Anti-misoperation monitoring system based on big data

Abstract

The invention relates to the technical field of anti-misoperation monitoring, in particular to an anti-misoperation monitoring system based on big data. The system comprises a data acquisition module, a historical database, a data analysis module and an isolation verification module. According to the invention, the data of each device is collected and processed and analyzed by using a big data technology to automatically identify potential faults of the device, such as overvoltage, short circuit and the like of the device, so that early warning information is sent in time, an operator is reminded to take corresponding maintenance measures, and fault points are accurately positioned by using big data analysis; an intelligent switch, a circuit breaker and other devices are adopted to quickly isolate fault points, and meanwhile, the response speed in the isolation process is monitored, so that the influence range on adjacent devices is dynamically adjusted, it is ensured that potential safety risks can be effectively dealt with under different environment conditions, and the flexibility and adaptability of the system are improved; by considering a fault diffusion phenomenon caused in a complex environment, an isolation strategy is updated in real time, and the fault processing efficiency and safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of error-proof monitoring, and in particular to an error-proof monitoring system based on big data. Background Art

[0002] Smart substation is a new type of substation that uses modern information technology to digitally transform and upgrade traditional substations. Compared with traditional substations, smart substations have the advantages of high automation, low operation and maintenance costs, and strong safety. However, the complexity and technical difficulty of smart substations are also increasing, making the maintenance and repair of substations more complicated and cumbersome. Therefore, the safety and error prevention requirements of secondary maintenance operations of smart substations have become particularly important. The demand for secondary safety and error prevention is mainly reflected in three aspects: high safety requirements, high difficulty in operation, and high efficiency requirements. Smart substations are an important part of the power system, and their stable operation is related to the safety and stability of the entire power system. Therefore, the safety requirements for secondary maintenance operations are very high. Once an erroneous operation occurs, it may cause serious safety accidents. At the same time, the equipment and systems in smart substations are complex and diverse, and the technical level of maintenance personnel is very high. Moreover, the small working space, complex environment, and large workload bring great difficulties to secondary maintenance operations. In addition, smart substations are the core facilities of the power system, and their fault repair needs to be completed as soon as possible to ensure the normal operation of the power grid. Therefore, secondary maintenance operations require operators to complete the operation quickly and accurately. In response to the above needs, it is necessary to develop smart substation secondary maintenance safety measures and error prevention technologies to improve the efficiency and safety of maintenance operations.

[0003] A Chinese patent document with publication number CN112698127A discloses an online monitoring system for substation anti-error locking equipment based on a networked five-defense system. The management and control platform includes a provincial company management and control platform, a prefectural company operation and maintenance center, and a substation or switchgear workstation; based on the network architecture of the network five-defense system, the anti-error system at each station end and the network anti-error master station system of the operation and maintenance center are interconnected; the Internet, smart chips and Internet of Things technologies are used to realize automatic identification and data penetration of anti-error equipment and comprehensive status perception of core components, and realize real-time monitoring of working conditions, statistical analysis of operations and ledger statistics; unified protocols are used to upload and download information, realize intelligent integration and related applications of business logic, and capture and intelligent analysis of big data; it can be seen that in the existing anti-error monitoring technology, due to the lack of accurate positioning of fault points and dynamic adjustment of the fault impact range in complex environments, and feedback verification at the same time, it is difficult to ensure the effectiveness of isolation processing. Summary of the invention

[0004] To this end, the present invention provides an anti-error monitoring system based on big data to overcome the problem in the prior art that the fault impact range cannot be dynamically adjusted in a complex environment due to the lack of multi-level monitoring and analysis, resulting in low fault handling efficiency.

[0005] To achieve the above object, the present invention provides a big data-based anti-error monitoring system, comprising:

[0006] Data acquisition module, used to collect the current operating parameters of each device, including real-time operating voltage and real-time operating current;

[0007] A historical database for storing preventive maintenance data, including historical normal ranges and failure frequencies of preventive maintenance equipment in the preventive maintenance list;

[0008] A data analysis module, which is connected to the data acquisition module and the historical database respectively, and is used to determine the fault point device based on the deviation between the current operating parameters and the historical normal range, and to determine the initial isolation area according to the fault point device;

[0009] An isolation verification module, which is connected to the data analysis module and is used to expand the initial isolation area to the target isolation area based on the real-time response speed of each device in the initial isolation area and the current environmental parameters, or to adjust the historical normal range;

[0010] Among them, the current environmental parameters include real-time ambient temperature and real-time gas concentration.

