain the device location information of the offline gateway device, obtain the coverage area of ​​the offline gateway device based on the device location information, and determine the fault level of the gateway device based on the coverage area, including: using a geographic information system to divide and calculate the area, and pre-setting the fault level of the gateway device corresponding to different coverage area sizes.

7. A fault handling device for data acquisition equipment, characterized in that, include: The historical data processing module is used to obtain a historical fault database after generating a device fault level based on the generation method for fault detection of the acquisition device according to any one of claims 1 to 2, and to extract historical processing operations for signal faults corresponding to the device signal fault level, historical processing operations for data transmission faults corresponding to the data transmission fault level, and historical processing operations for network faults corresponding to the network fault level from the historical fault database. The fault handling module is used to perform fault handling on the device to be analyzed based on the signal fault history handling operation, the data transmission fault history handling operation, and the network fault history handling operation.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the fault detection method for the acquisition device according to any one of claims 1 to 2 or the fault handling method for the acquisition device according to any one of claims 3 to 5.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the fault detection method for the acquisition device according to any one of claims 1 to 2 or the fault handling method for the acquisition device according to any one of claims 3 to 5.

Images