Intelligent equipment management method and system

Abstract

The invention relates to the technical field of automatic control, in particular to an intelligent equipment management method and system, and the method comprises the following steps: obtaining a control cabinet log, extracting an equipment temperature load pressure sequence, comparing a set offset to generate a deviation label, collecting an operation time length and load change mapping early warning table, and generating an abnormal record; and extracting task configuration and equipment state re-marking alternating points to generate load configuration, resetting an instruction sequence to generate a scheduling sequence, extracting an instruction time-consuming marking interference section, and switching a standby link to generate an evasion list. According to the invention, by collecting the temperature, the load rate and the pressure sequence in the operation cycle of the equipment, the dynamic state identification capability is improved, the scheduling path is optimized, the resource contention is relieved, the delay path is marked in combination with the original response data, and the standby link is switched to avoid interference, so that the flexibility and stability of linkage scheduling are improved, and the task execution beat accuracy is enhanced; the instruction blocking and interruption risks are effectively reduced, and the operation toughness and response efficiency of a production line are improved.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and in particular to an intelligent equipment management method and system. Background Art

[0002] The field of automated control technology encompasses the technical means for automated scheduling and operational management of various types of equipment in industrial production processes. The core content of this technical field primarily involves monitoring equipment operating status, setting control logic, executing automated instructions, and implementing human-machine interaction. Automated control is widely used in multiple industrial scenarios, including mechanical manufacturing, assembly, and processing. In particular, in manufacturing systems involving multi-station, multi-process coordination, it uses sensors to collect equipment status data, and outputs instructions after judgment by the control system, enabling precise control and efficient collaboration of equipment. For example, in the processing or production of casters, which involve injection molding, stamping, assembly, and other processes, automated control can effectively manage the operating parameters and timing logic of equipment in each process, and meet production efficiency and product quality requirements.

[0003] Among them, the intelligent equipment management method refers to the management method for the operation process of caster processing or production equipment, which uses automated control technology to collect information, identify status and schedule operation of each workstation equipment. The technical matters covered in the topic include recording and analysis of equipment operation data, identification of equipment fault status, task scheduling based on processing sequence, and rule setting for equipment operation behavior. It clarifies the operation logic, operation cycle and task dependency of each device through parameter setting, builds a unified scheduling framework in combination with the rule table drive method, and sets the operating conditions and triggering methods of specific equipment based on the process flow, so as to realize the coordinated management and operation control of production equipment such as stamping machines, injection molding machines, assembly platforms, etc. involved in caster processing.

[0004] Although the existing technology has the ability to collect and control the status of equipment, it uses static set values ​​as a reference, which makes it difficult to reflect the fluctuations in operating parameters within the task cycle, resulting in a delay in the identification of critical abnormal conditions. Risk warnings rely on simple threshold triggers, ignoring the load accumulation trend of equipment in long-cycle tasks, making it impossible to intervene in advance for some potential fault hazards. In terms of task scheduling, the existing technology generally uses preset scheduling templates, which lack the ability to dynamically adapt to the current operating status and task characteristics, resulting in low resource allocation efficiency and serious delays in the execution of some tasks. At the same time, the communication paths of existing equipment are fixed. Once link congestion or response delay occurs, the transmission path cannot be switched in time, resulting in scheduling command blockage and affecting the production rhythm. For example, when multiple devices are working in parallel, a transmission delay in a control link will cause the entire process node to wait, forming a chain reaction. In severe cases, manual intervention may be required to restore the scheduling order, reducing the overall stability and automation level. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, the embodiments of the present invention provide an intelligent device management method and system. The technical solution is as follows:

[0006] In one aspect, a method for intelligent device management is provided, the method comprising:

[0007] S1: Obtain the operation log records of the control cabinet data nodes and signal acquisition ports in the caster processing production line, extract the time series of equipment temperature, load rate, and processing pressure values ​​within the task cycle, compare the fluctuation range of the series with the set range offset value in the initial control configuration, and generate a state deviation identification label;

[0008] S2: Calling the state deviation identification tag to collect the running time and load change times of the equipment in the continuous task batches, and mapping the sum to the risk warning table of the monitoring configuration to generate a warning record of abnormal equipment status;

[0009] S3: Based on the abnormal equipment status warning record, extract the task allocation configuration, equipment control status interface and load allocation command, extract the warning equipment task type and remaining operation time, compare the execution time ratio of tasks of the same type, re-mark the task rotation points between devices, and generate a task load dynamic configuration table;

[0010] S4: calling the task load dynamic configuration table, resetting the transmission order of the scheduling dependent device instructions according to the device linkage path and execution order, adjusting according to the channel number and load priority, and generating a task scheduling linkage sequence.

[0011] As a further solution of the present invention, the state deviation identification label includes a temperature deviation label, a load rate deviation label, and a processing pressure deviation label; the equipment state abnormal warning record includes an operating time abnormal mark, a load change frequency abnormal mark, and a risk level warning label; the task load dynamic configuration table includes task type matching parameters, job duration allocation parameters, and task alternation point adjustment parameters; the task scheduling linkage sequence includes instruction transmission priority parameters, linkage channel number parameters, and load dependent path parameters.

