A bus module offline control method, device, equipment and storage medium

Abstract

The present invention discloses a bus module offline control method, device, equipment and storage medium. The present invention controls the host computer terminal to send status query instructions regularly, so as to monitor the operation status of the bus module master station and the bus module slave station in real time; if the bus module master station and the bus module slave station are monitored to be abnormal in operation, the fault prediction mechanism is triggered; the working stage and load condition of the bus module master station and the bus module slave station are obtained; the prediction parameters of the fault prediction model are dynamically adjusted; the adjusted prediction parameters are applied to the fault prediction model; the host computer terminal is controlled to perform fault prediction on the bus module master station and the bus module slave station according to the fault prediction model to predict the offline condition of the bus module master station and the bus module slave station; the host computer terminal is controlled to take measures to prevent the bus module master station and the bus module slave station from actually being offline according to the prediction result. The present invention can effectively realize the anti-offline control of the bus module and improve the working stability of the bus module.

Description

Technical Field

[0001] The present invention relates to the field of bus technology, and in particular to a bus module offline control method, device, equipment and storage medium. Background Art

[0002] The bus module, also known as the bus device, can control the bus and is the bottom-level data communication network in the field of automation. The bus module is used in the production site to connect the intelligent field equipment and the automatic control system with a two-way transmission and branch structure communication network, and can exchange data with other communicative devices.

[0003] In the existing industrial automation and network communication systems, the bus module is an important part of data transmission, and its stability and reliability are crucial to the operation of the entire system. However, due to hardware failure, software error, network congestion or external interference, the bus module may be disconnected, resulting in data transmission interruption, affecting the production process and causing economic losses.

[0004] In the existing technology, the monitoring and control methods for the bus module offline problem have the following deficiencies: 1) Static monitoring: Traditional monitoring methods mostly use static monitoring methods, and cannot dynamically adjust the monitoring strategy according to the actual operating status of the bus module, resulting in poor monitoring results in complex and changeable industrial environments. 2) Reactive processing: When the bus module is offline, the existing system can often only perform reactive processing after the problem occurs, lacking effective preventive measures and unable to fundamentally avoid the occurrence of offline events. 3) Lack of predictability: Existing control methods rarely achieve the prediction of bus module offline situations, so it is impossible to take action in advance to prevent potential offline risks.

[0005] Therefore, how to reliably and efficiently implement the bus module offline control problem has become a problem that needs to be solved urgently by technicians in this field. Summary of the invention

[0006] In view of the above defects, the embodiments of the present invention provide a bus module offline control method, device, equipment and storage medium to solve the problems existing in the prior art.

[0007] In order to solve the above technical problems, an embodiment of the present invention provides a bus module offline control method, the method comprising:

[0008] Control the host computer terminal to send status query instructions regularly, so as to monitor the operating status of the bus module master station and bus module slave station in real time;

[0009] If it is detected that the bus module master station and the bus module slave station are operating abnormally, a fault prediction mechanism is triggered;

[0010] Obtaining the current working stage and load status of the bus module master station and the bus module slave station;

[0011] Dynamically adjusting prediction parameters of a fault prediction model according to the current working stages of the bus module master station and the bus module slave station and the load conditions;

[0012] Applying the adjusted prediction parameters to the fault prediction model;

[0013] Controlling the host computer terminal to perform fault prediction on the bus module master station and the bus module slave station according to the fault prediction model, so as to predict the offline conditions of the bus module master station and the bus module slave station;

[0014] The host computer terminal is controlled to take measures in advance according to the fault prediction result to prevent the bus module master station and the bus module slave station from actually being disconnected.

[0015] Preferably, dynamically adjusting the prediction parameters of the fault prediction model according to the current working stage of the bus module master station and the bus module slave station and the load condition includes:

[0016] Confirming that the current working phase of the bus module master station and the bus module slave station is a startup phase, a stable operation phase or a heavy load phase;

[0017] Confirming that the current load conditions of the bus module master station and the bus module slave station are light load, standard load or heavy load;

[0018] According to the preset prediction parameter adjustment rules, the adjustment direction of the prediction parameters under different working stages and load conditions is clarified;

[0019] The prediction parameter is actually adjusted according to the adjustment direction of the prediction parameter.

[0020] Preferably, before the control host terminal actively sends a status query instruction at a regular interval so as to monitor the operating status of the bus module master station and the bus module slave station in real time, the bus module offline control method further includes:

[0021] Control the bus module to automatically apply for a communication address after the slave station accesses the communication bus, and monitor the communication data of the communication bus;

[0022] If the communication address received by the bus module from the station is the same as its own address, an address conflict reporting process is performed;

[0023] If the bus module slave station does not receive conflicting signals from other module nodes, it is determined that the bus module slave station address application is successful and enters a normal communication state.

