A wind power equipment fault early warning management feedback method and system

Abstract

The application discloses a kind of wind power equipment fault early warning management feedback method and system, it is related to equipment safety monitoring technical field, including: collection wind power equipment operating data and environmental parameters, design 8-bit binary+3-bit decimal hybrid coding system, judge whether fault occurs, if it occurs, corresponding fault code is generated, realize fine distinction and fast identification: based on environmental parameters and fault code, establish multidimensional dynamic adjustment rule, determine the output strategy of audible and visual alarm; control deployment in wind power equipment field and audible and visual alarm at fault position execute local alarm, it is sent to remote operation and maintenance terminal simultaneously, build edge computing-cloud storage-terminal interaction three-level platform, realize alarm information remote transmission and collaborative processing;Set up with the dynamic check cycle of fault level linkage, start feedback timing, if no confirmation feedback is received within the period, automatically execute alarm upgrade operation.The application improves fault early warning efficiency.

Description

Technical Field

[0001] This invention relates to the field of equipment safety monitoring technology, specifically to a method and system for early warning management and feedback of wind power equipment faults. Background Technology

[0002] As the wind power industry expands into complex scenarios such as large capacity, high altitude, and offshore operations, the reliability and identification accuracy of wind power equipment fault early warning and alarm systems have become crucial for ensuring the safe operation of wind farms. Traditional wind power alarm systems suffer from three core problems: First, alarm information coding is simplistic, often using a single audible and visual signal to correspond to all faults. Maintenance personnel cannot directly determine the fault type, level, and location through audible and visual signals and must rely on SCADA systems for querying, leading to delayed fault response. Second, audible and visual output lacks environmental adaptability. Under harsh conditions such as strong winds, low temperatures, and strong sunlight, the audible and visual signals of traditional alarms are easily interfered with by the environment, resulting in insufficient alarm information transmission success rate. Third, there is a lack of linkage verification mechanisms. Traditional systems rely solely on local audible and visual alarms, which can easily lead to missed alarm information if maintenance personnel are not on-site. The absence of an information feedback verification process makes it impossible to confirm whether maintenance personnel have received the alarm, resulting in delays in fault handling. Although some existing wind power alarm-related patents attempt to optimize audible and visual designs, limitations still exist.

[0003] Chinese patent CN202210876543.2 discloses a wind power equipment audible and visual alarm device, which only distinguishes the fault level by adjusting the LED flashing frequency, without involving the coding of fault type and location; Chinese patent CN202310123456.7 designed a wind power remote alarm system, but did not consider the impact of harsh environment on audible and visual signals, nor did it set up a linkage verification mechanism; therefore, there is an urgent need to design an audible and visual alarm design method with multi-dimensional information coding, environmental adaptive adjustment, and remote linkage verification functions to solve the defects of traditional systems. Summary of the Invention

[0004] To address the aforementioned technical problems, a method and system for wind power equipment fault early warning management feedback are provided. This technical solution resolves the problems mentioned above.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method and system for fault early warning management and feedback of wind power equipment, comprising: S1. Real-time acquisition of wind turbine operating data and environmental parameters, designing an 8-bit binary + 3-bit decimal hybrid coding system. Based on the wind turbine operating data, it determines whether a fault has occurred. If a fault occurs, a corresponding fault code is generated based on the hybrid coding system, achieving refined differentiation and rapid identification of fault type, level, location, and component. S2. Based on the environmental parameters of the wind power equipment and the corresponding fault codes, establish multi-dimensional dynamic adjustment rules and determine the output strategy of the audible and visual alarm. S3. Based on the determined output strategy of the audible and visual alarm, control the audible and visual alarms deployed at the wind power equipment site and corresponding to the fault location to execute local alarms and send them to the remote operation and maintenance terminal simultaneously. Construct a three-level platform of edge computing, cloud storage and terminal interaction to realize remote transmission and collaborative processing of alarm information. S4. Set a dynamic verification cycle linked to the fault level and start the feedback timer. If no confirmation feedback of the alarm information is received from the remote operation and maintenance terminal within the preset verification cycle, the alarm escalation operation will be executed automatically.

[0006] Preferably, step S1 specifically includes: Based on the SCADA system for wind power equipment, and using vibration sensors, temperature sensors, current sensors, and power sensors, the sampling frequency is set to 5 seconds to collect real-time vibration, temperature, current, and power data of the wind power equipment. The data is then integrated to obtain the wind power equipment operation data stream and preprocessed. Based on environmental sensors, the sampling frequency is set to 10 seconds to collect wind speed, ambient temperature and light intensity in the target area in real time, and integrate them to obtain the environmental data stream of the wind power equipment, and perform data preprocessing on it. Based on the different sampling frequencies of wind power equipment operation data and environmental data, using the high-frequency timestamp of wind power equipment operation data as a benchmark, for each operation data point, the environmental record with the smallest timestamp difference of no more than 10 seconds is searched from the environmental data. The record is then matched to each wind power equipment operation data time point using the nearest neighbor interpolation method to form a synchronized data record. Based on the expert knowledge base and known historical fault records, an 8-bit binary + 3-bit decimal hybrid coding system is designed. For the 3-bit binary fault type coding in the hybrid coding system, it covers the mechanical, electrical, hydraulic and control system fault types of wind power equipment. Each type corresponds to a unique binary code. 001 represents mechanical faults, including main shaft and gearbox faults, and 010 represents electrical faults, including generator and converter faults. For the 3-bit binary fault level code in the hybrid coding system, three alarm levels are divided according to the degree of impact of the fault on the operation of the wind power equipment. If it is determined to be a level 1 warning, the operation can continue and attention is required. If it is determined to be a level 2 alarm, the machine needs to be shut down for maintenance. If it is determined to be a level 3 emergency shutdown, the machine should be shut down immediately to avoid damage. For the 2-bit binary fault location coding in the hybrid coding system, the wind turbine is divided into three areas: nacelle area, tower area, and hub area. Each area corresponds to a unique code, enabling rapid fault location. For the 3-bit binary component numbering system in the hybrid coding system, specific components in each area are numbered 001-150. The faulty component is directly associated through the code, without the need to consult the equipment drawings. Specific components include: the main shaft and gearbox in the nacelle area, and the cables and control cabinet in the tower area; A two-digit decimal fault duration code has been added. If the fault lasts for more than 60 minutes, an emergency linkage signal will be automatically triggered, and information will be pushed to the regional emergency command center to prevent the fault from escalating.

