Intelligent monitoring method and device for converter station network and equipment, equipment and medium

Abstract

The invention discloses an intelligent monitoring method, device and equipment for a converter station network and equipment, and a medium. The method comprises the following steps: acquiring equipment monitoring data reported by intelligent sensors deployed in a converter station, and network state data of corresponding network transmission equipment when each intelligent sensor reports the equipment monitoring data; based on the network state data, determining first monitoring conditions of the network transmission equipment and the intelligent sensors when the intelligent sensors report the equipment monitoring data; and determining a second monitoring condition of the intelligent equipment included in the converter station based on the equipment monitoring data and a preset alarm fault library. According to the embodiment of the invention, by monitoring the equipment contained in the converter station and monitoring the sensor and the network thereof installed in the converter station, the management of the equipment in the converter station is more intelligent, the daily maintenance work is simplified, the alarm is more timely when the equipment has a fault, the fault positioning is accurate, and the accident loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of converter station monitoring, and in particular to an intelligent monitoring method, device, equipment and medium for a converter station network and equipment. Background Art

[0002] At present, the monitoring of each device in the converter station is usually carried out by optimizing the inspection plan of the equipment in the converter station, such as using drones to improve the efficiency of personnel inspections for fault monitoring. However, due to the existence of many devices and huge amounts of data in the converter station, when the various devices and networks in the converter station fail, the fault alarm is not timely and the fault location is not clear, resulting in accident losses. Summary of the invention

[0003] In view of this, the present invention provides an intelligent monitoring method, device, equipment and medium for a converter station network and equipment, which can realize more intelligent management of equipment in the converter station, simplify daily maintenance work, and make the alarm more timely and the fault location more accurate when equipment fails, thereby reducing the loss caused by accidents.

[0004] According to one aspect of the present invention, an embodiment of the present invention provides an intelligent monitoring method for a converter station network and equipment, the method comprising:

[0005] Acquire equipment monitoring data respectively reported by the smart sensors deployed in the converter station, and network status data of the corresponding network transmission equipment when each of the smart sensors reports the equipment monitoring data;

[0006] Determine, based on the network status data, a first monitoring condition of the network transmission device and the smart sensor when each of the smart sensors reports the device monitoring data;

[0007] A second monitoring condition of the intelligent device included in the converter station is determined based on the device monitoring data and a preset alarm fault library.

[0008] According to another aspect of the present invention, an embodiment of the present invention further provides an intelligent monitoring device for a converter station network and equipment, the device comprising:

[0009] A data acquisition module, used to acquire the equipment monitoring data reported by the smart sensors deployed in the converter station, and the network status data of the corresponding network transmission equipment when each of the smart sensors reports the equipment monitoring data;

[0010] A network monitoring module, configured to determine, based on the network status data, a first monitoring condition of the network transmission device and the smart sensor when each of the smart sensors reports the device monitoring data;

[0011] The device monitoring module is used to determine the second monitoring status of the intelligent device included in the converter station based on the device monitoring data and a preset alarm fault library.

[0012] According to another aspect of the present invention, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the intelligent monitoring method for converter station networks and equipment described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the intelligent monitoring method for converter station networks and equipment described in any embodiment of the present invention when executed.

[0017] According to another aspect of the present invention, an embodiment of the present invention further provides a computer program product, characterized in that the computer program product includes a computer program, and when the computer program is executed by a processor, it implements the intelligent monitoring method of the converter station network and equipment described in any embodiment of the present invention.

[0018] The technical effect of the present invention is that the network status data determines the first monitoring situation of the network transmission equipment and the intelligent sensor when each intelligent sensor reports the equipment monitoring data, and the second monitoring situation of the intelligent equipment included in the converter station is determined based on the equipment monitoring data and the preset alarm fault library, so as to realize the monitoring of the original equipment of the converter station, and at the same time, the sensors installed in the converter station and their networks are monitored and fault processing is carried out, so as to realize more intelligent management of the equipment in the converter station, simplify daily maintenance work, and make the alarm more timely and the fault location more accurate when the equipment fails, thereby reducing the loss of the accident.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A flow chart of an intelligent monitoring method for a converter station network and equipment provided by one embodiment of the present invention;

[0022] Figure 2 A flow chart of another intelligent monitoring method for converter station network and equipment provided by one embodiment of the present invention;

[0023] Figure 3 A flowchart of another method for monitoring faults of a converter station network and equipment provided by an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of a process for monitoring sensors and their networks installed in a converter station provided by an embodiment of the present invention;

[0025] Figure 5 A structural block diagram of an intelligent monitoring device for a converter station network and equipment provided by an embodiment of the present invention;

[0026] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] In one embodiment, Figure 1 A flow chart of a method for intelligent monitoring of a converter station network and equipment provided in one embodiment of the present invention. This embodiment can be applied to situations when monitoring existing equipment included in a converter station, as well as sensors and their networks installed in the converter station. The method can be executed by an intelligent monitoring device for the converter station network and equipment. The intelligent monitoring device for the converter station network and equipment can be implemented in the form of hardware and / or software, and the intelligent monitoring device for the converter station network and equipment can be configured in an electronic device.

[0030] like Figure 1 As shown, the intelligent monitoring method of the converter station network and equipment in this embodiment specifically includes the following steps:

[0031] S110, acquiring equipment monitoring data respectively reported by the smart sensors deployed in the converter station, and network status data of the corresponding network transmission equipment when each smart sensor reports the equipment monitoring data.

[0032] Among them, the equipment monitoring data can be understood as the equipment monitoring data of the sensors installed in the converter station that monitor various intelligent devices. In this embodiment, the intelligent devices in the converter station may include but are not limited to various devices such as intermediate converter transformers, converter valves, and reactors. Each device corresponds to a corresponding intelligent sensor to collect data from the corresponding device and perform intelligent monitoring of the device. In this embodiment, multiple intelligent sensors can correspond to one monitoring device, or one intelligent sensor can correspond to one monitoring device. This embodiment is not limited here.

[0033] The deployed intelligent sensors in this embodiment may include but are not limited to non-contact intelligent sensing terminals, contact intelligent sensors, photoacoustic spectroscopy oil dissolved gas monitoring devices and auxiliary systems, wherein the non-contact intelligent sensing terminals may include but are not limited to integrated infrared thermal imaging, ultra-high frequency partial discharge, visible light image, sound and other sensing terminals; contact intelligent sensors may include but are not limited to high frequency partial discharge, ultrasonic partial discharge, transient ground voltage, ground current, vibration, etc.; auxiliary systems may include but are not limited to temperature and humidity, smoke, water level, gas, water immersion, etc., for data monitoring of different equipment in the converter station. It should be noted that the deployment and installation of intelligent sensors can be deployed according to the design documents, which can be provided by the designer.

[0034] In this embodiment, the network transmission equipment at least includes: an optical network unit (Optical Network Unit, OUN), an access controller (Access Controller, AC), and at least two wireless access points (Access Point AP); wherein the OUN communicates with the host computer; the AC communicates with the OUN; the AC communicates with at least two APs; at least two APs correspond to at least one smart sensor respectively; each smart sensor is deployed next to each smart device included in the converter station according to a preset design file to monitor the data of the corresponding smart device; wherein the preset design file is a sensor deployment file for the smart device included in the converter station, which is a pre-deployed smart sensor installation location file. In this embodiment, the design document of the preset design file includes the monitoring device points deployed by the equipment included in the converter station.

[0035] The network status data in this embodiment includes: communication status and network equipment operation status; wherein the communication status is the status of wireless communication parameters, and the communication status includes at least one of the following: signal quality, number of online network transmission devices, and online rate of smart sensors; the network equipment operation status includes at least one of the following: memory usage, CPU occupancy, data packet loss rate, network connection status, and power status. Among them, signal quality can also be called signal strength.