[0024] Preferably, the controlling the host computer terminal to perform fault prediction on the bus module master station and the bus module slave station according to the fault prediction model so as to predict the offline conditions of the bus module master station and the bus module slave station includes:

[0025] Collecting historical operation data of the bus module master station and the bus module slave station;

[0026] Training the historical operation data of the bus module master station and the bus module slave station according to a machine learning algorithm to establish the fault prediction model;

[0027] The real-time monitoring data of the bus module master station and the bus module slave station are input into the fault prediction model, and the fault prediction result is output.

[0028] Preferably, the bus module offline control method further includes:

[0029] Feedback the fault prediction result and the actual offline situation to the host computer terminal;

[0030] The host computer terminal is controlled to optimize the fault prediction model according to the fault prediction result and the actual offline situation.

[0031] Preferably, the adjusting direction of the prediction parameters under different working stages and load conditions is clarified according to the preset prediction parameter adjustment rules, including:

[0032] Determining whether the prediction parameters need to be adjusted according to different working stages and load conditions;

[0033] If necessary, according to the preset prediction parameter adjustment rules, the adjustment direction of the prediction parameters under different working stages and load conditions is clarified.

[0034] Preferably, the step of clarifying the adjustment direction of the prediction parameters under different working stages and load conditions according to the preset prediction parameter adjustment rules further includes:

[0035] If not necessary, the current prediction parameters are applied to the fault prediction model.

[0036] In order to solve the above technical problems, an embodiment of the present invention provides a bus module offline control device, the device comprising:

[0037] The monitoring module is used to control the host computer terminal to periodically send status query instructions so as to monitor the operating status of the bus module master station and the bus module slave station in real time;

[0038] A trigger module, used for triggering a fault prediction mechanism if abnormal operation of the bus module master station and the bus module slave station is detected;

[0039] An information confirmation module, used to obtain the current working stage and load status of the bus module master station and the bus module slave station;

[0040] A dynamic adjustment module, used to dynamically adjust the prediction parameters of the fault prediction model according to the current working stage of the bus module master station and the bus module slave station and the load condition;

[0041] an optimization module, configured to apply the adjusted prediction parameters to the fault prediction model so as to optimize the fault prediction model;

[0042] A fault prediction module, used for controlling the host computer terminal to perform fault prediction on the bus module master station and the bus module slave station according to the fault prediction model, so as to predict the offline conditions of the bus module master station and the bus module slave station;

[0043] The prevention module is used to control the host computer terminal to take measures in advance according to the prediction results to prevent the bus module master station and the bus module slave station from actually going offline.

[0044] To solve the above technical problems, an embodiment of the present invention provides a bus module offline control device, comprising: at least one processor, at least one memory and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, the method of the first aspect in the above embodiment is implemented.

[0045] To solve the above technical problem, an embodiment of the present invention provides a storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method of the first aspect in the above embodiment is implemented.

[0046] In summary, the embodiments of the present invention provide a bus module offline control method, device, equipment and storage medium. The present invention controls the host computer terminal to send status query instructions regularly, so as to monitor the operating status of the bus module master station and the bus module slave station in real time; if the bus module master station and the bus module slave station are monitored to be abnormal, the fault prediction mechanism is triggered; the current working stage and load condition of the bus module master station and the bus module slave station are obtained; according to the current working stage and load condition of the bus module master station and the bus module slave station, the prediction parameters of the fault prediction model are dynamically adjusted; the adjusted prediction parameters are applied to the fault prediction model to optimize the fault prediction model; the host computer terminal is controlled to perform fault prediction on the bus module master station and the bus module slave station according to the fault prediction model, so as to predict the offline condition of the bus module master station and the bus module slave station; the host computer terminal is controlled to take measures in advance according to the prediction results to prevent the bus module master station and the bus module slave station from actually being offline. Therefore, the present invention can effectively realize the anti-offline control of the bus module and improve the stability of the bus module operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the following briefly introduces the drawings required for use in the embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 The present invention is a flowchart of a bus module offline control method according to an embodiment of the present invention.

[0049] Figure 2 It is a schematic diagram of a process of dynamically adjusting prediction parameters of a fault prediction model according to an embodiment of the present invention.

[0050] Figure 3 The present invention is a flowchart of an embodiment of the present invention before controlling the host computer terminal to actively send a status query instruction at a regular interval.

