CN117993828A - Intelligent storage-based problem tray searching method and system - Google Patents
Intelligent storage-based problem tray searching method and system Download PDFInfo
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Abstract
The invention relates to the technical field of warehouse management and discloses a problem tray searching method and a system based on intelligent warehouse, wherein the method is used for arranging corresponding warehouse trays by collecting data of warehouse objects, analyzing the warehouse objects and the warehouse trays to obtain expected reaction indexes of the warehouse trays, collecting actual reaction indexes of the warehouse trays in actual warehouse treatment, and realizing problem diagnosis of the warehouse trays by comparing and analyzing the expected reaction indexes and the actual reaction indexes, thereby solving the problem that the problem of the warehouse trays in the prior art cannot be found in time.
Description
Technical Field
The invention relates to the technical field of warehouse management, in particular to a problem tray searching method and system based on intelligent warehouse.
Background
With the rapid development of globalization and electronic commerce, the warehouse logistics industry is faced with unprecedented challenges and pressures, the amount of goods to be processed is greatly increased, and the requirements of customers on distribution speed and accuracy are also increasing. Traditional warehouse logistics management mode often relies on manual operation, and this not only inefficiency, and easily lead to goods damage or storage space waste because of the operation is improper or the tray is selected inappropriately moreover. In addition, the pallet is the most basic and important loading tool in warehouse logistics, and the state of the pallet directly influences the safety and the transportation efficiency of goods.
Conventional pallet management often lacks an effective monitoring and maintenance system, and damage or inapplicability to pallets may not be timely discovered and resolved, which may result in reduced downstream logistics efficiency and may even pose a threat to cargo security. Moreover, due to the lack of tracking of tray usage status and historical data, efficient tray maintenance and replacement planning is difficult to perform, thereby shortening the service life of the tray and increasing the operating costs of the enterprise.
Disclosure of Invention
The invention aims to provide a problem tray searching method and system based on intelligent storage, and aims to solve the problem that a storage tray cannot be found in time in the prior art.
The invention is realized in such a way that in a first aspect, the invention provides a problem tray searching method based on intelligent storage, comprising the following steps:
Data acquisition is carried out on the warehouse objects in the warehouse logistics transfer area so as to obtain warehouse characteristics of the warehouse objects; the warehouse logistics transfer area is used for carrying out logistics transfer on the warehouse objects;
Analyzing the storage characteristics of the storage objects to obtain the bearing characteristics of the storage objects, and selecting corresponding storage trays for the storage objects according to the bearing characteristics of the storage objects so as to load the storage objects; the storage tray is used for loading, transporting and storing the storage objects;
Calculating the expected reaction of the storage tray according to the bearing characteristics of the storage object to obtain the expected reaction index of the storage tray for the storage object;
Carrying out multidimensional data acquisition on the warehouse tray loaded with the warehouse objects so as to obtain actual reaction indexes of the warehouse tray;
And comparing, analyzing and processing the actual reaction index according to the expected reaction index to obtain a reaction deviation index of the storage tray, and analyzing and processing the reaction deviation index of the storage tray according to a preset standard to diagnose the problem of the storage tray.
Preferably, data acquisition is performed on the warehouse objects in the warehouse logistics transfer area to obtain warehouse characteristics of the warehouse objects, and the method comprises the following steps:
Data acquisition of basic characteristics is carried out on the warehouse objects in the warehouse logistics transfer area so as to obtain the basic characteristics of the warehouse objects; wherein the basic characteristics comprise the cargo type, weight, bottom area and height of the storage object;
performing deep analysis processing on the basic characteristics of the storage object to obtain the deep characteristics of the storage object; wherein the depth features include a pressure, a center of gravity of the warehouse object;
Analyzing and processing the basic characteristics and the depth characteristics of the storage object according to preset standards to obtain a preparation storage position of the storage object; wherein the preparation storage position is a specific position for storing the storage object;
and taking the basic characteristics, the depth characteristics and the preparation storage positions of the storage objects as storage characteristics of the storage objects.
Preferably, the analyzing and processing are performed on the basic features and the depth features of the warehouse object according to a preset standard to obtain a prepared warehouse position of the warehouse object, and the steps include:
Analyzing and processing the basic characteristics of the storage objects according to preset standards to determine storage areas corresponding to the cargo types of the storage objects;
data is acquired from the database for the warehouse area so as to acquire the residual space distribution of the warehouse area; the residual space distribution is used for describing a space in the storage area, wherein the space can store the storage objects;
And analyzing and processing the depth characteristics of the storage objects according to preset standards to determine the optimal storage space of the storage objects in the residual space distribution, and taking the optimal storage space as a preparation storage position of the storage objects.
Preferably, the step of analyzing the storage characteristics of the storage object to obtain the bearing characteristics of the storage object includes:
Analyzing and processing the basic characteristics of the storage object according to a preset standard to obtain tray specification data of the storage object; wherein the pallet specification data is part of the load bearing characteristics of the warehouse object;
performing comprehensive analysis processing according to the basic characteristics and the depth characteristics of the storage object to obtain the stable characteristics and the pressing characteristics of the storage object; the stability feature is used for describing the stability of the storage object when the storage object is placed on the storage tray, the pressure feature is used for describing the pressure condition caused to the storage tray when the storage object is placed on the storage tray, and the stability feature and the pressure feature are part of the bearing feature of the storage object.
Preferably, the calculating process of the expected reaction of the warehouse tray is performed according to the bearing characteristics of the warehouse object, so as to obtain the expected reaction index of the warehouse tray to the warehouse object, and the steps include:
constructing a tray model corresponding to the warehouse tray according to the tray specification data of the warehouse object;
According to the stable characteristics and the pressing characteristics of the storage object, an object model corresponding to the storage object is constructed;
And carrying out combination treatment on the tray model and the object model, and carrying out analysis treatment on expected reaction of the tray model according to the object model subjected to the combination treatment so as to obtain the expected reaction index of the warehouse tray to the warehouse object.
