Automatic equipment information acquisition management method and system

Abstract

The invention relates to the technical field of industrial internet of things, in particular to an automatic equipment information collection management method and system, and the method comprises the steps: 1, transmitting a multi-protocol detection instruction sequence to target equipment, analyzing response data, and dynamically generating a protocol feature code; 2, analyzing a production process logic to generate an equipment action dependency chain, and creating a time sequence trigger condition expression according to a dependency relationship; step 3, equipment fault events are detected in real time, a dynamic acquisition strategy is generated based on the fault conduction relation, and the strategy comprises an acquisition frequency adjustment rule of associated equipment; and 4, executing a dynamic acquisition strategy, starting data acquisition of specified equipment when a time sequence triggering condition is met, and dynamically adjusting the acquisition frequency according to the fault conduction strength. The dynamic acquisition strategy generated based on the fault conduction relation can adjust the acquisition frequency in real time, the flexibility and timeliness of fault response are improved, and efficient and safe operation of the production line is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial internet of things, and in particular to an automation equipment information acquisition management method and system. BACKGROUND

[0002] In the field of industrial internet of things, especially for discrete manufacturing production lines (such as automobile parts assembly lines and 3C electronic product production lines) where multiple protocols coexist, the existing equipment information acquisition technology has the following structural defects: 1. Protocol compatibility defects lead to equipment access barriers: Discrete manufacturing production lines often mix equipment of different years and different manufacturers, for example: new Japanese FANUC numerical control systems use private FOCAS protocols, old German Siemens PLCs only support S7COMM protocols, and American Rockwell equipment uses EtherNet / IP protocols. The existing technical solution adopts a polling acquisition mechanism of a preset protocol library, which requires pre-entering all device protocol specifications. When a device (such as a private protocol device) that is not pre-provisioned is connected, time-consuming manual protocol analysis and code development are required, which prolongs the production line modification and upgrade cycle by 35%-60%. This defect is particularly prominent in small-batch and multi-variety flexible manufacturing scenarios, and seriously hinders production line reorganization efficiency.

[0003] 2. Missing time sequence correlation causes process chain data to break; The core feature of discrete manufacturing is that there is a strong process time sequence dependency between device actions, for example: The slide block of the punch must be lowered to ≤10mm, the conveyor belt can only be started, and the hot melt welding machine can only start working when the mechanical hand gripper pressure ≥5MPa; The existing technical solution adopts independent device monitoring threads to collect device data at a fixed period (such as every 5 seconds). This approach results in: Key state switching events are not captured (such as the conveyor belt being mistakenly started before the punch is in place), the cause-and-effect relationship between devices is lost, and the root cause cannot be traced back during fault analysis. A case from an automobile welding plant showed that because the electrode pressure of the spot welding machine and the robot position data were not collected in association, the average positioning time for a virtual welding fault was as long as 4.2 hours.

[0004] Therefore, there is an urgent need for an automation equipment information acquisition management method and system to solve the above problems. SUMMARY

[0005] Based on the above purpose, the present application provides an automation equipment information acquisition management method and system, wherein the automation equipment information acquisition management method comprises: Step 1: Dynamically generate protocol signature by sending multi-protocol probe instruction sequence to target device and analyzing response data; Step 2: Analyze production process logic to generate device action dependency chain, and create timing trigger condition expression based on dependency relationship; Step 3: Real-time detect device fault events, and generate dynamic acquisition strategy based on fault conduction relationship, which contains acquisition frequency adjustment rules of associated devices; Step 4: Execute dynamic acquisition strategy, start data acquisition of specified devices when timing trigger condition is met, and dynamically adjust acquisition frequency according to fault conduction strength.

[0006] Preferably, the generation process of protocol signature in step 1 includes: Construct a probe sequence containing at least three industrial standard protocol instructions, and send them in order of protocol popularity priority; Start instruction fragmentation and recombination mechanism for non-responsive devices: split the probe instructions into multiple sub-pieces, and identify the protocol structure by combining the response piece features; Extract core identification features of response data, including: Calculate the distribution randomness measure of data packet byte value, and mark it as a private protocol when the randomness exceeds the set threshold, locate the response data offset position corresponding to the request instruction function code, and match the error response structure in the preset abnormal code pattern library; Combine the above features into a protocol signature that uniquely identifies the device protocol.

[0007] Preferably, the expression of timing trigger condition in step 2 is constructed as follows: Extract the physical constraint relationship of device action from the production process file, including: The sequence constraint of action execution, the spatial position constraint of material transfer between devices, and the start-stop dependency relationship of energy supply; Convert the physical constraints into executable logic expressions, which include: State monitoring parameters and their threshold ranges of the trigger source device, address identification and target parameters to be collected of the triggered device, and action delay time parameters after condition satisfaction; Support logical operator combination conditions, including "and" operation and "or" operation.

