Electric pipeline fault early warning method and system

Abstract

The invention discloses an electrical pipeline fault early warning method and system, and relates to the technical field of electrical pipelines, and the method comprises the steps: collecting electrical pipeline data, carrying out the preprocessing of the collected data, packaging the processed data, transmitting the packaged data to a central processing unit, analyzing the data, extracting characteristic parameters, and combining a predefined rule base, the method comprises the following steps: carrying out fault feature identification and abnormity determination, matching a fault mode based on identification features, evaluating a fault type and a severity degree, predicting a fault probability in combination with a historical data trend, automatically generating an early warning message when real-time data exceeds a preset threshold value, sending structured early warning information through a multi-mode communication channel, and tracking and confirming a state. And triggering preset emergency operation, and meanwhile, providing a manual intervention interface and recording a processing process. By monitoring the parameters of the electrical pipeline in real time and triggering an early warning mechanism, the safety problem is identified and processed before the fault occurs, the downtime is reduced, the reliability of the pipeline is improved, and preventive maintenance is realized so as to reduce the emergency maintenance cost.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical pipelines, and in particular to an electrical pipeline fault early warning method and system. Background Art

[0002] An electrical conduit system refers to a complete network of pipes used to transmit electricity, electrical signals or control signals, including cable conduit systems, wire trough systems and conduit systems. These systems are widely used inside buildings and industrial facilities. Their main function is to install and protect cables or wires to ensure that they transmit electricity or signals safely and reliably. Cable conduit systems usually use metal or non-metal, while wire trough systems are specially designed for easy installation of wires and are usually installed on walls or ceilings. Conduit systems provide greater physical protection and fire resistance and are usually used in industrial environments. In addition, equipment such as electrical boxes, junction boxes and distribution boxes are also indispensable components of electrical conduit systems, used to connect, distribute and protect electrical equipment and lines, and ensure the stable operation and safety of electrical systems.

[0003] The existing electrical pipeline fault warning system only issues alarm notifications for faults that have occurred or foreshadowing of faults. The fault response and processing speed is poor, resulting in long system downtime and high maintenance costs, which in turn leads to large losses from the fault. Summary of the Invention

[0004] In view of the above-mentioned existing problems, the present invention provides an electrical pipeline fault warning method and system to solve the problems in the prior art of poor fault response and processing speed, long system downtime and high maintenance costs, which lead to large fault losses.

[0005] In order to solve the above technical problems, an electrical pipeline fault early warning method is proposed, including:

[0006] Collect electrical pipeline data, pre-process the collected data, package the processed data and transmit it to the central processing unit; parse the data, extract characteristic parameters, and combine with the predefined rule base to identify fault characteristics and abnormality judgment, match fault modes based on identification characteristics, evaluate fault type and severity, and predict fault probability based on historical data trends; when real-time data exceeds the preset threshold, automatically generate an early warning message, send structured early warning information through multi-modal communication channels, track and confirm the status, trigger preset emergency operations, and provide a manual intervention interface and record the processing process.

[0007] As a preferred solution of the electrical pipeline fault early warning method described in the present invention, the collection of electrical pipeline data includes determining the location of key safety nodes in the electrical pipeline, analyzing the type and quantity of current, voltage, temperature and vibration parameters to be monitored, and deploying adaptive sensors according to the parameter characteristics to monitor the current, voltage, temperature and vibration parameters in real time.

[0008] As a preferred solution of the electrical pipeline fault early warning method described in the present invention, the preprocessing includes collecting sensor data, performing noise filtering, invalid data elimination and signal correction processing;

[0009] The preprocessing specifically includes configuring the interface of the data acquisition unit and connecting the sensor, collecting signals at a set frequency, performing analog-to-digital conversion on the analog signal, applying digital filtering to process noise, correcting data in combination with the sensor calibration curve, integrating multi-source data into a unified structure, performing compression processing, temporarily storing in local memory and verifying data integrity and accuracy.

[0010] As a preferred solution of the electrical pipeline fault early warning method described in the present invention, the transmission to the central processing unit includes evaluating the communication mode based on transmission distance, environmental adaptability and reliability, selecting a combination of Ethernet, optical fiber and wireless network technologies, adding timestamps and identity codes to the data, and packaging and transmitting according to the communication protocol.

