A power distribution power equipment real-time monitoring system and a recommended maintenance method

Abstract

The application discloses a kind of based on power distribution power equipment real-time monitoring system and recommended maintenance method, and the application relates to the electric power equipment monitoring technical field, based on power distribution power equipment real-time monitoring system includes data acquisition module, data preprocessing module, condition monitoring module, fault diagnosis module, early warning module, maintenance decision module and resource scheduling module, the advantages of the application are that: subsequent maintenance decision module and resource scheduling module will formulate corresponding maintenance plan according to different fault information and call corresponding maintenance resource, so that the power equipment of abnormal equipment can be repaired according to different fault categories and different fault levels, avoid the occurrence of small failure waste manpower and material resources and the influence of equipment normal operation condition due to poor preparation in big fault, greatly improve the pertinence of maintenance power equipment, improve maintenance efficiency and reduce maintenance cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment monitoring, in particular to a real-time monitoring system for power distribution power equipment and a recommended maintenance method. BACKGROUND

[0002] In modern society, electricity has become an indispensable form of energy for people's life and production. As an important part of the power system, power distribution power equipment undertakes the key task of efficiently and safely transmitting electric energy from the power generation end to the user terminal. With the continuous progress of science and technology and the continuous growth of power demand, power distribution power equipment is also developing and evolving. Early power equipment was relatively simple and had relatively single functions. However, with the increasing complexity of the power system and the diversified power demand, higher requirements are put forward for the performance, reliability and intelligent level of power distribution power equipment.

[0003] The existing real-time monitoring system for power equipment often uses a single maintenance method when detecting abnormal conditions of electrical equipment, which makes it difficult to classify and maintain the electrical equipment according to the specific fault type. When facing small faults of electrical equipment, resource waste is caused by excessive resource scheduling. When facing large electrical equipment faults, timely and targeted maintenance of the equipment cannot be performed due to insufficient resource scheduling, resulting in a mismatch between resource scheduling and electrical faults, causing waste of manpower and resources. Therefore, we propose a real-time monitoring system for power distribution power equipment and a recommended maintenance method. SUMMARY

[0004] The purpose of the present application is to provide a real-time monitoring system for power distribution power equipment and a recommended maintenance method.

[0005] To solve the problems raised in the background art, the present application provides the following technical solution: a real-time monitoring system for power distribution power equipment, comprising a data acquisition module, a data preprocessing module, a state monitoring module, a fault diagnosis module, an early warning module, a maintenance decision module and a resource scheduling module.

[0006] Data acquisition module: real-time acquisition of multi-element operation data of power distribution power equipment through various high-precision sensors, including voltage, current, power, vibration, sound and temperature data.

[0007] Data preprocessing module: cleaning, filtering and normalization of received data to remove abnormalities and noise and improve data quality before passing to the state monitoring module.

[0008] State monitoring module: use data to build device state model, real-time analysis, determine the device running state, when found abnormal, the information is transmitted to the fault diagnosis module;

[0009] Fault diagnosis module: responsible for analyzing abnormal data, combining historical fault data and expert experience, determining fault type, location and severity, and feeding back the results to the warning module and maintenance decision module;

[0010] Early warning module: responsible for issuing corresponding warning signals according to the diagnosis results;

[0011] Maintenance decision module: responsible for making reasonable maintenance plan by comprehensively considering various factors;

[0012] Resource scheduling module: responsible for coordinating the required resources according to the maintenance plan to ensure the smooth progress of maintenance work.

[0013] As a further scheme of the present application: the data acquisition module includes a data acquisition unit and an acquisition cache unit, the data acquisition unit is provided with a voltage sensor, a current sensor, a power sensor, a vibration sensor, a sound sensor and a temperature sensor, through these sensors, the voltage, current, power, vibration, sound and temperature parameters of the power distribution power equipment are monitored and collected in real time, in the data acquisition process, the adaptive sampling technology is used in the data acquisition unit, the data acquisition unit will dynamically adjust the sampling frequency according to the running state and demand of the equipment, so as to reduce the data amount and transmission pressure as much as possible while ensuring the data integrity, for the collected data, the data acquisition unit will carry out preliminary arrangement and special information marking, and assign timestamp information, so as to facilitate subsequent analysis and tracing, the acquisition cache unit will cache the data collected by the data acquisition unit to cope with possible transmission interruption and delay, so as to ensure the continuity and reliability of the data, the data acquisition module will transmit the collected data to the data preprocessing module.

[0014] As a further scheme of the present application: the data preprocessing module can receive the data transmitted by the data acquisition module, the data preprocessing module removes the abnormal values, noise and repeated data in the data by using data cleaning technology, improves the quality and reliability of the data, after data cleaning, data filtering technology is used, valuable data is screened out according to the rules and conditions preset by the management personnel, unnecessary data processing amount is reduced, then different types and different orders of data are uniformly converted into a standard format through data normalization technology, which is convenient for subsequent analysis and processing, the module adopts mean filtering algorithm, in the data processing process, the module will continuously evaluate and verify the quality of the data, to ensure that the voltage, current, power, vibration, sound and temperature data after preprocessing meet the requirements of the system, the data preprocessing module will transmit the power distribution and power consumption equipment data of voltage, current, power, vibration, sound and temperature after processing to the state monitoring module.

