Container floor production whole-process monitoring method and system based on internet of things

Abstract

The application discloses a container bottom plate production whole-process monitoring method and system based on the Internet of Things, and particularly relates to the technical field of intelligent manufacturing process monitoring, and is used for solving the problem of untimely production line scheduling response; the application collects data through a collecting device arranged on a container bottom plate production line, carries out time alignment based on a unified time reference, constructs a multi-station time sequence data frame set with task numbers and process identifiers, extracts task processing behavior characteristic vectors such as running time, process parameter offset and equipment use state, compares the characteristic vectors with preset rhythm stability threshold values and equipment load rate threshold values, dynamically identifies task rhythm abnormality and load unevenness problems, generates a task resource scheduling diagram, calculates equipment load rate and task accumulation density, and generates processing sequence adjustment and buffer area shunt control strategies in combination with task priority levels, so that the production efficiency and delivery quality are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing process monitoring technology, and more specifically, to a method and system for monitoring the entire production process of container flooring based on the Internet of Things. Background Technology

[0002] Currently, the container manufacturing industry has higher requirements for the dimensional accuracy, load-bearing capacity, and corrosion resistance of the base plate structure. As a core load-bearing component, the processing quality of the base plate directly affects the stability of the entire container structure and its long-term service safety. In actual production, the base plate needs to undergo multiple stages of collaborative operation, including cutting, welding, spraying, corrosion protection, and testing. The process path is complex, and the equipment types are diverse. Traditional Manufacturing Execution Systems (MES) often lack real-time connections with workshop equipment, making it impossible to form a task-oriented data flow link. With the development of Industrial Internet of Things (IIoT) technology, integrating production line equipment status, process parameters, and logistics rhythm into the data chain has become a key direction for realizing intelligent manufacturing.

[0003] The shortcomings of existing technologies are as follows: Base plate manufacturing mainly relies on fixed production scheduling and manual dispatching, lacking the ability to track the dynamic status of tasks across multiple work sections. Especially when multiple orders are staggered, equipment loads are uneven, or abnormal delays occur, the system cannot identify the imbalance in task rhythm in a timely manner, resulting in the accumulation of bottleneck work sections, idle equipment resources, and delayed task switching. At the same time, there is a lack of effective linkage mechanism between the equipment control system and the business scheduling system. Task status, quality feedback, and resource availability information are not integrated in real time, causing delays in scheduling response and imbalances in resource allocation. There is a lack of the ability to construct a full-process status chain based on task number, making it impossible to accurately trace quality anomalies back to the corresponding process parameters and equipment nodes. This leads to unclear quality causes, missing response actions, and the inability to form a closed loop in task execution, which seriously restricts the process adaptability and resource coordination efficiency of the base plate manufacturing process. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, this invention provides a method and system for monitoring the entire production process of container flooring based on the Internet of Things, in order to solve the problem of untimely production line scheduling response in the aforementioned background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The IoT-based method for monitoring the entire production process of container flooring includes the following steps:

[0007] Collect operating status data and process parameter data of equipment deployed in each section of the base plate production line, standardize the data format and align the time based on a unified time base, and construct a multi-section time sequence data frame set with task number and process identifier;

[0008] Extract the running time, process parameter offset and equipment usage status of each base plate task in different sections from the multi-section time sequence data frame set, construct the task processing behavior feature vector, and determine whether there is abnormal task rhythm or uneven equipment load based on preset rules.

[0009] Based on the abnormal task rhythm information and the current availability of equipment, a task resource scheduling diagram is generated to identify whether there are processing bottlenecks, task conflicts, or equipment occupancy conflicts in the current work section.

[0010] By combining the task resource scheduling diagram and task priority levels, an optimization strategy sequence is generated that includes processing order adjustment, equipment task reordering and buffer diversion control, and corresponding scheduling instructions are sent to the equipment control terminal.

[0011] During strategy execution, execution feedback data from each device is collected, including whether the task is completed according to the schedule, whether the processing status has returned to normal, and whether resources are released in a timely manner. The feedback results are then matched and compared with the optimized strategy sequence to determine whether the current scheduling strategy has failed or been interrupted.

[0012] When a scheduling strategy failure, task delay, or unresolved processing bottleneck is detected, the task resource scheduling diagram is regenerated and the optimization strategy is updated based on the latest equipment status and task execution status, so as to realize adaptive adjustment of rhythm and collaborative control of work sections during the base plate processing.

[0013] In a preferred embodiment, the collected equipment operating status data includes the equipment start / stop status, operating current, operating temperature, processing time and alarm information, and the process parameter data includes cutting path, welding current, coating thickness, conveying speed and detection threshold.

[0014] In a preferred embodiment, a multi-segment time-series data frame set with task numbers and process identifiers is constructed, as follows:

[0015] The collected equipment operating status data and process parameter data are timestamped and time-aligned according to a unified time base.

[0016] Each data record is divided according to its work section and bound to the corresponding base plate task number and work section process identifier to form a structured data item;

[0017] Sliding window segmentation is performed at fixed time intervals to organize continuously collected data into data frame units with temporal characteristics;

[0018] The data frame units of each work section are classified according to the task number to form a multi-section time-series data frame set that covers at least cutting, welding, spraying, and inspection.

[0019] In a preferred embodiment, the runtime, process parameter offset, and equipment usage status of each base plate task in different work sections are extracted from the multi-section time-series data frame set to construct a task processing behavior feature vector. Based on preset rules, it is determined whether there are abnormal task rhythms or uneven equipment loads. The specific process is as follows:

[0020] Extract the multi-segment time sequence data frame corresponding to each base plate task number;

[0021] Calculate the start and end times of the tasks in each work section to determine the running time;

[0022] Analyze the degree of deviation between the process parameters and the standard process template to obtain the process parameter deviation;

[0023] The frequency of device start-stop, the duration of device occupation, and the idle interval during task execution are statistically analyzed to extract the device usage status.

[0024] The runtime, process parameter offset, and equipment usage status are combined to construct a feature vector of task processing behavior.

[0025] The task processing behavior feature vector is compared with the preset rhythm stability threshold and the preset equipment load rate threshold to determine whether there is abnormal task rhythm or uneven equipment load.

[0026] In a preferred embodiment, a task resource scheduling diagram is generated based on the task rhythm anomaly information and the current equipment availability status to identify whether there are processing bottlenecks, task conflicts, or equipment occupancy conflicts in the current work section. The specific process is as follows:

[0027] Obtain the rhythm anomaly marker and the section identifier corresponding to each base plate task;

[0028] Collect the current number of available equipment, the number of tasks in the queue, and the remaining processing time for each task in each work section;

[0029] Calculate the equipment load rate and task accumulation density for each work section;

[0030] Construct a task resource scheduling diagram based on task number, work section identifier, and equipment number;

[0031] Mark areas of equipment occupancy conflicts, overlapping task execution times, and equipment waiting for idling in the task resource scheduling diagram;

[0032] When the task accumulation density of a work section exceeds the preset task accumulation density threshold and the equipment load rate is consistently higher than the preset equipment load rate threshold, the work section is determined to be a processing bottleneck.

[0033] If at least two tasks correspond to the same device within the same time period, or if the device waits continuously for more than a preset period, it is determined that there is a task conflict or device occupancy conflict.