[0007] Preferably, step S1 further includes: Set a 5-minute sliding time window and calculate the mean and standard deviation of wind power equipment operation data within the time window in real time. Based on the wind power equipment manual and historical safety data records, a static threshold is set. If any operating data of the wind power equipment exceeds the static threshold within a time window, a primary alarm is immediately triggered, and the alarm parameters, values ​​and timestamps are recorded. Based on a 5-minute time window, a dynamic threshold is set. If the current instantaneous value of a certain operating data of the wind power equipment exceeds 3 consecutive sampling points and exceeds this dynamic threshold within the time window, it indicates that the operating data of the wind power equipment is showing an abnormal upward or downward trend, and a trend alarm is automatically triggered, recording the window statistics, current value and timestamp. The dynamic threshold is: ɑ±n Where α is the average operating data of wind power equipment within the time window, and n is a configurable sensitivity coefficient. This represents the standard deviation of wind power equipment operating data within this time window. Based on the trigger alarm parameters, values ​​and timestamps, and combined with an 8-bit binary + 3-bit decimal hybrid coding system, fault judgment is performed. If a fault is determined to have occurred, a corresponding fault code is generated according to the fault type-level-location-component, so as to achieve fine differentiation and rapid identification of fault type-level-location-component.

[0008] Preferably, step S2 specifically includes: Based on the environmental data stream of the wind power equipment, the wind speed in the target area is obtained in real time. If the wind speed is greater than or equal to 15 / s, two instructions are generated: the basic volume of the buzzer is increased by 30% and the basic brightness of the LED is increased by 40%, and the wind speed compensation rule is executed. Based on the environmental data stream of the wind power equipment, the ambient temperature of the target area is obtained in real time. If the ambient temperature is less than or equal to -20℃, the LED adopts pulsed high brightness output, with a brightness of 2000cd / m² and a flashing frequency of 5Hz, to avoid the light source brightness decay caused by low temperature and to implement the low temperature enhancement rule. Based on the environmental data stream of the wind power equipment, the illumination of the target area is obtained in real time. If the illumination is greater than or equal to 10,000 lux, it is marked as strong light, and the basic brightness of the LED is increased by 50%. If the illumination is less than or equal to 500 lux, it is marked as weak light, and the basic brightness of the LED is reduced to 100 cd / m². The buzzer is turned on in a boost mode with a volume of 120dB to balance energy consumption and visibility, and the illumination adaptation rules are executed.

[0009] Preferably, step S2 further includes: Priority rules are defined as follows: Low temperature enhancement rule > Lighting adaptation rule > Wind speed compensation rule; If there is a conflict in LED control, the higher priority rule will override the lower priority rule's LED control instruction. If different rules adjust the buzzer, the maximum value will be taken, and a set of conflict-free final instructions will be generated for the LED and the buzzer. Based on the execution of wind speed compensation rules, low temperature enhancement rules, and illumination adaptation rules, a multi-dimensional dynamic adjustment rule is established. Based on the generated corresponding fault codes, the fault type is mapped to the LED color. If the LED displays red, it indicates a mechanical fault; if the LED displays yellow, it indicates an electrical fault; if the LED displays blue, it indicates a hydraulic fault; and if the LED displays purple, it indicates a fault in the surface control system. A fault type-color mapping rule is established. The fault level is mapped to the buzzer tone and flashing frequency. If the buzzer sounds intermittently at a low frequency and the LED flashes at a low frequency in sync with the fault color, it is determined to be a level 1 warning. If the buzzer sounds intermittently at a medium frequency and the LED flashes at a medium frequency in sync with the fault color, it is determined to be a level 2 warning. If the buzzer sounds continuously at a high frequency and the LED flashes continuously at a high frequency in sync with the fault color, it is determined to be a level 3 emergency shutdown. A level-sound and light mode mapping rule is established. Based on the fault type-color mapping rule and the level-sound and light mode mapping rule, establish the fault diagnosis and sound and light alarm rules for wind power equipment, and determine the output strategy of the sound and light alarm.

[0010] Preferably, step S3 specifically includes: Based on the wind turbine tower nacelle, an industrial-grade edge computing gateway is deployed, with a sampling frequency of 5 seconds, to receive fault data uploaded by the wind turbine SCADA system in real time, and to filter out abnormal data and fill in missing values ​​in the fault data. The fault data is encoded using an 8-bit binary + 3-bit decimal hybrid encoding system, and the encoded command is sent to the local audible and visual alarm to trigger on-site audible and visual prompts. Use Alibaba Cloud ECS servers to build a cloud database to store historical alarm data of wind power equipment. The storage period must be greater than 5 years. Based on 10,000 sets of historical alarm data of wind power equipment in the cloud database, a BP neural network model was established with fault codes, current environmental parameters and wind power equipment running time as inputs. The model was trained and iterated 500 times with a prediction accuracy of greater than or equal to 92% as the training objective and the prediction of fault development trend as the output. The fault development trend is as follows: short-term stability ≤ 24h - short-term deterioration, long-term stability > 24h - long-term deterioration.

[0011] Preferably, step S3 further includes: Based on the predicted fault development trend, it is stored in association with the original alarm records, and each record is marked with a warning level; The alarm information, status of the audible and visual alarms, and fault handling knowledge base suggestions are displayed in real time through the web and mobile APP. For alarms with high level or worsening trend, notifications are automatically pushed to the APP of relevant maintenance personnel. On each alarm message interface, work order status buttons are set to accept, process, and resolve. After the operations and maintenance manager sees the alarm, he / she clicks "accept" and the task is claimed. When personnel arrive at the scene or begin handling the situation, clicking "In Progress" will update the progress in the remarks section. After troubleshooting and verification, on-site personnel click "resolved," simultaneously recording the actual cause of the fault and the replaced parts. The terminal operation is synchronized to the cloud server, enabling alarm record status updates. This builds a three-tier platform of edge computing, cloud storage, and terminal interaction, enabling remote transmission and collaborative processing of alarm information.