Preferably, the tray model and the object model are combined, and the analysis processing of the expected reaction of the tray model is performed according to the object model subjected to the combination processing, so as to obtain the expected reaction index of the warehouse tray for the warehouse object, and the method comprises the following steps:
Determining a relative positional relationship between the tray model and the object model; wherein the relative positional relationship is used for describing the relative positional relationship between the object model and the tray model when the object model is placed on the tray model;
according to the relative position relation, combining the object model and the tray model to obtain a storage integral model; the storage overall model is used for describing the overall conditions of the storage objects and the storage trays when the storage objects are loaded on the storage trays;
according to the stability characteristics of the object model, analyzing and processing the stability of the storage integral model to obtain stability parameters of the storage integral model;
Acquiring a preliminary storage position of the storage object, and calculating according to the preliminary storage position to obtain a transportation path of the storage overall model; the transportation path is a path required by the warehouse objects to be transported from the warehouse logistics transfer area to the preparation warehouse position;
dividing the transportation path into path sections with a plurality of path attributes; the path attribute is used for describing the transportation condition of the warehouse object and the warehouse tray in the transportation process of the path section;
analyzing and processing stability parameters of the warehouse integral model according to path attributes of the path sections to obtain theoretical jitter data of the warehouse integral model in the path sections;
According to the pressing characteristics of the object model, analyzing and processing the pressing vector of the object model on the tray model by the storage overall model to obtain the distribution of the pressing vector of the object model on the tray model in the storage overall model; the distribution of the pressing vectors is used for describing the distribution condition of the pressing vectors of the object model to each position of the tray model, and the pressing vectors are used for describing the magnitude and the angle of the pressing force applied by the object model to one position of the tray model;
calculating theoretical deformation distribution of the tray model according to the pressure vector distribution so as to obtain theoretical deformation distribution of the tray model;
And jointly using theoretical shaking data of the warehouse integral model in each path section and theoretical deformation distribution of the tray model as the expected reaction index.
Preferably, the multi-dimensional data acquisition is performed on the warehouse tray loaded with the warehouse objects so as to obtain the actual reaction index of the warehouse tray, and the steps include:
Performing first-round appearance detection on the warehouse tray without the warehouse object so as to acquire initial appearance data of the warehouse tray;
Performing second-round appearance detection on the warehouse tray loaded with the warehouse objects to acquire strain appearance data of the warehouse tray;
Performing differential analysis processing on the strain appearance data and the initial appearance data to obtain deformation response indexes of the warehouse tray;
Continuously collecting shaking data of the warehouse tray loaded with the warehouse object to obtain shaking data of the warehouse tray loaded with the warehouse object in each path section, and taking the collected shaking data as shaking response indexes of the warehouse tray;
and taking the deformation reaction index and the shaking reaction index of the storage tray as actual reaction indexes of the storage tray.
Preferably, the actual reaction index is subjected to comparative analysis processing according to the expected reaction index to obtain a reaction deviation index of the warehouse tray, and the reaction deviation index of the warehouse tray is subjected to analysis processing according to a preset standard to diagnose the problem of the warehouse tray, which comprises the following steps:
Performing comparative analysis treatment on the actual reaction index according to the expected reaction index to obtain a reaction deviation index of the storage tray;
The method comprises the steps of calling response deviation indexes of the same type of histories corresponding to storage objects from a database, and comparing the response deviation indexes of the storage trays according to the response deviation indexes of the same type of histories to obtain a first deviation degree of the response deviation indexes of the storage trays; wherein the same type of history record is a history storage processing process of a storage object similar to the storage object;
the reaction deviation index of the historical working record of the storage tray is called from a database, and the reaction deviation index of the storage tray and the reaction deviation index of the historical working record of the storage tray are subjected to integral correlation analysis processing to obtain a second deviation degree of the reaction deviation index of the storage tray; the history work record is a history process of carrying out warehousing treatment on other warehouse objects by the warehouse pallet;
and comprehensively analyzing and processing the reaction deviation index, the first deviation degree and the second deviation degree to realize the problem diagnosis of the warehouse tray.
Preferably, the reaction deviation index, the first deviation degree and the second deviation degree are comprehensively analyzed to realize the problem diagnosis of the warehouse tray, and the steps include:
constructing a problem overall map of the warehouse tray according to the reaction deviation index; the problem overall map is used for describing problems integrally displayed by the warehouse tray in the warehouse treatment process;
Constructing an external influence map of the warehouse tray according to the first deviation degree; the external influence map is used for describing influence of an external device on problems displayed by the warehouse tray in the warehouse treatment process;
Performing differential analysis processing on the problem overall spectrum and the external influence spectrum, and obtaining a body influence spectrum of the storage tray according to the differential analysis processing result; the body influence map is used for describing the actual influence of the storage tray on the problems displayed in the storage process;
Constructing a multi-level influence map of the warehouse pallet according to the reaction deviation index and the second deviation degree; the multi-level influence map is used for describing the relation between the problems displayed by the warehouse trays in each warehouse process and the warehouse objects;
And analyzing and processing the body influence map and the multi-level influence map so as to realize the problem diagnosis of the storage tray.
In a second aspect, the present invention provides an intelligent warehouse-based problem tray searching system, including:
The data acquisition module is used for acquiring data of the warehouse objects in the warehouse logistics transfer area so as to obtain warehouse characteristics of the warehouse objects; the warehouse logistics transfer area is used for carrying out logistics transfer on the warehouse objects;
The data analysis module is used for analyzing the storage characteristics of the storage objects to obtain the bearing characteristics of the storage objects, and selecting corresponding storage trays for the storage objects according to the bearing characteristics of the storage objects so as to load the storage objects; the storage tray is used for loading, transporting and storing the storage objects;
the reaction prediction module is used for carrying out calculation processing on expected reaction of the storage tray according to the bearing characteristics of the storage object so as to obtain an expected reaction index of the storage tray for the storage object;
the reaction acquisition module is used for carrying out multidimensional data acquisition on the warehouse tray loaded with the warehouse objects so as to obtain actual reaction indexes of the warehouse tray;
And the problem diagnosis module is used for carrying out comparison analysis processing on the actual reaction index according to the expected reaction index to obtain a reaction deviation index of the storage tray, and carrying out analysis processing on the reaction deviation index of the storage tray according to a preset standard to carry out problem diagnosis on the storage tray.