[0008] Preferably, the generation process of dynamic acquisition strategy in step 3 includes: Build a fault conduction network model: analyze the device alarm time series in historical fault logs, calculate the causal association strength between alarm events, and determine the association strength by fault co-occurrence frequency and conduction time interval. When the association strength exceeds the set confidence threshold, establish the conduction path; Generating frequency adjustment coefficient according to conduction path: the depth of the conduction path from the fault source device to the associated device determines the adjustment amplitude, and the historical fault influence weight of the nodes in the path participates in the coefficient calculation; Generating fault recovery attenuation rules: monitoring the stable duration of the associated device parameters, when the stable duration reaches the device type related threshold, starting the progressive recovery of the collection frequency.

[0009] Preferably, the updating mechanism of the fault conduction network model comprises: Real-time recording of the conduction path of the newly occurring fault event; When the conduction time interval of the new path is shorter than the historical average, the association strength level of the path is improved; Recalculating the confidence weight of the conduction path every quarter, and eliminating the low weight path that has not been triggered continuously.

[0010] Preferably, the calculation process of the frequency adjustment coefficient comprises: Obtaining the influence weight factor of each node in the conduction path, which is determined by the criticality of the device in the process flow; Calculating the conduction depth weighted value of the path: the weight decays according to the device type with each additional layer of depth; Mapping the weighted value to the collection interval adjustment ratio, which is positively correlated with the weighted value.

[0011] Preferably, the implementation of the progressive recovery is: Setting the baseline time window for the recovery of the collection frequency, and the window length is determined according to the historical data of device fault recovery; Adjusting the collection interval according to the non-linear decay curve within the time window: using fast recovery strategy in the initial stage, and switching to slow recovery when the parameter approaches the stable threshold; Continuously monitoring the parameter fluctuation amplitude during the recovery process, and resetting the recovery process if it exceeds the tolerance range.

[0012] Preferably, the execution logic of step 4 comprises: Creating a double-channel collection control thread: the main thread continuously monitors the timing trigger condition, and the auxiliary thread receives the fault event interrupt signal; When the timing trigger condition is met: send the collection instruction corresponding to the protocol feature code to the target device, and start the data packet integrity verification mechanism; When receiving the fault interrupt signal: suspend the current timing collection task, and switch to high frequency collection mode according to the dynamic collection strategy.

[0013] Preferably, the extraction process of the physical constraint relationship comprises: Identifying the device action keywords in the process file, including "start", "stop", "transfer", "positioning"; Establish a space-time correlation matrix between actions: Time dimension: analyze the timestamp interval of the action instruction; Space dimension: analyze the material movement trajectory in the device coordinate system; Generate a minimum dependency path set through matrix operation, and eliminate redundant dependency relationships.

[0014] Correspondingly, the embodiment of the application provides an automatic device information acquisition management system, which comprises a memory configured to store instructions, and a processor configured to call the instructions from the memory and capable of realizing the automatic device information acquisition management method according to any one of the embodiments of the application when executing the instructions.

[0015] The beneficial effects of the application are as follows: 1. The method for generating a dynamic protocol feature code through a multi-protocol detection instruction sequence can effectively identify device protocols and realize dynamic adaptation. Even if a device uses a private protocol, it can be automatically identified and analyzed through a protocol detection mechanism without human intervention, greatly shortening the device access time and improving the flexibility and compatibility of device access. This method is particularly suitable for production lines with heterogeneous devices, significantly improving the efficiency of line reorganization and reducing the time and cost required for upgrading.

[0016] 2. The construction of the time sequence trigger condition expression accurately extracts and converts the physical constraint relationship between device actions, such as the sequence of device actions, the spatial position of material transfer, and the dependency relationship of energy supply, thereby creating a time sequence trigger condition reflecting the dependency relationship between devices. This method not only ensures that the time sequence dependency between devices is accurately recorded, but also captures changes in device state in a timely manner, avoiding data breaks in the process chain caused by incorrect capture of action dependency between devices. In this way, critical state switching events can be identified and responded to in a timely manner, greatly improving the accuracy and positioning efficiency of fault analysis and shortening the fault troubleshooting time.

[0017] 3. The dynamic acquisition strategy generation mechanism based on the fault propagation network model dynamically adjusts the acquisition frequency according to the propagation relationship and historical fault logs of device faults. The system can automatically adjust the acquisition frequency according to the propagation path depth and influence weight of the fault source device and associated devices, ensuring that critical devices are prioritized for monitoring when a fault occurs. In addition, the system also has a gradual recovery mechanism that gradually adjusts the acquisition frequency after fault recovery, avoiding the waste of excessive acquisition resources. This dynamic acquisition frequency adjustment method ensures that devices can be fully monitored when a fault occurs, improving the timeliness and accuracy of fault response.

[0018] 4、The application can flexibly adjust the collection interval from fast recovery to slow recovery to adapt to different recovery states of the equipment according to the set time window and the nonlinear decay curve after equipment failure recovery through the progressive recovery mechanism. At the same time, the system continuously monitors the parameter fluctuation of the equipment during the recovery process. If abnormal fluctuation occurs, the system will reset the recovery process to avoid potential failure problems during the equipment recovery process. This method effectively improves the collection efficiency during the equipment recovery process, reduces resource waste, and improves the intelligence and flexibility of the system in fault recovery. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0020] Fig. 1 The step flowchart of the method of the application; Fig. 2 The step flowchart of the updating mechanism of the fault conduction network model of the method of the application; Fig. 3 The step flowchart of the execution logic of step 4 of the method of the application. DETAILED DESCRIPTION

[0021] The application will be described in detail below with reference to the drawings and specific embodiments. It should be noted here that, in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and other alternative ways can also be used by those skilled in the art to implement some known technologies; and the drawings are only used to more specifically describe the embodiments, and are not intended to specifically limit the application.

