A fault recording-based early warning display method

Abstract

The embodiment of the application discloses a kind of early warning display methods based on fault recording, it is applied to including at least one to be monitored equipment, data acquisition component and display equipment early warning system;The to-be-monitored equipment, the data acquisition component and the display equipment are connected in turn;The method comprises: obtaining the waveform data of each to-be-monitored equipment in preset time period;Determine whether there is multiple repeated abnormal waveform data in the waveform data;In the case where there is multiple repeated abnormal waveform data in the waveform data, based on the abnormal waveform data and the fault early warning threshold value stored in the data acquisition component, determine the sequence number of the to-be-monitored equipment corresponding to the fault early warning threshold value that the waveform data exceeds triggers the early warning information related to fault;The data acquisition component is used to issue the sequence number of the early warning information and the fault early warning display data stored in the display equipment, and the display equipment displays the fault early warning display data corresponding to the sequence number of the early warning information.

Description

Technical Field

[0001] This application relates to the field of fault recording technology, and in particular to a method for early warning display based on fault recording. Background Technology

[0002] Fault recording technology is a key technology commonly used in power systems to monitor and record electrical faults occurring in the power grid. Current technology only begins fault recording after a fault alarm is triggered (i.e., there is a lag in alarm reporting), and it cannot store all recorded data in real time, nor can it promptly display fault information to equipment maintenance personnel. Due to the large volume of fault recorded data, the query cycle is long, and fault information is not displayed in a timely manner. Currently, there is no effective solution to this problem. Summary of the Invention

[0003] In view of this, the embodiments of this application aim to provide a method for early warning display based on fault recording waveforms.

[0004] The technical embodiments of this application are implemented as follows:

[0005] This application provides an early warning display method based on fault recording, applied to an early warning system including at least one device to be monitored, a data acquisition component, and a display device; the device to be monitored, the data acquisition component, and the display device are connected sequentially; the method includes:

[0006] Acquire waveform data for each of the monitored devices within a preset time period;

[0007] Determine whether there are multiple repetitions of abnormal waveform data in the waveform data;

[0008] In the case where there are multiple repeated abnormal waveform data in the waveform data, based on the abnormal waveform data and the fault warning threshold stored in the data acquisition component, the sequence number of the monitoring device that triggers the fault-related warning information corresponding to the waveform data exceeding the fault warning threshold is determined;

[0009] The data acquisition component uses the sequence number of the warning information and the fault warning display data stored in the display device to issue the warning information. The display device then displays the fault warning display data corresponding to the sequence number of the warning information.

[0010] In the above scheme, the fault warning threshold is located in the fault warning model library; the fault warning threshold is determined based on the normal value and alarm value of the waveform data of each device to be monitored; the fault warning threshold corresponds to the warning information corresponding to each fault waveform; the method further includes:

[0011] The acquired waveform data is processed in the time domain to obtain the fault waveform corresponding to the waveform data;

[0012] Feature extraction is performed on the fault recording waveform to obtain at least a first feature characterizing the amplitude characteristics of the fault recording waveform and a second feature characterizing the time series characteristics of the fault recording waveform; the first feature corresponds to first information related to the first fault corresponding to the fault recording waveform; the second feature corresponds to second information related to the second fault corresponding to the fault recording waveform.

[0013] The fault early warning model library is generated based on the first feature and the second feature.

[0014] In the above scheme, the feature extraction of the fault recording waveform to obtain at least a first feature characterizing the amplitude characteristics of the fault recording waveform and a second feature characterizing the time series characteristics of the fault recording waveform includes:

[0015] Obtain the first peak value, the first trough value, and the first time corresponding to the first trough value to the first peak value in the fault recording, as well as the scaling factor corresponding to the amplitude characteristic;

[0016] The first feature is determined based on the first peak value, the first trough value, and the scaling factor;

[0017] The second feature is determined based on the first time.

[0018] In the above scheme, determining the first feature based on the first peak value, the first trough value, and the scaling factor includes:

[0019] Determine the difference between the absolute value of the first peak value and the absolute value of the first trough value;

[0020] The first feature is determined based on the scaling factor and the difference.

[0021] In the above scheme, the method further includes:

[0022] Determine whether the waveform data meets the matching conditions of the fault early warning model library;

[0023] If the waveform data meets the matching conditions of the fault early warning model library, the monitored device is determined to trigger a fault-related early warning information when the waveform data exceeds the fault early warning threshold.

[0024] In the above scheme, the matching conditions include at least one of the following:

[0025] The first value of the feature characterizing the amplitude characteristics of the waveform data is greater than or equal to the value corresponding to the first feature in the fault early warning model library;

[0026] The second value of the feature characterizing the time series characteristics of the waveform data is less than or equal to the value corresponding to the second feature in the fault early warning model library.

[0027] In the above scheme, the early warning system further includes a host computer; the host computer includes a database storing the early warning information; the early warning information includes record information and sampling information related to the fault; the record information includes at least the fault code and the fault occurrence time; the method further includes:

[0028] Export the fault code, the fault occurrence time, and the sampling information from the database;

[0029] The fault code, the fault occurrence time, and the sampling information are displayed in a graphical form.

[0030] In the above scheme, the early warning system further includes a digital signal processor (DSP); the DSP is connected to the device to be monitored via serial communication; the method further includes:

[0031] After the early warning system completes the periodic serial communication, it performs fault recording and query on the device to be monitored.

[0032] In the above scheme, the step of performing fault recording and querying on the device to be monitored after the early warning system completes the periodic serial communication includes:

[0033] Obtain the first time of the periodic query and the second time of the serial communication query in the early warning system;

[0034] Determine the difference between the second time and the first time;

[0035] The difference is used to perform fault recording query on the device to be monitored.

[0036] In the above scheme, when performing fault recording query on the monitored device, the method further includes:

[0037] Obtain the priority strategy for fault recording query of the monitored equipment;

[0038] Based on the priority strategy, query the overall information of all fault filters to be queried; the overall information includes identification information indicating whether the synchronization status of each fault filter is successful;

[0039] If the identification information indicates that the synchronization status of each fault filter is unsuccessful, detailed information is queried for the fault filter whose synchronization status is unsuccessful.