[0012] As a further solution of the present invention, the step of identifying the state deviation tag is specifically as follows:

[0013] S101: Obtain operation log records of control cabinet data nodes and signal acquisition ports in the caster processing production line, monitor the device temperature, load rate, and processing pressure signal values ​​recorded by the acquisition ports during the task cycle, merge and organize the signal values ​​according to node numbers and timestamps, and establish a task node operation status sequence;

[0014] S102: Based on the task node operation state sequence, the set threshold intervals of temperature, rate, and pressure in the initial control configuration are called, and upper and lower limits are set for each signal item according to the time sequence comparison. The out-of-bounds value segments and corresponding time periods are extracted, and the interval deviation trends of the parameters are counted to obtain the signal deviation change distribution;

[0015] S103: Based on the signal offset change distribution and according to the device node identifier, the deviation state is associated with the task time period, the node state is recorded, and the device parameter abnormality registration component is updated to generate a state deviation identification tag.

[0016] As a further solution of the present invention, the step of recording abnormal warning of equipment status is specifically as follows:

[0017] S201: calling the state deviation identification tag, extracting the state deviation segment of the device node in the continuous task batch, collecting the running start and end time of the segment and the task execution time, accumulating the running time by batch number, and generating the total value of the node task running time;

[0018] S202: Extracting the load rate sequence within the task batch based on the total running time of the node task, the number of segments where the difference between any three consecutive points is greater than the rate change threshold, and accumulating the total number of segments that meet the conditions to obtain the total number of load changes;

[0019] S203: Based on the total number of load changes, identify the operating load ratio of the device node, map the ratio to the corresponding interval in the monitoring configuration table, extract the corresponding warning level status identification field, and generate a device status abnormality warning record.

[0020] As a further solution of the present invention, the steps of dynamically configuring the task load table are specifically as follows:

[0021] S301: Extracting the task allocation configuration and control status interface based on the device status abnormality warning record, screening the task number, scheduling time, and execution status corresponding to the warning device number, synchronizing the control and allocation status according to the task number, and generating a device task status matching set;

[0022] S302: Extracting the task type number and the remaining operation duration field based on the device task status matching set, aggregating the device execution duration ratio and the remaining operation amount in tasks of the same type, identifying the task switching identifier field, calculating the task alternation time interval, and generating a task type load occupancy rate group value;

[0023] S303: Call the task type load occupancy rate group value, filter the device numbers whose load exceeds the control threshold, re-mark the task alternation points according to the alternation time and device status, and generate a task load dynamic configuration table.

[0024] As a further solution of the present invention, the task alternation time interval adopts the formula:

[0025] ;

[0026] in, Represents the task alternation time interval, Represents task type Next The remaining operating time of each device, Represents task type Next The execution time of the device in the current cycle, Represents task type Next The time point when the task switching flag of each device changes, Represents task type The average value of the device task switching flag change time point, Represents task type Next The scheduling delay time of each device, For task type The total number of corresponding devices.

[0027] As a further solution of the present invention, the steps of the task scheduling linkage sequence are specifically as follows:

[0028] S401: calling the device number, task start and end time period, and load level fields in the task load dynamic configuration table, filtering the associated device path and task sorting identifier according to the device number, reorganizing the node device task number sequence according to the path order, and generating a device path task sequence table;

[0029] S402: Extracting the scheduling channel number and load priority corresponding to the node task number according to the device path task sequence table, sorting the task numbers by priority, and calling the matching control instructions to perform task instruction docking to generate a task channel priority mapping set;

[0030] S403: calling the task channel priority mapping set, serializing and rearranging the control instruction numbers according to the device task execution order field in the device path, determining the sequence interval order number according to the path interruption point and the priority mapping relationship, and generating a task scheduling linkage sequence.

[0031] As a further embodiment of the present invention, the method further comprises step S5:

[0032] S5: According to the task scheduling linkage sequence, the transmission time of three rounds of task instructions and the difference in device response are extracted from the device management data, the long-time path is marked as an interference section, and the backup communication link is switched to, and an interference avoidance scheduling path list is generated;

[0033] The interference avoidance scheduling path list includes a long time-consuming path identifier, an interference section shielding identifier, and a backup communication link selection.

[0034] As a further solution of the present invention, the step of scheduling the interference avoidance path list is specifically as follows:

[0035] S501: Identify the transmission time and response time difference of each round of tasks according to the task scheduling linkage sequence, match tasks according to device numbers and task rounds, select task paths whose transmission time differences within three rounds exceed the communication response benchmark value, and generate an abnormal communication time-consuming path set;

[0036] S502: calling the abnormal communication time-consuming path set, dividing the transmission time of the task path into segments, identifying the path segment numbers that continuously exceed the response tolerance threshold, marking the excessive time-consuming segment numbers according to the task sequence, and generating a task interference path number table;

[0037] S503: According to the task interference path number table, the task segment channel number is screened, the backup link configuration is extracted, the channel switching identifier and the task number are matched, the path mapping replacement is performed, and an interference avoidance scheduling path list is generated.

[0038] On the other hand, an electric vehicle state monitoring system is provided, wherein the electric vehicle state monitoring system is used to execute the above electric vehicle state monitoring method, and the system includes:

[0039] The condition monitoring module obtains the operation log records of the control cabinet data nodes and signal acquisition ports in the caster processing production line, extracts the equipment temperature, load rate and processing pressure within the task cycle, calculates the difference and determines the interval with the upper and lower limits of the initial control configuration, identifies the operation nodes according to the degree of deviation, and establishes a device operation deviation label set;

[0040] The risk identification module calls the equipment operation deviation label set, summarizes the operation time and load fluctuation number of abnormal nodes in the continuous task batch, selects the equipment number and task number in the trigger record, and generates an equipment early warning identification list;

[0041] The task control module calls the equipment warning identification list, extracts the abnormal task type and remaining duration in the task allocation configuration, calculates the proportion of the current execution period of similar tasks in all devices, strips and reallocates the task execution rights of the warning device, updates the task identification table, and generates a task scheduling reconstruction matrix;

[0042] The scheduling linkage module calls the task scheduling reconstruction matrix, sorts the tasks according to the linkage order and execution dependency path, device channel number and load priority value, readjusts the transmission sequence position of devices with conflicting numbers, sets the control signaling priority, and generates a command linkage sorting sequence;

[0043] The link switching module calls the command linkage sorting sequence, extracts the command transmission time and response time difference between devices in the last three rounds of tasks, marks the path with the excessive time difference as the interference path, and replaces the device number with the channel with the response time in the backup channel to generate an interference avoidance scheduling path list.