[0022] Please refer to Figs. 1-3 The embodiment of the application provides an automatic equipment information collection and management method. In step 1, the system sends a series of multi-protocol detection instructions to the target equipment. These instructions are designed for multiple communication protocols that the equipment may use. The target equipment generates corresponding response data according to the different protocols. The system analyzes the received response data, extracts the communication characteristics of the equipment, and dynamically generates the protocol characteristic code of the equipment. The key of this process is not to rely on a preset protocol library, but to identify the protocol type of the equipment through real-time analysis of the response data, so as to realize seamless connection between the equipment and the system. This method effectively avoids the equipment access problem caused by protocol incompatibility in the traditional technology, and is especially suitable for discrete manufacturing scenes where multiple manufacturers and different year equipment coexist, reducing the time and labor cost required for equipment access.

[0023] In Step 2, during the production process, there are strict time sequence dependencies between the actions of the devices. This step identifies the action dependency relationships between devices by analyzing the production process logic, establishing a dependency chain of device actions. Based on this dependency chain, the system can generate a time sequence trigger condition expression. This expression is used to represent which device actions need to occur first and which actions must wait for other devices to complete before starting under certain conditions. This method ensures the coordination of each link in the production line and avoids failures caused by misaligned or unsynchronized device actions. For example, the conveyor belt can only start after the punch press completes its action, avoiding production accidents caused by not starting in the correct order.

[0024] The core of Step 3 is real-time monitoring of device failures and intelligent analysis of fault transmission. When the system detects a failure in a device, it generates a dynamic acquisition strategy based on the fault transmission relationship between devices. This strategy can automatically adjust the acquisition frequency based on the fault propagation path between devices, prioritizing the monitoring of devices associated with the fault, and ensuring that the status of critical devices is fed back in a timely manner. For example, if a device fails and its failure may affect the normal operation of downstream devices, the system will automatically increase the acquisition frequency of downstream devices to detect potential problems as soon as possible. This intelligent fault response mechanism improves the efficiency of fault diagnosis and troubleshooting.

[0025] In Step 4, the system performs data acquisition tasks according to the dynamic acquisition strategy generated in Step 3. Only when the time sequence trigger condition is met will the system start the data acquisition task of the device. At this time, the system will dynamically adjust the data acquisition frequency based on the fault transmission intensity of the device and the working state of the related devices. For example, when a device fails, the system will preferentially increase the acquisition frequency of that device, and adjust the acquisition frequency of other devices based on the scope of the fault. This adaptive acquisition frequency adjustment mechanism can ensure that critical devices are monitored frequently during the production process, and the acquisition frequency gradually decreases after the device returns to normal to save resources and improve system efficiency.

[0026] By dynamically generating protocol feature codes, the system can quickly adapt to the access needs of different devices, significantly reducing device access time and labor costs. During the production process, the time sequence dependencies between devices are accurately captured, avoiding failures caused by unsynchronized device actions. In addition, the dynamic acquisition strategy generated based on the fault transmission relationship can adjust the acquisition frequency in real time, improving the flexibility and timeliness of fault response, and ensuring the efficient and safe operation of the production line. Through these innovative measures, the system's intelligence and automation level are improved, and the production line's transformation efficiency and device reliability are effectively enhanced.

[0027] In one possible implementation, in this embodiment, a probe sequence containing at least three industrial standard protocol instructions is first constructed. Different protocols have different applications in industrial equipment, so constructing a multi-protocol probe sequence helps quickly identify the type of protocol supported by the equipment. To optimize the equipment identification process, the instructions of the probe sequence are sorted according to the popularity priority of the protocol, that is, the instructions of the protocol that is more widely or more commonly used are sent first, which can increase the possibility of device response and improve the efficiency of device protocol identification.

[0028] In the case of encountering non-responsive equipment, the system adopts a fragmentation recombination mechanism. Specifically, when the equipment does not respond to the probe instruction, the system splits the probe instruction into multiple sub-pieces for sending, and relies on the combination characteristics of the response data pieces to identify the protocol structure of the equipment. The core purpose of this step is to solve the problem that the equipment cannot handle the complete instruction at one time due to response delay or the characteristics of some protocol structures. Through fragmentation sending, different parts of the protocol can be more efficiently covered, thereby improving the success rate of response.

[0029] After the equipment responds, the system will extract the core identification characteristics of the response data. This includes randomicity degree measurement analysis of the distribution of response data packet byte values. When the randomicity of the data packet byte values exceeds a certain set threshold, the system will mark the equipment as a private protocol equipment. This means that the protocol used by the equipment is customized or does not conform to the mainstream industrial standard protocol. Next, the system will also locate the response data offset position corresponding to the request instruction function code, and compare it with the preset abnormal code mode library to match whether there is a known error response structure. This process can effectively detect the abnormal behavior of the equipment and help identify potential problems in its protocol structure.

