Scheduling method for cross-process automatic transfer of cone yarns in spinning workshop

Abstract

The invention discloses a scheduling method for cross-process automatic transfer of cone yarns in a spinning workshop, and belongs to the technical field of intelligent spinning manufacturing. The method comprises the steps that a workshop is divided into a plurality of neighborhoods, and cone yarn information is initialized; forming a transfer marshalling through negotiation of the cone yarns in the neighborhood; dynamically planning an AGV path based on conventional, congestion and emergency pheromones; a process pressure value model is established, and transfer is triggered when the pressure difference exceeds a threshold value; batch transfer is realized by adopting a tidal rhythm comprising a goods collecting stage, a transfer stage and a buffering stage; and anomaly detection and hierarchical adaptive adjustment are carried out through multi-source information fusion. According to the method, distributed decision making is realized through neighborhood negotiation, path selection is optimized through a pheromone mechanism, pressure difference driven capacity balancing is realized, batch efficiency is improved through tidal rhythm, and system robustness is ensured through exception handling. All the mechanisms act synergistically to form a complete self-adaptive scheduling system, the cone yarn transfer efficiency can be effectively improved, the AGV empty driving rate can be reduced, productivity balance between procedures can be achieved, and the method adapts to the complex and changeable production environment of spinning workshops.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of textile intelligent manufacturing, and particularly relates to a scheduling method for automatic transfer of cross-process bobbin yarn in a spinning workshop. BACKGROUND

[0002] The spinning production process includes multiple processes such as roving, spinning and bobbin winding, and frequent transfer of bobbin yarn is required between processes. The traditional manual transfer method is labor-intensive and inefficient, and cannot meet the production needs of modern textile enterprises. Although the existing AGV automatic transfer system realizes automation to some extent, it still has the following technical defects: Firstly, the existing scheduling system mostly adopts centralized control, and all scheduling decisions are made by the central server. Once the central node fails, the entire system will be paralyzed, and the centralized decision-making cannot quickly respond to the dynamic changes in the workshop. Secondly, the existing path planning method mostly uses fixed paths or simple shortest path algorithms, which cannot be dynamically adjusted according to real-time road conditions, easily causing local congestion and reducing overall transfer efficiency. Thirdly, the existing system lacks effective batch transfer strategies, and AGVs frequently shuttle between processes, resulting in high empty running rate and low resource utilization. Fourthly, the imbalance of production capacity between processes has not been effectively solved, and there are often cases of bobbin yarn accumulation in upstream processes and material waiting in downstream processes, affecting production continuity.

[0003] Therefore, there is an urgent need for a bobbin yarn transfer scheduling method that can realize local autonomous decision-making, dynamic path optimization, intelligent batch scheduling, and automatically balance the production capacity between processes. SUMMARY

[0004] In view of the deficiencies of the prior art, the application provides a scheduling method for automatic transfer of cross-process bobbin yarn in a spinning workshop. The method realizes distributed autonomous decision-making through a neighborhood negotiation mechanism, intelligent path planning through dynamic optimization of pheromones, automatic balancing of process capacity through a differential pressure driving model, batch transfer through tidal rhythm control, and sets up an abnormal adaptive mechanism to ensure system robustness.