[0034] In a preferred embodiment, the equipment load rate and task accumulation density of each work section are calculated, and the specific process is as follows:

[0035] Count the number of devices in each work section that are currently in use or processing, and count the total number of available devices in that work section;

[0036] The equipment load rate is obtained by calculating the ratio of the number of currently running devices to the total number of available devices.

[0037] Count the number of tasks in the pending state of a work section and record the maximum task cache capacity of the work section;

[0038] The task backlog density is obtained by calculating the ratio of the current number of tasks to be processed to the upper limit of the task cache capacity.

[0039] In a preferred embodiment, by combining the task resource scheduling graph and task priority levels, an optimization strategy sequence is generated that includes processing order adjustment, equipment task reordering, and buffer diversion control. Corresponding scheduling instructions are then sent to the equipment control terminal. The specific process is as follows:

[0040] Based on the equipment load rate and task backlog density of each section in the task resource scheduling diagram, identify the target section that currently has bottleneck risks or resource conflicts.

[0041] Retrieve the task priority levels of all tasks within the target section and sort them from highest to lowest priority level;

[0042] Based on the sorting results and resource conflict types, a processing order adjustment strategy is generated to reorder the processing order of tasks to be processed in terms of time.

[0043] For equipment involved in conflicting tasks, a task rescheduling strategy is generated based on the predicted idle time period of the equipment and the historical task duration records.

[0044] If the task accumulation density of the target section exceeds the preset buffer occupancy threshold, a buffer diversion control strategy is generated to guide the tasks to other spare sections or buffers with available resources.

[0045] The generated processing sequence adjustment strategy, equipment task reordering strategy and buffer diversion control strategy are merged into an optimization strategy sequence;

[0046] The optimization strategy sequence is converted into recognizable scheduling instructions through the communication interface and sent to the device control terminal to drive task scheduling and execution.

[0047] In a preferred embodiment, the feedback result is matched and compared with the optimization strategy sequence to determine whether the current scheduling strategy has failed or been interrupted. The specific process is as follows:

[0048] Collect task execution status data fed back from the equipment control terminal, including equipment start / stop flags, task completion flags, processing start and end times, actual processing time, and resource release time;

[0049] The feedback results are mapped to the processing order adjustment strategy, equipment task rearrangement strategy and buffer diversion control strategy that have been issued in the optimization strategy sequence according to the task number.

[0050] Compare the actual processing start and end times in the feedback with the planned processing times in the optimization strategy sequence. If the time deviation exceeds the preset time tolerance, it is recorded as a scheduling delay event.

[0051] Check whether the task completion flag in the feedback is consistent with the target task list of the optimization strategy. If there are any omissions, duplicate executions, or outdated statuses, mark them as scheduling interruption events.

[0052] Determine whether the resource release time is completed within the time limit specified by the policy. If the delayed release causes downstream tasks to be unable to access the system, mark it as a resource blocking event.

[0053] If any of the following types of events exist: scheduling delay event, scheduling interruption event, or resource blocking event, the current scheduling policy is determined to be either execution failure or interruption.

[0054] In a preferred embodiment, when a scheduling strategy failure, task lag, or unresolved processing bottleneck is detected, the task resource scheduling graph is regenerated and the optimization strategy is updated based on the latest equipment status and task execution status. The specific process is as follows:

[0055] Collect current equipment availability status, task execution progress and processing feedback data for each section, and update the operating status and resource usage information of each piece of equipment;

[0056] Analyze the task execution progress to identify the task number and its corresponding work section that is currently lagging behind;

[0057] Determine whether the task backlog density of the work section marked as a bottleneck in the previous round in the task resource scheduling diagram is still higher than the preset task backlog density threshold and the equipment load rate is still higher than the preset equipment load rate threshold after the update. If so, it is confirmed that the bottleneck has not been resolved.

[0058] Based on the latest task and device status, recalculate the device load rate and task backlog density, and construct an updated task resource scheduling graph;

[0059] Based on the updated task resource scheduling graph and task priority level, the processing order adjustment strategy, equipment task rearrangement strategy and buffer diversion control strategy are regenerated to form a new optimization strategy sequence.

[0060] The new optimization strategy sequence is issued as a replacement strategy, overriding the previous failed strategy, thereby realizing the dynamic reconstruction of scheduling strategy and adaptive updating of resource configuration.

[0061] An IoT-based container floor production process monitoring system is used to implement the aforementioned IoT-based container floor production process monitoring method, including:

[0062] The production line data acquisition module is used to collect the operating status data and process parameter data of the equipment deployed in each section of the base plate production line. Based on a unified time base, the data is formatted and time-aligned to construct a multi-section time-series data frame set with task number and process identifier.

[0063] The task analysis module is used to extract the running time, process parameter offset and equipment usage status of each base plate task in different sections from the multi-section time series data frame set, construct the task processing behavior feature vector, and determine whether there is abnormal task rhythm or uneven equipment load based on preset rules.

[0064] The equipment status analysis module is used to generate a task resource scheduling diagram based on the abnormal task rhythm information and the current available status of the equipment, and to identify whether there are processing bottlenecks, task conflicts or equipment occupancy conflicts in the current work section.

[0065] The scheduling generation module is used to combine the task resource scheduling graph and task priority level to generate an optimized strategy sequence that includes processing order adjustment, equipment task reordering and buffer diversion control, and send corresponding scheduling instructions to the equipment control terminal.

[0066] The scheduling strategy analysis module is used to collect execution feedback data from each device during strategy execution, including whether the task was completed according to the schedule, whether the processing status has returned to normal, and whether the resources were released in a timely manner. The module also matches and compares the feedback results with the optimized strategy sequence to determine whether the current scheduling strategy has failed or been interrupted.

[0067] The monitoring and control module is used to regenerate the task resource scheduling diagram and update the optimization strategy based on the latest equipment status and task execution status when the scheduling strategy is detected to be ineffective, the task is delayed, or the processing bottleneck is not resolved. This enables adaptive adjustment of the rhythm and collaborative control of the work section during the base plate processing.

[0068] The technical effects and advantages of this invention are as follows:

[0069] This invention deploys various devices, including laser cutting machines, automatic welding devices, spraying platforms, vision inspection terminals, and conveying equipment, on a container floor production line to collect operational status data and process parameter data. Based on a unified time reference, it completes time alignment and format standardization, constructing a multi-segment time-series data frame set with task numbers and process identifiers. This enables refined data modeling of the entire floor processing process. On this basis, it extracts task processing behavior feature vectors, including runtime, process parameter offset, and equipment usage status. These vectors are then compared with preset rhythm stability thresholds and equipment load rate thresholds to dynamically identify abnormal task rhythms and uneven equipment loads, ensuring that the monitoring results are real-time and interpretable.

[0070] Furthermore, by generating a task resource scheduling graph, equipment load rate, task backlog density, and conflict markers are mapped into a visual relationship. When a bottleneck or conflict is detected, optimization strategy sequences such as processing order adjustment, equipment task reordering, and buffer diversion control are generated based on task priority levels. This ensures the flexibility and rationality of resource allocation and task scheduling. By matching and comparing the scheduling command issuance and feedback results, it is possible to determine in real time whether there is a delay, interruption, or blockage in the scheduling strategy. When the strategy fails, it automatically triggers reconstruction and generates alternative strategies based on the latest equipment status and task progress. This achieves closed-loop adaptive adjustment of the production process, thereby improving the efficiency and delivery quality of base plate production, while enhancing the system's scalability and intelligent management level. Attached Figure Description

[0071] Figure 1 This is a flowchart of the IoT-based method for monitoring the entire production process of container flooring according to the present invention.