[0036] In this embodiment, it may happen that the sensor data cannot be obtained. At this time, it is necessary to determine whether the AP to which the smart sensor belongs has status data uploaded. If the AP does not have current status data uploaded, it is necessary to query whether the AC to which the AP belongs has current status data, so as to determine whether the sensor is disconnected from the network, the AP is disconnected from the network, or the AC is disconnected from the network. It can be understood that when each smart sensor reports device monitoring data, the corresponding network transmission device may be in a normal state or an abnormal state. Therefore, it is necessary to obtain the network status data of the corresponding network transmission device when each smart sensor reports device monitoring data. In this embodiment, each smart sensor deployed in the converter station will monitor the device monitoring data of the corresponding smart device, and when the OUN, AC, and at least two APs are online, the device monitoring data reported by the smart sensors deployed in the converter station is obtained. It can be understood that the host computer obtains the corresponding data from the optical network unit OUN, and performs different monitoring and analysis according to the corresponding data type. The data type may include network status data and device monitoring data.

[0037] In this embodiment, since the intelligent sensor can be divided into a broadband sensor and a narrowband sensor, when data transmission is performed, the network signal strength and other data of the narrowband sensor can be aggregated to the ONU through the narrowband AC and AP, and then the background application communicates with the ONU to obtain data. If the sensor data cannot be obtained, it is determined whether the AP to which it belongs has data uploaded. If the AP does not upload the current status data, it is queried whether the AC to which the AP belongs has the current status data to determine whether the sensor is disconnected from the network, the AP is disconnected from the network, or the AC is disconnected from the network. In this embodiment, broadband sensors, broadband APs, ACs, ONUs and other devices are managed using an existing platform for real-time monitoring of network performance in existing projects. The online status of the device, CPU and memory usage, etc. can be determined by communicating with the platform and obtaining the current device list.

[0038] S120: Determine, based on the network status data, a first monitoring situation of the network transmission device and the smart sensor when each smart sensor reports device monitoring data.

[0039] Among them, the first monitoring situation refers to the monitoring of network status data and intelligent sensors corresponding to the network transmission equipment. The monitoring situation may include one or at least two faults occurring in the network transmission equipment or the intelligent sensor, or the network transmission equipment is in a normal state.

[0040] In this embodiment, the network status data can determine the first monitoring status of the network transmission equipment and the smart sensor when each smart sensor reports the device monitoring data. The monitoring status can include but is not limited to being determined by signal strength, the online number of network transmission equipment, the online rate of smart sensors, and memory usage, CPU occupancy, data packet loss rate, network connection status, etc.; in some embodiments, if the online number of ACs and / or the online number of APs do not meet the expected online number, it may indicate that the AC or AP is faulty and needs to be repaired or replaced. If all APs are not online and have been powered on and added to the whitelist, it is possible that the AC is faulty and needs to be repaired or replaced. In other embodiments, when the network status of ONU, AC and AP in the network transmission device are all in normal connection and the online number reaches a preset online threshold, the CPU usage and memory usage of the network transmission device can be determined by the number of smart sensors connected to the online AP and the business data transmitted by each smart sensor, so as to determine the operating status of the network transmission device based on the CPU usage and memory usage. If the operating status is normal, it can be determined whether the smart sensor has a fault based on the number of online smart sensors, the signal quality of the location where the smart sensor is installed, and the power status, or the packet loss rate of the monitoring data reported by the smart sensor can be determined by the sensor type of the smart sensor, the number of device data monitoring times and the preset monitoring data reporting times, and whether the smart sensor has a fault can be determined based on the packet loss rate and the preset packet loss rate threshold.

[0041] S130. Determine a second monitoring condition of the intelligent devices included in the converter station based on the device monitoring data and the preset alarm fault library.

[0042] The second monitoring situation refers to the monitoring situation of the monitoring data corresponding to each smart device transmitted by the smart sensor. The preset alarm fault library includes the historical fault sensor data corresponding to the smart sensor, the data range of the historical fault sensor data, the fault type and the fault alarm method.

[0043] In this embodiment, each smart sensor corresponds to a data collection range, and the data collection range can be set appropriately according to the needs of data collection, the type of specific sensor, and the application scenario in which the sensor is deployed. In this embodiment, for sensors of the same type, if the application scenarios are different, the data collection range corresponding to the sensor is also different. For example, for a temperature sensor, if the deployed application scenarios include indoors, next to a generator, and other locations, the data collection range set for the temperature sensor at the two locations is different.

[0044] In this embodiment, for monitoring of the smart devices included in the converter station, the first judgment can be made through the device monitoring data reported by each smart sensor and the pre-configured data collection range, so as to make a second judgment based on the result of the first judgment and the historical maximum and historical minimum values ​​corresponding to the device monitoring data. If it is within the range of the historical maximum and historical minimum values, then according to the installation position of the smart sensor, find out whether the abnormal monitoring data reported by other smart sensors in the same group and / or nearby groups as the smart sensor is found, and based on the abnormal monitoring data reported by other smart sensors in the same group and / or nearby groups of the smart sensor, and the preset alarm fault library, determine the monitoring status of the smart sensor. If it is not within the range of the historical maximum and historical minimum values, then determine whether the smart sensor has a false alarm based on the device monitoring data and the preset standard data range. In other embodiments, the data of the device status sensor can also be subjected to intelligent trend classification, missing value intelligent filling, abnormal value processing and data standardization, and based on the processed data, the long short-term memory network is used to train, predict and evaluate the device parameter fault monitoring data to determine the second monitoring status of the smart devices included in the converter station.

[0045] The technical solution of the embodiment of the present invention determines the first monitoring status of the network transmission equipment and the intelligent sensor when each intelligent sensor reports the equipment monitoring data through the network status data, and determines the second monitoring status of the intelligent equipment included in the converter station based on the equipment monitoring data and the preset alarm fault library, so as to realize the monitoring of the equipment included in the converter station, and the monitoring of the sensors installed in the converter station and their network, so as to realize more intelligent management of the equipment in the converter station, simplify the daily maintenance work, and make the alarm more timely and the fault location more accurate when the equipment fails, thereby reducing the loss caused by the accident.

[0046] In one embodiment, Figure 2 A flow chart of another intelligent monitoring method for a converter station network and equipment provided in one embodiment of the present invention. Based on the above embodiments, this embodiment further refines the first monitoring situation of the network transmission equipment and the intelligent sensor when each intelligent sensor reports the equipment monitoring data based on the network status data, and the second monitoring situation of the intelligent equipment included in the converter station based on the equipment monitoring data and the preset alarm fault library.

[0047] like Figure 2 As shown, the intelligent monitoring method of the converter station network and equipment in this embodiment may specifically include the following steps:

[0048] S210, acquiring equipment monitoring data respectively reported by the smart sensors deployed in the converter station, and network status data of the corresponding network transmission equipment when each smart sensor reports the equipment monitoring data.

[0049] S220. When the network connection status of ONU, AC and AP in the network transmission equipment are all in normal connection and the online number of network transmission equipment reaches the preset online threshold, determine the number of smart sensors connected to the online AP and the service data transmitted by each smart sensor.

[0050] The preset online threshold can be understood as the preset expected online number. If the expected online number is not reached, it is necessary to check the reason why the currently offline network transmission device is offline. The business data transmitted by each smart sensor may include but is not limited to the speed and frequency of data transmission and the size of data transmission.

[0051] In this embodiment, the number of online APs is associated with the number of nodes powered on in the station and the number of APs in the preset whitelist, wherein the preset whitelist is a list of APs pre-configured to access the AC. If you want an AP to go online in the network, you first need to add the unique identifier corresponding to the AP to the whitelist, and then after the AP goes online, it will perform a frequency scan, select the channel with the strongest signal strength to try to access, and the AC will perform access authentication. If the AP is powered on and attempts to access without being added to the whitelist, it will be added to the yellow list by the AC and will be prohibited from re-authentication within a set time, wherein the yellow list is a list of names that prohibit re-authentication within a preset time. In this embodiment, if the number of online APs is less than the expected number of online APs, first check whether the AP has been powered on and whether it has been added to the preset whitelist. If both of the above conditions are met, it is possible that the AP status is abnormal and needs to be repaired or replaced. If all APs are not online, and have been powered on and added to the preset whitelist, it is possible that the AC has a fault and needs to be repaired or replaced.