[0012] Preferably, step S4 specifically includes: Based on a three-level platform of edge computing, cloud storage and terminal interaction, according to the real-time fault location code, combined with the rules of wind power equipment fault diagnosis and audible and visual alarm, the activation command is only sent to the local audible and visual alarm in the corresponding area to avoid information interference caused by false triggering of alarms in irrelevant areas and reduce energy consumption. If a start command is sent to the local audible and visual alarm in the corresponding area, an audible and visual signal will be emitted according to the preset level. The local audible and visual alarm will be triggered synchronously with the operation and maintenance terminal, ensuring that operation and maintenance personnel can receive the alarm regardless of whether they are on site.

[0013] Preferably, step S4 further includes: The verification cycle can be set from 1 to 5 minutes, and can be adjusted according to the fault level. If a level 1 warning is triggered, the verification cycle is 5 minutes; if a level 2 warning is triggered, the verification cycle is 3 minutes; if a level 3 emergency shutdown is triggered, the verification cycle is 1 minute. Triggered by an alarm signal, the system starts a countdown verification cycle. The duration is set according to the alarm level. If the maintenance terminal does not respond with confirmation within the cycle, the local audible and visual alarm automatically increases the alarm level, brightness and volume by 30%-50%, and sends an unconfirmed signal to the cloud server. The cloud server automatically pushes the alarm information a second time via SMS and telephone until the maintenance personnel provide confirmation.

[0014] Furthermore, a wind power equipment fault early warning management feedback system includes: The module includes a hybrid coding system module, an adaptive audio-visual output adjustment module, a remote collaborative control module, and an audio-visual alarm linkage verification mechanism module. Among them, the adaptive sound and light output adjustment module is electrically connected to the hybrid coding system module, the remote collaborative control module is electrically connected to the adaptive sound and light output adjustment module, and the sound and light alarm linkage verification mechanism module is electrically connected to the remote collaborative control module. The hybrid coding system module collects real-time operating data of wind power equipment and environmental parameters of the wind power equipment. It designs an 8-bit binary + 3-bit decimal hybrid coding system to determine whether a fault has occurred based on the wind power equipment operating data. If a fault occurs, a corresponding fault code is generated based on the hybrid coding system, realizing fine differentiation and rapid identification of fault type, level, location, and component. The adaptive sound and light output adjustment module establishes multi-dimensional dynamic adjustment rules and determines the output strategy of the sound and light alarm based on the environmental parameters of the wind power equipment and the corresponding fault codes. The remote collaborative control module, based on the determined output strategy of the audible and visual alarm, controls the audible and visual alarms deployed at the wind power equipment site and corresponding to the fault location to execute local alarms and simultaneously send them to the remote operation and maintenance terminal. It constructs a three-level platform of edge computing, cloud storage and terminal interaction to realize remote transmission and collaborative processing of alarm information. The audible and visual alarm linkage verification mechanism module is configured with a dynamic verification cycle linked to the fault level and a feedback timer is started. If no confirmation feedback of the alarm information is received from the remote operation and maintenance terminal within the preset verification cycle, the alarm escalation operation is automatically executed.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes a fault early warning management feedback method and system for wind power equipment. Its advantages include: high alarm information recognition; a multi-dimensional coding system enables precise differentiation of fault type, level, location, and component; and, combined with color-type and tone-level audio-visual mapping, maintenance personnel can quickly determine the core fault information through audio-visual signals. It also boasts strong environmental adaptability; the adaptive audio-visual adjustment module maintains a high alarm information transmission success rate under conditions of gale-force winds (level 8), -30℃ low temperatures, and strong sunlight, representing a significant improvement over traditional systems. Furthermore, it offers efficient operation and maintenance collaboration; a remote collaboration platform and linkage verification mechanism achieve closed-loop management of fault alarm, remote reception, and feedback confirmation, shortening fault response time. Finally, it demonstrates broad compatibility; this design method can be adapted to wind power equipment of different capacities and manufacturers without requiring large-scale modifications to existing SCADA systems, only the addition of edge computing nodes and audio-visual alarms, reducing modification costs and possessing promising prospects for industrial application. Attached Figure Description

[0016] Figure 1 A flowchart of a fault early warning management feedback method for wind power equipment; Figure 2 This is a framework diagram of a wind power equipment fault early warning management feedback system. Detailed Implementation

[0017] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0018] Reference Figure 1 As shown, a wind power equipment fault early warning management feedback method includes: S1. Real-time acquisition of wind turbine operating data and environmental parameters, designing an 8-bit binary + 3-bit decimal hybrid coding system. Based on the wind turbine operating data, it determines whether a fault has occurred. If a fault occurs, a corresponding fault code is generated based on the hybrid coding system, achieving refined differentiation and rapid identification of fault type, level, location, and component. Step S1 specifically includes: Based on the SCADA system for wind power equipment, and using vibration sensors, temperature sensors, current sensors, and power sensors, the sampling frequency is set to 5 seconds to collect real-time vibration, temperature, current, and power data of the wind power equipment. The data is then integrated to obtain the wind power equipment operation data stream and preprocessed. Based on environmental sensors, the sampling frequency is set to 10 seconds to collect wind speed, ambient temperature and light intensity in the target area in real time, and integrate them to obtain the environmental data stream of the wind power equipment, and perform data preprocessing on it. Based on the different sampling frequencies of wind power equipment operation data and environmental data, using the high-frequency timestamp of wind power equipment operation data as a benchmark, for each operation data point, the environmental record with the smallest timestamp difference of no more than 10 seconds is searched from the environmental data. The record is then matched to each wind power equipment operation data time point using the nearest neighbor interpolation method to form a synchronized data record. Based on the expert knowledge base and known historical fault records, an 8-bit binary + 3-bit decimal hybrid coding system is designed. For the 3-bit binary fault type coding in the hybrid coding system, it covers the mechanical, electrical, hydraulic and control system fault types of wind power equipment. Each type corresponds to a unique binary code. 001 represents mechanical faults, including main shaft and gearbox faults, and 010 represents electrical faults, including generator and converter faults. For the 3-bit binary fault level code in the hybrid coding system, three alarm levels are divided according to the degree of impact of the fault on the operation of the wind power equipment. If it is determined to be a level 1 warning, the operation can continue and attention is required. If it is determined to be a level 2 alarm, the machine needs to be shut down for maintenance. If it is determined to be a level 3 emergency shutdown, the machine should be shut down immediately to avoid damage. For the 2-bit binary fault location coding in the hybrid coding system, the wind turbine is divided into three areas: nacelle area, tower area, and hub area. Each area corresponds to a unique code, enabling rapid fault location. For the 3-bit binary component numbering system in the hybrid coding system, specific components in each area are numbered 001-150. The faulty component is directly associated through the code, without the need to consult the equipment drawings. Specific components include: the main shaft and gearbox in the nacelle area, and the cables and control cabinet in the tower area; A two-digit decimal fault duration code has been added. If the fault lasts for more than 60 minutes, an emergency linkage signal will be automatically triggered, and information will be pushed to the regional emergency command center to prevent the fault from escalating.

