Sensor abnormity monitoring method and device, equipment, storage medium and program product

Abstract

The invention relates to the technical field of sensor abnormity monitoring, in particular to a sensor abnormity monitoring method and device, electronic equipment, a storage medium and a program product. The sensor data transmitted to the controller is acquired through the network communication equipment, and the network communication equipment has data copying and data sending functions; acquiring sensor data sent by the network communication equipment in a monitoring or inquiry mode; according to the method, the abnormal data in the sensor data are identified based on the preset sensor abnormity identification rule, so that the abnormal data such as sensor jitter and flash which are fast in occurrence time, short in process and random in occurrence can be sensitively and conveniently obtained, effective monitoring of sensor faults is realized, and normal operation of an industrial production line is ensured.

Description

Technical Field

[0001] The present disclosure relates to the technical field of sensor monitoring, and in particular, to a method and device for monitoring sensor anomalies, an electronic device, a storage medium, and a program product. Background Art

[0002] At present, data collection in industrial Internet mainly captures various data on the production line in real time through devices such as sensors, such as temperature, pressure, etc., and transmits them to the cloud platform or local data center. Since most sensors are located in harsh industrial sites, affected by factors such as high temperature, high pressure, various electromagnetic interferences, or high-frequency vibrations at the site, the sensors may have faults such as poor contact, short circuit of the circuit, or displacement of the sensor position, resulting in rapid or very short jitters in the sensor signals. These jitters occur quickly, have a short process, and appear randomly, causing most data collection devices with low acquisition accuracy or sensitivity to be unable to capture these sensor jitter signals in time.

[0003] However, these sensor jitter signals are often in the early state of major defects or faults. If these sensor jitter signals are ignored or cannot be collected and anomalies are not detected from them, it is bound to create a blind spot for fault monitoring, making it impossible to effectively detect sensor anomalies and continuously monitor the anomalies, and thus impossible to take timely measures to prevent the sudden occurrence of faults and the serious economic losses caused to the enterprise. In fact, there are indeed many cases of sudden faults and serious economic losses during industrial production, or sudden mutations in product quality that occur randomly, and many of these are due to the early random sensor jitter anomalies being ignored and gradually evolving into obvious sudden faults or accidents.

[0004] There are various ways to monitor existing sensor faults, including the method of querying or subscribing to a PLC or other controller to obtain preset sensor data. The querying method requires the data collection device to send a query to a PLC or other controller, and then receive the pre-configured sensor data sent by the PLC, and judge whether the sensor is normal by monitoring the obtained data from these queries. The subscribing method does not need to query the PLC controller every time and can obtain preset sensor data sent at a fixed period. However, both the querying and subscribing methods are affected by the industrial site network bandwidth and the maximum load of the PLC. The sensor data collection period is long, and only specific types of data can be obtained. The collected sensor data is limited or less, resulting in many sensor jitter data that occur quickly, have a short process, and appear randomly cannot be collected in time, and thus it is impossible to effectively monitor sensor anomalies or faults.

[0005] Therefore, how to sensitively and conveniently collect and obtain signals such as sensor jitter and flash interruption that occur quickly, have a short process, and appear randomly, and achieve effective monitoring of sensor anomalies or faults to ensure the normal operation of industrial production lines is a technical problem that urgently needs to be solved. Summary of the Invention

[0006] To solve the above technical problems, the present disclosure provides a sensor anomaly monitoring method, device, equipment, storage medium, and program product.

[0007] The first aspect of the embodiments of the present disclosure provides a sensor anomaly monitoring method, including: Obtaining sensor data transmitted to a controller through a network communication device, where the network communication device has a data replication and data sending function; Obtaining the sensor data sent by the network communication device by using a listening or querying method; Identifying abnormal data in the sensor data based on a preset sensor anomaly identification rule.

[0008] The second aspect of the embodiments of the present disclosure provides a sensor anomaly monitoring device, including: A data replication module, configured to obtain sensor data transmitted to a controller through a network communication device, where the network communication device has a data replication and data sending function; A data acquisition module, configured to obtain the sensor data sent by the network communication device by using a listening or querying method; An anomaly identification module, configured to identify abnormal data in the sensor data based on a preset sensor anomaly identification rule.