[0015] As a further scheme of the present application: the state monitoring module includes a feature database unit and a data comparison unit, the feature database unit adopts a deep neural network learning algorithm, establishes a feature database of normal operation of the equipment by learning and training a large amount of historical data, the data comparison unit can receive the power distribution and power consumption equipment data transmitted by the data preprocessing module, and real-time extract key feature data in the power distribution and power consumption equipment data, the key feature data includes specific numerical value, change trend data and fluctuation data of voltage, current, power, vibration, sound and temperature, and compares the key feature data with the data in the feature database unit, once the key feature data of the power distribution and power consumption equipment data exceeds the threshold value set by the management personnel in the feature database, the abnormal data will be marked and processed, and the marked abnormal data will be transmitted to the fault diagnosis module.

[0016] As a further scheme of the present application: the fault diagnosis module includes a diagnosis analysis unit and a management editing unit, the diagnosis analysis unit is constructed with an expert knowledge base, the expert knowledge base stores equipment model data, equipment fault data and equipment fault codes, the diagnosis analysis unit can receive the abnormal information data marked by the state monitoring module, then matches the abnormal information data with the equipment fault data in the expert knowledge base, outputs the fault equipment, fault type and fault position corresponding to the abnormal information data according to the matching result, when the fault equipment, fault type and fault position are determined, the corresponding equipment fault coefficient is calculated according to the abnormal information data, the specific equipment fault coefficient calculation formula is as follows:

[0017]

[0018] Wherein: C represents the equipment fault coefficient, V, I, P, Z, S and T represent the voltage, current, power, vibration, sound and temperature values, And These represent the average values ​​of voltage, current, power, vibration, sound, and temperature when the equipment is operating normally. a, b, c, d, e, and f represent the weighting coefficients of voltage, current, power, vibration, sound, and temperature input by the management personnel, respectively.

[0019] The management editing unit provides a simple information input window, through which data update and requirement input functions can be realized. The data update function allows backend administrators to edit the expert knowledge base stored in the diagnostic analysis unit in real time through this information input window, continuously optimizing and updating the data in the expert knowledge base based on new fault conditions, ensuring the accuracy and completeness of the equipment model data, equipment fault data, and equipment fault codes stored in the expert knowledge base. The requirement input function allows backend administrators to enter the weighting coefficients of voltage, current, power, vibration, sound, and temperature as needed through this information input window. The fault diagnosis module will then transmit the diagnosed data to the early warning module.

[0020] As a further aspect of the present invention: the early warning module has multiple early warning methods, including audible and visual alarms, SMS notifications, email notifications, and system pop-ups, ensuring that relevant personnel can receive information about equipment malfunctions as soon as possible. Regarding the early warning level settings, there are mild, moderate, and severe early warning levels, with different notification methods corresponding to different levels. The early warning levels are classified according to different faults, as detailed below:

[0021] Mild warning: (1) There is 1 faulty device, (2) C < C0, the alarm corresponding to the mild warning is a system pop-up window;

[0022] Medium warning: (1) There are 2 faulty devices, (2) C0≤C≤1.2C0, the corresponding alarms for medium warning are SMS notification, email notification and system pop-up;

[0023] Severe warning: (1) More than 3 faulty devices, (2) C > 1.2C0, the corresponding alarms for moderate warning are sound and light alarm, SMS notification, email notification and system pop-up;

[0024] Where: C0 represents the warning threshold set by the administrator;

[0025] The early warning module also has early warning recording and query functions, which facilitates management personnel to trace and analyze early warning events. The early warning module adopts 5G communication technology and TCP data transmission mechanism to ensure that early warning signals can be accurately and timely delivered to relevant personnel.

[0026] As a further scheme of the present application: the maintenance decision module comprises a maintenance decision unit and a device group data unit, the device group data unit stores connection data between devices, layout data of the entire device group, device detailed data, maintenance personnel information, maintenance tool data, device performance data and device importance data, the maintenance decision unit formulates a maintenance scheme according to the fault information provided by the fault diagnosis module, in combination with the actual situation and operation requirements of the device, the maintenance decision unit evaluates the influence degree of the fault on the operation of the device, determines the urgency and priority of maintenance, then the maintenance decision unit determines the specific method and technical requirements of maintenance by analyzing the structure and working principle of the device, in terms of maintenance personnel arrangement, the maintenance decision unit reasonably allocates maintenance personnel with corresponding experience according to the difficulty of the maintenance task, at the same time, for the preparation of maintenance tools and materials, the maintenance decision unit calculates and plans according to the maintenance scheme to ensure that the maintenance work can proceed smoothly, the maintenance decision module transmits data to the resource scheduling module and interacts with the resource scheduling module in real time.

[0027] As a further scheme of the present application: the resource scheduling module is responsible for analyzing the maintenance scheme formulated by the maintenance decision module, and determining the required human and material resources, then the management personnel communicate and coordinate with the maintenance personnel according to the analysis results, reasonably arrange the work tasks and time of the maintenance personnel, and ensure the full use of personnel resources, in terms of material resources, the module is responsible for unified management and allocation of maintenance tools and materials, and timely supplement and adjustment according to the maintenance progress and demand.

