Abnormal reason determination method and device, storage medium and electronic equipment

Abstract

The invention provides an exception cause determination method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining exception information data and industrial production data, the exception information data being used for representing the exception of target equipment in the industrial production process, and the industrial production data being used for representing the exception of the target equipment in the industrial production process; the industrial production data comprises operation technology (OT) data and information technology (IT) data. And according to the industrial production data, determining the cause of the abnormal condition. According to the abnormal reason determination method provided by the embodiment of the invention, the server can associate the OT data with the IT data, rapid diagnosis of the abnormal reason is realized, and compared with diagnosis by adopting unilateral data in a related technical scheme, the accuracy of abnormal reason diagnosis can be improved.

Description

Technical Field

[0001] The technical solution disclosed herein relates to the field of industrial production technology, and in particular to a method and device for determining an abnormality cause, a storage medium, and an electronic device. Background Art

[0002] In related technical solutions, when determining the cause of an abnormal situation occurring in a target device during an industrial production process, it is usually based on unilateral data, for example, based on basic parameter information of the target device.

[0003] In practical applications, since there are often multiple reasons for abnormal conditions in target devices, the method of determining the cause of the abnormality based on unilateral data has low accuracy. Summary of the invention

[0004] In view of this, embodiments of the present disclosure provide a method and device for determining an abnormality cause, a storage medium, and an electronic device.

[0005] According to a first aspect of the present disclosure, a method for determining an abnormality cause is proposed, the method comprising:

[0006] Acquire abnormal information data and industrial production data; wherein the abnormal information data is used to characterize abnormal conditions occurring in the target device during the industrial production process, and the industrial production data includes operational technology (OT) data and information technology (IT) data;

[0007] The cause of the abnormal situation is determined based on the industrial production data.

[0008] In combination with any embodiment provided by the present disclosure,

[0009] The OT data includes basic parameter information data of the target device.

[0010] In combination with any embodiment provided in the present disclosure, the IT data includes at least one of the following:

[0011] Maintenance information data of the target device; the maintenance information data is obtained based on the maintenance operation performed on the target device;

[0012] The process control data of the target device; the process control data is used to characterize the manufacturing process of the target device.

[0013] In combination with any embodiment provided in the present disclosure, the determining the cause of the abnormal situation according to the industrial production data includes:

[0014] Inputting the abnormal information data and the industrial production data into a Markov logic network; the Markov logic network is pre-built based on a rule library constructed for the target device;

[0015] The output of the Markov logic network is determined as the cause of the abnormal situation.

[0016] In combination with any of the embodiments provided in the present disclosure, the number of reasons for the occurrence of the abnormal situation determined by the Markov logic network is multiple;

[0017] The method further comprises:

[0018] Displaying the determined multiple causes on a visual interface;

[0019] After receiving a first reason selected by a user from the multiple reasons, increasing a weight parameter of a first rule in the rule base that causes the abnormal situation due to the first reason;

[0020] Based on the adjusted rule base, a new Markov logic network is rebuilt.

[0021] In combination with any embodiment provided by the present disclosure,

[0022] The IT data includes maintenance information data of the target device; the maintenance information data is obtained based on the maintenance operation performed on the target device;

[0023] The method further comprises:

[0024] After determining the maintenance operation corresponding to the first cause, displaying the maintenance operation on the visualization interface;

[0025] After receiving a confirmation operation of the user for the maintenance operation, the maintenance information data is updated according to the maintenance operation.

[0026] In combination with any embodiment provided in the present disclosure, the method further includes:

[0027] Displaying the cause of the abnormal situation determined based on the Markov logic network on a visualization interface;

[0028] After receiving a second reason input by the user based on the visual interface, adding a second rule in the rule base that the abnormal situation occurs due to the second reason; the second reason does not exist in the reasons for the abnormal situation determined by the Markov logic network;

[0029] Based on the adjusted rule base, a new Markov logic network is rebuilt.

