Iot-based lcd display screen production whole-process collaborative management method and system

Abstract

The application discloses an LCD display screen production whole-process collaborative management method and system based on an Internet of Things, and particularly relates to the technical field of production process collaborative management, and is used for solving the problems of circular waiting and resource deadlock caused by the lack of global resource allocation dependency relationship analysis in the complex production scene of the existing collaborative management method; by acquiring the equipment state data and material position data of each process in real time, a resource allocation relationship graph taking production equipment as a resource node and production task as a task node is constructed, the resource access sequence of each production task is analyzed to identify a circular waiting path, the blocking propagation risk is evaluated in combination with a process correlation network and a production flow network, and the urgency is quantitatively released according to the task priority, and finally, the production task is selected based on the release urgency to realize the resource re-allocation of the circular waiting path, so that the overall efficiency and resource utilization of the production line are improved.

Description

Technical Field

[0001] This invention relates to the field of collaborative management technology for production processes, and in particular to a method and system for collaborative management of the entire production process of LCD displays based on the Internet of Things. Background Technology

[0002] In the field of LCD manufacturing, building a collaborative management system for the entire production process based on IoT technology can improve operational efficiency. By deploying sensors and identification devices in each process, status data of materials, work-in-process, production equipment and environment are collected and transmitted to a central management platform. The aim is to achieve centralized monitoring and scheduling of production plans, material flow, equipment tasks and quality control. Existing technologies usually dynamically allocate production line resources and prioritize tasks based on real-time feedback of equipment status and order information to cope with changing production demands.

[0003] However, existing collaborative management methods have inherent flaws in their dynamic scheduling logic when facing complex production scenarios with multiple orders and multiple resources running in parallel. Due to the lack of insight and forward-looking analysis of the global dependency relationship of resource allocation, when the system responds to local state changes and performs real-time scheduling, it is easy to cause multiple production tasks to fall into a stalemate due to the cyclical waiting for occupied key resources. This will lead to a decline in local or overall production line efficiency, order delivery delays, and a reduction in resource utilization, which will restrict the further improvement of the overall collaborative efficiency. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a collaborative management method and system for the entire LCD display production process based on the Internet of Things.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0006] The IoT-based collaborative management method for the entire LCD display production process includes:

[0007] S1. Real-time acquisition of equipment status data and material location data for each process on the LCD display production line;

[0008] S2. Construct a resource allocation relationship diagram based on equipment status data and material location data, with production equipment as resource nodes and production tasks as task nodes;

[0009] S3. Analyze the resource access sequence formed by each production task based on its preset process route, identify the circular constraint relationship between different task sequences to detect cyclic waiting paths;

[0010] S4. When a circular waiting path is detected, analyze the set of processes with logical dependencies affected by the circular waiting path in the process association network constructed by the resource allocation relationship graph, and comprehensively assess the risk of congestion propagation by combining the changes in network structure performance indicators caused by the circular waiting path in the production flow network.

[0011] S5. Assess the urgency of resolving the circular waiting path by combining the risk of congestion propagation and the priority characteristics of each production task in the circular waiting path.

[0012] S6. Based on the urgency of the release, select production tasks in the circular waiting path and adjust resource allocation to release the circular waiting path by reallocating the resources occupied by the production tasks.

[0013] Furthermore, real-time acquisition of equipment status data and material location data for each process on the LCD display production line, including:

[0014] The IoT sensors deployed in each process of the LCD display production line collect real-time equipment status data, which includes equipment identification and equipment operating status.

[0015] The material location data is collected in real time by identification devices deployed on the production line. The material location data includes the material identification and the material's position information in the current process.

[0016] Furthermore, based on equipment status data and material location data, a resource allocation relationship diagram is constructed with production equipment as resource nodes and production tasks as task nodes, including:

[0017] Based on the device identifier and device operating status in the device status data, production devices in the running or standby state are mapped as resource nodes;

[0018] Based on the material identifier in the material location data and the material's location information in the current process, production tasks that are in the processing or waiting state are mapped as task nodes;

[0019] Based on the location information of materials in the current process and the operating status of equipment, directed edges representing resource occupation or resource application relationships are established between production task nodes and production equipment resource nodes to complete the construction of the resource allocation relationship graph.

[0020] Furthermore, the resource access sequence formed by each production task based on its preset process route is analyzed to identify cyclic constraint relationships between different task sequences in order to detect circular waiting paths, including:

[0021] For each production task node in the resource allocation diagram, an ordered sequence of production equipment resource access is generated based on the preset process route corresponding to the production task.

[0022] In the resource allocation relationship graph, the production equipment resource access sequence of each production task node is traversed to identify the circular waiting dependency formed between the resource access sequences of different production task nodes due to the sharing of production equipment resource nodes.

[0023] When a circular wait dependency exists, a set of production task nodes and production equipment resource nodes that constitute a circular wait dependency, along with their connection relationships, are determined to be a circular wait path.

[0024] Furthermore, when a circular waiting path is detected, the set of logically dependent processes affected by the circular waiting path in the process association network constructed by the resource allocation relationship graph is analyzed. Combined with the changes in network structure performance indicators caused by the circular waiting path in the production flow network, the risk of congestion propagation is comprehensively assessed, including:

[0025] A process association network is constructed based on the resource allocation relationship graph. The process association network is used to represent the logical dependencies between production task nodes based on the preset process route.

[0026] In the process association network, traverse the production task nodes involved in the loop waiting path to determine the set of processes that have logical dependencies on them.

[0027] A production flow network is constructed based on a resource allocation relationship graph. The production flow network is used to represent the connection relationship between production equipment resource nodes based on material flow.

[0028] In a production flow network, analyze the changes in network structure performance indicators caused by circular waiting paths;

[0029] By combining the set of processes with logical dependencies and changes in network structure performance indicators, a risk assessment of congestion propagation is performed.

[0030] Furthermore, by combining the set of processes with logical dependencies and changes in network structure performance indicators, the risk assessment of congestion propagation includes:

[0031] The first risk factor is determined based on the size of the set of processes with logical dependencies.

[0032] The second risk factor is determined based on the degree of change in the network structure performance indicators of the production flow network.

[0033] The first and second risk factors are weighted and fused to generate the risk assessment result of blocking propagation.

[0034] Furthermore, considering the risk of congestion propagation and the priority characteristics of each production task in the circular waiting path, the urgency of resolving the circular waiting path is assessed, including:

[0035] The global impact weight of the circular waiting path is determined based on the results of the obstruction propagation risk assessment.