[0011] Furthermore, the data analysis module includes a voltage difference analysis unit, a voltage fluctuation analysis unit and a fault analysis unit, wherein:

[0012] The voltage difference analysis unit is used to obtain the current operating voltage of the device to be monitored, calculate the current voltage difference according to the current operating voltage and the historical normal range, and determine whether the operating state of the device to be monitored is voltage abnormality according to the comparison result between the current voltage difference and the standard voltage difference;

[0013] The voltage fluctuation analysis unit is used to compare the current operating voltage when the operating state of the device to be monitored is abnormal voltage, and obtain a first comparison result and a second comparison result;

[0014] The fault analysis unit is used to determine the fault point device according to the comparison result and the current operating current.

[0015] Furthermore, the pressure difference analysis unit includes a calculation subunit and a pressure difference analysis subunit, wherein:

[0016] The calculation subunit is used to calculate the current voltage difference according to the current operating voltage and the historical normal range, including:

[0017] Compare the current operating voltage with the historical normal range.

[0018] If the current operating voltage is within the historical normal range, the output current voltage difference is zero;

[0019] If the current operating voltage is not within the historical normal range, calculate the difference between the current operating voltage and the upper and lower limits of the historical normal range, and select the smaller difference as the current voltage difference;

[0020] The voltage difference analysis subunit is used to determine whether the operating state of the device to be monitored is abnormal voltage according to the comparison result between the current voltage difference and the standard voltage difference, including:

[0021] If the current voltage difference is less than or equal to the standard voltage difference, it is determined that the operating status of the monitored equipment is normal;

[0022] If the current voltage difference is greater than the standard voltage difference, it is determined that the operating state of the device to be monitored is abnormal voltage.

[0023] Furthermore, the voltage fluctuation analysis unit is used to compare and analyze the current operating voltage when the operating state of the device to be monitored is abnormal voltage, including:

[0024] If the current operating voltage is less than or equal to the lower limit of the historical normal range, a first comparison result is obtained;

[0025] If the current operating voltage is greater than or equal to the upper limit value of the historical normal range, a second comparison result is obtained.

[0026] Furthermore, the fault analysis unit includes an overvoltage fault analysis subunit, wherein:

[0027] The overvoltage fault analysis subunit is used to calculate the real-time voltage transformation rate when the first comparison result is obtained, determine the real-time voltage transformation rate according to the standard voltage transformation rate, and determine whether it is an overvoltage fault based on the determination result;

[0028] If the real-time voltage transformation rate is greater than the standard voltage transformation rate, it is determined to be an overvoltage fault, and the corresponding device to be monitored is determined as the fault point device;

[0029] If the real-time voltage transformation rate is less than or equal to the standard voltage transformation rate, continue to monitor the operating status of the monitored equipment.

[0030] Furthermore, the fault analysis unit further includes a short circuit fault analysis subunit, wherein:

[0031] The short-circuit fault analysis subunit is used to obtain the current current when the second comparison result is obtained, compare the standard current with the current current, determine the fault point device based on the comparison result, or enter the environmental temperature analysis;

[0032] If the current current is greater than the standard current, it is determined to be a short circuit fault, and the corresponding device to be monitored is determined as the fault point device;

[0033] If the current current is less than or equal to the standard current, enter the ambient temperature analysis.

[0034] Furthermore, the data analysis module also includes a fault isolation unit, wherein:

[0035] The fault isolation unit is used to determine a number of fault propagation paths according to the fault point device and the shared critical path, and to determine an initial isolation area according to each fault propagation path.

[0036] Furthermore, the isolation verification module includes a cut-off test unit and a dynamic adjustment unit, wherein:

[0037] The cut-off test unit is used to cut off the power supply to the initial isolation area, and obtain the real-time response speed of each device in the initial isolation area, and determine the preventive maintenance equipment according to the real-time response speed and the device type;

[0038] The dynamic adjustment unit determines whether to expand the initial isolation area to the target isolation area according to the real-time response speed and the current environmental parameters.