[0051] Figure 4 The present invention is a flowchart of a method for predicting faults of a bus module master station and a bus module slave station according to an embodiment of the present invention.

[0052] Figure 5 It is another flowchart of performing fault prediction on a bus module master station and a bus module slave station according to an embodiment of the present invention.

[0053] Figure 6 It is a flow chart of an embodiment of the present invention for clarifying the adjustment direction of prediction parameters under different working stages and load conditions.

[0054] Figure 7 It is a structural schematic diagram of a bus module offline control device according to an embodiment of the present invention.

[0055] Figure 8 It is a structural schematic diagram of a bus module offline control device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The features and exemplary embodiments of various aspects of the present invention will be described in detail below. In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and Examples. It should be understood that the specific embodiments described herein are only configured to explain the present invention and are not configured to limit the present invention. For those skilled in the art, the present invention can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present invention by illustrating examples of the present invention.

[0057] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0058] See also Figure 1 , Figure 1 A bus module offline control method provided in this application comprises the following steps:

[0059] S1. Control the host computer terminal to periodically send status query instructions to monitor the operating status of the bus module master station and the bus module slave station in real time.

[0060] Specifically, in this embodiment, the bus module includes a bus module master station and a bus module slave station. The bus module master station acts as a control center in the network, responsible for managing network communications and operations, and exchanging data with each bus module slave station. The bus module slave station acts as an execution unit, responding to the instructions of the master station and completing the specified tasks. A one-to-many control relationship is formed between the bus module master station and the bus module slave station.

[0061] Specifically, in this embodiment, the bus module master station, each bus module slave station and the host computer terminal communicate and transmit data through communication technologies such as CAN protocol, Modbus protocol, CANopen protocol, Profinet protocol, EtherCAT protocol, etc. The communication method between the bus module master station, each bus module slave station and the host computer terminal is not specifically limited here.

[0062] Specifically, in this embodiment, the host terminal is configured as the main control center of the system, which is used to periodically send status query instructions. The sending period of the status query instruction is set according to the actual network environment and system requirements, and is not specifically limited here. It can be understood that through the status query instruction, the host terminal can monitor the operating status of the bus module master station and the bus module slave station in real time to ensure that the status of the bus module can be effectively monitored without excessively occupying network resources.

[0063] S2. If it is detected that the bus module master station and the bus module slave station are operating abnormally, the fault prediction mechanism is triggered.

[0064] Specifically, in this embodiment, after the status query instruction is issued, the host terminal receives the response signal from the bus module master station and the bus module slave station. On the one hand, the host terminal determines whether the bus module master station and the bus module slave station are operating normally through the response signal. If no response is received within a predetermined time, it means that the bus module is at risk of being disconnected. On the other hand, after receiving the corresponding signal, the host terminal performs data analysis on the response signal to obtain the operating data of the bus module master station and the bus module slave station. If the bus module master station and the bus module slave station operate abnormally, the fault prediction mechanism is triggered.

[0065] Specifically, in this embodiment, the judgment parameters for abnormal operation of the bus module master station and the bus module slave station include the communication status, which determines whether the communication is abnormal by monitoring parameters such as whether the communication signal exists, signal strength, and communication delay; it also includes electrical parameters such as current, voltage, power, etc. If these parameters exceed the normal operating range, it may indicate that the equipment is operating abnormally; in addition, the judgment parameters also include temperature. An abnormal increase in the temperature of the equipment may indicate that the equipment is overloaded or faulty, etc., which is not specifically limited here.

[0066] S3. Obtain the current working stage and load status of the bus module master station and the bus module slave station.

[0067] Specifically, in this embodiment, after the fault prediction mechanism is triggered, the host computer terminal further sends a status query instruction to obtain more detailed status information such as the current working stage and load status of the bus module master station and the bus module slave station.

[0068] It is worth noting that the bus module master and the bus module slave have different operating characteristics in different working stages. For example, the current and temperature in the startup stage and the stable operation stage are different. In addition, the load of the bus module master and the bus module slave directly affects the workload and wear. For example, high load may increase the risk of equipment failure. Therefore, the working stage and load condition are key factors affecting the operating state of the equipment. When performing fault prediction, in addition to considering the abnormal operating parameters of the module, the present application also uses the working stage and load condition of the bus module master and the bus module slave as feature inputs, so that the fault prediction model is more adapted to the current operating environment, thereby improving the accuracy of the prediction.