[0040] Once the synchronization status of each fault filter is successfully identified by the identification information, the fault recording query ends.

[0041] The fault warning threshold information is stored in the data acquisition component, and the fault warning display data is stored in the display device;

[0042] The data acquisition component outputs early warning information to the display component;

[0043] The display component updates the fault warning display data based on the obtained warning information.

[0044] This application provides an early warning display device based on fault recording, which is installed on an early warning system including at least one device to be monitored, a data acquisition component, and a display device; the data acquisition component is connected to the device to be monitored; the early warning device includes:

[0045] The first acquisition unit is used to acquire multiple intelligent subsystems to be used in the intelligent system;

[0046] A classification processing unit is used to classify the plurality of intelligent subsystems to obtain at least one first subsystem of the same type among the plurality of intelligent subsystems;

[0047] The second acquisition unit is used to acquire data information, service information and business information of each type of first subsystem;

[0048] A modular processing unit is used to perform modular processing on the data information, service information, and business information respectively to obtain a data module, a service module, and a business module; the data module, the service module, and the business module are used to access the business application system so that the business application system can execute business logic.

[0049] This application provides an early warning device based on fault recording, comprising:

[0050] Memory, used to store executable instructions;

[0051] A processor, when executing executable instructions stored in the memory, implements any step of the method described above.

[0052] This application provides a computer program product, including a computer program that, when executed by a processor, implements any step of the method described above.

[0053] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any step of the method described above.

[0054] This application provides a method, apparatus, device, product, and storage medium for early warning display based on fault waveform recording. It is applied to an early warning system comprising at least one device to be monitored, a data acquisition component, and a display device; the device to be monitored, the data acquisition component, and the display device are connected sequentially; the method includes: acquiring waveform data of each device to be monitored within a preset time period; determining whether there are multiple repetitions of abnormal waveform data in the waveform data; if multiple repetitions of abnormal waveform data are found, determining, based on the abnormal waveform data and a fault early warning threshold stored in the data acquisition component, the sequence number of the device to be monitored that triggers a fault-related early warning information corresponding to the waveform data exceeding the fault early warning threshold; using the data acquisition component to issue the sequence number of the early warning information and the fault early warning display data stored in the display device, and the display device displays the fault early warning display data corresponding to the sequence number of the early warning information. The technical solution of this application embodiment determines whether there are repeated abnormal waveform data in the waveform data of each monitored device within a preset time period. If repeated abnormal waveform data are found, based on the abnormal waveform data and the fault warning threshold stored in the data acquisition component, the monitoring device that triggers a fault-related warning information sequence number is determined when the waveform data exceeds the fault warning threshold. The data acquisition component issues the sequence number of the warning information and the fault warning display data stored in the display device. The display device displays the fault warning display data corresponding to the sequence number of the warning information, so as to display the fault in advance, conduct fault investigation and processing, and thus avoid the problem of fault lag. Attached Figure Description

[0055] Figure 1 This is a schematic diagram illustrating the implementation process of an early warning display method based on fault recording in an embodiment of this application;

[0056] Figure 2 This is a schematic diagram of the early warning system in the embodiments of this application;

[0057] Figure 3 This is a schematic diagram of the fault filtering process in an embodiment of this application;

[0058] Figure 4 This is a schematic diagram of the fault early warning model matching mechanism in the embodiments of this application;

[0059] Figure 5 This is a schematic diagram of the time-domain analysis process of fault recording data in an embodiment of this application;

[0060] Figure 6 This is a schematic diagram of the fault recording event list in an embodiment of this application;

[0061] Figure 7This is a schematic diagram of the fault waveform detailed information table in the embodiments of this application;

[0062] Figure 8 This is a schematic diagram of the fault waveform export information table in an embodiment of this application;

[0063] Figure 9 This is a schematic diagram of alarm notification in an embodiment of this application;

[0064] Figure 10 This is a schematic diagram illustrating the principle of the non-blocking query mechanism for fault recording in the embodiments of this application;

[0065] Figure 11 This is a schematic diagram of the structure of an early warning device based on fault recording according to an embodiment of this application;

[0066] Figure 12 This is a schematic diagram of the structure of an early warning device based on fault recording according to an embodiment of this application. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the specific technical solutions of the invention will be further described in detail below with reference to the accompanying drawings of the embodiments of this application. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application.

[0068] Fault recording technology is a key technology commonly used in power systems to monitor and record electrical faults occurring in the power grid. Due to the large volume of fault recording data, the query cycle is long. During fault troubleshooting, the next step can only proceed after the query is completed. If the fault is not handled promptly, it may affect the normal operation of the system or even cause equipment damage. Furthermore, fault recording only begins after a fault alarm (i.e., there is a lag in alarm processing), and it is impossible to store all recorded data in real time.

[0069] Based on this, this embodiment proposes an early warning display method based on fault recording waveforms. This method can be applied to early warning devices based on fault recording waveforms. The functions implemented by this method can be achieved by the processor in the early warning device based on fault recording waveforms calling program code. Of course, the program code can be stored in the memory of the processing device. It can be seen that the processing device includes at least a processor and a memory.

[0070] Figure 1 This is a schematic diagram illustrating the implementation process of an early warning display method based on fault recording, according to an embodiment of this application. It is applied to an early warning system including at least one device to be monitored, a data acquisition component, and a display device; the device to be monitored, the data acquisition component, and the display device are connected sequentially. Figure 1 As shown, the method includes:

[0071] Step S101: Obtain waveform data for each of the devices to be monitored within a preset time period.

[0072] Step S102: Determine whether there are multiple repeated abnormal waveform data in the waveform data.

[0073] Step S103: In the case that there are multiple repeated abnormal waveform data in the waveform data, based on the abnormal waveform data and the fault warning threshold stored in the data acquisition component, determine the sequence number of the monitoring device that triggers the fault-related warning information corresponding to the waveform data exceeding the fault warning threshold.