[0052] In this embodiment, the number of online APs connected to the AC in the network transmission device is obtained. If the number of online APs is less than the preset AP online number threshold, it is checked whether the online APs are powered on and whether they are in the pre-configured whitelist. If the online APs are powered on and in the whitelist, an alarm is triggered to display that the AP status is abnormal. If the network status of the ONU, AC, and AP in the network transmission device are all in normal connection and the number of online APs reaches the preset online threshold, the number of smart sensors connected to the online APs and the service data transmitted by each smart sensor are determined.

[0053] S230, determining the CPU usage and memory usage of the network transmission device according to the number of smart sensors connected to the online AP and the business data transmitted by each smart sensor, and determining the operating status of the network transmission device according to the CPU usage and the memory usage.

[0054] In this embodiment, the CPU usage and memory usage of the network transmission device are determined based on the number of smart sensors connected to the online AP and the service data transmitted by each smart sensor, and the operating status of the network transmission device is determined based on the CPU usage and memory usage. Specifically, a range corresponding to the CPU usage and memory usage can be set. When the range is exceeded, the operating status of the network transmission device is considered abnormal, and the network transmission device is repaired or replaced.

[0055] In one embodiment, the operating status of the network transmission device is determined based on the CPU usage rate and the memory usage rate, including: when the CPU usage rate is within the first preset range, or the memory usage rate is within the preset second preset range, the operating status of the network transmission device is determined to be normal; when the CPU usage rate is not within the first preset range, or the memory usage rate is not within the preset second preset range, the operating status of the network transmission device is determined to be abnormal, and the network transmission device is repaired or replaced. Among them, the preset first preset range and the preset second preset range are both thresholds customized by demand or experience, and can be the same threshold or different thresholds, which are not limited in this embodiment.

[0056] In this embodiment, the operating state of the device has a relatively large relationship with the number of sensor devices and the number of sensor services in the network. In this embodiment, when the CPU usage is within the first preset range, or the memory usage is within the preset second preset range, it is determined that the operating state of the network transmission device is normal. When the CPU usage is not within the first preset range, or the memory usage is not within the preset second preset range, it is determined that the operating state of the network transmission device is abnormal, and the network transmission device is repaired or replaced. Exemplarily, the first preset range is preset to 50%; the second preset range is preset to 80%. Under normal circumstances, the CPU usage of the device should be below 50%, and the memory usage should be below 80%. If it is found that the CPU or memory usage exceeds this range for a long time, it is necessary to repair or replace the device.

[0057] S240. When the operating status is normal, obtain the number of online smart sensors connected to each AP, and determine the first fault condition of the smart sensor according to the number of online smart sensors, signal quality of the location where the smart sensor is installed, and power status.

[0058] In this embodiment, the online rate of the sensor is related to the signal quality near the sensor, the node signal coverage, the sensor power status, and whether it is in the white list of the AC. When the operating status is normal, the number of online smart sensors connected to each AP is obtained, and whether the smart sensor is faulty is determined based on the number of online smart sensors, the signal quality of the location where the smart sensor is installed, and the power status. Exemplarily, a sensor online rate of more than 85% would be a relatively good state. If the sensor online rate does not meet expectations, first check whether the offline faulty sensor is already in the white list, and secondly confirm that there is node signal coverage at the faulty sensor, and the signal quality is good, where the node signal coverage can be understood as the signal coverage of the wireless router installed near the sensor, the signal coverage at the location of the sensor, and the signal strength when the sensor reports through the wireless network. If all the above conditions are met, then you should check whether it is a sensor problem, such as checking whether the battery is sufficient, or directly replace the sensor.

[0059] In one embodiment, determining the first fault condition of the smart sensor according to the number of online smart sensors, the signal quality of the location where the smart sensor is installed, and the power condition includes:

[0060] When the number of online smart sensors exceeds a preset first threshold, determine the signal quality of the location where the currently online smart sensors are installed, and compare the signal quality with a preset signal strength threshold to obtain a comparison result, wherein the comparison result includes smart sensors with qualified signals and smart sensors with unqualified signals;

[0061] Define smart sensors with unqualified signals as faulty sensors, triggering an alarm mechanism so that users can adjust the installation location of the smart sensors or enhance the signal quality;

[0062] For smart sensors with qualified signals, the power status of the smart sensors with qualified signals is determined according to a preset battery voltage-power comparison table. When the power status is lower than a preset power threshold, the smart sensor is determined to be abnormal, triggering an alarm mechanism to prompt the smart sensor to be replaced.

[0063] The preset battery voltage-capacity comparison table includes the capacity corresponding to the battery voltage. The estimated capacity set based on experience may be an exhaustive table, that is, the corresponding capacity is the battery voltage.

[0064] In this embodiment, whether there is a problem with the state of the sensor itself can be determined by the preset battery voltage-power comparison table and the signal quality of the location where the smart sensor is installed. Specifically, when the number of online smart sensors exceeds the preset first threshold, the signal quality of the location where the current online smart sensor is installed is determined, and the signal quality is compared with the preset signal strength threshold to obtain the comparison result. In this embodiment, the signal strength represents the strength of the network signal to which the device is connected. If the signal strength is poor, the reported data packet loss and other problems will increase significantly, and the device will be disconnected more frequently. Therefore, when the signal quality is low, it is necessary to adjust the installation location of the device or use means to enhance the signal; define the smart sensor with unqualified signal as a faulty sensor, trigger the alarm mechanism, so that the user can adjust the installation location of the smart sensor or enhance the signal quality; for the smart sensor with qualified signal, determine the power status of the smart sensor with qualified signal according to the preset battery voltage-power comparison table, and when the power status is lower than the preset power threshold, determine that the smart sensor is abnormal, trigger the alarm mechanism, and prompt to replace the smart sensor. Exemplary, when the battery power is less than 10%, it prompts to replace the sensor.

[0065] In one embodiment, the signal quality is compared with a preset signal strength threshold to obtain a comparison result, including: when the signal quality is greater than or equal to a preset first signal strength threshold, determining the signal quality to be a first level; when the signal quality is less than the preset first signal strength threshold and greater than a preset second signal strength threshold, determining the signal quality to be a second level; when the signal quality is less than or equal to the preset second signal strength threshold, determining the signal quality to be a third level; wherein the preset first signal strength threshold is greater than the preset second signal strength threshold; the first level is greater than the second level, and the second level is greater than the third level.

[0066] In this embodiment, the first level, the second level, and the third level are set as levels defined by the user according to the signal quality. When the signal quality is greater than or equal to the preset first signal strength threshold, the signal quality is determined to be the first level, and the first level is expressed as excellent; when the signal quality is less than the preset first signal strength threshold and greater than the preset second signal strength threshold, the signal quality is determined to be the second level, and the second level is expressed as quantity; when the signal quality is less than or equal to the preset second signal strength threshold, the signal quality is determined to be the third level, and the third level is expressed as poor; exemplarily, RSSI≥-80: excellent, -90≤RSSI<-80: good, RSSI<-90: poor.

[0067] S250. Obtain the number of equipment data monitoring times reported by each smart sensor according to a preset business cycle and a preset control cycle, and determine the packet loss rate of the monitoring data reported by the smart sensor based on the number of equipment data monitoring times and the preset monitoring data reporting times, and determine the second fault condition of the smart sensor based on the packet loss rate and the preset packet loss rate threshold.

[0068] The preset packet loss rate threshold can be customized according to the needs. The preset service cycle is how often the data reporting of the monitoring device is performed; the preset control cycle can be understood as the cycle of the user issuing commands to control data reporting. The preset monitoring data reporting number can be understood as the number of monitoring data reports that should be received.

[0069] In this embodiment, sensors with different traffic volumes have different packet loss rates. For example, for online monitoring sensors with smaller traffic volumes, the data packet loss rate should be controlled below 10% by comprehensively considering the signal quality. For sensors with larger traffic volumes, the traffic packet loss rate should be below 20%. However, if there is a retransmission mechanism, the packet loss rate should be controlled below 10%. If a sensor has a high packet loss rate for several consecutive days, it is necessary to check whether the network to which it is connected and whether the sensor itself has any faults.