[0069] It is worth noting that the present application triggers the fault prediction mechanism when it detects abnormal status of the bus module master station and the bus module slave station, and performs fault prediction after triggering the fault prediction mechanism, thereby achieving an effective balance between network communication efficiency and fault prediction, which not only ensures the network communication rate when the bus module master station and the bus module slave station are working normally, but also realizes fault warning when abnormalities occur in the bus module master station and the bus module slave station, effectively preventing the bus module master station and the bus module slave station from actually being disconnected.

[0070] S4. Dynamically adjust the prediction parameters of the fault prediction model according to the current working stage and load conditions of the bus module master station and the bus module slave station.

[0071] S5. Apply the adjusted prediction parameters to the fault prediction model.

[0072] Specifically, in this embodiment, the host computer terminal dynamically adjusts the prediction parameters of the preset fault prediction model according to the current working stages and load conditions of the received bus module master station and the bus module slave station, and applies the adjusted prediction parameters to the fault prediction model, so that the fault prediction model can output more accurate prediction results when performing fault prediction, thereby improving the bus module network communication quality.

[0073] S6. Control the host computer terminal to perform fault prediction on the bus module master station and the bus module slave station according to the fault prediction model, so as to predict the offline situation of the bus module master station and the bus module slave station.

[0074] S7. Control the host terminal to take measures in advance according to the prediction results to prevent the bus module master station and the bus module slave station from actually going offline.

[0075] Specifically, in this embodiment, the host terminal uses the updated fault prediction model to predict the bus module master station and the bus module slave station. If the prediction result shows that the bus module master station or the bus module slave station has the risk of being offline, the host terminal will take measures to prevent the bus module master station or the bus module slave station from actually being offline.

[0076] Specifically, in this embodiment, measures to prevent the bus module master station and the bus module slave station from actually going offline include sending an alarm to the operator, reallocating the network load to reduce the pressure on high-risk modules, or starting backup modules or redundant paths to maintain system stability, etc., which are not specifically limited here.

[0077] Therefore, the bus module offline control method provided in the present application can actively prevent and respond to potential offline problems of the bus module master station and the bus module slave station, thereby improving the reliability and stability of the overall network.

[0078] In summary, the present application provides a bus module offline control method. In this solution, the host computer terminal is controlled to send status query instructions regularly to monitor the operating status of the bus module master station and the bus module slave station in real time, so as to detect abnormalities in time. If the bus module master station and the bus module slave station are monitored to operate abnormally, the fault prediction mechanism is triggered to predict potential faults and reduce the risk of system operation. The current working stage and load conditions of the bus module master station and the bus module slave station are obtained to provide accurate data support for fault prediction. According to the current working stage and load conditions of the bus module master station and the bus module slave station, the prediction parameters of the fault prediction model are dynamically adjusted to improve the accuracy of fault prediction. The adjusted prediction parameters are applied to the fault prediction model to optimize the fault prediction model and ensure that the fault prediction model can more accurately reflect the actual operation of the bus module. The host computer terminal is controlled to perform fault prediction on the bus module master station and the bus module slave station according to the fault prediction model, so as to predict the offline situation of the bus module master station and the bus module slave station. The host computer terminal is controlled to take measures in advance according to the prediction result to prevent the bus module master station and the bus module slave station from actually dropping offline, thereby ensuring the stable operation of the bus module master station and the slave station. Therefore, the present invention can effectively realize the anti-dropping control of the bus module and improve the working stability of the bus module.

[0079] Based on the above embodiments:

[0080] See also Figure 2 , Figure 2 A flow chart of dynamically adjusting prediction parameters of a fault prediction model according to the current working stage and load conditions of a bus module master station and a bus module slave station provided in the present application.

[0081] As a preferred embodiment, according to the current working stage and load conditions of the bus module master station and the bus module slave station, dynamically adjusting the prediction parameters of the fault prediction model includes:

[0082] S41. Confirm that the current working phase of the bus module master station and the bus module slave station is the startup phase, the stable operation phase or the heavy load phase.

[0083] Specifically, in this embodiment, by monitoring the operating parameters of the bus module master station and the bus module slave station, it is automatically identified whether the bus module master station and the bus module slave station are in the startup stage, the stable operation stage or the heavy load stage.

[0084] Furthermore, in this embodiment, if the system has just started, it is determined to be in the startup phase; if the system runs smoothly without abnormal fluctuations, it is determined to be in the stable operation phase; if the system load exceeds a preset threshold, it is determined to be in the heavy load phase.

[0085] Specifically, in this embodiment, different working stages may affect the accuracy and efficiency of the fault prediction model. For example, the startup stage may require more sensitive prediction parameters to capture initial instability, while the stable operation stage may require more stable prediction parameters to reduce false alarms. Therefore, the present application confirms that the current working stage of the bus module master station and the bus module slave station helps to adjust the prediction model more accurately, thereby improving the accuracy of the prediction.

