Industrial internet of things based automobile door hinge production line resource state scheduling system

Abstract

The application discloses an automobile door hinge production line resource state scheduling system based on an industrial Internet of Things, which comprises the following modules: a production line data module: collecting production line global real-time data, and obtaining a fusion resource state matrix through multi-modal feature synchronization and alignment preprocessing operation; a resource analysis module: performing dynamic resource calculation on the fusion resource state matrix to obtain hinge production stability features and hinge production fluctuation trend features, and obtaining a dynamic performance vector of each resource entity based on the hinge production stability features and the hinge production fluctuation trend features; a decision analysis module: constructing a discrete event simulation engine based on the dynamic performance vector, combining a current work order queue to generate a resource future conflict probability atlas through multi-round Monte Carlo deduction, and generating an optimal scheduling instruction sequence based on the resource future conflict probability atlas and a directed graph network; and a scheduling execution module: realizing automobile door hinge production line resource state scheduling based on the optimal scheduling instruction sequence.

Description

Technical Field

[0001] This invention relates to the field of next-generation information technology, and in particular to a resource status scheduling system for automotive door hinge production lines based on the Industrial Internet of Things. Background Technology

[0002] As a core component of automotive parts manufacturing, the scheduling technology of automotive door hinge production lines has evolved from manual, experience-based scheduling to digital and intelligent scheduling. Initially, production lines relied on manual resource allocation based on work order experience, resulting in low efficiency and slow response times. With the widespread adoption of Industrial Internet of Things (IIoT) technology, production lines have achieved real-time data collection, gradually developing scheduling systems based on static resource models. By allocating resources through preset rules, production efficiency has been improved to some extent.

[0003] Currently, existing resource status scheduling systems for automotive door hinge production lines rely on static resource models, which cannot perceive real-time changes in resource dynamic efficiency. This leads to discrepancies between the resource status description and actual operation. Furthermore, the prediction of resource conflicts is based solely on static analysis of historical data, lacking probabilistic extrapolation and propagation path analysis of future conflicts. After a conflict occurs, the system can only respond passively and cannot proactively avoid cascading effects. Consequently, scheduling decisions often rely on single rules and fail to incorporate precise pruning based on the dynamic critical path of conflict propagation. The rescheduling instructions lack specificity and are prone to triggering new resource imbalances, making it difficult to meet the flexible production needs of automotive door hinge production lines for multiple varieties and small batches. Therefore, this paper proposes a resource status scheduling system for automotive door hinge production lines based on the Industrial Internet of Things. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art and to achieve the above objectives, the present invention proposes the following technical solution:

[0005] The resource status scheduling system for automotive door hinge production lines based on the Industrial Internet of Things includes:

[0006] Production line data module: Collects real-time data from the entire production line and obtains a fused resource status matrix through multimodal feature synchronization and alignment preprocessing operations;

[0007] Resource Analysis Module: Performs dynamic resource calculations on the fused resource state matrix to obtain hinge production stability characteristics and hinge production fluctuation trend characteristics, and obtains the dynamic efficiency vector of each resource entity based on the hinge production stability characteristics and hinge production fluctuation trend characteristics.

[0008] Decision Analysis Module: Based on dynamic performance vector, a discrete event simulation engine is constructed and combined with the current work order queue to perform multi-round Monte Carlo simulation to generate a probability map of future resource conflicts. Based on the probability map of future resource conflicts, a directed graph network is combined to generate the optimal scheduling instruction sequence.

[0009] Scheduling and execution module: Implements resource status scheduling for the automotive door hinge production line based on the optimal scheduling instruction sequence.

[0010] The process of obtaining the fusion resource state matrix is ​​as follows:

[0011] The industrial IoT sensing network is activated to collect real-time operating parameters of the target object. The data collection targets include processing equipment, logistics and transportation units, and tooling fixtures within the production line.

[0012] The clock signal of the production line's main controller is selected as the global time base to generate a time stamp sequence with equal intervals. For each acquisition node i, interpolation or resampling operations are performed on the original data sequence based on a global time base T to obtain a time-aligned dataset. ;

[0013] Time-aligned dataset for each acquisition node i Perform min-max normalization, and then map the normalized single-node feature vectors to the resource entity dimension using production line process association rules to obtain the aligned dataset. ;

[0014] Let the total number of resource entities in the automotive door hinge production line be . Assign a unique index to each resource entity Obtain fusion state value Arrange all fusion state values ​​according to the time dimension and the resource entity dimension to construct a fusion resource state matrix. .