[0036] Obtain the production task priority characteristics of each production task in the circular waiting path;

[0037] By combining the global impact weight and the priority characteristics of production tasks, a weighted calculation is used to generate an assessment value for the urgency of resolving the circular waiting path.

[0038] Furthermore, determining the global impact weight of the circular waiting path based on the congestion propagation risk assessment results includes: pre-setting the mapping relationship between the congestion propagation risk assessment results and the global impact weight; querying the mapping relationship based on the congestion propagation risk assessment results; and outputting the corresponding queried value as the global impact weight of the circular waiting path.

[0039] Obtaining the production task priority characteristics of each production task in the circular waiting path includes: extracting the order delivery deadline urgency parameter from the production task attributes; extracting the customer level priority parameter from the production task attributes; and combining the order delivery deadline urgency parameter and the customer level priority parameter to calculate and generate a quantitative value of the production task priority characteristics.

[0040] Furthermore, based on the urgency of easing restrictions, resource allocation adjustments are made to production tasks within the circular waiting path. This process, which reallocates the resources used by the production tasks, resolves the circular waiting path. This includes:

[0041] Sort all production tasks in the cyclic waiting path according to their urgency assessment value;

[0042] Select a production task whose urgency assessment value meets the preset conditions as the target adjustment task;

[0043] In the resource allocation relationship diagram, the resource occupation relationship between the target adjustment task and the currently occupied production equipment resource node is removed;

[0044] The target adjustment task is reassigned to an available alternative production equipment resource node to eliminate circular wait dependencies, thereby removing the circular wait path.

[0045] On the other hand, the present invention provides a collaborative management system for the entire LCD display production process based on the Internet of Things, including:

[0046] The data acquisition module is used to acquire real-time equipment status data and material location data for each process on the LCD display production line.

[0047] The allocation construction module is used to construct a resource allocation relationship diagram with production equipment as resource nodes and production tasks as task nodes based on equipment status data and material location data;

[0048] The path detection module is used to analyze the resource access sequence formed by each production task based on its preset process route, identify the circular constraint relationship between different task sequences, and detect cyclic waiting paths.

[0049] The congestion assessment module is used to analyze the set of logically dependent processes affected by the circular waiting path in the process association network constructed by the resource allocation relationship graph when a circular waiting path is detected, and to comprehensively assess the risk of congestion propagation by combining the changes in network structure performance indicators caused by the circular waiting path in the production flow network.

[0050] The urgency assessment module is used to assess the urgency of resolving the circular waiting path by combining the risk of congestion propagation and the priority characteristics of each production task in the circular waiting path.

[0051] The allocation adjustment module is used to adjust the resource allocation of production tasks in the circular waiting path based on the urgency of the task, and to release the circular waiting path by reallocating the resources occupied by the production tasks.

[0052] The beneficial effects of this invention are:

[0053] 1. By constructing a resource allocation relationship graph and identifying cyclic waiting paths, a deep insight and forward-looking analysis of the global dependencies in production resource allocation are achieved. Based on real-time collected equipment status and material location data, production equipment is mapped to resource nodes and production tasks are mapped to task nodes. By analyzing the preset process routes of each task to generate resource access sequences, the circular constraint relationships formed between different task sequences due to shared resources are systematically revealed. Potential cyclic waiting paths can be proactively detected, fundamentally avoiding the multi-task resource deadlock problem caused by local scheduling decisions. This enables the production line to maintain the coordination and continuity of resource allocation when dealing with multiple order parallel scenarios, significantly improving the stability and response efficiency of the entire process collaborative management.

[0054] 2. By combining the logical dependencies in the process association network with the performance index changes of the production flow network, the risk of congestion propagation is comprehensively assessed, and the urgency of relief is quantified based on task priority characteristics. This enables precise intervention in cyclical waiting paths, taking into account not only the propagation range of congestion impact but also the urgency of production tasks and customer value factors. As a result, global efficiency and local needs are balanced in resource reallocation decisions, effectively preventing production interruptions and resource idleness caused by cyclical waiting, ensuring the synchronous optimization of equipment utilization and order delivery timeliness, and ultimately achieving a systematic improvement in production operation efficiency at the management level. Attached Figure Description

[0055] Figure 1This is a flowchart of the IoT-based collaborative management method for the entire LCD display production process according to the present invention.

[0056] Figure 2 This is a schematic diagram of the collaborative management system for the entire LCD display production process based on the Internet of Things (IoT) of this invention. Detailed Implementation

[0057] 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.

[0058] Example 1: Figure 1 This invention presents a collaborative management method for the entire LCD display production process based on the Internet of Things, comprising:

[0059] S1. Real-time acquisition of equipment status data and material location data for each process on the LCD display production line;

[0060] S2. Construct a resource allocation relationship diagram based on equipment status data and material location data, with production equipment as resource nodes and production tasks as task nodes;

[0061] S3. Analyze the resource access sequence formed by each production task based on its preset process route, identify the circular constraint relationship between different task sequences to detect cyclic waiting paths;

[0062] S4. When a circular waiting path is detected, analyze the set of processes with logical dependencies affected by the circular waiting path in the process association network constructed by the resource allocation relationship graph, and comprehensively assess the risk of congestion propagation by combining the changes in network structure performance indicators caused by the circular waiting path in the production flow network.

[0063] S5. Assess the urgency of resolving the circular waiting path by combining the risk of congestion propagation and the priority characteristics of each production task in the circular waiting path.

[0064] S6. Based on the urgency of the release, select production tasks in the circular waiting path and adjust resource allocation to release the circular waiting path by reallocating the resources occupied by the production tasks.