[0011] As a preferred embodiment of the electrical pipeline fault early warning method of the present invention, the fault feature identification and abnormality determination includes cleaning missing and abnormal data, standardizing the data format, extracting periodic and trend characteristic parameters, and detecting fault features through a pattern recognition algorithm and comparing them with the rule base threshold.

[0012] The method of predicting the probability of failure by combining historical data trends includes inputting features into a diagnostic engine, matching historical failure patterns, determining the type of failure, prioritizing failures based on feature strength and impact range, analyzing historical data patterns, identifying current abnormal patterns and potential hidden dangers, and predicting the probability and type of failure based on time series.

[0013] As a preferred solution of the electrical pipeline fault early warning method described in the present invention, the automatic generation of the early warning message includes automatically generating an early warning message containing a level, parameter value and timestamp when real-time data exceeds a preset threshold;

[0014] The automatic generation of warning messages also includes setting dynamic warning thresholds based on historical data and operating parameters, analyzing transmission data in real time, determining whether thresholds and abnormal trends are triggered, constructing warning event messages containing levels, parameter deviation values ​​and timestamps, and dynamically determining warning types as parameter exceeding standards, abnormal trends and emergencies.

[0015] As a preferred embodiment of the electrical pipeline fault early warning method described in the present invention, the tracking and confirmation status includes aggregating the real-time values, thresholds, and trigger time information of abnormal parameters, configuring a combined notification strategy of SMS, email, mobile application, and sound and light alarms according to the warning level, generating structured notification content with handling suggestions, and monitoring the receipt confirmation status;

[0016] The recording and processing process includes automatically executing power cut-off and backup system startup operations, providing a manual operation interface, executing fault handling, and recording response actions and processing results throughout the entire process; visual monitoring includes designing a graphical interface to dynamically display equipment status and warning information, providing the function of querying historical data by time, equipment and parameters, and identifying risk points through trend charts, supporting view customization and highlighting of warning information.

[0017] As a preferred solution of the electrical pipeline fault early warning system described in the present invention, it is characterized by including a sensor detection module, a data acquisition and transmission module, a central processing and analysis module, and an early warning and notification module.

[0018] The sensor detection module is used to provide the system with raw data on current fluctuations, temperature anomalies, mechanical vibrations and voltage deviations through physical signal acquisition, covering the safety monitoring needs of areas with high fault incidence.

[0019] The data acquisition and transmission module is used to integrate multi-source heterogeneous sensor data into a unified format, select wired or wireless technology based on transmission distance and environmental adaptability, add timestamps and sensor IDs, and then transmit to the central unit.

[0020] The central processing and analysis module is used to parse the transmitted data, determine the type of anomaly, drive the fault diagnosis engine to match historical patterns to determine the type and severity of the fault, and predict the probability of future faults.

[0021] The warning and notification module is used to trigger warnings based on dynamic thresholds or abnormal trends, construct messages containing levels, parameter deviation values ​​and timestamps, combine SMS, email, mobile applications and sound and light alarm channels according to the warning level, send structured notifications with processing suggestions and track receipt status.

[0022] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a method for early warning of electrical pipeline faults when executing the computer program.

[0023] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for early warning of electrical pipeline faults.

[0024] Beneficial effects of the present invention: The present invention monitors current, voltage, temperature and vibration parameters in real time, combined with an early warning mechanism, to identify and address potential problems before a fault occurs, significantly reducing system downtime and improving the reliability and stability of electrical pipelines; at the same time, it supports preventive maintenance, avoids emergency repairs and replacements caused by sudden faults, effectively reduces maintenance costs and resource waste, and prevents electrical fires or other safety accidents by timely intervening in abnormal situations, ensuring the safety of personnel and equipment; the early warning notification function uses multiple channels such as SMS, email, and mobile applications to quickly transmit structured information, ensuring the timeliness of fault response and minimizing the impact on the system; the diagnostic prediction report generated by data collection and analysis provides a decision-making basis for maintenance strategy optimization, and the visual monitoring module simplifies historical data query and trend analysis through a graphical interface, helping users to efficiently identify risks; the automated fault detection and processing process reduces manual intervention and improves operation and maintenance efficiency; the flexible design of the system supports on-demand selection of sensor types and wired and wireless transmission methods, facilitating expansion and upgrading; the application of machine learning and deep learning algorithms enhances the accuracy of fault feature recognition and achieves more accurate diagnostic predictions; after the early warning is triggered, emergency measures are automatically executed and the manual intervention interface is retained to ensure that the failure loss is minimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 The present invention provides an overall flow chart of an electrical pipeline fault early warning method according to an embodiment of the present invention.