[0009] The third aspect of the embodiments of the present disclosure provides an electronic device, including: A processor; A memory; and A computer program; Wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the sensor anomaly monitoring method provided in the first aspect above.

[0010] The fourth aspect of the embodiments of the present disclosure provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor is caused to implement the sensor anomaly monitoring method provided in the first aspect above.

[0011] The fifth aspect of the embodiments of the present disclosure provides a computer-readable program product, which includes a computer program or instruction. When the computer program or instruction is executed by a processor, the processor is caused to implement the sensor anomaly monitoring method provided in the first aspect above.

[0012] The technical solutions provided by the embodiments of the present disclosure have the following advantages compared with the prior art: The sensor anomaly monitoring method, device, equipment, storage medium and program product provided by the embodiments of the present disclosure obtain sensor data transmitted to a controller through a network communication device, and the network communication device has a data replication and data sending function; obtain the sensor data sent by the network communication device by means of listening or querying; and identify abnormal data in the sensor data based on a preset sensor anomaly recognition rule. Thus, abnormal data such as sensor jitter and flash interruption that occur quickly, have a short process and appear randomly can be obtained sensitively and conveniently, effectively monitoring sensor faults and ensuring the normal operation of industrial production lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure and, together with the specification, used to explain the principles of the present disclosure.

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0015] Figure 1 is a flowchart of a sensor anomaly monitoring method provided by an embodiment of the present disclosure; Figure 2 is a schematic diagram of the flow of sensor data provided by an embodiment of the present disclosure; Figure 3 is a flowchart of another sensor anomaly monitoring method provided by an embodiment of the present disclosure; Figure 4 is a schematic diagram of the display of sensor anomaly data provided by an embodiment of the present disclosure; Figure 5 is a schematic diagram of the structure of a sensor anomaly monitoring device provided by an embodiment of the present disclosure; Figure 6 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] In order to better understand the above objects, features and advantages of the present disclosure, the solutions of the present disclosure will be further described below. It should be noted that, without conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other.

[0017] In the following description, numerous specific details are set forth to provide a thorough understanding of the present disclosure. However, the present disclosure may be practiced in other ways different from those described herein. Obviously, the embodiments in the specification are only a part of the embodiments of the present disclosure, rather than all of the embodiments.

[0018] It should be understood that the various steps recited in the embodiments of the present disclosure may be executed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this regard.

[0019] It should be noted that, in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0020] It should be noted that the modifiers "a" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise clearly specified in the context, it should be construed as "one or more".

[0021] Generally, the data of the sensors to be monitored is obtained from a controller such as a PLC by means of interrogation or subscription. Among them, when using the interrogation method, the data acquisition device needs to send an interrogation to the controller such as a PLC, and then receive the sensor data pre-configured by the PLC, and judge whether the sensor is normal by monitoring the data obtained from these interrogations. When using the subscription method, there is no need to interrogate the PLC controller every time, and the preset sensor data sent by the controller at a fixed period can be obtained. Whether it is the interrogation method or the subscription method, it is affected by the industrial field network bandwidth and the maximum load of the PLC. The data acquisition cycle is long, and the sensor data acquired is limited or small, resulting in many sensor jitter data with fast occurrence time, short process and random appearance cannot be obtained in time, and thus the effective monitoring of sensor anomalies cannot be achieved. In view of this problem, the embodiments of the present disclosure provide a method for monitoring sensor anomalies, which will be introduced below in combination with specific embodiments.

[0022] Figure 1It is a flowchart of a method for monitoring sensor anomalies provided by an embodiment of the present disclosure. This method can be executed by a sensor anomaly monitoring device, which can be implemented in software and / or hardware, and can be configured in an electronic device, such as a server or a terminal.

[0023] As Figure 1 shown, the method for monitoring sensor anomalies provided in this embodiment includes the following steps.

[0024] S110. Obtain sensor data transmitted to the controller through a network communication device, and the network communication device has a data replication and data transmission function.