The invention provides a problem tray searching method based on intelligent storage, which has the following beneficial effects:
According to the invention, the corresponding warehouse trays are arranged by collecting data of the warehouse objects, the expected reaction indexes of the warehouse trays are obtained by analyzing the warehouse objects and the warehouse trays, then the actual reaction indexes of the warehouse trays in the actual warehouse treatment are collected, and the problem diagnosis of the warehouse trays is realized by comparing and analyzing the expected reaction indexes and the actual reaction indexes, so that the problem of the warehouse trays cannot be found in time in the prior art is solved.
Drawings
Fig. 1 is a schematic step diagram of a problem tray searching method based on intelligent storage according to an embodiment of the present invention;
Fig. 2 is a schematic structural diagram of a problem tray searching system based on intelligent warehouse according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The implementation of the present invention will be described in detail below with reference to specific embodiments.
Referring to fig. 1 and 2, a preferred embodiment of the present invention is provided.
In a first aspect, the present invention provides a method for searching a problem tray based on intelligent storage, including:
S1: data acquisition is carried out on the warehouse objects in the warehouse logistics transfer area so as to obtain warehouse characteristics of the warehouse objects; the warehouse logistics transfer area is used for carrying out logistics transfer on the warehouse objects;
s2: analyzing the storage characteristics of the storage objects to obtain the bearing characteristics of the storage objects, and selecting corresponding storage trays for the storage objects according to the bearing characteristics of the storage objects so as to load the storage objects; the storage tray is used for loading, transporting and storing the storage objects;
S3: calculating the expected reaction of the storage tray according to the bearing characteristics of the storage object to obtain the expected reaction index of the storage tray for the storage object;
S4: carrying out multidimensional data acquisition on the warehouse tray loaded with the warehouse objects so as to obtain actual reaction indexes of the warehouse tray;
S5: and comparing, analyzing and processing the actual reaction index according to the expected reaction index to obtain a reaction deviation index of the storage tray, and analyzing and processing the reaction deviation index of the storage tray according to a preset standard to diagnose the problem of the storage tray.
Specifically, in step S1 of the embodiment provided by the present invention, data acquisition is performed on the warehouse objects in the warehouse logistics transfer area, so that corresponding warehouse trays are selected for the warehouse objects in the subsequent steps, so as to load, transport and store the warehouse objects.
It can be understood that the warehouse logistics transfer area is an area for carrying out logistics transfer on warehouse objects, the warehouse objects carry out data acquisition of warehouse characteristics in the area and are loaded on a warehouse tray, and the warehouse tray and the warehouse objects are carried by an automatic device so as to realize automatic warehouse processing.
It should be noted that, in the process of this warehouse treatment, data acquisition is performed on the warehouse pallet, so as to realize problem diagnosis on the warehouse pallet, and specific acquisition methods and diagnosis methods are detailed in the subsequent steps.
Specifically, in step S2 of the embodiment provided by the present invention, the storage characteristics of the storage object are analyzed to obtain the bearing characteristics of the storage object, and the corresponding storage tray is selected for the storage object according to the bearing characteristics of the storage object, so as to load the storage object.
It should be noted that the storage object is an object to be subjected to storage processing, that is, storage goods, and storage characteristics of the storage object are characteristics of the storage object to be considered in the storage processing, that is, a series of data of a goods type, a weight, a volume and the like of the storage object.
More specifically, the warehouse objects need to be stored on the warehouse tray, and the warehouse tray is transported by the equipment convenient to automatic, so that the convenient transportation of the warehouse objects is realized, and it is easy to understand that the warehouse tray needs to be selected by considering the bearing requirement of the warehouse objects, for example, the warehouse objects with larger volumes need larger warehouse trays, and the warehouse objects with higher weight need stronger warehouse trays with higher strength and higher loading capacity.
Therefore, the storage characteristics of the storage objects are analyzed to obtain the bearing characteristics of the storage objects, namely the characteristics of the storage objects, which are needed to be dealt with by the storage tray for loading the storage objects.
Specifically, in step S3 of the embodiment provided by the present invention, after the storage tray corresponding to the storage object is obtained, the calculation processing of the expected reaction of the storage tray may be performed according to the load-bearing characteristics of the storage object, so as to obtain the expected reaction index of the storage tray corresponding to the storage object.
It should be noted that the range of expected response indicators includes variations in loading the warehouse objects as well as variations in transporting the warehouse objects.
Specifically, in step S4 of the embodiment provided by the present invention, multidimensional data acquisition is performed on the warehouse tray loaded with the warehouse object, so as to obtain an actual reaction index of the warehouse tray.
It can be seen that this step corresponds to the last step in which the reaction is expected and which is actually detected.
Specifically, in step S5 of the embodiment provided by the present invention, through the reaction expectation and reaction detection of the warehouse tray, the expected reaction index and the actual reaction index of the warehouse tray may be analyzed to obtain the reaction deviation index of the warehouse tray, that is, the difference between the actual reaction and the expected reaction of the warehouse tray, and through the analysis and judgment of the difference, the problem judgment of the warehouse tray may be realized.
The invention provides a problem tray searching method based on intelligent storage, which has the following beneficial effects:
According to the invention, the corresponding warehouse trays are arranged by collecting data of the warehouse objects, the expected reaction indexes of the warehouse trays are obtained by analyzing the warehouse objects and the warehouse trays, then the actual reaction indexes of the warehouse trays in the actual warehouse treatment are collected, and the problem diagnosis of the warehouse trays is realized by comparing and analyzing the expected reaction indexes and the actual reaction indexes, so that the problem of the warehouse trays cannot be found in time in the prior art is solved.
Preferably, data acquisition is performed on the warehouse objects in the warehouse logistics transfer area to obtain warehouse characteristics of the warehouse objects, and the method comprises the following steps:
S11: data acquisition of basic characteristics is carried out on the warehouse objects in the warehouse logistics transfer area so as to obtain the basic characteristics of the warehouse objects; wherein the basic characteristics comprise the cargo type, weight, bottom area and height of the storage object;
S12: performing deep analysis processing on the basic characteristics of the storage object to obtain the deep characteristics of the storage object; wherein the depth features include a pressure, a center of gravity of the warehouse object;
S13: analyzing and processing the basic characteristics and the depth characteristics of the storage object according to preset standards to obtain a preparation storage position of the storage object; wherein the preparation storage position is a specific position for storing the storage object;
S14: and taking the basic characteristics, the depth characteristics and the preparation storage positions of the storage objects as storage characteristics of the storage objects.