[0019] Step S1 also includes: Set a 5-minute sliding time window and calculate the mean and standard deviation of wind power equipment operation data within the time window in real time. Based on the wind power equipment manual and historical safety data records, a static threshold is set. If any operating data of the wind power equipment exceeds the static threshold within a time window, a primary alarm is immediately triggered, and the alarm parameters, values ​​and timestamps are recorded. Based on a 5-minute time window, a dynamic threshold is set. If the current instantaneous value of a certain operating data of the wind power equipment exceeds 3 consecutive sampling points and exceeds this dynamic threshold within the time window, it indicates that the operating data of the wind power equipment is showing an abnormal upward or downward trend, and a trend alarm is automatically triggered, recording the window statistics, current value and timestamp. The dynamic threshold is: ɑ±n Where α is the average operating data of wind power equipment within the time window, and n is a configurable sensitivity coefficient. This represents the standard deviation of wind power equipment operating data within this time window. Based on the trigger alarm parameters, values ​​and timestamps, and combined with an 8-bit binary + 3-bit decimal hybrid coding system, fault judgment is performed. If a fault is determined to have occurred, a corresponding fault code is generated according to the fault type-level-location-component, so as to achieve fine differentiation and rapid identification of fault type-level-location-component.

[0020] When using it, please refer to the steps outlined above: Traditional wind power fault monitoring relies heavily on single threshold alarms and manual experience-based judgment, resulting in vague fault descriptions, coarse location, and a lack of systematic coding. This leads to inaccurate fault type identification, slow location, and delayed emergency response, hindering refined fault management and rapid handling. The beneficial effect of this step lies in constructing an 8-bit binary + 3-bit decimal hybrid coding system, which achieves structured coding of fault type, level, location, component, and duration. Combined with multi-sensor data fusion and a dynamic threshold alarm mechanism, it can automatically generate accurate fault identification codes, supporting refined differentiation and rapid identification of faults from multiple dimensions, including mechanical faults, secondary alarms, nacelle areas, gearboxes, and faults lasting over 60 minutes. This improves fault location efficiency and emergency response accuracy.

[0021] S2. Based on the environmental parameters of the wind power equipment and the corresponding fault codes, establish multi-dimensional dynamic adjustment rules and determine the output strategy of the audible and visual alarm. Step S2 specifically includes: Based on the environmental data stream of the wind power equipment, the wind speed in the target area is obtained in real time. If the wind speed is greater than or equal to 15 / s, two instructions are generated: the basic volume of the buzzer is increased by 30% and the basic brightness of the LED is increased by 40%, and the wind speed compensation rule is executed. Based on the environmental data stream of the wind power equipment, the ambient temperature of the target area is obtained in real time. If the ambient temperature is less than or equal to -20℃, the LED adopts pulsed high brightness output, with a brightness of 2000cd / m² and a flashing frequency of 5Hz, to avoid the light source brightness decay caused by low temperature and to implement the low temperature enhancement rule. Based on the environmental data stream of the wind power equipment, the illumination of the target area is obtained in real time. If the illumination is greater than or equal to 10,000 lux, it is marked as strong light, and the basic brightness of the LED is increased by 50%. If the illumination is less than or equal to 500 lux, it is marked as weak light, and the basic brightness of the LED is reduced to 100 cd / m². The buzzer is turned on in a boost mode with a volume of 120dB to balance energy consumption and visibility, and the illumination adaptation rules are executed.

[0022] Step S2 also includes: Priority rules are defined as follows: Low temperature enhancement rule > Lighting adaptation rule > Wind speed compensation rule; If there is a conflict in LED control, the higher priority rule will override the lower priority rule's LED control instruction. If different rules adjust the buzzer, the maximum value will be taken, and a set of conflict-free final instructions will be generated for the LED and the buzzer. Based on the execution of wind speed compensation rules, low temperature enhancement rules, and illumination adaptation rules, a multi-dimensional dynamic adjustment rule is established. Based on the generated corresponding fault codes, the fault type is mapped to the LED color. If the LED displays red, it indicates a mechanical fault; if the LED displays yellow, it indicates an electrical fault; if the LED displays blue, it indicates a hydraulic fault; and if the LED displays purple, it indicates a fault in the surface control system. A fault type-color mapping rule is established. The fault level is mapped to the buzzer tone and flashing frequency. If the buzzer sounds intermittently at a low frequency and the LED flashes at a low frequency in sync with the fault color, it is determined to be a level 1 warning. If the buzzer sounds intermittently at a medium frequency and the LED flashes at a medium frequency in sync with the fault color, it is determined to be a level 2 warning. If the buzzer sounds continuously at a high frequency and the LED flashes continuously at a high frequency in sync with the fault color, it is determined to be a level 3 emergency shutdown. A level-sound and light mode mapping rule is established. Based on the fault type-color mapping rule and the level-sound and light mode mapping rule, establish the fault diagnosis and sound and light alarm rules for wind power equipment, and determine the output strategy of the sound and light alarm.

[0023] When using it, please refer to the steps outlined above: In existing technologies, wind power equipment status indication systems typically employ fixed thresholds and single-dimensional alarm modes, failing to dynamically adjust the intensity and mode of audible and visual outputs according to complex and changing environmental conditions. This results in insufficient alarm visibility and recognizability under conditions of strong light, low temperature, or high wind speed, and a lack of multi-rule priority coordination mechanisms, easily leading to command conflicts. This step achieves adaptive optimization of the audible and visual alarm system by establishing multi-dimensional dynamic adjustment rules based on environmental perception. It adjusts LED brightness and buzzer volume in real time according to wind speed, low temperature, and light intensity, ensuring alarm effectiveness in extreme environments. Through clear rule priorities and command fusion logic, control conflicts are eliminated. Combining fault type-color mapping and level-audible and visual mode mapping, a hierarchical and intuitive fault diagnosis and alarm output are formed, improving the efficiency of maintenance personnel's perception of equipment status and the accuracy of emergency response.