[0025] In the embodiment of the present disclosure, a control program runs on the controller, and this control program controls various types of configuration devices in the field through digital or analog input and output data. The controller can be any controller capable of device control, including but not limited to: Programmable Logic Controller (PLC), Distributed Control System (DCS), and robot special controllers, etc. Different types of controllers have different control methods and application scenarios.

[0026] The devices connected to and controlled by the target controller here include but are not limited to automation devices such as sensors, cameras, motors, frequency converters, and robots in the industrial field. These devices are controlled by the target controller and execute in an orderly manner according to a predetermined program.

[0027] The network communication device is a device with a data replication and data transmission function. The data here includes sensor data. One end of the network communication device is connected to the signal access device for obtaining input signals including sensor signals, and the other end of the network communication device is connected to the controller for sending the obtained sensor signals to the controller, realizing the normal transmission of the sensor data corresponding to the sensor signals among the input device, the signal access device, and the controller. The flow direction of the sensor data can be seen in Figure 2 .

[0028] By Figure 2It can be known that input devices 21 such as sensors generate input signals including sensor signals. The sensor signals are accessed to network communication device 23 through signal access device 22, and the sensor data corresponding to the sensor signals are transmitted to controller 24 through network communication device 23. Furthermore, the controller performs logical operations based on the acquired sensor data and forms output data to achieve automatic control of various on-site devices. In the prior art, data acquisition unit 25 is connected to the controller to obtain sensor data from the controller. In this patent, the data acquisition unit is connected to the network communication device and obtains sensor data from the network communication device. Compared with the acquisition method in the prior art, adopting this patent can be unrestricted by the maximum communication load of controllers such as PLCs, and will not affect the control performance of the controller. While ensuring the normal operation of the controller to a large extent, all sensor data can be obtained.

[0029] Optionally, network communication device 23 includes one or more of a serial bus, an Ethernet switch, or a wireless communication device. Different types of network communication devices are applicable to different usage scenarios. Among them, the serial bus includes a serial connector, such as an RS485 serial connector, an RS232 serial connector, etc. The serial bus is applicable to serial communication protocols such as profibus and modbus. The Ethernet switch is applicable to Ethernet industrial protocols such as Profinet and Ethernet. The wireless communication device includes devices capable of wireless communication such as WIFI, Bluetooth, Zigbee, 5G / 6G mobile communication, and LoRaWAN.

[0030] Optionally, when network communication device 23 is a serial bus, the serial bus includes a serial connector. The serial connector includes a module with data replication and data sending functions, and this module can perform data mirror replication on the sensor data sent to the controller and send it externally. The devices receiving the externally sent data include but are not limited to the data acquisition unit.

[0031] Optionally, when network communication device 23 is an Ethernet switch, the Ethernet switch has a port mirroring function and an external sending function. It can replicate the data sent to the port connected to a controller such as a PLC in a mirrored manner to another port connected to other external devices through the port mirroring function of the switch. The other external devices include but are not limited to the data acquisition unit. The port mirroring function of the Ethernet switch enables the Ethernet switch to have a data replication function, and it replicates data in a mirrored manner.

[0032] The above switch can support different types of communication protocols and different types of data acquisition units, but the corresponding switch ports need to be configured in advance. Specifically, how to perform differential configuration and the configuration method are not specifically limited here.

[0033] Optionally, the types of data copied and sent from the switch include input data, output data, and intermediate process data. The input data includes, but is not limited to, the data corresponding to I variables. The output data includes, but is not limited to, the data corresponding to Q variables. The intermediate process data includes, but is not limited to, the data corresponding to intermediate process variables such as M variables, DB variables, C variables, and T variables.

[0034] When the network communication device 23 is a wireless communication device, the wireless communication device includes WIFI, Bluetooth, Zigbee, 5G / 6G mobile communication, LoRaWAN and other wireless communication devices with modules having data replication and data sending functions, which can mirror and copy the sensor data sent to the controller and send it externally. The devices receiving the externally sent data include, but are not limited to, data acquisition units.

[0035] Optionally, the signal access device 22 is used to perform format conversion processing on the generated input signals of different formats and access the network communication device. The signal access device includes an IO module or an IO module and a device with IO acquisition function. Among them, the devices with IO acquisition function include, but are not limited to, frequency converters, robots and other devices with IO acquisition functions.