[0044] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0045] By collecting time series such as equipment temperature, load rate, and processing pressure during the processing task operation cycle, and generating state identification tags by comparing the set value offset, it is helpful to quickly quantify the difference between the equipment operating status and the ideal value, and enhance the granularity of identifying performance fluctuations. Based on this identification tag, the aggregate mapping of multiple batches of operating time and the number of load changes can accurately reflect the load evolution trend of the equipment in continuous tasks, and associate the trend with the early warning logic of the risk level, realizing state prediction for dynamic operating environments. Based on the early warning results, the current control configuration and task characteristics are extracted, the execution time distribution of the task type is analyzed, and the task alternation point is reset, so that task scheduling is no longer fixed to a static template, with greater adaptability and response flexibility. The order of instruction transmission between devices is rearranged according to the updated scheduling configuration table, and the equipment collaborative path is optimized through the dual dimensions of channel number and load priority, effectively alleviating task conflicts and resource contention. Further collect original task response data, mark the transmission paths with significant response delays, and actively switch to backup links to avoid the impact of high-interference areas on scheduling timeliness, improve the real-time and stability of instruction transmission, and build a closed-loop mechanism from state perception, trend identification, task reorganization, scheduling optimization to communication obstacle avoidance in the processing flow, enhance the flexible control capability of equipment linkage scheduling, and significantly reduce the risk of delays and interruptions in the scheduling process while improving the accuracy of operation rhythm, ensuring that the production line operation has higher resilience and fault response efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0047] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0048] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0049] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0050] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0051] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0052] Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0053] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0054] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0055] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0056] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0057] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0058] See also Figure 1 The embodiment of the present invention provides an intelligent device management method, the processing flow of which may include the following steps:

[0059] S1: Obtain the operation log records of the control cabinet data nodes and signal acquisition ports in the caster processing production line, extract the time series of equipment temperature, load rate, and processing pressure values ​​within the task cycle, compare the fluctuation range of the series with the set range offset value in the initial control configuration, and generate a state deviation identification label;

[0060] S2: Call the state deviation identification tag to collect the device's operating time and load change times in continuous task batches, and map their sum to the risk warning table of the monitoring configuration to generate a device state abnormality warning record;

[0061] S3: Based on the abnormal equipment status warning records, extract the task allocation configuration, equipment control status interface and load allocation commands, extract the warning equipment task type and remaining operation time, compare the execution time ratio of tasks of the same type, re-mark the task rotation points between devices, and generate a dynamic task load configuration table;

[0062] S4: Call the task load dynamic configuration table, reset the transmission order of the scheduling dependent device instructions according to the device linkage path and execution order, adjust according to the channel number and load priority, and generate the task scheduling linkage sequence;

[0063] S5: According to the task scheduling linkage sequence, the transmission time of three rounds of task instructions and the differences in device responses are extracted from the device management data. The long-time path is marked as an interference section, and the backup communication link is switched to generate an interference avoidance scheduling path list.

[0064] State deviation identification labels include temperature deviation labels, load rate deviation labels, and processing pressure deviation labels. Equipment state abnormal warning records include operating time abnormality labels, load change frequency abnormality labels, and risk level warning labels. The task load dynamic configuration table includes task type matching parameters, job duration allocation parameters, and task alternation point adjustment parameters. The task scheduling linkage sequence includes instruction transmission priority parameters, linkage channel number parameters, and load dependent path parameters. The interference avoidance scheduling path list includes long-time path identification, interference section shielding identification, and backup communication link selection.

[0065] Specifically, if Figure 2 As shown, the steps of state deviation identification tag are as follows:

[0066] S101: Obtain operation log records of control cabinet data nodes and signal acquisition ports in the caster processing production line, monitor the device temperature, load rate, and processing pressure signal values ​​recorded by the acquisition ports during the task cycle, merge and organize the signal values ​​according to node numbers and timestamps, and establish a task node operation status sequence;

[0067] By acquiring the operational logs of the control cabinet's data nodes, the temperature, load rate, and process pressure signal values ​​of each signal acquisition port are analyzed one by one. Recording each signal acquisition point, a real-time data reading system and acquisition module are used to collect each device parameter, capturing the device's operating status in real time. Data is then aggregated and processed based on each node's number and timestamp to ensure the consistency of each signal's time series. Dynamic monitoring is performed based on the actual needs of the task cycle to ensure the comprehensiveness and integrity of data collection. For example, when the temperature acquisition port in the control cabinet reads a data point, the temperature value is automatically recorded at a predetermined time interval. The load rate and process pressure values ​​are recorded similarly. Based on the acquisition protocol of the specific device model, the signal output of each sensor is acquired and saved in a standardized data format, ensuring that the data can be used for subsequent comparison and analysis. During data processing, precise timestamp alignment is required to ensure that each signal value corresponds to the same time point for subsequent merging and analysis. The purpose of merging and sorting is to integrate the signals in chronological order, ensuring that no important data is missed during the analysis process. Ultimately, the operational status sequence of the task node is generated, ensuring efficiency from signal acquisition to data integration and avoiding the omission of important collected information.