[0030] According to a second aspect of the present disclosure, a device for determining an abnormality cause is provided, the device comprising:

[0031] A data acquisition module, used to acquire abnormal information data and industrial production data; wherein the abnormal information data is used to characterize abnormal conditions occurring in the target device during the industrial production process, and the industrial production data includes operational technology OT data and information technology IT data;

[0032] The cause determination module is used to determine the cause of the abnormal situation based on the industrial production data.

[0033] In combination with any embodiment provided by the present disclosure,

[0034] The OT data includes basic parameter information data of the target device.

[0035] In combination with any embodiment provided in the present disclosure, the IT data includes at least one of the following:

[0036] Maintenance information data of the target device; the maintenance information data is obtained based on the maintenance operation performed on the target device;

[0037] The process control data of the target device; the process control data is used to characterize the manufacturing process of the target device.

[0038] In combination with any embodiment provided by the present disclosure, the cause determination module, when used to determine the cause of the abnormal situation according to the industrial production data, includes:

[0039] Inputting the abnormal information data and the industrial production data into a Markov logic network; the Markov logic network is pre-built based on a rule library constructed for the target device;

[0040] The output of the Markov logic network is determined as the cause of the abnormal situation.

[0041] In combination with any of the embodiments provided in the present disclosure, the number of reasons for the occurrence of the abnormal situation determined by the Markov logic network is multiple;

[0042] The abnormality cause determination device may further include:

[0043] A first display module, used to display the determined multiple reasons on a visual interface;

[0044] A weight parameter increasing module, configured to increase a weight parameter of a first rule in the rule base that causes the abnormal situation due to the first reason after receiving a first reason selected by a user from the multiple reasons;

[0045] The first building module is used to rebuild the new Markov logic network based on the adjusted rule base.

[0046] In combination with any embodiment provided by the present disclosure, the IT data includes maintenance information data of the target device; the maintenance information data is obtained based on a maintenance operation performed on the target device;

[0047] The abnormality cause determination device may further include:

[0048] A display module, configured to display the maintenance operation on the visualization interface after determining the maintenance operation corresponding to the first cause;

[0049] The data updating module is used to update the maintenance information data according to the maintenance operation after receiving the user's confirmation operation on the maintenance operation.

[0050] In combination with any embodiment provided in the present disclosure, the abnormality cause determination device may further include:

[0051] A second display module, used for displaying the cause of the abnormal situation determined based on the Markov logic network on a visual interface;

[0052] a rule adding module, configured to add, in the rule base, a second rule causing the abnormal situation to occur due to the second reason after receiving the second reason input by the user based on the visual interface; the second reason does not exist in the reasons for the abnormal situation to occur determined by the Markov logic network;

[0053] The second building module is used to rebuild the new Markov logic network based on the adjusted rule base.

[0054] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, wherein the machine-readable storage medium stores machine-readable instructions, and when the machine-readable instructions are called and executed by a processor, the processor is prompted to implement the abnormality cause determination method of any embodiment of the present disclosure.

[0055] According to a fourth aspect of the present disclosure, there is provided an electronic device, comprising:

[0056] processor;

[0057] a memory for storing processor-executable instructions;

[0058] The processor is configured to execute the abnormality cause determination method of any embodiment of the present disclosure.

[0059] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0060] In the abnormal cause determination method and device, storage medium and electronic device provided by the embodiments of the present disclosure, the server can obtain abnormal information data and industrial production data of the target device, wherein the abnormal information data is used to characterize the abnormal situation that occurs in the industrial production process of the target device, and the industrial production data includes operating technology OT data and information technology IT data. Then, the server can determine the cause of the abnormal situation based on the industrial production data. In the abnormal cause determination method provided by the embodiments of the present disclosure, the server can associate OT data and IT data to achieve rapid diagnosis of the abnormal cause, which can improve the accuracy of abnormal cause diagnosis compared to the use of unilateral data for diagnosis in related technical solutions.