[0086] S42. Confirm that the current load conditions of the bus module master station and the bus module slave station are light load, standard load or heavy load.

[0087] Specifically, in this embodiment, by monitoring the load conditions of the bus module master station and the bus module slave station, such as CPU usage, memory usage, etc., it is identified whether the bus module master station and the bus module slave station are currently lightly loaded, standard loaded, and heavily loaded.

[0088] Furthermore, if the CPU usage of the bus module master station and the bus module slave station is less than 30%, it is determined to be a light load; if the CPU usage of the bus module master station and the bus module slave station is between 30% and 70%, it is determined to be a standard load; if the CPU usage of the bus module master station and the bus module slave station exceeds 70%, it is determined to be a heavy load. It can be understood that the distinction between light load, standard load and heavy load can be set as needed and is not specifically limited here.

[0089] Specifically, in this embodiment, different load conditions will affect the probability and type of bus module master and bus module slave failures. Therefore, the present application can effectively predict potential failures by adjusting the prediction parameters according to the load conditions, so as to take preventive measures.

[0090] S43. According to the preset prediction parameter adjustment rules, the adjustment direction of the prediction parameters under different working stages and load conditions is clarified.

[0091] Specifically, in this embodiment, the preset adjustment rules are used to guide the parameter adjustment of the fault prediction model in different working stages and load conditions to ensure the accuracy of the prediction.

[0092] S44. Actually adjust the prediction parameters according to the adjustment direction of the prediction parameters.

[0093] Specifically, in this embodiment, the preset prediction parameter adjustment rules specifically include:

[0094] In the startup phase, if the load is light, the sensitivity of the prediction parameters is increased; in the stable operation phase, if the load is standard, the prediction parameters are kept unchanged; in the heavy load phase, regardless of the working phase, the sensitivity of the prediction parameters is reduced to avoid false alarms.

[0095] See also Figure 3 , Figure 3 The present application provides a flow chart of controlling the host computer terminal to periodically and actively send status query instructions so as to monitor the operating status of the bus module master station and the bus module slave station in real time.

[0096] As a preferred embodiment, before controlling the host computer terminal to actively send a status query instruction at a regular interval so as to monitor the operating status of the bus module master station and the bus module slave station in real time, a bus module offline control method further includes:

[0097] S01. After the control bus module slave station is connected to the communication bus, it automatically applies for a communication address and monitors the communication data of the communication bus.

[0098] Specifically, in a bus communication system, each bus module slave station has a unique communication address to communicate with the bus module master station. When the bus module slave station is powered on or reset, it automatically sends a specific address application frame to the communication bus, and starts to monitor the data on the bus, ready to receive the address assigned by the host terminal. It can be understood that automatically applying for a communication address can ensure that the bus slave station module can smoothly access the communication network.

[0099] S02: If the communication address received by the bus module from the station is the same as its own address, an address conflict reporting process is performed.

[0100] Specifically, address conflicts will lead to communication confusion, and multiple bus slave modules using the same address will not be able to communicate normally. In this embodiment, if the bus slave module finds that the address in an address allocation instruction is the same as the address applied for by itself when monitoring bus data, it will immediately stop communication and report the address conflict to the host terminal or bus module master station, requesting reallocation of the address.

[0101] S03. If the bus module slave station does not receive conflicting signals from other module nodes, it is determined that the bus module slave station address application is successful and enters a normal communication state.

[0102] Specifically, in this embodiment, if the bus module slave station does not receive a conflicting signal from other module nodes, it is determined that the bus module slave station has obtained a unique communication address and enters a normal communication state. Therefore, the present application ensures that the slave station module can quickly enter a normal working state without conflict, thereby improving the response speed and stability of the system.

[0103] See also Figure 4 , Figure 4 The present application provides a flow chart of a control host terminal for predicting faults of a bus module master station and a bus module slave station according to a fault prediction model so as to predict offline conditions of the bus module master station and the bus module slave station.

[0104] As a preferred embodiment, controlling the host computer terminal to perform fault prediction on the bus module master station and the bus module slave station according to the fault prediction model so as to predict the offline conditions of the bus module master station and the bus module slave station includes:

[0105] S61. Collect historical operation data of the bus module master station and the bus module slave station.

[0106] S62. Train the historical operation data of the bus module master station and the bus module slave station based on the machine learning algorithm to establish a fault prediction model.

[0107] S63, input the real-time monitoring data of the bus module master station and the bus module slave station into the fault prediction model, and output the fault prediction result.