[0072] Figure 2 This is a schematic diagram of the IoT-based container floor production process monitoring system of the present invention. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] Example 1: As Figure 1 As shown, the IoT-based method for monitoring the entire production process of container flooring includes the following steps:

[0075] Collect operating status data and process parameter data of equipment deployed in each section of the base plate production line, standardize the data format and align the time based on a unified time base, and construct a multi-section time sequence data frame set with task number and process identifier;

[0076] Extract the running time, process parameter offset and equipment usage status of each base plate task in different sections from the multi-section time sequence data frame set, construct the task processing behavior feature vector, and determine whether there is abnormal task rhythm or uneven equipment load based on preset rules.

[0077] Based on the abnormal task rhythm information and the current availability of equipment, a task resource scheduling diagram is generated to identify whether there are processing bottlenecks, task conflicts, or equipment occupancy conflicts in the current work section.

[0078] By combining the task resource scheduling diagram and task priority levels, an optimization strategy sequence is generated that includes processing order adjustment, equipment task reordering and buffer diversion control, and corresponding scheduling instructions are sent to the equipment control terminal.

[0079] During strategy execution, execution feedback data from each device is collected, including whether the task is completed according to the schedule, whether the processing status has returned to normal, and whether resources are released in a timely manner. The feedback results are then matched and compared with the optimized strategy sequence to determine whether the current scheduling strategy has failed or been interrupted.

[0080] When a scheduling strategy failure, task delay, or unresolved processing bottleneck is detected, the task resource scheduling diagram is regenerated and the optimization strategy is updated based on the latest equipment status and task execution status, so as to realize adaptive adjustment of rhythm and collaborative control of work sections during the base plate processing.

[0081] Step 1: Collect operating status data and process parameter data of equipment deployed in each section of the base plate production line. Based on a unified time base, standardize the data format and align the time, and construct a multi-section time-series data frame set with task numbers and process identifiers. The specific implementation is as follows:

[0082] Industrial IoT data acquisition nodes and edge gateways are deployed in the cutting, welding, painting, and inspection sections of the container floor production line. For each device, the system collects its start / stop status, operating current, operating temperature, processing time, and alarm information. Simultaneously, it collects process parameter data including cutting path, welding current, painting thickness, conveyor speed, and inspection thresholds. A unified time base is provided by the production line's master clock, and the clocks of each device controller are calibrated via network time synchronization services. Each piece of equipment operating status data and process parameter data collected is timestamped with the unified time base after entering the edge gateway. Then, it adheres to format specifications, including unified field naming, data type, and unit of measurement, and undergoes time alignment processing based on the unified time base.

[0083] Time alignment processing uses the sampling time of a unified time base as a reference: when multiple source records exist at a certain moment, the record with the timestamp closest to the unified time base sampling time is retained, and the selection is based on the priority that the original controller data of the device is greater than the direct data collected by the edge gateway, which is greater than the forwarded data of the upper-layer system; for records at the same level, the record with the closest timestamp is selected; when a record is missing at a certain moment, the record with the closest timestamp from the same source is used to fill in the gap; when there are inconsistent records in the same field, the valid value is determined by the priority criterion of the device alarm information. To ensure the consistency of subsequent cross-section tracking, each record is bound to a unique identifier of the base plate as a task number, and is also bound to the name of the section and process as a section and process identifier. The task number is used to collect data for a single base plate throughout the entire process, and the section and process identifier is used to distinguish the upstream and downstream relationships of the same task in different processing stages.

[0084] After completing timestamp marking, time alignment, and identifier binding, a sliding window segmentation process is performed based on a fixed time interval. The fixed time interval is consistent with the sampling step size of the unified time reference and is defined by the production line master clock. The start and end times of the sliding window are aligned with the sampling scale of the unified time reference, and the window sliding step size is equal to the fixed time interval. Continuous records within the same sliding window with the same task number and work section / process identifier are organized to form data frame units with temporal characteristics. Each data frame unit records the start and end times, task number, and work section / process identifier of the sliding window, retains the original detailed records by field, and provides representative values ​​based on the sequential aggregation of detailed records: the representative value of the operating current is obtained by adding the operating currents in the window one by one and dividing by the number of records; the representative value of the operating temperature is obtained in the same way; the processing time is obtained by accumulating the effective processing time periods covered by the window; the cutting path is obtained by connecting the trajectory segments in the window in chronological order; and the representative values ​​of the welding current, coating thickness, and conveying speed are all obtained in the aforementioned order.

[0085] For alarm information, all alarm entries within the window are retained, and the first and last alarms are marked chronologically to reflect the abnormal context during the window period. After the data frame unit is generated, the data frame units generated by the cutting, welding, painting, and inspection sections are categorized and sequentially linked according to task number to form a multi-section time-series data frame set that includes at least cutting, welding, painting, and inspection. Each task number in the set corresponds to a cross-section time-series chain. Each node in the time-series chain is distinguished by the section process identifier, and the chain order is determined by the start and end times of each data frame unit. When data frame units from different sections overlap in time, they are sorted according to the priority of the production line logistics direction to ensure that the multi-section time-series relationship of the same task number is consistent with the actual logistics direction.

[0086] It should be noted that the unified time base is the time scale of the production line's master clock. The master clock is calibrated periodically through network time synchronization services, and once calibrated, it becomes the unified time base. The fixed time interval is given by the sampling step size of the production line's master clock, and the sampling step size remains unchanged during a production task. The task number is a unique identification code generated for each base plate when it enters the production line, and it runs through the cutting, welding, painting, and inspection sections. The section process identifier is formed by concatenating the section name and the process name, and is automatically written by the edge gateway based on the equipment controller status change each time the equipment is switched between sections or processes. The cutting path is formed by connecting the set of trajectory points recorded in the window in chronological order within the cutting section. The conveying speed is a representative value obtained by adding the speed records of the conveying equipment in the window one by one and dividing by the number of records. The representative values ​​of the coating thickness and welding current are obtained by the same sequential aggregation method as the representative value of the operating current. The detection threshold is the most stringent threshold in the set of quality judgment thresholds used by the detection equipment in the window. The most stringent threshold refers to the threshold with the highest requirement for the qualified judgment condition. Alarm information is used to indicate equipment abnormalities or process abnormalities. The edge gateway does not delete alarm information and maintains the original time sequence.

[0087] The multi-segment time-series data frame set obtained through the above steps has met the public requirements for constructing a multi-segment time-series data frame set with task number and process identifier, and provides a consistent data foundation and time reference for subsequent extraction of runtime, process parameter offset and equipment usage status, construction of task processing behavior feature vectors and generation of task resource scheduling diagrams based on the multi-segment time-series data frame set.