[0027] Figure 2 A system solution flow chart of an electrical pipeline fault early warning system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0028] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0029] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0030] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it individually or selectively refer to an embodiment that is mutually exclusive of other embodiments.

[0031] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0032] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0033] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0034] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides an electrical pipeline fault early warning method, comprising:

[0035] S1: Collect electrical pipeline data, pre-process the collected data, package the processed data and transmit it to the central processing unit.

[0036] The collection of electrical pipeline data includes determining the location of key safety nodes in the electrical pipeline, analyzing the types and quantities of current, voltage, temperature and vibration parameters that need to be monitored, and deploying adaptive sensors based on parameter characteristics to monitor current, voltage, temperature and vibration parameters in real time.

[0037] Sensor data collection includes determining the location of key nodes in electrical pipelines. Nodes are high-fault areas and areas that are critical to the safe operation of the electrical system. Analyzing the parameters that need to be monitored, including current, voltage, temperature, and vibration, and determining the type and quantity of sensors, the sensor type is selected based on demand analysis. Hall effect sensors are used to measure current, thermocouples or thermistors to measure temperature, accelerometers to measure vibration, and voltage sensors to measure voltage. Sensors are then installed at key nodes in electrical pipelines.

[0038] The preprocessing includes collecting sensor data, performing noise filtering, invalid data elimination and signal correction processing; data collection and preliminary processing include loading and configuring the operating system and software module of the data acquisition unit, connecting the sensor to the input interface of the data acquisition unit, the data acquisition unit reading analog signals and digital signals from the sensor according to the set sampling frequency, converting the analog signals into digital signals through an analog-to-digital converter, applying digital filtering technology to eliminate noise and interference in the collected signals, retaining valid signals, preliminarily detecting and eliminating abnormal data points, correcting the collected data according to the calibration curve and calibration constant of the sensor, converting the data output by the sensor into actual physical quantities, integrating the sensor data into a unified data structure, compressing the data using data compression technology to reduce the storage space and transmission bandwidth occupied, temporarily storing the filtered and processed data in the local memory of the data acquisition unit, checking the integrity and consistency of the collected data, and verifying the accuracy of the collected data by comparing with known reference data and standard data.

[0039] The preprocessing specifically includes configuring the interface of the data acquisition unit and connecting the sensor, collecting signals at a set frequency, performing analog-to-digital conversion on the analog signal, applying digital filtering to process noise, correcting data in combination with the sensor calibration curve, integrating multi-source data into a unified structure, performing compression processing, temporarily storing in local memory and verifying data integrity and accuracy.

[0040] The digital filtering formula is expressed as:

[0041]

[0042] Where y(n) is the output signal after filtering, x(ni) is the input signal, N is the number of sampling points involved in averaging, and i is the variable index.

[0043] In the embodiment of the present application, processing the noise includes applying a moving average filter formula to suppress the noise.

[0044] In an optional embodiment, processing noise includes sorting N consecutive sampling values ​​by size and taking the median of the sequence as a filtering output, which is applicable to the temperature sensor signal.

[0045] In another optional embodiment, processing the noise includes designing FIR filter coefficients of a cutoff frequency, calculating a convolution output, and performing denoising on high-frequency noise of the vibration sensor.

[0046] Eliminate noise and interference in the collected signal, retain the valid signal, preliminarily detect and eliminate abnormal data points, and calibrate the collected data according to the sensor's calibration curve and calibration constant. The calibration curve and calibration constant are obtained in the sensor's specification and through calibration experiments. According to the working principle and characteristics of the sensor, determine whether the relationship between the output and the actual physical quantity is linear or nonlinear. For linear relationships, directly use the calibration constant for calculation. The calibration curve equation for nonlinear relationships is:

[0047] Y=aX 2 +bX+c

[0048] Where X is the actual physical quantity, a, b, and c are coefficients determined through calibration experiments, and Y is the sensor output value.