[0024] S3. Based on the determined output strategy of the audible and visual alarm, control the audible and visual alarms deployed at the wind power equipment site and corresponding to the fault location to execute local alarms and send them to the remote operation and maintenance terminal simultaneously. Construct a three-level platform of edge computing, cloud storage and terminal interaction to realize remote transmission and collaborative processing of alarm information. Step S3 specifically includes: Based on the wind turbine tower nacelle, an industrial-grade edge computing gateway is deployed, with a sampling frequency of 5 seconds, to receive fault data uploaded by the wind turbine SCADA system in real time, and to filter out abnormal data and fill in missing values ​​in the fault data. The fault data is encoded using an 8-bit binary + 3-bit decimal hybrid encoding system, and the encoded command is sent to the local audible and visual alarm to trigger on-site audible and visual prompts. Use Alibaba Cloud ECS servers to build a cloud database to store historical alarm data of wind power equipment. The storage period must be greater than 5 years. Based on 10,000 sets of historical alarm data of wind power equipment in the cloud database, a BP neural network model was established with fault codes, current environmental parameters and wind power equipment running time as inputs. The model was trained and iterated 500 times with a prediction accuracy of greater than or equal to 92% as the training objective and the prediction of fault development trend as the output. The fault development trend is as follows: short-term stability ≤ 24h - short-term deterioration, long-term stability > 24h - long-term deterioration.

[0025] Step S3 also includes: Based on the predicted fault development trend, it is stored in association with the original alarm records, and each record is marked with a warning level; The alarm information, status of the audible and visual alarms, and fault handling knowledge base suggestions are displayed in real time through the web and mobile APP. For alarms with high level or worsening trend, notifications are automatically pushed to the APP of relevant maintenance personnel. On each alarm message interface, work order status buttons are set to accept, process, and resolve. After the operations and maintenance manager sees the alarm, he / she clicks "accept" and the task is claimed. When personnel arrive at the scene or begin handling the situation, clicking "In Progress" will update the progress in the remarks section. After troubleshooting and verification, on-site personnel click "resolved," simultaneously recording the actual cause of the fault and the replaced parts. The terminal operation is synchronized to the cloud server, enabling alarm record status updates. This builds a three-tier platform of edge computing, cloud storage, and terminal interaction, enabling remote transmission and collaborative processing of alarm information.

[0026] When using it, please refer to the steps outlined above: Traditional wind power equipment alarm systems typically employ simple threshold triggering mechanisms, resulting in limited alarm information and a lack of intelligent analysis. This leads to a disconnect between on-site alarms and remote maintenance, reliance on manual experience for fault handling, delayed responses, and a lack of comprehensive alarm data closed-loop management and trend prediction capabilities, resulting in low maintenance efficiency and hindering preventative maintenance. The benefits of this approach are: by constructing a three-tiered platform of edge computing, cloud storage, and terminal interaction, real-time intelligent processing and collaborative closed-loop management of alarm information are achieved. Real-time preprocessing of fault data and local audible and visual alarms are implemented based on the edge computing gateway, ensuring timely on-site response. A cloud-based BP neural network model intelligently analyzes historical alarm data, predicts fault development trends, and assigns warning levels. Real-time synchronization of alarm information, processing status, and knowledge base suggestions across multiple terminals is achieved via web and mobile apps, with embedded work order status tracking and closed-loop recording functions, improving maintenance collaboration efficiency and fault handling transparency, and providing data support for preventative maintenance.

[0027] S4. Set a dynamic verification cycle linked to the fault level and start the feedback timer. If no confirmation feedback of alarm information is received from the remote operation and maintenance terminal within the preset verification cycle, the alarm escalation operation will be executed automatically. Step S4 specifically includes: Based on a three-level platform of edge computing, cloud storage and terminal interaction, according to the real-time fault location code, combined with the rules of wind power equipment fault diagnosis and audible and visual alarm, the activation command is only sent to the local audible and visual alarm in the corresponding area to avoid information interference caused by false triggering of alarms in irrelevant areas and reduce energy consumption. If a start command is sent to the local audible and visual alarm in the corresponding area, an audible and visual signal will be emitted according to the preset level. The local audible and visual alarm will be triggered synchronously with the operation and maintenance terminal, ensuring that operation and maintenance personnel can receive the alarm regardless of whether they are on site.

[0028] Step S4 also includes: The verification cycle can be set from 1 to 5 minutes, and can be adjusted according to the fault level. If a level 1 warning is triggered, the verification cycle is 5 minutes; if a level 2 warning is triggered, the verification cycle is 3 minutes; if a level 3 emergency shutdown is triggered, the verification cycle is 1 minute. Triggered by an alarm signal, the system starts a countdown verification cycle. The duration is set according to the alarm level. If the maintenance terminal does not respond with confirmation within the cycle, the local audible and visual alarm automatically increases the alarm level, brightness and volume by 30%-50%, and sends an unconfirmed signal to the cloud server. The cloud server automatically pushes the alarm information a second time via SMS and telephone until the maintenance personnel provide confirmation.

[0029] When using it, please refer to the steps outlined above: Traditional wind turbine alarm systems often employ a global broadcast alarm mechanism, where simultaneous triggering of alarms in unrelated areas can easily cause on-site noise and light pollution and energy waste. Furthermore, alarm responses rely on manual confirmation, lacking a tiered, progressive mandatory confirmation mechanism. This can easily lead to delayed fault response due to missed information or delayed processing, potentially causing minor faults to escalate into serious equipment damage. This approach uses edge computing positioning linked to fault levels to achieve precise, localized noise and light alarms, reducing interference and energy consumption in unrelated areas. A dynamic verification cycle and feedback escalation mechanism are established, setting differentiated confirmation time limits based on fault levels. If the maintenance terminal fails to respond promptly, the system automatically escalates the alarm and pushes secondary information via the cloud server, forming a mandatory confirmation closed loop. This improves the reliability and timeliness of fault response, avoiding processing delays caused by human negligence.