[0015] The process of obtaining hinge production stability characteristics through dynamic resource calculation is as follows:

[0016] The hinge production stability characteristics are obtained by calculating the standard deviation and the coefficient of variation of the state sequence.

[0017] The standard deviation of the state sequence is expressed as: ;

[0018] The coefficient of variation of the state sequence is expressed as ;

[0019] in, Indicates the first The mean of the state sequence corresponding to each resource entity. Indicates the first The standard deviation of each resource entity.

[0020] The process of obtaining the hinge production fluctuation trend characteristics through dynamic resource calculation is as follows:

[0021] The hinge production fluctuation trend characteristics consist of the production trend slope and the fitting priority.

[0022] The process of obtaining the production trend slope includes:

[0023] Calculate the first data acquisition time. For each resource entity, the time deviation and state deviation are calculated by multiplying the time deviation at all times by the corresponding state deviation, and then summing all the products to obtain the numerator. ;

[0024] Then calculate the number of data acquisition moments at each acquisition time. The denominator is the square of the time deviation corresponding to each resource entity, plus the sum of all the squared values. Based on the numerator With denominator Get the Production trend slope corresponding to each resource entity ;

[0025] The process of obtaining the fitting priority includes:

[0026] Based on the obtained production trend slope Combine time mean and state mean to obtain the first Fitting state values ​​of each resource entity ;

[0027] Get the The actual state value of each resource entity at each moment and corresponding fitted state values The sum of squares of the deviations is expressed as the first deviation. and the actual state value at each moment. with state mean The sum of squares of the deviations and expressed as the second deviation Based on the first deviation And the second deviation Get the Resource entity fitting priority , represented as: .

[0028] The process of obtaining the dynamic performance vector is as follows:

[0029] Based on the The hinge production stability characteristics and hinge production fluctuation trend characteristics corresponding to each resource entity constitute the first... Dynamic performance vector of each resource entity The hinge production stability characteristics corresponding to all resource entities. Characteristics of hinge production fluctuations The final dynamic performance vector is then integrated. .

[0030] The process of building a discrete event simulation engine is as follows:

[0031] The discrete event simulation engine is based on a pre-set discrete event simulation algorithm model. As a basic infrastructure, let the current work order queue be... The work order execution cycle is ;

[0032] Combine the current work order queue with the dynamic performance vector set of all resource entities. The current work order queue is With work order execution cycle As input, and setting the total number of rounds in the multi-round Monte Carlo simulation to... Call a uniformly distributed random number generation function Combined with dynamic performance vector The process processing capacity of the limited operating state generates a state trajectory. ,in For the generated first All resource entities in the round of simulation The state trajectory, , A discrete event simulation engine is obtained by using a random sampling function based on a dynamic performance vector.

[0033] The process of generating a probability map of future resource conflicts through multiple rounds of Monte Carlo simulations is as follows:

[0034] Define the work order task scheduling rules and conflict event detection rules for the discrete event simulation engine;

[0035] The work order task scheduling is based on all sampled resource entities. State Trajectory , and work order queue Match the task requirements;

[0036] The conflict event detection rule applies to the same resource entity in two or more work orders at the same time. When a demand for occupancy arises, it is identified as a conflict event;

[0037] Then, multiple rounds of Monte Carlo simulations are performed, assuming the [number]th [round]... In the Monte Carlo simulation, within the time interval The set of conflict events detected internally is Statistics on each resource entity in this round of simulation Number of conflicts ;

[0038] The probability of future resource conflicts is calculated based on the results of multiple rounds of simulation, assuming a total number of simulation rounds. Then the resource entity The probability of future conflict is:

[0039] ;

[0040] Among them, The duration of the work order execution cycle;

[0041] Map the conflict probabilities of all resource entities to the time dimension and the resource dimension to generate a future conflict probability map of resources. .

[0042] The process of generating the optimal scheduling instruction sequence is as follows:

[0043] Based on the probability map of future resource conflicts As input, when the resource entity At any moment Conflict probability If the conflict exceeds the preset threshold, then... Marked as the initial conflict node And combined with a probability map of future conflicts Establish a directed graph of conflict propagation ,in, For all conflicting and related nodes, This represents a directed edge representing conflict propagation, i.e., the probability of future conflicts.