[0061] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0063] Figure 1 is a flow chart of a method for determining an abnormality cause according to an exemplary embodiment of the present disclosure;

[0064] Figure 2 is a flow chart of another method for determining an abnormality cause according to an exemplary embodiment of the present disclosure;

[0065] Figure 3 is a schematic diagram of a rule base according to an exemplary embodiment of the present disclosure;

[0066] Figure 4 is a schematic diagram of an output result of a Markov logic network according to an exemplary embodiment of the present disclosure;

[0067] Figure 5 is a flow chart of another method for determining an abnormality cause according to an exemplary embodiment of the present disclosure;

[0068] Figure 6 is a flow chart of another method for determining an abnormality cause according to an exemplary embodiment of the present disclosure;

[0069] Figure 7 is a flow chart of another method for determining an abnormality cause according to an exemplary embodiment of the present disclosure;

[0070] Figure 8is a flowchart of a method for determining an abnormality cause according to an exemplary embodiment of the present disclosure;

[0071] Fig. 9 is a structural schematic diagram of a device for determining an abnormality cause according to an exemplary embodiment of the present disclosure;

[0072] Fig.10 It is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0073] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0074] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. The singular forms of "a", "said" and "the" used in this disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0075] It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0076] In related technical solutions, when determining the cause of an abnormal situation occurring in a target device during an industrial production process, it is usually based on unilateral data, for example, based on basic parameter information of the target device.

[0077] In practical applications, since there are often multiple reasons for abnormal conditions in target devices, the method of determining the cause of the abnormality based on unilateral data has low accuracy.

[0078] In view of this, an embodiment of the present disclosure provides a method for determining the cause of an abnormality, in which the server can associate OT (Operational Technology) data and IT (Information Technology) data to determine the cause of the abnormality, thereby improving the accuracy of determining the cause of the abnormality.

[0079] The abnormality cause determination method of the embodiment of the present disclosure is described in detail below with reference to the accompanying drawings.

[0080] Figure 1 The present disclosure is a flowchart of a method for determining the cause of an abnormality according to an exemplary embodiment. The method is used to determine the cause of an abnormality in a target device during an industrial production process. The target device may be, for example, a printer, a fax machine, etc., which is not limited in the present disclosure. For the convenience of description, the following embodiments are described by taking the target device as a printer as an example. In a specific implementation, the method may be executed by a server, such as Figure 1 As shown, the exemplary embodiment method may include the following steps:

[0081] In step 101, abnormal information data and industrial production data are acquired.

[0082] The abnormal information data is used to characterize the abnormal situation of the target device during the industrial production process. When the target device is a printing machine, the abnormal information data may be, for example, no tin, little tin, or too much tin.

[0083] The industrial production data may include OT data and IT data. Among them, the OT data includes basic parameter information data of the target device. When the target device is a printing press, the basic parameter information data may include but is not limited to printing speed, scraper pressure, demolding speed, demolding distance, printing gap, wiping frequency, offset coordinates, fiducial coordinates and other data. The IT data may include maintenance information data and process control data of the target device, the maintenance information data is obtained based on the maintenance operations performed on the target device, and the process control data is used to characterize the manufacturing process of the target device. When the target device is a printing press, the maintenance information data is obtained based on the user's maintenance operations on the printing press, and the process control data is used to characterize the production date, origin, and specific production line of the printing press.

[0084] In step 102, the cause of the abnormal situation is determined based on the industrial production data.

[0085] When the target device is a printing press, the server may determine the cause of the abnormality of the printing press based on the OT data and IT data of the printing press obtained above.

[0086] In the method for determining the cause of an abnormality provided by the embodiment of the present disclosure, the server can obtain abnormal information data and industrial production data of the target device, wherein the abnormal information data is used to characterize abnormal conditions occurring in the industrial production process of the target device, and the industrial production data includes operating technology OT data and information technology IT data. Then, the server can determine the cause of the abnormal condition based on the industrial production data. In the method for determining the cause of an abnormality provided by the embodiment of the present disclosure, the server can associate OT data and IT data to achieve rapid diagnosis of the cause of the abnormality, which can improve the accuracy of diagnosis of the cause of the abnormality compared to the use of unilateral data for diagnosis in related technical solutions.

[0087] In one embodiment, if Figure 2 As shown, the above step 102 may specifically include:

[0088] In step 201, the abnormal information data and the industrial production data are input into a Markov logic network.