[0030] Reference Figure 2 As shown, a wind power equipment fault early warning management feedback system includes: The module includes a hybrid coding system module, an adaptive audio-visual output adjustment module, a remote collaborative control module, and an audio-visual alarm linkage verification mechanism module. Among them, the adaptive sound and light output adjustment module is electrically connected to the hybrid coding system module, the remote collaborative control module is electrically connected to the adaptive sound and light output adjustment module, and the sound and light alarm linkage verification mechanism module is electrically connected to the remote collaborative control module. The hybrid coding system module collects real-time operating data of wind power equipment and environmental parameters of the wind power equipment. It designs an 8-bit binary + 3-bit decimal hybrid coding system to determine whether a fault has occurred based on the wind power equipment operating data. If a fault occurs, a corresponding fault code is generated based on the hybrid coding system, realizing fine differentiation and rapid identification of fault type, level, location, and component. The adaptive sound and light output adjustment module establishes multi-dimensional dynamic adjustment rules and determines the output strategy of the sound and light alarm based on the environmental parameters of the wind power equipment and the corresponding fault codes. The remote collaborative control module, based on the determined output strategy of the audible and visual alarm, controls the audible and visual alarms deployed at the wind power equipment site and corresponding to the fault location to execute local alarms and simultaneously send them to the remote operation and maintenance terminal. It constructs a three-level platform of edge computing, cloud storage and terminal interaction to realize remote transmission and collaborative processing of alarm information. The audible and visual alarm linkage verification mechanism module is configured with a dynamic verification cycle linked to the fault level and a feedback timer is started. If no confirmation feedback of the alarm information is received from the remote operation and maintenance terminal within the preset verification cycle, the alarm escalation operation is automatically executed.

[0031] Based on the above, the specific implementation method is as follows: At 14:05 in the afternoon, the SCADA system of the No. 2 wind turbine of a certain wind farm detected abnormal gearbox temperature data. The edge computing gateway detected that the temperature value of three consecutive sampling points exceeded the dynamic threshold calculated based on a 5-minute sliding window for 15 seconds with a 5-second cycle, and automatically triggered a trend alarm. The system immediately initiates the fault diagnosis logic, combines it with the expert knowledge base, determines it to be a mechanical fault, and generates a fault code 001-010-01-037-075 according to the 8-bit binary + 3-bit decimal mixed coding rule. This code represents: 001 is the mechanical fault type, 010 is a level 2 alarm requiring shutdown and maintenance, 01 is the engine compartment area, 037 is the gearbox component number, and 075 is that the fault has lasted for 75 minutes, triggering a linkage signal for more than 60 minutes. The system instantly completes a detailed and structured description of the mechanical fault, from abnormal temperature data to the engine compartment gearbox requiring shutdown and maintenance, and the fault has lasted for a considerable period of time. At this time, the on-site environmental monitoring showed a wind speed of 20 m / s, an ambient temperature of -22℃, and a low light level of 400 lux (overcast). After receiving fault codes 001-010, the audible and visual alarm control module first determined the basic output strategy: the LED displayed red, indicating a mechanical fault, and the buzzer sounded intermittently at a medium frequency as a level two alarm. The system dynamically adjusted the output intensity based on real-time environmental parameters: it prioritized the low-temperature enhancement rule, switching the LED to a brightness of 2000 cd per square meter and a 5Hz pulsed red flashing to overcome light decay caused by low temperature; secondly, it implemented the light adaptation rule, as the light level was below 500 lux, the buzzer activated its enhancement mode, increasing the volume to 120 dB; finally, it implemented the wind speed compensation rule, further increasing the buzzer volume by 30% to counteract wind noise. The audible and visual alarm on the top of the No. 2 wind turbine nacelle provides clear warnings in the midst of strong winds and snowstorms with its high-brightness pulsed red light and ultra-high volume intermittent alarm, ensuring the effective transmission of alarm information under adverse conditions. The fault code and environmental data are uploaded to the cloud server. The cloud-based BP neural network model calls the historical operating data of the wind turbine and similar fault cases. Combined with the current high wind speed of 28 m / s, it predicts that the gearbox fault trend will deteriorate in the short term, and the warning level is marked as high. The alarm notification popped up simultaneously on the mobile APP of maintenance team leader Zhang San and the Web screen in the duty room. The interface clearly displayed the fault code, the location as the No. 2 wind turbine nacelle, the predicted trend as short-term deterioration, and the preliminary handling suggestions recommended by the knowledge base as checking the gearbox cooling system and oil level. Zhang San immediately clicked "Accepted Work Order" on the APP and notified the maintenance team. After the maintenance personnel arrived at the site, they updated the work order status to "Processing" and wrote in the remarks column that they were checking the cooling fan. One hour later, they confirmed that the cause was a damaged cooling fan. After the fan was replaced, the fault was eliminated. The on-site personnel clicked "Resolved" on the APP and filled in the final cause and replacement part record, completing the work order loop. All statuses and records were synchronized to the cloud for archiving in real time. At the initial stage of the fault, the system was set with a 3-minute verification cycle based on the level 2 alarm. After the alarm was issued, due to the loud wind noise at the scene and Zhang San's ongoing emergency operation, he was unable to confirm the alarm via the APP within 3 minutes. After the feedback timer returned to zero, the alarm escalation mechanism was automatically activated. Local escalation: The output intensity of the local audible and visual alarm on wind turbine No. 2 was further increased, with brightness and volume increased by 40% and the flashing pattern becoming more rapid. Remote escalation: Upon receiving the unconfirmed signal, the cloud server immediately pushed the upgraded emergency alarm information again to Zhang San and his backup supervisor, Li Si, via SMS and automated voice call. After receiving the voice call, Zhang San immediately checked his phone and completed the confirmation. The system then stopped pushing the upgrade. This mechanism ensured that critical alarms would never be missed due to human negligence, forcibly forming a closed loop for fault response confirmation, and effectively preventing the potential escalation of equipment damage due to processing delays.