[0070] In this embodiment, the preset business cycle and preset control cycle corresponding to different sensor types may be the same or different, and can be defined according to the reporting requirements. The packet loss rate of the monitoring data reported by the intelligent sensor is determined according to the number of equipment data monitoring times and the preset monitoring data reporting times, and whether the intelligent sensor fails is determined according to the packet loss rate and the preset packet loss rate threshold. It can be understood that all sensors can calculate the number of reports that should be made each natural day according to the set business cycle and control cycle. For sensor types with simple reporting data such as temperature, humidity, and water immersion, the number of reports of each sensor in the past day can be counted at a fixed time every day and compared with the expected number of reports. If the number is different, the data is missed; for sensors with complex reporting data such as high frequency, ultra-high frequency, and local ground voltage, the amount of data reported in this case is relatively large, so it will be divided into multiple data packets for uploading. It is necessary to parse and splice the data packets according to the reported data packets. If all data exists and the quantity is normal, it is used as valid data for calculation. If the data is missing, it is considered that the transmission packet is lost and discarded.

[0071] In one embodiment, determining the packet loss rate of monitoring data reported by the smart sensor according to the number of device data monitoring times and the preset number of monitoring data reporting times includes:

[0072] For the first type of sensor, the device data monitoring times and the preset monitoring data reporting times are compared to obtain a comparison result. When the comparison result is that the device data monitoring times and the preset monitoring data reporting times do not match, it is determined that data packet loss has occurred, and a first packet loss rate is determined based on the device data monitoring times and the preset monitoring data reporting times. For the second type of sensor, each received device monitoring data packet is parsed and spliced ​​to obtain a spliced ​​target monitoring data packet. When the amount of data included in the target monitoring data packet is normal and the data format is normal, it is determined to be valid data. If the amount of data included in the target monitoring data packet is abnormal, or the data format is abnormal, it is determined that data packet loss has occurred, the device data monitoring times of the device monitoring data packet are counted, and the second packet loss rate is determined based on the device data monitoring times and the preset monitoring data reporting times.

[0073] In this embodiment, the first type of sensor is a sensor type that reports simple data such as temperature, humidity, and water immersion. The first type of sensor includes at least one of the following: temperature and humidity sensor, water immersion sensor, smoke sensor, water level sensor, and gas sensor; the second type of sensor is a sensor type that reports complex data such as high frequency, ultra-high frequency, etc. The second type of sensor includes at least one of the following: high frequency sensor, ultra-high frequency sensor, and transient ground voltage sensor.

[0074] S260: Take the operating status, the first fault condition and the second fault condition as the first monitoring condition.

[0075] S270, determining whether the equipment monitoring data reported by each smart sensor is within a pre-configured data collection range, if so, executing S280; if not, executing S290; wherein each smart sensor corresponds to a data collection range.

[0076] The pre-configured data collection range is a data collection range set by the user based on historical collection data or data collection requirements. If necessary, each sensor corresponds to a corresponding data collection range.

[0077] In this embodiment, the collection range of sensor data is constrained to ensure the accuracy and reliability of the data. By limiting the range of sensor data, the influence of data beyond the limit on the analysis results can be avoided. In this embodiment, the monitoring threshold of the equipment monitoring data reported by each sensor stored in the database may include but is not limited to the preset standard data range, the historical maximum value and historical minimum value of non-fault received, and different sensors may have different collection ranges, such as the collection range of sensors such as temperature, humidity, and pressure. Therefore, it is necessary to set a suitable collection range according to the specific sensor type and application scenario. At the same time, for data beyond the collection range, it is necessary to process and identify it. For example, if the temperature sensor transmits an extremely high temperature value, it will alarm and require the inspection personnel to go to the site to check. If it is found to be a false alarm, the data will be identified as a false alarm and will not be included in the subsequent adjustment of the collection range; if it is a real equipment failure, the current data will be set to the maximum value of the temperature, and then if the data with a temperature between the maximum value and the collection range is received, it will also directly alarm.

[0078] In this embodiment, when the equipment monitoring data reported by each smart sensor is within the pre-configured data collection range, the equipment monitoring data is saved in a preset database, so that the sensor collection range can be adjusted using historical data. When the equipment monitoring data reported by each smart sensor is not within the pre-configured data collection range, the corresponding historical maximum value and historical minimum value are searched from the preset database to determine whether the equipment monitoring data is within the range of the historical maximum value and the historical minimum value. If it is within the range of the historical maximum value and the historical minimum value, the monitoring status of the smart sensor is determined based on whether other smart sensors in the same group and / or nearby groups as the smart sensor report abnormal monitoring data, and a preset alarm fault library.

[0079] S280: Save the equipment monitoring data to a preset database, and optimize the data collection scope based on the equipment monitoring data stored in the preset database.

[0080] In this embodiment, after data comparison, the equipment monitoring data is saved in a preset database, and the data collection range is optimized based on the equipment monitoring data stored in the preset database. In one embodiment, the data collection range is optimized based on the equipment monitoring data stored in the preset database, including: searching the target equipment monitoring data whose time correlation does not exceed the preset correlation threshold from the equipment monitoring data, determining the corresponding average value and variance based on the target equipment monitoring data; adjusting the collection range of the equipment monitoring data by the average value and variance. It can be understood that the collection range of the sensor is adjusted using historical data. For basic data with less time correlation, such as indoor temperature, humidity, lightning arrester leakage current, high-frequency sampling data, ultrasonic transient ground wave sampling data, etc., the historical data can be used to calculate the average value and variance, and the data collection range is adjusted by data calculation. If the new data received is not within the variance range, it can be temporarily determined as fault data or an accident, and an alarm will be issued and the inspection personnel will be required to go to the site for inspection.

[0081] S290. Obtain the historical maximum and minimum values ​​corresponding to the equipment monitoring data, and determine whether the equipment monitoring data is within the range of the historical maximum and minimum values. If so, execute S2100; if not, execute S2110.

[0082] In this embodiment, the historical maximum and minimum values ​​corresponding to the equipment monitoring data are obtained, and it is determined whether the equipment monitoring data is within the range of the historical maximum and minimum values. If it is within the range of the historical maximum and minimum values, it can be saved based on the abnormal type and data reported by the sensors in the same group and nearby groups to form a data model for fault judgment, so that when the abnormality is reported later, it can be compared in the saved preset alarm fault library to find various similar conditions, and weighted judgments are made according to time, matching degree and the number of matches, and possible faults are listed according to possibility to assist in the judgment of the fault. If it is not within the range of the historical maximum and minimum values, the comparison result of the equipment monitoring data and the preset standard data range is determined, and whether the intelligent sensor has a false alarm is determined based on the comparison result.

[0083] S290: Obtain the installation location of the smart sensor, and determine the fault monitoring result of the smart sensor based on the installation location and a preset alarm fault library.

[0084] In this embodiment, the abnormal monitoring data reported by other smart sensors in the same group and / or nearby groups as the smart sensor is searched based on the installation position of the smart sensor, and the monitoring status of the smart sensor is determined based on the abnormal monitoring data reported by other smart sensors in the same group and / or nearby groups of the smart sensor, and a preset alarm fault library. Specifically, the placement positions of all sensors can be obtained and divided into various groups according to the distance from the equipment where they are installed. When various accidents occur, the abnormal types and data reported by the sensors in the same group and nearby groups are checked and saved to form a data model for fault judgment, so that when an abnormality is reported later, a comparison is made in the saved library to find various similar conditions, and a weighted judgment is made according to time, matching degree and number of matches, and possible faults are listed according to likelihood to assist in fault judgment.