[0036] Optionally, the input device 21 is used to generate input signals, and the input signals include, but are not limited to, sensor signals. The input device includes, but is not limited to, sensors, buttons, relays, indicators, contactors, radars, cameras and other devices. The function of these input devices is to convert physical signals such as temperature, pressure, and liquid level of field devices into electrical signals, and then use the electrical signals as input signals for controllers such as PLCs for the controller to process, realizing industrial automation control.

[0037] S120. Obtain the sensor data sent by the network communication device by means of listening or querying.

[0038] In the embodiments of the present disclosure, the data acquisition device 25 obtains the sensor data copied and sent externally by the network communication device by means of listening. Compared with the existing querying or subscribing methods, there is no need to limit the data type by pre-configuring, nor to send queries to controllers such as PLCs, and all sensor data can be obtained by listening. Among them, when using the querying or subscribing method, it is necessary to pre-configure the types of sensor data to be obtained, that is, to clarify which sensor data needs to be obtained. In addition, the all sensor data here refers to all sensor data, regardless of the sensor type or name.

[0039] Optionally, to enable the data acquisition device to obtain all sensor data through listening, it is necessary to pre-configure the ports of the network communication device so that the data acquisition device only needs to wait and receive the sensor data sent by the network communication device. Using this method, there is no need to modify the control program of controllers such as PLCs, which will not affect the control performance of the controller, and it is not restricted by network bandwidth and the maximum load of the PLC. All sensor data can be obtained, improving the flexibility of sensor anomaly monitoring.

[0040] Optionally, the data acquisition device 25 obtains the sensor data copied and sent externally by the network communication device. This sensor data comes from an intelligent controller with the function of sending all sensor data. This intelligent controller does not limit the maximum communication load and does not limit the external sending of all sensor data in full. Therefore, compared with the existing query or subscription methods, all sensor data can be obtained by sending a query signal for obtaining all data to the intelligent controller. Here, all sensor data refers to all sensor data, regardless of the type or name of the sensor.

[0041] Optionally, after the data acquisition device obtains the sensor data, the obtained sensor data is stored, and the storage method is not further limited.

[0042] Optionally, the acquisition period of the data acquisition device by listening varies depending on the data generation periods of different devices on-site. When the data generation period of the device is short, the acquisition period of the listening method is also short, so as to match the data generation period of the device and ensure that all data generated by the device can be obtained.

[0043] Optionally, the acquisition period range of the data acquisition device is 0.1 - 1000 ms. Preferably, the acquisition period range of the data acquisition device is 0.1 - 10 ms. More preferably, the acquisition period of the data acquisition device is 0.1 ms, 0.5 ms, 1 ms, 2 ms, 3 ms, 4 ms, 5 ms, 6 ms, 7 ms, 8 ms, 9 ms, 10 ms. Here, ms refers to milliseconds.

[0044] Optionally, the acquired sensor data is data with an acquisition period of 0.1 - 10 ms. Preferably, the sensor data is data with an acquisition period of 0.1 ms, 0.5 ms, 1 ms, 2 ms, 3 ms, 4 ms, 5 ms, 6 ms, 7 ms, 8 ms, 9 ms, 10 ms. Here, ms refers to milliseconds. Further, the above-acquired sensor data includes fault sensor data that is small and rapidly changing.

[0045] Compared with the prior art which obtains sensor data from controllers such as PLCs and DCSs in an interrogative manner, obtaining sensor data through network communication devices such as switches can greatly shorten the acquisition cycle of sensor data. For example, the acquisition cycle can be shortened from the existing 20 ms to 1 ms, and the amount of sensor data collected will increase exponentially. As a result, it is possible to collect fault signals such as sensor jitter that are small and rapidly changing, effectively monitor sensor faults, improve the accuracy and sensitivity of data acquisition, and thus identify small anomalies that cannot be identified by existing acquisition technologies and continuously monitor the anomalies. Furthermore, it is possible to take timely measures to prevent the sudden occurrence of faults and the resulting losses.

[0046] Optionally, after acquiring the sensor data and before identifying the abnormal data, the acquired sensor data is stored. The storage method is not specifically limited.