[0032] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for fault early warning management feedback of wind power equipment, characterized in that, include: S1. Real-time acquisition of wind turbine operating data and environmental parameters, designing an 8-bit binary + 3-bit decimal hybrid coding system. Based on the wind turbine operating data, it determines whether a fault has occurred. If a fault occurs, a corresponding fault code is generated based on the hybrid coding system, achieving refined differentiation and rapid identification of fault type, level, location, and component. S2. Based on the environmental parameters of the wind power equipment and the corresponding fault codes, establish multi-dimensional dynamic adjustment rules and determine the output strategy of the audible and visual alarm. S3. Based on the determined output strategy of the audible and visual alarm, control the audible and visual alarms deployed at the wind power equipment site and corresponding to the fault location to execute local alarms and send them to the remote operation and maintenance terminal simultaneously. Construct a three-level platform of edge computing, cloud storage and terminal interaction to realize remote transmission and collaborative processing of alarm information. S4. Set a dynamic verification cycle linked to the fault level and start the feedback timer. If no confirmation feedback of the alarm information is received from the remote operation and maintenance terminal within the preset verification cycle, the alarm escalation operation will be executed automatically.

2. The wind power equipment fault early warning management feedback method according to claim 1, characterized in that, Step S1 specifically includes: Based on the SCADA system for wind power equipment, and using vibration sensors, temperature sensors, current sensors, and power sensors, the sampling frequency is set to 5 seconds to collect real-time vibration, temperature, current, and power data of the wind power equipment. The data is then integrated to obtain the wind power equipment operation data stream and preprocessed. Based on environmental sensors, the sampling frequency is set to 10 seconds to collect wind speed, ambient temperature and light intensity in the target area in real time, and integrate them to obtain the environmental data stream of the wind power equipment, and perform data preprocessing on it. Based on the different sampling frequencies of wind power equipment operation data and environmental data, using the high-frequency timestamp of wind power equipment operation data as a benchmark, for each operation data point, the environmental record with the smallest timestamp difference of no more than 10 seconds is searched from the environmental data. The record is then matched to each wind power equipment operation data time point using the nearest neighbor interpolation method to form a synchronized data record. Based on the expert knowledge base and known historical fault records, an 8-bit binary + 3-bit decimal hybrid coding system is designed. For the 3-bit binary fault type coding in the hybrid coding system, it covers the mechanical, electrical, hydraulic and control system fault types of wind power equipment. Each type corresponds to a unique binary code. 001 represents mechanical faults, including main shaft and gearbox faults, and 010 represents electrical faults, including generator and converter faults. For the 3-bit binary fault level code in the hybrid coding system, three alarm levels are divided according to the degree of impact of the fault on the operation of the wind power equipment. If it is determined to be a level 1 warning, the operation can continue and attention is required. If it is determined to be a level 2 alarm, the machine needs to be shut down for maintenance. If it is determined to be a level 3 emergency shutdown, the machine should be shut down immediately to avoid damage. For the 2-bit binary fault location coding in the hybrid coding system, the wind turbine is divided into three areas: nacelle area, tower area, and hub area. Each area corresponds to a unique code, enabling rapid fault location. For the 3-bit binary component numbering system in the hybrid coding system, specific components in each area are numbered 001-150. The faulty component is directly associated through the code, without the need to consult the equipment drawings. Specific components include: the main shaft and gearbox in the nacelle area, and the cables and control cabinet in the tower area; A two-digit decimal fault duration code has been added. If the fault lasts for more than 60 minutes, an emergency linkage signal will be automatically triggered, and information will be pushed to the regional emergency command center to prevent the fault from escalating.

3. The wind power equipment fault early warning management feedback method according to claim 21, characterized in that, Step S1 also includes: Set a 5-minute sliding time window and calculate the mean and standard deviation of wind power equipment operation data within the time window in real time. Based on the wind power equipment manual and historical safety data records, a static threshold is set. If any operating data of the wind power equipment exceeds the static threshold within a time window, a primary alarm is immediately triggered, and the alarm parameters, values ​​and timestamps are recorded. Based on a 5-minute time window, a dynamic threshold is set. If the current instantaneous value of a certain operating data of the wind power equipment exceeds 3 consecutive sampling points and exceeds this dynamic threshold within the time window, it indicates that the operating data of the wind power equipment is showing an abnormal upward or downward trend, and a trend alarm is automatically triggered, recording the window statistics, current value and timestamp. The dynamic threshold is: ɑ±n Where α is the average operating data of wind power equipment within the time window, and n is a configurable sensitivity coefficient. This represents the standard deviation of wind power equipment operating data within this time window. Based on the trigger alarm parameters, values ​​and timestamps, and combined with an 8-bit binary + 3-bit decimal hybrid coding system, fault judgment is performed. If a fault is determined to have occurred, a corresponding fault code is generated according to the fault type-level-location-component, so as to achieve fine differentiation and rapid identification of fault type-level-location-component.

4. The wind power equipment fault early warning management feedback method according to claim 3, characterized in that, Step S2 specifically includes: Based on the environmental data stream of the wind power equipment, the wind speed in the target area is obtained in real time. If the wind speed is greater than or equal to 15 / s, two instructions are generated: the basic volume of the buzzer is increased by 30% and the basic brightness of the LED is increased by 40%, and the wind speed compensation rule is executed. Based on the environmental data stream of the wind power equipment, the ambient temperature of the target area is obtained in real time. If the ambient temperature is less than or equal to -20℃, the LED adopts pulsed high brightness output, with a brightness of 2000cd / m² and a flashing frequency of 5Hz, to avoid the light source brightness decay caused by low temperature and to implement the low temperature enhancement rule. Based on the environmental data stream of the wind power equipment, the illumination of the target area is obtained in real time. If the illumination is greater than or equal to 10,000 lux, it is marked as strong light, and the basic brightness of the LED is increased by 50%. If the illumination is less than or equal to 500 lux, it is marked as weak light, and the basic brightness of the LED is reduced to 100 cd / m². The buzzer is turned on in a boost mode with a volume of 120dB to balance energy consumption and visibility, and the illumination adaptation rules are executed.