Specifically, data acquisition is carried out on the storage object through a preset sensor group so as to obtain the basic characteristics of the storage object; the basic characteristics comprise the cargo type, weight, bottom area and height of the storage objects.
More specifically, the sensor group includes an image sensor, a weight sensor, and a tag identification sensor.
More specifically, the pressure (weight divided by bottom area) is calculated by a calculation formula using the acquired basic feature data, and the center of gravity position of the cargo is determined by designing a calculation model.
More specifically, the preliminary warehouse location is determined in consideration of the stability, distribution efficiency, and access convenience of goods by analyzing the basic features and the depth features according to preset criteria (e.g., safety specifications, optimal access paths, inventory rotation strategies, etc.).
More specifically, as the warehouse characteristics of goods, basic characteristics, depth characteristics and a preliminary warehouse location coding or entry system are processed and stored using a Warehouse Management System (WMS) or an enterprise resource planning system (ERP), and the warehouse characteristics are used in a decision support system to guide logistics operations and inventory management.
It can be understood that the steps realize accurate goods storage, reduce goods loss and space waste, reduce goods taking time through scientific storage position distribution, improve warehouse in and out operating efficiency, consider depth characteristics such as pressure and focus, ensure goods storage stability, reduce accident risk, the characteristic of datamation can cooperate automated equipment (such as automated forklift, robot etc.), realize efficient automated storage.
Preferably, the analyzing and processing are performed on the basic features and the depth features of the warehouse object according to a preset standard to obtain a prepared warehouse position of the warehouse object, and the steps include:
S131: analyzing and processing the basic characteristics of the storage objects according to preset standards to determine storage areas corresponding to the cargo types of the storage objects;
S132: data is acquired from the database for the warehouse area so as to acquire the residual space distribution of the warehouse area; the residual space distribution is used for describing a space in the storage area, wherein the space can store the storage objects;
S133: and analyzing and processing the depth characteristics of the storage objects according to preset standards to determine the optimal storage space of the storage objects in the residual space distribution, and taking the optimal storage space as a preparation storage position of the storage objects.
Specifically, the warehouse objects are stored in the warehouse area, and in the warehouse area, different types of warehouse objects can be stored in a partitioned mode so as to be convenient to manage, so that the basic characteristics of the warehouse objects are analyzed and processed according to preset standards to determine the warehouse area corresponding to the cargo types of the warehouse objects.
More specifically, the data of the warehouse area is called from the database to acquire the residual space distribution of the warehouse area; the residual space distribution is used for describing the space in the warehouse area, which can store the warehouse objects, namely the distribution condition of the space in the warehouse area, which is not stored yet and can be used for storing the warehouse objects.
More specifically, the pressure (weight divided by bottom area) and the gravity center position of the cargo are calculated to determine whether the cargo is suitable for being stored on some specific type of shelves, for example, the higher the gravity center, the cargo tends to be stored in an area close to the ground, so as to avoid accidents during transportation, ensure the stability of cargo storage and the safety of operation by deeply analyzing the depth characteristics such as the pressure and the gravity center, reduce unnecessary carrying and storage operations, and control the operation cost.
Preferably, the step of analyzing the storage characteristics of the storage object to obtain the bearing characteristics of the storage object includes:
s21: analyzing and processing the basic characteristics of the storage object according to a preset standard to obtain tray specification data of the storage object; wherein the pallet specification data is part of the load bearing characteristics of the warehouse object;
S22: performing comprehensive analysis processing according to the basic characteristics and the depth characteristics of the storage object to obtain the stable characteristics and the pressing characteristics of the storage object; the stability feature is used for describing the stability of the storage object when the storage object is placed on the storage tray, the pressure feature is used for describing the pressure condition caused to the storage tray when the storage object is placed on the storage tray, and the stability feature and the pressure feature are part of the bearing feature of the storage object.
Specifically, analyzing and processing basic characteristics of the storage object according to a preset standard to obtain tray specification data of the storage object; the pallet specification data is a part of the bearing characteristics of the warehouse object, and represents the data of the pallet specification of the warehouse pallet required by the warehouse object loading, namely the specification data of the warehouse pallet arranged for the warehouse object.
More specifically, the method comprises the steps of evaluating the placement mode (center placement, uniform distribution and the like) and the ratio of the height to the area of a substrate of the goods on the tray, predicting the influence of the placement mode of the goods on the stability of the goods, applying a physical principle such as gravity center analysis, determining the stability of the goods on the tray, calculating the pressure value applied to the tray on the unit area of the goods, ensuring the pressure value to be within the bearing range of the tray, analyzing the weight distribution of the goods, judging whether the non-uniform pressure is caused on the tray or not, thereby affecting the integrity and the service life of the tray, comprehensively analyzing the stability characteristics and the pressure characteristics to determine the optimal goods placement scheme and whether additional stabilizing measures (such as wrapping films, strapping and the like) are needed.
It should be noted that the stabilizing feature is used to describe the stability of the storage object when the storage object is placed on the storage tray, and the pressing feature is used to describe the pressure condition caused to the storage tray when the storage object is placed on the storage tray, where the stabilizing feature and the pressing feature are part of the bearing feature of the storage object.
Preferably, the calculating process of the expected reaction of the warehouse tray is performed according to the bearing characteristics of the warehouse object, so as to obtain the expected reaction index of the warehouse tray to the warehouse object, and the steps include:
S31: constructing a tray model corresponding to the warehouse tray according to the tray specification data of the warehouse object;
s32: according to the stable characteristics and the pressing characteristics of the storage object, an object model corresponding to the storage object is constructed;
s33: and carrying out combination treatment on the tray model and the object model, and carrying out analysis treatment on expected reaction of the tray model according to the object model subjected to the combination treatment so as to obtain the expected reaction index of the warehouse tray to the warehouse object.
Specifically, according to the specification data (size, bearing capacity, material and the like) of the tray, a CAD software or other modeling tools are used for establishing a digital model of the tray, and key bearing points and bearing limits are marked in the model.