[0047] S130. Based on a preset sensor anomaly recognition rule, identify the abnormal data in the sensor data.

[0048] In the embodiments of the present disclosure, the sensor anomaly recognition rule needs to be set in advance. To preset the sensor anomaly recognition rule, it is necessary to pre-statistically analyze the data pattern of the sensor to be monitored and identify the sensor data that does not conform to the data pattern as the sensor abnormal data.

[0049] The abnormal data here refers to the data that does not conform to the normal data pattern of the sensor. The abnormal forms include but are not limited to jitter, missing, error, etc.

[0050] Since the types of sensors are different, their sensor data types are also different, and the corresponding data patterns are also different, so the corresponding sensor anomaly recognition rules are also different. Among them, the sensor types include but are not limited to: pulse signals with a constant change period, pulse signals with an indefinite period change, sine waves / cosine waves, step change pulse signals, signals that appear periodically with a fixed value, etc.

[0051] By analyzing different types of sensor data and identifying the data pattern of each type of sensor, it is possible to determine the anomaly recognition rule for this type of sensor. The core idea of the recognition rule here is: identify the data that does not conform to the normal sensor data pattern as the abnormal data, and the normal sensor data pattern refers to the preset rule or correct rule of the sensor data under normal circumstances.

[0052] Exemplarily, for this type of sensor that uses a pulse signal with a constant change period, the data pattern is that the data changes between high level and low level according to a constant period. If the data at this moment is high-level data under normal circumstances, and the data collected at this moment is low-level data, then the low-level data at this moment is considered abnormal data that does not conform to the data pattern; or, if the data at this moment is low-level data under normal circumstances, and the data collected at this moment is high-level data, then the high-level data at this moment is considered abnormal data that does not conform to the data pattern. These abnormal data may be abnormal data caused by sensor jitter.

[0053] Optionally, before identifying abnormal data in sensor data based on a preset sensor abnormality recognition rule, it also includes identifying the type of the sensor and matching the corresponding type of sensor abnormality recognition rule.

[0054] Optionally, after preferentially judging the sensor data type, for different types of sensor data, match the corresponding type of sensor abnormality recognition rule, and identify abnormal data that does not conform to the data pattern based on the corresponding rule.

[0055] Specifically, by comparing the sensor data obtained by listening at the current moment with the theoretical sensor data, if the data does not match, the sensor data is considered abnormal data. The theoretical sensor data is data that conforms to the normal sensor data pattern.

[0056] Optionally, through algorithms such as artificial intelligence and machine learning, automatically identify abnormal data caused by sensor jitter that is different from the normal data pattern.

[0057] Through the machine learning algorithm, learn the historical data of the corresponding sensor, identify the data type of the sensor, the normal data rule and the abnormal data rule of the sensor, and then based on the learned results, automatically identify and judge the current sensor data, and automatically identify whether the current data belongs to abnormal data.

[0058] In the embodiments of the present disclosure, sensor data transmitted to a controller is obtained through a network communication device, and the network communication device has functions of data replication and data transmission; the sensor data sent by the network communication device is obtained by means of listening or querying; based on a preset sensor anomaly recognition rule, the anomaly data in the sensor data is recognized. Thus, the data listening method and the network communication device with functions of data replication and transmission can be applied to the field of sensor jitter fault monitoring, and all sensor data can be obtained. In particular, it is possible to collect abnormal data such as sensor jitter and flash interruption that are small and rapidly changing and cannot be obtained by existing data collection methods, so as to sensitively collect fault signals of sensor jitter with a short collection period, effectively monitor sensor faults, improve data collection accuracy and sensitivity, continuously monitor small anomalies, and facilitate taking measures to prevent the sudden occurrence of faults and the resulting losses.

[0059] Based on the above embodiments, Figure 3 is a flowchart of another sensor anomaly monitoring method provided by the embodiments of the present disclosure. As Figure 3 shown, the specific steps are as follows: S310. Obtain sensor data transmitted to a controller through a network communication device, and the network communication device has functions of data replication and data transmission; S320. Obtain the sensor data sent by the network communication device by means of listening or querying; S330. Based on a preset sensor anomaly recognition rule, recognize the anomaly data in the sensor data.