[0065] S1. Real-time acquisition of equipment status data and material location data for each process on the LCD display production line, specifically implemented as follows:

[0066] In the end-to-end collaborative management approach for LCD display production lines, real-time acquisition of equipment status data and material location data for each process is achieved through physical hardware deployed on the production line. Specifically, IoT sensors are installed at every process location on the production line, including but not limited to cleaning, coating, exposure, etching, demolding, cell assembly, and module assembly processes. These sensors are either directly attached to the production equipment or integrated into the equipment control system. The type of IoT sensor is selected based on the process and equipment characteristics. For example, current and voltage sensors are deployed in processes requiring monitoring of power equipment, temperature sensors are deployed in processes requiring monitoring of thermal management equipment, and vibration or speed sensors are deployed in processes requiring monitoring of mechanical motion equipment. The equipment identifier in the equipment status data is pre-assigned during production system initialization. This identifier is a unique string sequence, such as a combination of equipment type code and serial number, for example, ArrayEtchMachine001 represents array etching equipment number 001. The equipment's operating status is determined by comparing real-time parameter values ​​collected by sensors with preset thresholds. For example, if the current value read by the current sensor is within the normal operating range, the status is determined to be running; if the current value is below the minimum operating current threshold, the status is determined to be stopped; and if the current value exceeds the maximum safe current threshold, the status is determined to be faulty. These thresholds are set based on the equipment manufacturer's specifications and statistical analysis of historical operating data. The minimum operating current threshold is determined by analyzing the equipment's no-load current value; for example, 90% of the no-load current value is used as the minimum operating current threshold. The maximum safe current threshold is determined by the equipment's rated current value; for example, 120% of the rated current value is used as the maximum safe current threshold. Sensor data is transmitted in real time via industrial communication protocols such as Modbus TCP or OPC UA. The sampling frequency is set according to the criticality of the process; for example, high-precision processes are sampled 10 times per second, while ordinary processes are sampled once per second. The data packets contain equipment identification, timestamps, and status values ​​to ensure that the central management platform can continuously receive and process the data.

[0067] Material location data acquisition relies on identification devices deployed at key nodes of the production line. These devices include RFID readers, QR code scanners, or visual recognition cameras, installed at process entrances, internal workstations, and process exits. Material identification is stored through electronic or physical tags attached to the material carrier. For example, UHF RFID tags store 96-bit electronic product codes as material identifiers, or QR codes store a combination of material batch numbers and serial numbers. The material's location within the current process is directly defined by the physical installation location of the identification devices. For instance, when material enters the cleaning process, the RFID reader at the entrance reads the tag and records the location as the cleaning process entrance. If the material moves within the process, the precise location is determined by the reading sequence of multiple readers. For example, in the coating process, three readers identify the material's location at the feeding, coating, and discharging points, respectively. The identification devices connect to the central system via fieldbuses such as PROFIBUS or Ethernet, triggered by events. When a tag enters the reading range, data is immediately read and uploaded, including material identification, location identification, and a timestamp. To ensure data accuracy, the identification equipment is calibrated and maintained regularly. For example, standard test tags are used monthly to verify read / write distance and sensitivity, preventing misreads or missed reads. Location information is linked to process logic. For instance, if material stays at a certain process location for more than a preset material dwell timeout threshold, the system marks it as abnormal. This material dwell timeout threshold is based on the standard cycle time of the process, which is determined by averaging the completion time of that process in historical production data. For example, the standard cycle time for a cleaning process is 30 seconds, and the material dwell timeout threshold is set to 60 seconds. All collected equipment status data and material location data are encrypted and verified during transmission. For example, AES encryption is used to ensure data security, and CRC verification is used to ensure data integrity, thus providing reliable input for the subsequent construction of resource allocation relationship diagrams.

[0068] S2. Construct a resource allocation diagram based on equipment status data and material location data, with production equipment as resource nodes and production tasks as task nodes. The specific implementation is as follows:

[0069] Based on the device identifier and device operating status in the device status data, production devices in the running or standby state are mapped as resource nodes. The device operating status is determined by comparing the real-time parameter values ​​collected by IoT sensors with preset status thresholds. For example, if the current value read by the current sensor is between the minimum operating current threshold and the maximum safe current threshold, it is determined to be in the running state. If the current value is lower than the minimum operating current threshold but the device power signal is not turned on, it is determined to be in the standby state. Resource nodes are represented by device identifiers as unique identifiers in the graph. Node attributes include device type code and device operating status. The mapping process is implemented by parsing the real-time transmitted device status data stream. Whenever a new data packet containing device identifier and device operating status is received, the system checks whether the device already exists in the resource node set. If it does not exist, a new resource node is created. If it exists, the node status is updated to ensure that the resource node set dynamically reflects all available production devices on the production line.

[0070] Based on the material identifier and its location information in the current process from the material location data, production tasks in processing or waiting states are mapped to task nodes. The processing state is determined jointly by the material location information and the equipment operating status. For example, if the material location information indicates that the material is located at the processing station of the process and the corresponding production equipment is in running state, it is determined to be in processing state. The waiting state is determined by the material location information indicating that the material is located at the buffer or entrance position of the process and the corresponding production equipment is in standby or running state. Task nodes are represented in the diagram by the material identifier as a unique identifier. Node attributes include material type code, current process code, and task status. The mapping process is conducted through… The system analyzes the correlation between material location data streams and equipment status data. For example, when the identification device reads the material identifier and records the location information, the system determines the process code based on the location information, then queries the status of the production equipment corresponding to the process, and thus sets the task node status. If the material stays at a certain process location for more than the material stay timeout threshold, the task status is marked as abnormal. The material stay timeout threshold is set based on the standard cycle time of the process. For example, it is determined by adding a buffer time to the average completion time of the process in the historical production data. The standard cycle time is the average value, and the buffer time is 50% of the standard cycle time. For example, if the standard cycle time of the cleaning process is 30 seconds, then the material stay timeout threshold is set to 45 seconds.

[0071] Based on the material's location information and equipment operating status in the current process, directed edges representing resource occupancy or resource request relationships are established between production task nodes and production equipment resource nodes. A resource occupancy relationship is defined as the task node currently using a resource node, determined by the task node being in a processing state and the corresponding production equipment being in an "operating" state. For example, when the material is located at the coating position in the coating process and the coating equipment is running, a directed edge from the resource node to the task node is established to represent resource occupancy. A resource request relationship is defined as the task node requesting to use a resource node but not yet occupying it, determined by the task node being in a waiting state and the corresponding production equipment being in a standby or operating state. For example, when the material is located at the entrance of the cleaning process and the cleaning equipment is in standby mode, a directed edge is established between the task node and the resource node... Resource requests are represented by directed edges connecting task nodes to resource nodes. These edges are established by matching the process code in the material location information with the equipment identifier of the resource node. A process-equipment mapping table is used to associate process codes with equipment identifiers. This mapping table is preset during production system initialization and contains a list of possible equipment identifiers for each process code. For example, the cleaning process is mapped to cleaning equipment 001 and cleaning equipment 002. After the edges are established, the resource allocation graph is stored in a graph data structure. The node set includes all resource nodes and task nodes, and the edge set includes all directed edges. Graph updates are triggered when equipment status data or material location data changes. For example, when the equipment's operating status changes from running to stopped, all directed edges related to that resource node are removed, and the task node status is re-evaluated to ensure that the resource allocation graph is synchronized with the production line status in real time.