[0005] To achieve the above purpose, the application adopts the following technical solutions: A scheduling method for automatic transfer of cross-process bobbin yarn in a spinning workshop, the method comprising: S1, neighborhood division and bobbin yarn information initialization: the workshop is divided into several neighborhoods according to the spatial distribution of processes and the process flow relationship, each neighborhood contains spatially adjacent and technologically related process nodes; each bobbin yarn is assigned a unique identity code and a multi-dimensional attribute information including position coordinates, time stamp, target process, variety code, batch number, quality grade and state vector is established; S2, Neighborhood Cylinder Yarn Negotiation Grouping: When the cylinder yarn waiting time exceeds the dynamically adjusted time threshold, the negotiation initiation right is obtained, and the grouping invitation message containing the identity, target process, current position, expected departure time, waiting time and search radius is broadcasted through the local communication network; The target consistency test, distance constraint test and benefit evaluation are carried out on the receiving cylinder yarn, and when the comprehensive benefit is positive, the invitation is accepted; The final grouping members are determined according to the grouping size constraint and comprehensive score, and the loading order is determined according to the greedy algorithm; S3, Dynamic path planning based on pheromone: In the path network abstracted as a directed graph, three kinds of pheromone concentrations of normal, congestion and emergency are maintained for each edge; AGV updates the normal pheromone according to the actual time consumption, task quality and load condition after completing the task, dynamically updates the congestion pheromone according to the number of queued AGVs, and temporarily sets the emergency pheromone for emergency tasks; All pheromones naturally volatilize according to the time decay law; AGV calculates the path selection probability according to the comprehensive pheromone concentration and heuristic information; S4, Process pressure difference driven scheduling: The process pressure value is defined as the ratio of the number of cylinder yarns to be processed to the dynamic processing capacity, and the processing capacity is adjusted in real time according to the equipment efficiency, personnel configuration and quality requirements; When the pressure difference between adjacent processes exceeds the trigger threshold, the transfer instruction is generated; The transfer quantity considers the pressure difference, downstream processing capacity, balance time window and available resource constraints; The priority queue management is used to realize the coordination of pressure difference scheduling and negotiation grouping; S5, Tidal rhythm batch transfer control: The production time is divided into periodic scheduling units containing collection, transfer and buffer; The collection stage counts the cylinder yarns to be transferred, triggers negotiation grouping and allocates AGV resources; In the transfer stage, AGV performs batch transfer according to priority, and the speed is dynamically adjusted according to the load and road conditions; In the buffer stage, AGV charging scheduling, pheromone decay update and operation index statistics are carried out; When the pressure difference exceeds the emergency threshold, the tidal rhythm can be temporarily broken; S6, Adaptive adjustment of abnormal conditions: Through multi-source information fusion, equipment failure, emergency order and AGV fault abnormal event are detected and processed hierarchically; When the equipment fails, the processing capacity is recalculated and the path pheromone is adjusted; The emergency order raises the priority of related cylinder yarns and can trigger the transfer immediately; When the AGV fails, the tasks are redistributed through the optimization model; After the abnormality is removed, the system gradually restores the normal operation parameters according to the recovery rate curve.

[0006] Further, the neighborhood correlation degree is calculated by weighted combination of physical distance reciprocal, material flow and process correlation coefficient, and when the correlation degree exceeds the threshold, the corresponding process is divided into the same neighborhood.

[0007] Further, the benefit evaluation of negotiation grouping considers the time saving ratio, path optimization ratio and waiting cost ratio, and the weighted combination is carried out through the normalization weight coefficient.

[0008] Further, the pheromone update includes three mechanisms of positive feedback enhancement of task completion, negative feedback inhibition of congestion detection and time decay, ensuring that the pheromone field dynamically reflects the road network state.

[0009] Further, the transshipment quantity of the differential pressure scheduling is jointly constrained by the transshippable cone yarn quantity, AGV carrying capacity and downstream buffer capacity.

[0010] Further, the tidal cycle is dynamically adjusted according to production load and order urgency, and the proportion of the length of each stage is flexibly configured according to the actual situation of the workshop. Advantages

[0011] 1. Distributed decision-making improves system robustness: local autonomous decision-making is achieved through neighborhood negotiation mechanism, avoiding system paralysis caused by central node failure, enhancing system fault tolerance and response speed; 2. Dynamic path optimization reduces congestion probability: pheromone-based path planning can dynamically adjust the AGV travel path according to real-time traffic conditions, effectively avoiding local congestion and improving road network efficiency; 3. Batch transshipment strategy improves carrying efficiency: tidal batch transshipment concentrates scattered transportation demand for centralized processing, reduces AGV empty running rate and improves equipment utilization; 4. Pressure difference driving realizes self-balancing of production capacity: the differential pressure scheduling model between processes can automatically identify production capacity bottlenecks and actively allocate transshipment resources to maintain dynamic balance between processes; 5. Multi-mechanism coordination enhances scheduling flexibility: neighborhood negotiation, pheromone optimization, differential pressure driving, tidal control and exception handling five mechanisms work together to ensure efficient and orderly regular production and flexible response to abnormal situations. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 A step flowchart of the method of the present application is shown; DETAILED DESCRIPTION