More specifically, a three-dimensional model of the warehouse object is constructed according to basic characteristics (size, weight, etc.) and depth characteristics (stability, gravity center position, pressure distribution, etc.) of the warehouse object, and key parameters affecting stability and pressure application characteristics are marked in the model.
More specifically, the tray model and the object model are combined on the same modeling platform so as to simulate the placement state of the warehouse objects on the tray, and the model is adjusted until the actual placement situation is reflected by considering the stacking mode and the bearing point of the goods.
More specifically, structural analysis is performed by using simulation software, reactions (such as deformation, stress distribution and the like) of the tray and the goods under the action of gravity and external force are evaluated, durability and stability performances of the tray under different loading conditions are analyzed, and key indexes are extracted from simulation results to obtain expected reaction indexes.
It should be noted that this step may also include optimizing the tray design or the stack strategy according to the desired reaction criteria, and cycling through simulations until a satisfactory performance criteria is achieved.
Preferably, the tray model and the object model are combined, and the analysis processing of the expected reaction of the tray model is performed according to the object model subjected to the combination processing, so as to obtain the expected reaction index of the warehouse tray for the warehouse object, and the method comprises the following steps:
S331: determining a relative positional relationship between the tray model and the object model; wherein the relative positional relationship is used for describing the relative positional relationship between the object model and the tray model when the object model is placed on the tray model;
S332: according to the relative position relation, combining the object model and the tray model to obtain a storage integral model; the storage overall model is used for describing the overall conditions of the storage objects and the storage trays when the storage objects are loaded on the storage trays;
S333: according to the stability characteristics of the object model, analyzing and processing the stability of the storage integral model to obtain stability parameters of the storage integral model;
s334: acquiring a preliminary storage position of the storage object, and calculating according to the preliminary storage position to obtain a transportation path of the storage overall model; the transportation path is a path required by the warehouse objects to be transported from the warehouse logistics transfer area to the preparation warehouse position;
S335: dividing the transportation path into path sections with a plurality of path attributes; the path attribute is used for describing the transportation condition of the warehouse object and the warehouse tray in the transportation process of the path section;
s336: analyzing and processing stability parameters of the warehouse integral model according to path attributes of the path sections to obtain theoretical jitter data of the warehouse integral model in the path sections;
S337: according to the pressing characteristics of the object model, analyzing and processing the pressing vector of the object model on the tray model by the storage overall model to obtain the distribution of the pressing vector of the object model on the tray model in the storage overall model; the distribution of the pressing vectors is used for describing the distribution condition of the pressing vectors of the object model to each position of the tray model, and the pressing vectors are used for describing the magnitude and the angle of the pressing force applied by the object model to one position of the tray model;
s338: calculating theoretical deformation distribution of the tray model according to the pressure vector distribution so as to obtain theoretical deformation distribution of the tray model;
S339: and jointly using theoretical shaking data of the warehouse integral model in each path section and theoretical deformation distribution of the tray model as the expected reaction index.
Specifically, CAD or modeling software is used for placing the object model on the tray model, and the optimal position of the object model on the tray is determined so as to ensure uniform load distribution, and the integral 3D model of the tray and the object model is created by combining the determined relative position relationship, so that the integral model is ensured to reflect the actual loading condition.
More specifically, the overall model was subjected to stability analysis using structural analysis software, assessing the position of the center of gravity of the model, the possible overturning points and the stability under different conditions.
More specifically, an optimal transportation path from the staging area to the storage location is calculated based on predetermined warehouse locations, and the transportation path is divided into different sections, each section having specific properties (e.g., slope, curvature, etc.), considering the factors of turning, slope, etc. in the path, and properties that may affect stability and pressure distribution during transportation are defined for each section.
More specifically, each path segment is analyzed to predict jitter and vibration that may occur during transportation, and the impact of these jitter on the overall model stability parameters is evaluated.
More specifically, the pressure application characteristics of the object model to the tray model are analyzed, the position, size, and direction of the pressure application points are determined, and a pressure application vector distribution map is created to represent the distribution of pressure on the tray.
More specifically, the theoretical deformation of the tray model is predicted from the distribution of the pressing vector, for evaluating the performance of the tray model when pressed.
More specifically, theoretical shake data and deformation distribution are combined as expected response indicators for evaluating expected performance of the tray model throughout transportation.
It will be appreciated that by analyzing the transport path, it is determined what changes the bin Chu Tuopan will exhibit during transport due to the transport path and the stocker's subjects, and based on this expected change, the stocker tray is monitored and problem diagnosed.
Preferably, the multi-dimensional data acquisition is performed on the warehouse tray loaded with the warehouse objects so as to obtain the actual reaction index of the warehouse tray, and the steps include:
s41: performing first-round appearance detection on the warehouse tray without the warehouse object so as to acquire initial appearance data of the warehouse tray;
S42: performing second-round appearance detection on the warehouse tray loaded with the warehouse objects to acquire strain appearance data of the warehouse tray;
S43: performing differential analysis processing on the strain appearance data and the initial appearance data to obtain deformation response indexes of the warehouse tray;
S44: continuously collecting shaking data of the warehouse tray loaded with the warehouse object to obtain shaking data of the warehouse tray loaded with the warehouse object in each path section, and taking the collected shaking data as shaking response indexes of the warehouse tray;
S45: and taking the deformation reaction index and the shaking reaction index of the storage tray as actual reaction indexes of the storage tray.
Specifically, a 3D scanning technique or other precision measuring tool is used to detect a warehouse pallet that is not loaded with warehouse objects, and after the warehouse pallet is loaded with warehouse objects, 3D scanning is performed again or similar techniques are used to acquire the shape data of the pallet, and this detection should focus on the shape change of the pallet after loading and any deformation that may be caused by loading.
More specifically, the first wheel profile detection is used to obtain initial profile data for the warehouse pallet and the second wheel profile detection is used to obtain strain profile data for the warehouse pallet, and the deformation of the pallet is analyzed by comparing the profile data of the first wheel and the second wheel.
More specifically, the means for shape detection may include image acquisition, infrared laser ray detection, and the like.
More specifically, specialized software is used to process the data, possibly including digital image processing and finite element analysis, to quantify the extent of deformation.
More specifically, vibration sensors or accelerometers are used to continuously collect shake data during the transport of the pallet, which data should include accelerations, frequencies, amplitudes etc. in various directions, particularly specific shake characteristics at different path segments.
