CN-121978998-A - Kitchen electricity group cooperative work intelligent scheduling control method and system

CN121978998ACN 121978998 ACN121978998 ACN 121978998ACN-121978998-A

Abstract

The invention provides an intelligent scheduling control method and system for kitchen electric group cooperative work, and relates to the technical field of intelligent home, wherein the intelligent scheduling control method and system comprise the steps of obtaining the running state of kitchen electric equipment and cooking task information, carrying out procedure disassembly by combining a cooking process constraint rule to form a task execution diagram, calculating a key degree quantization index, carrying out equipment allocation based on a feasibility mapping relation and the key degree quantization index, identifying a parallel executable procedure combination, and re-allocating equipment to generate a scheduling sequence, so that efficient cooperative work of the kitchen electric equipment is realized, and the cooking task execution efficiency is improved.

Inventors

SHAO LINGLING
HE GUANGBING
LI XIANG
YAO YUNJUN
XIE MIAOJUN
ZHU LI
Ying Liqian
MAO GUANGJUN
LV JIANLIANG
QIAN YANGYANG
YUAN YINGBIN

Assignees

宁波舜韵电子有限公司

Dates

Publication Date: 20260505
Application Date: 20251219

Claims (10)

1. The intelligent dispatching control method for the cooperative work of the kitchen electric groups is characterized by comprising the following steps: acquiring the running state information and the to-be-executed cooking task information of each kitchen electric device in a kitchen electric group, carrying out process disassembly on the to-be-executed cooking task information by combining a preset cooking process constraint rule, identifying time sequence dependency relations among different processes to obtain a task execution diagram, extracting capacity parameters of the kitchen electric devices according to the running state information, and carrying out matching judgment on the capacity parameters and the resource requirement parameters of process nodes in the task execution diagram to obtain a feasibility mapping relation; Traversing backwards from each process node along a time sequence dependent path in a task execution diagram, counting the number of subsequent processes corresponding to the current process node, accumulating the longest time span from the current process node to the end point of the task execution diagram, and calculating to obtain a key degree quantization index based on the number of the subsequent processes and the longest time span; And distributing kitchen electric equipment to the process nodes according to the feasibility mapping relation and the key degree quantification index to obtain a preliminary scheduling distribution result, extracting unassigned process nodes in the preliminary scheduling distribution result, tracing forward along the time sequence dependency relation in a task execution diagram to obtain resource release dependency information, identifying process node combinations which have no time sequence constraint with the unassigned process nodes to obtain parallel executable process combinations, and re-distributing the kitchen electric equipment to the parallel executable process combinations by combining the feasibility mapping relation to generate a kitchen electric equipment scheduling sequence.
2. The method of claim 1, wherein obtaining operational status information of each kitchen appliance in a kitchen appliance group and information of a cooking task to be performed, performing process disassembly on the information of the cooking task to be performed in combination with a preset cooking process constraint rule, and identifying time sequence dependency relationships among different processes to obtain a task execution graph comprises: receiving a cooking task to be executed, which is input by a user, determining a dish identification and a cooking target parameter in the cooking task to be executed, inquiring a preset cooking process constraint rule according to the dish identification, extracting a procedure sequence corresponding to the dish identification, disassembling the procedure sequence, and identifying an operation type and a resource demand parameter corresponding to each procedure; Analyzing a precursor procedure and a subsequent procedure of each procedure in the procedure sequence based on a procedure definition in a cooking procedure constraint rule, determining a state transfer relation between different procedures based on the precursor procedure and the subsequent procedure, and constructing and obtaining a preliminary dependency graph; Acquiring operation state information of each kitchen electric device in the kitchen electric group, extracting device occupation time period and residual processing capacity in the operation state information, traversing process nodes in the preliminary dependency graph, extracting resource demand parameters corresponding to each process node, comparing the resource demand parameters with the residual processing capacity corresponding to each kitchen electric device, identifying processes of which the resource demand parameters exceed the residual processing capacity, and determining time sequence dependency relations among different processes to obtain a task execution graph.
3. The method of claim 1, wherein extracting capability parameters of the kitchen electric equipment according to the operation state information and performing matching judgment with resource demand parameters of process nodes in the task execution graph to obtain a feasibility mapping relation comprises: analyzing the running state information, extracting equipment type identification and historical execution records of kitchen electric equipment, determining capability parameters of each kitchen electric equipment, wherein the capability parameters comprise a functional operation set and performance boundary values, traversing process nodes in the task execution graph, extracting operation types and resource demand parameters of each process node, matching the operation types with the functional operation set, and identifying candidate kitchen electric equipment containing the process node operation types in the functional operation set; Retrieving the history procedure nodes which are executed by the candidate kitchen electric equipment and have the same operation