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CN-121742407-B - Intelligent warehouse automation control optimization method and system based on dynamic environment perception

CN121742407BCN 121742407 BCN121742407 BCN 121742407BCN-121742407-B

Abstract

The invention discloses an intelligent storage automation control optimization method and system based on dynamic environment awareness, and relates to the technical field of intelligent storage control. The method comprises the steps of firstly obtaining specific scene types of a designated warehouse, determining a corresponding dynamic perception allocation scheme through scene matching, further determining a collaborative operation association object to achieve accurate adaptation of dynamic environment perception and the warehouse scene, then collecting dynamic environment perception data of a warehouse area where the collaborative operation association object is located, generating a structured fusion data set adapting to different timing scenes after collaborative association processing, carrying out dynamic warehouse parameter calibration to generate an initial control strategy and complete validity assessment, deciding whether to dynamically adjust a path planning scheme and an operation execution sequence according to an assessment result, achieving dynamic adaptation and accurate regulation of intelligent warehouse automation control, and effectively improving adaptation of warehouse control, smoothness of flow operation and stability of operation execution under complex dynamic scenes.

Inventors

  • LI QI
  • LI PEISONG
  • ZHANG LIFU

Assignees

  • 北京奇步自动化控制设备有限公司

Dates

Publication Date
20260512
Application Date
20260226

Claims (10)