[0074] Step S104: The data acquisition component sends out the sequence number of the warning information and the fault warning display data stored in the display device, and the display device displays the fault warning display data corresponding to the sequence number of the warning information.

[0075] It should be noted that the aforementioned early warning system can be determined based on actual circumstances, and no limitation is made here. In practical applications, the early warning system can also be referred to as a visual early warning system.

[0076] The device to be monitored can be determined according to the actual situation and is not limited here. As an example, the device to be monitored can be an inverter, a motor, etc.

[0077] The data acquisition component can be determined according to actual conditions and is not limited here. As an example, the data acquisition component can be a data acquisition device. The data acquisition component can be used to query waveform data, store it, and make judgments. For ease of understanding, it can be combined with... Figure 2 To understand, Figure 2 This is a schematic diagram of the early warning system in an embodiment of this application.

[0078] The data acquisition component is connected to the device under monitoring. This connection can be determined according to the actual situation and is not limited here. As an example, the connection can be a wired connection or a wireless connection; wherein, the wired connection can be a wire connection capable of transmitting data; the wireless connection can use short-range communication technology, such as Bluetooth, Zigbee, etc.; or it can use long-range communication technology, such as WiFi (Wireless Fidelity) connection.

[0079] The display device can be determined according to the actual situation and is not limited here. As an example, the display device can be a host computer, a computer or a mobile phone, etc.

[0080] In step S101, the preset time period can be determined according to the actual situation, and is not limited here. As an example, the preset time period can be understood as a certain time period.

[0081] The acquisition of waveform data for each of the monitored devices within a preset time period may be described as acquiring waveform data for a first sampling point on each of the monitored devices within the preset time period; the first sampling point is any sampling point on the monitored device. In practical applications, acquiring waveform data for the first sampling point on each of the monitored devices within a preset time period can be understood as acquiring waveform data for a specific sampling point on each of the monitored devices within the preset time period. The waveform data can be understood as sampled waveform data.

[0082] In step S102, it is determined whether there are multiple repetitions of abnormal waveform data in the waveform data; wherein, the abnormal waveform data can be determined according to the actual situation, and is not limited here. As an example, the abnormal waveform data can be understood as abnormal data.

[0083] In practical applications, determining whether there are multiple repeated abnormal waveform data in the waveform data can be understood as determining whether there are abnormal waveform data in the waveform data where a certain fault occurs repeatedly.

[0084] In step S103, the fault warning threshold can be determined according to the actual situation, and is not limited here. As an example, the fault warning threshold can be determined based on the normal value and alarm value of the waveform data of each of the monitored devices.

[0085] The presence of repeated abnormal waveform data in the waveform data can be understood as the presence of abnormal waveform data in which a certain fault occurs repeatedly.

[0086] Based on the abnormal waveform data and the fault warning threshold stored in the data acquisition component, the specific determination process for triggering fault-related warning information for the monitored device corresponding to the waveform data exceeding the fault warning threshold can be determined according to the actual situation and is not limited here. As an example, determining whether the abnormal waveform data exceeds the fault warning threshold and triggering fault-related warning information for the monitored device can be done by judging whether the abnormal waveform data exceeds the fault warning threshold. If the waveform data exceeds the fault warning threshold, warning information related to the fault corresponding to the fault warning threshold is triggered. The fault warning threshold can be determined according to the actual situation and is not limited here. As an example, the fault warning threshold can be 80% of the peak / valley value threshold as the warning point. 80% of the peak / valley value threshold refers to a complete waveform, where the difference between the absolute value of the peak and the absolute value of the valley is multiplied by 80%. When this condition is met, the alarm corresponding to the recorded waveform is triggered.

[0087] In this embodiment, the system determines whether there are multiple repetitions of abnormal waveform data in the waveform data of each monitored device within a preset time period. If multiple repetitions of abnormal waveform data are found, based on the abnormal waveform data and the fault warning threshold stored in the data acquisition component, the system determines that the monitored device whose waveform data exceeds the fault warning threshold will trigger a fault-related warning message. The data acquisition component then issues the sequence number of the warning message and the fault warning display data stored in the display device. The display device displays the fault warning display data corresponding to the sequence number of the warning message. This allows for timely display of fault information before a fault alarm is triggered, enabling maintenance personnel to visually identify the fault and troubleshoot it, thus avoiding delays in fault diagnosis.

[0088] In one optional embodiment of this application, the fault warning threshold is located in a fault warning model library; the fault warning threshold is determined based on the normal value and alarm value of the waveform data of each of the monitored devices; the fault warning threshold corresponds to the warning information corresponding to each fault waveform; the method further includes:

[0089] The acquired waveform data is processed in the time domain to obtain the fault waveform corresponding to the waveform data;

[0090] Feature extraction is performed on the fault recording waveform to obtain at least a first feature characterizing the amplitude characteristics of the fault recording waveform and a second feature characterizing the time series characteristics of the fault recording waveform; the first feature corresponds to first information related to the first fault corresponding to the fault recording waveform; the second feature corresponds to second information related to the second fault corresponding to the fault recording waveform.

[0091] The fault early warning model library is generated based on the first feature and the second feature.

[0092] In this embodiment, the fault warning threshold located in the fault warning model library can be understood as the fault warning model library storing the fault warning threshold. Simultaneously, the fault warning model library updates the fault information in the model library based on real-time collected fault waveform information and transmits the updated information to the display device. The display device updates the fault warning display data based on the data transmitted by the data acquisition component. Each warning information sequence number in the fault warning model library corresponds one-to-one with the sequence number of the fault warning display data. The data acquisition component only needs to transmit the sequence number of the fault information to the display device, and the display device can retrieve the corresponding fault information to be displayed based on that sequence number. This results in a small data transmission volume, thereby achieving rapid display of fault information so that maintenance personnel can begin processing.