[0039] Furthermore, the cut-off test unit determines the preventive maintenance equipment according to the real-time response speed and the equipment type, including:

[0040] Determine the real-time response speed based on the standard response speed:

[0041] If the real-time response speed is less than or equal to the standard response speed,

[0042] If the real-time response speed is greater than the standard response speed, the corresponding equipment type is obtained, the preventive maintenance equipment is determined according to the equipment type, and the preventive maintenance equipment is included in the preventive maintenance list;

[0043] If it is determined that the device type is not a fault point device, the corresponding device to be monitored is determined as a preventive maintenance device.

[0044] Further, the dynamic adjustment unit determines whether to expand the initial isolation area to the target isolation area according to the real-time response speed and the current environmental parameters, including:

[0045] When it is determined that the real-time response speed is greater than the standard response speed, the real-time ambient temperature is obtained and the real-time ambient temperature is determined according to the standard ambient temperature.

[0046] If the real-time ambient temperature is lower than the standard ambient temperature, obtain the real-time gas concentration and determine the real-time gas concentration based on the allowable gas concentration:

[0047] If the real-time gas concentration is less than or equal to the allowed gas concentration, the historical normal range is adjusted;

[0048] If the real-time gas concentration is greater than the allowable gas concentration, the initial isolation area is expanded to the target isolation area;

[0049] If the real-time ambient temperature is greater than or equal to the maximum value of the standard ambient temperature, the initial isolation area is expanded to the target isolation area.

[0050] Compared with the prior art, the beneficial effect of the present invention lies in that data of each monitored device, including current, voltage, temperature and other parameters, is collected through various sensors and monitoring equipment, the data is stored in a cloud historical database, and processed and analyzed using big data technology. Through the analysis of these data, the system can automatically identify potential faults of the equipment, such as equipment overvoltage, short circuit, etc., so as to issue early warning information in time to remind operators to take corresponding maintenance measures, accurately locate the fault point by big data analysis, and quickly isolate the fault point by using intelligent switches, circuit breakers and other equipment. At the same time, the response speed during the isolation process is monitored to dynamically adjust the impact range on adjacent devices, ensure that potential safety risks can be effectively responded to under different environmental conditions, improve the flexibility and adaptability of the system, and update the isolation strategy in real time by considering the fault propagation phenomenon caused by complex environments, thereby improving fault handling efficiency and safety.

[0051] Furthermore, by acquiring the current operating voltage of each device to be monitored in real time and calculating the difference between the current operating voltage and the rated voltage, the degree of voltage fluctuation can be analyzed, and the operating status of the device when the voltage fluctuation is large can be identified in time. In addition, by analyzing the current operating voltage, the current or voltage transformation rate can be further analyzed when the current operating voltage increases or decreases, so as to identify the fault point device and the corresponding fault cause.

[0052] Furthermore, the insulation score of the adjacent lines is reduced due to the short-circuit current shock. Therefore, by determining the preventive maintenance equipment in the adjacent operating equipment, the executors can clearly understand the safety requirements and ensure the correctness of the operation. Since the historical normal range is not fixed, the historical normal range is dynamically adjusted as the equipment is used and environmental conditions change to ensure its accuracy and effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a structural schematic diagram of an anti-error monitoring system based on big data according to an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of the structure of a data analysis module according to an embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram of the structure of a pressure difference analysis unit according to an embodiment of the present invention;

[0056] Figure 4 The figure is a schematic diagram of the structure of a fault analysis unit according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0058] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0059] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0060] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0061] See also Figure 1 As shown, it is a schematic diagram of the structure of an anti-error monitoring system based on big data according to an embodiment of the present invention. The present invention provides an anti-error monitoring system based on big data, including:

[0062] Data acquisition module, used to collect the current operating parameters of each device, including real-time operating voltage and real-time operating current;

[0063] A historical database for storing preventive maintenance data, including historical normal ranges and failure frequencies of preventive maintenance equipment in the preventive maintenance list;

[0064] A data analysis module, which is connected to the data acquisition module and the historical database respectively, and is used to determine the fault point device based on the deviation between the current operating parameters and the historical normal range, and to determine the initial isolation area according to the fault point device;

[0065] An isolation verification module, which is connected to the data analysis module and is used to expand the initial isolation area to the target isolation area based on the real-time response speed of each device in the initial isolation area and the current environmental parameters, or to adjust the historical normal range;

[0066] Among them, the current environmental parameters include real-time ambient temperature and real-time gas concentration.