[0108] Specifically, in this embodiment, the fault prediction model is a logistic Sigmoid function, and the specific formula is:

[0109] ;

[0110] Where y represents the probability of a fault occurring, 0≤y≤1, and y is used to indicate the possibility of a fault occurring;

[0111] A set of input features representing the working phase, load conditions and abnormal operation parameters of the bus module master and bus module slave during operation;

[0112] wi represents the weight of each input feature, i.e., the fault prediction parameter. The fault prediction parameter reflects the importance of the input feature for fault prediction.

[0113] b represents the bias term of the fault prediction model, which is used to further adjust the accuracy of the prediction results; n represents the number of input features.

[0114] Specifically, in this embodiment, the working stage, load condition and abnormal operation parameters of the bus module are converted into input features of the fault prediction model. Working stage encoding: The working stage (startup, stable operation, heavy load) is encoded as a numerical feature, for example, using One-Hot Encoding. Load condition quantification: The load condition (light load, standard load, heavy load) is quantified into a numerical value, directly using the percentage of the actual load or the graded numerical value. Abnormal operation parameters are standardized or normalized: Different abnormal parameters have different dimensions and numerical ranges, and need to be standardized or normalized to eliminate the dimension effect and make each feature contribute equally to the model.

[0115] Specifically, in this embodiment, when designing the input layer of the logical Sigmoid function, the characteristics of the working stage, load condition and abnormal operation parameters of the bus module are used as input nodes (xi). The number of input layer nodes should be equal to the sum of all characteristics, including working stage coding, load condition quantization value and other possible monitoring parameters.

[0116] Specifically, in this embodiment, an adjustment strategy for the prediction parameters is defined according to the working stage and load conditions.

[0117] Specifically, in this embodiment, the weight of each input feature, i.e., the prediction parameter adjustment, is predefined according to different working stages, load conditions, and abnormal operating parameters. For example, if the fault prediction model needs to strengthen the response to certain features in the heavy load stage, the weight of these features is increased.

[0118] Specifically, in this embodiment, the bias item can be adjusted according to the characteristics of different stages, which is not specifically limited here.

[0119] Specifically, during the model training process, the adjustment of the prediction parameters is achieved according to the following steps: During the model operation, the working stage and load of the module can be monitored in real time, and the parameters can be adjusted dynamically.

[0120] Specifically, in this implementation, a Sigmoid activation function is used to compress the output to between 0 and 1, that is, the output of the fault prediction model is converted into the probability of a fault occurring, where fault is coded as 1 and no fault is coded as 0.

[0121] Specifically, in this implementation, after the model training is completed, the output y is converted into the actual fault prediction result using the following method:

[0122] Threshold setting: Set a threshold (for example, 0.5). If y is greater than this threshold, it is predicted to be a fault, otherwise it is predicted to be no fault. For example, when predicting:

[0123] Model output: The model output y is 0.65, indicating that there is a 65% probability of failure.

[0124] Threshold decision: If the threshold is set to 0.5, the prediction result is failure because 0.65 > 0.5.

[0125] See also Figure 5 , Figure 5 The present application provides a flow chart of a control host terminal for predicting faults of a bus module master station and a bus module slave station according to a fault prediction model so as to predict offline conditions of the bus module master station and the bus module slave station.

[0126] As a preferred embodiment, a bus module offline control method of the present application further includes:

[0127] S64. Feedback the fault prediction result and the actual offline situation to the host computer terminal.

[0128] S65. Control the host computer terminal to optimize the fault prediction model according to the fault prediction result and the actual offline situation.

[0129] Specifically, the present application can optimize and train the fault prediction model based on the historical operation data of the bus module master station and the bus module slave station, including the fault prediction results and the actual offline situation. For example, assuming that in the heavy load stage, the historical data shows that the fault prediction results correspond to the actual offline situation, the prediction is accurate, and the historical data shows that the correlation between certain features and faults increases. The system will increase the weights of these features so that the fault prediction model pays more attention to these features. The system applies these weight adjustments to the fault prediction model and retrains the fault prediction model.

[0130] See also Figure 6 , Figure 6 A flow chart of a method provided in the present application before clarifying the adjustment direction of prediction parameters under different working stages and load conditions according to preset prediction parameter adjustment rules.

[0131] As a preferred embodiment, according to the preset prediction parameter adjustment rules, the adjustment direction of the prediction parameters under different working stages and load conditions is clarified, including:

[0132] S431. Determine whether the prediction parameters need to be adjusted according to different working stages and load conditions.

[0133] S432. If necessary, according to the preset prediction parameter adjustment rules, clarify the adjustment direction of the prediction parameters under different working stages and load conditions.