[0093] The specific determination process of the fault warning threshold based on the normal value and alarm value of the waveform data of each of the monitored devices can be determined according to the actual situation and is not limited here. As an example, the determination of the fault warning threshold based on the normal value and alarm value of the waveform data of each of the monitored devices can be understood as the fault warning threshold taking a value between the normal value and alarm value of the waveform data of each of the monitored devices.

[0094] The correspondence between the fault warning threshold and the warning information corresponding to each fault waveform can be understood as a correspondence between the fault warning threshold and the warning information corresponding to each fault waveform. This correspondence can be determined according to the actual situation and is not limited here. As an example, this correspondence can be a one-to-one correspondence.

[0095] The specific processing procedure for performing time-domain processing on the acquired waveform data to obtain the fault waveform corresponding to the waveform data can be determined according to the actual situation and is not limited here. As an example, the time-domain processing on the acquired waveform data to obtain the fault waveform corresponding to the waveform data can be represented by the acquired waveform data in the time domain to obtain the fault waveform corresponding to the waveform data.

[0096] The specific extraction process for extracting features from the fault recording to obtain at least a first feature characterizing the amplitude characteristics of the fault recording and a second feature characterizing the time series characteristics of the fault recording can be determined according to actual circumstances and is not limited here. As an example, the feature extraction process for extracting features from the fault recording to obtain at least a first feature characterizing the amplitude characteristics of the fault recording and a second feature characterizing the time series characteristics of the fault recording may include: obtaining a first peak value, a first trough value, and a first time corresponding to the first trough value to the first peak value in the fault recording, as well as a scaling factor corresponding to the amplitude characteristics; determining the first feature based on the first peak value, the first trough value, and the scaling factor; and determining the second feature based on the first time.

[0097] The first feature corresponds to the first information related to the first fault in the fault recording. The first fault can be determined according to the actual situation and is not limited here. As an example, the first fault can be a power grid phase sequence abnormality. The first information can be determined according to the actual situation and is not limited here. As an example, the first information may include the name, type, code, number, etc. of the first fault. For ease of understanding, this is illustrated by an example, assuming that the device to be monitored is a parallel control card, the corresponding fault code is power grid phase sequence abnormality, the corresponding fault number is 674, and the corresponding channel number is 4.

[0098] The second feature corresponds to the second information related to the second fault in the fault recording waveform; the second fault can be determined according to the actual situation and is not limited here. As an example, the second fault can be a motor malfunction. As an example, the second information can include the name, type, code, number, etc. of the second fault. For ease of understanding, this example assumes that the device to be monitored is a motor, the corresponding fault code is motor malfunction, the corresponding fault number is 671, and the corresponding channel number is 3.

[0099] The phrase "generating the fault early warning model library based on the first feature and the second feature" can be understood as "constructing the fault early warning model library based on the first feature and the second feature".

[0100] In one optional embodiment of this application, the step of extracting features from the fault recording to obtain at least a first feature characterizing the amplitude characteristics of the fault recording and a second feature characterizing the time series characteristics of the fault recording includes:

[0101] Obtain the first peak value, the first trough value, and the first time corresponding to the first trough value to the first peak value in the fault recording, as well as the scaling factor corresponding to the amplitude characteristic;

[0102] The first feature is determined based on the first peak value, the first trough value, and the scaling factor;

[0103] The second feature is determined based on the first time.

[0104] In this embodiment, the first peak value and the first trough value can be determined according to the actual situation, and are not limited here. In practical applications, the first peak value can be simply referred to as the peak value, and the first trough value can be simply referred to as the trough value.

[0105] The first time from the first trough to the first peak can be understood as the first time from the trough to the peak, or as the first time from consecutive first troughs to the first peak. In practical applications, assuming a complete waveform is a time interval t, the peak will be reached at time t / 2 from the trough.

[0106] The scaling factor corresponding to the amplitude characteristic can be determined according to the actual situation and is not limited here. As an example, the scaling factor corresponding to the amplitude characteristic can be 80%.

[0107] The specific determination process for determining the first feature based on the first peak value, the first trough value, and the scaling factor can be determined according to actual circumstances and is not limited here. As an example, determining the first feature based on the first peak value, the first trough value, and the scaling factor may include: determining the difference between the absolute value of the first peak value and the absolute value of the first trough value; and determining the first feature according to the scaling factor and the difference. The first feature can be determined according to actual circumstances and is not limited here. As an example, the first feature can be called a warning point.

[0108] The specific determination process for determining the second feature based on the first time can be determined according to the actual situation and is not limited here. As an example, determining the second feature based on the first time can be that the sampled value reaches the peak value of the previous waveform within a time interval of t / 2 (t is the minimum time interval for sampling).

[0109] In one optional embodiment of this application, determining the first feature based on the first peak value, the first trough value, and the scaling factor includes:

[0110] Determine the difference between the absolute value of the first peak value and the absolute value of the first trough value;

[0111] The first feature is determined based on the scaling factor and the difference.

[0112] In this embodiment, determining the difference between the absolute value of the first peak value and the absolute value of the first trough value can be understood as subtracting the absolute value of the first trough value from the absolute value of the first peak value to obtain the difference.

[0113] The specific determination process for determining the first feature based on the scaling factor and the difference can be determined according to the actual situation and is not limited here. As an example, determining the first feature based on the scaling factor and the difference can be done by multiplying the scaling factor and the difference to obtain the first feature.

[0114] In practical applications, for a complete waveform, the difference between the absolute value of the peak and the absolute value of the valley is multiplied by 80%. When this condition is met, the alarm corresponding to that waveform is triggered.

[0115] In one optional embodiment of this application, the method further includes:

[0116] Determine whether the waveform data meets the matching conditions of the fault early warning model library;

[0117] If the waveform data meets the matching conditions of the fault early warning model library, the monitored device is determined to trigger a fault-related early warning information when the waveform data exceeds the fault early warning threshold.