[0013] The exemplary embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0014] In combination Figure 1 , the present application provides a scheduling method for automatic transshipment of cone yarn across processes in a spinning workshop, comprising: S1, neighborhood division and cone yarn information initialization In the actual production environment of the spinning workshop, each process presents a specific distribution pattern in space, and the processes are connected through logistics channels. According to this spatial distribution characteristic and process flow relationship, the entire workshop is divided into several neighborhoods. The neighborhood is defined as a set of process nodes that are adjacent in physical space and have direct material transfer relationship in process flow.

[0015] The specific neighborhood division process is as follows: a topological structure diagram of the workshop is established, wherein a node represents each process, and an edge represents the material flow relationship between processes. For any two process nodes i and j, the neighborhood correlation degree index thereof is defined as follows: Wherein: is the neighborhood correlation degree of process i and process j; is the physical distance between process i and process j, in meters; is the material flow from process i to process j per unit time, indicating the frequency of cone yarn transfer; is the process correlation coefficient of process i and process j, with a value range of [0, 1], which is close to 1 when the two processes have a direct upstream and downstream relationship, and close to 0 when there is no direct relationship; , , is the corresponding weight coefficient, satisfying the normalization condition .

[0016] When the neighborhood correlation degree exceeds the preset correlation degree threshold , process i and process j are classified into the same neighborhood. By traversing all process node pairs, a neighborhood set is formed, wherein represents the kth neighborhood, and the set of process nodes contained therein is . This neighborhood division is not fixed and can be dynamically updated according to the adjustment of the production plan and the change of the process layout. The update period is determined according to the production batch switching frequency.

[0017] After completing the neighborhood division, the system needs to initialize the information of each cone yarn. Each cone yarn entering the scheduling system is assigned a unique identity code , which is generated by combining a timestamp and an incremental serial number: Wherein: is the timestamp of the cone yarn entering the system, accurate to seconds; is the incremental serial number within the day, starting from 1 and increasing; is a separation coefficient to ensure that the timestamp and the serial number are not confused.

[0018] At the same time, an information vector containing multiple attributes is established for each cone yarn: Wherein: is the attribute information vector of cone yarn i; is the three-dimensional position coordinate of cone yarn i in the workshop coordinate system, with the coordinate origin set at the southwest corner of the workshop, the x-axis pointing east, the y-axis pointing north, and the z-axis pointing vertically upward; is the timestamp of cone yarn i entering the current process, using the system unified clock; is the target process number of cone yarn i; is the yarn variety code of cone yarn i, used to identify the yarn specification; is the production batch number to which cone yarn i belongs; is the quality grade parameter of cone yarn i, divided into three grades of first-class, second-class and third-class; is the state vector of cone yarn i, including three states of waiting, transferring and processing.

[0019] These attribute information is stored and updated through the electronic tag installed on the cone yarn. The electronic tag maintains real-time communication with the wireless communication base station arranged in the workshop, and the communication protocol uses the industrial standard wireless protocol. When the position or state of the cone yarn changes, the corresponding attribute information will be immediately updated and synchronized to the database of the scheduling system, with an update delay of less than 100 milliseconds. This real-time information synchronization mechanism provides an accurate data basis for subsequent negotiation grouping and path planning.

[0020] S2, neighborhood cone yarn negotiation grouping After completing the neighborhood division and information initialization in step S1, this step realizes the autonomous negotiation and dynamic grouping of neighborhood cone yarns. The design purpose of the negotiation grouping mechanism is to combine multiple cone yarns with the same transfer demand into a batch, thereby reducing the number of AGV round trips and improving transfer efficiency.

[0021] The trigger mechanism of negotiation grouping is based on the waiting time of the cone yarn. The system continuously monitors the waiting time of each cone yarn , which starts counting from the time when the cone yarn completes the processing of the current process: Wherein: is the current system time; is the time point when cone yarn i completes the processing of the current process.