[0085] In one embodiment, the preset alarm fault library includes: historical fault sensor data corresponding to the smart sensor, the data range of the historical fault sensor data, the fault type, and the fault alarm mode; accordingly, the fault monitoring result of the smart sensor is determined based on the installation location and the preset alarm fault library, including:

[0086] According to the installation location, find out whether other smart sensors in the same group and / or nearby groups as the smart sensor have reported historical abnormal monitoring data in the preset alarm faults;

[0087] When historical abnormal monitoring data is found, the equipment monitoring data is matched with the historical abnormal monitoring data to obtain a matching result table; wherein the matching result table is a matching degree table sorted according to the fault occurrence time, fault matching degree and matching similarity times;

[0088] The first weight ratio of the fault occurrence time is set according to the time proximity order, the second weight ratio is set according to the fault matching degree, and the third weight ratio is set according to the number of matching similarities;

[0089] Calculate the first product corresponding to each fault occurrence time and the first weight ratio, the second product corresponding to each fault matching degree and the second weight ratio, and the third product of each matching similarity number and the third weight ratio, and calculate the sum of the first product, the second product and the third product to obtain a fault type ranking result with a weighted ranking feature;

[0090] The highest ranked fault type in the fault type sorting result is used as the fault type of the equipment monitoring data, and the fault alarm mode corresponding to the fault type is triggered;

[0091] When no historical abnormal monitoring data can be found, the data corresponding to the sensor is put into the preset alarm fault.

[0092] It can be understood that there may be multiple possibilities of time sorting for a fault corresponding to a data in the database. The closer the time, the greater the weight value, that is, the same data range and fault are screened according to time. The matching degree in this embodiment is the matching degree of the data range, alarm mode and fault type recorded by other smart sensors of the same group and / or nearby group installed around the sensor and the corresponding fault alarm recorded in the fault alarm database. For example, there are 3 sensor alarms of other smart sensors of the same group and / or nearby group around. According to the matched current sensor alarm type, occurrence time sequence, matching degree, and matching number, weighted calculation is performed. For example, the fault alarm database matches 3 sensors and alarms at the same time, which is a fire. Each fault is recorded and searched in the fault alarm database. If the type of fire appears more, the weight increases, and it is preliminarily judged to be a fire; according to the weighting of these three weights, the maximum possibility, the second possibility and the third possibility are obtained; for example, the time proportion is 20%, the matching degree is 40%, and the matching number is 40%, and a possibility ranking is calculated for each possible result listed in the matching result table.

[0093] S2100, determining a comparison result between the equipment monitoring data and a preset standard data range, and determining whether a false alarm occurs in the smart sensor based on the comparison result.

[0094] In this embodiment, the device monitoring data is compared with the preset standard data range to determine the comparison result of the device monitoring data and the preset standard data range, and whether the smart sensor has a false alarm is determined based on the comparison result. Specifically, if it is not within the preset standard data range, it is determined that the smart sensor has a false alarm, and an alarm mechanism is activated to allow the user to check the smart sensor; if it is within the preset standard data range, the device monitoring data is recorded, classified and added to the preset database.

[0095] The technical solution of the embodiment of the present invention is to inspect and maintain the data network established by the sensors in the converter station, warn and maintain the possible faults, and provide processing suggestions; and to bind the reported data of multiple sensors in the converter station with the installation location and the preset alarm fault library, so that when similar faults occur later, the reported abnormal data and the abnormal type can be used for matching, and various data can be collected to automatically optimize the analysis and processing of the reported abnormal data, and provide possible fault analysis for the staff.

[0096] In one embodiment, in order to better understand the intelligent monitoring method of the converter station network and equipment, Figure 3 A flowchart of another method for fault monitoring of a converter station network and equipment provided by an embodiment of the present invention.

[0097] like Figure 3 As shown, in this embodiment, the intelligent monitoring method of the converter station network and equipment specifically includes the following steps:

[0098] a1. Sensor reports data.

[0099] a2. The background receives data.

[0100] a3. Determine the data type. When the data type is device monitoring data, execute a9; when the data type is network status data of a network transmission device, execute a4.

[0101] a4. Check the wireless communication status.

[0102] a5. Check whether the network status is normal. If so, execute a6; if not, execute a7.

[0103] a6. Check the network status.

[0104] a7. Is the sensor status normal? If so, execute a8; if not, monitor the data normally.

[0105] a8. Check the corresponding sensor.

[0106] a9. Check whether the equipment monitoring data is within the collection range. If yes, execute a10; if not, execute a11.

[0107] a10. Save the equipment monitoring data to the preset database to optimize the data collection scope.

[0108] a11. Whether the equipment monitoring data is within the range of the historical maximum value and the historical minimum value, if so, execute a12; if not, execute a13.

[0109] a12. Determine whether nearby sensors report abnormal monitoring data. If so, execute a14; if not, execute a17.

[0110] a13. Check whether the equipment monitoring data is within the normal range. If so, execute a15; if not, execute a16.

[0111] a14. Is there a similar situation in the preset alarm database? If so, execute a18; if not, execute a17.

[0112] a15. Record the report and classify it according to the inspection results after the inspection is completed.

[0113] a16. Think the sensor is giving a false alarm, check the sensor.

[0114] a17. Check the preset alarm fault library.

[0115] a18. Based on the abnormal monitoring data reported by other smart sensors in the same group and / or nearby groups of the smart sensor, and the preset alarm fault library, the monitoring status of the smart sensor is determined, and an alarm is issued according to the monitoring status, and the analysis results are issued together.

[0116] In this embodiment, not only the monitoring of the original equipment of the converter station is supported, but also the monitoring and fault handling of the sensors and their networks installed in the converter station are performed. Among them, the sensors installed in the converter station support comprehensive analysis of sensors such as temperature, humidity, water immersion, water level, lightning arrester leakage current, high-frequency partial discharge, ultra-high-frequency partial discharge, ultrasonic ground voltage, voiceprint, noise, etc., and support the continued expansion of other types of sensors.

[0117] In this embodiment, the sensor data of the converter station is collected, and the reported data is judged and preprocessed based on mathematical methods. The current state of the sensor and the sensor network is judged according to the missing of the reported data, the abnormality of the network state, the processing result of the sensor reporting its own state data, etc. If the state is poor, the alarm is analyzed and issued. If the abnormal data reported by the sensor is received, the pre-stored fault type and the actual fault database that actually occurred are matched according to the type of sensor nearby, whether the nearby sensor reports abnormal data and the type of abnormal data reported, and the matched faults are sorted according to the degree of matching, and an alarm is issued to the operation and maintenance personnel in the station. In this embodiment, because the installation positions of all sensors are saved, it is possible to analyze and judge whether it is a sensor false alarm or a real fault based on the data reported by the sensor and multiple sensors installed nearby, and analyze and process using historical data and pre-stored fault types. At the same time, the judgment threshold used in the present invention is a threshold calculated by the historical value of long-term operation based on the threshold set by the expert, which will be different according to the region and environment where it is located, and is more accurate than the prediction threshold calculated using a small amount of data.

[0118] In one embodiment, in order to facilitate a better understanding, the sensors and their networks installed in the converter station are monitored and faults are handled. Figure 4 A schematic diagram of a flow chart of monitoring sensors and their networks installed in a converter station provided by an embodiment of the present invention, such as Figure 4 As shown, the specific process is as follows:

[0119] b1. Check the online status of network devices (AP, ONU, etc.).

[0120] b2. Are all network devices online? If so, execute b3; if not, execute b4.

[0121] b3. Check the operating status of network devices.

[0122] b4. Check offline network devices on site.

[0123] b5. Check whether the CPU usage and memory usage of the device are normal. If yes, execute b6; if no, execute b7.

[0124] b6. Check the online status of the sensor.

[0125] b7. On-site maintenance of network equipment.

[0126] b8: Are all sensors online? If yes, execute b9; if not, execute b10.

[0127] b9. Check the sensor network signal strength.

[0128] b10. Check the offline sensors on site.

[0129] b11: Is the signal strength qualified? If yes, execute b12; if no, execute b13.

[0130] b12. Check the remaining power of the sensor.

[0131] b13. Check the sensors with unqualified signals.

[0132] b14: ​​Is the remaining power sufficient? If so, execute b15; if not, execute b16.

[0133] b15. Calculate the packet loss rate of each sensor based on the number of times data is received and the number of times it should be reported per unit time.

[0134] b16. Replace the sensor whose battery is running out.

[0135] b17. Check whether the packet loss rate is too high. If so, execute b18; if not, execute b19.

[0136] b18. Complete a round of self-check, wait for a unit time, and return to b1 for re-monitoring.