[0044] Directed graph of conflict propagation In the middle, calculate the cumulative conflict probability for each path:

[0045] ;

[0046] in, This is a propagation path in the diagram. This represents the cumulative conflict probability of the path.

[0047] Identify the set of critical paths with the highest cumulative conflict probability. For each critical path, adjust the resource allocation strategy of the core nodes on the path until the cumulative conflict probability of all paths is less than a preset pruning threshold. , This is the pruning threshold;

[0048] Extract all resource adjustment actions during the pruning process and generate the optimal scheduling instruction sequence. .

[0049] The present invention has the following beneficial effects:

[0050] 1. Through dynamic resource profiling and dynamic performance vector extraction, the system can quantify dynamic indicators such as the effective capacity and failure probability of each resource entity in real time, accurately reflect the current operating status of the resources, and provide accurate basic data support for subsequent scheduling.

[0051] 2. Based on multi-round Monte Carlo simulations to generate a probability map of future resource conflicts, combined with the construction of a directed graph of conflict propagation, the probability of conflict occurrence, time nodes, and propagation paths can be identified in advance. Compared with the static conflict analysis that relies solely on historical data in existing technologies, this solution can predict the chain reaction of potential conflicts from the root, realizing the transformation from passively responding to conflicts to actively preventing conflicts, significantly reducing the occurrence rate of production line conflicts, and reducing work order delays and resource waste caused by conflicts.

[0052] 3. Through dynamic critical path weight pruning, the system can generate a precise rescheduling instruction sequence for the links most severely affected by conflicts. The adjustment actions are directly applied to the core conflict nodes, effectively avoiding ineffective scheduling and improving resource utilization and work order execution efficiency.

[0053] 4. The entire scheduling process is based on real-time data and dynamic simulation, which can quickly respond to changes in production scenarios such as work order changes and resource fluctuations. It is adapted to the flexible production mode of multiple varieties and small batches in the automotive door hinge production line, ensuring the stability and efficiency of the production line operation. Attached Figure Description

[0054] Figure 1 This is a system block diagram of the resource status scheduling system for automotive door hinge production line based on the Industrial Internet of Things proposed in this invention. Detailed Implementation

[0055] 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.

[0056] Example 1: As Figure 1 As shown, the resource status scheduling system for automotive door hinge production lines based on the Industrial Internet of Things proposed in this invention includes:

[0057] Production line data module: Collects real-time data from the entire production line and obtains a fused resource status matrix through multimodal feature synchronization and alignment preprocessing operations;

[0058] The industrial Internet of Things (IoT) sensing network is activated to collect real-time data on all production resources of the automotive door hinge production line. The data collection targets include processing equipment, logistics and conveying units, and tooling fixtures within the production line.

[0059] Real-time operating parameters of the collected objects Perform multimodal feature synchronization and alignment preprocessing:

[0060] The clock signal of the production line's main controller is selected as the global time base to generate a time stamp sequence with equal intervals. For each acquisition node i, the original data sequence Perform interpolation or resampling operations based on the global time base T:

[0061] For nodes with a sampling frequency higher than the reference frequency, downsampling is used to preserve key feature points;

[0062] For nodes with acquisition frequencies lower than the baseline frequency, linear interpolation or spline interpolation is used to fill in the missing data points. After synchronization processing, a time-aligned dataset is obtained. ;

[0063] Time-aligned datasets Feature vectors containing different physical meanings, such as equipment temperature, vibration amplitude, material inventory, and process duration, have significant differences in their numerical range and dimensions. To eliminate the influence of dimensions, all features need to be mapped to a unified feature space.

[0064] For each data acquisition node Time-aligned datasets Perform min-max normalization and map the normalized single-node feature vectors to the resource entity dimension using production line process association rules, for example:

[0065] The sensor features of a certain processing equipment, such as temperature and vibration, are associated and mapped to the feature vector of the corresponding resource entity.

[0066] The location and speed characteristics of a logistics unit are associated and mapped to the feature vector of the corresponding resource entity;

[0067] After feature alignment, the aligned dataset is obtained. The feature vectors of this dataset already possess a unified dimension and spatial mapping relationship;

[0068] All resource entities within the production line, such as processing equipment, logistics units, tooling fixtures, and work-in-process, are counted, with a total number. Assign a unique index to each resource entity ;

[0069] For each base timestamp With resource entities The aligned dataset The fusion state value is obtained by weighting and fusing all feature vectors associated with the resource entity. ;

[0070] Arrange all fusion state values ​​according to the time dimension and the resource entity dimension to construct a fusion resource state matrix. ,in, Representing the Time of the first The matrix stores the fusion status values ​​of each resource entity in a structured form, providing complete and accurate input data for subsequent resource analysis modules, and ensuring the accuracy of subsequent dynamic resource profiling and conflict inference.