[0028] In addition, the present application also provides a power distribution power equipment recommended maintenance method, comprising the following steps:

[0029] S1, when the early warning module issues a warning signal, the maintenance decision module evaluates the fault influence and determines the maintenance priority according to the results of the fault diagnosis module and the information of the device group data unit;

[0030] S2, if it is a mild warning, arrange the corresponding maintenance personnel to carry the necessary tools to the scene, and perform basic inspection and maintenance on the faulty device, including basic inspection, cleaning the device, and tightening the connection, if it is a moderate warning, in addition to arranging the maintenance personnel to perform basic inspection, prepare replacement parts, and perform part replacement and maintenance circuit operation on the faulty device, if it is a severe warning, in addition to arranging the maintenance personnel to perform basic inspection, more maintenance personnel and resources are urgently allocated to perform comprehensive overhaul and maintenance on the faulty device, including complex circuit repair and core component replacement;

[0031] S3, the basic inspection includes checking the power supply line, transformer for the fault caused by voltage anomaly, repairing and replacing the damaged parts, checking short circuit and overload problem when current anomaly, adjusting load and repairing the fault circuit, checking the running state and load condition of the equipment for power anomaly, adjusting power factor and replacing related devices, checking the fixing device and bearing of the equipment for vibration anomaly, tightening and replacing, checking whether there is mechanical failure and part loosening for sound anomaly, corresponding maintenance, checking the heat dissipation system and cooling device for temperature anomaly, and ensuring good heat dissipation of the equipment;

[0032] S4, during the maintenance process, the resource scheduling module coordinates the human and material resources in real time to ensure the smooth progress of the maintenance work, and after the maintenance is completed, the equipment is detected through the data acquisition module and the state monitoring module to ensure that the equipment returns to the normal running state.

[0033] Compared with the prior art, the beneficial effects of the present application are as follows:

[0034] 1. The present application collects the relevant data of the power equipment through the data acquisition module, and checks the data through the state detection module after preprocessing the data, marks the data when detecting abnormal data, and then transmits the data to the fault diagnosis module and abnormal data, the fault diagnosis module analyzes the abnormal data in combination with the equipment and the database, calculates the fault coefficient, the warning module issues different types of warnings according to different scales of fault degree, and then the maintenance decision module and the resource scheduling module formulate corresponding maintenance plans and call corresponding maintenance resources according to different fault information, so that the power equipment with abnormal equipment can be repaired according to different fault categories and different fault levels, the waste of manpower and material resources caused by small faults and the influence on the normal operation of the equipment caused by the poor preparation of large faults are avoided, the pertinence of the maintenance of the power equipment is greatly improved, the maintenance efficiency is improved and the maintenance cost is reduced;

[0035] 2. The present application has multiple early warning methods through the early warning module, including audible and visual alarm, short message notification, email notification and system pop-up, which enables relevant personnel to receive equipment abnormal information in the first time through multiple ways, greatly improving the timeliness and reliability of information transmission, compared with the single early warning method in the prior art, this diversified early warning mechanism can better adapt to different scenes and needs, at the same time, the early warning level is set as mild early warning, moderate early warning and severe early warning, and is classified according to different faults, different levels correspond to different notification methods, mild early warning is only system pop-up, while severe early warning contains audible and visual alarm, short message notification, email notification and system pop-up, this intelligent early warning level division can enable relevant personnel to accurately judge the severity of equipment abnormality according to the early warning level, so as to take corresponding measures, in addition, the early warning module also has early warning record and query function, which is convenient for managers to trace and analyze early warning events, which provides important data support for subsequent equipment maintenance and management, and helps to continuously optimize the system and improve the monitoring effect;

[0036] 3. The present application can comprehensively consider various factors to formulate a reasonable maintenance scheme through the maintenance decision module, which can fully evaluate the influence degree of the fault on the equipment operation, determine the urgency and priority of maintenance, and then accurately determine the specific method and technical requirements of maintenance according to the structure and working principle of the equipment, reasonably allocate maintenance personnel with corresponding experience according to the task difficulty in the aspect of maintenance personnel arrangement, and scientifically plan and prepare maintenance tools and materials, to ensure the smooth progress of maintenance work, while the resource scheduling module is responsible for in-depth analysis of the maintenance scheme, and clear human, material and resource requirements, and efficient communication and coordination with maintenance personnel, reasonable arrangement of work tasks and time, and full use of personnel resources, this flexible and efficient resource scheduling and maintenance decision mechanism not only improves the maintenance efficiency and shortens the equipment fault repair time, but also optimizes the resource allocation to the greatest extent, avoiding resource waste and idling. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The system flowchart in the embodiment of the present application is shown in the figure;

[0038] Figure 2 The method step diagram in the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0039] The specific embodiments of the present application will be further described in combination with the drawings, and it should be noted that the description of these embodiments is used to help understand the present application, but does not constitute a limitation on the present application.

[0040] In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0041] Please refer to the attached Figure 1 - attached Figure 2 The application is a real-time monitoring system for power distribution equipment based on power distribution equipment real-time monitoring system, which includes data acquisition module, data preprocessing module, state monitoring module, fault diagnosis module, early warning module, maintenance decision module and resource scheduling module;

[0042] Data acquisition module: real-time acquisition of multi-element operation data of power distribution equipment through various high-precision sensors, including voltage, current, power, vibration, sound and temperature data;

[0043] Data preprocessing module: clean, filter and normalize the received data, remove abnormal and noise, and improve data quality before passing to the state monitoring module;

[0044] State monitoring module: use data to build device state model, real-time analysis, judge device running state, when found abnormal, pass information to fault diagnosis module;

[0045] Fault diagnosis module: responsible for analyzing abnormal data, combining historical fault data and expert experience, determining fault type, location and severity, and feeding back the results to the early warning module and maintenance decision module;

[0046] Early warning module: responsible for issuing corresponding warning signals according to the diagnosis results;

[0047] Maintenance decision module: responsible for developing reasonable maintenance plan by considering various factors;

[0048] Resource scheduling module: responsible for coordinating the required resources according to the maintenance plan to ensure the smooth progress of maintenance work.