[0060] It should be noted that the specific implementation manners of S310 - S330 are similar to the above implementation manners and will not be elaborated here.

[0061] S340. Display the obtained anomaly data of the sensor.

[0062] In the embodiments of the present disclosure, to facilitate the user to more intuitively recognize the anomaly data of the sensor, the anomaly data of the sensor is displayed through a data display device.

[0063] Optionally, in order to compare the normal data and the anomaly data of the sensor, all sensor data obtained by the data collection device is displayed. Among them, all sensor data includes normal data and anomaly data.

[0064] Exemplarily, Figure 4Displays the data of this type of sensor that uses a pulse signal with a constant change period. The normal data pattern of the sensor is to change the data between high level and low level according to a constant period. If the data is high-level data under normal circumstances, and the data collected at the current moment is low-level data, then the low-level data is considered abnormal data that does not conform to the data pattern; or, if the data is low-level data under normal circumstances, and the data collected at the current moment is high-level data, then the high-level data is considered abnormal data that does not conform to the data pattern. These abnormal data may be abnormal data generated by sensor jitter.

[0065] Specifically, Figure 4 Displays the data of this type of sensor that uses a pulse signal with a constant change period, specifically the data of a Boolean-type sensor signal. Among them, 0 represents low level and 1 represents high level, and the sensor signal alternates between high level and low level at a fixed period. As time goes by, if the sensor data conforms to the above data pattern obtained through analysis, then the data is considered normal data; if the sensor data does not conform to the above data pattern, then the data is considered abnormal data. As shown in the figure, the data highlighted by the red box is abnormal data.

[0066] Optionally, the display method of the abnormal data of the sensor includes, but is not limited to, one or a combination of forms such as graphs, tables, texts, voices, videos, etc.

[0067] Optionally, in order to more intuitively display the abnormal data, the abnormal data is displayed in a highlighted manner. The highlighting methods include, but are not limited to, different color displays, shape magnification, adding reminder marks, etc.

[0068] Optionally, in order to facilitate the positioning of the faulty sensor location, the network topology diagram of the configuration device in the industrial field can also be combined, and the sensor with abnormal data is displayed on the network topology diagram, which is beneficial to intuitively display the sensor location and facilitate subsequent fault repair.

[0069] In this disclosure implementation, sensor data transmitted to the controller is obtained through a network communication device, and the network communication device has data replication and data sending functions; the sensor data sent by the network communication device is obtained by using a listening or querying method; based on a preset sensor anomaly recognition rule, the abnormal data in the sensor data is recognized; the obtained abnormal data of the sensor is displayed. Thus, it is possible to sensitively and conveniently obtain abnormal data such as sensor jitter and flash interruption that occur quickly in time, have a short process, and occur randomly, and display them intuitively, realizing effective monitoring of sensor faults, being beneficial to the identification and repair of sensor faults, and further ensuring the normal operation of the industrial production line.

[0070] Figure 5It is a schematic structural diagram of a sensor anomaly monitoring device provided by an embodiment of the present disclosure. The sensor anomaly monitoring device provided by the embodiment of the present disclosure can execute the processing flow provided by the embodiment of the sensor anomaly monitoring method. For example, Figure 5 As shown, the sensor anomaly monitoring device 40 includes: a data replication module 41, a data acquisition module 42, and an anomaly recognition module 43.

[0071] Among them, the data replication module 41 can be used to obtain sensor data transmitted to the controller through a network communication device, and the network communication device has the functions of data replication and data sending.

[0072] The data acquisition module 42 can be used to obtain sensor data sent by the network communication device by means of listening or querying.

[0073] The anomaly recognition module 43 can be used to identify abnormal data in the sensor data based on a preset sensor anomaly recognition rule.

[0074] In the embodiment of the present disclosure, sensor data transmitted to the controller is obtained through a network communication device, and the network communication device has the functions of data replication and data sending; sensor data sent by the network communication device is obtained by means of listening or querying; abnormal data in the sensor data is identified based on a preset sensor anomaly recognition rule. Thus, abnormal data such as sensor jitter that occurs quickly, has a short process, and appears randomly can be obtained sensitively and conveniently, realizing effective monitoring of sensor faults and ensuring the normal operation of the industrial production line.