[0067] In this embodiment, data of each monitored device is collected through various sensors and monitoring devices, including various parameters such as current, voltage, temperature, etc., and the data is stored in a cloud historical database, and processed and analyzed using big data technology. Through the analysis of these data, the system can automatically identify potential faults of the equipment, such as equipment overvoltage, short circuit, etc., so as to issue early warning information in time to remind operators to take corresponding maintenance measures, accurately locate the fault point using big data analysis, and use intelligent switches, circuit breakers and other equipment to quickly isolate the fault point. At the same time, the response speed during the isolation process is monitored to dynamically adjust the impact range on adjacent devices, ensure that potential safety risks can be effectively responded to under different environmental conditions, and improve the flexibility and adaptability of the system. By considering the fault propagation phenomenon caused by complex environments, the isolation strategy can be updated in real time to improve the efficiency and safety of fault handling. Through multi-level monitoring and analysis, the system can take preventive measures before a fault occurs, thereby enhancing the reliability and stability of the overall system.

[0068] See also Figure 2 As shown, it is a schematic diagram of the structure of the data analysis module of an embodiment of the present invention;

[0069] Specifically, the data analysis module includes a voltage difference analysis unit, a voltage fluctuation analysis unit and a fault analysis unit, wherein:

[0070] The voltage difference analysis unit is used to obtain the current operating voltage of the device to be monitored, calculate the current voltage difference according to the current operating voltage and the historical normal range, and determine whether the operating state of the device to be monitored is voltage abnormality according to the comparison result between the current voltage difference and the standard voltage difference;

[0071] The voltage fluctuation analysis unit is used to compare the current operating voltage when the operating state of the device to be monitored is abnormal voltage, and obtain a first comparison result and a second comparison result;

[0072] The fault analysis unit is used to determine the fault point device according to the comparison result and the current operating current.

[0073] In this embodiment, the historical normal range indicates the upper and lower limits of the normal working state determined by monitoring and recording the equipment operating parameters (current operating voltage) within a certain period of time. The historical normal range is set to 220V to 240V.

[0074] By acquiring the current operating voltage of each monitored device in real time and calculating the difference between the current operating voltage and the rated voltage, the voltage fluctuation degree can be analyzed, and the operating status of the device when the voltage fluctuation is large can be identified in time. In addition, by analyzing the current operating voltage, the current or voltage transformation rate can be further analyzed when the current operating voltage increases or decreases, so as to identify the fault point device and the corresponding fault cause.

[0075] See also Figure 3 As shown, it is a schematic diagram of the structure of the pressure difference analysis unit according to an embodiment of the present invention;

[0076] Specifically, the pressure difference analysis unit includes a calculation subunit and a pressure difference analysis subunit, wherein:

[0077] The calculation subunit is used to calculate the current voltage difference according to the current operating voltage and the historical normal range, including:

[0078] Compare the current operating voltage with the historical normal range.

[0079] If the current operating voltage is within the historical normal range, the output current voltage difference is zero;

[0080] If the current operating voltage is not within the historical normal range, calculate the difference between the current operating voltage and the upper and lower limits of the historical normal range, and select the smaller difference as the current voltage difference;

[0081] The voltage difference analysis subunit is used to determine whether the operating state of the device to be monitored is abnormal voltage according to the comparison result between the current voltage difference and the standard voltage difference, including:

[0082] If the current voltage difference is less than or equal to the standard voltage difference, it is determined that the operating status of the monitored equipment is normal;

[0083] If the current voltage difference is greater than the standard voltage difference, it is determined that the operating state of the device to be monitored is abnormal voltage.

[0084] Specifically, the voltage fluctuation analysis unit is used to compare and analyze the current operating voltage when the operating state of the device to be monitored is abnormal voltage, including:

[0085] If the current operating voltage is less than or equal to the lower limit of the historical normal range, a first comparison result is obtained;

[0086] If the current operating voltage is greater than or equal to the upper limit value of the historical normal range, a second comparison result is obtained.