[0022] When the waiting time of cone yarn i exceeds the preset time threshold , that is, the condition is met, the cone yarn obtains the negotiation initiation right and becomes the leading cone yarn. Here is not a fixed value, but is dynamically determined according to the process type and production urgency: Wherein: is the basic waiting time threshold; is a process adjustment coefficient, different processes set different coefficient values according to their production rhythm; is an emergency coefficient, dynamically adjusted according to order delivery time and production plan.

[0023] The leading bobbin yarn that obtains the initiation right broadcasts a marshalling invitation message through the local communication network in the neighborhood. The propagation range of the invitation message is limited within the neighborhood where the leading bobbin yarn is located, avoiding invalid communication across neighborhoods. The message adopts a structured data format: Among them: is the identity of the leading bobbin yarn that initiates the negotiation; is the target process number; is the current position coordinate of the leading bobbin yarn; is the estimated departure time, calculated according to the current time plus the preset assembly time; is the length of time the leading bobbin yarn has been waiting; is the expected marshalling scale; is the search radius, defining the maximum distance between the bobbin yarn responding to the invitation and the leading bobbin yarn.

[0024] After receiving the marshalling invitation message, other bobbin yarns j in the neighborhood execute a multi-level decision-making process. The first level of judgment is the consistency test of the target process: Among them: is the target consistency indicator variable; is the target process of bobbin yarn j.

[0025] If , bobbin yarn j directly rejects the invitation; if , it enters the second level of distance constraint test: Among them: is the current position of bobbin yarn j; is the Euclidean distance between bobbin yarn j and the leading bobbin yarn.

[0026] After meeting the distance constraint, it enters the third level of benefit evaluation. The benefit evaluation comprehensively considers time saving, path optimization and waiting cost: Among them, the calculation of time benefit is: The calculation of path benefit is: Waiting cost is calculated as: where: is the expected completion time of the individual transfer of cone j; is the expected completion time of the batch transfer after joining the group; is the path length that cone j needs to go through for individual transfer; is the path length that cone j shares with other cones after joining the group; is the additional waiting time due to waiting for other members in the group; , , are the time benefit weight, path benefit weight and waiting cost weight, respectively, satisfying the normalization condition .

[0027] When the calculated comprehensive benefit value , cone j sends a confirmation message to the leading cone to accept the invitation: where: is the identity of cone j; is the current location of cone j; is the time when cone j is expected to arrive at the collection point; is the current state information of cone j.

[0028] After the leading cone collects all the confirmation messages, it determines the final group members according to the group size constraints: where: is the number of cones in the actual formed group; is the lower limit of the group size; is the upper limit of the group size.

[0029] If the number of cones accepting the invitation exceeds , the cone with the highest score is selected to join the group according to the comprehensive score. The comprehensive score considers waiting time, distance, and quality level: where: is the comprehensive score of cone j; is the waiting time of cone j; is the maximum waiting time among all candidate cones; is the distance between cone j and the leading cone; is the search radius; is the quality level value of cone j; For the highest quality level value; 、 、 For the corresponding score weight, satisfying .

[0030] After the formation of the group, the loading sequence of the tube yarn in the group needs to be determined. The determination of the loading sequence follows the principle of minimizing the total waiting time, and the greedy algorithm is used to determine the access sequence, so that the travel path of the AGV is the shortest.

[0031] S3, dynamic path planning based on pheromone After the tube yarn grouping in step S2, the optimal path for the transfer task needs to be planned. In this step, the pheromone field is constructed and maintained in the workshop path network to realize the intelligent path selection of the AGV. This mechanism draws on the pheromone communication principle of ant foraging, and through positive feedback, it strengthens the high-quality path, and through negative feedback, it suppresses the congestion path.

[0032] The path network of the workshop is abstracted as a directed graph , where the node set represents the key position points in the workshop, including the inlet and outlet of each process, the path intersection point, and the charging station position. The directed edge set represents the feasible path between nodes, and each edge has directionality, reflecting the allowed driving direction of the AGV.