[0071] Resource Analysis Module: Performs dynamic resource calculations on the fused resource state matrix to obtain hinge production stability characteristics and hinge production fluctuation trend characteristics, and obtains the dynamic efficiency vector of each resource entity based on the hinge production stability characteristics and hinge production fluctuation trend characteristics.

[0072] Based on the fusion resource state matrix For each resource entity Extract its time series State sequence on ;

[0073] Extract a dynamic performance vector that reflects the dynamic behavior of resources. The dynamic performance vector includes hinge production stability characteristics. Characteristics of hinge production fluctuations The specific acquisition process is as follows:

[0074] Hinge manufacturing stability characteristics The fluctuation degree of the operational state of a resource entity is quantified by calculating the standard deviation and coefficient of variation of the state sequence. The process is as follows:

[0075] No. The production standard deviation corresponding to each resource entity is represented as follows: , No. The coefficient of variation for each resource entity is expressed as: ;

[0076] in, Indicates the first The mean of the state sequence corresponding to each resource entity. Indicates the first The standard deviation of each resource entity;

[0077] Finally, the first Hing production stability characteristics of individual resource entities By the Production standard deviation of individual resource entities With the Coefficient of variation of each resource entity The composition reflects the reliability of the continuous operation of resource entities;

[0078] Furthermore, the first Hing production stability characteristics of individual resource entities By the Production standard deviation corresponding to each resource entity With coefficient of variation The vectors can be directly concatenated to obtain the result, which is represented as: ;

[0079] Hinge production fluctuation trend characteristics From the slope of the production trend and fitting priority The composition reflects the direction and rate of change of resource load over time, and the implementation process is as follows:

[0080] Get the Production trend slope corresponding to each resource entity :

[0081] Calculate the first data acquisition time. Time deviation and state deviation corresponding to each resource entity;

[0082] The time deviation is the difference between that moment and the time mean. The difference between the state value and the mean state value at that moment is the state deviation. The difference;

[0083] Multiply the time deviations at all times by the corresponding state deviations, and then sum all the products to obtain the numerator. ;

[0084] Calculate the first data acquisition time. The denominator is the square of the time deviation corresponding to each resource entity, plus the sum of all the squared values. ;

[0085] numerator With denominator The ratio of is used as the first Production trend slope corresponding to each resource entity ,when When this occurs, it indicates that the resource load is on the rise. This indicates that the resource load is showing a downward trend. The larger the absolute value of , the more significant the th . The faster a resource entity responds to load changes;

[0086] Get the Fitting priority for each resource entity First, based on the obtained production trend slope Combined with time average with state mean Calculate the first Fitted state values ​​corresponding to each resource entity ;

[0087] Get the The actual state value of each resource entity and corresponding fitted state values The sum of squares of the deviations is expressed as the first deviation. and the actual state value at each moment. with state mean The sum of squares of the deviations and expressed as the second deviation Based on the first deviation And the second deviation Get the Resource entity fitting priority , represented as: ;

[0088] in, The value ranges from 0 to 1. The closer it is to 1, the stronger the linear trend explains the load fluctuations and the more significant the trend. The closer it is to 0, the stronger the randomness of the load fluctuations and the less obvious the linear trend.

[0089] Finally, the first Characteristics of hinge production fluctuation trends of individual resource entities By the Production trend slope of individual resource entities and the Fitting priority of each resource entity composition;

[0090] Furthermore, the first Characteristics of hinge production fluctuation trends of individual resource entities By the Production trend slope corresponding to each resource entity With fit priority The vectors can be directly concatenated to obtain the result, which is represented as: ;

[0091] No. The hinge production stability characteristics corresponding to each resource entity Characteristics of hinge production fluctuations The first is formed by vector concatenation. Dynamic performance vector of each resource entity ,Right now ;

[0092] The hinge production stability characteristics of all resource entities Characteristics of hinge production fluctuations The vectors are then concatenated and integrated again to form the final dynamic performance vector. ,in, This reflects the maximum production capacity that a resource entity can stably output under its current state.