[0088] Step 2: Extract the runtime, process parameter offset, and equipment usage status of each base plate task in different work sections from the multi-section time-series data frame set, construct a task processing behavior feature vector, and determine whether there are abnormal task rhythms or uneven equipment loads based on preset rules. Specifically, the implementation is as follows:

[0089] Based on the aforementioned multi-segment time sequence data frame set, the task number of each base plate is processed one by one;

[0090] First, time-series data frames with the same task number are grouped according to the work section process identifier. Data frame units belonging to the processing state within each work section are identified. The start and end times of each data frame unit are connected chronologically to obtain continuous and non-continuous processing sections. The runtime is determined by accumulating effective processing time: only data frame units in the processing state are accumulated; idle or waiting data frame units are not included in the runtime. For work sections with multiple start and stop times, the start time of the first entry into the processing state is taken as the start of the current processing cycle, and the end time of the last exit from the processing state is taken as the end of the current processing cycle. The runtime is obtained by accumulating effective processing time, thus avoiding the inclusion of waiting time and tooling changeover time in the runtime, ensuring that the runtime objectively reflects the actual processing load.

[0091] After the runtime is determined, the process parameter offset is calculated based on the standard process template. The standard process template is a set of target parameters and allowable ranges formed during the stable operation phase of the production line. It includes at least the cutting path and allowable path deviation range of the cutting section, the target value of welding current and allowable current deviation range of the welding section, the target value of spraying thickness and allowable thickness deviation range of the spraying section, the target value of conveying speed and allowable speed deviation range of the conveying equipment, and the detection threshold and judgment conditions of the detection section.

[0092] For each data frame unit, the representative values ​​of cutting path, welding current, coating thickness, and conveying speed are first obtained according to the aforementioned representative value rules, and the detection threshold used in the data frame unit is read. Then, deviation judgment is performed on each parameter: when the representative value is within the allowable range of the corresponding parameter of the standard process template, the deviation result of the parameter is recorded as within the allowable range; when the representative value exceeds the allowable range, the deviation result of the parameter is recorded as exceeding the allowable range, and the magnitude of exceeding the allowable range is described as the amount by which the representative value exceeds the boundary of the allowable range.

[0093] For the deviation results of multiple parameters within a process section, the process parameter offset is expressed as a parameter deviation list, that is, listing whether each parameter is within the allowable range and the corresponding excess amount. The excess amount is defined by the difference between the representative value and the nearest boundary of the allowable range. The difference is obtained by first determining whether the representative value is above or below the allowable range, and then recording the difference between the representative value and the corresponding boundary value.

[0094] Equipment usage status is extracted from the same data frame set by task number and work section / process identifier:

[0095] The start / stop frequency is the sum of the number of times the task switches from non-processing state to processing state and from processing state to non-processing state during the processing period;

[0096] The occupied time is the cumulative value of the duration of the data frame unit that is in the processing state within the section for this task; the idle interval is the cumulative value of the duration of the data frame unit that can be accessed but has not entered the processing state within the section for this task.

[0097] Equipment usage status is used to reflect the resource occupancy of the task in this work section, and is subsequently compared with the equipment load rate at the work section level.

[0098] The equipment load rate at the section level is obtained by the ratio of the number of equipment currently in use or processing within the same section to the total number of available equipment within that section. The threshold judgment of the equipment load rate is performed using a preset equipment load rate threshold. The preset equipment load rate threshold is the upper limit of allowable load set by the production line based on historical data during the stable operation phase, which is used to distinguish between normal load and overload trends.

[0099] Based on the runtime, process parameter offset, and equipment usage status mentioned above, a task processing behavior feature vector is constructed. The task processing behavior feature vector is organized in a fixed field order, including the runtime field, process parameter offset field, and equipment usage status field:

[0100] The runtime field records the cumulative effective processing time; the process parameter offset field records the list of parameter deviations and the excess amount of each parameter (if any); the equipment usage status field records the start-up and shutdown frequency, occupied time, and idle interval.

[0101] The judgment of abnormal task rhythm is based on the preset rhythm stability threshold: the preset rhythm stability threshold is derived from the cycle target corresponding to the standard process template and the historical stable runtime distribution. During the production line debugging stage, the upper limit value that can cover the main stable range is selected from the runtime set of stable task samples as the rhythm stability threshold.

[0102] When the runtime of a task exceeds the preset rhythm stability threshold multiple times consecutively and the equipment load rate of the section where the task is located exceeds the preset equipment load rate threshold consecutively, a task rhythm abnormality flag is generated; when the runtime does not exceed the preset rhythm stability threshold consecutively but the equipment load rate of the section where the task is located exceeds the preset equipment load rate threshold consecutively, and the idle interval of the equipment usage status of the task exceeds the preset idle interval threshold or the start-stop frequency exceeds the preset start-stop frequency threshold, an uneven equipment load flag is generated.

[0103] Task rhythm anomaly markers and equipment load unevenness markers are written back to the multi-segment time sequence data frame set along with the task number, and used as input for subsequent generation of task resource scheduling diagrams and identification of processing bottlenecks, task conflicts, or equipment occupancy conflicts.

[0104] Step 3: Based on the abnormal task rhythm information and the current availability of equipment, generate a task resource scheduling diagram to identify whether there are processing bottlenecks, task conflicts, or equipment occupancy conflicts in the current work section. The specific implementation is as follows:

[0105] Based on the formed multi-segment time sequence data frame set, the rhythm abnormality marker and the segment process identifier are read one by one according to the task number, and the time period is divided with a unified time base and consistent with the fixed time interval as the judgment time period.

[0106] For each time period, the number of available devices, the number of tasks in the queue, and the remaining processing time of tasks are counted for each work segment: the number of available devices is the number of devices in the work segment that are in an accessible state and are not shut down or under maintenance; the number of tasks in the queue is the number of tasks in the queue of the work segment whose task status is pending processing (including tasks already assigned to the work segment in the buffer); the remaining processing time of tasks is the estimated processing time of tasks in execution in the work segment minus the processing time already completed. The estimated processing time is derived from the target cycle time in the standard process template or the representative value of the stable interval of similar historical tasks. The above statistical results, together with the rhythm anomaly marker, are used as input data to generate the task resource scheduling diagram.

[0107] When calculating equipment load rate and task backlog density, a consistent calculation process is performed for each work segment within the same judgment time period: First, the number of equipment currently in operation or processing status is counted, and the total number of available equipment in the work segment is recorded; the ratio of the number of currently running equipment to the total number of available equipment is calculated, and the equipment load rate is obtained by the ratio of the number of currently running equipment to the total number of available equipment; then, the number of tasks in the work segment that are in the pending processing status is counted, and the upper limit of the task cache capacity of the work segment is read.

[0108] The task backlog density is calculated by comparing the current number of tasks to be processed with the upper limit of the task cache capacity.

[0109] The preset equipment load rate threshold is used to distinguish between normal load and overload trends, and is derived from the upper limit of the stable range of equipment load rate distribution during the stable operation phase of this section; the preset task accumulation density threshold is used to distinguish between normal queuing and congestion trends, and is derived from the upper limit of the stable range of task accumulation density distribution during the stable operation phase of this section; the preset cycle is the maximum length of time that equipment can wait continuously, measured with a unified time base scale, and is derived from the comprehensive setting of process cycle time and changeover requirements.