[0030] Finally, the system integrates the various features extracted above to generate a unique protocol feature code for identifying the protocol type of the equipment. This protocol feature code can accurately represent the protocol structure of the equipment, making it easy for the system to quickly identify the equipment and perform data exchange in subsequent operations.

[0031] By this method, the system can effectively solve the identification difficulty caused by protocol incompatibility when industrial equipment is connected. Especially in the case of multi-protocol support and unstable device response, the fragmentation reorganization mechanism and abnormal code matching can greatly improve the success rate of protocol identification. The extraction of core identification features can help the system accurately identify private protocol devices, thereby avoiding communication failures or data loss caused by protocol mismatch. In addition, by generating a unique protocol feature code, the system can efficiently manage the compatibility and data exchange of different device protocols, improving the automation and intelligence level of device information collection management. These innovative technical features make this method have obvious advantages in handling complex and heterogeneous industrial equipment access, and can adapt to various device protocol types, further improving the efficiency of device access and the scalability of the system.

[0032] In one possible implementation, in the management process of automated device information collection, production process files are an important source of information, which contain the operation process and mutual relationship between devices. The system first extracts the physical constraint relationship of device action from these process files. Specifically, the physical constraint relationship can be divided into the following aspects: The constraint of the execution sequence of actions: This involves the timing logic of device operation, which clearly indicates which device actions must be executed first and which must wait for other devices to complete before execution. For example, the cutting action must be performed first, and then the assembly operation can be performed.

[0033] The spatial position constraint of material transfer between devices: Different devices may involve the transfer and handover of materials, and the sequence and spatial position of material transfer must meet the specific production requirements. For example, some devices can only receive materials located at a certain position, or materials need to be transferred along a certain path.

[0034] The start-stop dependency relationship of energy supply: The energy supply of a device is sometimes dependent on the working state of other devices, such as some devices must be started and stopped when the previous device is working. Such relationships ensure the rational allocation and use of energy between devices.

[0035] Converting the extracted physical constraint relationship into an executable logical expression is one of the key steps. In this process, the system constructs the corresponding trigger condition expression through the analysis of physical constraints. These expressions contain the following core contents: The state monitoring parameters of the trigger source device and their threshold range: Each device has a series of monitorable parameters (such as temperature, pressure, speed, etc.), and the change of these parameters may trigger the action of other devices. For example, when the temperature of device A exceeds the set threshold, the action of device B can be triggered. The system constructs the corresponding monitoring conditions according to the running state of the device and the set threshold.

[0036] Address identification of triggered devices and target parameters to be collected: This indicates which device needs to perform data collection and which specific parameters to collect when the trigger condition is met. These target parameters can be device operating data, status information, etc.

[0037] Action delay time parameter after condition satisfaction: In some cases, the action of a device may not occur immediately, but with a certain delay. For example, device B may start collecting data after device A completes a certain operation with a delay. This delay time needs to be explicitly stated in the logical expression.

[0038] To construct more complex timing trigger conditions, the system supports the use of logical operators (such as "and" and "or" operations) to combine multiple conditions. For example, when the temperature and pressure of device A both exceed the threshold, the action of device B can be triggered, in which case the "and" operator is used to indicate that both conditions must be met simultaneously. Another example is that as long as any one of the states of device A or device B meets the set condition, the collection action of device C can be triggered, in which case the "or" operator is used to connect multiple conditions.

[0039] By converting the physical constraints in the production process file into executable logical expressions, the system can accurately control the operation sequence and data collection timing of the devices, thereby improving production efficiency and coordination of device operation. For example, through the precise construction of timing trigger conditions, it can ensure that devices perform appropriate actions at the right time, avoiding unnecessary waiting or incorrect operation between devices. In addition, the support of logical operators enables the system to flexibly respond to complex process requirements, dynamically adjusting trigger conditions in different situations, effectively improving the accuracy and automation level of device information collection. Overall, this method greatly improves the intelligence and accuracy of automated device information collection, making device management more efficient and highly adaptable.

[0040] In one possible implementation, first, the system constructs a fault propagation network model by analyzing the time series of device alarms in historical fault logs. In this step, the system calculates the causal correlation strength between alarm events based on the time relationship between fault events and the pattern of fault occurrence. The causal correlation strength between alarm events is mainly determined by the following two factors: Fault co-occurrence frequency: that is, the frequency of simultaneous occurrence of device fault events in historical data. Frequent simultaneous occurrence of fault events indicates a strong correlation between these devices.

[0041] Conduction time interval: that is, the time interval from the failure of one device to the failure of another device. Shorter time intervals between device failures are more likely to have direct or indirect causal relationships.

[0042] When the correlation strength between two alarm events exceeds a preset confidence threshold, the system considers that there is a significant fault conduction relationship between the two devices, and establishes a conduction path in the fault conduction network model.