[0093] Decision Analysis Module: Based on dynamic performance vector, a discrete event simulation engine is constructed and combined with the current work order queue to perform multi-round Monte Carlo simulation to generate a probability map of future resource conflicts. Based on the probability map of future resource conflicts, a directed graph network is combined to generate the optimal scheduling instruction sequence.

[0094] First, we build a discrete event simulation engine:

[0095] The discrete event simulation engine is based on a pre-set discrete event simulation algorithm model. As infrastructure:

[0096] Let the current work order queue be ,in The total number of work orders. Representing the The processing task requirements of each work order;

[0097] First, resource status sampling is performed. Let the work order execution cycle be represented as... Input the set of dynamic performance vectors of all resource entities Current work order queue and work order execution cycle And set the total number of rounds of Monte Carlo simulation as follows: ;

[0098] Then call a uniformly distributed random number generation function. Combined with dynamic performance vector The process handling capacity of a given operating state generates a state trajectory, namely: ;

[0099] in, For the generated first All resource entities in the round of simulation The state trajectory, , It is a random sampling function based on dynamic performance vector. Uniformly distributed random numbers are used to implement Monte Carlo randomization sampling, and the output includes resources. In the cycle The discrete event simulation engine is constructed by defining the running / fault / switching / idle states and corresponding time intervals within the system.

[0100] Then, multiple rounds of Monte Carlo simulations are performed to generate a probability map of future resource conflicts.

[0101] Define the work order task scheduling rules and conflict event detection rules for the discrete event simulation engine, where:

[0102] Work order task scheduling: This involves scheduling all sampled resource entities. State Trajectory , and work order queue The task requirements are matched, and resource entities are allocated to each work order process according to work order priority and process sequence to simulate the resource occupation and release process in actual production.

[0103] Conflict event detection rules: During the task matching process, resource conflict events are detected in real time. When two or more work orders conflict with the same resource entity at the same time, the conflict will be detected. When a demand for occupancy arises, it is determined to be a conflict event;

[0104] Then the dynamic performance vector set Import a discrete event simulation engine to provide performance constraints for the simulation of each resource entity;

[0105] For example: dynamic performance vector The effective capacity index determines the number of processes that the resource entity can process per unit time, and the failure probability index determines the random trigger probability of the resource entity failing within the simulation cycle.

[0106] Secondly, multiple rounds of Monte Carlo simulations were conducted, and in each round, based on the... The probability distribution of the dynamic performance vector of each resource entity is obtained by randomly sampling the future operating state of each resource entity, i.e.:

[0107] In the round In the process of task matching, resource conflict events are detected in real time. When two or more work orders conflict with the same resource entity at the same time, the conflict is detected. When a demand for occupancy arises, it is determined to be a conflict event;

[0108] Let the first In the Monte Carlo simulation, within the time interval The set of conflict events detected internally is Statistics on each resource entity in this round of simulation Number of conflicts ;

[0109] The probability of future resource conflicts is calculated based on the results of multiple rounds of simulation, assuming a total number of simulation rounds. Then the resource entity The probability of future conflict is:

[0110] ;

[0111] Among them, The duration of the work order execution cycle;

[0112] Map the conflict probabilities of all resource entities to the time dimension and the resource dimension to generate a future conflict probability map of resources. ;

[0113] Then, based on the resource future conflict probability map and the directed graph network, the optimal scheduling instruction sequence is generated:

[0114] Based on the probability map of future resource conflicts As input, the initial set of conflicting nodes is first identified. The process is as follows:

[0115] If resource entity At any moment Conflict probability θ is a preset conflict threshold, then... Marked as the initial conflict node;

[0116] Then, based on the resource future conflict probability map To establish a directed graph of conflict propagation, we need to determine the strength of the relationship between the conflict probabilities of resource entities. ,in, For all conflicting and related nodes, This represents a directed edge for conflict propagation, i.e., the probability of future conflict. The stronger the probability of future conflict, the stronger the connection between the associated nodes.

[0117] Then, in the directed graph of conflict propagation... In the middle, calculate the cumulative conflict probability for each path:

[0118] ;

[0119] in, This is a propagation path in the diagram. This represents the cumulative conflict probability of the path.