More specifically, the deformation response indicators and the shake response indicators are combined to form a comprehensive view of the actual performance of the tray, and these indicators can be used to evaluate the overall durability and applicability of the tray.
It can be understood that through two-wheel appearance detection, accurately know the physical condition of tray under unloaded and loading state, deformation analysis helps discernment tray possible weakness, in time maintains or changes to the emergence of prevention accident, and the data of meeting an emergency provides the quantitative information that the loading goods influences the tray structure, helps designing more reasonable loading scheme, and shake data acquisition provides the dynamic performance information of tray in actual transportation, realizes the problem diagnosis to the storage tray from another aspect.
More specifically, the actual reaction index may also be used to adjust the transportation scheme to reduce damage to the cargo caused by abnormal vibration or deformation of the pallet during transportation.
Preferably, the actual reaction index is subjected to comparative analysis processing according to the expected reaction index to obtain a reaction deviation index of the warehouse tray, and the reaction deviation index of the warehouse tray is subjected to analysis processing according to a preset standard to diagnose the problem of the warehouse tray, which comprises the following steps:
S51: performing comparative analysis treatment on the actual reaction index according to the expected reaction index to obtain a reaction deviation index of the storage tray;
s52: the method comprises the steps of calling response deviation indexes of the same type of histories corresponding to storage objects from a database, and comparing the response deviation indexes of the storage trays according to the response deviation indexes of the same type of histories to obtain a first deviation degree of the response deviation indexes of the storage trays; wherein the same type of history record is a history storage processing process of a storage object similar to the storage object; s53: the reaction deviation index of the historical working record of the storage tray is called from a database, and the reaction deviation index of the storage tray and the reaction deviation index of the historical working record of the storage tray are subjected to integral correlation analysis processing to obtain a second deviation degree of the reaction deviation index of the storage tray; the history work record is a history process of carrying out warehousing treatment on other warehouse objects by the warehouse pallet;
s54: and comprehensively analyzing and processing the reaction deviation index, the first deviation degree and the second deviation degree to realize the problem diagnosis of the warehouse tray.
Specifically, the actual reaction index and the expected reaction index are compared and analyzed to identify the deviation of the tray performance in actual use, and the reaction deviation index of the warehouse tray is obtained.
More specifically, the reaction deviation indexes of the same type of histories corresponding to the warehouse objects are called from the database, and the reaction deviation indexes of the warehouse trays are compared according to the reaction deviation indexes of the same type of histories, so that a first deviation degree of the reaction deviation indexes of the warehouse trays is obtained.
It can be understood that the data object called in this step is the reaction deviation index of other warehouse trays in the past warehouse processing process, and the warehouse objects of these other warehouse trays have similar warehouse objects with the warehouse object of this time, that is to say, for the same warehouse object of this time, observe the reaction deviation index of each warehouse tray, judge whether there is too big deviation in the reaction deviation index of this time warehouse tray, so as to judge whether the warehouse tray has a problem or the automation equipment that transports the warehouse tray has a problem.
It will be appreciated that if all of the warehouse trays have similar reaction deviation indicators, then a greater probability is that the automated equipment transporting the warehouse trays is problematic, whereas a greater probability is that the warehouse trays are problematic.
More specifically, the reaction deviation index of the historical working record of the storage tray is called from the database, and the analysis processing of the integral correlation is carried out on the reaction deviation index of the storage tray and the reaction deviation index of the historical working record of the storage tray, so that the second deviation degree of the reaction deviation index of the storage tray is obtained; the history work record is a process of warehousing other warehousing objects, which is performed before the warehouse pallet.
It can be understood that the data object retrieved in this step is a reaction deviation index of the same warehouse tray in the past warehouse process, in the warehouse process of loading, transporting and storing other warehouse objects, and the variation difference of the reaction deviation index of the warehouse tray to each warehouse object is judged by analyzing the reaction deviation index of the same warehouse tray to each warehouse object, if the variation difference is larger, a new problem appears in the representation of a larger probability, otherwise, no problem appears in the representation of a larger probability.
In conclusion, the deviation between the performance and the expected performance of the tray under the actual condition can be accurately identified through multiple comparison analysis, and the problem diagnosis is helpful for finding and solving the problem in time, so that the downtime is reduced, and the overall operation efficiency is improved.
Preferably, the reaction deviation index, the first deviation degree and the second deviation degree are comprehensively analyzed to realize the problem diagnosis of the warehouse tray, and the steps include:
s541: constructing a problem overall map of the warehouse tray according to the reaction deviation index; the problem overall map is used for describing problems integrally displayed by the warehouse tray in the warehouse treatment process;
s542: constructing an external influence map of the warehouse tray according to the first deviation degree; the external influence map is used for describing influence of an external device on problems displayed by the warehouse tray in the warehouse treatment process;
S543: performing differential analysis processing on the problem overall spectrum and the external influence spectrum, and obtaining a body influence spectrum of the storage tray according to the differential analysis processing result; the body influence map is used for describing the actual influence of the storage tray on the problems displayed in the storage process;
S544: constructing a multi-level influence map of the warehouse pallet according to the reaction deviation index and the second deviation degree; the multi-level influence map is used for describing the relation between the problems displayed by the warehouse trays in each warehouse process and the warehouse objects;
S545: and analyzing and processing the body influence map and the multi-level influence map so as to realize the problem diagnosis of the storage tray.
Specifically, a problem overall map is created by using the reaction deviation index, and is used for visually representing all problems encountered by the tray in the storage process, an external influence map is constructed by using the first deviation degree data, and influences of external factors such as equipment faults, misoperation, environmental conditions and the like on the performance of the tray are displayed.
More specifically, comparing the overall problem map with the external influence map, performing difference analysis to identify problems caused by the properties of the tray (such as material fatigue, structural problems, etc.), and obtaining a body influence map according to the result of the difference analysis to show how the characteristics of the tray affect the problems in the storage process.
More specifically, a multi-level influence map is created that reveals the problem of trays during different warehouse processes and the interrelationship with warehouse objects, combining the reaction deviation index and the second deviation degree.
More specifically, the body influence spectrum and the multi-level influence spectrum are analyzed and processed to realize the problem diagnosis of the warehouse tray.