[0049] Convert the sensor output data into actual physical quantities and integrate the data from different sensors into a unified data structure. For data collected at high frequency, apply data compression technology to reduce storage space and transmission bandwidth. The two-dimensional discrete cosine transform formula is:

[0050]

[0051] Where F(u,v) is the result of the two-dimensional discrete cosine transform (DCT), which represents the coefficients in the frequency domain, f(s,j) is the original image block, s and j are the indexes of the pixel positions, M is the size of the image block, that is, the number of pixels on each side, u and v are the indices of the DCT coefficients in the frequency domain, C(u) and C(v) are normalization coefficients used to adjust the amplitude of the DCT coefficients, and π is pi.

[0052] The transmission to the central processing unit includes evaluating the communication mode based on transmission distance, environmental adaptability and reliability, selecting a combination of Ethernet, optical fiber and wireless network technologies, adding a timestamp and identity code to the data, and packaging and transmitting according to the communication protocol.

[0053] Data transmission includes evaluating the applicability of wired and wireless transmission methods based on on-site environmental conditions, selecting transmission technology based on the evaluation results, including Ethernet, optical fiber, 4G, 5G, Wi-Fi, and LoRa, installing and connecting the communication module, configuring the parameters of the communication module, and packaging the processed data in a predetermined format, including adding timestamps, sensor IDs, and data value information, encoding the data, selecting a transmission protocol based on data transmission requirements, and implementing the protocol sending and receiving functions on the data acquisition unit and the central processing unit. The packaged and encoded data is sent to the central processing unit through the communication module according to the selected transmission protocol.

[0054] S2: Analyze data, extract characteristic parameters, and combine with the predefined rule base to perform fault feature identification and anomaly judgment. Match fault modes based on the identified features, evaluate the fault type and severity, and predict the fault probability based on historical data trends.

[0055] In the embodiment of the present application, the fault feature identification and abnormality judgment include cleaning missing and abnormal data, standardizing data format, extracting periodic and trend feature parameters, and detecting fault features through pattern recognition algorithms and comparing them with rule base thresholds.

[0056] In an optional embodiment, the fault feature identification and abnormality judgment include constructing a multidimensional feature vector for current and temperature parameters, and using K-means clustering to divide normal / abnormal state clusters, and marking data points far away from the center of the normal cluster as abnormal.

[0057] In another optional embodiment, the fault feature identification and abnormality determination includes constructing multiple randomly segmented isolation trees, calculating the path length of isolated data points, and determining an abnormality when the path length is significantly lower than the average value.

[0058] The fault diagnosis engine analyzes the identified fault features to determine the type and severity of the fault. By recognizing patterns in historical data, it predicts possible fault trends and provides preventive maintenance recommendations. Fault diagnosis and prediction also include inputting features extracted from the data parsing and fault feature identification stages into the fault diagnosis engine, using the models and rule base in the fault diagnosis engine to match the input features with known fault patterns, determine the fault type, assess the severity of the fault, determine the urgency and processing priority of the fault based on the strength and impact range of the fault features, analyze historical data, identify patterns and regularities that appear during system operation, identify abnormal patterns that lead to faults by comparing current data with historical data, identify trends and potential hidden dangers that lead to faults based on time series analysis, use historical data and machine learning algorithms to build a prediction model to predict possible future faults, input current and recent data into the prediction model to predict fault trends, analyze the prediction results, and determine the types and probabilities of possible system faults in the future.

[0059] The method of predicting the probability of failure by combining historical data trends includes inputting features into a diagnostic engine, matching historical failure patterns, determining the type of failure, prioritizing failures based on feature strength and impact range, analyzing historical data patterns, identifying current abnormal patterns and potential hidden dangers, and predicting the probability and type of failure based on time series.

[0060] S3: When real-time data exceeds the preset threshold, an early warning message is automatically generated, structured early warning information is sent through multimodal communication channels, and confirmation status is tracked, triggering preset emergency operations. At the same time, a manual intervention interface is provided and the processing process is recorded.

[0061] Furthermore, the automatic generation of warning messages includes automatically generating warning messages containing levels, parameter values ​​and timestamps when real-time data exceeds a preset threshold; after the warning is triggered, automatically executing preset emergency response measures, including cutting off power, starting the backup system, providing an interface for operators to manually intervene, executing fault handling operations, and recording fault responses and handling operations.