[0087] See also Figure 4 As shown, it is a schematic diagram of the structure of a fault analysis unit according to an embodiment of the present invention;

[0088] Specifically, the fault analysis unit includes an overvoltage fault analysis subunit, wherein:

[0089] The overvoltage fault analysis subunit is used to calculate the real-time voltage transformation rate when the first comparison result is obtained, determine the real-time voltage transformation rate according to the standard voltage transformation rate, and determine whether it is an overvoltage fault based on the determination result;

[0090] If the real-time voltage transformation rate is greater than the standard voltage transformation rate, it is determined to be an overvoltage fault, and the corresponding device to be monitored is determined as the fault point device;

[0091] If the real-time voltage transformation rate is less than or equal to the standard voltage transformation rate, continue to monitor the operating status of the monitored equipment.

[0092] The real-time voltage transformation rate in this embodiment is the rate at which the voltage changes within a period of time, and the standard voltage transformation rate is set to 5V / s; by comparing the real-time voltage transformation rate with the standard voltage transformation rate, it can be determined whether the equipment has an overvoltage fault.

[0093] Specifically, the fault analysis unit further includes a short circuit fault analysis subunit, wherein:

[0094] The short-circuit fault analysis subunit is used to obtain the current current when the second comparison result is obtained, compare the standard current with the current current, determine the fault point device based on the comparison result, or enter the environmental temperature analysis;

[0095] If the current current is greater than the standard current, it is determined to be a short circuit fault, and the corresponding device to be monitored is determined as the fault point device;

[0096] If the current current is less than or equal to the standard current, enter the ambient temperature analysis.

[0097] Specifically, the data analysis module also includes a fault isolation unit, wherein:

[0098] The fault isolation unit is used to determine a number of fault propagation paths according to the fault point device and the shared critical path, and to determine an initial isolation area according to each fault propagation path.

[0099] When a short circuit occurs, the short circuit current is transmitted to the adjacent lines through the high-voltage busbar. Therefore, by analyzing the connection relationship between the devices, a device connection diagram is constructed to determine the shared critical path between the fault point device and other devices. A graph algorithm (depth-first search algorithm or breadth-first search algorithm) is used to start from the fault point device, traverse the graph, find all devices that may be affected by the fault, and obtain several fault propagation paths. According to the fault propagation paths, the devices that need to be isolated are determined, and the affected devices are marked as initial isolation areas to ensure that these devices do not affect the normal operation of other devices during the fault handling process. For example, the system detects that device A has a fault and marks it as a fault point device. After analyzing the device connection relationship, it is found that device A shares a critical path with devices B, C, and D. It is calculated that the fault may propagate to devices B and C, and devices A, B, and C are marked as initial isolation areas to ensure that these devices are isolated during the fault handling process.

[0100] By setting up fault isolation units, the system can effectively identify fault point equipment, analyze critical paths, calculate fault propagation paths, and determine the initial isolation area. When a fault occurs, measures can be taken quickly to prevent the fault from spreading and ensure the safe and stable operation of the system.

[0101] Specifically, the isolation verification module includes a cut-off test unit and a dynamic adjustment unit, wherein:

[0102] The cut-off test unit is used to cut off the power supply to the initial isolation area, and obtain the real-time response speed of each device in the initial isolation area, and determine the preventive maintenance equipment according to the real-time response speed and the device type;

[0103] The dynamic adjustment unit determines whether to expand the initial isolation area to the target isolation area according to the real-time response speed and the current environmental parameters.

[0104] Specifically, the cut-off test unit determines the preventive maintenance equipment according to the real-time response speed and the equipment type, including:

[0105] Determine the real-time response speed based on the standard response speed:

[0106] If the real-time response speed is less than or equal to the standard response speed,

[0107] If the real-time response speed is greater than the standard response speed, the corresponding equipment type is obtained, the preventive maintenance equipment is determined according to the equipment type, and the preventive maintenance equipment is included in the preventive maintenance list;

[0108] If it is determined that the device type is not a fault point device, the corresponding device to be monitored is determined as a preventive maintenance device.

[0109] Specifically, the dynamic adjustment unit determines whether to expand the initial isolation area to the target isolation area according to the real-time response speed and the current environmental parameters, including:

[0110] When it is determined that the real-time response speed is greater than the standard response speed, the real-time ambient temperature is obtained and the real-time ambient temperature is determined according to the standard ambient temperature.