[0137] b19. Check whether the sensor is damaged.

[0138] In one embodiment, Figure 5 This is a structural block diagram of an intelligent monitoring device for a converter station network and equipment provided in one embodiment of the present invention. The device is suitable for monitoring the original equipment included in the converter station, as well as the sensors installed in the converter station and their networks. The device can be implemented by hardware / software. It can be configured in an electronic device to implement an intelligent monitoring method for a converter station network and equipment in an embodiment of the present invention.

[0139] like Figure 5As shown, the device includes: a data acquisition module 510, a network monitoring module 520 and a device monitoring module 530;

[0140] The data acquisition module 510 is used to acquire the equipment monitoring data reported by the smart sensors deployed in the converter station, and the network status data of the corresponding network transmission equipment when each of the smart sensors reports the equipment monitoring data;

[0141] A network monitoring module 520, configured to determine, based on the network status data, a first monitoring condition of the network transmission device and the smart sensor when each of the smart sensors reports the device monitoring data;

[0142] The device monitoring module 530 is used to determine the second monitoring status of the intelligent device included in the converter station based on the device monitoring data and the preset alarm fault library.

[0143] In the embodiment of the present invention, the network monitoring module determines the first monitoring status of the network transmission equipment and the intelligent sensor when each intelligent sensor reports the equipment monitoring data through the network status data; the equipment monitoring module determines the second monitoring status of the intelligent equipment included in the converter station based on the equipment monitoring data and the preset alarm fault library, thereby realizing the monitoring of the original equipment of the converter station, and at the same time, monitoring and fault processing are performed on the sensors installed in the converter station and their networks, thereby realizing more intelligent management of the equipment in the converter station, simplifying daily maintenance work, and making the alarm more timely and the fault location more accurate when the equipment fails, thereby reducing the loss caused by the accident.

[0144] In one embodiment, the network transmission equipment at least includes: an optical network unit OUN, an access controller AC, and at least two wireless access nodes AP; the OUN is connected to a host computer; the AC communicates with the OUN; the AC communicates with the at least two APs; the at least two APs correspond to at least one smart sensor respectively; each of the smart sensors is deployed next to each smart device included in the converter station according to a preset design file to monitor data of the corresponding smart device;

[0145] The network status data includes: communication status quantity and network equipment operation status quantity; the communication status quantity includes at least one of the following: signal quality, number of online network transmission equipment, online rate of intelligent sensors; the network equipment operation status quantity includes at least one of the following: memory utilization rate, CPU occupancy rate, data packet loss rate.

[0146] In one embodiment, the network monitoring module 520 includes:

[0147] A data determination unit, configured to determine the number of smart sensors connected to the online AP and the service data transmitted by each of the smart sensors when the network connection states of the ONU, AC and AP in the network transmission device are all in normal connection and the number of online network transmission devices reaches a preset online threshold;

[0148] An operation status determination unit, configured to determine a CPU usage rate and a memory usage rate of the network transmission device according to the number of smart sensors connected to the online AP and the service data transmitted by each of the smart sensors, and determine an operation status of the network transmission device according to the CPU usage rate and the memory usage rate;

[0149] A first fault determination unit is used to obtain the number of online smart sensors connected to each of the APs when the operating state is normal, and determine the first fault condition of the smart sensor according to the number of online smart sensors, the signal quality of the location where the smart sensor is installed, and the power condition;

[0150] A second fault determination unit is used to obtain the number of device data monitoring times reported by each of the smart sensors according to a preset business cycle and a preset control cycle, and determine a packet loss rate of the monitoring data reported by the smart sensor according to the sensor type of the smart sensor, the number of device data monitoring times and the preset monitoring data reporting times, and determine a second fault condition of the smart sensor according to the packet loss rate and a preset packet loss rate threshold;

[0151] The detection condition determination unit is used to use the operating state condition, the first fault condition and the second fault condition as the first monitoring condition.

[0152] In one embodiment, the operation status determination unit includes:

[0153] A first operation status determination subunit, configured to determine that the operation status of the network transmission device is normal when the CPU usage rate is within a first preset range or the memory usage rate is within a second preset range;

[0154] The second operation status determination subunit is used to determine that the operation status of the network transmission device is abnormal when the CPU usage is not within the first preset range or the memory usage is not within the preset second preset range.

[0155] In one embodiment, the first fault determination unit includes:

[0156] A comparison subunit, configured to determine the signal quality of the installation position of the currently online smart sensor when the number of online smart sensors exceeds a preset first threshold, and compare the signal quality with a preset signal strength threshold to obtain a comparison result, wherein the comparison result includes smart sensors with qualified signals and smart sensors with unqualified signals;

[0157] A first alarm subunit, used for defining the smart sensor with unqualified signals as a faulty sensor and triggering an alarm mechanism;

[0158] The second alarm sub-unit is used to determine the power status of the smart sensor with a qualified signal according to a preset battery voltage-power comparison table. When the power status is lower than a preset power threshold, it is determined that the smart sensor is abnormal and an alarm mechanism is triggered.

[0159] In one embodiment, the second fault determination unit includes:

[0160] a first packet loss rate determination subunit, configured to compare the device data monitoring times and the preset monitoring data reporting times for the first type of sensor to obtain a comparison result, and determine that data packet loss occurs when the comparison result is that the device data monitoring times and the preset monitoring data reporting times do not match, and determine a first packet loss rate according to the device data monitoring times and the preset monitoring data reporting times;

[0161] The second packet loss rate determination subunit is used to parse and splice each received device monitoring data packet for the second type of sensor to obtain a spliced ​​target monitoring data packet, and determine it as valid data when the data amount and data format of the target monitoring data packet are normal; if the data amount or data format of the target monitoring data packet is abnormal, it is determined that data packet loss has occurred, and the number of device data monitoring times of the device monitoring data packet is counted, and the second packet loss rate is determined based on the number of device data monitoring times and the preset monitoring data reporting times.

[0162] In one embodiment, the first type of sensor includes at least one of the following: a temperature and humidity sensor, a water immersion sensor, a smoke sensor, a water level sensor, and a gas sensor;

[0163] The second type of sensor includes at least one of the following: a high frequency sensor, an ultra-high frequency sensor, and a transient voltage sensor.

[0164] In one embodiment, the comparison subunit is specifically used for:

[0165] When the signal quality is greater than or equal to a preset first signal strength threshold, determining that the signal quality is at a first level;

[0166] When the signal quality is less than the preset first signal strength threshold and greater than the preset second signal strength threshold, determining that the signal quality is at the second level;

[0167] When the signal quality is less than or equal to the preset second signal strength threshold, the signal quality is determined to be the third level; wherein the preset first signal strength threshold is greater than the preset second signal strength threshold; the first level is greater than the second level, and the second level is greater than the third level.

[0168] In one embodiment, the equipment monitoring module 530 includes:

[0169] A first judgment unit, used to judge whether the equipment monitoring data reported by each of the smart sensors is within a pre-configured data collection range;

[0170] an optimization unit, configured to save the device monitoring data into a preset database if the device monitoring data is within the data collection range, and optimize the data collection range according to the device monitoring data stored in the preset database;

[0171] A second judgment unit is used to obtain the historical maximum value and the historical minimum value corresponding to the device monitoring data if the device monitoring data is not within the data collection range, and judge whether the device monitoring data is within the range of the historical maximum value and the historical minimum value;

[0172] a fault determination unit, configured to obtain the installation location of the smart sensor if the value is within the range of the historical maximum value and the historical minimum value, and determine the fault monitoring result of the smart sensor based on the installation location and a preset alarm fault library;

[0173] The comparison unit is used to determine the comparison result of the equipment monitoring data with the preset standard data range if it is not within the range of the historical maximum value and the historical minimum value, and determine whether the intelligent sensor has a false alarm based on the comparison result.