[0068] S102: Based on the task node operation state sequence, the set threshold intervals for temperature, rate, and pressure in the initial control configuration are called. For each signal item, upper and lower limits are set according to the time sequence comparison. The out-of-bounds value segments and corresponding time periods are extracted. The interval deviation trends of the parameters are counted to obtain the signal deviation change distribution.

[0069] Real-time comparison is performed using the set thresholds of temperature, rate, and pressure, and the set upper and lower limits of each signal are extracted from the control configuration. Each signal item will be refined according to the specific equipment model and operating parameters to determine the specific upper and lower limits of each parameter. For example, in a certain processing process, the temperature setting upper limit is 100°C and the lower limit is 30°C, the upper limit of the load rate is 20 units / second and the lower limit is 5 units / second, and the upper limit of the processing pressure is 50MPa and the lower limit is 10MPa. Based on the acquisition timestamp of each signal, each signal item is compared in time according to the preset time period to determine whether each signal value is out of bounds. If a signal exceeds the set threshold, the out-of-bounds time period and the corresponding numerical range are recorded. Taking the pressure signal as an example, if the collected pressure signal is 55MPa and exceeds the set upper limit of 50MPa, the data point is marked as out of bounds, and the specific timestamp and out-of-bounds segment are recorded (for example, 55MPa lasted for 10 minutes). Then, the interval offset trend of the parameter is counted, that is, the amplitude change of each out-of-limit segment. By comparing the signal changes in different time periods, a signal offset change distribution graph is generated to further analyze whether there is an abnormal fluctuation trend, which helps detect potential equipment failures.

[0070] S103: Based on the signal offset change distribution and the device node identifier, the deviation state is associated with the task time period, the node state is recorded, and the device parameter anomaly registration component is updated to generate a state deviation identification tag;

[0071] The system associates device node identifiers and records deviations. Using the device identifier, each node's status is associated with the task time period, ensuring accurate recording of device status and signal changes within each time period. This system also updates the device's parameter anomaly registration component. Specifically, the device node ID is used to calibrate the runtime of each task and incorporate the deviation trend. For example, if a load rate signal deviation exceeding a set threshold is detected during the task period for a device identified as "Node 001," a deviation tag is generated and labeled "Node 001-Abnormal" along with the time period (e.g., 8:00 AM to 10:00 AM). This abnormal signal information is synchronized to the device management system, marking the device as deviating from its operating state. The anomaly registration component updates the device's original fault record, ensuring that maintenance personnel can promptly understand the device's operating status and conduct further inspections. A state deviation identification tag is generated to prevent production accidents caused by device deviations.

[0072] Specifically, if Figure 3 As shown in the figure, the specific steps for recording abnormal equipment status warnings are as follows:

[0073] S201: Calling the state deviation identification tag, extracting the state deviation segment of the device node in the continuous task batch, collecting the running start and end time of the segment and the task execution time, accumulating the running time by batch number, and generating the total value of the node task running time;

[0074] The device node's state deviation segments are extracted from the state deviation identification tag. Execution requires analyzing the device node status within each task batch. Each device node will experience multiple deviation segments during task execution. These deviation segments are determined based on whether the node's status exceeds the set operating range. For example, if the temperature exceeds the upper or lower limit, or if the load rate fluctuates dramatically, the data collection process involves the device's start and end times. Within each task batch, the task start and end times are automatically recorded for each device node, representing the entire process from the device's start of operation to its state deviation and return to normal. The task execution time is correlated with the node's start and end times and used in subsequent time interval calculations. Task execution duration is calculated by accumulating the data by batch number. For example, if a device node's run time in a task batch is 4 hours, the run time for that batch is 4 hours. If the device experiences another anomaly in a subsequent task batch and its run time is 3 hours, the total run time is 7 hours. This method generates a total task run time value for each node, reflecting the node's operating status and degree of deviation over multiple task cycles.

[0075] S202: Based on the total running time of the node tasks, extract the load rate sequence within the task batch, the number of segments where the difference between any three consecutive points is greater than the rate change threshold, and accumulate the total number of segments that meet the conditions to obtain the total number of load changes;

[0076] The load rate sequence within each task batch is extracted. The load rate data for each batch is filtered from the task records. This data is collected in real time through the load acquisition port on the device. The load rate sequence is continuous numerical data. The difference between every three consecutive data points is analyzed. If the difference between any three consecutive data points exceeds the set rate change threshold, the segment is identified as a load rate change segment. For example, if the rate data sequence is [10, 20, 50, 70, 80] and the set rate change threshold is 15 units / second, the difference from 10 to 20 is 10, which is less than the threshold, and the difference from 20 to 50 is 30, which is greater than the threshold. Therefore, the segment is marked as a change segment. This operation is performed by iterating through the rate change data within all task batches. The total number of segments that meet the rate change criteria is counted. If five rate change segments occur within a task batch, the total number of load changes is obtained. By counting these changes, a quantitative understanding of load rate fluctuations can be obtained and data support is provided for subsequent anomaly identification.