[0043] After the fault conduction network is constructed, the system generates a frequency adjustment coefficient according to the depth of the conduction path. The depth of the conduction path refers to the number of levels between the fault source device and the associated device, which determines the amplitude of the frequency adjustment. Each node device in the path may have affected other devices in the history, and the historical fault influence weight of these devices will be used in the calculation of the adjustment coefficient. Generally speaking, the closer the device is to the fault source device, the greater its influence, so the frequency adjustment coefficient of these devices will be higher.

[0044] In order to effectively respond to the recovery process of device failure, the system monitors various parameters of the associated device and calculates its stable duration. The stable duration refers to the time during which the device remains stable after failure recovery. When the stable duration of the device reaches a certain threshold (which is set according to the device type and process requirements), the system gradually restores the acquisition frequency of the device. Specifically, the restoration of acquisition frequency is gradual, that is, the acquisition frequency is gradually increased as the device runs stably for a certain period of time, rather than immediately restored to the high-frequency acquisition state before the failure. This gradual recovery helps to avoid the burden on the system caused by too frequent data acquisition, while ensuring the stability of the device in the early stage of recovery.

[0045] By constructing the fault conduction network model and generating the frequency adjustment coefficient according to the conduction path, this method can dynamically adjust the frequency of device information acquisition, effectively balancing the real-time performance of device data acquisition and the utilization rate of system resources. The system not only can predict the fault conduction path between devices according to the historical data of device failure, but also can adjust the acquisition frequency according to these paths, effectively preventing excessive or insufficient data acquisition. By generating the fault recovery decay rule, the system can gradually restore the data acquisition frequency during device recovery, avoiding the impact on the stability of the device caused by excessive monitoring. In addition, this dynamic acquisition strategy can adapt to the changes in the state of the device in real time, making the information acquisition management more flexible and efficient, thereby improving the accuracy of device monitoring and the efficiency of the system. These technical features make this method have significant advantages in complex and variable automation environments, especially in scenarios that require efficient fault warning and rapid recovery, which can effectively improve the reliability and response ability of the automation system.

[0046] In one possible implementation, during the production process, new failure events can occur, which can lead to the formation of new failure conduction paths. The system needs to record these newly occurring failure events and their conduction paths in real time. Through timely monitoring and data collection of failure events, the system can capture the sequence of each failure occurrence, the causal relationship between devices, and the conduction path after device failure. The conduction path of a new failure event includes the causal relationship between the source device and the affected device, which is immediately recorded when the failure occurs and updated into the failure conduction network.

[0047] After recording the newly occurring failure events, the system determines the importance of the paths according to the conduction time interval. If the conduction time interval of a new path is shorter than the average time interval of historical failure events, the system considers that the failure conduction relationship of this path is more closely related and has a higher correlation. Therefore, the system will upgrade the correlation strength level of this path. This upgrade helps to accelerate the attention to this conduction path and gives it a higher weight in subsequent data collection and analysis, thereby ensuring that the failure conduction path is more accurately monitored and processed.

[0048] To maintain the accuracy and real-time nature of the failure conduction network model, the system regularly updates and optimizes the model. Specifically, the system recalculates the confidence weight of each conduction path every quarter. This calculation is based on historical failure data between devices, the frequency of failure occurrence, and their correlation strength, etc. If some conduction paths have not occurred failure events or triggered any associated device actions in the past period of time, the system will judge that the correlation of these paths is weak and gradually reduce their weights. Low-weight paths that have not been triggered for a long time will be eliminated or deleted from the failure conduction network model, avoiding the network being filled with invalid or redundant information. Through this mechanism, the system can optimize the dynamic management of failure conduction paths, so that the network always remains efficient and accurate.

[0049] Real-time recording of newly occurring failure events and updating of conduction paths ensures that the system can timely reflect device failures and their mutual relationships. This enables the identification and conduction of failures to be quickly responded to and effectively handled. Secondly, by upgrading the correlation strength level of new paths, the system can flexibly respond to new device failure patterns, prioritize important failure conduction paths, and improve the system's failure warning capability. Thirdly, regularly recalculating the confidence weight of conduction paths and eliminating low-weight paths helps to keep the network simple and efficient, avoiding invalid paths affecting data analysis and decision-making. At the same time, this updating mechanism can also ensure the self-optimization of the failure conduction network, continuously improving its accuracy and reliability over time, thereby improving the overall management level and failure response efficiency of the devices.

[0050] In one possible implementation, when calculating the frequency adjustment factor, the system first needs to obtain the impact weight factor of each node (device) in the conduction path. The impact weight factor is determined by the criticality of the device in the entire process flow. The criticality in the process flow reflects the degree of influence of the device on the entire production or operation process. Generally, the more critical the device, the greater the impact of its failure or anomaly on the production process, and therefore the higher its weight factor in the conduction path. For example, host devices or control systems usually have a high impact weight, while auxiliary devices or non-core devices may have a lower weight.