[0072] S3. Analyze the resource access sequence formed by each production task based on its preset process route, identify the circular constraint relationship between different task sequences to detect cyclic waiting paths, specifically implemented as follows:

[0073] For each production task node in the resource allocation diagram, an ordered sequence of production equipment resource accesses is generated based on the preset process route corresponding to the production task. The preset process route is stored in the process route configuration database of the production line management system. Each production task is associated with a specific process route identifier through its material type attribute. Each process route identifier uniquely corresponds to a sequence of operation codes, defining the order in which the production task must proceed, such as cleaning, coating, and exposure operation codes. When generating the production equipment resource access sequence, the system first queries the process route configuration database based on the material type of the production task node to obtain the operation code sequence. Then, it uses an operation equipment mapping table to convert each operation code into a specific production equipment resource node identifier. This operation equipment mapping table is preset during production system initialization and includes... Each process code corresponds to a list of optional production equipment identifiers. For example, the cleaning process code may be mapped to cleaning equipment 001 and cleaning equipment 002. The system selects the equipment identifier that is running or in standby state from the optional list based on the current equipment operating status of the production equipment resource node. The selection takes into account the equipment load factor. The equipment load is determined by calculating the number of production task nodes currently associated with the equipment. For example, the equipment load is equal to the number of production task nodes connected to the equipment resource node in the resource allocation relationship diagram. Equipment identifiers with lower equipment loads are selected first to fill the sequence. After the sequence is generated, it is stored as an ordered list structure. The list elements are production equipment resource node identifiers. The order is completely consistent with the process route process code sequence. The sequence length is the same as the number of processes in the process route. The sequence generation process is triggered when the resource allocation relationship diagram is updated to ensure synchronization with the current production line status.

[0074] In the resource allocation graph, the system traverses the resource access sequences of each production task node to identify circular waiting dependencies formed between resource access sequences of different production task nodes due to shared production equipment resource nodes. The traversal process starts from all production task nodes in the processing or waiting state in the resource allocation graph, and processes the resource access sequences of each production task node in turn. When identifying circular waiting dependencies, the system constructs a resource dependency graph, where the nodes are production equipment resource nodes and the edges represent the access order dependencies of production task nodes to production equipment resource nodes. Specifically, for each production task node, consecutive device access pairs are extracted from its production equipment resource access sequence. For example, if device A is followed by device B in the sequence, a directed edge from device A to device B is added to the resource dependency graph, and the associated production task node is labeled. The identification of circular waiting dependencies is achieved by detecting whether there is a cycle in the resource dependency graph. Cycle detection uses a depth-first search algorithm, which starts from the resource dependency graph... Any node begins recursively visiting its neighboring nodes, maintaining an access status flag and a path stack. The access status flag records the access status of each node, including unvisited, being visited, and visited. The path stack stores the sequence of nodes on the current recursive path. When a visited node that is still in the path stack is encountered during the search, a cycle is determined to exist. The sequence of nodes in the cycle is a potential circular wait dependency. However, it is necessary to further verify whether the cycle forms a valid wait condition in the actual resource allocation graph. During verification, it is checked whether each production equipment resource node in the cycle has a resource occupation relationship or resource request relationship associated with the production task nodes in the cycle. For example, if the cycle contains equipment X and equipment Y, and there is a production task node A occupying equipment X and requesting equipment Y, while production task node B occupies equipment Y and requests equipment X, then a circular wait dependency is determined to be formed. The complete definition of a circular wait dependency requires that the cycle contains at least two different production task nodes and two different production equipment resource nodes, and each production equipment resource node is occupied or requested by at least one production task node.

[0075] When a circular wait dependency exists, a group of production task nodes and production equipment resource nodes that constitute a circular wait dependency, along with their connections, are identified as a circular wait path. The identification process includes extracting the identifiers of all production task nodes and production equipment resource nodes involved in the circular wait dependency, as well as the set of directed edges between these nodes in the resource allocation graph. These directed edges include resource occupancy relationships and resource request relationships. The circular wait path is stored as a path object, containing a node list and an edge list. The node list is arranged in an ordered manner to reflect the circular structure; for example, starting from any production task node, production task nodes and production equipment resource nodes are listed alternately in a circular order. The edge list... The system records the type and direction of directed edges between nodes. The path object also contains a strength index for circular waiting dependencies. The strength index is determined by calculating the ratio of the number of resource request relationships to the number of resource occupancy relationships in the ring. For example, if there are two resource request relationships and two resource occupancy relationships in the ring, the ratio is one. The identifier of the circular waiting path is generated based on the hash value of the identifiers of all nodes in the ring to ensure uniqueness. After determination, the circular waiting path is added to the system's circular waiting path set. This set is dynamically maintained. When the resource allocation relationship graph changes, the circular waiting dependency is re-evaluated. If the ring is destroyed, the corresponding path is removed from the set. Circular waiting paths are used for subsequent congestion propagation risk assessment and urgency assessment.

[0076] S4. When a circular waiting path is detected, analyze the set of logically dependent processes affected by the circular waiting path in the process association network constructed by the resource allocation relationship graph, and comprehensively assess the risk of congestion propagation by combining the changes in network structure performance indicators caused by the circular waiting path in the production flow network. Specifically, the implementation is as follows:

[0077] A process association network is constructed based on the resource allocation graph. This network represents the logical dependencies between production task nodes based on preset process routes. The construction process uses production task nodes from the resource allocation graph as nodes in the process association network. Node attributes include the material identifier and current process code of the production task node. Logical dependencies are determined by analyzing the preset process routes of the production task nodes. These preset process routes are stored in a process route configuration database, defining the sequence of process codes required for each material type. For example, the preset process route for LCD panel production includes cleaning, coating, exposure, etching, stripping, cell assembly, and module assembly process codes. The edges of the process association network represent the logical dependencies between production task nodes, including sequence dependencies and resource dependencies. Sequence dependencies are determined by comparing the position of the current process code of a production task node within the preset process route. For example, if the current process code of production task node A... If the code is located before the current process code of production task node B in the preset process route, a directed edge from production task node A to production task node B is added. Resource dependency is determined by checking whether production task nodes share the same production equipment resource node in the resource allocation relationship graph. For example, if two production task nodes have resource occupation or resource application relationships with the same production equipment resource node in the resource allocation relationship graph, a bidirectional edge is added to indicate resource competition dependency. The process association network is stored using a graph data structure, and the edge weight represents the dependency strength. The dependency strength is determined by calculating the process distance and resource competition degree between production task nodes. The process distance is defined as the difference in the number of steps between two process codes in the preset process route. The resource competition degree is calculated by the load ratio of the shared production equipment resource node. The load ratio is equal to the number of production task nodes associated with the production equipment resource node divided by the maximum allowable load. The maximum allowable load is preset based on the production equipment type. For example, the maximum allowable load for cleaning equipment is 5.