  1. 1. The intelligent warehouse automation control optimization method based on dynamic environment perception is characterized by comprising the following steps: S1, acquiring a warehouse scene type in a designated warehouse, matching to obtain a corresponding dynamic perception allocation scheme, and simultaneously marking automation equipment, a perception terminal and goods in a corresponding warehouse area in the designated warehouse as a collaborative operation association object; the method for obtaining the warehouse scene type in the appointed warehouse and obtaining the corresponding dynamic perception allocation scheme by matching specifically comprises the following steps: Collecting operation characterization data corresponding to the warehouse scene type by taking a preset statistical period as a unit, and summarizing the operation characterization data into a scene judgment data set, wherein the operation characterization data comprise cross operation conflict frequency, total input and output frequency in unit time and task handover delay time; Calculating characteristic indexes of corresponding operation characterization data in a scene judgment data set, wherein the characteristic indexes comprise a cooperative adaptation coefficient, an operation intensity coefficient and a flow stability coefficient; respectively carrying out ratio operation on the calculated characteristic indexes and corresponding preset reference characteristic indexes, and then taking a geometric average value to obtain a dynamic scene index for quantifying the dynamic complexity of the storage scene, and matching a dynamic perception allocation scheme based on the obtained dynamic scene index; S2, acquiring dynamic environment sensing data of a storage area where the collaborative operation association object is located according to a dynamic sensing allocation scheme, and performing collaborative association processing to generate a structured fusion data set; s3, executing dynamic warehousing parameter calibration according to the generated structured fusion data set, generating an initial control strategy, and judging the effectiveness of the initial control strategy based on the executing process of the initial control strategy, wherein the specific process is as follows: In a preset monitoring period, based on the ratio of the accumulated time length of the cargo flow rate in the rate target value allowable range to the preset monitoring total time length, obtaining a rate balance standard-reaching duty ratio reflecting the allocation rationality of the automatic equipment, and simultaneously, based on the ratio of the difference value of the unit time operation completion amount after the policy execution and the reference operation amount before the policy execution to the reference operation amount, obtaining an operation efficiency lifting duty ratio reflecting the operation flow optimization effect; If the obtained rate balance standard-reaching duty ratio is larger than the preset rate balance standard-reaching duty ratio and the obtained operation efficiency lifting duty ratio is larger than the preset operation efficiency lifting duty ratio, judging that the initial control strategy is effective and maintaining an execution state corresponding to the initial control strategy; otherwise, the initial control strategy is judged to be optimized, and the dynamic change condition of the corresponding real-time sensing data in the designated warehouse is monitored so as to dynamically adjust the path planning scheme and the operation execution sequence.
  2. 2. The intelligent warehouse automation control optimization method based on dynamic environment sensing as claimed in claim 1, wherein the collaborative adaptation coefficient is obtained by combining response synchronization rate correction of an automation device and a sensing terminal based on a ratio of a cross operation conflict frequency to a total number of collaborative operations in a period; the operation intensity coefficient is obtained by combining the complexity coefficient of the overlapped order batch based on the ratio of the actual warehouse-in and warehouse-out frequency to the maximum bearing operation frequency in unit time; And the flow stability coefficient is obtained based on the statistical characteristics of task handover delay time length and by combining the frequency occupation ratio of the task handover delay time length exceeding the corresponding allowable delay time length.
  3. 3. The intelligent warehouse automation control optimization method based on dynamic environment sensing as claimed in claim 2, wherein the dynamic scene index matching dynamic sensing allocation scheme based on the acquisition is specifically as follows: invoking a scene classification threshold corresponding to the dynamic scene index, wherein the scene classification threshold comprises a basic scene threshold and an enhanced scene threshold, and the basic scene threshold is smaller than the enhanced scene threshold; If the dynamic scene index is not greater than the basic scene threshold, judging that the current storage scene is a basic dynamic scene, and matching a basic perception allocation scheme; If the dynamic scene index is between the basic scene threshold and the enhanced scene threshold, judging that the current storage scene is a conventional dynamic scene, and matching with a further perception allocation scheme; If the dynamic scene index is larger than the enhanced scene threshold, judging that the current storage scene is a dense dynamic scene, and matching an enhanced perception allocation scheme; In a preset monitoring period in the matching process of the dynamic sensing distribution scheme, delay time sequence data of sensing response of each sensing terminal to the operation characterization data in the current storage scene is obtained, a delay change rate sequence is obtained by performing domain difference operation, and a time domain standard deviation of the sequence is used as a sensitivity influence factor; The jitter time sequence data of the operation characterization data in the transmission process after the perception response is obtained, fast Fourier transformation is carried out to obtain a frequency domain power spectrum, and an energy value corresponding to the main peak frequency is extracted to be used as a real-time influence factor; After the sensitivity influence factors and the real-time influence factors are respectively normalized, obtaining a comprehensive value of the matching process of the quantized dynamic perception allocation scheme and the adaptation degree of the dynamic scene through geometric average operation, and recording the comprehensive value as a comprehensive value of the perception response influence degree; If the comprehensive value of the perceived response influence degree is larger than a preset perceived response influence degree threshold, sending a scene adaptation abnormal verification prompt; if the comprehensive value of the perception response influence degree is not greater than the preset perception response influence degree threshold value, the matching of the corresponding dynamic perception allocation scheme is maintained unchanged, and the cooperative association processing is carried out.