[0033] Three types of pheromone concentrations are maintained on each directed edge , forming a pheromone vector: Wherein: is the pheromone concentration vector on the edge ; is the regular pheromone concentration, reflecting the historical passing efficiency of the path; is the congestion pheromone concentration, indicating the current real-time congestion degree; is the emergency pheromone concentration, used to guide the rapid passing of emergency tasks.

[0034] The initialization of the pheromone is carried out at the start of the system, and the initial pheromone concentration of all edges is set as: Wherein: is the initial reference value of the regular pheromone; is the physical length of the edge .

[0035] The pheromone updating mechanism contains three aspects: positive feedback update after task completion, negative feedback update of congestion detection, and time decay.

[0036] When AGV_k successfully completes the transfer task from node i to node j, the regular pheromone is updated according to the following rules: Pheromone increment The calculation considers the task completion time and transportation quality: Where: P is the pheromone intensity constant; T is the actual time AGV_k takes to travel from node i to node j; Q is the task completion quality coefficient, which is 1 when the transportation process is smooth and abnormal, and decreases accordingly when vibration, collision, etc. occurs; L is the load coefficient, which is greater when full than when empty.

[0037] The congestion pheromone update is based on real-time monitoring of AGV queuing: Where: Q is the number of AGVs currently queuing on the edge , which is real-time counted by sensors at both ends of the path; C is the congestion determination threshold; G is the congestion aggravation coefficient, which controls the growth rate of congestion pheromone; R is the congestion relief coefficient, which satisfies so that the congestion pheromone gradually decreases when the road condition improves.

[0038] Emergency pheromone is used to handle high-priority tasks. When there is an urgent transfer task, temporary emergency pheromone is added on its planned path: Where: E_max is the maximum value of emergency pheromone; P_e is the set of planned paths for emergency tasks.

[0039] All pheromones follow the time decay rule to simulate the natural evaporation of pheromones: Where: P represents any type of pheromone; D is the decay rate, with a value range of ; ΔP is the pheromone increment at this time step.

[0040] When AGV needs to select the next node j from the current node i, the path selection probability is calculated according to the comprehensive pheromone concentration: Wherein: is the probability of selecting the path ; is the comprehensive pheromone concentration; is the heuristic information, indicating the inherent attractiveness of the path; and are the pheromone importance and heuristic information importance parameters, respectively; is the set of all feasible paths starting from node i.

[0041] The calculation of the comprehensive pheromone concentration considers the weighted combination of the three pheromones: Wherein: , , are the weight coefficients of the regular, emergency and congestion pheromones, respectively.

[0042] Through this dynamic pheromone update and path selection mechanism, the system can adaptively discover and strengthen high-quality paths while avoiding congestion areas, achieving global path optimization.

[0043] S4, differential pressure driven scheduling between processes After determining the path planning mechanism in S3, this step establishes a differential pressure driven scheduling model by monitoring and analyzing the production status of each process, actively balancing the capacity differences between processes. This mechanism analogizes processes to containers with different pressures, and the cheese is like fluid flowing from high pressure area to low pressure area, achieving automatic balancing of capacity.

[0044] The pressure value of each process is defined as the ratio of the number of cheese to be processed to the processing capacity: Wherein: is the pressure value of process i at time t; is the number of cheese to be processed by process i at time t; is the processing capacity of process i at time t, in cheese / hour; is the pressure correction coefficient of process i, reflecting the importance and bottleneck characteristics of the process.

[0045] The processing capacity is not a fixed value, but changes dynamically according to the equipment state and personnel configuration: Wherein: is the base processing capacity of process i; is the equipment efficiency coefficient, which is 1 when the equipment is in normal operation, and decreases when the equipment is in failure or maintenance; is the personnel allocation coefficient, which reflects the current personnel allocation situation of the shift; is the quality influence coefficient, which decreases the processing speed when the quality requirement is improved.

[0046] The pressure difference between adjacent processes is defined as: wherein: is the pressure difference between process i and process j at time t; process i is the upstream process, and process j is the downstream process.