[0077] S203: Based on the total number of load changes, identify the operating load ratio of the device node, map the ratio to the corresponding interval in the monitoring configuration table, extract the corresponding warning level status identification field, and generate a device status abnormality warning record;

[0078] Identify the operating load ratio of the device node. The operating load ratio is calculated by analyzing the relationship between the frequency of load rate changes and task execution duration. The load ratio calculation formula is: Load ratio = Number of load change segments / Task execution duration. For example, in a task batch, the number of load rate change segments is 5, and the total task duration is 10 hours, so the load ratio is 0.5. The calculated load ratio is mapped to the corresponding interval in the monitoring configuration table. Based on this interval, the corresponding warning level status indicator is extracted. The monitoring configuration table sets different warning levels for different load ratio intervals. For example, if the load ratio is less than 0.3, the device is operating normally and the warning level is green. If the load ratio is between 0.3 and 0.6, the device is experiencing a problem and the warning level is yellow. If the load ratio is greater than 0.6, the device is experiencing a serious anomaly and the warning level is red. A device status anomaly warning record is generated, which contains information such as the device node ID, load ratio, warning level, and task execution time period for reference by equipment maintenance personnel.

[0079] Specifically, if Figure 4 As shown, the steps for dynamically configuring the task load table are as follows:

[0080] S301: Extract the task allocation configuration and control status interface based on the equipment status abnormality warning record, filter the task number, scheduling time and execution status corresponding to the warning equipment number, synchronize the control and allocation status according to the task number, and generate a device task status matching set;

[0081] Based on the abnormal equipment status warning record, the abnormal status is identified and the number of the abnormal equipment is extracted. Then, the task information matching the equipment number is queried through the task allocation configuration and control status interface, and the task number, scheduling time and execution status of each task are extracted. The task number is used as a unique identifier for further synchronization operations to obtain the control and allocation status related to the task. The device control status and allocation status corresponding to the task number are synchronized through matching logic. Multiple status information of the task number are compared to ensure that the status update is correct, and then a set of data sets matching the equipment task status is generated. For example, if a certain equipment (numbered 001) has a task number of T1000, a scheduling time of 10:00, and an execution status of "in progress", the control and allocation status information of the task is synchronized, and finally a matching set is formed. The generated equipment task status matching set contains information such as equipment number, task number, scheduling time, execution status, etc., which is helpful for further processing of subsequent task allocation and execution status.

[0082] S302: Extract the task type number and remaining operation duration fields based on the device task status matching set, aggregate the device execution time ratio and remaining operation volume of tasks of the same type, identify the task switching identifier field, calculate the task alternation time interval, and generate a task type load occupancy rate group value;

[0083] Extract the task type number and remaining job duration fields from the device task status matching set data. For each task, classify it according to the task type number and calculate the execution time ratio of each device in the same type of task. For example, if the execution time of the device with task number T1000 is 3 hours and the remaining job duration is 2 hours, then the execution time ratio of this task is 60%. Calculate the execution time ratio and remaining job volume of all devices in this type of task, and determine the alternation time interval of the task based on the data. For example, assuming that the execution time period of the device with task type number T1 is 10:00-14:00, and the execution time period of the device with task number T2 is 14:00-18:00, then the task switching identification field is used to determine that the alternation time interval occurs at 12:00, forming the relevant data of the alternation time interval. Through calculation and judgment, the task type load occupancy rate group value is finally generated, indicating the load occupancy of the task type on the device;

[0084] The task alternation time interval uses the formula:

[0085] ;

[0086] in, Represents the task alternation time interval, Represents task type Next The remaining operating time of each device, Represents task type Next The execution time of the device in the current cycle, Represents task type Next The time point when the task switching flag of each device changes, Represents task type The average value of the device task switching flag change time point, Represents task type Next The scheduling delay time of each device, For task type The total number of corresponding devices;

[0087] The task alternation time interval is a key indicator for measuring the degree of difference between the start and end times of the same task type when switching between different devices. It is used to reflect the time distribution fluctuations caused by factors such as resource scheduling, job remaining, and execution intensity during the parallel execution of tasks on multiple devices. This indicator comprehensively considers the degree of match between the remaining job duration and the device execution time, the device response delay during task alternation, and the distribution dispersion of the task switching time points. A larger value indicates a less concentrated distribution of task switching periods and a less coordinated scheduling execution. Conversely, a lower value indicates a more stable task alternation process between devices and a more consistent scheduling execution.

[0088] This formula is used to calculate the task type Task alternation time interval , to evaluate the stability of task switching;

[0089] : The unit is hour (h), which is obtained through real-time monitoring of the equipment management system;

[0090] : The unit is hour (h), which is obtained from the device operation log record;

[0091] : The unit is hour (h), obtained from the timestamp recorded by the task scheduling system;

[0092] :The unit is hour (h), by calculating all The average value of is obtained;

[0093] : The unit is hour (h), which is calculated by the difference between the planned and actual start time recorded by the scheduling system;

[0094] : Obtained through statistics of the equipment management system;

[0095] Consider the task type Below The parameters of the device are as follows:

[0096] Device 1: ;

[0097] Device 2: ;

[0098] Device 3: ;

[0099] calculate : ;

[0100] Calculate the molecular part :

[0101] ;

[0102] Calculate the denominator :

[0103] ;

[0104] Calculate the first term: ;

[0105] Calculate the second term : ;

[0106] calculate : ;

[0107] This result shows that the task type The task alternation time interval is about 17.01 hours, which indicates that the time fluctuation of task switching is large.

[0108] S303: Calling the task type load occupancy rate group value, filtering the device number whose load exceeds the control threshold, re-marking the task rotation point according to the rotation time and device status, and generating a task load dynamic configuration table;

[0109] Using the task type load occupancy group value, we filter out device numbers whose load exceeds the control threshold. The control threshold is set to 70% of the device load. That is, when the proportion of the device load to the total task load is greater than 70%, it is judged to be overloaded. If the device load exceeds this threshold, the device is selected and further analysis is conducted on the alternation time interval and device status. For example, if the load of a certain device accounts for 75%, it is considered overloaded. Based on this, the task alternation points are remarked according to the alternation time period. The marking of the alternation points depends on the task execution status and time difference calculation. For example, if the alternation time of a task is between 12:00 and 14:00, the alternation point at 12:30 is remarked as the starting time for device load adjustment. A dynamic task load configuration table is generated, indicating the load and adjustment point of each device, ensuring that the dynamic control system can adjust the task execution strategy in real time based on load information.