type as the current procedure node from the history execution record, extracting the history resource demand parameters corresponding to the history procedure nodes and the actual consumption resource quantity, carrying out regression fitting to obtain a regression mapping relation, substituting the resource demand parameters of the current procedure node into the regression mapping relation, and calculating to obtain the predicted consumption resource quantity; Determining the executability of the pre-obtained precursor process and the subsequent process on the candidate kitchen electric equipment, counting the total number of executable nodes, calculating to obtain a topological consistency score based on the total number of nodes, calculating to obtain a resource satisfaction degree based on the predicted consumed resource quantity and the performance boundary value, carrying out weighted summation on the resource satisfaction degree and the topological consistency score to obtain a matching adaptation value, and establishing executable association between the candidate kitchen electric equipment with the matching adaptation value higher than a preset matching threshold and the process nodes to obtain a feasibility mapping relation.
4. The method of claim 1, wherein traversing backwards from each process node along a time-dependent path in the task execution graph, counting a number of subsequent processes corresponding to a current process node, accumulating a maximum time span from the current process node to a task execution graph endpoint, and calculating a criticality quantization index based on the number of subsequent processes and the maximum time span comprises: Traversing backwards from each process node along a time sequence dependent path in the task execution diagram, extracting estimated execution time length of each subsequent process node until reaching a destination node, accumulating the estimated execution time length of each process node in each reachable path to obtain path time length, determining the longest time span, and counting the number of all the subsequent process nodes reachable from the current process node to obtain the number of the subsequent processes; Extracting a historical procedure node set with the same operation type as the current procedure node from a pre-obtained historical execution record, obtaining a time sequence extending depth of each historical procedure node and a corresponding task completion deviation value, quantitatively analyzing the correlation to obtain a time dimension sensitive factor, and obtaining a topology coverage breadth of each historical procedure node and a corresponding task completion deviation value, quantitatively analyzing the correlation to obtain a breadth dimension sensitive factor; And carrying out nonlinear coupling on the longest time span and the time dimension sensitive factor to obtain a time sequence criticality measure, carrying out nonlinear coupling on the number of subsequent procedures and the breadth dimension sensitive factor to obtain a topology influence measure, calculating to obtain a basic priority score based on the time sequence criticality measure and the topology influence measure, encoding the basic priority score into a particle position vector, and carrying out iterative optimization on the particle position vector by a particle swarm algorithm in combination with a resource demand parameter corresponding to a current procedure node and a residual capacity parameter corresponding to kitchen electric equipment to obtain the criticality quantization index.
5. The method of claim 4, wherein encoding the base priority value into a particle location vector, and iteratively optimizing the particle location vector by a particle swarm algorithm in combination with a resource demand parameter corresponding to a current process node and a remaining capacity parameter corresponding to a kitchen electrical appliance to obtain the criticality quantization index comprises: vectorizing the basic priority values of all the process nodes according to the arrangement sequence of the process nodes in the task execution diagram to obtain an initial position vector, determining constraint penalty items based on matching tension between the resource demand parameters and the residual capacity parameters, taking task completion time as a target, calculating fitness corresponding to the initial position vector as individual optimal fitness by combining preset constraint penalty items, and selecting a particle position vector with the largest individual optimal fitness as a global optimal vector; Determining an individual cognition component based on the deviation of a particle current position vector and an individual optimal vector and a preset individual learning factor, determining a social cognition component based on the deviation of a particle current position vector and a global optimal vector and a preset global influence coefficient, updating a particle speed based on the individual cognition component and the social cognition component to obtain an updating speed vector, and fusing the particle current position vector and the updating speed vector to obtain a candidate position vector; And carrying out feasible domain inspection on the candidate position vectors, determining a new position vector according to an inspection result, calculating the fitness corresponding to the new position vector, comparing the fitness with the individual optimal fitness, updating the individual optimal vector and the global optimal vector according to a comparison result, repeating the updating until the preset maximum iteration times are reached, and extracting the priority values corresponding to all process nodes in the global optimal vector to obtain a key quantization index.