[0049] In an embodiment of the present application: the data acquisition module includes a data acquisition unit and an acquisition cache unit, the data acquisition unit is provided with a voltage sensor, a current sensor, a power sensor, a vibration sensor, a sound sensor and a temperature sensor, through which the voltage, current, power, vibration, sound and temperature parameters of the power distribution electrical equipment are monitored and collected in real time, in the data acquisition process, the adaptive sampling technology is used by the data acquisition unit, the sampling frequency of which is dynamically adjusted according to the operating state and demand of the equipment, so as to reduce the data volume and transmission pressure as much as possible while ensuring data integrity, for the collected data, the data acquisition unit will carry out preliminary arrangement and special information marking, and assign timestamp information, so as to facilitate subsequent analysis and tracing, the acquisition cache unit will cache the data collected by the data acquisition unit to cope with possible transmission interruption and delay, and ensure the continuity and reliability of the data, and the data acquisition module will transmit the collected data to the data preprocessing module.

[0050] In an embodiment of the present application: the data preprocessing module can receive the data transmitted by the data acquisition module, the data preprocessing module uses data cleaning technology to remove abnormal values, noise and repeated data in the data, and improves the quality and reliability of the data, after data cleaning, data filtering technology is used to filter out valuable data according to the rules and conditions preset by the management personnel, reduce unnecessary data processing amount, then through data normalization technology, different types and different orders of data are uniformly converted into standard format, which is convenient for subsequent analysis and processing, the module uses mean filter algorithm, in the data processing process, the module will constantly evaluate and verify the quality of the data, to ensure that the preprocessed voltage, current, power, vibration, sound and temperature data meet the requirements of the system, and the data preprocessing module will transmit the processed voltage, current, power, vibration, sound and temperature data of the power distribution electrical equipment to the state monitoring module.

[0051] In an embodiment of the present application: the state monitoring module includes a feature database unit and a data comparison unit, the feature database unit uses a deep neural network learning algorithm, through learning and training of a large amount of historical data, a feature database of normal operation of the equipment is established, the data comparison unit can receive the power distribution electrical equipment data transmitted by the data preprocessing module, and extract the key feature data in the power distribution electrical equipment data in real time, the key feature data includes specific numerical value, change trend data and fluctuation data of voltage, current, power, vibration, sound and temperature, and the key feature data is compared with the data in the feature database unit, once the key feature data of the power distribution electrical equipment data exceeds the threshold set by the management personnel in the feature database, the abnormal data will be marked and processed, and the marked abnormal data will be transmitted to the fault diagnosis module.

[0052] In one embodiment of the present application: the fault diagnosis module comprises a diagnostic analysis unit and a management editing unit, the diagnostic analysis unit is constructed with an expert knowledge base, the expert knowledge base stores device model data, device fault data and device fault codes, the diagnostic analysis unit can receive abnormal information data marked by the state monitoring module, then match the abnormal information data with the device fault data in the expert knowledge base, output the corresponding fault device, fault type and fault location according to the matching result, when the fault device, fault type and fault location are determined, the corresponding device fault coefficient is calculated according to the abnormal information data, and the specific device fault coefficient calculation formula is as follows:

[0053]

[0054] Wherein: C represents the device fault coefficient, V, I, P, Z, S and T represent the voltage, current, power, vibration, sound and temperature values respectively, And V, I, P, Z, S and T represent the average values of voltage, current, power, vibration, sound and temperature when the device is working normally, and a, b, c, d, e and f represent the weight coefficients of voltage, current, power, vibration, sound and temperature input by the management personnel;

[0055] The management editing unit can provide a simple information input window, through which the data updating function and the demand input function can be realized, the data updating function is that the background management personnel can edit the expert knowledge base stored in the diagnostic analysis unit in real time through the information input window, and can continuously optimize and update the data of the expert knowledge base according to new fault conditions, so as to ensure the accuracy and perfection of the device model data, device fault data and device fault codes stored in the expert knowledge base, the demand input function is that the background management personnel can input the weight coefficients of voltage, current, power, vibration, sound and temperature as needed through the information input window, and the fault diagnosis module will transmit the diagnosed data to the early warning module.

[0056] In one embodiment of the present application: the early warning module has multiple early warning modes, the early warning modes include audible and visual alarm, short message notification, email notification and system pop-up window, so that relevant personnel can receive device abnormal information in the first time, in the early warning level setting, the early warning levels include mild early warning, moderate early warning and severe early warning, different notification modes correspond to different early warning levels, the early warning levels are classified according to different faults, and the specific classification conditions are as follows:

[0057] Mild early warning: (1) the fault device is 1, (2) C < C0, the mild early warning corresponds to the system pop-up window as the alarm;

[0058] Moderate early warning: (1) 2 fault devices, (2) C0≤C≤1.2C0, moderate early warning corresponding alarm is short message notification, email notification and system pop-up window;

[0059] Severe early warning: (1) more than 3 fault devices, (2) C>1.2C0, moderate early warning corresponding alarm is audible and visual alarm, short message notification, email notification and system pop-up window;

[0060] Wherein: C0 represents the early warning threshold set by the management personnel;

[0061] The early warning module also has early warning record and query function, which is convenient for the management personnel to trace and analyze the early warning event, and the early warning module adopts 5G communication technology and TCP data transmission mechanism, so that the early warning signal can be accurately and timely transmitted to the relevant personnel.