[0134] As a preferred embodiment, according to the preset prediction parameter adjustment rules, clarifying the adjustment direction of the prediction parameters under different working stages and load conditions also includes:

[0135] S433: If not necessary, apply the current prediction parameters to the fault prediction model.

[0136] See also Figure 7 The embodiment of the present invention provides a bus module offline control device, the device comprising:

[0137] The monitoring module 1 is used to control the upper computer terminal to periodically send status query instructions so as to monitor the operating status of the bus module master station and the bus module slave station in real time.

[0138] The trigger module 2 is used to trigger the fault prediction mechanism if it is detected that the bus module master station and the bus module slave station are operating abnormally.

[0139] The information confirmation module 3 is used to obtain the current working stage and load status of the bus module master station and the bus module slave station.

[0140] The dynamic adjustment module 4 is used to dynamically adjust the prediction parameters of the fault prediction model according to the current working stage and load conditions of the bus module master station and the bus module slave station.

[0141] The optimization module 5 is used to apply the adjusted prediction parameters to the fault prediction model.

[0142] The fault prediction module 6 is used to control the upper computer terminal to perform fault prediction on the bus module master station and the bus module slave station according to the fault prediction model, so as to predict the offline situation of the bus module master station and the bus module slave station.

[0143] The prevention module 7 is used to control the upper computer terminal to take measures in advance according to the prediction results to prevent the bus module master station and the bus module slave station from actually going offline.

[0144] In addition, combined Figure 1 The bus module offline control method described in the embodiment of the present invention can be implemented by a network bus module offline control device. Figure 8 A schematic diagram of the hardware structure of a bus module offline control device provided in an embodiment of the present invention is shown.

[0145] The bus module offline control device may include a processor 401 and a memory 402 storing computer program instructions.

[0146] Specifically, the processor 401 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0147] The storage 402 may include a large capacity memory for data or instructions. For example, but not limitation, the storage 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the storage 402 may include a removable or non-removable (or fixed) medium. In appropriate cases, the storage 402 may be inside or outside the data processing device. In a specific embodiment, the storage 402 is a non-volatile solid-state memory. In a specific embodiment, the storage 402 includes a read-only memory (ROM). In appropriate cases, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM) or a flash memory or a combination of two or more of these.

[0148] The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any one of the bus module offline control methods in the above embodiments.

[0149] In one example, the bus module offline control device may further include a communication interface 403 and a bus 410. Figure 8 As shown, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 410 and communicate with each other.

[0150] The communication interface 403 is mainly used to implement the communication between the modules, devices, units and / or equipment in the embodiment of the present invention.

[0151] Bus 410 includes hardware, software or both, and the components of the bus module offline control device are coupled to each other. For example, but not limitation, the bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 410 may include one or more buses. Although the embodiment of the present invention describes and shows a specific bus, the present invention considers any suitable bus or interconnection.

[0152] In addition, in combination with a bus module offline control method in the above embodiment, an embodiment of the present invention can provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any bus module offline control method in the above embodiment is implemented.

[0153] It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in a different order from the embodiments, or several steps can be performed simultaneously.

[0154] The above is only a specific implementation of the present invention. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be covered within the protection scope of the present invention.

Claims

1. A bus module offline control method, characterized in that: The bus module offline control method comprises: Control the host computer terminal to send status query instructions regularly, so as to monitor the operating status of the bus module master station and bus module slave station in real time; If it is detected that the bus module master station and the bus module slave station are operating abnormally, a fault prediction mechanism is triggered; Obtaining the current working stage and load status of the bus module master station and the bus module slave station; According to the current working stage and load conditions of the bus module master station and the bus module slave station, the prediction parameters of the fault prediction model are dynamically adjusted, including: Confirming that the current working phase of the bus module master station and the bus module slave station is a startup phase, a stable operation phase or a heavy load phase; Confirming that the current load conditions of the bus module master station and the bus module slave station are light load, standard load or heavy load; According to the preset prediction parameter adjustment rules, the adjustment direction of the prediction parameters under different working stages and load conditions is clarified; Actually adjusting the prediction parameter according to the adjustment direction of the prediction parameter; The adjusted prediction parameters are applied to the fault prediction model; wherein the fault prediction model is a logistic Sigmoid function, and the specific formula is: ； Where y represents the probability of a fault occurring, 0≤y≤1, and y is used to indicate the possibility of a fault occurring; A set of input features representing the working phase, load condition and abnormal operation parameters of the bus module master station and the bus module slave station during operation; wi represents the weight of each input feature, i.e., the prediction parameter, which reflects the importance of the input feature for fault prediction; b represents the bias term of the fault prediction model, which is used to further adjust the accuracy of the prediction results; n represents the number of input features; Controlling the host computer terminal to perform fault prediction on the bus module master station and the bus module slave station according to the fault prediction model, so as to predict the offline conditions of the bus module master station and the bus module slave station; The host computer terminal is controlled to take measures in advance according to the fault prediction result to prevent the bus module master station and the bus module slave station from actually being disconnected.