[0089] The Markov logic network is pre-built based on a rule base constructed for the target device.

[0090] When the target device is a printing press, the definitions of each term and the corresponding examples in the rule base constructed for the printing press can be shown in the following Table 1:

[0091] Table 1

[0092]

[0093] Exemplarily, the rule base constructed for the printing press can be as follows: Figure 3 As shown, the following rules may be included (arrows are used to describe a relationship, and from left to right represents "leading to"):

[0094] Scraper absorbs solder paste → less solder is fixed

[0095] Steel mesh plugging → less tin fixation

[0096] There is paper lint on the steel mesh → less tin fixation

[0097] Main board and steel mesh deviate → insufficient tin fixation

[0098] Abnormal mainboard size → insufficient solder fixation

[0099] Based on the rule base, a Markov logic network corresponding to the printing press can be constructed. The process of constructing a Markov logic network based on the rule base can be found in the introduction of the relevant article, which will not be introduced in detail here.

[0100] In this step, after acquiring the abnormal information data and industrial production data of the printing press, the server may input the acquired abnormal information data and industrial production data into a pre-built Markov logic network.

[0101] In step 202, the output of the Markov logic network is determined as the cause of the abnormal situation.

[0102] The Markov logic network can perform a series of processing on the input abnormal information data and industrial production data to obtain the corresponding output, which is the reason why the printing press has an abnormal situation.

[0103] Exemplarily, the output may be: basic parameter information of the printing press is abnormal.

[0104] In the abnormality cause determination method provided in the embodiment of the present disclosure, a Markov logic network can be used to diagnose the abnormality cause, which can further improve the accuracy of abnormality cause diagnosis compared to the traditional method of using a decision tree or deep learning method.

[0105] In one embodiment, the number of reasons for the occurrence of the abnormal situation determined by the Markov logic network may be multiple.

[0106] like Figure 5 As shown, in Figure 2 Based on the process shown, the abnormality cause determination method may further include the following steps:

[0107] In step 501, the determined multiple reasons are displayed on a visualization interface.

[0108] Exemplarily, the multiple reasons determined may be as follows: Figure 4 As shown, it may specifically include:

[0109] 47.400% motherboard and steel mesh deviation (timestamp)

[0110] 50.000% of the steel mesh has paper lint (time stamp)

[0111] 48.800% steel mesh plugging (timestamp)

[0112] 47.400% motherboard size abnormal (timestamp)

[0113] 47.800% of the holes have foreign matter (timestamp)

[0114] 47.200% scraper adsorbs solder paste (time stamp)

[0115] The server may display the above-mentioned multiple reasons on a visual interface.

[0116] In step 502, after receiving a first reason selected by a user from the plurality of reasons, a weight parameter of a first rule in the rule base that causes the abnormal situation due to the first reason is increased.

[0117] In this example, for different rules in the rule base, corresponding weight parameters can be set respectively. For example, the weight parameters can be set to 10.000000 for different rules, as follows:

[0118] 10.000000 scraper absorbs solder paste → less tin fixed

[0119] 10.000000 Steel mesh plugging → less tin fixation

[0120] 10.000000 There is paper lint on the steel mesh → less tin fixation

[0121] 10.000000 Mainboard deviates from steel mesh → less tin fixation

[0122] 10.000000 The mainboard size is abnormal → insufficient tin fixation

[0123] After the above-mentioned multiple reasons are displayed on the visualization interface, the user can determine the real reason why the printing press has an abnormality based on the multiple reasons displayed on the visualization interface.

[0124] For example, the user can check the percentages corresponding to the above multiple reasons in descending order. After the user determines the real reason for the abnormality of the printing machine, the user can select the reason from the multiple reasons displayed on the visual interface, which is the first reason mentioned above.

[0125] After receiving the first reason selected by the user from the multiple reasons, the server may increase the weight parameter of the first rule in the aforementioned rule base, which causes the abnormal situation due to the first reason, based on the preset rule.