  4. 4. The intelligent warehouse automation control optimization method based on dynamic environment sensing as claimed in claim 3, wherein the performing collaborative association process specifically comprises: calculating storage collaborative indexes of storage areas where different sensing terminals acquire collaborative operation association objects so as to quantify the collaborative adaptation degree of the sensing terminals in storage management and cargo safety monitoring links; When the warehouse collaborative index is larger than a preset warehouse collaborative index, carrying out warehouse data standardization control on the collected dynamic environment perception data, wherein the dynamic environment perception data comprises corresponding multidimensional data for reflecting the environmental state of a warehouse area, the running state of a collaborative operation related object and the dynamic change of an operation scene; When the storage collaborative index is not greater than the preset storage collaborative index, if the environment parameter abnormal feedback of the storage area is received, the corresponding dimension data re-acquisition prompt is sent after the environment parameter control is carried out, and if the environment parameter abnormal feedback of the storage area is not received, the corresponding dimension data re-acquisition is directly triggered; The environment parameter abnormal feedback indicates that the environment parameter in the storage area does not meet the expected permission condition corresponding to the operation safety of the automatic equipment and corresponds to a feedback signal that the abnormal operation time length reaches a preset judging period; the environmental parameter control comprises the steps of sending an automatic equipment regulation prompt to an equipment control terminal corresponding to the storage area based on a specific environmental parameter type in environmental parameter abnormal feedback until the feedback and regulation are recovered from the environmental parameter abnormal feedback of the storage area, and triggering the corresponding dimension data to be re-acquired.
  5. 5. The intelligent warehouse automation control optimization method based on dynamic environment sensing as claimed in claim 4, wherein the warehouse collaborative index is obtained by the following steps: Summarizing the real-time occupied state data of the storage bits collected by the sensing terminal and the reference storage bit occupied state data in a preset storage bit planning scheme, obtaining the frequency occupation ratio of the two storage bit occupied states in a preset statistical period through the state comparison of storage bit by bit and time node by time node, and recording the frequency occupation ratio as the storage bit occupied matching degree; The method comprises the steps of performing ratio processing on the frequency of superposition of a cargo displacement anomaly monitoring signal captured by a sensing terminal and a historical anomaly monitoring signal in a historical feature library and the total early warning frequency of cargo displacement anomaly sent by the sensing terminal in a preset statistical period to obtain cargo displacement early warning accuracy; After geometric average processing is carried out on the acquired storage occupancy matching degree and the cargo displacement early warning accuracy, a storage synergy index is obtained by combining a scene dynamic synergy correction coefficient; the scene dynamic collaborative correction coefficient is obtained based on the operation intensity coefficient and the flow stability coefficient.
  6. 6. The intelligent warehouse automation control optimization method based on dynamic environment sensing as set forth in claim 4, wherein when the acquisition time sequences corresponding to the dynamic environment sensing data acquired by different sensing terminals are consistent, the warehouse data standardization control is specifically as follows: In a preset acquisition period, acquiring time sequence feature characteristics of the same storage parameter acquired by each sensing terminal, and calculating a collaborative deviation value between the corresponding time sequence feature characteristics of adjacent sensing terminals; if the cooperative deviation value is not greater than the preset allowable cooperative deviation, performing light-weight standardization processing on the dynamic environment sensing data to generate a structured fusion data set, and performing dynamic storage parameter calibration; Otherwise, based on the quantization interval of the collaborative deviation value, the cumulative influence degree of the collaborative deviation on the stability of the warehouse operation flow in the time dimension is quantized, specifically: taking time in a preset acquisition period as an integral variable, and respectively taking cooperative deviation values of adjacent perception terminals as integrated functions to calculate a deviation accumulated integral value in a preset statistical period; If the deviation accumulated integral value is not larger than the preset deviation accumulated integral value, carrying out smooth correction on the acquired dynamic environment sensing data through a moving average algorithm; And if the accumulated deviation integral value is larger than the preset accumulated deviation integral value, carrying out noise reduction correction on the acquired dynamic environment sensing data through a Kalman filtering algorithm.
  7. 7. The intelligent warehouse automation control optimization method based on dynamic environment sensing as claimed in claim 4, wherein when the acquisition time sequences corresponding to the dynamic environment sensing data acquired by different sensing terminals are inconsistent, the warehouse data standardization control is specifically as follows: Inputting a time sequence consistency deviation value corresponding to the dynamic environment sensing data into a data standardization association table, mapping by combining the storage scene types to obtain time sequence calibration priority, and converting to obtain a time sequence calibration priority score according to a preset deviation interval-score correspondence rule; The time sequence consistency deviation value represents the standard deviation of the time stamp difference value of the same dimension data in the dynamic environment perception data in a preset acquisition period; The data standardization association table represents a structured data table for storing the mapping relation of the time sequence consistency deviation value, the storage scene type and the time sequence calibration priority score; the storage scene type comprises a basic dynamic scene, a conventional dynamic scene and a dense dynamic scene; If the data meeting the preset conditions does not exist in the dynamic environment sensing data, carrying out light-weight standardization processing on the dynamic environment sensing data to generate a structured fusion data set, and carrying out dynamic storage parameter calibration, wherein the preset conditions indicate that the acquired time sequence calibration priority score is larger than the reference time sequence calibration priority score; if the single dimension data meeting the preset condition exists in the dynamic environment sensing data, adopting a real-time stream processing mode to perform standardized control, wherein the method specifically comprises the following steps: Determining a time stamp