[0062] In an embodiment of the present application: the maintenance decision module includes a maintenance decision unit and a device group data unit, the device group data unit stores the connection data between devices, the layout data of the entire device group, the device detailed data, the maintenance personnel information, the maintenance tool data, the device performance data and the device importance data, the maintenance decision unit formulates a maintenance scheme according to the fault information provided by the fault diagnosis module, combined with the actual situation and operation requirements of the device, the maintenance decision unit will evaluate the influence degree of the fault on the operation of the device, determine the urgency and priority of maintenance, then the maintenance decision unit determines the specific method and technical requirements of maintenance by analyzing the structure and working principle of the device, in terms of maintenance personnel arrangement, the maintenance decision unit will reasonably allocate maintenance personnel with corresponding experience according to the difficulty of the maintenance task, at the same time, for the preparation of maintenance tools and materials, the maintenance decision unit will calculate and plan according to the maintenance scheme to ensure the smooth progress of the maintenance work, the maintenance decision module will transmit data to the resource scheduling module and interact with the resource scheduling module in real time.

[0063] In an embodiment of the present application: the resource scheduling module is responsible for analyzing the maintenance scheme formulated by the maintenance decision module, and determining the required human and material resources, then the management personnel communicates and coordinates with the maintenance personnel according to the analysis result, reasonably arranges the work tasks and time of the maintenance personnel, and ensures the full use of personnel resources, in terms of material resources, the module is responsible for unified management and allocation of maintenance tools and materials, and timely supplement and adjustment according to the maintenance progress and demand.

[0064] Example one, please refer to the attached Figure 1In actual application, the data acquisition module will set different types of high-precision sensors according to different types of power equipment. These sensors work together to provide the system with rich and detailed equipment operation information. The setting of the acquisition and cache unit is crucial. It can effectively store data when transmission problems occur, avoiding data loss. When the network experiences temporary fluctuations or interruptions, the cache unit can store the data collected during this period, and then transmit it after the network is restored, ensuring the continuity and integrity of the data.

[0065] Example Two, please refer to the attached Figure 1 The various warning methods of the warning module ensure that relevant personnel can learn about equipment abnormal information in the first time. The strong warning of sound and light alarm is suitable for severe warning situations and can quickly attract the attention of surrounding personnel. SMS notification and email notification ensure the timely transmission of information, even if the relevant personnel are not on site. The system pop-up window is directly displayed on the system operation interface, making it convenient for operators to learn about the situation in a timely manner. The setting of the warning level is classified according to the severity and impact of the fault. Mild warning may only remind relevant personnel to pay attention and check, while moderate and severe warning means that more urgent and decisive measures need to be taken. When mild warning occurs, the system pop-up window can display simple prompt information, while when severe warning occurs, sound and light alarms will sound at the same time, and SMS and email will provide detailed information about the fault and the urgency. The warning record and query function facilitates subsequent tracing and analysis, helping managers to summarize experience and lessons and continuously optimize the warning strategy.

[0066] Example Three, please refer to the attached Figure 1 The rich information stored in the equipment group data unit provides a comprehensive basis for maintenance decisions. Through analysis of the connection data and layout data between devices, the maintenance decision unit can consider the overall operation of the equipment group. The assessment of the impact of the fault determines the urgency of maintenance. For example, if the fault will affect the normal operation of other devices, maintenance needs to be carried out as soon as possible. The determination of maintenance methods and technical requirements is based on the structure and working principle of the equipment, ensuring the effectiveness and correctness of the maintenance work. In terms of maintenance personnel arrangement, reasonable allocation is made according to the task difficulty and the experience of maintenance personnel to ensure the quality of maintenance. For the preparation of maintenance tools and materials, accurate calculation and planning are carried out according to the maintenance plan to avoid delays due to the lack of necessary tools or materials. When a key device is being repaired, the maintenance decision unit will allocate experienced maintenance personnel according to its importance and fault condition, and ensure that the necessary special tools and spare parts are ready, in order to improve the efficiency and quality of maintenance.

[0067] Specifically, the relevant data of the power equipment is collected by the data acquisition module, and after the data is preprocessed, it is checked by the state detection module, and when abnormal data is detected, the data is marked, and then the data is transmitted to the fault diagnosis module and abnormal data, the fault diagnosis module analyzes the abnormal data in combination with the equipment and the database, and calculates the fault coefficient, the warning module issues different types of warnings according to different scales of fault degree, and then the maintenance decision module and the resource scheduling module formulate corresponding maintenance plans and call corresponding maintenance resources according to different fault information, so that the power equipment with abnormal equipment can be repaired according to different fault categories and different fault levels, avoiding the waste of manpower and material resources caused by small faults and the impact on the normal operation of the equipment caused by the lack of preparation of large faults, greatly improving the pertinence of repairing the power equipment, improving the maintenance efficiency and reducing the maintenance cost.