6. The method of claim 1, wherein assigning kitchen electrical equipment to the process nodes according to the feasibility mapping relationship and the criticality quantization index to obtain a preliminary scheduling assignment result, extracting unassigned process nodes in the preliminary scheduling assignment result and tracing forward along the time sequence dependency relationship in a task execution graph to obtain resource release dependency information comprises: arranging the process nodes in descending order according to the key degree quantization index, sequentially extracting an executable kitchen electric equipment set corresponding to the process nodes from the feasibility mapping relation according to the ordering result, and distributing kitchen electric equipment for the process nodes based on the matching relation between the residual capacity parameter and the resource demand parameter to obtain a preliminary scheduling distribution result; Extracting all unassigned process nodes in the preliminary scheduling allocation result, constructing a precursor tracing path in a task execution diagram in a forward trace manner along a time sequence dependency relationship, determining a resource release propagation depth based on the allocation state of each process node, and selecting allocation target equipment corresponding to the allocated process node with the largest resource release propagation depth as preferential candidate equipment of the unassigned process node; And extracting priority candidate devices and corresponding resource release time points in each precursor tracing path for unallocated process nodes with a plurality of precursor tracing paths, calculating the time sequence matching degree between the path duration of each precursor tracing path and the resource release time points, determining the path priority based on the time sequence matching degree, taking the priority candidate device corresponding to the precursor tracing path with the highest path priority as the optimal candidate device of the unallocated process node, and establishing a delay binding mapping relation between the current unallocated process node and the priority candidate device and the corresponding resource release time points to obtain resource release dependency information.
7. The method of claim 1, wherein identifying a process node combination for which there is no timing sequence constraint with an unassigned process node to obtain a parallel executable process combination, reassigning kitchen electrical equipment to the parallel executable process combination in combination with the feasibility map, and generating a kitchen electrical equipment scheduling sequence comprises: Constructing an adjacency matrix of a task execution graph based on the resource release dependency information, calculating a transfer closure matrix, extracting row vectors and column vectors corresponding to unassigned process nodes from the transfer closure matrix, taking position indexes corresponding to process nodes with zero element values in the row vectors and the column vectors as candidate parallel nodes, screening process nodes with identical priority candidate equipment and a resource release time point difference value smaller than a preset time threshold from the candidate parallel nodes, carrying out resource feasibility verification on resource demand parameters of the process nodes and residual capacity parameters of the priority candidate equipment by combining the feasibility mapping relation, and combining the process nodes passing the verification to obtain a parallel executable process combination; Calculating the time dispersion of the resource release time points corresponding to all process nodes in the parallel executable process combination, calculating the resource adequacy based on the residual capacity parameter of the priority candidate device and the combined resource demand corresponding to the parallel executable process combination, calculating the time sequence resource coordination degree based on the time dispersion and the resource adequacy, selecting the priority candidate device with the highest time sequence resource coordination degree as the target parallel device, distributing the execution time window based on the execution time of the process nodes and the resource release time points of the target parallel device, and carrying out topological sorting on all the process nodes, the corresponding distribution target device and the execution time window according to the time sequence dependency relationship to generate the kitchen electric device scheduling sequence.
8. Kitchen electricity group cooperative work intelligent scheduling control system for implementing the method of any one of the preceding claims 1-7, characterized by comprising: The first unit is used for acquiring the running state information of each kitchen electric device in the kitchen electric group and the cooking task information to be executed, carrying out procedure disassembly on the cooking task information to be executed by combining a preset cooking process constraint rule, identifying time sequence dependency relations among different procedures to obtain a task execution diagram, extracting capacity parameters of the kitchen electric devices according to the running state information, and carrying out matching judgment on the capacity parameters and the resource requirement parameters of procedure nodes in the task execution diagram to obtain a feasibility mapping relation; The second unit is used for traversing backwards from each process node along a time sequence dependent path in the task execution diagram, counting the number of subsequent processes corresponding to the current process node, accumulating the longest time span from the current process node to the end point of the task execution diagram, and calculating to obtain a key degree quantization index based on the number of the subsequent processes and the longest time span; And the third unit is used for distributing kitchen electric equipment to the process nodes according to the feasibility mapping relation and the key degree quantification index to obtain a preliminary scheduling distribution result, extracting unassigned process nodes in the preliminary scheduling distribution result, tracing back to the task execution diagram along the time sequence dependency relation to obtain resource release dependency information, identifying process node combinations which have no time sequence constraint with the unassigned process nodes to obtain parallel executable process combinations, and reassigning the kitchen electric equipment to the parallel executable process combinations by combining the feasibility mapping relation to generate a kitchen electric equipment scheduling sequence.
9. An electronic device, comprising: A processor; A memory for storing processor-executable instructions; Wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1 to 7.