[0118] In this embodiment, the matching conditions of the fault early warning model library can be determined according to the actual situation, and are not limited here. As an example, the matching conditions include at least one of the following: the first value of the feature characterizing the amplitude characteristics of the waveform data is greater than or equal to the value corresponding to the first feature in the fault early warning model library; the second value of the feature characterizing the time series characteristics of the waveform data is less than or equal to the value corresponding to the second feature in the fault early warning model library.

[0119] In one optional embodiment of this application, the matching conditions include at least one of the following:

[0120] The first value of the feature characterizing the amplitude characteristics of the waveform data is greater than or equal to the value corresponding to the first feature in the fault early warning model library;

[0121] The second value of the feature characterizing the time series characteristics of the waveform data is less than or equal to the value corresponding to the second feature in the fault early warning model library.

[0122] In this embodiment, the value corresponding to the first feature in the fault early warning model library can be called the early warning point; the early warning point can be understood as 80% of the difference between the absolute value of the peak and the absolute value of the valley for a complete waveform. In practical applications, the early warning point can be called 80% of the threshold for selecting peak / valley values.

[0123] The first value of the feature characterizing the amplitude characteristics of the waveform data can be understood as the sampled value.

[0124] In practical applications, the first value of the feature characterizing the amplitude characteristics of the waveform data being greater than or equal to the value corresponding to the first feature in the fault warning model library can be understood as selecting 80% of the threshold of the peak / valley value as the warning point, when the sampled value is greater than or equal to the warning point.

[0125] The value corresponding to the second feature in the fault early warning model library can be recorded as t / 2, where t is the minimum sampling time interval. A complete waveform is a time interval of t. Then, according to the normal t\2, the trough will reach the position of the peak.

[0126] The second value representing the time-series characteristics of the waveform data can be understood as a sampled value.

[0127] In practical applications, the second value of the feature characterizing the time series characteristics of the waveform data being less than or equal to the value corresponding to the second feature in the fault warning model library can be understood as the sampled value reaching the peak of the previous waveform within a time interval of t / 2. This refers to the position where a complete waveform would normally reach its peak within a time interval of t. If the time interval is less than t / 2, an alarm corresponding to that waveform is triggered. Alternatively, it can be understood as the sampled value reaching the peak of the previous waveform within a time interval of t / 2 (t being the minimum sampling time interval).

[0128] In one optional embodiment of this application, the early warning system further includes a host computer; the host computer includes a database storing the early warning information; the early warning information includes record information and sampling information related to the fault; the record information includes at least a fault code and the fault occurrence time; the method further includes:

[0129] Export the fault code, the fault occurrence time, and the sampling information from the database;

[0130] The fault code, the fault occurrence time, and the sampling information are displayed in a graphical form.

[0131] In this embodiment, the fault code, the fault occurrence time, and the sampling information can all be determined according to the actual situation, and are not limited here. As an example, the fault occurrence time can be called the fault occurrence point; the sampling information may include sampling values.

[0132] In practical applications, the early warning system not only displays the waveform data graphically but also supports data export. Key information from the fault waveform data is stored in a database. The host computer can retrieve the data from the database and display it in a fault waveform event list. This list gathers various key information, enabling operators to understand the cause and nature of the fault more intuitively and quickly. Key information includes: 1. Record name, alarm trigger, and synchronization status; 2. Clicking "Details" displays the fault waveform curve composed of sampled values ​​for each channel; 3. The exported Excel data table displays the fault code, fault location, and sampled values.

[0133] In one optional embodiment of this application, the early warning system further includes a digital signal processor (DSP); the DSP is connected to the device to be monitored via serial communication; the method further includes:

[0134] After the early warning system completes the periodic serial communication, it performs fault recording and query on the device to be monitored.

[0135] In this embodiment, fault waveform query is performed without affecting device communication. This method fragments the entire fault waveform query process into multiple execution steps, inserting each step into the idle time of serial communication. Fault waveform query is performed after the completion of periodic serial communication, thus prioritizing serial communication and ensuring that it is not affected while still allowing for normal querying of fault waveform data.

[0136] In one optional embodiment of this application, the step of performing fault recording query on the device to be monitored after the early warning system completes a periodic serial communication includes:

[0137] Obtain the first time of the periodic query and the second time of the serial communication query in the early warning system;

[0138] Determine the difference between the second time and the first time;

[0139] The difference is used to perform fault recording query on the device to be monitored.

[0140] In this embodiment, the first time can be referred to as the periodic query time; the second time can be referred to as the serial communication query time.

[0141] Determining the difference between the second time and the first time can be understood as subtracting the second time from the first time to obtain the difference. In practical applications, subtracting the second time from the first time to obtain the difference can be achieved by subtracting the serial communication query time from the periodic query time. In practical applications, the difference can be understood as the idle time of the serial communication.

[0142] Using the difference to perform fault waveform query on the device under monitoring can be understood as using the idle time of serial communication to perform fault waveform query on the device under monitoring.

[0143] In practical applications, fault waveform queries are performed on the monitored device based on priority scheduling and idle time utilization, enabling fault waveform queries without affecting device communication. This method fragments the entire fault waveform query process into multiple execution steps, inserting each step into the idle time of serial communication in a "squeezing-in" manner. Fault waveform queries are performed after the completion of a periodic serial communication cycle, thus treating serial communication as a high priority and ensuring that serial communication is not affected while still allowing normal querying of fault waveform data. Because the fault waveform queries are fragmented and distributed throughout the idle cycle in a "squeezing-in" manner, the system continuously monitors for fault occurrences during the idle period. When a fault occurs, the fault waveform query immediately begins, thereby improving the query response speed.

[0144] In one optional embodiment of this application, when performing fault recording query on the device to be monitored, the method further includes:

[0145] Obtain the priority strategy for fault recording query of the monitored equipment;

[0146] Based on the priority strategy, query the overall information of all fault filters to be queried; the overall information includes identification information indicating whether the synchronization status of each fault filter is successful;

[0147] If the identification information indicates that the synchronization status of each fault filter is unsuccessful, detailed information is queried for the fault filter whose synchronization status is unsuccessful.