[0068] Specifically, the warning module has multiple warning methods, including audible and visual alarms, SMS notifications, email notifications, and system pop-ups, which enable relevant personnel to receive equipment abnormal information in the first time through multiple channels, greatly improving the timeliness and reliability of information transmission. Compared with the single warning method in the prior art, this diversified warning mechanism is more adaptable to different scenarios and needs, and at the same time, the warning levels are set as light warning, medium warning and heavy warning, and are classified according to different faults, and different levels correspond to different notification methods. Light warning is only a system pop-up, while heavy warning includes audible and visual alarms, SMS notifications, email notifications and system pop-ups. This intelligent warning level division can enable relevant personnel to accurately judge the severity of equipment abnormalities according to the warning level, so as to take corresponding measures. In addition, the warning module also has a warning record and query function, which facilitates the management personnel to trace and analyze the warning events, which provides important data support for subsequent equipment maintenance and management, and helps to continuously optimize the system and improve the monitoring effect.

[0069] Specifically, the maintenance decision module can comprehensively consider various factors to formulate a reasonable maintenance plan. It fully assesses the impact of the fault on the operation of the equipment, determines the urgency and priority of maintenance, and then accurately determines the specific method and technical requirements of maintenance according to the structure and working principle of the equipment. In terms of maintenance personnel arrangement, it can reasonably allocate maintenance personnel with corresponding experience according to the task difficulty, and scientifically plan and prepare maintenance tools and materials to ensure the smooth progress of maintenance work. The resource scheduling module is responsible for in-depth analysis of the maintenance plan, clarifying the required human and material resources, and efficiently communicating and coordinating with maintenance personnel to reasonably arrange work tasks and time, realizing the full utilization of personnel resources. This flexible and efficient resource scheduling and maintenance decision mechanism not only improves maintenance efficiency and shortens equipment fault repair time, but also maximizes resource allocation optimization, avoiding resource waste and idling.

[0070] Working principle:

[0071] Firstly, after the warning module sends out a warning signal, the maintenance decision module assesses the impact of the fault and determines the maintenance priority based on the results of the fault diagnosis module and the information of the equipment group data unit.

[0072] Secondly, if it is a light warning, the corresponding maintenance personnel carrying necessary tools are arranged to go to the scene to conduct basic inspection and maintenance of the faulty equipment, including basic inspection, cleaning of the equipment, and tightening of the connection. If it is a moderate warning, in addition to arranging maintenance personnel for basic inspection, replacement parts are prepared for the faulty equipment for component replacement and maintenance circuit operation. If it is a severe warning, in addition to arranging maintenance personnel for basic inspection, more maintenance personnel and resources are urgently allocated for comprehensive overhaul and maintenance of the faulty equipment, including complex circuit repair and core component replacement.

[0073] Then, the basic inspection includes checking the power supply line, transformer for faults caused by voltage abnormalities, repairing and replacing damaged parts, checking short circuit and overload problems for current abnormalities, adjusting load and repairing faulty circuits, checking the running state and load of the equipment for power abnormalities, adjusting power factor and replacing related devices, checking the fixing device and bearing of the equipment for vibration abnormalities, tightening and replacing, checking for mechanical failure and component loosening for sound abnormalities, and performing corresponding maintenance, checking the cooling system and cooling device for temperature abnormalities to ensure good heat dissipation of the equipment.

[0074] Finally, during the maintenance process, the resource scheduling module coordinates human and material resources in real time to ensure the smooth progress of maintenance work. After maintenance is completed, the equipment is detected through the data acquisition module and the state monitoring module to ensure that the equipment returns to normal operating state. Thus, the entire work flow is completed.

[0075] Also, certain terminology can also be used in the description for the sake of brevity. For example, the terms "some" and "one" or "another" can mean one or more than one. Also, the term "exemplary" can mean that an aspect, structure, or characteristic following the term is a non-limiting example. Moreover, the phrases "this" and "that" can be used to describe an aspect, structure, or characteristic of one or more of the same or similar elements or features. Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or" that excludes the possibility that the aforesaid components, features, elements or concepts, for example, can be combined under any circumstance. The term "coupled" generally means to be directly or indirectly connected so that the components, features, elements, or concepts so

[0076] Aspects of the present application can be implemented in, completely, by hardware, completely by software (including firmware, resident software, micro-code, etc.), or by combinations of hardware and software. The above described hardware or software can be referred to as a "block", "module", "engine", "unit", "component", or "system". The processor can be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, or combinations thereof. In addition, aspects of the present application can be embodied as a computer product located in one or more computer readable media, which includes computer readable program code. For example, the computer readable media can include, but is not limited to, magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips...), optical disks (e.g., compact disk (CD), digital versatile disk (DVD)...), smart cards, and flash memory devices (e.g., card, stick, key drive...).

[0077] The computer readable media can include a propagated data signal with computer program code embodied therein, for example, in baseband or as a carrier wave. The computer readable media can be any available media that can be accessed by a general purpose or special purpose computer including the Internet, an optical or electrical carrier wave, electromagnetic signal, or the like. The computer readable media can be a computer program product that can include any suitable combination of hardware and / or software. The computer program product can be a computer readable storage medium having computer readable program code embodied thereon, which can be read by a computer. The computer readable storage medium can be a magnetic storage device, optical disk, flash memory device, or the like. The computer readable storage medium can be a memory device, such as a hard disk drive, floppy disk drive, RAM, ROM, EEPROM, CD-ROM or the like. The computer readable storage medium can also be a propagated data signal, including any suitable combination of electromagnetic, optical, or the like that can be generated during the execution of the computer program code.