[0110] Specifically, if Figure 5 As shown in the figure, the steps of the task scheduling linkage sequence are as follows:

[0111] S401: Call the device number, task start and end time period, and load level fields in the task load dynamic configuration table, filter the associated device path and task sorting identifier according to the device number, reorganize the node device task number sequence according to the path order, and generate a device path task sequence table;

[0112] The device number, task start and end periods, and load level fields are extracted from the task load dynamic configuration table. The device number identifies the specific device. Each device has a corresponding task start and end period. The task execution time interval clearly defines the device's duty cycle, and the load level field indicates the load intensity of the device during task execution. During execution, the device number is used to filter all tasks associated with the device, and then the associated device paths and task sequence identifiers are selected. This matching operation is performed based on the device path identifier. A device path refers to the different processing stages a device passes through during the production process, and each stage has different tasks. For example, if the device number is "Device 01" and the path identifier is "Path A," all related tasks are first found by device number. Based on the task sequence identifier, the tasks are reorganized according to the device path sequence. The purpose of task sequencing is to ensure that each task is executed sequentially on the appropriate device path, avoiding conflicts or misordering of different tasks. Tasks are then reorganized and organized into a device path task sequence table to ensure that each task is associated with the device path, providing clear guidance for subsequent task execution.

[0113] S402: Extract the scheduling channel number and load priority corresponding to the node task number according to the device path task sequence table, sort the task numbers by priority, call the matching control instructions to perform task instruction docking, and generate a task channel priority mapping set;

[0114] The scheduling channel number and load priority corresponding to the task number are extracted. Each task has a scheduling channel number, which represents the specific execution channel within the equipment path, while the load priority indicates the priority of the task during scheduling. The corresponding scheduling channel number and load priority are extracted from the path task sequence table based on the task number. The load priority is determined based on the task's importance, required resources, and current equipment status. For example, Task A might have a high load priority because the material it processes has a greater impact on the production line, while Task B might have a low priority because the material it processes is less important. Task numbers are sorted by load priority, with higher-priority tasks placed first and lower-priority tasks placed last. Once sorted, the control instructions matching each task are called to connect the task instructions. Each task's control instruction is selected and matched based on the task's requirements and equipment status to ensure that the task's execution is initiated at the appropriate time. A task-channel priority mapping set is generated, which contains all tasks, their corresponding scheduling channel numbers, and control instructions, ensuring smooth execution and scheduling according to the predetermined order.

[0115] S403: Calling the task channel priority mapping set, serializing and reordering the control instruction numbers according to the device task execution order field in the device path, determining the sequence interval order number based on the path interruption point and the priority mapping relationship, and generating a task scheduling linkage sequence;

[0116] According to the device task execution order field in the device path, the control instruction numbers are serialized and rearranged. During this process, the task execution order field in the device path is referenced to ensure that tasks are executed in a reasonable order. Each device path has its own specific execution order. If a task is paused due to device interruption or other reasons, the interval sequence number in the task sequence needs to be determined based on the breakpoint and priority mapping relationship in the path. For example, in path A, after Task 1 is executed, Task 3 will be executed in advance due to the adjustment of the device status, and Task 2 will need to wait until Task 3 is completed before it can begin. In this way, the execution order of tasks is optimized, ensuring the continuity and rationality of tasks. During this process, the task sequence numbers are adjusted, and the execution order of tasks is corrected through path breakpoints, thereby ensuring the efficiency and smoothness of the entire task scheduling, generating a task scheduling linkage sequence, and ensuring that the tasks are scheduled according to the actual execution status of the device path.

[0117] Specifically, if Figure 6 As shown, the steps of the interference avoidance scheduling path list are as follows:

[0118] S501: Based on the task scheduling linkage sequence, identify the transmission time and response time difference of each round of tasks, match tasks according to device numbers and task rounds, select task paths whose transmission time difference within three rounds exceeds the communication response benchmark value, and generate an abnormal communication time path set;

[0119] Identify the transmission time and response time difference for each round of tasks. Transmission time refers to the communication time between nodes from task initiation to task execution, while response time difference refers to the time it takes for a device to complete the actual response after receiving the task instruction. By calculating the transmission time and response time difference for each round of tasks, we can identify which tasks have transmission time differences exceeding the communication response baseline over multiple rounds. The communication response baseline is a pre-set standard value used to determine whether the task transmission process is normal. Assuming the baseline value is set to 100ms, when the task transmission time exceeds this baseline, it is recorded as an abnormal task. For example, if the transmission time of the task in the first round is 120ms and the second round is 150ms, and the baseline value is 100ms, then the transmission time of Task 1 and Task 2 exceeds the baseline value, and the tasks are marked as abnormal paths. Through this process, we can accurately identify which task paths have abnormalities and generate a set of abnormal communication time paths for subsequent analysis and scheduling adjustments.