[0148] Once the synchronization status of each fault filter is successfully identified by the identification information, the fault recording query ends.

[0149] In this embodiment, the priority strategy can be determined according to the actual situation, and is not limited here. As an example, the priority strategy can be to prioritize querying overall information.

[0150] The overall information includes identification information indicating whether the synchronization status of each fault filter is successful; the identification information can be determined according to the actual situation and is not limited here.

[0151] In practical applications, the implementation of cache management and intelligent query strategies for fault waveform recording results is crucial. The fault waveform query process distinguishes between overall information queries and detailed information queries. Overall information refers to the number of fault waveforms to be queried in the system, their synchronization status, and trigger time; detailed information refers to the sampling points, sample values, number of channels, and number of packets for each fault waveform. When querying fault waveform data, overall information is queried first to obtain the synchronization status of each fault waveform. The synchronization status indicates whether a detailed information query is needed for this fault waveform, thus avoiding duplicate queries of already queried fault waveforms. This significantly reduces query time and serial port resource pressure, thereby improving query efficiency and overall system performance.

[0152] This application's embodiment employs modular data management for intelligent systems. This allows for modular management of each type of data and service requiring integration into the intelligent system. Different manufacturers, brands, and models can quickly identify which data and services need to be integrated during the integration process. Furthermore, it addresses standardized requirements regarding the timeliness, stability, and security of the integrated data and services, making intelligent system integration more accurate and convenient. By adopting modular data management, the upper-layer business system and the lower-layer intelligent system are isolated in terms of business logic and data application. This ensures that changes, replacements, and additions to the lower-layer intelligent system during the project will not affect the execution of the upper-layer business, guaranteeing uninterrupted business operations, continuous application data, and consistent business logic.

[0153] To facilitate understanding of this application, the specific example of this application is a fault recording-based early warning display method, which is applied to a fault recording-based display and early warning system.

[0154] The specific implementation steps are as follows:

[0155] 1. Fault warning:

[0156] Time-domain analysis is performed on waveform data from repeated fault recordings to extract key features such as peak values, valley values, and waveform time-series characteristics. A fault early warning model library is then built for fault prediction, diagnosis, and management in power systems. The library includes two matching mechanisms: one selects 80% of the peak / valley threshold as the warning point when the sampled value exceeds this threshold; the other determines when the sampled value reaches the peak value of the previous waveform within a time interval t / 2 (t being the minimum sampling time interval). Real-time queries of fault recording data are matched against the fault early warning model library. When either of these conditions is triggered, the name of the potential fault is displayed in advance, and an alarm is issued, allowing maintenance personnel to detect potential faults early and implement corresponding protective measures. The sampled value can be determined as current or voltage based on the type of waveform being recorded; it could be input / output voltage / current or grid voltage / current. Eighty percent of the peak / valley threshold refers to a complete waveform. The difference between the absolute value of the peak and the absolute value of the valley, multiplied by 80%, triggers an alarm for that waveform when this condition is met. The sampled value reaching the peak of the previous waveform within t / 2 time means that a complete waveform would normally reach its peak within t / 2 time intervals. If it reaches the peak within t / 2, an alarm for that waveform is triggered. Alarms can be sent to the system from the parallel control card (DSP). Therefore, each fault waveform has a corresponding alarm name. If the same alarm occurs multiple times, the warning system is activated. For easier understanding, this can be combined with... Figure 3 , Figure 4 , Figure 5 To understand, Figure 3 This is a schematic diagram of the fault filtering process in an embodiment of this application; Figure 4 This is a schematic diagram of the fault early warning model matching mechanism in the embodiments of this application; Figure 5 This is a schematic diagram of the time-domain analysis process of fault recording data in an embodiment of this application.

[0157] 2. Warning Information Display:

[0158] This paper describes a fault waveform recording information system that graphically outputs data to a display device. The system not only displays the waveform data graphically but also supports data export. Key information from the fault waveform data is stored in the display device's database. The display device can retrieve data from the database and display it in a fault waveform event list. This list gathers various key information, enabling operators to understand the cause and nature of the fault more intuitively and quickly. Simultaneously, key information from the data acquisition components includes:

[0159] 1. Record the name, alarm trigger, and synchronization status, etc.;

[0160] 2. Click on details to view the fault waveform curve composed of sampled values ​​in each channel;

[0161] 3. In the exported Excel data table, you can see information such as fault code, fault location, and sampled value.

[0162] To make it easier to understand, this can be combined with... Figure 6 , Figure 7 , Figure 8 To understand, Figure 6 This is a schematic diagram of the fault recording event list in an embodiment of this application; Figure 7 This is a schematic diagram of the fault waveform detailed information table in the embodiments of this application; Figure 8 This is a schematic diagram of the fault waveform export information table in an embodiment of this application.

[0163] ③ Non-blocking query:

[0164] This paper proposes a method based on priority scheduling and idle time utilization to enable fault waveform querying without affecting device communication. The method fragments the entire fault waveform querying process into multiple execution steps, inserting each step into the idle time of serial communication. Fault waveform querying is performed after the completion of periodic serial communication, thus treating serial communication as a high priority and ensuring that serial communication is not affected while still allowing normal querying of fault waveform data.

[0165] To make it easier to understand, this can be combined with... Figure 9 , Figure 10 To understand, Figure 9 This is a schematic diagram of alarm notification in an embodiment of this application; Figure 10 This is a schematic diagram illustrating the principle of the non-blocking query mechanism for fault recording in the embodiments of this application.

[0166] ④ Intelligent query strategy:

[0167] This paper describes an intelligent fault waveform query method, including the caching management of fault waveform results and the implementation of an intelligent query strategy. The fault waveform query process distinguishes between overall information query and detailed information query. Overall information refers to the number of fault waveforms to be queried in the system, their synchronization status, and trigger time; detailed information refers to the sampling points, sampling values, number of channels, and number of packets for each fault waveform. When querying fault waveform data, the overall information is queried first to obtain the synchronization status of each fault waveform. The synchronization status indicates whether a detailed information query is needed for this fault waveform, thus avoiding duplicate queries of already queried fault waveforms. This significantly reduces query time and serial port resource pressure, thereby improving query efficiency and overall system performance. This can be combined with... Figure 6 , Figure 7 , Figure 8 To understand.