[0174] In one embodiment, the preset alarm fault library includes: historical fault sensor data corresponding to the smart sensor, the data range of the historical fault sensor data, the fault type and the fault alarm mode;

[0175] Accordingly, the fault determination unit includes:

[0176] A matching subunit, configured to search, according to the installation location, in the preset alarm fault to see whether other smart sensors in the same group and / or nearby groups as the smart sensor have reported historical abnormal monitoring data;

[0177] A matching result determination subunit is used for matching the equipment monitoring data with the historical abnormal monitoring data to obtain a matching result table when the historical abnormal monitoring data is found; wherein the matching result table is a matching degree table sorted according to the fault occurrence time, fault matching degree and matching similarity number;

[0178] A weight determination subunit, configured to set a first weight ratio of the fault occurrence time according to a time proximity order, set a second weight ratio according to a fault matching degree, and set a third weight ratio according to a matching similarity number;

[0179] a sorting subunit, configured to calculate a first product corresponding to each of the fault occurrence times and the first weight ratio, a second product corresponding to each of the fault matching degrees and the second weight ratio, and a third product of each of the matching similarity times and the third weight ratio, and calculate the sum of the first product, the second product and the third product to obtain a fault type sorting result having a weight sorting feature;

[0180] The fault determination subunit is used to use the highest ranked fault type in the fault type sorting result as the fault type of the equipment monitoring data, and trigger the fault alarm mode corresponding to the fault type.

[0181] In one embodiment, the optimization unit is specifically configured to:

[0182] Searching for target device monitoring data whose time correlation does not exceed a preset correlation threshold from the device monitoring data, and determining a corresponding average value and variance based on the target device monitoring data;

[0183] The collection range of the equipment monitoring data is adjusted according to the mean value and the variance.

[0184] The intelligent monitoring device for converter station network and equipment provided in the embodiment of the present invention can execute the intelligent monitoring method for converter station network and equipment provided in any embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method.

[0185] In one embodiment, Figure 6A schematic diagram of the structure of an electronic device provided for an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0186] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0187] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0188] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the intelligent monitoring method of the converter station network and equipment.

[0189] In some embodiments, the intelligent monitoring method of the converter station network and equipment can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the intelligent monitoring method of the converter station network and equipment described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the intelligent monitoring method of the converter station network and equipment in any other appropriate manner (for example, by means of firmware).

[0190] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0191] The computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable converter station network and equipment intelligent monitoring device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0192] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0193] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0194] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0195] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0196] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0197] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent monitoring method for converter station network and equipment, characterized in that: include: Acquire equipment monitoring data respectively reported by the smart sensors deployed in the converter station, and network status data of the corresponding network transmission equipment when each of the smart sensors reports the equipment monitoring data; Determine, based on the network status data, a first monitoring condition of the network transmission device and the smart sensor when each of the smart sensors reports the device monitoring data; A second monitoring condition of the intelligent device included in the converter station is determined based on the device monitoring data and a preset alarm fault library.

2. The method according to claim 1, characterized in that The network transmission device at least includes: an optical network unit OUN, an access controller AC, and at least two wireless access nodes AP; the OUN is connected to a host computer; the AC communicates with the OUN; the AC communicates with the at least two APs; the at least two APs correspond to at least one smart sensor respectively; The network status data includes: communication status quantity and network equipment operation status quantity; the communication status quantity includes at least one of the following: signal quality, number of online network transmission equipment, online rate of intelligent sensors; the network equipment operation status quantity includes at least one of the following: memory usage rate, CPU occupancy rate, data packet loss rate, power status.

3. The method according to claim 2, characterized in that The determining, based on the network status data, a first monitoring condition of the network transmission device and the smart sensor when each of the smart sensors reports the device monitoring data includes: When the network connection status of the ONU, AC and AP in the network transmission device are all in normal connection, and the online number of the network transmission device reaches a preset online threshold, determine the number of smart sensors connected to the online AP and the service data transmitted by each of the smart sensors; Determine the CPU usage and memory usage of the network transmission device according to the number of smart sensors connected to the online AP and the service data transmitted by each of the smart sensors, and determine the operating status of the network transmission device according to the CPU usage and the memory usage; When the operating status is normal, the number of online smart sensors connected to each of the APs is obtained, and the first fault condition of the smart sensor is determined according to the number of online smart sensors, the signal quality of the location where the smart sensor is installed, and the power condition; Obtain the number of device data monitoring times reported by each of the smart sensors according to a preset business cycle and a preset control cycle, determine a packet loss rate of the monitoring data reported by the smart sensor according to the number of device data monitoring times and the preset monitoring data reporting times, and determine a second fault condition of the smart sensor according to the packet loss rate and a preset packet loss rate threshold; The operating status, the first fault condition and the second fault condition are used as the first monitoring condition.

4. The method according to claim 3, characterized in that The determining the operating status of the network transmission device according to the CPU usage and the memory usage includes: When the CPU usage is within a first preset range, or the memory usage is within a second preset range, determining that the operation state of the network transmission device is normal; When the CPU usage is not within the first preset range, or the memory usage is not within the second preset range, it is determined that the operating state of the network transmission device is abnormal.

5. The method according to claim 3, characterized in that: The determining the first fault condition of the smart sensor according to the number of online smart sensors, the signal quality of the location where the smart sensor is installed, and the power condition includes: When the number of online smart sensors exceeds a preset first threshold, determine the signal quality of the location where the currently online smart sensors are installed, and compare the signal quality with a preset signal strength threshold to obtain a comparison result, wherein the comparison result includes smart sensors with qualified signals and smart sensors with unqualified signals; The smart sensor with unqualified signal is defined as a faulty sensor, and an alarm mechanism is triggered; For the smart sensor with qualified signal, the power status of the smart sensor with qualified signal is determined according to a preset battery voltage-power comparison table. When the power status is lower than a preset power threshold, the smart sensor is determined to be abnormal and an alarm mechanism is triggered.

6. The method according to claim 3, characterized in that The determining of the packet loss rate of the monitoring data reported by the smart sensor according to the number of device data monitoring times and the preset number of monitoring data reporting times includes: For the first type of sensor, the device data monitoring times and the preset monitoring data reporting times are compared to obtain a comparison result, and when the comparison result is that the device data monitoring times and the preset monitoring data reporting times do not match, it is determined that data packet loss occurs, and a first packet loss rate is determined according to the device data monitoring times and the preset monitoring data reporting times; For the second type of sensor, each received device monitoring data packet is parsed and spliced ​​to obtain a spliced ​​target monitoring data packet. When the data amount included in the target monitoring data packet is normal and the data format is normal, it is determined to be valid data; if the data amount included in the target monitoring data packet is abnormal, or the data format is abnormal, it is determined that data packet loss has occurred, and the number of device data monitoring times of the device monitoring data packet is counted, and the second packet loss rate is determined based on the number of device data monitoring times and the preset monitoring data reporting times.

7. The method according to claim 6, characterized in that The first type of sensor includes at least one of the following: a temperature and humidity sensor, a water immersion sensor, a smoke sensor, a water level sensor, and a gas sensor; The second type of sensor includes at least one of the following: a high frequency sensor, an ultra-high frequency sensor, and a transient voltage sensor.

8. The method according to claim 5, characterized in that The comparing the signal quality with a preset signal strength threshold to obtain a comparison result includes: When the signal quality is greater than or equal to a preset first signal strength threshold, determining that the signal quality is at a first level; When the signal quality is less than the preset first signal strength threshold and greater than the preset second signal strength threshold, determining that the signal quality is at the second level; When the signal quality is less than or equal to the preset second signal strength threshold, the signal quality is determined to be the third level; wherein the preset first signal strength threshold is greater than the preset second signal strength threshold; the first level is greater than the second level, and the second level is greater than the third level.

9. The method according to claim 1, characterized in that: The determining the second monitoring condition of the intelligent device included in the converter station based on the device monitoring data and the preset alarm fault library includes: Determining whether the equipment monitoring data reported by each of the smart sensors is within a pre-configured data collection range; If it is within the data collection range, the device monitoring data is saved in a preset database, and the data collection range is optimized according to the device monitoring data stored in the preset database; If it is not within the data collection range, obtaining the historical maximum value and the historical minimum value corresponding to the device monitoring data, and determining whether the device monitoring data is within the range of the historical maximum value and the historical minimum value; If it is within the range of the historical maximum value and the historical minimum value, obtaining the installation location of the smart sensor, and determining the fault monitoring result of the smart sensor based on the installation location and the preset alarm fault library; If it is not within the range of the historical maximum value and the historical minimum value, the comparison result of the equipment monitoring data and the preset standard data range is determined, and whether the smart sensor has a false alarm is determined based on the comparison result.