[0126] For example, when the first reason is that there are paper scraps on the steel mesh, the weight parameter corresponding to the rule "There are paper scraps on the steel mesh → Less tin fixation" in the aforementioned rule base can be increased. For example, it can be increased from 10.00000 to 10.100000, or it can be increased from 10.000000 to 10.010000. The present disclosure does not limit the way to increase the weight parameter, which can be set by relevant staff based on actual conditions.

[0127] In step 503, based on the adjusted rule base, a new Markov logic network is rebuilt.

[0128] When the weight parameter is increased from 10.000000 to 10.100000 based on the preset rule, the adjusted rule base may be as follows:

[0129] 10.000000 scraper absorbs solder paste → less tin fixed

[0130] 10.000000 Steel mesh plugging → less tin fixation

[0131] 10.100000 steel mesh has paper shavings → less tin fixation

[0132] 10.000000 Mainboard deviates from steel mesh → less tin fixation

[0133] 10.000000 The mainboard size is abnormal → insufficient tin fixation

[0134] Furthermore, the server can rebuild a new Markov logic network based on the adjusted rule base.

[0135] In the abnormal cause determination method provided by the embodiment of the present disclosure, the server can continuously optimize the existing rule base by adjusting the weight parameters of the rules in the rule base based on actual application conditions, and then continuously optimize the Markov logic network, thereby achieving the effect of continuously improving the accuracy of abnormal cause diagnosis.

[0136] In one embodiment, if Figure 6 As shown, after receiving the first reason selected by the user from the multiple reasons, the following operations may also be performed:

[0137] In step 601, after determining the maintenance operation corresponding to the first cause, the maintenance operation is displayed on the visualization interface.

[0138] Exemplarily, when the first reason is that the scraper absorbs solder paste, the corresponding maintenance operation may be wiping the scraper. When the first reason is that the steel mesh is blocked, the corresponding maintenance operation may be clearing the steel mesh aperture or replacing the steel mesh. When the first reason is that there are paper scraps on the steel mesh, the corresponding maintenance operation may be wiping the steel mesh. When the first reason is that the mainboard deviates from the steel mesh, the corresponding maintenance operation may be aligning the mainboard with the steel mesh. When the first reason is that the mainboard size is abnormal, the corresponding maintenance operation may be replacing the mainboard.

[0139] It should be noted that the above description of the correspondence between the causes and the maintenance operations is only illustrative, and is intended to enable those skilled in the art to better understand the technical solutions of the embodiments of the present disclosure. In practical applications, the maintenance operations corresponding to the above causes may also be other operations, which can be specifically set by relevant staff based on actual conditions, and the present disclosure does not limit this.

[0140] In this example, after receiving the first reason selected by the user from the multiple reasons, the maintenance operation corresponding to the first reason can be determined based on the aforementioned correspondence, and the maintenance operation can be displayed on the visual interface.

[0141] In step 602, after receiving a user confirmation operation for the maintenance operation, the maintenance information data is updated according to the maintenance operation.

[0142] After the maintenance operation corresponding to the aforementioned first reason is displayed on the visual interface, the user can execute the corresponding maintenance operation, and after the execution is completed, perform a confirmation operation for the maintenance operation on the visual interface.

[0143] After receiving the user's confirmation operation, the server can update the maintenance information data of the printing press according to the maintenance operation.

[0144] In the abnormality cause determination method provided by the embodiment of the present disclosure, the server can update the maintenance information data of the target equipment in real time based on the user's maintenance operations, thereby ensuring that the maintenance information data input into the Markov logic network is the latest, thereby improving the accuracy of determining the abnormality cause based on the Markov logic network.

[0145] In one embodiment, if Figure 7 As shown, in Figure 2 Based on the process shown, the abnormality cause determination method may further include the following steps:

[0146] In step 701: the cause of the abnormal situation determined based on the Markov logic network is displayed on a visualization interface.

[0147] The abnormal situation may be: insufficient tin fixation.

[0148] Exemplarily, the causes of the abnormal situation may include:

[0149] 47.400% motherboard and steel mesh deviation (timestamp)

[0150] 50.000% of the steel mesh has paper lint (time stamp)

[0151] 48.800% steel mesh plugging (timestamp)

[0152] In step 702, after receiving a second reason input by the user based on the visual interface, a second rule for causing the abnormal situation to occur due to the second reason is added to the rule base.