[0168] ⑤ High response speed:

[0169] Because fault waveform queries are fragmented and scattered throughout idle periods in a "slot-by-slot" manner, fault occurrence is continuously monitored during idle periods. When a fault occurs, fault waveform queries immediately begin, thereby improving query response speed. This can be combined with... Figure 9 , Figure 10 To understand.

[0170] In this embodiment, the fault recording-based early warning display system can detect potential faults in advance and visualize them, allowing maintenance personnel to intuitively understand the system's health status and potential risks, and quickly take corresponding measures, effectively reducing time and economic losses caused by faults. Furthermore, when a fault occurs repeatedly, the system will proactively allocate resources for continuous querying. When conditions in the fault early warning model are triggered, a corresponding alarm will be generated for the entire system, enabling staff to perceive the alarm in advance and implement appropriate preventative strategies.

[0171] This application also provides an early warning device based on fault recording, which is installed on an early warning system including at least one device to be monitored and a data acquisition component; such as Figure 11 As shown, Figure 11 This is a schematic diagram of a fault recording-based early warning device according to an embodiment of this application. The early warning device 1100 includes:

[0172] Acquisition unit 1101 is used to acquire waveform data of each of the devices to be monitored within a preset time period;

[0173] The judgment unit 1102 is used to determine whether there are multiple repeated abnormal waveform data in the waveform data;

[0174] The determining unit 1103 is used to determine, in the case that there are multiple repeated abnormal waveform data in the waveform data, the sequence number of the monitoring device that triggers a fault-related warning information when the waveform data exceeds the fault warning threshold, based on the abnormal waveform data and the fault warning threshold stored in the data acquisition component.

[0175] The display unit 1104 is used to send the sequence number of the warning information and the fault warning display data stored in the display device using the data acquisition component, and the display device displays the fault warning display data corresponding to the sequence number of the warning information.

[0176] In one embodiment, the fault warning threshold is located in a fault warning model library; the fault warning threshold is determined based on the normal value and alarm value of the waveform data of each device to be monitored; the fault warning threshold corresponds to the warning information corresponding to each fault waveform; the device further includes a processing unit, an extraction unit, and a generation unit; wherein,

[0177] The processing unit is used to perform time-domain processing on the acquired waveform data to obtain the fault waveform corresponding to the waveform data.

[0178] The extraction unit is used to extract features from the fault recording waveform to obtain at least a first feature characterizing the amplitude characteristics of the fault recording waveform and a second feature characterizing the time series characteristics of the fault recording waveform; the first feature corresponds to first information related to the first fault corresponding to the fault recording waveform; the second feature corresponds to second information related to the second fault corresponding to the fault recording waveform.

[0179] The generation unit is used to generate the fault early warning model library based on the first feature and the second feature.

[0180] In one embodiment, the extraction unit is further configured to acquire a first peak value, a first trough value, and a first time corresponding to the transition from the first trough value to the first peak value in the fault recording, as well as a scaling factor corresponding to the amplitude characteristic; determine the first feature based on the first peak value, the first trough value, and the scaling factor; and determine the second feature based on the first time.

[0181] In one embodiment, the extraction unit is further configured to determine the difference between the absolute value of the first peak value and the absolute value of the first trough value; and to determine the first feature based on the scaling factor and the difference.

[0182] In one embodiment, the judgment unit 1102 is further configured to determine whether the waveform data meets the matching conditions of the fault early warning model library;

[0183] The determining unit 1103 is further configured to determine, when the waveform data meets the matching conditions of the fault warning model library, that the monitored device triggers a fault-related warning information when the waveform data exceeds the fault warning threshold.

[0184] In one embodiment, the matching criteria include at least one of the following:

[0185] The first value of the feature characterizing the amplitude characteristics of the waveform data is greater than or equal to the value corresponding to the first feature in the fault early warning model library;

[0186] The second value of the feature characterizing the time series characteristics of the waveform data is less than or equal to the value corresponding to the second feature in the fault early warning model library.

[0187] In one embodiment, the early warning system further includes a host computer; the host computer includes a database storing the early warning information; the early warning information includes record information and sampling information related to the fault; the record information includes at least a fault code and a fault occurrence time; the device 1100 further includes a display unit for exporting the fault code, the fault occurrence time and the sampling information from the database; and displaying the fault code, the fault occurrence time and the sampling information in a graphical form.

[0188] In one embodiment, the early warning system further includes a digital signal processor (DSP); the DSP is connected to the device under monitoring via serial communication; the device 1100 further includes a query unit, used to perform fault recording query on the device under monitoring after the early warning system has completed a period of serial communication.

[0189] In one embodiment, the query unit is further configured to obtain the first time of periodic query and the second time of serial communication query in the early warning system; determine the difference between the second time and the first time; and use the difference to perform fault recording query on the device to be monitored.

[0190] In one embodiment, when performing fault waveform query on the device under monitoring, the query unit is further configured to: obtain a priority strategy for performing fault waveform query on the device under monitoring; query the overall information of all fault filters to be queried based on the priority strategy; the overall information includes identification information indicating whether the synchronization status of each fault filter is successful; if the identification information indicates that the synchronization status of each fault filter is unsuccessful, perform detailed information query on the fault filters with unsuccessful synchronization status; and if the identification information indicates that the synchronization status of each fault filter is successful, end the fault waveform query.

[0191] It should be noted that, in the embodiments of this application, if the above-described fault waveform-based early warning display method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a fault waveform-based early warning system (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0192] Based on the hardware implementation of the above program modules, this application embodiment also provides an early warning device based on fault recording waveforms, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements the steps in the early warning display method based on fault recording waveforms provided in the above embodiment.