[0120] Then identify the set of critical paths with the highest cumulative conflict probability. For each critical path, adjust the resource allocation strategy of the core nodes on the path (such as shifting load or adjusting process sequence) until the cumulative conflict probability of all paths is less than the preset pruning threshold. , This is the pruning threshold;

[0121] Extract all resource adjustment actions during the pruning process and generate the optimal scheduling instruction sequence. ;

[0122] Each instruction It includes the execution time, target resource entity, and adjustment method, which directly serve as the basis for the operation of the scheduling and execution module.

[0123] Scheduling and execution module: Implements resource status scheduling for the automotive door hinge production line based on the optimal scheduling instruction sequence;

[0124] Receive the optimal scheduling instruction sequence Each instruction is parsed to extract the instruction execution time, target resource entity, adjustment method and parameters. After parsing, the feasibility of the instruction is verified by combining it with the current integrated resource status matrix of the production line, ensuring that the adjustment action has no logical conflict with the current resource status and work order execution progress.

[0125] For instructions that pass verification, they are converted into control signals that the production line control system can recognize; the instructions for the processing equipment are then converted into... Executable operating parameter adjustment instructions are converted from the instructions of the logistics unit into conveying path and cycle time adjustment instructions, and the instructions of tooling fixtures are converted into installation position and switching timing adjustment instructions. Through the industrial Internet of Things control network, the control signals are sent to the controllers of the corresponding resource entities in real time.

[0126] After receiving the control signal, the controller of each resource entity executes the adjustment action:

[0127] The processing equipment updates its task queue and operating parameters, the logistics unit optimizes its conveying path and cycle time, and the tooling fixtures adjust their installation positions and switching sequences. During the adjustment process, the system collects real-time status feedback data of resource entities, updates and merges the resource status matrix, and forms a closed-loop control.

[0128] In the application, several formulas are calculated by removing dimensions and taking their numerical values. The formulas are established by collecting a large amount of data and simulating the most recent real situation. Some coefficients or weights in the formulas are set by those skilled in the art according to the actual situation, so they will not be elaborated here.

[0129] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. 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.

[0130] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A resource status scheduling system for an automotive door hinge production line based on the Industrial Internet of Things, characterized in that, include: Production line data module: Collects real-time data from the entire production line and obtains a fused resource status matrix through multimodal feature synchronization and alignment preprocessing operations; Resource Analysis Module: Performs dynamic resource calculations on the fused resource state matrix to obtain hinge production stability characteristics and hinge production fluctuation trend characteristics, and obtains the dynamic efficiency vector of each resource entity based on the hinge production stability characteristics and hinge production fluctuation trend characteristics. Decision Analysis Module: Based on dynamic performance vector, a discrete event simulation engine is constructed and combined with the current work order queue to perform multi-round Monte Carlo simulation to generate a probability map of future resource conflicts. Based on the probability map of future resource conflicts, a directed graph network is combined to generate the optimal scheduling instruction sequence. The process of generating a probability map of future resource conflicts through multiple rounds of Monte Carlo simulations is as follows: Define the work order task scheduling rules and conflict event detection rules for the discrete event simulation engine; The work order task scheduling rule is based on all sampled resource entities. State Trajectory , and work order queue Match the task requirements; The conflict event detection rule applies to the same resource entity in two or more work orders at the same time. When a demand for occupancy arises, it is identified as a conflict event; Then, multiple rounds of Monte Carlo simulations are performed, assuming the [number]th [round]... In the Monte Carlo simulation, within the time interval The set of conflict events detected internally is Statistics on each resource entity in this round of simulation Number of conflicts ; The probability of future resource conflicts is calculated based on the results of multiple rounds of simulation, assuming a total number of simulation rounds. Then the resource entity The probability of future conflict is: ； in The duration of the work order execution cycle; Map the conflict probabilities of all resource entities to the time dimension and the resource dimension to generate a future conflict probability map of resources. ; The process of generating the optimal scheduling instruction sequence is as follows: Based on the probability map of future resource conflicts As input, when the resource entity At any moment Conflict probability If the conflict exceeds the preset threshold, then... Marked as the initial conflict node And combined with a probability map of future conflicts Establish a directed graph of conflict propagation ,in, For all conflicting and related nodes, This represents a directed edge representing conflict propagation, i.e., the probability of future conflicts. Directed graph of conflict propagation In the middle, calculate the cumulative conflict probability for each path: ； in, This is a propagation path in the diagram. This represents the cumulative conflict probability of the path. Identify the set of critical paths with the highest cumulative conflict probability. For each critical path, adjust the resource allocation strategy of the core nodes on the path until the cumulative conflict probability of all paths is less than a preset pruning threshold. , This is the pruning threshold; Extract all resource adjustment actions during the pruning process and generate the optimal scheduling instruction sequence. ; Scheduling and execution module: Implements resource status scheduling for the automotive door hinge production line based on the optimal scheduling instruction sequence.