[0153] The second cause does not exist in the causes of the abnormal situation determined by the Markov logic network.

[0154] When the user determines that the real reason for the abnormality of the printing press does not exist among the multiple reasons displayed on the visualization interface, the user can input the real reason for the abnormality of the printing press based on the visualization interface, which is the aforementioned second reason.

[0155] After receiving the second reason input by the user, the server may add a second rule in the rule base indicating that the abnormal situation occurs due to the second reason.

[0156] Exemplarily, when the second reason is the critical life of the steel mesh tension, a second rule of critical life of the steel mesh tension→less tin fixation can be added to the rule library.

[0157] In step 703, based on the adjusted rule base, a new Markov logic network is rebuilt.

[0158] The server can rebuild a new Markov logic network based on the adjusted rule base.

[0159] In the abnormal cause determination method provided by the embodiment of the present disclosure, the server can continuously optimize the existing rule base by adding new rules based on the actual application situation, and then continuously optimize the Markov logic network, so as to achieve the effect of continuously improving the accuracy of abnormal cause diagnosis based on the Markov logic network.

[0160] In order to better understand the abnormality cause determination method provided by the embodiment of the present disclosure, Figure 8 The process is further described in the following manner.

[0161] like Figure 8 As shown, the equipment layer includes a printer and SPI (Solder Paste Inspection), and SPI is used to detect abnormal information of the printer. The industrial control layer is a software unit, and the printer industrial control is a software unit of the printer, which is used to collect basic parameter information data of the printer and transmit the basic parameter information data to the edge server. SPI industrial control is a software unit of SPI, which is used to collect abnormal information data detected by SPI and transmit the abnormal information data to the edge server.

[0162] After cleaning the received basic parameter information data and abnormal information data, the edge server can transmit the cleaned basic parameter information data and abnormal information data to the data preprocessing platform. At the same time, the data preprocessing platform can receive the process control data and maintenance information data for the printing press saved by the system end, wherein the process control data is used to characterize the manufacturing process of the printing press, and the maintenance information data is obtained based on the maintenance operations performed on the printing press. Then, the data preprocessing platform can perform data preprocessing on the received data, including but not limited to denoising and other operations.

[0163] After the data preprocessing operation is completed, the data preprocessing platform can transmit the acquired basic parameter information data, abnormal information data, process control data and maintenance information data to the pre-built Markov logic network, and determine the output of the Markov logic network as the cause of the abnormal situation of the printing machine.

[0164] Furthermore, the server may display the above-obtained reasons on a visualization interface, and after the user (production line caretaker) determines the real reason for the abnormality of the printing machine based on the multiple reasons displayed on the visualization interface, the user may select the reason on the visualization interface. After receiving the reason selected by the user, the server may display the maintenance operation corresponding to the reason on the visualization interface, and the user may perform the maintenance operation on the printing machine.

[0165] Finally, the server can update the above maintenance information data of the printing press stored in the system based on the user's maintenance operations.

[0166] For the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited by the described order of actions, because according to the present disclosure, certain steps can be performed in other orders or simultaneously.

[0167] Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0168] Corresponding to the aforementioned application function implementation method embodiment, the present disclosure also provides an application function implementation device and a corresponding terminal embodiment.

[0169] Fig. 9 is a schematic diagram of a structure of an abnormality cause determination device according to an exemplary embodiment of the present disclosure. Fig. 9 As shown, the abnormality cause determination device may include:

[0170] The data acquisition module 91 is used to acquire abnormal information data and industrial production data; wherein the abnormal information data is used to characterize abnormal conditions occurring in the target device during the industrial production process, and the industrial production data includes operational technology OT data and information technology IT data.

[0171] The cause determination module 92 is used to determine the cause of the abnormal situation based on the industrial production data.

[0172] Optionally, the OT data includes basic parameter information data of the target device.

[0173] Optionally, the IT data includes at least one of the following:

[0174] The maintenance information data of the target device; the maintenance information data is obtained based on the maintenance operations performed on the target device.