[0078] In the process association network, the production task nodes involved in the circular waiting path are traversed to determine the set of processes with logical dependencies. The traversal process starts from each production task node in the circular waiting path and performs a breadth-first search in the process association network. The starting node of the breadth-first search is a production task node in the circular waiting path. The search depth is limited by a preset maximum search depth, which is set based on the average path length of the process association network. For example, twice the average path length is taken as the maximum search depth. During the search, all reachable production task nodes are collected. These nodes constitute the set of potentially affected nodes. Then, nodes with logical dependencies are filtered out. Logical dependencies include direct dependencies and indirect dependencies. Direct dependencies... Dependencies are determined directly by the edges existing in the process association network, while indirect dependencies are determined by checking whether there is a path between nodes. Dependencies with a path length exceeding 1 hop are considered indirect dependencies. The process set with logical dependencies ultimately includes all selected production task nodes and their associated process codes. The size of the process set is determined by counting the number of unique process codes in the set. For example, if the process set includes cleaning process codes, coating process codes, and exposure process codes, the size is 3. The process set also records the degree to which each process code is affected. The degree of impact is determined by calculating the proportion of the number of production task nodes that the process code appears in the set to the total number of production task nodes for that process code. The total number of production task nodes is counted from the resource allocation relationship graph.

[0079] A production flow network is constructed based on a resource allocation graph. This network represents the connections between production equipment resource nodes formed by material flow. The construction process uses the production equipment resource nodes in the resource allocation graph as nodes in the production flow network. Node attributes include the equipment identifier and equipment type. Connections are determined by analyzing the material flow path on the production line. The flow path is derived based on a preset process route and a process equipment mapping table. For example, the material flow from the cleaning process to the coating process may correspond to a connection between the cleaning equipment and the coating equipment. Edges in the production flow network represent the material flow direction between production equipment resource nodes, and edge weights represent the flow frequency. The flow frequency is obtained through historical production data statistics, such as counting the number of times material flows from the cleaning equipment to the coating equipment over a period of time. The production flow network is stored using a directed graph data structure, with edge directions consistent with the material flow direction. The production flow network also includes virtual start nodes and virtual end nodes. Virtual start nodes connect to all inbound process production equipment resource nodes, and virtual end nodes connect to all outbound process production equipment resource nodes, thus fully representing the entire material flow process from inlet to outlet.

[0080] In a production flow network, the changes in network structure performance indicators caused by cyclic waiting paths are analyzed. These indicators include network efficiency, node betweenness centrality, and connectivity. Network efficiency is determined by calculating the inverse average of the shortest path lengths for all pairs of nodes in the production flow network. The shortest path lengths are calculated using Dijkstra's algorithm, considering edge weights as a distance metric. The change in network efficiency caused by cyclic waiting paths is obtained by comparing the network efficiency values ​​before and after the introduction of cyclic waiting paths. Node betweenness centrality is determined by calculating the proportion of shortest paths passing through each node in the production flow network to the total number of shortest paths. Cyclic waiting paths may significantly increase the betweenness centrality of some nodes, indicating that these nodes become bottlenecks. The risk of bottlenecks increases. Connectivity is determined by calculating the ratio of the largest connected subgraph size of the production flow network to the entire network size. Cyclic waiting paths may lead to decreased connectivity, indicating an increased risk of network splitting. The analysis process first establishes a baseline production flow network, which represents the ideal state without cyclic waiting paths. This is simulated by removing the production equipment resource nodes and their connecting edges involved in cyclic waiting paths from the current production flow network. Then, the differences between the current production flow network and the baseline production flow network in various performance indicators are calculated. The absolute value of the difference represents the degree of change. The degree of change is normalized to the range of 0 to 1. The normalization uses the minimum-maximum scaling method, and the minimum and maximum values ​​are set based on the maximum range of change of the indicator in historical data.

[0081] By combining the sets of logically dependent processes with changes in network structure performance indicators, a congestion propagation risk assessment is performed. A first risk factor is determined based on the size of the logically dependent process sets. This first risk factor is obtained by mapping the size of the process sets to a risk value range of 0 to 1 using a linear function. For example, a process set size of 1 corresponds to a risk value of 0.1, and a size of 5 corresponds to a risk value of 0.9. The correspondence between size and risk value is established based on statistical analysis of historical congestion event data. This data records past congestion events and the number of processes they affected. Regression analysis is used to determine the linear relationship between size and risk. A second risk factor is determined based on the degree of change in the network structure performance indicators of the production flow network. This second risk factor is calculated by comprehensively considering changes in network efficiency, node betweenness centrality, and connectivity. First, the degree of change in each performance indicator is converted into a risk component. An exponential function is used for the conversion to emphasize the impact of larger changes. For example, a change of 0.5 in network efficiency is converted into a risk component of 0.75. Then, the average of the three risk components is taken to obtain the second risk factor. The first and second risk factors are then weighted and fused to generate the congestion propagation risk assessment result. The weighted fusion uses a weighted average formula. The weights of the first and second risk factors are set by an expert evaluation method. The expert evaluation method invites production management experts to score the importance of the two factors. The scores are averaged and normalized to a weight value. For example, the weight of the first risk factor is 0.6 and the weight of the second risk factor is 0.4. The congestion propagation risk assessment result is a value between 0 and 1. The higher the value, the greater the risk of congestion propagation. The assessment result is used for subsequent assessment of the urgency of resolution.