[0047] When the pressure difference exceeds the preset trigger threshold, the system generates a transfer instruction: wherein: is the transfer trigger flag; is the pressure difference trigger threshold.

[0048] After triggering the transfer, the transfer quantity needs to be determined. The calculation of the transfer quantity not only relieves the pressure difference, but also avoids excessive adjustment causing reverse imbalance: wherein: is the planned transfer quantity of the cone yarn; is the adjustment coefficient, the value range is to avoid excessive adjustment; is the expected balance time window; is the floor function.

[0049] The transfer quantity is also limited by multiple constraint conditions: wherein: is the transferable cone yarn quantity of the upstream process; is the available AGV quantity; is the carrying capacity of a single AGV; is the buffer capacity of the downstream process.

[0050] The pressure difference scheduling works in coordination with the aforementioned negotiation grouping mechanism. When the pressure difference triggers the transfer demand, the cone yarns that have formed groups are preferentially selected for transfer; if the number of groups is insufficient, a new negotiation grouping process is triggered. This coordinated mechanism is realized through priority queue management: wherein: is the transfer priority of cone yarn i; Contribution value for normalized pressure difference; For marshalling state, marshalled is 1, unmarshalled is 0; Normalized waiting time; , , Corresponding weight coefficient.

[0051] The update period of pressure value is dynamically adjusted according to the production rhythm: Wherein: Pressure value update period; And The minimum and maximum update periods, respectively; Adjustment parameter.

[0052] Through this pressure difference driving mechanism, the system can actively identify the capacity bottleneck, and allocate transfer resources in advance to avoid the situation of upstream backlog and downstream material interruption.

[0053] S5, tidal rhythm batch transfer control The pressure difference driving mechanism established in step S4 provides the triggering condition of transfer demand, and this step realizes the timing coordination of batch transfer through tidal rhythm control. Tidal scheduling divides the continuous production time into periodic scheduling units, and completes the centralized transfer of cones in each period according to the predetermined rhythm, so as to avoid the frequent scattered scheduling of AGV and improve the overall transfer efficiency.

[0054] The setting of tidal period is based on the production characteristics and logistics intensity of the workshop. Define the tidal period And its internal stage division: Wherein: The length of the complete tidal period; The length of the collection stage; The length of the transfer stage; The length of the buffer stage.

[0055] The proportional relationship of the length of each stage is dynamically adjusted according to the actual situation of the workshop: Wherein: , , The length ratio coefficient of each stage satisfies .

[0056] The dynamic adjustment mechanism of tidal period considers the production load and order urgency: Wherein: is the length of the k+1th cycle; is the length of the kth cycle; is the load adjustment factor, which shortens the cycle when the production load increases; is the urgency adjustment factor, which shortens the cycle when there are urgent orders.

[0057] In the collection phase , the system performs the following operations: (1) Statistics of each process to be transported cone yarn, generate transport demand list: Among them: is the transport demand list; is the cone yarn identification; and are the source location and target location respectively; is the cone yarn ready time; is the quality level; is the ready cone yarn set.

[0058] (2) Trigger the negotiation grouping process in the neighborhood, form a batch transport group: Among them: is the batch transport group set; is the jth grouping, containing a number of cone yarns with the same target.

[0059] (3) Assign AGV resources to each grouping and generate a task assignment matrix: The assignment process must meet the following constraints: Among them: is the total number of available AGVs; The first constraint ensures that each AGV is assigned to at most one grouping; The second constraint ensures that each grouping is assigned at least one AGV.

[0060] In the transport phase , AGVs collectively perform the transport task. Transport execution follows a priority scheduling strategy: Among them: is the execution priority of the grouping ; is the average waiting time of the cone yarn in the grouping ; is the grouping size; is the maximum grouping size; The average transport distance for grouping; , , The weight coefficient.

[0061] The driving speed of AGV is dynamically adjusted according to road conditions and load: Wherein: The actual driving speed of AGV; The basic driving speed; The load influence coefficient, the speed decreases when full load; The road condition influence coefficient, the speed decreases when congestion.