[0111] If the real-time ambient temperature is lower than the standard ambient temperature, obtain the real-time gas concentration and determine the real-time gas concentration based on the allowable gas concentration:

[0112] If the real-time gas concentration is less than or equal to the allowed gas concentration, the historical normal range is adjusted to 215V to 235V;

[0113] If the real-time gas concentration is greater than the allowable gas concentration, the initial isolation area is expanded to the target isolation area;

[0114] If the real-time ambient temperature is greater than or equal to the maximum value of the standard ambient temperature, the initial isolation area is expanded to the target isolation area;

[0115] The initial isolation area is expanded to the target isolation area by adding fault point devices. For example, the initial isolation area is device A and device B, and the target isolation area is device A, device B, device C, and device D.

[0116] In this embodiment, the allowable gas concentration is 100ppm, the standard ambient temperature is 30°C, and the standard response speed is 100ms. By effectively monitoring the response speed and environmental parameters of the equipment, timely measures are taken to ensure the safe operation of the equipment. For example, the equipment types include equipment A, equipment B, and equipment C. The initial isolation area includes equipment A and equipment B. The real-time response speed of equipment A is 120ms, and the real-time response speed of equipment B is 80ms.

[0117] The real-time ambient temperature is 25°C, and the real-time gas concentration is 90ppm. The cut-off test unit performs power-off processing, that is, power-off of device A and device B, and obtains the real-time response speed of device A as 120ms, and the real-time response speed of device B as 80ms. At this time, the response speed of device A is greater than the standard response speed and is included in the preventive maintenance list. The response speed of device B is less than the standard response speed and is recorded as normal; the dynamic adjustment unit performs a judgment on the response speed, that is, the response speed of device A is greater than the standard response speed, obtains the real-time ambient temperature (25°C), 25°C<30°C, obtains the real-time gas concentration (90ppm), gas concentration judgment: 90ppm≤100ppm, adjusts the historical normal range; temperature judgment: 25°C<30°C, decides not to expand the initial isolation area; dynamically decides whether to expand the initial isolation area to the target isolation area based on the real-time response speed and environmental parameters. This flexible adjustment mechanism helps to improve the safety and reliability of the system and ensures that the affected equipment can be effectively isolated in the event of equipment failure or abnormality.

[0118] The insulation score of the adjacent lines is reduced due to the short-circuit current impact. Therefore, by determining the preventive maintenance equipment in the adjacent operating equipment, the executors can clearly understand the safety requirements and ensure the correctness of the operation. Since the historical normal range is not fixed, the historical normal range is dynamically adjusted as the equipment is used and environmental conditions change to ensure its accuracy and effectiveness.

[0119] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0120] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A big data-based anti-error monitoring system, characterized in that: include, Data acquisition module, used to collect the current operating parameters of each device, including real-time operating voltage and real-time operating current; A historical database for storing preventive maintenance data, including historical normal ranges and failure frequencies of preventive maintenance equipment in the preventive maintenance list; A data analysis module, which is connected to the data acquisition module and the historical database respectively, and is used to determine the fault point device based on the deviation between the current operating parameters and the historical normal range, and to determine the initial isolation area according to the fault point device; An isolation verification module, which is connected to the data analysis module and is used to expand the initial isolation area to the target isolation area based on the real-time response speed of each device in the initial isolation area and the current environmental parameters, or to adjust the historical normal range; Among them, the current environmental parameters include real-time ambient temperature and real-time gas concentration.

2. The anti-error monitoring system based on big data according to claim 1 is characterized in that: The data analysis module includes a voltage difference analysis unit, a voltage fluctuation analysis unit and a fault analysis unit, wherein: The voltage difference analysis unit is used to obtain the current operating voltage of the device to be monitored, calculate the current voltage difference according to the current operating voltage and the historical normal range, and determine whether the operating state of the device to be monitored is voltage abnormality according to the comparison result between the current voltage difference and the standard voltage difference; The voltage fluctuation analysis unit is used to compare the current operating voltage when the operating state of the device to be monitored is abnormal voltage, and obtain a first comparison result and a second comparison result; The fault analysis unit is used to determine the fault point device according to the comparison result and the current operating current.

3. The anti-error monitoring system based on big data according to claim 2 is characterized in that: The pressure difference analysis unit includes a calculation subunit and a pressure difference analysis subunit, wherein: The calculation subunit is used to calculate the current voltage difference according to the current operating voltage and the historical normal range, including: Compare the current operating voltage with the historical normal range. If the current operating voltage is within the historical normal range, the output current voltage difference is zero; If the current operating voltage is not within the historical normal range, calculate the difference between the current operating voltage and the upper and lower limits of the historical normal range, and select the smaller difference as the current voltage difference; The voltage difference analysis subunit is used to determine whether the operating state of the device to be monitored is abnormal voltage according to the comparison result between the current voltage difference and the standard voltage difference, including: If the current voltage difference is less than or equal to the standard voltage difference, it is determined that the operating status of the monitored equipment is normal; If the current voltage difference is greater than the standard voltage difference, it is determined that the operating state of the device to be monitored is abnormal voltage.