synchronous adjustment step length and a time sequence correction coefficient through time sequence calibration priority deviation corresponding to single-dimension data, setting the time stamp synchronous adjustment step length as an adjustment amount of step-by-step reduction operation, setting the time sequence correction coefficient as an adjustment amount of step-by-step increase operation, and simultaneously executing time stamp step-by-step synchronous calibration and data time sequence deviation correction operation; If non-single dimension data meeting preset conditions exist in the dynamic environment sensing data, carrying out standardized control by adopting a batch processing mode, wherein the standardized control comprises the following specific steps: and determining a batch calibration window adjusting step length and a data integration weight through a time sequence calibration priority score deviation average value corresponding to non-single dimension data, setting the batch calibration window adjusting step length as an adjusting amount of step-by-step optimization operation, setting the data integration weight as an allocating amount of step-by-step adaptation operation, and executing batch time sequence calibration and heterogeneous data fusion operation based on dynamic adjusting parameters of each round of processing results.
  8. 8. The intelligent warehouse automation control optimization method based on dynamic environment sensing as claimed in claim 6 or 7, wherein the executing the dynamic warehouse parameter calibration specifically comprises: acquiring the goods storage density and the goods flow rate of the corresponding operation area in the appointed warehouse according to the generated structured fusion data set; The parameter target value is set, specifically, a preset cargo storage priority and a storage space utilization upper limit are called, a cargo storage density target value is determined, and a cargo flow rate target value is determined by combining a historical cargo flow peak value of a corresponding operation area and a storage operation capacity upper limit; taking the currently acquired cargo storage density, cargo flow rate and space diffusion coefficient as the input of a partial differential equation to obtain a storage density adjustment gradient so as to dynamically correct a cargo space allocation strategy; the load balancing objective function constructed based on the Lagrangian multiplier method takes the cargo flow rate target value as a constraint condition of the load balancing objective function, and obtains the optimal flow rate adjustment quantity by deviant guiding of cargo in-out warehouse resource allocation coefficients in the load balancing objective function so as to dynamically correct cargo in-out warehouse scheduling rules, wherein the load balancing objective function is used for reflecting the dynamic adaptation relation between the cargo flow rate and warehouse operation resource supply quantity; and feeding back an adjustment result, namely generating an initial control strategy adapting to a warehouse operation scene based on the cargo storage density and the cargo flow rate obtained after the dynamic correction if the cargo storage density and the cargo flow rate after the dynamic correction are in the corresponding allowable ranges, otherwise, sending a parameter regulation abnormal prompt.
  9. 9. The intelligent warehouse automation control optimization method based on dynamic environment sensing as claimed in claim 8, wherein the dynamic adjustment path planning scheme and the job execution sequence are specifically as follows: acquiring the real-time cargo idle space occupation ratio and the emergency degree weight of the order of each storage partition in the designated storage, and carrying out harmonic mean processing to obtain a balance coefficient; marking the idle goods space duty ratio corresponding to the equalization coefficient larger than the reference equalization coefficient as a first priority area idle duty ratio, marking the rest idle goods space duty ratios as a second priority area idle duty ratio, wherein the job allocation priority of the first priority area idle duty ratio is larger than the job allocation priority of the second priority area idle duty ratio; Calculating the product operation results of the idle duty ratio of the first priority area, the idle duty ratio of the second priority area and the emergency degree weight of the order respectively, if the results are the same, determining the execution sequence of the operation from the near to the far according to the current position of the AGV corresponding to the idle goods space, and if the results are different, distributing the operation tasks with the largest priority of the product operation results; Taking the space three-dimensional coordinates of the storage partition corresponding to the idle duty ratio of the first priority area and the cargo space dynamic occupied state parameters as independent variables of a jacobian matrix, solving coordinate mapping partial derivatives in path planning through matrix derivation operation so as to correct path deviation caused by space layout change in the path planning process, wherein the cargo space dynamic occupied state parameters comprise a numerical value representing the idle cargo space state and a numerical value representing the occupied cargo space state; And after the path planning scheme and the operation execution sequence are adjusted, re-acquiring the rate balance standard-reaching duty ratio and the operation efficiency lifting duty ratio in the designated warehouse, if the re-acquired rate balance standard-reaching duty ratio is larger than the preset rate balance standard-reaching duty ratio, and the acquired operation efficiency lifting duty ratio is larger than the preset operation efficiency lifting duty ratio, judging that the path planning scheme and the operation execution sequence are effectively adjusted, otherwise, performing the adjustment effect failure standard-reaching early warning.
  10. 10. An intelligent warehouse automation control optimization system based on dynamic environment sensing, which applies the intelligent warehouse automation control optimization method based on dynamic environment sensing as claimed in any one of claims 1-9, and is characterized by comprising: the warehouse scene dynamic sensing module is used for acquiring the warehouse scene type in the appointed warehouse, matching to obtain a corresponding dynamic sensing distribution scheme, and simultaneously marking automation equipment, a sensing terminal and goods in a corresponding warehouse area in the appointed warehouse as a collaborative operation association object; The collaborative association processing module is used for acquiring dynamic environment perception data of a storage area where a collaborative operation association object is located according to a dynamic perception allocation scheme, and performing collaborative association processing to generate a structured fusion data set; And the warehousing parameter calibration and effectiveness judgment module is used for executing dynamic warehousing parameter calibration according to the generated structured fusion data set, generating an initial control strategy, and judging the effectiveness of the initial control strategy based on the executing process of the initial control strategy.