[0082] S5. Based on the risk of congestion propagation and the priority characteristics of each production task in the circular waiting path, assess the urgency of resolving the circular waiting path. Specifically, this is implemented as follows:

[0083] The global impact weight of the cyclic waiting path is determined based on the congestion propagation risk assessment results. The congestion propagation risk assessment results are numerical results generated after the congestion propagation risk assessment performed in the previous steps. These numerical results are floating-point numbers between 0 and 1. The global impact weight represents the degree of impact of the cyclic waiting path on the overall operation of the production line; a higher value indicates a greater impact. A pre-defined mapping relationship between the congestion propagation risk assessment results and the global impact weight is established by creating a numerical correspondence table. This table is based on historical production data analysis. Historical production data includes records of past congestion events and their corresponding congestion propagation risk assessment results and actual impact data. The actual impact is quantified by the ratio of the production delay time caused by the event to the total planned time. For example, if the delay time accounts for 10% of the total planned time, the actual impact is 0.1; if the delay time accounts for 50% of the total planned time, the actual impact is 0.5. The mapping relationship is designed so that the higher the congestion propagation risk assessment result, the higher the global impact weight. The value of the weight is large, indicating a positive correlation. The specific mapping value is determined through statistical analysis. For example, linear regression analysis is performed on the risk assessment results and actual impact of the blockage and propagation in historical data to fit a conversion function from the assessment result to the weight. The conversion function can be a linear function. For example, a risk assessment result of 0.3 corresponds to a global impact weight of 0.4, and an assessment result of 0.7 corresponds to a global impact weight of 0.8. When querying the mapping relationship based on the risk assessment result of the blockage and propagation, the system uses the current assessment result as the input parameter and performs a matching search in the numerical correspondence table. The matching search uses a linear interpolation method. If the assessment result is between two values ​​in the table, the corresponding weight is calculated through interpolation. For example, if the assessment result is 0.5, and 0.3 corresponds to 0.4 and 0.7 corresponds to 0.8 in the table, then the interpolation weight is 0.6. The corresponding value found in the query is output as the global impact weight of the loop waiting path. The global impact weight is stored in floating-point format with a value range of 0 to 1, and is used for the subsequent calculation of the urgency assessment value.

[0084] The production task priority feature is obtained for each production task in the circular waiting path. This feature represents the relative importance of a single production task within the production line. The order delivery deadline urgency parameter is extracted from the production task attributes. This parameter is obtained by calculating the ratio of the remaining delivery time to the total allowed delivery time. The remaining delivery time is the time difference between the current time and the order delivery deadline, calculated in hours. The total allowed delivery time is the time difference between the order receipt time and the delivery deadline, also calculated in hours. A smaller ratio indicates higher urgency. For example, if the remaining delivery time is 48 hours and the total allowed delivery time is 240 hours, the ratio is 0.2. The order delivery deadline urgency parameter is obtained by subtracting this ratio from 1, so that a larger parameter value indicates higher urgency. For example, if the ratio is 0.2, the order delivery deadline urgency parameter is 0.8. The customer level priority parameter is also extracted from the production task attributes. This parameter is based on a customer level code mapping, stored in the order management database. The code ranges from 1 to 5, with larger numbers indicating higher customer levels and greater priority. Priority parameters are obtained by normalizing customer level codes to a range of 0 to 1. For example, if the customer level code is 3, the customer level priority parameter is 0.6. The mapping relationship between customer level codes and priority parameters is set through a customer importance strategy, which is formulated by the production management department based on the historical order value and cooperation stability of customers. For example, high-value customers correspond to higher level codes. Combining the order delivery deadline urgency parameter and the customer level priority parameter, a quantitative value of the production task priority characteristic is calculated. The calculation uses a weighted average method. The weights of the order delivery deadline urgency parameter and the customer level priority parameter are set through an expert evaluation method. The expert evaluation method invites production scheduling experts to score the importance of the two parameters. The scores are averaged and then normalized. For example, if the order delivery deadline urgency parameter weight is 0.7 and the customer level priority parameter weight is 0.3, the quantitative value of the production task priority characteristic is equal to the order delivery deadline urgency parameter multiplied by 0.7 plus the customer level priority parameter multiplied by 0.3. The quantitative value ranges from 0 to 1. The higher the value, the higher the priority of the production task. The quantitative value of the priority characteristic of each production task is calculated and stored independently.

[0085] By combining global impact weights and production task priority characteristics, a weighted calculation is used to generate a urgency assessment value for resolving circular waiting paths. This urgency assessment value indicates the degree of urgency in resolving the circular waiting path. The weighted calculation first requires obtaining the quantified values ​​of the production task priority characteristics of all production tasks within the circular waiting path. Then, the average of these quantified values ​​is calculated as the overall priority level of the path. For example, if a circular waiting path contains three production tasks with priority characteristic quantified values ​​of 0.8, 0.6, and 0.7 respectively, the average value is 0.7. The combination of global impact weights and overall priority level uses a multiplicative model. The urgency assessment value is equal to the global impact weight multiplied by the overall priority level. For example, if the global impact weight is 0.8 and the overall priority level is 0.7, then the urgency assessment value is 0.56. The multiplicative model emphasizes the synergistic effect of global impact and priority level, that is, the urgency is highest when both are high. The urgency assessment value generated after weighted calculation is stored as a floating-point number with a value range of 0 to 1. The higher the value, the higher the urgency of resolving the circular waiting path. The assessment value is used for subsequent resource allocation adjustment decisions, such as selecting the path with the highest urgency assessment value among multiple circular waiting paths for priority processing.

[0086] S6. Based on the urgency of easing, adjust the resource allocation for production tasks in the circular waiting path. The circular waiting path is eased by reallocating the resources occupied by the production tasks. Specifically, the implementation is as follows:

[0087] All production tasks in the circular waiting path are sorted according to their urgency assessment value. This urgency assessment value is a floating-point number between 0 and 1, generated through a weighted calculation in the previous steps; a higher value indicates higher urgency. The sorting process uses descending order, meaning the production task with the highest urgency assessment value is placed at the beginning of the sequence. The sorting algorithm used is quicksort. Quicksort recursively sorts the production task list into two sublists by selecting a pivot element. The pivot element is selected using the median method, taking the urgency assessment value of the production task at the middle position of the list. Based on this, during the sorting process, when comparing the relief urgency assessment values ​​of two production tasks, floating-point values ​​are directly compared. The sorting result is stored as an ordered list structure, where each element is a production task node identifier and its corresponding relief urgency assessment value. For example, if a circular waiting path contains three production task nodes with relief urgency assessment values ​​of 0.8, 0.6, and 0.9, the sorted sequence would be the production task nodes corresponding to 0.9, 0.8, and 0.6. The sorting operation is triggered immediately upon detecting a circular waiting path to ensure that the sorting result is based on the latest relief urgency assessment value. The sorted list is used for subsequent target adjustment task selection.