[0051] Next, the system calculates the conduction depth weighted value of each fault conduction path. Conduction depth refers to the number of device levels between the fault source device and the final affected device. With each additional level of depth in the path, the system attenuates the weight according to the device type. Specifically, the further away a device is from the fault source, the weaker its influence in the conduction path, so the system attenuates the weight of these devices according to the characteristics of the device type. For example, important devices may attenuate slowly, while less critical devices quickly attenuate the weight. This calculation of weighted values helps accurately reflect the diminishing effect of fault impact, ensuring that the collection frequency matches the position and importance of the device in the conduction path.

[0052] According to the weighted values calculated as described above, the system maps these weighted values to collection interval adjustment ratios. The collection interval adjustment ratio is directly positively related to the weighted value, i.e., the higher the weight of a device, the higher its collection frequency will be. Specifically, when a device is in an early layer of the fault conduction path and is highly important, its collection interval will be short, meaning that the system will collect its data more frequently to ensure timely capture of potential faults. Conversely, for devices that are deep in the conduction path and are less important, the collection interval will be longer, reducing the data collection frequency, thereby optimizing the use of system resources.

[0053] By determining the impact weight of the node according to the criticality of the device in the process flow, the system can dynamically adjust the collection frequency according to the actual importance of the device. This dynamic adjustment can avoid over-sparing monitoring of important devices, while reducing over-monitoring of unimportant devices, thereby improving the efficiency and accuracy of the system.

[0054] Secondly, by calculating the conduction depth weighting value and attenuating the weight according to the device type, the system can effectively distinguish the transmission depth of the fault impact, ensuring a higher monitoring priority for critical path devices. Finally, the adjustment of the collection interval is positively related to the weighting value, enabling the system to respond more flexibly to changes in the state of the device, ensuring timely data collection during the device fault conduction process, and thus achieving accurate fault warning and processing. These technical features can effectively improve the intelligent level of device information collection and management, reduce invalid data collection, improve the utilization efficiency of system resources, and optimize device fault detection and response time.

[0055] In one possible implementation, first, the system needs to set a baseline time window for collection frequency recovery. The length of this time window is determined based on historical data of device fault recovery. After a device fault occurs, its recovery process usually exhibits different time characteristics, and the time required for different devices to recover from a fault also varies. Therefore, the length of the baseline time window is reasonably set based on historical data to ensure that the system can adapt to the recovery characteristics of the device during the recovery period after a fault occurs. This time window helps to avoid premature or delayed execution of the frequency recovery strategy, making the collection frequency recovery process more consistent with the actual recovery process of the device.

[0056] Within the set time window, the system adjusts the collection interval according to a non-linear decay curve. The non-linear decay curve means that the initial recovery process is relatively fast, i.e., the collection interval shortens quickly, in order to obtain key data of the recovering device as soon as possible and respond quickly to possible fault changes. As the device gradually recovers, the system gradually slows down the adjustment speed of the collection interval according to the changes in the decay curve, which is called the slow recovery phase. The advantage of this is that when the device approaches a stable state, the collection frequency gradually stabilizes, avoiding unnecessary excessive data collection and improving the utilization efficiency of system resources.

[0057] During the gradual recovery process, the system continuously monitors the fluctuation amplitude of the device's parameters to ensure that the parameter changes during the device recovery process do not exceed the pre-set tolerance range. If the operating parameters of the device exhibit fluctuations beyond the tolerance range during the recovery process, the system will identify potential instability and restart the recovery process. This monitoring mechanism ensures that the system can respond in real-time to any possible abnormal fluctuations during the device recovery process, thereby ensuring the stability and effectiveness of the recovery process and avoiding the impact of excessive parameter fluctuations on the normal operation of the device.

[0058] If the parameter fluctuation exceeds the tolerance range during the recovery process of the device, the system will automatically reset the recovery process. This means that if the state of the device becomes unstable or fails to recover as expected, the system will re-evaluate the current recovery strategy and restart the recovery process from the initial stage. The function of resetting the recovery process can ensure that the system has enough flexibility to recover from device failures, avoid errors or mistakes in the adjustment of the device recovery process, and provide a more stable and reliable recovery mechanism.

[0059] By setting the reference time window and the nonlinear decay curve, the system can adjust the collection frequency according to the actual situation of device failure recovery, avoid excessive monitoring or insufficient collection, and optimize the accuracy and efficiency of data collection. Secondly, the switching mechanism of fast recovery strategy and slow recovery strategy can make the system use the most suitable collection frequency in different stages of device recovery, so that the system can capture important data in the early stage of device recovery, and reduce unnecessary resource consumption after the device is stable. In addition, the mechanism of continuously monitoring the parameter fluctuation amplitude and resetting the recovery process increases the robustness and fault tolerance of the system, ensuring that the parameter fluctuation during the device recovery process can be handled in time, avoiding misjudgment or loss of control of the production and device state. These features effectively improve the recovery efficiency of the system, reduce the risk of production interruption caused by device failure, and optimize the device management and operation process.

[0060] In one possible implementation, the system first creates a dual-channel collection control thread, which includes a main thread and an auxiliary thread. The task of the main thread is to continuously monitor the timing trigger condition, ensuring that the system collects information at the predetermined time interval. The timing trigger condition is usually related to the running period of the device, state change or other parameter change, and the main thread will periodically check these conditions to determine when to start the data collection task. At the same time, the auxiliary thread is responsible for receiving the interrupt signal of the fault event, and when the device appears abnormal or fails, the auxiliary thread will interrupt the current collection task and trigger the corresponding fault handling process.