[0062] The triggering of the early warning mechanism includes selecting key parameters that need to be monitored based on the operating parameters of the electrical pipeline, setting the early warning threshold for each parameter based on historical data and expert experience, collecting the operating data of the electrical pipeline in real time, and transmitting the collected data to the central processing unit for analysis through the set transmission method. The central processing unit analyzes the real-time transmitted data to determine whether it exceeds the set early warning threshold. When the monitoring data exceeds the preset threshold, the intelligent early warning system automatically triggers the early warning mechanism, records and processes the early warning event, and constructs an early warning message, including the early warning level, abnormal parameters, current value, threshold and timestamp. According to the early warning rules, the type of early warning is determined, including parameter exceeding the standard, trend abnormality, and emergency.

[0063] In the implementation manner of the present application, the triggering of the early warning mechanism includes setting a static threshold based on historical data and expert experience. When the monitoring data exceeds the preset threshold, the intelligent early warning system automatically triggers the early warning mechanism, records and processes the early warning event, and constructs an early warning message.

[0064] In an optional embodiment, triggering of the early warning mechanism includes statistically calculating the mean and standard deviation of the parameters in a 24-hour time window, setting a dynamic threshold, and periodically updating the threshold.

[0065] In another optional embodiment, triggering the early warning mechanism includes defining a membership function of normal, warning, and dangerous parameters, and comprehensively determining the early warning level according to a current-temperature association rule.

[0066] The automatic generation of warning messages also includes setting dynamic warning thresholds based on historical data and operating parameters, analyzing transmission data in real time, determining whether thresholds and abnormal trends are triggered, constructing warning event messages containing levels, parameter deviation values ​​and timestamps, and dynamically determining warning types as parameter exceeding standards, abnormal trends and emergencies.

[0067] The tracking confirmation status includes aggregating the real-time values, thresholds and trigger time information of abnormal parameters, configuring a combined notification strategy of SMS, email, mobile application and sound and light alarm according to the warning level, generating structured notification content with processing suggestions, and monitoring the reception confirmation status;

[0068] The recording and processing process includes automatically executing power cut-off and backup system startup operations, providing a manual operation interface, executing fault handling, and recording response actions and processing results throughout the entire process; visual monitoring includes designing a graphical interface to dynamically display equipment status and warning information, providing the function of querying historical data by time, equipment and parameters, and identifying risk points through trend charts, supporting view customization and highlighting of warning information.

[0069] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0070] Example 2, reference Figure 2 , which is the second embodiment of the present invention, provides an electrical pipeline fault early warning system, including a sensor detection module, a data acquisition and transmission module, a central processing and analysis module, and an early warning and notification module.

[0071] The sensor detection module is used to provide the system with raw data on current fluctuations, temperature anomalies, mechanical vibrations and voltage deviations through physical signal acquisition, covering the safety monitoring needs of areas with high fault incidence.

[0072] The data acquisition and transmission module is used to integrate multi-source heterogeneous sensor data into a unified format, select wired or wireless technology based on transmission distance and environmental adaptability, add timestamps and sensor IDs, and then transmit to the central unit.

[0073] The central processing and analysis module is used to parse the transmitted data, determine the type of anomaly, drive the fault diagnosis engine to match historical patterns to determine the type and severity of the fault, and predict the probability of future faults.

[0074] The warning and notification module is used to trigger warnings based on dynamic thresholds or abnormal trends, construct messages containing levels, parameter deviation values ​​and timestamps, combine SMS, email, mobile applications and sound and light alarm channels according to the warning level, send structured notifications with processing suggestions and track receipt status.

[0075] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0076] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0077] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0078] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0079] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0080] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. An electrical pipeline fault early warning method, characterized by: include, Collect electrical pipeline data, pre-process the collected data, package the processed data and transmit it to the central processing unit; Analyze data, extract characteristic parameters, and combine with predefined rule base to identify fault characteristics and abnormality judgment. Match fault modes based on identified characteristics, evaluate fault type and severity, and predict fault probability based on historical data trends. When real-time data exceeds the preset threshold, an early warning message is automatically generated, structured early warning information is sent through multimodal communication channels, and the confirmation status is tracked, triggering preset emergency operations. At the same time, a manual intervention interface is provided and the processing process is recorded.