[0075] In some embodiments of the present disclosure, the anomaly recognition module 43 further includes a sensor anomaly recognition rule preset sub-module, which is used to statistically analyze the data pattern of the sensor and identify data that does not conform to the data pattern as abnormal data of the sensor.

[0076] In some embodiments of the present disclosure, the anomaly recognition module 43 further includes a sensor type recognition sub-module, which is used to identify the type of the sensor and match the corresponding type of sensor anomaly recognition rule.

[0077] In some embodiments of the present disclosure, the sensor anomaly monitoring device further includes a display module 44, which can be used to display the abnormal data of the sensor.

[0078] Figure 5 The sensor anomaly monitoring device of the embodiment can be used to execute the technical solution of the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here.

[0079] Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. The electronic device provided by the embodiment of the present disclosure can execute the processing flow provided by the embodiment of the sensor anomaly monitoring method. For example,Figure 6 As shown in Figure 6 , the electronic device 5 includes: a processor 51, a memory 52, and a computer program 53; wherein, the computer program is stored in the memory 52 and is configured to read the computer program 53 from the memory 52 and execute the computer program 53 to implement the sensor anomaly monitoring method provided by the embodiments of the present disclosure. Figures 1 to 4 The sensor anomaly monitoring method provided by the embodiments of the present disclosure.

[0080] In addition, the embodiments of the present disclosure also provide a computer-readable storage medium, which can store a computer program. When the computer program is executed by a processor, the processor is enabled to implement the sensor anomaly monitoring method provided by the embodiments of the present disclosure. Figures 1 to 4 The sensor anomaly monitoring method provided by the embodiments of the present disclosure.

[0081] The above storage medium may include, for example, a memory for the computer program. The above computer program can be executed by the processor of the sensor anomaly monitoring device to complete the sensor anomaly monitoring method provided by the embodiments of the present disclosure. Figures 1 to 4 Optionally, the storage medium may be a non-transitory computer-readable storage medium. For example, the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage configuration device, etc.

[0082] Furthermore, the embodiments of the present disclosure also provide a computer program product, which includes a computer program or instruction. When the computer program or instruction is executed by a processor, the above sensor anomaly monitoring method is implemented.

[0083] The above are only specific implementation manners of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring sensor anomalies, characterized in that, Including: Obtaining sensor data transmitted to a controller through a network communication device, where the network communication device has data replication and data transmission functions; Obtaining the sensor data sent by the network communication device by using a listening or querying method; Identifying abnormal data in the sensor data based on a preset sensor abnormality recognition rule.

2. The method according to claim 1, characterized in that, The network communication device includes one or more of a serial bus, an Ethernet switch, or a wireless communication device.

3. The method according to claim 2, wherein The network communication device is respectively connected to a controller and a signal access device, and the signal access device includes an IO module or an IO module and a device with an IO acquisition function.

4. The method according to claim 1, characterized in that, The preset sensor abnormality recognition rule includes: statistically analyzing the data pattern of the sensor, and identifying the data that does not conform to the data pattern as the abnormal data of the sensor.

5. The method according to claim 4, wherein Based on the preset sensor abnormality recognition rule, identifying abnormal data in the sensor data further includes: identifying the type of the sensor and matching the corresponding type of sensor abnormality recognition rule.

6. The method according to claim 1, characterized in that After identifying abnormal data in the sensor data based on the preset sensor abnormality recognition rule, the method further includes: displaying the abnormal data of the sensor.

7. A sensor abnormality monitoring device, characterized in that, Including: A data replication module, configured to obtain sensor data transmitted to a controller through a network communication device, where the network communication device has data replication and data transmission functions; A data acquisition module, configured to obtain the sensor data sent by the network communication device by using a listening or querying method; An abnormality recognition module, configured to identify abnormal data in the sensor data based on a preset sensor abnormality recognition rule.

8. An electronic device, characterized in that, Including: A processor; A memory; And A computer program; Wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, the processor is caused to implement the method according to any one of claims 1-6 above.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instruction is executed by a processor, the method according to any one of claims 1-6 is implemented.

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