[0061] When the main thread detects that the timing trigger condition is met, it will send the collection instruction corresponding to the protocol feature code to the target device. These collection instructions are generated according to the communication protocol of the device, and the protocol feature code ensures that the instruction is compatible with the communication of the target device, and can accurately trigger data collection. At the same time, the system starts the data packet integrity verification mechanism to ensure that there is no packet loss or data corruption during data transmission. Data packet integrity verification can use various methods such as CRC verification, hash verification, etc., to ensure the accuracy and reliability of the data, which is crucial for device monitoring and fault diagnosis.

[0062] When the auxiliary thread receives the interrupt signal of the fault event, it immediately suspends the current timing collection task. Suspending the current task means that the system pauses the current data collection work to focus resources on handling the fault event. After suspending the task, the system switches to a high-frequency collection mode according to the dynamic collection strategy. High-frequency collection mode means that the system will increase the frequency of data collection to be able to monitor the state changes of the device more quickly and in more detail when a fault occurs. This switching to high-frequency collection mode can capture more fault data as early as possible when the device fails, providing more information for subsequent fault analysis, diagnosis and repair.

[0063] By continuously monitoring the timing trigger condition through the main thread, the system can perform data collection tasks in a timely and accurate manner, ensuring comprehensive monitoring of the device's operating state. Secondly, the auxiliary thread receives the interrupt signal of the fault event, enabling the system to respond to device faults or abnormal situations in a timely manner, avoiding the continuation of normal collection tasks when the device has problems, thereby avoiding the collection of irrelevant or unimportant data. Most importantly, by switching to a high-frequency collection mode, the system can improve the accuracy of fault detection and ensure that sufficient fault data is obtained at critical moments to provide a basis for fault diagnosis and recovery. This dual-channel control mechanism not only improves the system's adaptive ability, but also optimizes the use of resources, enabling the system to run efficiently both in normal operation and in fault handling.

[0064] In one possible implementation, first, the system needs to extract action keywords related to device operation from the process file, such as "start", "stop", "transfer" and "position". These keywords represent the basic actions of the device and describe the operating state and operation process of the device. For example, "start" means the device starts running, "stop" means the stop action of the device, "transfer" refers to the movement of materials between different positions, and "position" usually involves precise positioning or operation of materials by the device. These keywords will be used to identify key events of device operation to provide basic data for subsequent spatio-temporal correlation analysis.

[0065] Next, the system constructs a spatio-temporal correlation matrix between actions based on the timestamps and spatial coordinates of device actions. The spatio-temporal correlation matrix can combine time and space dimensions to analyze the relationship between device actions: The system analyzes the timestamps of each action instruction and their intervals to understand the temporal relationship between different device actions. This process can help determine the order of actions to ensure that the device's operation is performed according to the predetermined time requirements. For example, some operations may have to be completed within a specified time window, otherwise it will affect production efficiency or product quality.

[0066] The system also analyzes the coordinate system of the equipment and the movement trajectory of the material. In an automated production line, materials are transferred from one station to another, and the equipment actions usually involve the movement trajectory of the material in space. By analyzing the coordinate system of the equipment and the path of the material, the spatial correlation of each equipment action can be determined. For example, some equipment actions may need to be performed after the material is delivered to a specific location, while others may require the positioning of the equipment and the material to match.

[0067] Through the operation of the space-time correlation matrix, the system can identify the minimum dependent path set between equipment actions. These paths represent the indispensable dependencies between equipment actions, i.e., certain actions must be performed in a specific order or at a specific time point, while other operations can be performed in parallel. Through matrix operation, redundant dependencies are removed, optimizing the efficiency of equipment operation and reducing the execution of irrelevant or redundant actions. For example, if two equipment actions can be performed in parallel without affecting the final result, the redundant dependency between the two actions will be removed, thereby improving the response speed and efficiency of the system.

[0068] This process significantly improves the efficiency of information collection and management of automated equipment. First, by accurately identifying and analyzing the space-time relationship between equipment actions, the system can better understand the dependencies of each equipment action, ensuring efficient coordination of equipment operation. Second, generating the minimum dependent path set and removing redundant dependencies effectively reduces redundant steps and resource consumption in equipment operation, optimizing the production process. Finally, through space-time matrix-based dependency analysis, the system can more intelligently schedule equipment tasks, ensuring that there are no conflicts or delays when coordinating work between devices, thereby improving the efficiency and stability of the overall automated production line. This optimization not only improves production efficiency but also reduces the risk of equipment failure, improves equipment utilization and production quality.

[0069] Accordingly, the embodiments of the present application provide an automated equipment information collection and management system, including a memory configured to store instructions, a processor configured to call the instructions from the memory and capable of implementing an automated equipment information collection and management method as described in any of the embodiments of the present application when executing the instructions.

[0070] The present application encompasses any substitutions, modifications, equivalent methods and solutions made to the essence and scope of the present application. In order for the public to have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without these details by those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, flows, elements and circuits, etc. are not described in detail.