[0088] One production task whose urgency assessment value meets the preset condition is selected as the target adjustment task. The preset condition is defined as having the highest urgency assessment value in the sorted list. That is, the first production task in the sorted list is selected as the target adjustment task. If multiple production tasks have the same highest urgency assessment value, auxiliary conditions further filter based on the production task priority feature quantification value. The production task priority feature quantification value comes from the values ​​calculated in the previous steps. The production task with the largest priority feature quantification value is selected as the target adjustment task. For example, if two production tasks both have an urgency assessment value of 0.9 and priority feature quantification values ​​of 0.8 and 0.7 respectively, then the production task with the priority feature quantification value of 0.8 is selected. The preset condition is met. System configuration parameters are stored in the policy configuration file of the management platform. The parameter values ​​are adjusted by the production administrator according to the production line scheduling strategy. For example, in an emergency, the preset conditions can be modified to select production tasks whose urgency assessment value exceeds a specific urgency assessment threshold. The urgency assessment threshold is set through historical data analysis. Historical data includes past cases of successfully clearing loop waiting paths and their urgency assessment value distribution. The urgency assessment threshold is taken from the upper quartile of the distribution. For example, if the upper quartile is 0.7, the urgency assessment threshold is set to 0.7. The selection process traverses the sorted list of production tasks and checks whether the preset conditions are met starting from the first element. If they are met, the production task node identifier is immediately returned as the target adjustment task.

[0089] In the resource allocation graph, the resource occupancy relationship between the target adjustment task and the currently occupied production equipment resource node is removed. This resource occupancy relationship is represented in the resource allocation graph as a directed edge from the production equipment resource node to the production task node. The removal operation is achieved by deleting this directed edge from the edge set of the resource allocation graph. Before deletion, the existence of the directed edge must be verified. Verification is done by querying the resource allocation graph for edges from the target adjustment task node identifier to the production equipment resource node identifier, where the edge type is resource occupancy. For example, if the target adjustment task node identifier is PanelTask123 and the production equipment resource node identifier is EtchMachine005, then the edge pointing from EtchMachine005 to PanelTask123 is deleted. The directed edges of anelTask123 are updated after the release operation, updating the graph data structure of the resource allocation relationship graph to ensure that the edge set is synchronously removed from the relationship. At the same time, the status attributes of the production equipment resource nodes are updated, changing the equipment running status from occupied to available. The available status includes running or standby. For example, if the original status of the production equipment resource node was running and occupied, after release, the status is still running but marked as available. The task status attributes of the production task nodes are updated, changing the task status from processing to waiting. The waiting status indicates that the task has not yet been allocated resources. These status updates are completed by modifying the attribute values ​​of the nodes in the resource allocation relationship graph. The release operation triggers a version update of the resource allocation relationship graph, generating a new graph version for subsequent consistency checks.

[0090] The target adjustment task is reassigned to an available alternative production equipment resource node to eliminate circular wait dependencies and thus remove the circular wait path. An available alternative production equipment resource node must be in running or standby status and currently not associated with any production task node. When searching for an alternative node, the process equipment mapping table is queried based on the current process code corresponding to the target adjustment task. The process equipment mapping table stores a list of available production equipment resource node identifiers for each process code. For example, if the current process code is cleaning, the process equipment mapping table will retrieve cleaning equipment 001 and cleaning equipment 002. Nodes with an available running status are selected from the list. The selection strategy prioritizes the node with the lowest equipment load. Equipment load is determined by calculating the number of production task nodes associated with that production equipment resource node in the resource allocation graph; a lower number indicates a lower load. For example, cleaning equipment 001... If the number of associated tasks is 2 and the number of associated tasks for cleaning equipment 002 is 1, then cleaning equipment 002 is selected. The reallocation operation creates a new directed edge in the resource allocation graph, pointing from the alternative production equipment resource node to the target adjustment task node. The edge type is set to resource occupancy, indicating that the target adjustment task occupies the alternative resource. Simultaneously, the task status of the target adjustment task node is updated from waiting to processing, and the equipment running status of the alternative production equipment resource node is updated to marked as occupied. After these updates, the existence of a circular waiting path is rechecked. A circular waiting dependency is detected by traversing the resource allocation graph. If the circular structure in the original circular waiting path is broken, the circular waiting path is determined to be resolved. The resolution result is recorded in the system log for subsequent analysis. For example, if the original circular waiting path involves three production task nodes and two production equipment resource nodes, and the circular dependency disappears after reallocation, then the path resolution is successful.

[0091] Example 2: Figure 2 A schematic diagram of the IoT-based collaborative management system for the entire LCD display production process is provided. The IoT-based collaborative management system for the entire LCD display production process includes:

[0092] The data acquisition module is used to acquire real-time equipment status data and material location data for each process on the LCD display production line.

[0093] The allocation construction module is used to construct a resource allocation relationship diagram with production equipment as resource nodes and production tasks as task nodes based on equipment status data and material location data;

[0094] The path detection module is used to analyze the resource access sequence formed by each production task based on its preset process route, identify the circular constraint relationship between different task sequences, and detect cyclic waiting paths.

[0095] The congestion assessment module is used to analyze the set of logically dependent processes affected by the circular waiting path in the process association network constructed by the resource allocation relationship graph when a circular waiting path is detected, and to comprehensively assess the risk of congestion propagation by combining the changes in network structure performance indicators caused by the circular waiting path in the production flow network.

[0096] The urgency assessment module is used to assess the urgency of resolving the circular waiting path by combining the risk of congestion propagation and the priority characteristics of each production task in the circular waiting path.

[0097] The allocation adjustment module is used to adjust the resource allocation of production tasks in the circular waiting path based on the urgency of the task, and to release the circular waiting path by reallocating the resources occupied by the production tasks.

[0098] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0099] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0100] 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. A 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 according to 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. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. Computer-readable storage media can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0101] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0102] 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 modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules 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 modules may be electrical, mechanical, or other forms.

[0103] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0104] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0105] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0106] The above are merely specific embodiments 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.

[0107] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A collaborative management method for the entire LCD display production process based on the Internet of Things, characterized in that: include: S1. Real-time acquisition of equipment status data and material location data for each process on the LCD display production line; S2. Construct a resource allocation relationship diagram with production equipment as resource nodes and production tasks as task nodes based on equipment status data and material location data; S3. Analyze the resource access sequence formed by each production task based on its preset process route, identify the circular constraint relationship between different task sequences to detect cyclic waiting paths; S4. When a circular waiting path is detected, analyze the set of processes with logical dependencies affected by the circular waiting path in the process association network constructed by the resource allocation relationship graph, and comprehensively assess the risk of congestion propagation by combining the changes in network structure performance indicators caused by the circular waiting path in the production flow network. S5. Based on the risk of congestion propagation and the priority characteristics of each production task in the circular waiting path, assess the urgency of resolving the circular waiting path. S6. Based on the urgency of the release, select production tasks in the circular waiting path and adjust resource allocation to release the circular waiting path by reallocating the resources occupied by the production tasks.