4. The anti-error monitoring system based on big data according to claim 3 is characterized in that: The voltage fluctuation analysis unit is used to compare and analyze the current operating voltage when the operating state of the device to be monitored is abnormal voltage, including: If the current operating voltage is less than or equal to the lower limit of the historical normal range, a first comparison result is obtained; If the current operating voltage is greater than or equal to the upper limit value of the historical normal range, a second comparison result is obtained.

5. The anti-error monitoring system based on big data according to claim 2 is characterized in that: The fault analysis unit includes an overvoltage fault analysis subunit, wherein: The overvoltage fault analysis subunit is used to calculate the real-time voltage transformation rate when the first comparison result is obtained, determine the real-time voltage transformation rate according to the standard voltage transformation rate, and determine whether it is an overvoltage fault based on the determination result; If the real-time voltage transformation rate is greater than the standard voltage transformation rate, it is determined to be an overvoltage fault, and the corresponding device to be monitored is determined as the fault point device; If the real-time voltage transformation rate is less than or equal to the standard voltage transformation rate, continue to monitor the operating status of the monitored equipment.

6. The anti-error monitoring system based on big data according to claim 5 is characterized in that: The fault analysis unit also includes a short circuit fault analysis subunit, wherein: The short-circuit fault analysis subunit is used to obtain the current current when the second comparison result is obtained, compare the standard current with the current current, determine the fault point device based on the comparison result, or enter the environmental temperature analysis; If the current current is greater than the standard current, it is determined to be a short circuit fault, and the corresponding device to be monitored is determined as the fault point device; If the current current is less than or equal to the standard current, enter the ambient temperature analysis.

7. The anti-error monitoring system based on big data according to claim 2 is characterized in that: The data analysis module also includes a fault isolation unit, wherein: The fault isolation unit is used to determine a number of fault propagation paths according to the fault point device and the shared critical path, and to determine an initial isolation area according to each fault propagation path.

8. The anti-error monitoring system based on big data according to claim 7 is characterized in that: The isolation verification module includes a cut-off test unit and a dynamic adjustment unit, wherein: The cut-off test unit is used to cut off the power supply to the initial isolation area, and obtain the real-time response speed of each device in the initial isolation area, and determine the preventive maintenance equipment according to the real-time response speed and the device type; The dynamic adjustment unit determines whether to expand the initial isolation area to the target isolation area according to the real-time response speed and the current environmental parameters.

9. The anti-error monitoring system based on big data according to claim 8 is characterized in that: The cut-off test unit determines the preventive maintenance equipment according to the real-time response speed and the equipment type, including: Determine the real-time response speed based on the standard response speed: If the real-time response speed is less than or equal to the standard response speed, If the real-time response speed is greater than the standard response speed, the corresponding equipment type is obtained, the preventive maintenance equipment is determined according to the equipment type, and the preventive maintenance equipment is included in the preventive maintenance list; If it is determined that the device type is not a fault point device, the corresponding device to be monitored is determined as a preventive maintenance device.

10. The anti-error monitoring system based on big data according to claim 8, characterized in that: The dynamic adjustment unit determines whether to expand the initial isolation area to the target isolation area according to the real-time response speed and the current environmental parameters, including: When it is determined that the real-time response speed is greater than the standard response speed, the real-time ambient temperature is obtained and the real-time ambient temperature is determined according to the standard ambient temperature. If the real-time ambient temperature is lower than the standard ambient temperature, obtain the real-time gas concentration and determine the real-time gas concentration based on the allowable gas concentration: If the real-time gas concentration is less than or equal to the allowed gas concentration, the historical normal range is adjusted; If the real-time gas concentration is greater than the allowable gas concentration, the initial isolation area is expanded to the target isolation area; If the real-time ambient temperature is greater than or equal to the maximum value of the standard ambient temperature, the initial isolation area is expanded to the target isolation area.

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