5. The wind power equipment fault early warning management feedback method according to claim 4, characterized in that, Step S2 also includes: Priority rules are defined as follows: Low temperature enhancement rule > Lighting adaptation rule > Wind speed compensation rule; If there is a conflict in LED control, the higher priority rule will override the lower priority rule's LED control instruction. If different rules adjust the buzzer, the maximum value will be taken, and a set of conflict-free final instructions will be generated for the LED and the buzzer. Based on the execution of wind speed compensation rules, low temperature enhancement rules, and illumination adaptation rules, a multi-dimensional dynamic adjustment rule is established. Based on the generated corresponding fault codes, the fault type is mapped to the LED color. If the LED displays red, it indicates a mechanical fault; if the LED displays yellow, it indicates an electrical fault; if the LED displays blue, it indicates a hydraulic fault; and if the LED displays purple, it indicates a fault in the surface control system. A fault type-color mapping rule is established. The fault level is mapped to the buzzer tone and flashing frequency. If the buzzer sounds intermittently at a low frequency and the LED flashes at a low frequency in sync with the fault color, it is determined to be a level 1 warning. If the buzzer sounds intermittently at a medium frequency and the LED flashes at a medium frequency in sync with the fault color, it is determined to be a level 2 warning. If the buzzer sounds continuously at a high frequency and the LED flashes continuously at a high frequency in sync with the fault color, it is determined to be a level 3 emergency shutdown. A level-sound and light mode mapping rule is established. Based on the fault type-color mapping rule and the level-sound and light mode mapping rule, establish the fault diagnosis and sound and light alarm rules for wind power equipment, and determine the output strategy of the sound and light alarm.

6. The wind power equipment fault early warning management feedback method according to claim 5, characterized in that, Step S3 specifically includes: Based on the wind turbine tower nacelle, an industrial-grade edge computing gateway is deployed, with a sampling frequency of 5 seconds, to receive fault data uploaded by the wind turbine SCADA system in real time, and to filter out abnormal data and fill in missing values ​​in the fault data. The fault data is encoded using an 8-bit binary + 3-bit decimal hybrid encoding system, and the encoded command is sent to the local audible and visual alarm to trigger on-site audible and visual prompts. Use Alibaba Cloud ECS servers to build a cloud database to store historical alarm data of wind power equipment. The storage period must be greater than 5 years. Based on 10,000 sets of historical alarm data of wind power equipment in the cloud database, a BP neural network model was established with fault codes, current environmental parameters and wind power equipment running time as inputs. The model was trained and iterated 500 times with a prediction accuracy of greater than or equal to 92% as the training objective and the prediction of fault development trend as the output. The fault development trend is as follows: short-term stability ≤ 24h - short-term deterioration, long-term stability > 24h - long-term deterioration.

7. The wind power equipment fault early warning management feedback method according to claim 6, characterized in that, Step S3 also includes: Based on the predicted fault development trend, it is stored in association with the original alarm records, and each record is marked with a warning level; The alarm information, status of the audible and visual alarms, and fault handling knowledge base suggestions are displayed in real time through the web and mobile APP. For alarms with high level or worsening trend, notifications are automatically pushed to the APP of relevant maintenance personnel. On each alarm message interface, work order status buttons are set to accept, process, and resolve. After the operations and maintenance manager sees the alarm, he / she clicks "accept" and the task is claimed. When personnel arrive at the scene or begin handling the situation, clicking "In Progress" will update the progress in the remarks section. After troubleshooting and verification, on-site personnel click "resolved," simultaneously recording the actual cause of the fault and the replaced parts. The terminal operation is synchronized to the cloud server, enabling alarm record status updates. This builds a three-tier platform of edge computing, cloud storage, and terminal interaction, enabling remote transmission and collaborative processing of alarm information.

8. The wind power equipment fault early warning management feedback method according to claim 7, characterized in that, Step S4 specifically includes: Based on a three-level platform of edge computing, cloud storage and terminal interaction, according to the real-time fault location code, combined with the rules of wind power equipment fault diagnosis and audible and visual alarm, the activation command is only sent to the local audible and visual alarm in the corresponding area to avoid information interference caused by false triggering of alarms in irrelevant areas and reduce energy consumption. If a start command is sent to the local audible and visual alarm in the corresponding area, an audible and visual signal will be emitted according to the preset level. The local audible and visual alarm will be triggered synchronously with the operation and maintenance terminal, ensuring that operation and maintenance personnel can receive the alarm regardless of whether they are on site.

9. The wind power equipment fault early warning management feedback method according to claim 8, characterized in that, Step S4 also includes: The verification cycle can be set from 1 to 5 minutes, and can be adjusted according to the fault level. If a level 1 warning is triggered, the verification cycle is 5 minutes; if a level 2 warning is triggered, the verification cycle is 3 minutes; if a level 3 emergency shutdown is triggered, the verification cycle is 1 minute. Triggered by an alarm signal, the system starts a countdown verification cycle. The duration is set according to the alarm level. If the maintenance terminal does not respond with confirmation within the cycle, the local audible and visual alarm automatically increases the alarm level, brightness and volume by 30%-50%, and sends an unconfirmed signal to the cloud server. The cloud server automatically pushes the alarm information a second time via SMS and telephone until the maintenance personnel provide confirmation.

10. A wind power equipment fault early warning management feedback system, characterized in that, A method for implementing a wind power equipment fault early warning management feedback method as described in any one of claims 1-9, comprising: The module includes a hybrid coding system module, an adaptive audio-visual output adjustment module, a remote collaborative control module, and an audio-visual alarm linkage verification mechanism module. Among them, the adaptive sound and light output adjustment module is electrically connected to the hybrid coding system module, the remote collaborative control module is electrically connected to the adaptive sound and light output adjustment module, and the sound and light alarm linkage verification mechanism module is electrically connected to the remote collaborative control module. The hybrid coding system module collects real-time operating data of wind power equipment and environmental parameters of the wind power equipment. It designs an 8-bit binary + 3-bit decimal hybrid coding system to determine whether a fault has occurred based on the wind power equipment operating data. If a fault occurs, a corresponding fault code is generated based on the hybrid coding system, realizing fine differentiation and rapid identification of fault type, level, location, and component. The adaptive sound and light output adjustment module establishes multi-dimensional dynamic adjustment rules and determines the output strategy of the sound and light alarm based on the environmental parameters of the wind power equipment and the corresponding fault codes. The remote collaborative control module, based on the determined output strategy of the audible and visual alarm, controls the audible and visual alarms deployed at the wind power equipment site and corresponding to the fault location to execute local alarms and simultaneously send them to the remote operation and maintenance terminal. It constructs a three-level platform of edge computing, cloud storage and terminal interaction to realize remote transmission and collaborative processing of alarm information. The audible and visual alarm linkage verification mechanism module is configured with a dynamic verification cycle linked to the fault level and a feedback timer is started. If no confirmation feedback of the alarm information is received from the remote operation and maintenance terminal within the preset verification cycle, the alarm escalation operation is automatically executed.

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