2. The IoT-based collaborative management method for the entire LCD display production process according to claim 1, characterized in that, Real-time acquisition of equipment status data and material location data for each process on the LCD display production line, including: The IoT sensors deployed in each process of the LCD display production line collect real-time equipment status data, which includes equipment identification and equipment operating status. The material location data is collected in real time by identification devices deployed on the production line. The material location data includes the material identification and the material's position information in the current process.

3. The IoT-based collaborative management method for the entire LCD display production process according to claim 1, characterized in that, Based on equipment status data and material location data, a resource allocation diagram is constructed with production equipment as resource nodes and production tasks as task nodes, including: Based on the device identifier and device operating status in the device status data, production devices in the running or standby state are mapped as resource nodes; Based on the material identifier in the material location data and the material's location information in the current process, production tasks that are in the processing or waiting state are mapped as task nodes; Based on the location information of materials in the current process and the operating status of equipment, directed edges representing resource occupation or resource application relationships are established between production task nodes and production equipment resource nodes to complete the construction of the resource allocation relationship graph.

4. The IoT-based collaborative management method for the entire LCD display production process according to claim 1, characterized in that, Analyze the resource access sequences formed by each production task based on its preset process route, identify cyclic constraint relationships between different task sequences to detect circular waiting paths, including: For each production task node in the resource allocation diagram, an ordered sequence of production equipment resource access is generated based on the preset process route corresponding to the production task. In the resource allocation relationship graph, the production equipment resource access sequence of each production task node is traversed to identify the circular wait dependency formed between the resource access sequences of different production task nodes due to the sharing of production equipment resource nodes. When a circular wait dependency exists, a set of production task nodes and production equipment resource nodes that constitute a circular wait dependency, along with their connection relationships, are determined to be a circular wait path.

5. The IoT-based collaborative management method for the entire LCD display production process according to claim 1, characterized in that, When a circular waiting path is detected, the set of logically dependent processes affected by the circular waiting path in the process association network constructed by the resource allocation relationship graph is analyzed. Combined with the changes in network structure performance indicators caused by the circular waiting path in the production flow network, the risk of congestion propagation is comprehensively assessed, including: A process association network is constructed based on the resource allocation relationship graph. The process association network is used to represent the logical dependencies between production task nodes based on the preset process route. In the process association network, traverse the production task nodes involved in the loop waiting path to determine the set of processes that have logical dependencies on them. A production flow network is constructed based on a resource allocation relationship graph. The production flow network is used to represent the connection relationship between production equipment resource nodes based on material flow. In a production flow network, analyze the changes in network structure performance indicators caused by circular waiting paths; By combining the set of processes with logical dependencies and changes in network structure performance indicators, a risk assessment of congestion propagation is performed.

6. The IoT-based collaborative management method for the entire LCD display production process according to claim 5, characterized in that, The risk assessment of execution congestion propagation, taking into account the set of processes with logical dependencies and changes in network structure performance indicators, includes: The first risk factor is determined based on the size of the set of processes with logical dependencies. The second risk factor is determined based on the degree of change in the network structure performance indicators of the production flow network. The first and second risk factors are weighted and fused to generate the risk assessment result of blocking propagation.

7. The IoT-based collaborative management method for the entire LCD display production process according to claim 1, characterized in that, By combining the risk of congestion propagation with the priority characteristics of each production task in the circular waiting path, the urgency of resolving the circular waiting path is assessed, including: The global impact weight of the circular waiting path is determined based on the results of the obstruction propagation risk assessment. Obtain the production task priority characteristics of each production task in the circular waiting path; By combining the global impact weight and the priority characteristics of production tasks, a weighted calculation is used to generate an assessment value for the urgency of resolving the circular waiting path.

8. The IoT-based collaborative management method for the entire LCD display production process according to claim 7, characterized in that, Determining the global impact weight of the circular waiting path based on the congestion propagation risk assessment results includes: pre-setting the mapping relationship between the congestion propagation risk assessment results and the global impact weight; querying the mapping relationship based on the congestion propagation risk assessment results; and outputting the corresponding queried value as the global impact weight of the circular waiting path. Obtaining the production task priority characteristics of each production task in the circular waiting path includes: extracting the order delivery deadline urgency parameter from the production task attributes; extracting the customer level priority parameter from the production task attributes; and combining the order delivery deadline urgency parameter and the customer level priority parameter to calculate and generate a quantitative value of the production task priority characteristics.

9. The IoT-based collaborative management method for the entire LCD display production process according to claim 1, characterized in that, Based on the urgency of easing restrictions, resource allocation adjustments are made to production tasks within the circular waiting path. This involves reallocating resources used by production tasks to resolve the circular waiting path, including: Sort all production tasks in the cyclic waiting path according to their urgency assessment value; Select a production task whose urgency assessment value meets the preset conditions as the target adjustment task; In the resource allocation relationship diagram, the resource occupation relationship between the target adjustment task and the currently occupied production equipment resource node is removed; The target adjustment task is reassigned to an available alternative production equipment resource node to eliminate circular wait dependencies, thereby removing the circular wait path.

10. An IoT-based collaborative management system for the entire LCD display production process, used to implement the IoT-based collaborative management method for the entire LCD display production process as described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to acquire real-time equipment status data and material location data for each process on the LCD display production line. The allocation construction module is used to construct a resource allocation relationship diagram with production equipment as resource nodes and production tasks as task nodes based on equipment status data and material location data; The path detection module is used to analyze the resource access sequence formed by each production task based on its preset process route, identify the circular constraint relationship between different task sequences, and detect cyclic waiting paths. The congestion assessment module is used to analyze the set of logically dependent processes affected by the circular waiting path in the process association network constructed by the resource allocation relationship graph when a circular waiting path is detected, and to comprehensively assess the risk of congestion propagation by combining the changes in network structure performance indicators caused by the circular waiting path in the production flow network. The urgency assessment module is used to assess the urgency of resolving the circular waiting path by combining the risk of congestion propagation and the priority characteristics of each production task in the circular waiting path. The allocation adjustment module is used to adjust the resource allocation of production tasks in the circular waiting path based on the urgency of the task, and to release the circular waiting path by reallocating the resources occupied by the production tasks.

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