[0120] S502: Calling the abnormal communication time-consuming path set, dividing the transmission time of the task path into segments, identifying the path segment numbers that continuously exceed the response tolerance threshold, and marking the excessive time-consuming segment numbers according to the task sequence, and generating a task interference path number table;

[0121] The transmission time of the task path is divided into segments. The transmission time of the task path is subdivided according to the time distribution of the task during execution. Each task path is divided into several segments. The segment division is based on the continuity of the transmission time and the situation of exceeding the response tolerance threshold. The response tolerance threshold refers to the maximum time difference allowed for a task to exceed the baseline value. When the transmission time difference of a task exceeds this threshold, the segment is considered a timeout segment. For example, assuming the response tolerance threshold is 50ms, when the transmission time of a section in a task path is 120ms, exceeding the tolerance threshold of 50ms, the segment will be marked as a timeout segment. This method can identify which sections in the task path have obvious delay problems during the transmission process, and the problems will affect the normal scheduling of subsequent tasks. The over-consumption segments will be numbered according to the task order to form a task interference path number table. This number table can accurately reflect the timeout segments in each task path for subsequent interference avoidance and scheduling optimization.

[0122] S503: According to the task interference path number table, the task segment channel number is screened, the backup link configuration is extracted, the channel switching identifier and the task number are matched, the path mapping is replaced, and an interference avoidance scheduling path list is generated;

[0123] Filter the task segment channel number and extract the backup link configuration. In the task path, each channel number identifies a specific transmission channel. It is necessary to find the channel corresponding to each task segment based on the information in the interference path number table and extract the backup link configuration. The backup link configuration means that when a communication problem occurs, the backup channel will be used to take over the task execution to avoid task stagnation due to delays or failures in the main channel. By matching the channel switching identifier and the task number, it is possible to automatically determine which backup channel each task should switch to when a communication problem occurs. For example, if the channel of task path 1 times out in a certain section, it will switch to the backup channel of task path 1, and perform path mapping replacement to generate an interference avoidance scheduling path list. The list contains the scheduling paths of all tasks and indicates which paths need to use backup links to ensure that they can be automatically adjusted in the event of communication delays or failures, ensuring that the task is completed smoothly as planned.

[0124] like Figure 7 As shown, an intelligent equipment management system includes:

[0125] The condition monitoring module obtains the operation log records of the control cabinet data nodes and signal acquisition ports in the caster processing production line, extracts the equipment temperature, load rate and processing pressure within the task cycle, calculates the difference and determines the interval with the upper and lower limits of the initial control configuration, identifies the operation nodes according to the degree of deviation, and establishes a device operation deviation label set;

[0126] The risk identification module calls the equipment operation deviation label set, summarizes the operation time and load fluctuation number of abnormal nodes in continuous task batches, selects the equipment number and task number in the trigger record, and generates an equipment warning identification list;

[0127] The task control module calls the equipment warning identification list, extracts the abnormal task type and remaining duration in the task allocation configuration, calculates the proportion of similar tasks in the current execution period of all devices, strips and reallocates the task execution rights of the warning devices, updates the task identification table, and generates a task scheduling reconstruction matrix;

[0128] The scheduling linkage module calls the task scheduling reconstruction matrix, sorts the devices according to the linkage order and execution dependency path, and the device channel number and load priority value, readjusts the transmission sequence position of the devices with conflicting numbers, sets the control signaling priority, and generates the instruction linkage sorting sequence;

[0129] The link switching module calls the instruction linkage sorting sequence, extracts the instruction transmission time and response time difference between devices in the last three rounds of tasks, marks the path with excessive time difference as the interference path, and replaces the device number with the channel with the response time in the backup channel to generate an interference avoidance scheduling path list.

[0130] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An intelligent equipment management method, characterized in that: The following steps are involved: S1: Obtain the operation log records of the control cabinet data nodes and signal acquisition ports in the caster processing production line, extract the time series of equipment temperature, load rate, and processing pressure values ​​within the task cycle, compare the fluctuation range of the series with the set range offset value in the initial control configuration, and generate a state deviation identification label; S2: Calling the state deviation identification tag to collect the running time and load change times of the equipment in the continuous task batches, and mapping the sum to the risk warning table of the monitoring configuration to generate a warning record of abnormal equipment status; S3: Based on the abnormal equipment status warning record, extract the task allocation configuration, equipment control status interface and load allocation command, extract the warning equipment task type and remaining operation time, compare the execution time ratio of tasks of the same type, re-mark the task rotation points between devices, and generate a task load dynamic configuration table; S4: calling the task load dynamic configuration table, resetting the transmission order of the scheduling dependent device instructions according to the device linkage path and execution order, adjusting according to the channel number and load priority, and generating a task scheduling linkage sequence.

2. The intelligent device management method according to claim 1, characterized in that: The state deviation identification label includes a temperature deviation label, a load rate deviation label, and a processing pressure deviation label. The equipment state abnormal warning record includes an operation time abnormal mark, a load change frequency abnormal mark, and a risk level warning label. The task load dynamic configuration table includes task type matching parameters, job duration allocation parameters, and task alternation point adjustment parameters. The task scheduling linkage sequence includes instruction transmission priority parameters, linkage channel number parameters, and load dependent path parameters.

3. The intelligent device management method according to claim 1, characterized in that: The steps of the state deviation identification tag are specifically as follows: S101: Obtain operation log records of control cabinet data nodes and signal acquisition ports in the caster processing production line, monitor the device temperature, load rate, and processing pressure signal values ​​recorded by the acquisition ports during the task cycle, merge and organize the signal values ​​according to node numbers and timestamps, and establish a task node operation status sequence; S102: Based on the task node operation state sequence, the set threshold intervals of temperature, rate, and pressure in the initial control configuration are called, and upper and lower limits are set for each signal item according to the time sequence comparison. The out-of-bounds value segments and corresponding time periods are extracted, and the interval deviation trends of the parameters are counted to obtain the signal deviation change distribution; S103: Based on the signal offset change distribution and according to the device node identifier, the deviation state is associated with the task time period, the node state is recorded, and the device parameter abnormality registration component is updated to generate a state deviation identification tag.