[0175] The process control data of the target device; the process control data is used to characterize the manufacturing process of the target device.

[0176] Optionally, the cause determination module 92, when used to determine the cause of the abnormal situation according to the industrial production data, includes:

[0177] The abnormal information data and the industrial production data are input into a Markov logic network; the Markov logic network is pre-built based on a rule base constructed for the target device.

[0178] The output of the Markov logic network is determined as the cause of the abnormal situation.

[0179] Optionally, the number of reasons for the occurrence of the abnormal situation determined by the Markov logic network is multiple.

[0180] exist Fig. 9 Based on the modules shown, the abnormality cause determination device may further include:

[0181] The first display module is used to display the determined multiple reasons on a visual interface.

[0182] The weight parameter increasing module is used to increase the weight parameter of the first rule in the rule base that causes the abnormal situation due to the first reason after receiving the first reason selected by the user from the multiple reasons.

[0183] The first building module is used to rebuild the new Markov logic network based on the adjusted rule base.

[0184] Optionally, the IT data includes maintenance information data of the target device; the maintenance information data is obtained based on maintenance operations performed on the target device.

[0185] exist Fig. 9 Based on the modules shown, the abnormality cause determination device may further include:

[0186] The display module is used to display the maintenance operation on the visualization interface after determining the maintenance operation corresponding to the first cause.

[0187] The data updating module is used to update the maintenance information data according to the maintenance operation after receiving the user's confirmation operation on the maintenance operation.

[0188] exist Fig. 9 Based on the modules shown, the abnormality cause determination device may further include:

[0189] The second display module is used to display the cause of the abnormal situation determined based on the Markov logic network on a visual interface.

[0190] A rule adding module is used to add a second rule in the rule base for causing the abnormal situation to occur due to a second reason input by the user based on the visual interface after receiving the second reason; the second reason does not exist in the reasons for the abnormal situation determined by the Markov logic network.

[0191] The second building module is used to rebuild the new Markov logic network based on the adjusted rule base.

[0192] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial description of the method embodiments. The device embodiments described above are only schematic, wherein the units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art may understand and implement it without creative work.

[0193] Fig.10 FIG. 1 is a schematic diagram showing the structure of an electronic device 1000 according to an exemplary embodiment. For example, the electronic device 1000 may be a printer or other device.

[0194] Reference Fig.10, the electronic device 1000 may include one or more of the following components: a processing component 1002 , a memory 1004 , a power component 1006 , a multimedia component 1008 , an audio component 1010 , an input / output (I / O) interface 1012 , a sensor component 1014 , and a communication component 1016 .

[0195] The processing component 1002 generally controls the overall operation of the electronic device 1000, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 1002 may include one or more processors 1020 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 1002 may include one or more modules to facilitate the interaction between the processing component 1002 and other components. For example, the processing component 1002 may include a multimedia module to facilitate the interaction between the multimedia component 1008 and the processing component 1002.

[0196] The memory 1004 is configured to store various types of data to support operations on the device 1000. Examples of such data include instructions for any application or method operating on the electronic device 1000, contact data, phone book data, messages, pictures, videos, etc. The memory 1004 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0197] The power supply component 1006 provides power to various components of the electronic device 1000. The power supply component 1006 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 1000.

[0198] The multimedia component 1008 includes a screen that provides an output interface between the electronic device 1000 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1008 includes a front camera and / or a rear camera. When the electronic device 1000 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0199] The audio component 1010 is configured to output and / or input audio signals. For example, the audio component 1010 includes a microphone (MIC), and when the electronic device 1000 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 1004 or sent via the communication component 1016. In some embodiments, the audio component 1010 also includes a speaker for outputting audio signals.