[0110] It should be noted that the above three thresholds were obtained and solidified as configuration parameters through statistics of stable samples during the production line debugging phase, and were used in subsequent production according to the same criteria.

[0111] When generating the task resource scheduling diagram, a three-layer mapping structure is constructed using task number, section identifier, and equipment number: The task layer records the task number, task status (pending processing, in execution, completed), remaining processing time, and rhythm anomaly markers; the section layer records the section identifier, number of available equipment, number of tasks in queue, upper limit of task cache capacity, equipment load rate, and task backlog density; the equipment layer records the equipment number, current equipment status (idle, occupied, under maintenance), equipment waiting idle time, and the list of assigned tasks.

[0112] The three layers of data are linked according to the relationship of "task number - section identifier - equipment number" to form a task resource scheduling diagram that can be used for conflict retrieval and bottleneck identification. In the task resource scheduling diagram, the equipment occupancy conflict is marked based on whether at least two tasks are assigned to the same equipment within the same judgment time period; the overlapping of task execution times is marked based on whether the planned processing time ranges of two or more tasks overlap within the same judgment time period; the equipment waiting for idling is marked based on the cumulative continuous waiting time when the equipment is in an idle state and the number of tasks in the section is greater than zero.

[0113] When identifying processing bottlenecks and conflict events, a unified criterion is applied: if the task accumulation density of a certain section exceeds the preset task accumulation density threshold in the current judgment time period and the equipment load rate is higher than the preset equipment load rate threshold in consecutive judgment times and adjacent judgment time periods, then the section is marked as a processing bottleneck in the task resource scheduling diagram; if at least two tasks correspond to the same equipment in the same judgment time period, then the corresponding equipment node and the related task edge are simultaneously marked as equipment occupancy conflict and task execution time overlap; if the equipment waiting idle time of a certain equipment continuously accumulates to exceed the preset period, then the equipment waiting idle event is marked on the equipment node, and the event is associated with the number of tasks queued in that section, so as to distinguish between waiting caused by uneven task allocation and waiting caused by process rhythm control needs during subsequent scheduling.

[0114] After completing the above marking, the marking results of processing bottlenecks, equipment occupancy conflicts, overlapping task execution times, and equipment waiting for idling, along with equipment load rate, task backlog density, and remaining task processing time, are written back to the task resource scheduling graph as a direct basis for generating an optimization strategy sequence that includes processing order adjustment, equipment task rearrangement, and buffer diversion control.

[0115] Step 4: Combining the task resource scheduling diagram and task priority levels, generate an optimization strategy sequence that includes processing order adjustment, equipment task rearrangement, and buffer diversion control, and send corresponding scheduling instructions to the equipment control terminal. The specific implementation is as follows:

[0116] Based on the formed task resource scheduling diagram and the multi-segment time sequence data frame set, each segment is evaluated in the same period under a unified time base.

[0117] First, the equipment load rate and task backlog density of each work section are read, and the processing bottleneck, equipment occupation conflict, task execution time overlap, and equipment waiting idle are retrieved from the task resource scheduling diagram. Any work section whose equipment load rate is close to or exceeds the preset equipment load rate threshold and / or whose task backlog density is close to or exceeds the preset task backlog density threshold and / or whose conflict marker is true is identified as a target work section. For each target work section, the task priority level corresponding to all task numbers in its queue is retrieved. The task priority level is derived from the comprehensive setting of delivery time limit, customer level and process sensitivity in the production planning system, and is solidified as a configuration parameter during the production line debugging stage to ensure consistency. The tasks in the same target work section are sorted from high to low according to the task priority level, and the task sequence before and after sorting is retained for strategy write-back and reconciliation.

[0118] To ensure consistency between strategy generation and execution, the target work section, task number, task priority level, equipment load rate, task backlog density, and conflict flag are all written into the strategy context as input fields for this round of strategy generation.

[0119] When generating processing sequence adjustment strategies and equipment task reordering strategies, the processing sequence of the tasks to be processed is first rearranged on the time axis according to the sorting results, and processing sequence adjustment strategy entries are generated. Each entry includes at least the target section, task number, adjusted planned start time and planned end time, and time connection relationship with adjacent tasks, and is aligned with a unified time base.

[0120] Subsequently, for tasks with equipment occupancy conflicts or overlapping task execution times, a device task reordering strategy is generated by combining the predicted idle time period of the equipment with the historical task time records: The predicted idle time period of the equipment is obtained by reading the list of assigned tasks and the remaining processing time of the tasks within the current time period of the equipment, superimposing the stable representative value of the historical task time records, and deriving the available range of the equipment in chronological order; The historical task time records are the experience set of the processing time of the equipment to complete the same type of task under the same process identification and the same process parameter range in the same work section, and the stable representative value is determined by the value corresponding to the median position of the concentrated distribution range of the experience set.

[0121] When generating equipment task rescheduling strategies, the earliest available equipment idle time slot is prioritized for tasks with higher priority levels and in conflict. For multiple task conflicts on the same equipment, tasks are first migrated to the idle time slots of other equipment within the same work section. If no available time slot is available, the planned time is extended while keeping the work section unchanged, ensuring that equipment occupancy conflicts or overlapping task execution times are resolved. All processing sequence adjustment strategies and equipment task rescheduling strategies are recorded in the form of entries, including task number, target work section, target equipment number (if rescheduling is involved), planned start time, and planned end time, and are associated with the input fields in the strategy context for execution tracking.

[0122] When the task accumulation density of the target section exceeds the preset buffer occupancy threshold, a buffer diversion control strategy is generated: First, the upper limit of the task cache capacity and the current task accumulation density of the target section are read, and tasks with lower priority and less impact on the type change are selected as candidate diversion tasks.

[0123] Subsequently, a backup work section with the same process identification capability is retrieved. If the equipment load rate of the backup work section is lower than the preset equipment load rate threshold and its task accumulation density is lower than the preset task accumulation density threshold, the candidate diversion tasks are allocated to the equipment idle prediction period of the backup work section according to the principle of availability in the nearest time. When there is no equipment idle prediction period that meets the conditions in the backup work section, the task is instead allocated to the buffer corresponding to the target work section. The number of tasks entering the buffer is controlled by the upper limit of the task cache capacity. The tasks entering the buffer record the buffer entry time and keep their task number and work section process identification unchanged to ensure that they can be directly backfilled in subsequent rescheduling.

[0124] After merging the processing sequence adjustment strategy, equipment task reordering strategy, and buffer diversion control strategy into an optimized strategy sequence, the strategy is executed in the following order: bottleneck resolution first, conflict resolution second, and conventional sequence adjustment last. The optimized strategy sequence is then converted into scheduling instructions and sent to the equipment control terminal through the communication interface. Each scheduling instruction includes a task number, target section, target equipment number (if applicable), planned start time and planned end time, buffer entry / exit control flag, and strategy entry identifier. The equipment control terminal returns a receipt confirmation and an exception return code. Based on this, the instruction status is written back in the task resource scheduling diagram and the subsequent feedback collection and strategy matching comparison process begins.