4. The intelligent device management method according to claim 3, characterized in that: The steps of recording abnormal warning of equipment status are specifically as follows: S201: calling the state deviation identification tag, extracting the state deviation segment of the device node in the continuous task batch, collecting the running start and end time of the segment and the task execution time, accumulating the running time by batch number, and generating the total value of the node task running time; S202: Extracting the load rate sequence within the task batch based on the total running time of the node task, the number of segments where the difference between any three consecutive points is greater than the rate change threshold, and accumulating the total number of segments that meet the conditions to obtain the total number of load changes; S203: Based on the total number of load changes, identify the operating load ratio of the device node, map the ratio to the corresponding interval in the monitoring configuration table, extract the corresponding warning level status identification field, and generate a device status abnormality warning record.

5. The intelligent device management method according to claim 4, characterized in that: The steps of dynamically configuring the task load table are specifically as follows: S301: Extracting the task allocation configuration and control status interface based on the device status abnormality warning record, screening the task number, scheduling time, and execution status corresponding to the warning device number, synchronizing the control and allocation status according to the task number, and generating a device task status matching set; S302: Extracting the task type number and the remaining operation duration field based on the device task status matching set, aggregating the device execution duration ratio and the remaining operation amount in tasks of the same type, identifying the task switching identifier field, calculating the task alternation time interval, and generating a task type load occupancy rate group value; S303: Call the task type load occupancy rate group value, filter the device numbers whose load exceeds the control threshold, re-mark the task alternation points according to the alternation time and device status, and generate a task load dynamic configuration table.

6. The intelligent device management method according to claim 5, characterized in that: The task alternation time interval adopts the formula: ； in, Represents the task alternation time interval, Represents task type Next The remaining operating time of each device, Represents task type Next The execution time of the device in the current cycle, Represents task type Next The time point when the task switching flag of each device changes, Represents task type The average value of the device task switching flag change time point, Represents task type Next The scheduling delay time of each device, For task type The total number of corresponding devices.

7. The intelligent device management method according to claim 5, characterized in that: The steps of the task scheduling linkage sequence are specifically as follows: S401: calling the device number, task start and end time period, and load level fields in the task load dynamic configuration table, filtering the associated device path and task sorting identifier according to the device number, reorganizing the node device task number sequence according to the path order, and generating a device path task sequence table; S402: Extracting the scheduling channel number and load priority corresponding to the node task number according to the device path task sequence table, sorting the task numbers by priority, and calling the matching control instructions to perform task instruction docking to generate a task channel priority mapping set; S403: calling the task channel priority mapping set, serializing and rearranging the control instruction numbers according to the device task execution order field in the device path, determining the sequence interval order number according to the path interruption point and the priority mapping relationship, and generating a task scheduling linkage sequence.

8. The intelligent device management method according to claim 1, characterized in that: The method further comprises step S5: S5: According to the task scheduling linkage sequence, the transmission time of three rounds of task instructions and the difference in device response are extracted from the device management data, the long-time path is marked as an interference section, and the backup communication link is switched to, and an interference avoidance scheduling path list is generated; The interference avoidance scheduling path list includes a long time-consuming path identifier, an interference section shielding identifier, and a backup communication link selection.

9. The intelligent device management method according to claim 8, characterized in that: The steps of the interference avoidance scheduling path list are specifically as follows: S501: Identify the transmission time and response time difference of each round of tasks according to the task scheduling linkage sequence, match tasks according to device numbers and task rounds, select task paths whose transmission time differences within three rounds exceed the communication response benchmark value, and generate an abnormal communication time-consuming path set; S502: calling the abnormal communication time-consuming path set, dividing the transmission time of the task path into segments, identifying the path segment numbers that continuously exceed the response tolerance threshold, marking the excessive time-consuming segment numbers according to the task sequence, and generating a task interference path number table; S503: According to the task interference path number table, the task segment channel number is screened, the backup link configuration is extracted, the channel switching identifier and the task number are matched, the path mapping replacement is performed, and an interference avoidance scheduling path list is generated.

10. An intelligent equipment management system, characterized in that: The system is used to implement the intelligent device management method according to any one of claims 1 to 9, and the system includes: The condition monitoring module obtains the operation log records of the control cabinet data nodes and signal acquisition ports in the caster processing production line, extracts the equipment temperature, load rate and processing pressure within the task cycle, calculates the difference and determines the interval with the upper and lower limits of the initial control configuration, identifies the operation nodes according to the degree of deviation, and establishes a device operation deviation label set; The risk identification module calls the equipment operation deviation label set, summarizes the operation time and load fluctuation number of abnormal nodes in the continuous task batch, selects the equipment number and task number in the trigger record, and generates an equipment early warning identification list; The task control module calls the equipment warning identification list, extracts the abnormal task type and remaining duration in the task allocation configuration, calculates the proportion of the current execution period of similar tasks in all devices, strips and reallocates the task execution rights of the warning device, updates the task identification table, and generates a task scheduling reconstruction matrix; The scheduling linkage module calls the task scheduling reconstruction matrix, sorts the tasks according to the linkage order and execution dependency path, device channel number and load priority value, readjusts the transmission sequence position of devices with conflicting numbers, sets the control signaling priority, and generates a command linkage sorting sequence; The link switching module calls the command linkage sorting sequence, extracts the command transmission time and response time difference between devices in the last three rounds of tasks, marks the path with the excessive time difference as the interference path, and replaces the device number with the channel with the response time in the backup channel to generate an interference avoidance scheduling path list.

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