[0071] The above merely describes the preferred embodiments of the present application, and it should be pointed out that, for those skilled in the art, some improvements and refinements can be made without departing from the principles of the present application, and these improvements and refinements should also be considered as falling within the protection scope of the present application.

Claims

1. An automated device information collection management method, characterized by: Comprise: Step 1: dynamically generate protocol feature code by sending multi-protocol probe instruction sequence to target device and analyzing response data; Step 2: parse production process logic to generate device action dependency chain, and create timing trigger condition expression according to dependency relationship; Step 3: real-time detect device fault events, and generate dynamic acquisition strategy based on fault conduction relationship, which contains associated device acquisition frequency adjustment rules; Step 4: execute dynamic acquisition strategy, start data acquisition of specified device when timing trigger condition is met, and dynamically adjust acquisition frequency according to fault conduction strength.

2. The method of claim 1, wherein: The generation process of protocol feature code in step 1 includes: Construct a probe sequence containing at least three industrial standard protocol instructions, and send them in order of protocol popularity priority; Start instruction fragmentation and recombination mechanism for non-responsive devices: split the probe instruction into multiple sub-segments, and identify the protocol structure by combining the response segments; Extract the core identification features of the response data, including: Calculate the randomness measure of data packet byte value distribution, and mark it as a private protocol when the randomness exceeds the set threshold, locate the response data offset position corresponding to the request instruction function code, and match the error response structure in the preset abnormal code mode library; Combine the above features into a protocol feature code that uniquely identifies the device protocol.

3. The method of claim 1, wherein: The expression of timing trigger condition in step 2 is constructed as follows: Extract the physical constraint relationship of device action from the production process file, including: Sequence constraint of action execution, spatial position constraint of material transfer between devices, and start-stop dependency relationship of energy supply; Convert physical constraints into executable logic expressions, which include: State monitoring parameters and their threshold ranges of trigger source devices, address identification and target parameters to be collected of triggered devices, and action delay time parameters after condition satisfaction; Support logical operator combination conditions, including "and" operation and "or" operation.

4. The method of claim 1, wherein: The generation process of dynamic acquisition strategy in step 3 includes: Build a fault conduction network model: analyze the device alarm time series in historical fault logs, calculate the causal correlation strength between alarm events, and establish the conduction path when the correlation strength exceeds the set confidence threshold, which is determined by the frequency of fault co-occurrence and the conduction time interval; Generate frequency adjustment coefficient according to the conduction path: the adjustment amplitude is determined by the conduction path depth from the fault source device to the associated device, and the historical fault impact weight of the nodes in the path participates in the coefficient calculation; Generate fault recovery decay rules: monitor the stable duration of associated device parameters, and start the gradual recovery of acquisition frequency when the stable duration reaches the device type related threshold.

5. The method of claim 4, wherein: The update mechanism of the fault conduction network model includes: Real-time record the conduction path of new fault events; When the conduction time interval of the new path is shorter than the historical average, the correlation strength level of the path is improved; Recalculate the confidence weight of the conduction path every quarter, and eliminate low-weight paths that have not been triggered continuously.

6. The method of claim 4, wherein: The calculation process of frequency adjustment coefficient includes: Get the impact weight factor of each node in the conduction path, which is determined by the criticality of the device in the process flow; Calculate the conduction depth weighted value of the path: the weight decays according to the device type with each additional layer of depth; The adjustment ratio is adjusted according to the mapping of the weighting value, and the adjustment ratio is positively correlated with the weighting value.

7. The method of claim 4, wherein: The implementation mode of the gradual recovery is as follows: A reference time window for collecting frequency recovery is set, and the window length is determined according to the device fault recovery history data; In the time window, the collection interval is adjusted according to a non-linear decay curve: a fast recovery strategy is adopted in the initial stage, and a slow recovery is switched when the parameter approaches the stable threshold; During the recovery process, the parameter fluctuation amplitude is continuously monitored, and if it exceeds the tolerance range, the recovery process is reset.

8. The method of claim 1, wherein: The execution logic of step 4 includes: Create a dual-channel collection control thread: the main thread continuously monitors the timing trigger condition, and the auxiliary thread receives the fault event interrupt signal; When the timing trigger condition is met: send the collection instruction corresponding to the protocol feature code to the target device, and start the data packet integrity verification mechanism; When the fault interrupt signal is received: suspend the current timing collection task, and switch to the high-frequency collection mode according to the dynamic collection strategy.

9. The method of claim 3, wherein: The extraction process of the physical constraint relationship includes: Identify the device action keywords in the process file, including "start", "stop", "transfer", and "positioning"; Establish a space-time correlation matrix between actions: Time dimension: analyze the time stamp interval of the action instruction; Space dimension: analyze the material movement trajectory in the device coordinate system; Generate a minimum dependency path set through matrix operation and eliminate redundant dependency relationships.

10. An automated equipment information acquisition management system characterized by comprising: The memory is configured to store instructions, and the processor is configured to call the instructions from the memory and implement the automatic device information collection management method according to any one of claims 1-9 when executing the instructions.

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