[0193] Correspondingly, this application provides a computer program product, which, when executed by a processor, implements the steps in the fault recording-based early warning method provided in the above embodiments.

[0194] Correspondingly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the fault recording-based early warning display method provided in the above embodiments.

[0195] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0196] Based on the hardware implementation of the above program modules, this application embodiment also provides an information transmission device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the methods described above on the network device side; or, when the processor executes the program, it implements the steps of any of the methods described above on the terminal side.

[0197] Correspondingly, embodiments of this application provide a computer program product, including a computer program on which the computer program is stored, wherein when the computer program is executed by a processor, it implements the steps of any of the methods described above on the network device side; or, when the processor executes the program, it implements the steps of any of the methods described above on the terminal side.

[0198] Correspondingly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of any of the methods described above on the network device side; or, when the processor executes the program, it implements the steps of any of the methods described above on the terminal side.

[0199] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0200] It should be noted that, Figure 12 This is a schematic diagram of the structure of the early warning device based on fault recording according to an embodiment of this application, as shown below. Figure 12 As shown, the early warning device 1200 includes a processor 1201 and a memory 1203. Optionally, the early warning device 1200 may also include a communication interface 1202.

[0201] It is understood that memory 1203 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 1203 described in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0202] The methods disclosed in the embodiments of this application can be applied to or implemented by the processor 1201. The processor 1201 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 1201 or by instructions in the form of software. The processor 1201 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 1201 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly manifested as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, which is located in the memory 1203. The processor 1201 reads the information in the memory 1203 and completes the steps of the aforementioned method in conjunction with its hardware.

[0203] In an exemplary embodiment, the device may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0204] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential 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 this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0205] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0206] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0207] The features disclosed in the several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0208] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0209] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for early warning display based on fault recording waveforms, characterized in that, Applied to early warning systems that include at least one device to be monitored, a data acquisition component, and a display device; The device to be monitored, the data acquisition component, and the display device are connected sequentially; the method includes: Acquire waveform data for each of the monitored devices within a preset time period; Determine whether there are multiple repetitions of abnormal waveform data in the waveform data; In the case where there are multiple repeated abnormal waveform data in the waveform data, based on the abnormal waveform data and the fault warning threshold stored in the data acquisition component, the sequence number of the monitoring device that triggers the fault-related warning information corresponding to the waveform data exceeding the fault warning threshold is determined; The data acquisition component uses the sequence number of the warning information and the fault warning display data stored in the display device to issue the warning information. The display device then displays the fault warning display data corresponding to the sequence number of the warning information.

2. The method according to claim 1, characterized in that, The fault warning threshold is located in the fault warning model library; the fault warning threshold is determined based on the normal value and alarm value of the waveform data of each of the monitored devices. The fault warning threshold corresponds to the warning information for each type of fault recording waveform; the method further includes: The acquired waveform data is processed in the time domain to obtain the fault waveform corresponding to the waveform data; Feature extraction is performed on the fault recording waveform to obtain at least a first feature characterizing the amplitude characteristics of the fault recording waveform and a second feature characterizing the time series characteristics of the fault recording waveform; the first feature corresponds to first information related to the first fault corresponding to the fault recording waveform; the second feature corresponds to second information related to the second fault corresponding to the fault recording waveform. The fault early warning model library is generated based on the first feature and the second feature.

3. The method according to claim 2, characterized in that, The feature extraction of the fault recording waveform to obtain at least a first feature characterizing the amplitude characteristics of the fault recording waveform and a second feature characterizing the time series characteristics of the fault recording waveform includes: Obtain the first peak value, the first trough value, and the first time corresponding to the first trough value to the first peak value in the fault recording, as well as the scaling factor corresponding to the amplitude characteristic; The first feature is determined based on the first peak value, the first trough value, and the scaling factor; The second feature is determined based on the first time.

4. The method according to claim 3, characterized in that, Determining the first feature based on the first peak value, the first trough value, and the scaling factor includes: Determine the difference between the absolute value of the first peak value and the absolute value of the first trough value; The first feature is determined based on the scaling factor and the difference.

5. The method according to claim 4, characterized in that, The method further includes: Determine whether the waveform data meets the matching conditions of the fault early warning model library; If the waveform data meets the matching conditions of the fault early warning model library, the monitored device is determined to trigger a fault-related early warning information when the waveform data exceeds the fault early warning threshold.

6. The method according to claim 5, characterized in that, The matching criteria include at least one of the following: The first value of the feature characterizing the amplitude characteristics of the waveform data is greater than or equal to the value corresponding to the first feature in the fault early warning model library; The second value of the feature characterizing the time series characteristics of the waveform data is less than or equal to the value corresponding to the second feature in the fault early warning model library.

7. The method according to any one of claims 1-6, characterized in that, The early warning system also includes a digital signal processor (DSP); The DSP is connected to the device under monitoring via serial communication; the method further includes: After the early warning system completes the periodic serial communication, it performs fault recording and query on the device to be monitored.

8. The method according to claim 7, characterized in that, After the early warning system completes the periodic serial communication, the process of performing fault recording and querying on the monitored device includes: Obtain the first time of the periodic query and the second time of the serial communication query in the early warning system; Determine the difference between the second time and the first time; The difference is used to perform fault recording query on the device to be monitored.

9. The method according to claim 8, characterized in that, When performing fault recording queries on the monitored equipment, the method further includes: Obtain the priority strategy for fault recording query of the monitored equipment; Based on the priority strategy, query the overall information of all fault filters to be queried; the overall information includes identification information indicating whether the synchronization status of each fault filter is successful; If the identification information indicates that the synchronization status of each fault filter is unsuccessful, detailed information is queried for the fault filter whose synchronization status is unsuccessful. Once the synchronization status of each fault filter is successfully identified by the identification information, the fault recording query ends.

10. The method according to any one of claims 1-9, characterized in that, The fault warning threshold information is stored in the data acquisition component, and the fault warning display data is stored in the display device; The data acquisition component outputs early warning information to the display component; The display component updates the fault warning display data based on the obtained warning information.

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