[0062] In the buffer stage , the system performs maintenance and adjustment operations: (1) AGV charging scheduling, to ensure that the power meets the next cycle demand: Wherein: The required power state; The current power state; The estimated next cycle energy consumption; The battery capacity.

[0063] (2) pheromone decay update, clean up outdated information: Wherein: The pheromone concentration at the end of the buffer stage; The pheromone concentration at the end of the transport stage; The decay rate of the buffer stage.

[0064] (3) statistics of the operation index of this period, to provide basis for parameter adjustment of the next period: Wherein: The average utilization rate of AGV; The average transport time; The number of completed transport tubes; The number of delayed transport tubes.

[0065] The coordination mechanism of tidal rhythm and pressure difference driving is reflected in: when the pressure difference exceeds the emergency threshold, the tidal rhythm can be temporarily broken, and the emergency transport is started: Wherein: The emergency pressure difference threshold, significantly greater than the regular trigger threshold .

[0066] S6, Abnormal situation adaptive adjustment In the actual production process, various abnormal situations will inevitably occur, such as equipment failure, emergency order, AGV failure, etc. This step establishes an abnormal detection and adaptive adjustment mechanism to ensure that the system can still maintain basic operation under abnormal situations.

[0067] Abnormal event detection is achieved through multi-source information fusion: Among them: is the set of abnormal events; is the abnormal detection function; is the equipment state information; is the AGV state information; is the order state information; is the quality monitoring information.

[0068] Abnormal event classification processing, according to the impact degree to determine the response level: Among them: is the abnormal level; is the abnormal impact assessment value; , , are the threshold values of serious, major and minor abnormalities respectively.

[0069] For different levels of abnormality, take appropriate adjustment strategy: (1) Equipment failure handling: when detecting the equipment failure of the process , recalculate the processing capacity of the process: Among them: is the processing capacity after failure; is the failure impact rate.

[0070] At the same time, adjust the pheromone of the related path to guide the AGV to bypass the failure area: Among them: is the congestion pheromone increment caused by failure.

[0071] (2) Emergency order handling: when receiving an emergency order, the priority of the related tube yarn is raised: Among them: is the adjusted priority; is the emergency amplification coefficient; Emergency reward value.

[0072] Emergency tube yarn can break the tidal rhythm limit and trigger immediate transfer: Wherein: The priority threshold value for immediate transfer.

[0073] (3) AGV fault handling: when AGV_k fails, the tasks it undertakes need to be reassigned: Constraint condition: Wherein: The task set originally undertaken by the fault AGV_k; The cost of AGV_i executing task j; The reassignment decision variable; The load of task j; The remaining capacity of AGV_i.

[0074] The abnormal recovery mechanism monitors the change of abnormal state, and when the abnormality is removed, the system gradually recovers to normal operation mode: Wherein: The recovery rate; The recovery speed parameter; The time after the abnormality is removed.

[0075] The system parameters are gradually adjusted with the recovery rate: Wherein: The current parameter value; The normal parameter value; The emergency parameter value.

[0076] In summary, the present application constructs a distributed intelligent scheduling system for cross-process transfer of spinning mill tube yarn through the synergistic effect of neighborhood negotiation mechanism, pheromone dynamic optimization, differential pressure driven scheduling, tidal rhythm control and abnormal adaptive processing, effectively solving the problems of fragile central control, rigid path planning and low transfer efficiency existing in traditional methods.

[0077] The above merely describes the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any changes or replacements within the technical scope disclosed by the present application, which can be easily thought by any person skilled in the art, should be covered within the protection scope of the present application.