2. The electrical pipeline fault early warning method according to claim 1, characterized in that: The collection of electrical pipeline data includes determining the location of key safety nodes in the electrical pipeline, analyzing the types and quantities of current, voltage, temperature and vibration parameters that need to be monitored, and deploying adaptive sensors based on parameter characteristics to monitor current, voltage, temperature and vibration parameters in real time.

3. The electrical pipeline fault early warning method according to claim 2, characterized in that: The pre-processing includes collecting sensor data, performing noise filtering, invalid data elimination and signal correction processing; The preprocessing specifically includes configuring the interface of the data acquisition unit and connecting the sensor, collecting signals at a set frequency, performing analog-to-digital conversion on the analog signal, applying digital filtering to process noise, correcting data in combination with the sensor calibration curve, integrating multi-source data into a unified structure, performing compression processing, temporarily storing in local memory and verifying data integrity and accuracy.

4. The electrical pipeline fault early warning method according to claim 3, characterized in that: The transmission to the central processing unit includes evaluating the communication mode based on transmission distance, environmental adaptability and reliability, selecting a combination of Ethernet, optical fiber and wireless network technologies, adding a timestamp and identity code to the data, and packaging and transmitting according to the communication protocol.

5. The electrical pipeline fault early warning method according to claim 4, characterized in that: The fault feature identification and abnormality determination includes cleaning missing and abnormal data, standardizing data formats, extracting periodic and trend characteristic parameters, and detecting fault features through pattern recognition algorithms and comparing them with rule base thresholds; The method of predicting the probability of failure by combining historical data trends includes inputting features into a diagnostic engine, matching historical failure patterns, determining the type of failure, prioritizing failures based on feature strength and impact range, analyzing historical data patterns, identifying current abnormal patterns and potential hidden dangers, and predicting the probability and type of failure based on time series.

6. The electrical pipeline fault early warning method according to claim 5, characterized in that: The automatic generation of warning messages includes automatically generating warning messages containing levels, parameter values ​​and timestamps when real-time data exceeds a preset threshold; The automatic generation of warning messages also includes setting dynamic warning thresholds based on historical data and operating parameters, analyzing transmission data in real time, determining whether thresholds and abnormal trends are triggered, constructing warning event messages containing levels, parameter deviation values ​​and timestamps, and dynamically determining warning types as parameter exceeding standards, abnormal trends and emergencies.

7. The electrical pipeline fault early warning method according to claim 6, characterized in that: The tracking confirmation status includes aggregating the real-time values, thresholds and trigger time information of abnormal parameters, configuring a combined notification strategy of SMS, email, mobile application and sound and light alarm according to the warning level, generating structured notification content with processing suggestions, and monitoring the reception confirmation status; The recording and processing process includes automatically executing power cut-off and backup system startup operations, providing a manual operation interface, executing fault handling, and recording response actions and processing results throughout the entire process; visual monitoring includes designing a graphical interface to dynamically display equipment status and warning information, providing the function of querying historical data by time, equipment and parameters, and identifying risk points through trend charts, supporting view customization and highlighting of warning information.

8. A system using the electrical pipeline fault early warning method according to any one of claims 1 to 7, characterized in that: It includes sensor detection module, data acquisition and transmission module, central processing and analysis module, and early warning and notification module; The sensor detection module is used to provide the system with raw data on current fluctuations, temperature anomalies, mechanical vibrations, and voltage deviations through physical signal acquisition, covering the safety monitoring needs of high-fault areas; The data acquisition and transmission module is used to integrate multi-source heterogeneous sensor data into a unified format, select wired or wireless technology based on transmission distance and environmental adaptability, add timestamps and sensor IDs, and then transmit to the central unit; The central processing and analysis module is used to parse the transmitted data, determine the type of anomaly, drive the fault diagnosis engine to match historical patterns to determine the type and severity of the fault, and predict the probability of future faults; The warning and notification module is used to trigger warnings based on dynamic thresholds or abnormal trends, construct messages containing levels, parameter deviation values ​​and timestamps, combine SMS, email, mobile applications and sound and light alarm channels according to the warning level, send structured notifications with processing suggestions and track receipt status.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the electrical pipeline fault early warning method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electrical pipeline fault early warning method according to any one of claims 1 to 7 are implemented.

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