It can be understood that after external interference is eliminated, the body influence map is fed back to the deviation brought by the storage tray, so that more accurate problem diagnosis can be performed, the multi-level influence map is used for carrying out deviation relation between storage objects to be treated by the storage tray, so that the problem diagnosis of integrity is performed on the storage tray, and the diagnosis of the storage tray is more comprehensive and accurate.
Referring to fig. 2, in a second aspect, the present invention provides a problem tray searching system based on intelligent warehouse, including:
The data acquisition module is used for acquiring data of the warehouse objects in the warehouse logistics transfer area so as to obtain warehouse characteristics of the warehouse objects; the warehouse logistics transfer area is used for carrying out logistics transfer on the warehouse objects;
The data analysis module is used for analyzing the storage characteristics of the storage objects to obtain the bearing characteristics of the storage objects, and selecting corresponding storage trays for the storage objects according to the bearing characteristics of the storage objects so as to load the storage objects; the storage tray is used for loading, transporting and storing the storage objects;
the reaction prediction module is used for carrying out calculation processing on expected reaction of the storage tray according to the bearing characteristics of the storage object so as to obtain an expected reaction index of the storage tray for the storage object;
the reaction acquisition module is used for carrying out multidimensional data acquisition on the warehouse tray loaded with the warehouse objects so as to obtain actual reaction indexes of the warehouse tray;
And the problem diagnosis module is used for carrying out comparison analysis processing on the actual reaction index according to the expected reaction index to obtain a reaction deviation index of the storage tray, and carrying out analysis processing on the reaction deviation index of the storage tray according to a preset standard to carry out problem diagnosis on the storage tray.
In this embodiment, for specific implementation of each module in the above system embodiment, please refer to the description in the above method embodiment, and no further description is given here.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. The problem tray searching method based on intelligent storage is characterized by comprising the following steps of:
Data acquisition is carried out on the warehouse objects in the warehouse logistics transfer area so as to obtain warehouse characteristics of the warehouse objects; the warehouse logistics transfer area is used for carrying out logistics transfer on the warehouse objects;
Analyzing the storage characteristics of the storage objects to obtain the bearing characteristics of the storage objects, and selecting corresponding storage trays for the storage objects according to the bearing characteristics of the storage objects so as to load the storage objects; the storage tray is used for loading, transporting and storing the storage objects;
Calculating the expected reaction of the storage tray according to the bearing characteristics of the storage object to obtain the expected reaction index of the storage tray for the storage object;
Carrying out multidimensional data acquisition on the warehouse tray loaded with the warehouse objects so as to obtain actual reaction indexes of the warehouse tray;
And comparing, analyzing and processing the actual reaction index according to the expected reaction index to obtain a reaction deviation index of the storage tray, and analyzing and processing the reaction deviation index of the storage tray according to a preset standard to diagnose the problem of the storage tray.
2. The method for searching problem trays based on intelligent warehousing as set forth in claim 1, wherein the step of collecting data of the warehouse objects in the warehouse logistics transfer area to obtain the warehouse characteristics of the warehouse objects includes:
Data acquisition of basic characteristics is carried out on the warehouse objects in the warehouse logistics transfer area so as to obtain the basic characteristics of the warehouse objects; wherein the basic characteristics comprise the cargo type, weight, bottom area and height of the storage object;
performing deep analysis processing on the basic characteristics of the storage object to obtain the deep characteristics of the storage object; wherein the depth features include a pressure, a center of gravity of the warehouse object;
Analyzing and processing the basic characteristics and the depth characteristics of the storage object according to preset standards to obtain a preparation storage position of the storage object; wherein the preparation storage position is a specific position for storing the storage object;
and taking the basic characteristics, the depth characteristics and the preparation storage positions of the storage objects as storage characteristics of the storage objects.
3. The method for searching problem trays based on intelligent storage as claimed in claim 2, wherein the steps of analyzing the basic features and the depth features of the storage object according to a preset standard to obtain a preliminary storage position of the storage object include:
Analyzing and processing the basic characteristics of the storage objects according to preset standards to determine storage areas corresponding to the cargo types of the storage objects;
data is acquired from the database for the warehouse area so as to acquire the residual space distribution of the warehouse area; the residual space distribution is used for describing a space in the storage area, wherein the space can store the storage objects;
And analyzing and processing the depth characteristics of the storage objects according to preset standards to determine the optimal storage space of the storage objects in the residual space distribution, and taking the optimal storage space as a preparation storage position of the storage objects.
4. A problem tray searching method based on intelligent warehousing as set forth in claim 3, wherein the step of analyzing the warehousing characteristics of the warehousing object to obtain the bearing characteristics of the warehousing object includes:
Analyzing and processing the basic characteristics of the storage object according to a preset standard to obtain tray specification data of the storage object; wherein the pallet specification data is part of the load bearing characteristics of the warehouse object;
performing comprehensive analysis processing according to the basic characteristics and the depth characteristics of the storage object to obtain the stable characteristics and the pressing characteristics of the storage object; the stability feature is used for describing the stability of the storage object when the storage object is placed on the storage tray, the pressure feature is used for describing the pressure condition caused to the storage tray when the storage object is placed on the storage tray, and the stability feature and the pressure feature are part of the bearing feature of the storage object.
5. The method for searching problem trays based on intelligent warehousing as set forth in claim 4, wherein the calculating process of the expected reaction of the warehouse tray according to the bearing characteristics of the warehouse object is performed to obtain the expected reaction index of the warehouse tray to the warehouse object, and the steps include:
constructing a tray model corresponding to the warehouse tray according to the tray specification data of the warehouse object;
According to the stable characteristics and the pressing characteristics of the storage object, an object model corresponding to the storage object is constructed;
And carrying out combination treatment on the tray model and the object model, and carrying out analysis treatment on expected reaction of the tray model according to the object model subjected to the combination treatment so as to obtain the expected reaction index of the warehouse tray to the warehouse object.