[0078] For simplicity and to help with the understanding of one or more implementations of the application, the description of implementations of the application sometimes uses terms like "one implementation," "another implementation," "an implementation," and "some implementations" to describe various features. However, the use of these terms does not limit the application to the features described in the implementations using these terms. Rather, these terms have been used as shorthand notations for a single implementation or a combination of different implementations described throughout the detailed description of implementations of the application. The features described in the various implementations using these terms can be combined with each other to form implementations of the application. As used herein, "about" means approximately or nearly and in the context of a numerical value or range of values means ±20% of the numerical value or range of values. Accordingly, numerical values used in the description and claims of the application are approximations that can vary depending upon the desired properties sought to be obtained by the individual implementation. In some implementations, numerical values used in the description and claims of the application are approximations that will vary from the numerical values stated in the specification and claims due to, for example, variations in manufacturing tolerances, and the inherent inexactness found in measuring certain physical quantities. Notwithstanding these approximations, the above-described implementations of the application preferably fulfill the particular objectives and advantages set forth herein. In some implementations, the numerical values in the specification and claims have been presented in one or more generally accepted numerical formats and the application not to be construed as being limited to the foregoing.

[0079] While the application has been disclosed in its preferred forms with references to the drawings, it will be appreciated that numerous other modifications can be made without departing from the application. Accordingly, the application is not to be limited to the preferred forms described above but is intended to extend to all reasonable adaptations and modifications thereof.

Claims

1. A real-time monitoring system based on power distribution electrical equipment, characterized by: Comprise: Data acquisition module: real-time acquisition of multi-element operation data of power distribution electrical equipment through various high-precision sensors, including voltage, current, power, vibration, sound and temperature data; Data preprocessing module: clean, filter and normalize the received data, remove abnormal and noise, and improve data quality before passing to the state monitoring module; State monitoring module: use data to build a device state model for real-time analysis to determine the device operating state, and pass information to the fault diagnosis module when an anomaly is found; Fault diagnosis module: responsible for analyzing abnormal data, combining historical fault data and expert experience to determine fault type, location and severity, and feeding back the results to the warning module and maintenance decision module; Early warning module: responsible for issuing appropriate warning signals according to the diagnosis results; Maintenance decision module: responsible for developing a reasonable maintenance plan by considering various factors; Resource scheduling module: responsible for coordinating the required resources according to the maintenance plan to ensure smooth maintenance work; The fault diagnosis module includes a diagnostic analysis unit and a management editing unit. The diagnostic analysis unit has an expert knowledge base that stores device model data, device fault data, and device fault codes. The diagnostic analysis unit can receive abnormal information data marked by the state monitoring module, then match the abnormal information data with the device fault data in the expert knowledge base, and output the corresponding fault equipment, fault type and fault location according to the matching results. When the fault equipment, fault type and fault location are determined, the corresponding device fault coefficient is calculated according to the abnormal information data. The specific device fault coefficient calculation formula is as follows: wherein: C represents the equipment failure coefficient, V, I, P, Z, S and T represent the voltage, current, power, vibration, sound and temperature values, respectively, , , , , and represent the average values of voltage, current, power, vibration, sound and temperature when the equipment is working normally, and a, b, c, d, e and f represent the weight coefficients of voltage, current, power, vibration, sound and temperature input by the management personnel, respectively. The early warning module has multiple warning methods, including audible and visual alarms, SMS notifications, email notifications, and system pop-ups, to ensure that relevant personnel can receive device anomaly information in the first time. In terms of warning level settings, warning levels include mild warning, moderate warning and severe warning. Different warning levels correspond to different notification methods. Warning levels are classified according to different faults. The specific classification is as follows: Mild warning: (1) one faulty device, (2) C < C0, mild warning corresponds to system pop-up alarm; Moderate warning: (1) two faulty devices, (2) C0≤C≤1.2C0, moderate warning corresponds to SMS notification, email notification and system pop-up alarm; Severe warning: (1) more than three faulty devices, (2) C>1.2C0, severe warning corresponds to audible and visual alarm, SMS notification, email notification and system pop-up alarm; Wherein: C0 represents the warning threshold set by the management personnel.

2. The real-time monitoring system based on power distribution equipment according to claim 1, characterized in that: The data acquisition module comprises a data acquisition unit and an acquisition cache unit. The data acquisition unit is provided with a voltage sensor, a current sensor, a power sensor, a vibration sensor, a sound sensor and a temperature sensor. The voltage, current, power, vibration, sound and temperature parameters of the power distribution electrical equipment are monitored and collected in real time through these sensors. In the data acquisition process, the adaptive sampling technology is used by the data acquisition unit. The data acquisition unit dynamically adjusts the sampling frequency according to the operating state and requirements of the equipment, so as to reduce the data volume and transmission pressure as much as possible while ensuring the integrity of the data. The data acquisition unit will preliminarily arrange and mark special information for the collected data, and assign timestamp information, so as to facilitate subsequent analysis and tracing. The acquisition cache unit will cache the data collected by the data acquisition unit to cope with possible transmission interruption and delay, and ensure the continuity and reliability of the data. The data acquisition module will transmit the collected data to the data preprocessing module.

3. The real-time monitoring system for power distribution based power equipment according to claim 1, characterized in that: The data preprocessing module can receive the data transmitted by the data acquisition module. The data preprocessing module uses data cleaning technology to remove abnormal values, noise and repeated data in the data, improves the quality and reliability of the data, and uses data filtering technology to filter out valuable data according to the rules and conditions preset by the management personnel after data cleaning, reduces the data processing amount, and then converts different types and different magnitudes of data into a standard format through data normalization technology, so as to facilitate subsequent analysis and processing. The module uses the mean filtering algorithm. In the data processing process, the module will continuously evaluate and verify the quality of the data to ensure that the preprocessed voltage, current, power, vibration, sound and temperature data meet the requirements of the system. The data preprocessing module will transmit the processed power distribution electrical equipment data of voltage, current, power, vibration, sound and temperature to the state monitoring module.