[0125] Step 5: During strategy execution, collect execution feedback data from each device, including whether the task was completed according to schedule, whether the processing status returned to normal, and whether resources were released in a timely manner. Match and compare the feedback results with the optimized strategy sequence to determine whether the current scheduling strategy has failed or been interrupted. Specifically, this is implemented as follows:

[0126] Under a unified time base, task execution status data is collected from the equipment control terminal using the task number as the primary key. Data items include equipment start / stop flags, task completion flags, processing start and end times, actual processing time, and resource release time. To ensure correspondence with each optimization strategy sequence, the processing order adjustment strategy, equipment task reordering strategy, and buffer diversion control strategy already issued in the optimization strategy sequence are first read, and the above three types of strategy items are uniformly organized into an optimization strategy target task list. Each strategy item includes task number, target section, target equipment number (if applicable), planned start time, planned end time, and resource release requirements.

[0127] During the data alignment phase, the system verifies the time fields fed back from the device control terminal using a unified time benchmark: when there is a slight deviation between the feedback timestamp and the unified time benchmark scale, alignment is performed using the nearest scale; when a feedback record is missing a single time field, the previous valid record for that task is used to fill in the missing field and its source is marked; when multiple conflicting records exist for the same task within the same time period, the trusted record is selected based on the priority criteria of device start / stop flags and alarm information. After completing the time and field alignment, each feedback result is mapped to the corresponding strategy entry in the optimization strategy target task list according to the task number. If the mapping fails (e.g., the corresponding strategy entry cannot be found), the task is considered not to have entered the execution scope of the issued strategies in this round, and the record is retained for review.

[0128] The same criteria are used to identify scheduling delay events and scheduling interruption events. For each completed mapping task, the system compares the actual processing start and end times in the feedback with the planned start and end times in the optimization strategy sequence one by one. The time deviation is obtained as follows:

[0129] First, calculate the time difference between the actual start time and the planned start time using a unified time base. Then, calculate the time difference between the actual end time and the planned end time. If the absolute value of either time difference exceeds the preset time tolerance, it is recorded as a scheduling delay event. The preset time tolerance is derived from the upper limit of time error during the stable operation phase of the production line and is solidified as a configuration parameter during the debugging phase, applied uniformly to all work sections. Next, verify the task completion identifier against the expected task status in the optimization strategy target task list: if no task completion identifier is provided or the task status remains in execution, and the task has exceeded the planned end time plus the preset time tolerance, it is marked as a scheduling interruption event. If the same strategy item has two or more completion identifiers within the same judgment time period, it is marked as duplicate execution and categorized as a scheduling interruption event. For missing feedback due to abnormal equipment start / stop markings, at the end of the judgment time period, it is directly marked as a scheduling interruption event based on the principle of not receiving a valid completion identifier, while retaining the original cause of the missing information (such as communication abnormality or equipment alarm).

[0130] The identification of resource blocking events is based on a comparison between the resource release time and the time limit specified in the policy. The time limit specified in the policy is given by the policy entry and is equivalent to the planned end time plus the resource release buffer time. The resource release buffer time is set and fixed as a configuration parameter during the production line commissioning phase according to the changeover requirements and safety shutdown procedures. After obtaining the resource release time from the feedback, it is first determined whether the resource release time exists and whether it is aligned with a unified time base. If the resource release time is later than the time limit specified in the policy, it is marked as a resource blocking event. If the resource release time is missing and the equipment start / stop flag shows that the equipment is still in an occupied state, it is also marked as a resource blocking event.

[0131] The process of merging the set of events for the same task within the same time period is as follows:

[0132] If any type of scheduling delay event, scheduling interruption event, or resource blocking event is recorded, the current scheduling policy is determined to be ineffective or interrupted. This determination result, along with the event type, comparison time field, preset time tolerance, and policy-specified time limit, is written back to the task resource scheduling graph and the target task list of the optimization policy. This serves as the direct input for the next step of regenerating the task resource scheduling graph and updating the optimization policy based on the latest device status and task execution.

[0133] Step 6: When a scheduling strategy failure, task lag, or unresolved processing bottleneck is detected, a new task resource scheduling diagram is generated based on the latest equipment status and task execution status, and the optimization strategy is updated to achieve adaptive adjustment of the rhythm and collaborative control of work sections during the base plate processing. Specifically, the implementation is as follows:

[0134] Using the current judgment time period as the processing unit, the reconstruction process of this step is initiated when one of the following events is detected: scheduling delay event, scheduling interruption event, or resource blocking event, or when the processing bottleneck has been marked in the previous judgment time period.

[0135] First, the current equipment availability status, task execution progress, and processing feedback data of each work section are collected synchronously from the multi-section time-series data frame set and the task resource scheduling diagram:

[0136] Equipment availability status includes the current status of the equipment (idle, occupied, under maintenance), the list of assigned tasks, the remaining processing time of the tasks, and the equipment waiting idle time; the task execution progress uses the task number as the primary key and records the planned start time, planned end time, actual start time, actual end time, and actual processing time; the processing feedback data consists of the equipment start / stop flag, task completion flag, and resource release time reported by the equipment control terminal.

[0137] Update the operating status and resource usage information of each device: when a device is in an occupied state and there is remaining processing time for a task, record it as occupied and execute it; when a device is in an occupied state but there is no remaining processing time for a task and the resource release time has not reached the time limit specified by the policy, record it as occupied and waiting to be released; when a device is in a maintenance state, record it as unavailable and freeze its previous resource usage information for traceability.

[0138] After updating the equipment and task status, the task execution progress is analyzed to identify task lags and determine whether bottlenecks have not been resolved. The criteria for determining task lags remain consistent:

[0139] When the absolute value of the time difference between the actual start time and the planned start time exceeds the preset time tolerance, or when the actual end time is later than the planned end time plus the preset time tolerance, or when the task completion flag is not received after the planned end time plus the preset time tolerance, the task is marked as being in a delayed state, and a delayed flag is written in the corresponding task node of the task resource scheduling diagram.

[0140] Subsequently, for the work sections that were marked as processing bottlenecks in the previous round, the system recalculates the task backlog density and equipment load rate of the work sections based on the updated records: the task backlog density is obtained by the ratio of the number of tasks to be processed to the upper limit of the task cache capacity; the equipment load rate is obtained by the ratio of the number of devices currently in use or processing to the total number of available devices.

[0141] If the task backlog density of the work section is still higher than the preset task backlog density threshold, and the equipment load rate of the work section continues to be higher than the preset equipment load rate threshold during the current judgment time period, then the work section is marked as an unresolved bottleneck work section in the task resource scheduling diagram; if neither condition is met, then the bottleneck continuation mark of the work section is canceled and it is transferred to regular scheduling.

[0142] After identifying task delays and unresolved bottlenecks, the equipment load rate and task backlog density are recalculated for all work sections based on the latest task status and equipment status. An updated task resource scheduling diagram is constructed according to the three-level mapping relationship of task number - work section identifier - equipment number, and a new optimization strategy sequence is generated based on this and the task priority level.

[0143] The generation of the new optimization strategy sequence follows the same approach as described above: A processing order adjustment strategy is generated for the target section to reorder tasks in terms of time; for tasks with equipment occupancy conflicts or overlapping task execution times, a task reordering strategy is generated based on the predicted idle time period of the equipment and historical task time records; when the task accumulation density of the target section exceeds the preset buffer occupancy threshold, a buffer diversion control strategy is generated to guide low-priority tasks with minimal impact on the replacement process to a spare section with available resources or into the buffer. The above three types of strategy entries are merged into a new optimization strategy sequence and issued to the equipment control terminal as a replacement strategy, overriding the previous failed strategy; the issued content includes the task number, target section, target equipment number (if applicable), planned start time, planned end time, resource release requirements, and strategy entry identifier.