2. The resource status scheduling system for automotive door hinge production line based on the Industrial Internet of Things as described in claim 1, characterized in that, The process of obtaining the fused resource state matrix is ​​as follows: The industrial IoT sensing network is activated to collect real-time operating parameters of the target object. The data collection targets include processing equipment, logistics and transportation units, and tooling fixtures within the production line. The clock signal of the production line's main controller is selected as the global time base to generate a time stamp sequence with equal intervals. For each acquisition node i, interpolation or resampling operations are performed on the original data sequence based on a global time base T to obtain a time-aligned dataset. ; Time-aligned dataset for each acquisition node i Perform min-max normalization, and then map the normalized single-node feature vectors to the resource entity dimension using production line process association rules to obtain the aligned dataset. ; Let the total number of resource entities in the automotive door hinge production line be . Assign a unique index to each resource entity Obtain fusion state value Arrange all fusion state values ​​according to the time dimension and the resource entity dimension to construct a fusion resource state matrix. .

3. The resource status scheduling system for automotive door hinge production line based on the Industrial Internet of Things as described in claim 2, characterized in that, The process of obtaining hinge production stability characteristics through dynamic resource calculation is as follows: The hinge production stability characteristics are obtained by calculating the standard deviation and the coefficient of variation of the state sequence. The standard deviation of the state sequence is expressed as: ; The coefficient of variation of the state sequence is expressed as: ; in, Indicates the first The mean of the state sequence corresponding to each resource entity. Indicates the first The standard deviation of each resource entity.

4. The resource status scheduling system for automotive door hinge production line based on the Industrial Internet of Things as described in claim 2, characterized in that, The process of obtaining the hinge production fluctuation trend characteristics through dynamic resource calculation is as follows: The hinge production fluctuation trend characteristics consist of the production trend slope and the fitting priority. The process of obtaining the production trend slope includes: Calculate the first data acquisition time. For each resource entity, the time deviation and state deviation are calculated by multiplying the time deviation at all times by the corresponding state deviation, and then summing all the products to obtain the numerator. ; Calculate the first data acquisition time. The denominator is the square of the time deviation corresponding to each resource entity, plus the sum of all the squared values. Based on the numerator With denominator Get the Production trend slope corresponding to each resource entity ; The process of obtaining the fitting priority includes: Based on the obtained production trend slope Combine time mean and state mean to obtain the first Fitting state values ​​of each resource entity ; Get the The actual state value of each resource entity at each moment and corresponding fitted state values The sum of squares of the deviations is expressed as the first deviation. and the actual state value at each moment. with state mean The sum of squares of the deviations and expressed as the second deviation Based on the first deviation And the second deviation Get the Resource entity fitting priority , is represented as: .

5. The resource status scheduling system for automotive door hinge production line based on the Industrial Internet of Things as described in claim 4, characterized in that, The process of obtaining the dynamic performance vector is as follows: Based on the The hinge production stability characteristics and hinge production fluctuation trend characteristics corresponding to each resource entity constitute the first... Dynamic performance vector of each resource entity The hinge production stability characteristics corresponding to all resource entities. Characteristics of hinge production fluctuations The final dynamic performance vector is then integrated. .

6. The resource status scheduling system for automotive door hinge production line based on industrial Internet of Things as described in claim 5, characterized in that, The process of building a discrete event simulation engine is as follows: The discrete event simulation engine is based on a pre-set discrete event simulation algorithm model. As a basic infrastructure, let the current work order queue be... The work order execution cycle is ; Combine the current work order queue with the dynamic performance vector set of all resource entities. The current work order queue is With work order execution cycle As input, and setting the total number of rounds in the multi-round Monte Carlo simulation to... Call a uniformly distributed random number generation function Combined with dynamic performance vector The process processing capacity of the limited operating state generates a state trajectory. ,in For the generated first All resource entities in the round of simulation The state trajectory, , A discrete event simulation engine is obtained by using a random sampling function based on a dynamic performance vector.

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