Description

Intelligent warehouse automation control optimization method and system based on dynamic environment perception Technical Field The invention relates to the technical field of intelligent storage control, in particular to an intelligent storage automation control optimization method and system based on dynamic environment awareness. Background Under the premise of flexible production popularization in manufacturing industry, intelligent storage is a core hub for efficient operation of a supply chain, the core links penetrate through the whole flow of storage operation and cover four key modules including cargo storage, equipment scheduling, operation execution and state monitoring, wherein the cargo storage links are required to adapt to dynamic storage requirements of diversified cargo specifications, high-density storage is realized through high-level shelves and flexible cargo space layout, the equipment scheduling links relate to parallel operation of multiple equipment such as automatic guided vehicles (AGVs, automated Guided Vehicle), stackers and conveyors and are required to coordinate operation tracks and task allocation of all automation equipment, the operation execution links focus core operations such as warehouse-in and warehouse-out, carrying and stacking, the circulation is required to be completed efficiently according to order priority, and the state monitoring links provide data support for the whole flow optimization through multi-source sensing and capturing of dynamic information such as equipment states, cargo positions and environmental parameters. The dynamic challenges faced in the actual operation of the core links are just highlighted, the necessity of implementing the fine control on the intelligent storage is precisely highlighted, the storage requirements are frequently changed due to the diversification of the specifications of the goods in the goods storage link, the distribution of goods positions and the layout of goods shelves are required to be dynamically adjusted through a control strategy, the path congestion is easily caused by the parallel arrangement of multiple devices in the equipment scheduling link, the path planning and task coordination are required to be optimized through the real-time control, the rapid adaptation of the operation flow is required by the high-frequency order and the flexible production in the operation execution link, the execution sequence and the operation rhythm are required to be adjusted through the dynamic control, the preset flow is easily broken due to the sudden equipment faults or personnel intervention in the state monitoring link, the deviation is required to be timely corrected through the closed-loop control, the efficient operation of the storage system is maintained, and the controlled accurate landing is not separated, and the overall capture and the deep analysis of the core data of each link are not required. The popularization of sensing technologies such as the internet of things (IoT, internet of Things) and the laser radar realizes the comprehensive acquisition of multi-source dynamic data of environments, equipment and goods in a warehouse scene, wherein warehouse parameters such as environment parameters, goods parameters and operation parameters which penetrate through the whole flow of warehouse operation are covered, a precise data base is provided for warehouse automation control, the breakthrough development of artificial intelligence (AI, artificial Intelligence) and intelligent scheduling algorithms provides core support for the analysis of mass sensing parameters and the dynamic optimization of control strategies, the scattered warehouse parameters are converted into control instructions capable of falling to the ground, the research and development of the warehouse automation control technology driven by data is promoted, and the warehouse system is enabled to be capable of self-adapting and transforming from fixed execution to real-time parameter-based. Taking the Chinese patent with the publication number of CN119690020B as an example, the control method and the system of the automatic transport equipment of the special transportation line are disclosed, and the control method comprises the steps of constructing a local environment model based on micro-meshing to acquire and transmit equipment state information, carrying out task allocation and path planning on the automatic equipment according to the micro-meshing environment model, carrying out dynamic optimization on the task allocation and path planning in a global range according to the task allocation and path planning in each micro-meshing, monitoring the execution condition of the optimized task allocation and path planning in real time, dynamically adjusting the execution sequence of the tasks and the path planning through an intelligent scheduling algorithm, and carrying out flexible adaptive adjustment according to the resource state and enviro