6. The method for finding a problem tray based on intelligent warehousing as set forth in claim 5, wherein the step of combining the tray model and the object model and analyzing the expected reaction of the tray model according to the object model subjected to the combination to obtain the expected reaction index of the warehousing tray to the warehousing object comprises the steps of:
Determining a relative positional relationship between the tray model and the object model; wherein the relative positional relationship is used for describing the relative positional relationship between the object model and the tray model when the object model is placed on the tray model;
according to the relative position relation, combining the object model and the tray model to obtain a storage integral model; the storage overall model is used for describing the overall conditions of the storage objects and the storage trays when the storage objects are loaded on the storage trays;
according to the stability characteristics of the object model, analyzing and processing the stability of the storage integral model to obtain stability parameters of the storage integral model;
Acquiring a preliminary storage position of the storage object, and calculating according to the preliminary storage position to obtain a transportation path of the storage overall model; the transportation path is a path required by the warehouse objects to be transported from the warehouse logistics transfer area to the preparation warehouse position;
dividing the transportation path into path sections with a plurality of path attributes; the path attribute is used for describing the transportation condition of the warehouse object and the warehouse tray in the transportation process of the path section;
analyzing and processing stability parameters of the warehouse integral model according to path attributes of the path sections to obtain theoretical jitter data of the warehouse integral model in the path sections;
According to the pressing characteristics of the object model, analyzing and processing the pressing vector of the object model on the tray model by the storage overall model to obtain the distribution of the pressing vector of the object model on the tray model in the storage overall model; the distribution of the pressing vectors is used for describing the distribution condition of the pressing vectors of the object model to each position of the tray model, and the pressing vectors are used for describing the magnitude and the angle of the pressing force applied by the object model to one position of the tray model;
calculating theoretical deformation distribution of the tray model according to the pressure vector distribution so as to obtain theoretical deformation distribution of the tray model;
And jointly using theoretical shaking data of the warehouse integral model in each path section and theoretical deformation distribution of the tray model as the expected reaction index.
7. The method for finding a problem tray based on intelligent storage as claimed in claim 6, wherein the step of performing multidimensional data collection on the storage tray loaded with the storage object to obtain an actual reaction index of the storage tray comprises the steps of:
Performing first-round appearance detection on the warehouse tray without the warehouse object so as to acquire initial appearance data of the warehouse tray;
Performing second-round appearance detection on the warehouse tray loaded with the warehouse objects to acquire strain appearance data of the warehouse tray;
Performing differential analysis processing on the strain appearance data and the initial appearance data to obtain deformation response indexes of the warehouse tray;
Continuously collecting shaking data of the warehouse tray loaded with the warehouse object to obtain shaking data of the warehouse tray loaded with the warehouse object in each path section, and taking the collected shaking data as shaking response indexes of the warehouse tray;
and taking the deformation reaction index and the shaking reaction index of the storage tray as actual reaction indexes of the storage tray.
8. The method for searching for problem trays based on intelligent storage as claimed in claim 1, wherein the steps of comparing the actual reaction index according to the expected reaction index to obtain a reaction deviation index of the storage tray, and analyzing the reaction deviation index of the storage tray according to a preset standard to diagnose the problem of the storage tray include:
Performing comparative analysis treatment on the actual reaction index according to the expected reaction index to obtain a reaction deviation index of the storage tray;
The method comprises the steps of calling response deviation indexes of the same type of histories corresponding to storage objects from a database, and comparing the response deviation indexes of the storage trays according to the response deviation indexes of the same type of histories to obtain a first deviation degree of the response deviation indexes of the storage trays; wherein the same type of history record is a history storage processing process of a storage object similar to the storage object;
the reaction deviation index of the historical working record of the storage tray is called from a database, and the reaction deviation index of the storage tray and the reaction deviation index of the historical working record of the storage tray are subjected to integral correlation analysis processing to obtain a second deviation degree of the reaction deviation index of the storage tray; the history work record is a history process of carrying out warehousing treatment on other warehouse objects by the warehouse pallet;
and comprehensively analyzing and processing the reaction deviation index, the first deviation degree and the second deviation degree to realize the problem diagnosis of the warehouse tray.
9. The method for finding a problem tray based on intelligent storage as claimed in claim 8, wherein the steps of comprehensively analyzing the reaction deviation index, the first deviation degree and the second deviation degree to realize the problem diagnosis of the storage tray include:
constructing a problem overall map of the warehouse tray according to the reaction deviation index; the problem overall map is used for describing problems integrally displayed by the warehouse tray in the warehouse treatment process;
Constructing an external influence map of the warehouse tray according to the first deviation degree; the external influence map is used for describing influence of an external device on problems displayed by the warehouse tray in the warehouse treatment process;
Performing differential analysis processing on the problem overall spectrum and the external influence spectrum, and obtaining a body influence spectrum of the storage tray according to the differential analysis processing result; the body influence map is used for describing the actual influence of the storage tray on the problems displayed in the storage process;
Constructing a multi-level influence map of the warehouse pallet according to the reaction deviation index and the second deviation degree; the multi-level influence map is used for describing the relation between the problems displayed by the warehouse trays in each warehouse process and the warehouse objects;
And analyzing and processing the body influence map and the multi-level influence map so as to realize the problem diagnosis of the storage tray.
10. Problem tray seek system based on intelligent storage, characterized by comprising:
The data acquisition module is used for acquiring data of the warehouse objects in the warehouse logistics transfer area so as to obtain warehouse characteristics of the warehouse objects; the warehouse logistics transfer area is used for carrying out logistics transfer on the warehouse objects;
The data analysis module is used for analyzing the storage characteristics of the storage objects to obtain the bearing characteristics of the storage objects, and selecting corresponding storage trays for the storage objects according to the bearing characteristics of the storage objects so as to load the storage objects; the storage tray is used for loading, transporting and storing the storage objects;
the reaction prediction module is used for carrying out calculation processing on expected reaction of the storage tray according to the bearing characteristics of the storage object so as to obtain an expected reaction index of the storage tray for the storage object;
the reaction acquisition module is used for carrying out multidimensional data acquisition on the warehouse tray loaded with the warehouse objects so as to obtain actual reaction indexes of the warehouse tray;
And the problem diagnosis module is used for carrying out comparison analysis processing on the actual reaction index according to the expected reaction index to obtain a reaction deviation index of the storage tray, and carrying out analysis processing on the reaction deviation index of the storage tray according to a preset standard to carry out problem diagnosis on the storage tray.
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