[0144] After the device control terminal returns a receipt confirmation and an error return code, the system writes the policy status and issuance timestamp into the updated task resource scheduling graph, providing a continuous and consistent data context for feedback collection and policy matching comparison in the next judgment period.

[0145] By setting preset task backlog density thresholds, preset equipment load rate thresholds, and preset time tolerances, the executable conditions for identifying bottlenecks, delays, and conflicts are clearly defined. This enables the system to quickly reconstruct the scheduling graph and generate alternative strategies when scheduling delay events, scheduling interruption events, or resource blocking events are detected. The three types of optimization strategies generated—processing sequence adjustment, equipment task rearrangement, and buffer diversion control—are all managed in an itemized manner and include task numbers, target work sections, target equipment numbers, and planned time information, achieving transparency and traceability of the scheduling process.

[0146] It should be noted that the thresholds involved in the embodiments can be determined according to specific scenarios and needs.

[0147] This invention deploys various devices, including laser cutting machines, automatic welding devices, spraying platforms, vision inspection terminals, and conveying equipment, on a container floor production line to collect operational status data and process parameter data. Based on a unified time reference, it completes time alignment and format standardization, constructing a multi-segment time-series data frame set with task numbers and process identifiers. This enables refined data modeling of the entire floor processing process. On this basis, it extracts task processing behavior feature vectors, including runtime, process parameter offset, and equipment usage status. These vectors are then compared with preset rhythm stability thresholds and equipment load rate thresholds to dynamically identify abnormal task rhythms and uneven equipment loads, ensuring that the monitoring results are real-time and interpretable.

[0148] Furthermore, this invention generates a task resource scheduling graph, mapping equipment load rate, task backlog density, and conflict markers into a visual relationship. When a bottleneck or conflict is detected, it generates a sequence of optimization strategies based on task priority levels, such as adjusting processing order, reordering equipment tasks, and controlling buffer flow. This ensures the flexibility and rationality of resource allocation and task scheduling. By matching and comparing the scheduling command issuance with the feedback results, it can determine in real time whether there is a delay, interruption, or blockage in the scheduling strategy. When the strategy fails, it automatically triggers reconstruction and generates alternative strategies based on the latest equipment status and task progress. This achieves closed-loop adaptive adjustment of the production process, thereby improving the efficiency and delivery quality of base plate production, while enhancing the system's scalability and intelligent management level.

[0149] Example 2: A container floor production process monitoring system based on the Internet of Things, such as... Figure 2 As shown, it specifically includes:

[0150] The production line data acquisition module is used to collect the operating status data and process parameter data of the equipment deployed in each section of the base plate production line. Based on a unified time base, the data is formatted and time-aligned to construct a multi-section time-series data frame set with task number and process identifier.

[0151] The task analysis module is used to extract the running time, process parameter offset and equipment usage status of each base plate task in different sections from the multi-section time-series data frame set, construct the task processing behavior feature vector, and determine whether there is abnormal task rhythm or uneven equipment load based on preset rules.

[0152] The equipment status analysis module is used to generate a task resource scheduling diagram based on the abnormal task rhythm information and the current available status of the equipment, and to identify whether there are processing bottlenecks, task conflicts or equipment occupancy conflicts in the current work section.

[0153] The scheduling generation module is used to combine the task resource scheduling graph and task priority level to generate an optimized strategy sequence that includes processing order adjustment, equipment task reordering and buffer diversion control, and send the corresponding scheduling instructions to the equipment control terminal.

[0154] The scheduling strategy analysis module is used to collect execution feedback data from each device during the strategy execution process, including whether the task was completed according to the schedule, whether the processing status has returned to normal, and whether the resources were released in a timely manner. The module also matches and compares the feedback results with the optimized strategy sequence to determine whether the current scheduling strategy has failed or been interrupted.

[0155] The monitoring and control module is used to regenerate the task resource scheduling diagram and update the optimization strategy based on the latest equipment status and task execution status when the scheduling strategy is detected to be ineffective, the task is delayed, or the processing bottleneck is not resolved. This enables adaptive adjustment of the rhythm and collaborative control of the work section during the base plate processing.

[0156] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0157] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0158] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0159] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0160] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0161] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0162] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for monitoring the entire production process of container flooring based on the Internet of Things, characterized in that, Includes the following steps: Collect operating status data and process parameter data of equipment deployed in each section of the base plate production line, standardize the data format and align the time based on a unified time base, and construct a multi-section time sequence data frame set with task number and process identifier; Extract the running time, process parameter offset and equipment usage status of each base plate task in different sections from the multi-section time sequence data frame set, construct the task processing behavior feature vector, and determine whether there is abnormal task rhythm or uneven equipment load based on preset rules. Based on the abnormal task rhythm information and the current availability of equipment, a task resource scheduling diagram is generated to identify whether there are processing bottlenecks, task conflicts, or equipment occupancy conflicts in the current work section. By combining the task resource scheduling diagram and task priority levels, an optimization strategy sequence is generated that includes processing order adjustment, equipment task reordering and buffer diversion control, and corresponding scheduling instructions are sent to the equipment control terminal. During strategy execution, execution feedback data from each device is collected, including whether the task is completed according to the schedule, whether the processing status has returned to normal, and whether resources are released in a timely manner. The feedback results are then matched and compared with the optimized strategy sequence to determine whether the current scheduling strategy has failed or been interrupted. When the scheduling strategy fails, the task is delayed, or the processing bottleneck is not resolved, the task resource scheduling diagram is regenerated and the optimization strategy is updated based on the latest equipment status and task execution status, so as to realize the adaptive adjustment of the rhythm and the collaborative control of the work section during the base plate processing. The runtime, process parameter offset, and equipment usage status of each base plate task in different work sections are extracted from the multi-section time-series data frame set. A task processing behavior feature vector is constructed, and based on preset rules, it is determined whether there are abnormal task rhythms or uneven equipment loads. The specific process is as follows: Extract the multi-segment time sequence data frame corresponding to each base plate task number; Calculate the start and end times of the tasks in each work section to determine the running time; Analyze the degree of deviation between the process parameters and the standard process template to obtain the process parameter deviation; The frequency of device start-stop, the duration of device occupation, and the idle interval during task execution are statistically analyzed to extract the device usage status. The runtime, process parameter offset, and equipment usage status are combined to construct a feature vector of task processing behavior. The task processing behavior feature vector is compared with the preset rhythm stability threshold and the preset equipment load rate threshold to determine whether there is abnormal task rhythm or uneven equipment load. Based on the abnormal task rhythm information and the current availability of equipment, a task resource scheduling diagram is generated to identify whether there are processing bottlenecks, task conflicts, or equipment occupancy conflicts in the current work section. The specific process is as follows: Obtain the rhythm anomaly marker and the section identifier corresponding to each base plate task; Collect the current number of available equipment, the number of tasks in the queue, and the remaining processing time for each task in each work section; Calculate the equipment load rate and task accumulation density for each work section; Construct a task resource scheduling diagram based on task number, work section identifier, and equipment number; Mark areas of equipment occupancy conflicts, overlapping task execution times, and equipment waiting for idling in the task resource scheduling diagram; When the task accumulation density of a work section exceeds the preset task accumulation density threshold and the equipment load rate is consistently higher than the preset equipment load rate threshold, the work section is determined to be a processing bottleneck. If at least two tasks correspond to the same device within the same time period or the device waits for more than a preset period, it is determined that there is a task conflict or device occupancy conflict. The specific process for calculating the equipment load rate and task accumulation density for each work section is as follows: Count the number of devices in each work section that are currently in use or processing, and count the total number of available devices in that work section; The equipment load rate is obtained by calculating the ratio of the number of currently running devices to the total number of available devices. Count the number of tasks in the pending state of a work section and record the maximum task cache capacity of the work section; The task backlog density is obtained by calculating the ratio of the current number of tasks to be processed to the upper limit of the task cache capacity. Combining the task resource scheduling graph and task priority levels, an optimization strategy sequence is generated, which includes processing order adjustment, equipment task reordering, and buffer diversion control. Corresponding scheduling instructions are then sent to the equipment control terminal. The specific process is as follows: Based on the equipment load rate and task backlog density of each section in the task resource scheduling diagram, identify the target section that currently has bottleneck risks or resource conflicts. Retrieve the task priority levels of all tasks within the target section and sort them from highest to lowest priority level; Based on the sorting results and resource conflict types, a processing order adjustment strategy is generated to reorder the processing order of tasks to be processed in terms of time. For equipment involved in conflicting tasks, a task rescheduling strategy is generated based on the predicted idle time period of the equipment and the historical task duration records. If the task accumulation density of the target section exceeds the preset buffer occupancy threshold, a buffer diversion control strategy is generated to guide the tasks to other spare sections or buffers with available resources. The generated processing sequence adjustment strategy, equipment task reordering strategy and buffer diversion control strategy are merged into an optimization strategy sequence; The optimization strategy sequence is converted into recognizable scheduling instructions through the communication interface and sent to the device control terminal to drive task scheduling and execution.