2. A bus module offline control method according to claim 1, characterized in that: Before the control host terminal actively sends a status query instruction at a regular time so as to monitor the operation status of the bus module master station and the bus module slave station in real time, the bus module offline control method further includes: Control the bus module to automatically apply for a communication address after the slave station accesses the communication bus, and monitor the communication data of the communication bus; If the communication address received by the bus module from the station is the same as its own address, an address conflict reporting process is performed; If the bus module slave station does not receive conflicting signals from other module nodes, it is determined that the bus module slave station address application is successful and enters a normal communication state.

3. A bus module offline control method according to claim 1, characterized in that: The controlling the host computer terminal to perform fault prediction on the bus module master station and the bus module slave station according to the fault prediction model so as to predict the offline conditions of the bus module master station and the bus module slave station includes: Collecting historical operation data of the bus module master station and the bus module slave station; Training the historical operation data of the bus module master station and the bus module slave station according to a machine learning algorithm to establish the fault prediction model; The real-time monitoring data of the bus module master station and the bus module slave station are input into the fault prediction model, and the fault prediction result is output.

4. A bus module offline control method according to claim 1, characterized in that: The bus module offline control method further comprises: Feedback the fault prediction result and the actual offline situation to the host computer terminal; The host computer terminal is controlled to optimize the fault prediction model according to the fault prediction result and the actual offline situation.

5. A bus module offline control method according to claim 1, characterized in that: The adjustment direction of the prediction parameters under different working stages and load conditions is clarified according to the preset prediction parameter adjustment rules, including: Determining whether the prediction parameters need to be adjusted according to different working stages and load conditions; If necessary, according to the preset prediction parameter adjustment rules, the adjustment direction of the prediction parameters under different working stages and load conditions is clarified.

6. A bus module offline control method according to claim 5, characterized in that: The method of clarifying the adjustment direction of the prediction parameters under different working stages and load conditions according to the preset prediction parameter adjustment rules also includes: If not necessary, the current prediction parameters are applied to the fault prediction model.

7. A bus module offline control device, characterized in that: The device comprises: The monitoring module is used to control the host computer terminal to periodically send status query instructions so as to monitor the operating status of the bus module master station and the bus module slave station in real time; A trigger module, used for triggering a fault prediction mechanism if abnormal operation of the bus module master station and the bus module slave station is detected; An information confirmation module, used to obtain the current working stage and load status of the bus module master station and the bus module slave station; A dynamic adjustment module, used to dynamically adjust the prediction parameters of the fault prediction model according to the current working stage of the bus module master station and the bus module slave station and the load condition, including: Confirming that the current working phase of the bus module master station and the bus module slave station is a startup phase, a stable operation phase or a heavy load phase; Confirming that the current load conditions of the bus module master station and the bus module slave station are light load, standard load or heavy load; According to the preset prediction parameter adjustment rules, the adjustment direction of the prediction parameters under different working stages and load conditions is clarified; Actually adjusting the prediction parameter according to the adjustment direction of the prediction parameter; The optimization module is used to apply the adjusted prediction parameters to the fault prediction model, wherein the fault prediction model is a logical Sigmoid function, and the specific formula is: ； Where y represents the probability of a fault occurring, 0≤y≤1, and y is used to indicate the possibility of a fault occurring; A set of input features representing the working phase, load condition and abnormal operation parameters of the bus module master station and the bus module slave station during operation; wi represents the weight of each input feature, i.e., the prediction parameter, which reflects the importance of the input feature for fault prediction; b represents the bias term of the fault prediction model, which is used to further adjust the accuracy of the prediction results; n represents the number of input features; A fault prediction module, used for controlling the host computer terminal to perform fault prediction on the bus module master station and the bus module slave station according to the fault prediction model, so as to predict the offline conditions of the bus module master station and the bus module slave station; The prevention module is used to control the host computer terminal to take measures in advance according to the prediction results to prevent the bus module master station and the bus module slave station from actually going offline.

8. A bus module offline control device, characterized in that: include: At least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, implement the method according to any one of claims 1 to 6.

9. A storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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