Description

Kitchen electricity group cooperative work intelligent scheduling control method and system Technical Field The invention relates to the technical field of intelligent home furnishing, in particular to an intelligent dispatching control method and system for kitchen electric group cooperative work. Background Along with the improvement of living standard and the popularization of intelligent home, the intelligent degree of kitchen appliances is continuously improved. Modern household kitchens are commonly provided with a plurality of kitchen electric devices, such as electric cookers, ovens, electromagnetic ovens and steam boxes, which jointly form a kitchen electric group. In the cooking process, a plurality of kitchen electric devices are often required to work cooperatively to complete a series of complex cooking tasks. Most of traditional kitchen electric devices independently operate, and the use sequence and time of different kitchen electric devices are required to be manually planned to finish cooking; with the development of intelligent household technology, kitchen electric equipment starts to have an intelligent function, information interaction and simple coordination between the kitchen electric equipment can be realized through network connection, but the conventional kitchen electric cooperative work generally adopts a preset fixed working mode, and the problems that the process cannot be disassembled and resource scheduling is still carried out, the analysis capability of complex time sequence dependency relationship among the processes is lacking, key processes cannot be identified, resources are preferentially allocated, the parallel execution capability of the processes is limited, the scheduling strategy is difficult to dynamically adjust under the condition of resource competition and the like still exist. Disclosure of Invention The embodiment of the invention provides an intelligent scheduling control method and system for kitchen electricity group cooperative work, which at least can solve part of problems in the prior art. In a first aspect of the embodiment of the present invention, there is provided an intelligent scheduling control method for cooperative work of kitchen electric groups, including: acquiring the running state information and the to-be-executed cooking task information of each kitchen electric device in a kitchen electric group, carrying out process disassembly on the to-be-executed cooking task information by combining a preset cooking process constraint rule, identifying time sequence dependency relations among different processes to obtain a task execution diagram, extracting capacity parameters of the kitchen electric devices according to the running state information, and carrying out matching judgment on the capacity parameters and the resource requirement parameters of process nodes in the task execution diagram to obtain a feasibility mapping relation; Traversing backwards from each process node along a time sequence dependent path in a task execution diagram, counting the number of subsequent processes corresponding to the current process node, accumulating the longest time span from the current process node to the end point of the task execution diagram, and calculating to obtain a key degree quantization index based on the number of the subsequent processes and the longest time span; And distributing kitchen electric equipment to the process nodes according to the feasibility mapping relation and the key degree quantification index to obtain a preliminary scheduling distribution result, extracting unassigned process nodes in the preliminary scheduling distribution result, tracing forward along the time sequence dependency relation in a task execution diagram to obtain resource release dependency information, identifying process node combinations which have no time sequence constraint with the unassigned process nodes to obtain parallel executable process combinations, and re-distributing the kitchen electric equipment to the parallel executable process combinations by combining the feasibility mapping relation to generate a kitchen electric equipment scheduling sequence. In an alternative embodiment of the present invention, Acquiring operation state information and to-be-executed cooking task information of each kitchen electric device in a kitchen electric group, performing process disassembly on the to-be-executed cooking task information by combining a preset cooking process constraint rule, and identifying time sequence dependency relations among different processes to obtain a task execution diagram comprises the following steps: receiving a cooking task to be executed, which is input by a user, determining a dish identification and a cooking target parameter in the cooking task to be executed, inquiring a preset cooking process constraint rule according to the dish identification, extracting a procedure sequence corresponding to the dish identific