2. The method for monitoring the entire production process of container floor based on the Internet of Things as described in claim 1, characterized in that: The collected equipment operation status data includes the equipment's start / stop status, operating current, operating temperature, processing time, and alarm information. The process parameter data includes cutting path, welding current, coating thickness, conveying speed, and detection threshold.

3. The method for monitoring the entire production process of container floor based on the Internet of Things as described in claim 2, characterized in that: The following is the specific process for constructing a multi-segment time-series data frame set with task numbers and process identifiers: The collected equipment operating status data and process parameter data are timestamped and time-aligned according to a unified time base. Each data record is divided according to its work section and bound to the corresponding base plate task number and work section process identifier to form a structured data item; Sliding window segmentation is performed at fixed time intervals to organize continuously collected data into data frame units with temporal characteristics; The data frame units of each work section are classified according to the task number to form a multi-section time-series data frame set that covers at least cutting, welding, spraying, and inspection.

4. The method for monitoring the entire production process of container floor based on the Internet of Things as described in claim 3, characterized in that: The feedback results are then matched and compared with the optimization strategy sequence to determine whether the current scheduling strategy has failed or been interrupted. The specific process is as follows: Collect task execution status data fed back from the equipment control terminal, including equipment start / stop flags, task completion flags, processing start and end times, actual processing time, and resource release time; The feedback results are mapped to the processing order adjustment strategy, equipment task rearrangement strategy and buffer diversion control strategy that have been issued in the optimization strategy sequence according to the task number. Compare the actual processing start and end times in the feedback with the planned processing times in the optimization strategy sequence. If the time deviation exceeds the preset time tolerance, it is recorded as a scheduling delay event. Check whether the task completion flag in the feedback is consistent with the target task list of the optimization strategy. If there are any omissions, duplicate executions, or outdated statuses, mark them as scheduling interruption events. Determine whether the resource release time is completed within the time limit specified by the policy. If the delayed release causes downstream tasks to be unable to access the system, mark it as a resource blocking event. If any of the following types of events exist: scheduling delay event, scheduling interruption event, or resource blocking event, the current scheduling policy is determined to be either execution failure or interruption.

5. The method for monitoring the entire production process of container floor based on the Internet of Things as described in claim 4, characterized in that: When a scheduling strategy failure, task lag, or unresolved processing bottleneck is detected, the task resource scheduling graph is regenerated and the optimization strategy is updated based on the latest equipment status and task execution status. The specific process is as follows: Collect current equipment availability status, task execution progress and processing feedback data for each section, and update the operating status and resource usage information of each piece of equipment; Analyze the task execution progress to identify the task number and its corresponding work section that is currently lagging behind; Determine whether the task backlog density of the work section marked as a bottleneck in the previous round in the task resource scheduling diagram is still higher than the preset task backlog density threshold and the equipment load rate is still higher than the preset equipment load rate threshold after the update. If so, it is confirmed that the bottleneck has not been resolved. Based on the latest task and device status, recalculate the device load rate and task backlog density, and construct an updated task resource scheduling graph; Based on the updated task resource scheduling graph and task priority level, the processing order adjustment strategy, equipment task rearrangement strategy and buffer diversion control strategy are regenerated to form a new optimization strategy sequence. The new optimization strategy sequence is issued as a replacement strategy, overriding the previous failed strategy, thereby realizing the dynamic reconstruction of scheduling strategy and adaptive updating of resource configuration.

6. A container floor production process monitoring system based on the Internet of Things (IoT), used to implement the container floor production process monitoring method based on the IoT as described in any one of claims 1-5, characterized in that, include: The production line data acquisition module is used to collect the operating status data and process parameter data of the equipment deployed in each section of the base plate production line. Based on a unified time base, the data is formatted and time-aligned to construct a multi-section time-series data frame set with task number and process identifier. The task analysis module is used to extract the running time, process parameter offset and equipment usage status of each base plate task in different sections from the multi-section time-series data frame set, construct the task processing behavior feature vector, and determine whether there is abnormal task rhythm or uneven equipment load based on preset rules. The equipment status analysis module is used to generate a task resource scheduling diagram based on the abnormal task rhythm information and the current available status of the equipment, and to identify whether there are processing bottlenecks, task conflicts or equipment occupancy conflicts in the current work section. The scheduling generation module is used to combine the task resource scheduling graph and task priority level to generate an optimized strategy sequence that includes processing order adjustment, equipment task reordering and buffer diversion control, and send the corresponding scheduling instructions to the equipment control terminal. The scheduling strategy analysis module is used to collect execution feedback data from each device during the strategy execution process, including whether the task was completed according to the schedule, whether the processing status has returned to normal, and whether the resources were released in a timely manner. The module also matches and compares the feedback results with the optimized strategy sequence to determine whether the current scheduling strategy has failed or been interrupted. The monitoring and control module is used to regenerate the task resource scheduling diagram and update the optimization strategy based on the latest equipment status and task execution status when the scheduling strategy is detected to be ineffective, the task is delayed, or the processing bottleneck is not resolved. This enables adaptive adjustment of the rhythm and collaborative control of the work section during the base plate processing.

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