10. The method according to claim 9, characterized in that The preset alarm fault library includes: historical fault sensor data corresponding to the smart sensor, the data range of the historical fault sensor data, the fault type and the fault alarm mode; Accordingly, the determining of the fault monitoring result of the smart sensor based on the installation position and the preset alarm fault library includes: Searching, according to the installation location, in the preset alarm fault, whether other smart sensors in the same group and / or nearby groups as the smart sensor have reported historical abnormal monitoring data; When the historical abnormal monitoring data is found, the equipment monitoring data is matched with the historical abnormal monitoring data to obtain a matching result table; wherein the matching result table is a matching degree table sorted according to the fault occurrence time, fault matching degree and matching similarity times; The first weight ratio of the fault occurrence time is set according to the time proximity order, the second weight ratio is set according to the fault matching degree, and the third weight ratio is set according to the matching similarity number; Calculating a first product corresponding to each of the fault occurrence times and the first weight ratio, a second product corresponding to each of the fault matching degrees and the second weight ratio, and a third product of each of the matching similarity times and the third weight ratio, and calculating the sum of the first product, the second product and the third product to obtain a fault type ranking result with a weight ranking feature; The highest ranked fault type in the fault type sorting result is used as the fault type of the equipment monitoring data, and a fault alarm mode corresponding to the fault type is triggered.

11. The method according to claim 9, characterized in that The optimizing the data collection scope according to the equipment monitoring data stored in the preset database includes: Searching for target device monitoring data whose time correlation does not exceed a preset correlation threshold from the device monitoring data, and determining a corresponding average value and variance based on the target device monitoring data; The collection range of the equipment monitoring data is adjusted according to the mean value and the variance.

12. A fault monitoring device for converter station network and equipment, characterized in that: include: A data acquisition module, used to acquire the equipment monitoring data reported by the smart sensors deployed in the converter station, and the network status data of the corresponding network transmission equipment when each of the smart sensors reports the equipment monitoring data; A network monitoring module, configured to determine, based on the network status data, a first monitoring condition of the network transmission device and the smart sensor when each of the smart sensors reports the device monitoring data; The device monitoring module is used to determine the second monitoring status of the intelligent device included in the converter station based on the device monitoring data and a preset alarm fault library.

13. The device according to claim 12, characterized in that The network monitoring module comprises: A data determination unit, configured to determine the number of smart sensors connected to the online AP and the service data transmitted by each of the smart sensors when the network connection states of the ONU, AC and AP in the network transmission device are all in normal connection and the online number of the network transmission devices reaches a preset online threshold; An operation status determination unit, configured to determine a CPU usage rate and a memory usage rate of the network transmission device according to the number of smart sensors connected to the online AP and the service data transmitted by each of the smart sensors, and determine an operation status of the network transmission device according to the CPU usage rate and the memory usage rate; A first fault determination unit is used to obtain the number of online smart sensors connected to each of the APs when the operating state is normal, and determine the first fault condition of the smart sensor according to the number of online smart sensors, the signal quality of the location where the smart sensor is installed, and the power condition; a second fault determination unit, configured to obtain the number of device data monitoring times reported by each of the smart sensors according to a preset service cycle and a preset control cycle, and determine a packet loss rate of the monitoring data reported by the smart sensor according to the number of device data monitoring times and the preset monitoring data reporting times, and determine a second fault condition of the smart sensor according to the packet loss rate and a preset packet loss rate threshold; The detection condition determination unit is used to use the operating state condition, the first fault condition and the second fault condition as the first monitoring condition.

14. The device according to claim 13, characterized in that The first fault determination unit includes: A comparison subunit, configured to determine the signal quality of the installation position of the currently online smart sensor when the number of online smart sensors exceeds a preset first threshold, and compare the signal quality with a preset signal strength threshold to obtain a comparison result, wherein the comparison result includes smart sensors with qualified signals and smart sensors with unqualified signals; A first alarm subunit, used for defining the smart sensor with unqualified signals as a faulty sensor and triggering an alarm mechanism; The second alarm sub-unit is used to determine the power status of the smart sensor with a qualified signal according to a preset battery voltage-power comparison table. When the power status is lower than a preset power threshold, it is determined that the smart sensor is abnormal and an alarm mechanism is triggered.

15. The device according to claim 13, characterized in that The second fault determination unit includes: a first packet loss rate determination subunit, configured to compare the device data monitoring times and the preset monitoring data reporting times for the first type of sensor to obtain a comparison result, and determine that data packet loss occurs when the comparison result is that the device data monitoring times and the preset monitoring data reporting times do not match, and determine a first packet loss rate according to the device data monitoring times and the preset monitoring data reporting times; The second packet loss rate determination subunit is used to parse and splice each received device monitoring data packet for the second type of sensor to obtain a spliced ​​target monitoring data packet, and determine it as valid data when the data amount and data format of the target monitoring data packet are normal; if the data amount or data format of the target monitoring data packet is abnormal, it is determined that data packet loss has occurred, and the number of device data monitoring times of the device monitoring data packet is counted, and the second packet loss rate is determined based on the number of device data monitoring times and the preset monitoring data reporting times.

16. The device according to claim 12, characterized in that The equipment monitoring module comprises: A first judgment unit, used to judge whether the equipment monitoring data reported by each of the smart sensors is within a pre-configured data collection range; an optimization unit, configured to save the device monitoring data into a preset database if the device monitoring data is within the data collection range, and optimize the data collection range according to the device monitoring data stored in the preset database; A second judgment unit is used to obtain the historical maximum value and the historical minimum value corresponding to the device monitoring data if the device monitoring data is not within the data collection range, and judge whether the device monitoring data is within the range of the historical maximum value and the historical minimum value; a fault determination unit, configured to obtain the installation location of the smart sensor if the value is within the range of the historical maximum value and the historical minimum value, and determine the fault monitoring result of the smart sensor based on the installation location and a preset alarm fault library; The comparison unit is used to determine the comparison result of the equipment monitoring data with the preset standard data range if it is not within the range of the historical maximum value and the historical minimum value, and determine whether the intelligent sensor has a false alarm based on the comparison result.

17. The device according to claim 16, characterized in that The preset alarm fault library includes: historical fault sensor data corresponding to the smart sensor, the data range of the historical fault sensor data, the fault type and the fault alarm mode; Accordingly, the fault determination unit includes: A matching subunit, configured to search, according to the installation location, in the preset alarm fault to see whether other smart sensors in the same group and / or nearby groups as the smart sensor have reported historical abnormal monitoring data; A matching result determination subunit is used for matching the equipment monitoring data with the historical abnormal monitoring data to obtain a matching result table when the historical abnormal monitoring data is found; wherein the matching result table is a matching degree table sorted according to the fault occurrence time, fault matching degree and matching similarity number; A weight determination subunit, configured to set a first weight ratio of the fault occurrence time according to a time proximity order, set a second weight ratio according to a level of fault matching, and set a third weight ratio according to a level of similar matching times; a sorting subunit, configured to calculate a first product corresponding to each of the fault occurrence times and the first weight ratio, a second product corresponding to each of the fault matching degrees and the second weight ratio, and a third product of each of the matching similarity times and the third weight ratio, and calculate the sum of the first product, the second product and the third product to obtain a fault type sorting result having a weight sorting feature; The fault determination subunit is used to use the highest ranked fault type in the fault type sorting result as the fault type of the equipment monitoring data, and trigger the fault alarm mode corresponding to the fault type.

18. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the intelligent monitoring method for converter station networks and equipment according to any one of claims 1 to 11.

19. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the intelligent monitoring method for converter station networks and equipment according to any one of claims 1 to 11 when executed.

20. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the computer program implements the intelligent monitoring method for converter station networks and devices according to any one of claims 1 to 11.

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