[0200] I / O interface 1012 provides an interface between processing component 1002 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0201] The sensor assembly 1014 includes one or more sensors for providing various aspects of status assessment for the electronic device 1000. For example, the sensor assembly 1014 can detect the open / closed state of the electronic device 1000, the relative positioning of the components, such as the display and keypad of the electronic device 1000, and the sensor assembly 1014 can also detect the position change of the electronic device 1000 or a component of the electronic device 1000, the presence or absence of contact between the user and the electronic device 1000, the orientation or acceleration / deceleration of the electronic device 1000, and the temperature change of the electronic device 1000. The sensor assembly 1014 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 1014 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1014 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0202] The communication component 1016 is configured to facilitate wired or wireless communication between the electronic device 1000 and other devices. The electronic device 1000 can access a wireless network based on a communication standard, such as WiFi, 4G or 5G, 4G LTE, 5G NR or a combination thereof. In an exemplary embodiment, the communication component 1016 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1016 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0203] In an exemplary embodiment, the electronic device 1000 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0204] In an exemplary embodiment, a non-transitory computer-readable storage medium is also provided, such as a memory 1004 including instructions. When the instructions in the storage medium are executed by the processor 1020 of the electronic device 1000, the electronic device 1000 can perform the following abnormality cause determination method:

[0205] Acquire abnormal information data and industrial production data; wherein, the abnormal information data is used to characterize abnormal conditions occurring in the target device during the industrial production process, and the industrial production data includes operational technology OT data and information technology IT data.

[0206] The cause of the abnormal situation is determined based on the industrial production data.

[0207] The non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0208] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0209] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for determining an abnormality cause, characterized in that: The method is used to determine the cause of an abnormal situation of a target device in an industrial production process, and the method comprises: Acquire abnormal information data and industrial production data; wherein the abnormal information data is used to characterize abnormal conditions occurring in the target device during the industrial production process, and the industrial production data includes operational technology (OT) data and information technology (IT) data; The cause of the abnormal situation is determined based on the industrial production data.

2. The method according to claim 1, characterized in that The OT data includes basic parameter information data of the target device.

3. The method according to claim 1, characterized in that: The IT data includes at least one of the following: Maintenance information data of the target device; the maintenance information data is obtained based on the maintenance operation performed on the target device; Process control data of the target device; The process control data is used to characterize the manufacturing process of the target device.

4. The method according to claim 1, characterized in that Determining the cause of the abnormal situation according to the industrial production data includes: Inputting the abnormal information data and the industrial production data into a Markov logic network; the Markov logic network is pre-built based on a rule library constructed for the target device; The output of the Markov logic network is determined as the cause of the abnormal situation.

5. The method according to claim 4, characterized in that The number of reasons for the occurrence of the abnormal situation determined by the Markov logic network is multiple; The method further comprises: Displaying the determined multiple causes on a visual interface; After receiving a first reason selected by a user from the multiple reasons, increasing a weight parameter of a first rule in the rule base that causes the abnormal situation due to the first reason; Based on the adjusted rule base, a new Markov logic network is rebuilt.

6. The method according to claim 5, characterized in that The IT data includes maintenance information data of the target device; the maintenance information data is obtained based on the maintenance operation performed on the target device; The method further comprises: After determining the maintenance operation corresponding to the first cause, displaying the maintenance operation on the visualization interface; After receiving a confirmation operation of the user for the maintenance operation, the maintenance information data is updated according to the maintenance operation.

7. The method according to claim 4, characterized in that The method further comprises: Displaying the cause of the abnormal situation determined based on the Markov logic network on a visualization interface; After receiving a second reason input by the user based on the visual interface, adding a second rule in the rule base that the abnormal situation occurs due to the second reason; the second reason does not exist in the reasons for the abnormal situation determined by the Markov logic network; Based on the adjusted rule base, a new Markov logic network is rebuilt.

8. An abnormality cause determination device, characterized in that: The device is used to determine the cause of abnormal conditions occurring in a target device during an industrial production process, and the device comprises: A data acquisition module, used to acquire abnormal information data and industrial production data; wherein the abnormal information data is used to characterize abnormal conditions occurring in the target device during the industrial production process, and the industrial production data includes operational technology OT data and information technology IT data; The cause determination module is used to determine the cause of the abnormal situation based on the industrial production data.

9. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. An electronic device comprising: processor; a memory for storing processor-executable instructions; The processor is configured to execute the steps of any one of the methods of claims 1-7.

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