Claims

1. A method for scheduling automatic cross-process transfer of cones within a spinning mill, characterized in that, The method comprises the following steps: S1, neighborhood division and bobbin information initialization: calculate neighborhood correlation degree according to physical distance, material flow and process correlation between process nodes, and divide process nodes with correlation degree exceeding threshold value into the same neighborhood; assign unique identity code to each bobbin, and establish attribute information containing position coordinates, time stamp, target process, variety code, batch number, quality grade and state vector; S2, bobbin grouping negotiation in neighborhood: when bobbin waiting time exceeds dynamic time threshold value, the bobbin serves as leading bobbin to broadcast grouping invitation message; other bobbins in neighborhood successively perform target process consistency test, distance constraint test, and calculate comprehensive benefit value of joining grouping; the comprehensive benefit value is weighted combination of time saving proportion, path optimization proportion and waiting cost proportion; when comprehensive benefit value is positive, accept invitation and join grouping; S3, dynamic path planning based on pheromone: maintain three concentration values of conventional pheromone, congestion pheromone and emergency pheromone on each directed edge of path network; after AGV completes transfer task, update conventional pheromone according to ratio of actual time consumption to task quality; when number of AGVs queuing on path exceeds congestion threshold value, increase congestion pheromone; all pheromones decrease with time according to preset decay rate; AGV calculates comprehensive pheromone concentration according to weighted combination of three pheromones, and determines path selection probability in combination with heuristic information of path length; S4, differential pressure driven scheduling between processes: define ratio of number of bobbins to be processed to processing capacity of each process as pressure value; when pressure difference between adjacent processes exceeds trigger threshold value, determine transfer number according to product of pressure difference value, downstream processing capacity and balance time window, and generate transfer instruction; S5, tidal rhythm batch transfer control: divide production time into periodic scheduling units, each scheduling unit comprising collection phase, transfer phase and buffer phase; bobbins complete grouping negotiation and AGV resources are allocated in collection phase; AGVs collectively perform batch transfer in transfer phase; AGV charging and system parameter updating are performed in buffer phase; S6, adaptive adjustment of abnormal conditions: detect abnormal events through multi-source information fusion and process in stages; when equipment fails, recalculate processing capacity of corresponding process and increase congestion pheromone of fault area; when emergency order occurs, improve priority of related bobbins and allow immediate triggering of transfer; when AGV fails, reassign its task to other available AGV.

2. The method of claim 1, wherein, The neighborhood correlation degree is calculated through weighted sum of reciprocal of physical distance, material flow per unit time and process correlation coefficient, and weight coefficients satisfy normalization condition.

3. The method of claim 1, wherein, The dynamic time threshold value is equal to product of basic waiting time threshold value, process adjustment coefficient and emergency degree coefficient, and is dynamically adjusted according to production cycle of different processes and emergency degree of order.

4. The method of claim 1, wherein, The grouping invitation message comprises leading bobbin identity, target process number, current position coordinates, expected departure time, waiting time and search radius; grouping size is subject to upper and lower limit constraints; when number of bobbins accepting invitation exceeds upper limit, select grouping members according to comprehensive score of waiting time, distance, and quality grade.

5. The method of claim 1, wherein, The update increment of the conventional pheromone is equal to the pheromone intensity constant divided by the actual transit time, multiplied by the task completion quality coefficient and the load coefficient; the congestion pheromone is increased by the congestion aggravation coefficient when the number of AGVs in the queue exceeds the threshold, otherwise it is attenuated by the congestion alleviation coefficient.

6. The method of claim 1, wherein, The process processing capacity is equal to the basic processing capacity multiplied by the equipment efficiency coefficient, the personnel configuration coefficient and the quality influence coefficient; the transit quantity is constrained by the number of transferable cone yarns, the AGV carrying capacity and the downstream buffer capacity.

7. The method of claim 1, wherein, The tidal cycle is dynamically adjusted according to the production load adjustment factor and the urgency adjustment factor; when the inter-process pressure difference exceeds the emergency pressure difference threshold, the transit is immediately executed by temporarily breaking the tidal rhythm.

8. The method of claim 1, wherein, The pressure difference driven scheduling and the negotiation grouping are realized by the priority queue, and the cone yarn transit priority is determined by the weighted sum of the normalized pressure difference contribution value, the grouping state and the normalized waiting time.

9. The method of claim 1, wherein, The abnormal event is divided into four levels of serious, major, minor and slight according to the influence degree evaluation value; after the abnormality is removed, the system parameters gradually recover from the emergency parameters to the normal parameters according to the negative exponential recovery rate curve.

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