4. The real-time monitoring system for power distribution based power equipment according to claim 1, characterized in that: The state monitoring module comprises a feature database unit and a data comparison unit. The feature database unit uses a deep neural network learning algorithm to learn and train a large amount of historical data, and establishes a feature database of normal operation of the equipment. The data comparison unit can receive the power distribution electrical equipment data transmitted by the data preprocessing module, and real-time extract key feature data in the power distribution electrical equipment data. The key feature data includes specific numerical values, trend data and fluctuation data of voltage, current, power, vibration, sound and temperature. The key feature data is compared with the data in the feature database unit. Once the key feature data of the power distribution electrical equipment data exceeds the threshold set by the management personnel in the feature database, the abnormal data will be marked and transmitted to the fault diagnosis module.

5. The real-time monitoring system based on power distribution equipment according to claim 2 or 4, characterized in that: The management editing unit provides a simple information input window through which the data updating function and the demand input function can be realized, the data updating function is that the background management personnel can edit the expert knowledge base stored in the diagnosis analysis unit in real time through the information input window, can continuously optimize and update the data of the expert knowledge base according to the new fault condition, and ensure the accuracy and perfection of the equipment model data, equipment fault data and equipment fault code stored in the expert knowledge base, the demand input function is that the background management personnel can input the weight coefficients of voltage, current, power, vibration, sound and temperature according to needs through the information input window, and the fault diagnosis module will transmit the diagnosed data to the early warning module.

6. A real-time monitoring system for power distribution based electrical equipment as claimed in claim 1, wherein: The early warning module also has early warning record and query function, which is convenient for management personnel to trace and analyze the early warning event, the early warning module adopts 5G communication technology and TCP data transmission mechanism, which ensures that the early warning signal can be accurately and timely transmitted to the relevant personnel.

7. The real-time monitoring system for power distribution based power equipment according to claim 5, wherein: The maintenance decision module includes a maintenance decision unit and a device group data unit, the device group data unit stores the connection data between devices, the layout data of the entire device group, the device detailed data, the maintenance personnel information, the maintenance tool data, the device performance data and the device importance data, the maintenance decision unit formulates a maintenance scheme according to the fault information provided by the fault diagnosis module, combines the actual situation and operation requirements of the device, evaluates the influence degree of the fault on the operation of the device, determines the urgency and priority of maintenance, then the maintenance decision unit determines the specific method and technical requirements of maintenance by analyzing the structure and working principle of the device, in terms of maintenance personnel arrangement, the maintenance decision unit will reasonably allocate maintenance personnel with corresponding experience according to the difficulty of the maintenance task, at the same time, for the preparation of maintenance tools and materials, the maintenance decision unit will calculate and plan according to the maintenance scheme, to ensure that the maintenance work can be carried out smoothly, the maintenance decision module will transmit the data to the resource scheduling module and interact with the resource scheduling module in real time.

8. The real-time monitoring system for power distribution based power equipment according to claim 7, characterized in that: The resource scheduling module is responsible for analyzing the maintenance scheme formulated by the maintenance decision module, and determining the required human and material resources, then the management personnel communicates and coordinates with the maintenance personnel according to the analysis result, reasonably arranges the work tasks and time of the maintenance personnel, and ensures the full use of personnel resources, in terms of material resources, the module is responsible for the unified management and allocation of maintenance tools and materials, and timely supplement and adjustment according to the maintenance progress and demand.

9. A power distribution equipment recommended maintenance method suitable for the real-time monitoring system for power distribution equipment based on any one of claims 1 to 8, characterized by, The method comprises the following steps: S1, when the early warning module sends an early warning signal, the maintenance decision module evaluates the fault influence and determines the maintenance priority according to the results of the fault diagnosis module and the information of the device group data unit; S2, if it is a mild warning, arrange the corresponding maintenance personnel to carry the necessary tools to the scene, carry out basic inspection and maintenance of the faulty equipment, including basic inspection, cleaning equipment, tightening connection, if it is a moderate warning, in addition to arranging maintenance personnel to carry out basic inspection, prepare to replace parts, carry out part replacement and maintenance circuit operation of the faulty equipment, if it is a severe warning, in addition to arranging maintenance personnel to carry out basic inspection, urgently allocate more maintenance personnel and resources, carry out comprehensive overhaul and maintenance of the faulty equipment, including complex circuit repair and core component replacement; S3, the basic inspection includes checking the power supply line, transformer for the fault caused by voltage anomaly, repairing and replacing the damaged parts, checking the short circuit and overload problem when the current is abnormal, adjusting the load and repairing the faulty circuit, checking the running state and load condition of the equipment for power anomaly, adjusting the power factor and replacing the related devices, checking the fixing device and bearing of the equipment for vibration anomaly, tightening and replacing, checking whether there is mechanical failure and part loosening for sound anomaly, carrying out corresponding maintenance, checking the cooling system and cooling device for temperature anomaly, ensuring good heat dissipation of the equipment; S4, in the maintenance process, the resource scheduling module coordinates the human and material resources in real time to ensure the smooth progress of the maintenance work, after the maintenance is completed, the equipment is detected through the data acquisition module and the state monitoring module to ensure that the equipment returns to the normal running state.

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